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35820961 | PMC9277849 | pmc | 488 | {
"abstract": "Background Astaxanthin is a highly valuable ketocarotenoid with strong antioxidative activity and is natively accumulated upon environmental stress exposure in selected microorganisms. Green microalgae are photosynthetic, unicellular organisms cultivated in artificial systems to produce biomass and industrially relevant bioproducts. While light is required for photosynthesis, fueling carbon fixation processes, application of high irradiance causes photoinhibition and limits biomass productivity. Results Here, we demonstrate that engineered astaxanthin accumulation in the green alga Chlamydomonas reinhardtii conferred high light tolerance, reduced photoinhibition and improved biomass productivity at high irradiances, likely due to strong antioxidant properties of constitutively accumulating astaxanthin. In competitive co-cultivation experiments, astaxanthin-rich Chlamydomonas reinhardtii outcompeted its corresponding parental background strain and even the fast-growing green alga Chlorella vulgaris . Conclusions Metabolic engineering inducing astaxanthin and ketocarotenoids accumulation caused improved high light tolerance and increased biomass productivity in the model species for microalgae Chlamydomonas reinhardtii . Thus, engineering microalgal pigment composition represents a powerful strategy to improve biomass productivities in customized photobioreactors setups. Moreover, engineered astaxanthin accumulation in selected strains could be proposed as a novel strategy to outperform growth of other competing microalgal strains. Supplementary Information The online version contains supplementary material available at 10.1186/s13068-022-02173-3.",
"conclusion": "Conclusions The results presented here demonstrate that tailored pigments composition and addition of ketocarotenoids with higher antioxidant activity is a viable strategy to induce high light resistance and improve photosynthetic efficiency of C. reinhardtii and likely other green algae. Astaxanthin accumulated in engineered C. reinhardtii bkt5 strain was found mainly free in the thylakoid membranes or possibly bound to both PSI and PSII. The changes in pigments composition affect the photosynthetic machinery in low light condition, but the presence of ketocarotenoids gives a striking advantage in constant high light. Very high light (up to 3000 μmol photons m −2 s −1 ) natively occurs in outdoor cultivation strategies in extreme environments but induced strong photoinhibition in wild-type C. reinhardtii reducing overall efficiency. Engineered astaxanthin accumulation significantly improved growth under high light, reduced photoinhibition and could be proposed as a novel strategy to outperform growth of competing microalgae or cyanobacteria strains.",
"discussion": "Discussion The expression of an active BKT enzyme profoundly changed the specific pigment accumulation and composition in C. reinhardtii . In bkt5 more than 50% of carotenoids were converted into ketocarotenoids, ~ 74% and ~ 56% of which being astaxanthin for cells grown, respectively, in CL or HL. Moreover, Chl/Car ratios were decreased in both CL and HL conditions indicating, respectively, a ~ 17% and ~ 20% increase in total carotenoids accumulation on a Chl basis (Additional file 1 : Table S1, Table S2). It is important to note that overall pigment content per cell in bkt5 was reduced, with a decrease in both Chl and carotenoid accumulation on a cell basis (Additional file 1 : Table S1). However, the increased carotenoids content per Chl implies an increased availability of carotenoids as photoprotective agent for the Chl accumulated in the thylakoid membranes. The altered carotenoids composition with ketocarotenoids partially substituting xanthophylls and carotenes (Additional file 1 : Figure S1) destabilizes the photosynthetic complexes affecting mainly the trimerization state of LHCII, PSII core and PSI (Fig. 1 ). The PSII core complex was more affected by the alteration in carotenoids: in bkt5 the accumulation of PSII complexes was greatly impaired while the assembly of PSI was less sensitive to carotenoid availability [ 49 , 50 ]. The lack of appropriate carotenoids could result in a reduced efficiency in PSII assembly. Ketocarotenoids were found distributed throughout the overall fractions in sucrose gradient which could be related to contamination due to high accumulation of astaxanthin in the thylakoids. Alternatively, partial substitution of xanthophylls and carotenes by ketocarotenoids cannot be excluded, but the binding of these carotenoids to photosynthetic complexes need further work to be verified. It is likely that the molecular structure of astaxanthin (e.g., carboxylation of β-rings) does not fit with photosynthetic proteins or that their interaction is weaker than endogenous xanthophylls and it is disrupted by detergent solubilization. Accordingly, the distribution of the astaxanthin in thylakoid membranes (Additional file 1 : Figure S1A) showed that the main part of the ketocarotenoid was not bound to the photosynthetic complexes but free in the membranes. Similar consequences were also observed in A. thaliana and tobacco plants engineered to accumulate astaxanthin [ 31 , 51 , 52 ]. In the case of H. lacustris , astaxanthin is mainly accumulated outside the chloroplast, but even in this case the small fraction of this ketocarotenoid detected in the plastids is mainly free in the thylakoid membranes [ 26 ]. The changes in carotenoids composition and partial destabilization of the photosynthetic complexes affect the photosynthetic performance in bkt5 (Fig. 3 ). The Fv/Fm was reduced in bkt5 by ~ 18% while, upon exposure to actinic lights, ΦPSII, ETR and 1-qL were reduced at 100 μmol photons m −2 s −1 (Fig. 3 ). However, the reduced PSII efficiency had almost negligible effect on the growth of bkt5 at this light intensity (Fig. 6 ). This discrepancy between the negative impact of BKT expression on photosynthetic parameters at low light and comparable biomass productivity for bkt5 and UVM4 strains under autotrophic or mixotrophic conditions, could be explained with the “pale” phenotype of bkt5 . Decreasing the chlorophyll content of microalgae cells is a strategy successfully applied to increase solar energy conversion efficiency enhancing light penetration inside algal culture and minimizing feedback energy dissipation [ 53 , 54 ]. As reported in Additional file 1 : Figure S9, the reduction of both Chl and carotenoids per cell content in bkt5 caused a slightly enhanced penetration of blue and red light , possibly compensating the reduced efficiency of the PSII, even if this speculation requires further evidences to be supported. We cannot even exclude that the reduced penetration of the 500–560 nm wavelengths in bkt5 culture might contribute to some extent to the increased photoresistance of this strain, involving some signal transmission pattern related to some specific photoreceptors as channel rhodopsins [ 55 , 56 ], which perceives light at these wavelengths. At higher irradiances, PSI and PSII photosynthetic parameters as ΦPSI, ΦPSII, ETR and ETR PSI , were similar or even higher in bkt5 compared to UVM4. Moreover, the photosynthetic rate, measured as oxygen evolution, showed a higher P max and a higher half-saturation intensity (Additional file 1 : Table S3). These results indicate that the negative effect on the stability of photosynthetic complexes was compensated by an increased resistance to high light. Accordingly, the biomass productivity was strongly enhanced in bkt5 when grown at 3000 μmol photons m −2 s −1 either in autotrophy or in mixotrophy (Fig. 6 ). This effect is particularly evident from growth curves and productivity of the second cycles of growth when the cell were already exposed to high illumination and ketocarotenoid content was increased (Fig. 6 E, F). Moreover, bkt5 outcompeted its parental strain when competitively co-cultured in the same photobioreactor (Fig. 7 ). Surprisingly, bkt5 strain was able to become dominant even in a competition test with C. vulgaris , one of the green alga species more considered for industrial application due its fast-growing phenotype (Fig. 7 ). These results were quite surprising since in land plant expressing BKT, there were no reported evidence for an increased productivity while in some cases the transgenic plant showed a hampered growth compared to the control [ 31 , 51 , 52 ]. This is likely related to the simple structure of unicellular organism like microalgae that can cope better with the severe changes in carotenoids composition. It is interesting to note that even in the case of astaxanthin-rich H. lacustris cysts an increase in P max on a Chl basis was observed compared to the “green” vegetative cells [ 57 ]. In C. reinhardtii bkt5 astaxanthin accumulated constitutively during growth phase [ 28 ], whereas natively it requires environmental stress-related activation in H. lacustris , which simultaneously induces profound changes of cellular structure and a shift to the aplanospore phase [ 25 ]. Improved biomass productivity in C. reinhardtii could be mediated by the increased light penetration due to reduced chlorophyll contents, although application of higher light intensities should compensate diffusion limitations and ensures sufficient light energy supply in C. reinhardtii cultures. But it is likely that increased resistance to photoinhibition in bkt5 is assisting in tolerating high light, as measured from chlorophyll bleaching, oxygen evolution and singlet oxygen production upon exposure to photo-inhibiting irradiances (Fig. 4 ). The main mechanism for protection from high light is the NPQ that dissipates the excessive light energy as heat. A higher resistance to photoinhibition could be due to an improved quenching mechanism, but this was not the case of bkt5 where a strong reduction in NPQ was evident in cells acclimated either to CL or HL. Activation of NPQ in C. reinhardtii is dependent on generation of protonic gradient in the thylakoid lumen and accumulation of antenna protein LHCSR1 and LHCSR3 [ 11 , 15 , 40 ]. ECS measurements demonstrated that proton transport into the lumen was similar or even higher in bkt5 strains compared to its background strain. In bkt5 LHCSR1 is present both in CL and HL even if its level is reduced compared to UVM4. The reduction of LHCSR1 is likely not the cause for the impaired NPQ since LHCSR3 is primarily responsible for quenching activation in high light-acclimated cells [ 11 ] and C. reinhardtii mutant without LHCSR1 showed the same NPQ level as wild type [ 12 ]. LHCSR3 is synthetized in bkt5 only in HL: on chlorophyll basis LHCSR3 was reduced in bkt5 but on a PSII basis, there was no significant difference between the two genotypes (Fig. 2 ): because it was reported that the LHCSR3/PSII ratio is linearly correlated with the NPQ capability of C. reinhardtii [ 10 ], the different LHCSR3 expression on a chlorophyll basis is likely not sufficient to explain the NPQ phenotype of bkt5 strain. However, it is important to note that while LHCSR3/PSII ratio is essentially unchanged, bkt5 exhibits an increased LHCII/PSII ratio: being the energy absorbed by LHCII one of the main targets of NPQ, we cannot exclude that the reduced LHCSR3/LHCII ratio in bkt5 could at least partially contribute to the reduced NPQ phenotype in this engineered strain compared to UVM4. Astaxanthin accumulation in tobacco plants was previously reported to induce LHCII antenna proteins into a light-independent quenched state [ 58 ]. However, this possible constitutive quenching mechanism had a minor role in C. reinhardtii bkt5 because 77 K fluorescence measurements on dark or light-treated whole cells demonstrated that PSII in bkt5 cells is not in a constitutive quenching state (Additional file 1 : Figure S8). The reduction of NPQ was more probably due to the changes in carotenoids compositions; mutants with altered xanthophylls and carotenes composition showed a reduction of NPQ both in microalgae and land plant [ 59 , 60 ]. However, independently from the reason for its reduction, the NPQ mechanisms are not involved in the increased resistance to strong irradiances observed in bkt5 . The increased resistance to high light of bkt5 strain must then directly depend on astaxanthin and ketocarotenoids accumulation. Astaxanthin could probably exert this role with different mechanisms. (i) The increased carotenoid contents per chlorophyll observed in bkt5 , mainly related to ketocarotenoids accumulation, makes astaxanthin as a “sunscreen” for chlorophylls, reducing the penetration of blue light into the thylakoids, thereby reducing excessive light absorption by photosystems [ 61 ]. The reduced singlet oxygen production, measured by SOSG fluorescence, in bkt5 compared to UVM4 upon red light only exposure demonstrates, however, that the light-filtering effect of ketocarotenoids in the 500–560 nm (Additional file 1 : Figure S9) is not the only mechanism contributing to increased photoprotection in bkt5 . (ii) Astaxanthin is one of the most powerful antioxidant molecules and can scavenge ROS and could act as a barrier that prevents lipids, pigments, and photosynthetic complexes oxidation. Astaxanthin showed an antioxidant activity against singlet oxygen far higher than β-carotene [ 23 , 62 ]. According to this hypothesis, singlet oxygen was detected in far higher amount in UVM4 compared to the strain that accumulates astaxanthin (Fig. 4 C). This result is different from what was observed in H. lacustris where astaxanthin accumulating “red” cells showed a reduced Chl bleaching but the same amount of singlet oxygen generation. This difference could depend on the fact that in bkt5 ketocarotenoids are accumulated in the chloroplast, the main site of light-dependent ROS production, while in H. Lacustris astaxanthin is present in lipid droplets in the cytosol, having a major role as a light filter. (iiii) The extensive accumulation of astaxanthin could be an additional way to use the high level of reducing power generated during continuous illumination regenerating NADP + as electron acceptor for the photosynthetic apparatus, mitigating the risk of saturation of the electron transport chain [ 63 ]. All these aspects can contribute to the higher resistance of bkt5 in high light. It is important to consider a possible astaxanthin dose response for the high light tolerance in bkt strains: bkt5 strain herein investigated was selected for having high astaxanthin and accumulation (up to 2.5 mg/g dry weight), but further work is required to identify the best astaxanthin and ketocarotenoid cell concentration to improve biomass production in high light conditions."
} | 3,711 |
33330365 | PMC7717947 | pmc | 489 | {
"abstract": "Triboelectric Nanogenerators (TENGs) are a highly efficient approach for mechanical-to-electrical energy conversion based on the coupling effects of contact electrification and electrostatic induction. TENGs have been intensively applied as both sustainable power sources and self-powered active sensors with a collection of compelling features, including lightweight, low cost, flexible structures, extensive material selections, and high performances at low operating frequencies. The output performance of TENGs is largely determined by the surface triboelectric charges density. Thus, manipulating the surface chemical properties via appropriate modification methods is one of the most fundamental strategies to improve the output performances of TENGs. This article systematically reviews the recently reported chemical modification methods for building up high-performance TENGs from four aspects: functional groups modification, ion implantation and decoration, dielectric property engineering, and functional sublayers insertion. This review will highlight the contribution of surface chemistry to the field of triboelectric nanogenerators by assessing the problems that are in desperate need of solving and discussing the field's future directions.",
"conclusion": "Conclusions In this review, surface chemistry was systematically introduced to promote the mechanical to electrical energy conversion via triboelectric nanogenerators. Through chemical modifications and approaches, triboelectric materials' surface charge densities have been proven to be modulated and thus the output performances of TENGs are improved. Table 1 systematically summarizes the performance enhancements and mechanical durability of each chemical-modified TENGs. Methods that have been discussed in this review include functional groups grafting, ion implantation and decoration, dielectric properties engineering, and sublayers insertion. TENGs with broadened material choices, diversified operation modes and structural design have fully displayed the advantages in a variety of application scenarios, comparing with other mechanical energy harvesting techniques. Many challenges are waiting to be overcome to advance the field of surface chemistry for high-performance TENGs, as follows. Enhance the chemical stability of the surface chemical functional group grafting. Functional group grafting is a straightforward, cost-efficient, and easy-to-implement way to efficiently enhance the output performance of TENGs. Although that may be true, the results may lose its effectiveness if the surface is polished or worn out during the friction since the modification just takes place on the surfaces rather than deep into the bulk of triboelectric materials. To overcome this difficulty and limitation, further improvements should be found to enhance stability and effective duration of the method. Reduce the cost of ion injection and decoration methods for further scaling up. The ion injection and decoration methods provide effective approaches for improving the TENG's output performance with relatively high stability. However, the fabrication processes are relatively complicated and the instruments used during the processes, such as air-ionization guns, add extra cost. It may bring obstacles for further scaling up. Consequently, more flexible and cost-efficient techniques should be explored for ion implantation promotion. Deeply explore the mechanisms of dielectric engineering. Although the dielectric property modification is experimentally proven to be an effective and simple approach for obtaining high-performance TENGs, the correlation between dielectric properties of triboelectric materials and output performance of as-fabricated TENGs is not clearly unveiled yet. Further investigation should be done to explore the mechanisms of dielectric engineering and reveal the coefficient between embedded nanoparticles and other chemical and physical properties of the material itself, therefore optimizing the dose of dopants for high-performance TENGs. Investigate the inner mechanism of functional sublayers insertion. Functional sublayers insertion is a promising way to improve the output performance of the TENGs because it can provide an active intervention to the electrons transport and storage process. Nevertheless, the mechanism of the collaborative effect from the inserted sublayers, inner interfaces, and the triboelectric material has not yet been well-studied. Thus, further studies investigating the inner mechanism will have great guiding significance to further improve the output performance of TENGs. Incorporation of chemical and physical modifications. The synergy of chemical and physical modifications could be an effective approach to build up a high-performance TENGs. Applying them to a material of interest can not only increase the effective contact area between the two triboelectric surfaces by various surface morphologies, but also increase the gap between the two triboelectric materials' ability to gain or lose electrons as well. Therefore, more attempts should be done to apply both chemical and physical modifications together to triboelectric materials or to explore new techniques that can modify surface morphologies and chemical properties simultaneously. Seek for possible biochemical approaches. Chemical and biological approaches are usually highly correlated with each other. By introducing biomaterials as novel triboelectric materials, like silk fibroin film, TENG may have some satisfying superiorities, such as flexible, stretchable, bio-friendly and preferably transparent. Therefore, it should be wearable (or even implantable) to the human body. Through applying proper chemical modifications to these biomaterials, higher electrical performance and working stability can also be achieved, making TENG an efficient and reliable power source with wider application prospects. Explorations of versatile TENG-based self-powered devices. Both theoretical calculations and experimental endeavors are highly in need for promoting existing materials or potentially discovering new materials. which are not only with high triboelectric properties but also compatibility with various application scenarios, which seem to be a basis for the development of versatile TENG-based self-powered devices. Table 1 A summary of typical chemical modification methods for TENGs. Tribo-materials Modification method Output performance enhancement b Duration c References Charge Voltage Current Power Cl-PET, PEI(b)-PET Self-assembled monolayer - 19.80 a 21 a - 5,000 Shin et al., 2017 PDMS, ITO UVO-irradiation 15.88 11.97 17.46 - 20,000 Yun et al., 2015 PMMA − fiber, Cu Electrospinning - 1.28 a 147 a 585 a 125,000 Busolo et al., 2018 FEP, Al Ion Injection 5 5 3.33 25 400,000 Wang et al., 2014 FEP, Kapton Ion Irradiation 2.32 a - - 35,999 - Li et al., 2020 FOTS-PDMS, Li + film Ion Absorption - 2.40 5.25 20 50,000 Park et al., 2017 AlO X -PDMS, PDMS Sequential Infiltration Synthesis 8 6.67 6.33 - - Yu et al., 2015 BaTiO 3 -doped(PVDF-TrFE), Al High Dielectric Doping 2.52 2 a 5 a 150 - Seung et al., 2017 PI/MoS 2 :PI/PI, Al Caption-Layer Insertion 3 12.33 6 a 120 - Wu et al., 2017 PDMS-Au layer, Al Trapping-Layer Insertion 4 - - - - Lai et al., 2018 PVA-PSC-PS, PVDF-PSC-PS Multiple-Layer Structure 16.5 9 a 9 a - - Cui et al., 2018 a Estimated value . b Multiple of Enhancement . c Testing Cycle of Duration . Overall, the utilization of surface chemistry opens an emerging and effective route to build up high-performance triboelectric nanogenerator as a pervasive energy solution in the upcoming era of the Internet of Things. Challenges coexist with opportunities, and much more research efforts remain desired to improve surface chemical modification with the goals of improved stability, robustness, scalable and advanced surface modification. We anticipate that the wide application of surface chemistry can contribute largely to develop high-performance TENGs as both sustainable power sources and active sensors.",
"introduction": "Introduction The rapid development of wearable and portable electronic devices is greatly revolutionizing our conventional means of energy generation and consumption (Gubbi et al., 2013 ; Lee and Lee, 2015 ; Zhou et al., 2020a ; Zou et al., 2020 ). Miniaturized energy sources with high portability and sustainability are eagerly desired for powering billions of distributed devices in the era of the Internet of Things (Bai et al., 2014 ; Yang et al., 2015 ; Lin et al., 2017 ; Xu et al., 2017 ; Bedi et al., 2018 ; Wang, 2019 ). In the modern world, portable energy storage units, such as batteries, seem like the intuitive and most widely used solution to meet the power consumption needs of electronic devices (Grey and Tarascon, 2017 ; Gu et al., 2017 ; Liu W. et al., 2017 ; He et al., 2018 ; Zan et al., 2020 ). However, their limited lifetime (Ponrouch et al., 2016 ; Placke et al., 2017 ; Liu K. et al., 2018 ; Wan et al., 2019 ; Xu et al., 2019 ), rigid structure, toxic chemical components, and unsustainable working mode, which includes periodically recharging or even replacing the battery unit, deems portable energy storage units obsolete for wide-range adoption to mobile electronics, and more specifically wearable devices (Wang, 2013 ; Zang et al., 2015 ; Gao et al., 2016 ; Kenry and Lim, 2016 ; Trung and Lee, 2016 ; Liu Y. et al., 2017 ; Seneviratne et al., 2017 ; Gür, 2018 ; Kim et al., 2019 ; Yan et al., 2020 ; Zhang et al., 2020 ) and bio-integrated applications (Kang et al., 2013 ; Slater et al., 2013 ; Li and Dai, 2014 ; Yabuuchi et al., 2014 ; Fu et al., 2017 ; Zhang et al., 2017 ; Lin et al., 2018 ; Yan et al., 2018 ; Meng et al., 2019 ; Zhou et al., 2020b ). Converting the accessible, renewable energy from the human body and its surroundings into electricity is considered a great alternative solution (Wang Z. L. et al., 2015 ; Chen et al., 2020 ; Su et al., 2020 ). Electricity generation from biomechanical motions (Qin et al., 2008 ; Sun et al., 2011 ; Lee et al., 2012 ; Yang W. et al., 2013 ; Yi et al., 2015 ; Chen and Wang, 2017 ; Zhao et al., 2019 ), acoustic waves (Wang et al., 2007 ; Cha et al., 2010 ; Yang J. et al., 2014a ), solar irradiance (Stephen, 2006 ; Zheng et al., 2015 ; Chen et al., 2016b ; Dagdeviren et al., 2017 ), body heat (Niu et al., 2009 ; Yang et al., 2013b ; Zi et al., 2015a ; Wang et al., 2019 ), and biofuels (Zou et al., 2016 ), are just some examples of the conversion of energy from and around the human body. In 2012, the triboelectric nanogenerator (TENG) was invented as a highly efficient energy harvesting technology from human biomechanical motions (Bai et al., 2013a ; Chen et al., 2013 ; Hou et al., 2013 ; Zhu et al., 2013a , 2014b ; Yang J. et al., 2014b ; Cheng et al., 2015b ; Jeong et al., 2015 ; Chen, 2016 ; Jin et al., 2016 ; Wang Z. L. et al., 2016 ). Compared to other energy harvesting approaches, TENG has several advantages: light weight, low cost, flexible structures, extensive material selection, and great efficiency at low operating frequencies, all of which make TENGs one of the mainstream power supplies for self-powered devices (Jing et al., 2014 ; Yang W. et al., 2014a , b ; Kuang et al., 2015 ; Lin et al., 2016 ; Liu R. et al., 2018 ; Chu et al., 2020 ; Pu et al., 2020a ). TENGs is feasible for driving various electronic devices, ranging from light-emitting diodes (LEDs) (Yang et al., 2013d ; Lin et al., 2014 ; Chun et al., 2015 ; Kanik et al., 2015 ; Mao et al., 2015 ; Wu et al., 2016 ) to cell phones (Wang et al., 2012 ; Zhu et al., 2014c ) and from a large number of bio-sensors (Fan et al., 2015 ; Wen et al., 2015 ; Cai et al., 2018 ; Su et al., 2018 , 2020a , b ; Davoodi et al., 2020 ; Meng et al., 2020 ) to pacemakers (Zheng Q. et al., 2014 ). This shows showing their remarkable compatibility with a wide range of application in different settings, displaying that plentiful possibilities are remaining to be further explored (Wang, 2014 ; Hinchet et al., 2015 ; Wang S. et al., 2015 ; Zhang et al., 2015 ; Zhu et al., 2015 ; Zhang B. et al., 2016 ; Pu et al., 2020b ). Note that the triboelectric effect is a well-known phenomenon, in which two surfaces, having different triboelectric properties, become electrically charged during physical contact (Mizes et al., 1990 ; Liu and Bard, 2009 ). The principle of TENG is based on the coupling effect of contact electrification and electrostatic induction (Yang et al., 2013c ; Su et al., 2014b ; Wu et al., 2015 ; Li Z. et al., 2016 ; Zhang L. et al., 2016 ). The static polarized charges, resulting from the contact between the two friction surfaces with different charge affinities, are generated on the friction surfaces and cause different surface potentials, thereby bringing about inductive charges among the two attached electrodes (Su et al., 2014a ; Zhu et al., 2014a ; Chen et al., 2015a , b ). Then the inductive electrons are driven to flow between two electrodes via an external circuit to fulfill the conversion process from mechanical energy to electricity (Niu et al., 2013a , b , 2014a ; Chen et al., 2015c ; Niu and Wang, 2015 ; Zi et al., 2015b ). The output performance of TENGs is determined by the triboelectric charge density on the triboelectric material surfaces (Dharmasena et al., 2018 ). Thus, increasing the triboelectric charge density is the most fundamental strategy for building high-performance TENGs. Considerable efforts have been made to increase the triboelectric charge density of TENGs, including proper triboelectric materials selection (Zenkiewicz et al., 2015 ; Zhao et al., 2015 ; Kim et al., 2017 ; Lee et al., 2018 ), advanced device structural design (Bai et al., 2013b ; Lin L. et al., 2013 ; Wang et al., 2013 ; Yang et al., 2013a ; Zhang H. et al., 2014 ; Deng et al., 2020 ), and triboelectric materials surface physical/chemical modifications (Lin et al., 2009 ; Lin Z. H. et al., 2013 ; Niu et al., 2014b ; Jing and Kar-Narayan, 2018 ; Zhou Y. et al., 2020 ). The surface physical modification is primarily realized via material morphological manipulation. Namely, increasing the effective friction area through incorporating surface micro-/nano-structures (Jeong et al., 2014 ; Kim et al., 2015 ; Feng et al., 2016 ; Wang et al., 2017 ), such as nanowires (Zheng et al., 2014 ; Lin et al., 2015 ), nanoparticles (Zhu et al., 2013b ) and other nanoscale patterns (Zhang et al., 2013 ; Lee et al., 2014 ; Choi et al., 2015 ; Dudem et al., 2015 ). Furthermore, manipulating the surface chemistry of the friction layers through chemical modifications and consequent changes in surface potentials will enlarge the polarity between the two friction surfaces therefore contributing to the high-performance of TENGs (Wang S. et al., 2016 ). This review systematically reports the current advances in surface chemistry for high-performance TENGs. As shown in Figure 1 , the chemical modification methods can be summarized and classified into four categories: functional groups grafting, ion implantation and decoration, dielectric property engineering, and functional sublayers insertion. In addition, this review provides a critical analysis of surface chemistry for TENG and insights into remaining challenges and future directions. With worldwide efforts in innovations in chemistry and materials elaborated in this review, the frontiers of high-performance TENGs will be pushed forward, which could offer the era of Internet of Things a compelling pervasive energy solution. Figure 1 Surface chemistry for high-performance triboelectric nanogenerators. Reprinted with permission from Shin et al. ( 2017 ). Copyright 2017 American Chemical Society. Reprinted with permission from Shin and Kwon ( 2015 ). Copyright 2015 American Chemical Society. Reprinted with permission from Wang et al. ( 2014 ). Copyright 2014 Wiley-VCH. Reprinted with permission from Park et al. ( 2017 ). Copyright 2017 WILEY-VCH. Reprinted with permission from Yu et al. ( 2015 ). Copyright 2015 Wiley-VCH. Reprinted with permission from Seung et al. ( 2017 ). Copyright 2017 Wiley-VCH. Reprinted with permission from Lai et al. ( 2018 ). Copyright 2018 American Chemical Society. Reprinted with permission from Cui et al. ( 2018 ). Copyright 2018 American Chemical Society."
} | 4,124 |
25699064 | PMC4318275 | pmc | 490 | {
"abstract": "Environmental pollutants have received considerable attention due to their serious effects on human health. There are physical, chemical, and biological means to remediate pollution; among them, bioremediation has become increasingly popular. The nitrogen-fixing rhizobia are widely distributed in the soil and root ecosystems and can increase legume growth and production by supplying nitrogen, resulting in the reduced need for fertilizer applications. Rhizobia also possess the biochemical and ecological capacity to degrade organic pollutants and are resistant to heavy metals, making them useful for rehabilitating contaminated soils. Moreover, rhizobia stimulate the survival and action of other biodegrading bacteria, thereby lowering the concentration of pollutants. The synergistic action of multiple rhizobial strains enhances both plant growth and the availability of pollutants ranging from heavy metals to persistent organic pollutants. Because phytoremediation has some restrictions, the beneficial interaction between plants and rhizobia provides a promising option for remediation. This review describes recent advances in the exploitation of rhizobia for the rehabilitation of contaminated soil and the biochemical and molecular mechanisms involved, thereby promoting further development of this novel bioremediation strategy into a widely accepted technique.",
"conclusion": "CONCLUSION Rhizobia have been recognized as a potential strategy to simultaneously enhance soil nitrogen content, reduce the use of fertilizers, and increase H 2 concentration (hydrogen fertilizers) in the rhizosphere through symbiotic nitrogen fixation. Rhizobia also possess the biochemical and ecological capacity to degrade environmental organic chemicals and to decrease the risk associated with metals and metalloids in contaminated sites. Rhizobia-assisted phytoremediation provides further environmental and economic benefits for bioremediation. The exploitation of microbe–microbe or plant–microbe interactions between intra-species and inter-species communication in the rhizosphere could represent more integrative approaches to further facilitate bioremediation. Researchers have proposed that the wide adoption of these biological adaptation strategies would result in the development of environmentally friendly management techniques (i.e., biological carbon sequestration, bioenergy, and bioremediation: the “3B” technique) to further enhance biodiversity and relieve environmental stressors ( Teng et al., 2012 ). Symbiotic nitrogen fixation and the reductive dechlorination of organic pollutants are both oxygen-sensitive and energetically costly for rhizobia ( Morris and Schmidt, 2013 ). The presence of leghemoglobin maintains the low O 2 concentrations in the root nodules (within the nanomolar range) and protects nitrogenase from inhibition by O 2 ( Becker et al., 2004 ). Therefore, researchers have proposed that the micro-oxic environment formed in nodules might also provide the proper conditions for the reductive degradation of organic pollutants. Moreover, Rhizobium sp. have a demonstrated capacity for partial denitrification in soils; some strains have been associated with nitrate reductase (NAR) activities, especially under micro-oxic conditions ( Streeter and DeVine, 1983 ). Some reports have provided evidence of NAR-mediated dechlorination of PCB153 in free-living cells of Phanerochaete chrysosporium and crude enzyme extracts from Medicago sativa leaves, even under aerobic conditions ( De et al., 2006 ; Magee et al., 2008 ). O’Hara et al. (1983) reported that S. meliloti possessed denitrifying activities in both its free-living and symbiotic forms. However, Becker et al. (2004) only found two genes ( nir V and nor B) that were induced in the S. meliloti bacteroids ( Becker et al., 2004 ). Therefore, the involvement of metabolic enzymes in the potential degradation of organic compounds in rhizobia requires further study. Our understanding of the genetic and molecular influences of bioremediation effects is not complete, and the goal of transforming this strategy into practice has not yet been fully achieved. The suitable selection of rhizobial strains or consortia in combination with plant hosts, indicators of successful bioremediation under field conditions and the mechanisms involved constitute future work that should be pursued for the initiation of successful efforts in this area. The successful execution of this versatile bioremediation strategy also requires a thorough understanding of the factors regulating the growth, metabolism, and functions of degradative rhizobia and indigenous microbial communities at contaminated sites. Furthermore, to date the major work of the “Rhizobial bioremediation” field has been mostly conducted under controlled laboratory conditions and not in the field, where further practical investigations and testing are required before bioremediation can become a widely accepted technique. The selection of suitable rhizobial strains will be necessary for the remediation of certain polluted sites. In conclusion, this review provides a comprehensive framework for applying the versatile rhizobia to revitalize contaminated soils. The selective introduction of degradative rhizobia into hyperaccumulator plants could facilitate the accelerated removal of mixed pollutants from soils. Using this approach, the exploitation of these degradative, nitrogen-fixing and endophytic pollutant-cleaners could become a highly efficient, eco-friendly and low-input bioremediation technology for the future.",
"introduction": "INTRODUCTION Essential planetary functions such as primary production, the earth’s climate, biogeochemical and water cycling, and the maintenance of biodiversity have been severely undermined by anthropogenic activities ( Teng et al., 2012 ; Alloway and Trevors, 2013 ; Valentín et al., 2013 ). Approximately 30% of the terrene environment is estimated to be degraded or contaminated, threatening agricultural production, and the environment ( Alloway and Trevors, 2013 ; Valentín et al., 2013 ). In addition to contemporary pollutants such as heavy metals, hydrocarbons, and pesticides, a new generation of persistent organic pollutants (POPs) such as polybrominated diphenyl ethers (PBDEs), polychlorinated naphthalenes (PCNs), and perfluorooctanoic acid (PFOA) require urgent attention ( Lohmann et al., 2007 ). Thus, there have been intensive studies investigating physico-chemical processes and bioaugmentation for their exploitation in multipurpose remediation technologies. Although physico-chemical treatments (i.e., physical removal of contaminated soils, chemical extraction, and the application of chemical reagents) are still the most effective strategies to rapidly remediate heavily polluted sites, they are usually energy-intensive and intrusive for the environment ( Segura and Ramos, 2013 ). In contrast, the less energy-demanding bioremediation utilizes living organisms and/or their bioproducts to clean up or stabilize inorganic/organic contaminants from the environment. Therefore, bioremediation is a promising alternative due to its relative low-level disturbance of contaminated sites, low cost and higher public acceptance compared with conventional remediation methods. Among the various types of bioremediation, phytoremediation is an environmentally friendly and cost effective approach that provides intangible benefits for the soil ecosystem, including soil carbon sequestration, soil quality improvement, biomass and biofuel production, and biodiversity maintenance ( Rajkumar et al., 2012 ). Legumes are essential for nitrogen cycling in agriculture due to their symbiosis with the nitrogen-fixing rhizobia. Many reports have noted that some leguminous species are heavy-metal resistant and can significantly promote the dissipation of organic pollutants [i.e., polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and amide herbicides; Fan et al., 2008 ; Hamdi et al., 2012 ; Carrasco-Gil et al., 2013 ; Li et al., 2013 ]. Intercropping of multiple leguminous plants may also become a promising in situ bioremediation strategy for contaminated sites ( Sun et al., 2011a ; Li et al., 2013 ). The symbiosis between microorganisms and plants has been employed for the elimination of environmental contaminants to achieve high effectiveness and ecological sustainability. The effectiveness of phyto- or microbial-remediation is dependent on: (i) soil physio-chemical properties, such as pH, nutrient/organic matter content, soil surface properties, soil texture and bulk densities that influence plant–soil–water relationships and nutrient availability; (ii) toxicity or bioavailability of the targeted contaminants that reduce the productivity of the impacted soils, the biomass of plants and the degradative ability of microorganisms; (iii) plant species and traits; and (iv) the diversity and richness of the indigenous soil microbial communities or flora ( Segura et al., 2009 ). However, these limitations can be addressed through the exploitation of the chemical interactions between the plants and the related rhizospheric microbes or endophytes ( Abhilash et al., 2012 ). For the phytoremediation of heavy metals, heavy metal-resistant microbes can enhance plant growth, decrease metal phytotoxicity, and affect metal translocation and accumulation in plants ( Li et al., 2012 ). Rhizobiales, belonging to the alphaproteobacteria, are Gram-negative bacteria of agronomic importance because some species form nitrogen-fixing symbiotic relationships with leguminous plants ( Sato et al., 2005 ). Rhizobia invade the roots of legumes and form nodules to fix atmospheric nitrogen into ammonia, which is then provided to the host plants. This activity allows the plants to grow in the absence of an external nitrogen source ( Figure 1 ; Prell and Poole, 2006 ; Deakin and Broughton, 2009 ). Hydrogen (H 2 ) is a by-product of the symbiotic nitrogen fixation process and has recently been revealed to be a common element with novel bioactive properties that enhances plant tolerance to abiotic factors (i.e., oxidative stress and heavy metal toxicity; Cui et al., 2013 ; Jin et al., 2013 ). Because nitrogen is of the utmost importance for agricultural productivity, rhizobia have attained a special position in the field of agriculture as a plant growth promoter. FIGURE 1 Schematic drawing representing the nitrogen-fixing process associated with rhizobia. Rhizobia invade the roots of legumes (i.e., alfalfa) and form nodules. During the process of biological nitrogen fixation in nodules, dinitrogen (N 2 ) is reduced to two ammonia (NH 3 ) molecules by the rhizobial nitrogenase. Hydrogen (H 2 ) is a by-product of the symbiotic nitrogen fixation process. Recently, rhizobia have been demonstrated to be available for the elimination of various types of organic pollutants from the environment, ranging from aromatic to linear hydrocarbons, chlorinated compounds, phenolic compounds, pesticides, and others (Table S1 ). Kaiya et al. (2012) reported that genus Rhizobium was one of the most abundant members of the degrading microcosm in dibenzofuran-contaminated soil. However, the bacterial catabolic enzymes and the pathways involved in the degradation of these compounds are only partially known ( Figure 2 ). In addition to organic compounds, rhizobia have also been shown to have the potential to be a powerful tool for heavy metal bioremediation ( Hao et al., 2014 ). Potential mechanisms involved are: (i) adsorption and accumulation of heavy metals; (ii) microbial secretion of enzymes and bioactive metabolites (i.e., extracellular polymeric substance, siderophores, and organic acids) to lessen their toxicity by altering the redox state of metals and increasing the complexation and bioavailability of metals; these actions can also indirectly aid phytoremediation ( Hao et al., 2014 ); and (iii) microbial volatilization of heavy metals and their transformed products can also facilitate bioremediation, although this process has yet to be identified in rhizobia. FIGURE 2 Schematic representations of the proposed degradation pathways of several organic pollutants in rhizobial strains. Organic pollutants: phenanthrene ( Keum et al., 2006 ), chlorpyrifos ( Jabeen et al., 2014 ), aromatic compounds ( Muthukumar et al., 1982 ), acenaphthylene ( Poonthrigpun et al., 2006 ), and trihaloacetate ( Stringfellow et al., 1997 ). Structures in brackets represent the hypothetical metabolites. Due to copious production of plant biomass in terrestrial ecosystems, microbial symbionts constitute the ‘unseen majority’ during phytoremediation ( Fester et al., 2014 ). The nitrogen-fixing and plant growth-promoting traits of rhizobia directly improve plant biomass, soil fertility, bioavailability of contaminants, the uptake, and translocation of pollutants from soil to plant, and the ability to degrade organic pollutants and indirectly help phytostabilize metals. These traits could help rhizobia overcome the constraints associated with phytoremediation (see Assisted-phytoremediation) and achieve higher efficiency ( Hao et al., 2014 ); therefore, the symbiotic relationship between rhizobia and legumes results in an enhanced removal rate for pollutants ( Glick, 2010 ). In comparison with rhizospheric microorganisms (including non-symbiotic diazotrophs), the balanced and stable endophytic association between rhizobia and their host plants provides a sustainable way to improve the performance of host plants in the context of the plant life cycle ( Li et al., 2012 ). Thus, rhizobial remediation may represent a low-input biotechnology with no need for repeated inoculations of microbial agents. Some rhizospheric microbes capable of removing pollutants cannot survive and achieve bioremediation in the soil environment because they cannot compete with indigenous organisms. Bacterial inoculation agents also have advantages compared with fungal elicitors, such as a short period for culture and elicitation of responses in host plants ( Wang et al., 2015 ). Moreover, beneficial bacteria often trigger relatively weaker defense responses than fungal elicitors, which might facilitate the sustainable and balanced relationships between the bioremediation partners. This might be attributed to the smaller niches that bacteria occupied than fungi ( Wang et al., 2015 ). Another advantage of using rhizobia in organic pollutant bioremediation is that rhizobial nitrogen-fixation removes the limitation caused by nitrogen deficiency in sites. The deposition of hydrocarbon skeletons significantly increases the accumulation of organic carbon in the soil and generates a very high C/N imbalance during bioremediation ( Moreto et al., 2005 ; Dashti et al., 2010 ). The presence of rhizobia can also exert direct or indirect impacts on microbial-degrader communities in the soil, thereby comprehensively facilitating restoration ( Li et al., 2013 ). The mechanisms involved in this process include: (i) improvement of environmental conditions (i.e., pH) and nutrient availability (i.e., nitrogen) and (ii) changes in the amounts and constituents of root exudates due to the enhancement of plant metabolic activities following inoculation with rhizobia ( Johnson et al., 2005 ; Terrence et al., 2011 ). Nevertheless, because hyperaccumulator plants are often limited in their plant-metabolic capacities with toxic contaminants, researchers have proposed that in most cases it is the plant-associated microorganisms that are the real players mediating the plants’ impacts on the completed transformation of contaminant ( Fester et al., 2014 ). In this review, we will introduce the latest findings in the field of bioremediation by rhizobia and summarize the knowledge of the restoration mechanism and modified strategies for rhizobia involved in ecosystem revitalization in contaminated sites."
} | 3,993 |
29075481 | PMC5654505 | pmc | 491 | {
"abstract": "We present a nanostructured “super surface” fabricated using a simple recipe based on deep reactive ion etching of a silicon wafer. The topography of the surface is inspired by the surface topographical features of dragonfly wings. The super surface is comprised of nanopillars 4 μm in height and 220 nm in diameter with random inter-pillar spacing. The surface exhibited superhydrophobicity with a static water contact angle of 154.0° and contact angle hysteresis of 8.3°. Bacterial studies revealed the bactericidal property of the surface against both gram negative ( Escherichia coli ) and gram positive ( Staphylococcus aureus ) strains through mechanical rupture of the cells by the sharp nanopillars. The cell viability on these nanostructured surfaces was nearly six-fold lower than on the unmodified silicon wafer. The nanostructured surface also killed mammalian cells (mouse osteoblasts) through mechanical rupture of the cell membrane. Thus, such nanostructured super surfaces could find applications for designing self-cleaning and anti-bacterial surfaces in diverse applications such as microfluidics, surgical instruments, pipelines and food packaging.",
"conclusion": "4 Conclusion A super surface was prepared in this study that is inspired by the nanostructures present on insect wing surfaces. The super surface is shown to exhibit self-cleaning and superhydrophobic properties in nature. It is a super killer surface that kills both bacterial and mammalian cells by mechanical rupture. Such a super surface may find use in different applications requiring antifouling and self-cleaning properties.",
"introduction": "1 Introduction “Super surfaces” with a range of exceptional properties are researched in many areas of science. There is particular interest in the field of medicine given the stringent demands of the healthcare sector. Different areas in healthcare such as biosensing, 1 drug delivery, 2 biomaterials and implants, 3 therapeutics, 4 and medical devices and instruments 5 utilize the smartness of nanomaterials which exhibit self-healing, 6 self-cleaning, 7 superhydrophobic 8 and antibacterial activity. 9 Some of the most widely sought after properties include self-cleaning, superhydrophobicity and antibacterial activity. 10 – 13 These properties could be either triggered by some external stimuli or may be inherently present as is observed in some nanostructured surfaces in nature. Surfaces that exhibit more than one such property can be called “super surfaces”. Several strategies have been developed in an attempt to engineer surfaces with antibacterial activity to minimize biofouling. 9 , 12 , 13 Antibacterial surfaces are classified as either antibiofouling or bactericidal surfaces based on the underlying mechanism. Antibiofouling surfaces repel the attachment and proliferation of the bacteria through unfavorable conditions present on the surface whereas bactericidal surfaces kill the bacterial cells by inactivating them mainly through chemical mechanisms. 9 In order to eliminate the attached bacteria or inhibit the biofilm formation on the surfaces, new fabrication techniques have been devised and design improvements on the existing antibacterial surfaces have been proposed in the form of surface coating, surface chemical modification, and control of surface architecture. 9 , 14 Surface coatings and chemical modifications have been further characterized into surface polymerization, functionalization and derivatization. 9 However, the surface coatings or modifications have several significant drawbacks; firstly, the emergence of bacterial resistance against the antibiotics or antibacterial agents; secondly, the surface coatings or antibacterial agents can take a long time to leach from the surface; thirdly, the concentration of the surface coatings or antibiotics is limited and may not be maintained at optimum level to provide effective antibacterial activity over sustained periods of time; and, lastly, the durability of the surface may not be long enough to maintain the antibacterial activity. 9 , 15 – 19 Emerging strains of antibiotic resistant superbugs pose a serious biomedical challenge. Discovery of new antibiotics are infrequent with a recent report after a span of nearly 30 years. 20 Biomimicry can offer innovative and alternative solutions to overcome such challenges. The nano-architecture on surfaces of insect wings may offer an excellent platform for the design of super surfaces. Since both insect wings and prokaryotes are known to co-exist and evolve since millions of years, it is evident that the bactericidal insect wing nanostructures are able to consistently rupture the bacterial cells without encountering bacterial resistance in contrast to the chemical based antibacterial mechanisms. Two known nanostructured insect wing surfaces are those of cicada and dragonfly. The surface of such wings have been known to be self-cleaning, super-hydrophobic and antibacterial in addition to perhaps many as yet unexplored characteristics. 21 , 22 There have been a few attempts to fabricate surfaces that mimic at least some of the unique properties of the insect wing surfaces. 11 , 23 Whereas the antibacterial properties have been replicated in some recent studies, surfaces that are super-hydrophobic and yet bactericidal have not been accomplished. 21 , 24 Moreover, the interactions of such a super surface with mammalian cells is not reported precluding its potential application in design of surfaces for biomedical implants. The objective of this study was to engineer a super surface mimicking the superhydrophobic, self-cleaning and antibacterial characteristics of the surface of dragonfly wings through a facile fabrication technique. Deep reactive ion etching (DRIE) technique was utilized in this study because of its ease of use, high throughput and cost effectiveness when compared with other nanofabrication techniques. The water wettability and bacterial response to the surface was characterized. The cytocompatibility of the super surface generated was also evaluated.",
"discussion": "3 Results and discussion The nanostructured silicon surface ( Fig. 1 ) was fabricated by a simple DRIE technique or the Bosch process. In the last decade, the DRIE process has evolved as one of the widely used fabrication technique in the micro-electro mechanical system (MEMS) industry. 31 It is mainly used to produce high-aspectratio silicon surfaces for designing the semiconductors and photovoltaics materials. 32 Interestingly, the DRIE processes have also been used in some biological applications very recently. 21 , 33 Here, the silicon nanostructures are 4 μm tall and 220 nm in diameter with extremely sharp peaks. The width of the peak varies between 10–20 nm. The etched nanopillars are not smooth on its walls and exhibit a usual scalloping pattern because of the consecutive etching and passivation steps using SF 6 and C 4 F 8 gases, respectively ( Fig. 1A ). 34 Due to the high aspect ratios, the etched silicon sometimes is antireflective and turns black in color after the etching and is therefore termed as ‘black silicon’. Black silicon has been used for solar cell applications. 35 The nanostructured surface is superhydrophobic in nature with a static water contact angle of 154.0° ± 2.3° ( Fig. 1B ). The contact angle hysteresis (CAH) value of the nanostructured surface is 8.3° with advancing and receding values at 154.3° and 146.0°, respectively. This indicates that the superhydrophobic surface has a low adhesion and is thereby self-cleaning. The CAH is closely related to the roll-off angle and CAH values less than 10.0° are termed self-cleaning in nature. 10 , 11 The surface free energy, calculated by the van Oss–Chaudhury–Good method, was calculated to be 18.8 mJ m −2 ( Table 1 ). The surface energy value is significantly lower in comparison to the control surface and other reported surfaces further confirming the low adhesive nature of the super surface prepared herein. 36 – 39 In order to understand the chemical composition of the super surface, EDX analysis of the nanostructured surface was performed. The presence of fluorine, oxygen and carbon along with silicon is evident that may be attributed to the use of the etching and passivating gases during fabrication ( Fig. 1C and ESI, Table S1† ). The silicon control surface exhibited only the presence of silicon and carbon (ESI, Fig. S1 and Table S2† ). It is widely believed that it is difficult to achieve superhydrophobicity and self-cleaning property due to nanoscale topography alone and hierarchical roughness is essential to impart these properties. 40 , 41 However, there are a few natural surfaces that exhibit superhydrophobicity and self-cleaning ability based only on their nanotopography such as insect wings. 21 , 22 , 42 – 44 Recently, the self-cleaning property of a nanostructured surface has been demonstrated by the jumping mechanism rather than the usual sliding or tilt angle measurement of the water droplet. 44 Here, due to the superhydrophobicity and low adhesion, the surface exhibits a similar behavior as the cicada wing 44 as the water droplet was unable to adhere and rolled off the surface (ESI, Video S1† ). The superhydrophobicity of the nanostructured surface is also facilitated by the C 4 F 8 gas discharges during the fabrication stage that deposits a hydrophobic Teflon-like (polytetrafluoroethylene, PTFE) passivation layer on the silicon material 45 , 46 as confirmed by the EDAX measurements ( Fig. 1C ). Thus, a combination of chemical composition of the outer layer and the nanostructured topography imparts superhydrophobicity and self-cleaning ability to this super surface in contrast to other such surfaces based on multi-scale topography. Bacterial attachment studies were performed to test if the nanostructured superhydrophobic surfaces exhibit any antibacterial activity. Viability of E. coli and S. aureus were tested as model Gram negative and Gram positive strains on contact with the nanostructured surfaces. The bacterial cell response was assessed using a number of measures. The fate of adhered cells was evaluated by electron and fluorescence microscopy. Independently, the fate of cells in suspension in contact with the nanostructured surface was also measured. SEM images reveal that the nanopillars stretch the bacterial cells of both strains to the limit until they are ruptured ( Fig. 2 ). A similar observation was reported for cicada wings that stretch the membrane of Gram-negative rod shaped cells. 22 , 47 Interestingly, herein even the thicker membranous coccoid-shaped cells are seen to be ruptured in the same manner of stretching ( Fig. 2B ). It seems that the spherical shaped cells exhibit morphological deformations in the same manner as they are ruptured by the previously studied black silicon material. 21 However, here the stretching of the membrane and the cellular disruption is to a markedly greater extent and evident in both the cellular strains ( Fig. 2 ), when compared from the black silicon material reported previously. It appears that the cells whilst trying to adjust in the nanostructured surface are captured by the individual nanopins as the nanopins hold on to the cellular membrane. As the cells further move due to their motile behavior, the cells stretch themselves while few boundaries are still held by the nanopins. Upon reaching the limit of stretching, the cells are no longer able to survive and are therefore ruptured and killed. The intact bacterial cells attached to smooth control surfaces are shown in ESI ( Fig. S2 and S3† ) exhibiting intact morphology. These results are consistent with the previous bactericidal reports of wings and wing inspired studies. 21 , 22 , 47 To test the killing efficiency of the nanostructured surfaces, live and dead adhered cells were counted using the fluorescent microscopy images. Of the total number of the bacterial strains attached on the nanostructured surfaces, 86% of S. aureus and 83% of E. coli cells were found to be non-viable after 3 hours of incubation. In contrast, on the control surfaces, cell viability was high with the non-viable fraction of only 11% of S. aureus and 13% of E. coli strains ( Fig. 3 ). The over six-fold increase in the killing rate demonstrates the super killing nature of the nanostructured surfaces compared to the control smooth surfaces. However, this anti-bacterial activity was observed to be time dependent with only a quarter of the cells killed in 5 min. This fraction increased to >60% by 30 min. Thereafter, the fraction of dead cells increased with time to >80% by 3 h. Thus, approximately 30 min of contact with the super surface is required to induce inactivity of a substantial fraction of bacterial cells. In order to further confirm the inactivity of the bacterial cells on the nanostructured surfaces, the absorbance values of the cell suspension was measured after incubation of 1 min, 5 min, 10 min, 30 min, 1 h, 3 h and 5 h. The suspensions for either strains exhibit lower absorbance compared to the smooth silicon surface control and negative control (no surface) suggesting reduced cell viability (ESI, Fig. S4 and S5† ). The growth of the bacterial cells was also examined over a period of 30 h when the adhered cells on the nanostructured and control surface were allowed to grow in the nutrient media. The growth of both the strains in the nutrient medium containing the control surface was higher than the growth on the nanostructured surface further corroborating the antibacterial property of the nanostructured surface (ESI, Fig. S6 and S7† ). The inactivity of bacterial cells were also assayed by the plate count method. The number of colonies of both the cell types in suspension were significantly reduced on the nanostructured surfaces compared to the silicon surfaces (ESI Fig. S8† ). Thus, all measures of bacterial response confirmed the excellent bactericidal property of the super surface fabricated herein mediated by mechanical rupture of the cell membrane. This is in sharp contrast to other strategies based on the use of micro/nano structured surfaces that aim to reduce bacterial infection by minimizing cell adhesion. 48 , 49 Toward exploring the potential utility of such a super surfaces for use on biomedical implants such as prosthetic joints, stents, and fracture fixation devices, etc. , the cytocompatibility of the surface was evaluated. The viability and morphology of mouse osteoblasts on the surfaces was characterized. SEM and fluorescent micrographs indicate mechanical disruption of the membrane and low viability of cells on the super surface ( Fig. 4 and 5 ). On the control surfaces, the cells were well spread and viable unlike the nanostructured surface where the excessive stretching of the cells on the nanopillars eventually led to the cell rupture and death. Live/dead staining confirmed that cell viability was only 12% on the nanostructured surfaces ( Fig. 5 ). Few reports have shown that different kinds of mammalian cells can proliferate on vertical nanostructured surfaces. 50 – 52 However, the nanostructures on these reports differ in the aspect ratio and the close-packing when compared to this study. Moreover the bacterial attachment experiments were not reported. In a recent study, cicada wing inspired nanowired surfaces were fabricated that discriminate between the bacterial cells and osteoblasts such that the bacterial cells were significantly reduced whereas the osteoblasts were shown to adhere and proliferate. 24 In the reported study, the nano-features were not vertically aligned when compared with the natural cicada wing nanoarchitecture. 22 Also, because of the brush type or individual nanowires, the surface was unable to lyse the mammalian cells but in due course the antibacterial efficiency was also compromised as the nanowires were unable to pierce the Gram positive S. aureus cells. Nevertheless, there is a growing interest in the fabrication of vertical nanostructures for biological applications 53 and leading to optimal designs for super surfaces. Taken together, the findings of this study suggest that the super surface prepared herein exhibits superhydrophobicity and self-cleaning properties along with super killing properties for both bacterial and mammalian cells. Thus, such a super surface will be well suited for a variety of anti-bacterial surfaces but not on biomedical implants requiring intimate contact with mammalian cells in vivo . However in the field of medicine, the potential of such super surfaces extends to surgical instruments, tubing, and diagnostics tools, etc. to maintain ultraclean, dry and aseptic conditions. 5 In addition, such super surfaces can be utilized for food packaging and microfluidics where contamination, dust, dirt, moisture and infections are serious concerns. This study proposes the functionality and potential utility of such nanostructured surfaces. Further work is required to develop strategies to engineer such super surfaces on materials used to fabricate instruments and products for biomedical use."
} | 4,289 |
33643237 | PMC7905023 | pmc | 493 | {
"abstract": "To better predict the consequences of environmental change on aquatic microbial ecosystems it is important to understand what enables community resilience. The mechanisms by which a microbial community maintain its overall function, for example, the cycling of carbon, when exposed to a stressor, can be explored by considering three concepts: biotic interactions, functional adaptations, and community structure. Interactions between species are traditionally considered as, e.g., mutualistic, parasitic, or neutral but are here broadly defined as either coexistence or competition, while functions relate to their metabolism (e.g., autotrophy or heterotrophy) and roles in ecosystem functioning (e.g., oxygen production, organic matter degradation). The term structure here align with species richness and diversity, where a more diverse community is though to exhibit a broader functional capacity than a less diverse community. These concepts have here been combined with ecological theories commonly used in resilience studies, i.e., adaptive cycles, panarchy, and cross-scale resilience, that describe how the status and behavior at one trophic level impact that of surrounding levels. This allows us to explore the resilience of a marine microbial community, cultivated in an outdoor photobioreactor, when exposed to a naturally occurring seasonal stress. The culture was monitored for 6weeks during which it was exposed to two different temperature regimes (21 ± 2 and 11 ± 1°C). Samples were taken for metatranscriptomic analysis, in order to assess the regulation of carbon uptake and utilization, and for amplicon (18S and 16S rRNA gene) sequencing, to characterize the community structure of both autotrophs (dominated by the green microalgae Mychonastes ) and heterotrophs (associated bacterioplankton). Differential gene expression analyses suggested that community function at warm temperatures was based on concomitant utilization of inorganic and organic carbon assigned to autotrophs and heterotrophs, while at colder temperatures, the uptake of organic carbon was performed primarily by autotrophs. Upon the shift from high to low temperature, community interactions shifted from coexistence to competition for organic carbon. Network analysis indicated that the community structure showed opposite trends for autotrophs and heterotrophs in having either high or low diversity. Despite an abrupt change of temperature, the microbial community as a whole responded in a way that maintained the overall level of diversity and function within and across autotrophic and heterotrophic levels. This is in line with cross-scale resilience theory describing how ecosystems may balance functional overlaps within and functional redundancy between levels in order to be resilient to environmental change (such as temperature).",
"conclusion": "Conclusions Responses within a PBR with a mixed community of both microalgae and bacteria, when faced with changed environmental conditions, suggest that interlevel interactions, decoupling function and taxonomy, have a strong impact on the resilience of the system. The two-level system shifted from coexistence, with separate resource niches (inorganic carbon for microalgae and organic carbon for bacteria), to competition for organic carbon, with overlapping resource niches (where both microalgae and bacteria utilized organic carbon), when relieved from temperature stress. By analyzing these results with resilience theory sensu \n Holling (1973) , cross-scale resilience and modern coexistence theory we may describe the mechanisms by which this system of medium complexity adapted to temperature stress through overlapping functional diversity within and functional redundancy across levels. Knowledge about these mechanisms may help improve studies related to environmental change through improved models of aquatic microbial ecosystems, and their behavior when faced with environmental perturbations.",
"introduction": "Introduction Microorganisms make up ≈70% of the aquatic biomass and their interactions in the microbial loop are vital for the recycling of energy and nutrients that ensure the ecosystem services provided by aquatic food webs ( Azam et al., 1983 ; Bar-On et al., 2018 ). In addition, aquatic microorganisms contribute ≈50% of the O 2 in the atmosphere today ( Field et al., 1998 ; Behrenfeld et al., 2001 ). The impact of current and predicted environmental changes on aquatic microorganisms, including the increasing sea surface temperatures ( Collins and Knutti, 2013 ), is difficult to assess due to the lack of studies using high-resolution molecular methods of microbial community interactions. The ability of aquatic microbial ecosystems to be resilient to disturbances, on shorter or longer scales, depends on the interplay of multiple factors ( Allison and Martiny, 2008 ; Shade et al., 2012a ). Identifying the behavior of key resilience mechanisms in response to changed environmental conditions may lead to more accurate predictions of the effects of environmental changes on biogeochemical cycles. Such knowledge could for instance enable the implementation of more locally adapted monitoring and management programs of aquatic microbial ecosystems ( Bernhardt and Leslie, 2013 ; Andersson et al., 2015 ). Several studies have suggested that the functional capabilities of experimental microbial ecosystems, and thus their resilience, were not found to be related to the composition of the communities ( Fernandez et al., 2000 ; Wang et al., 2011 ; Vanwonterghem et al., 2014 ; Louca and Doebeli, 2016 ), which might be explained by the large functional redundancy and diversity that exists among microbial species ( Louca et al., 2017 , 2018 , 2020 ). Microbial ecosystems are complex and consist of several interacting levels, such as trophic levels, that enable the transfer of energy and nutrients within the microbial loop and further up in the food web. Adaptations to changed conditions seen at one level likely have an influence on the levels above or below ( Gunderson and Holling, 2002 ). Thus, in order to gain a deeper understanding of the underlying mechanisms of the resilience of microbial communities, it is important to link experimental results with theories. In this study, we focused on three interlinked mechanisms that together have the potential to influence microbial ecosystem resilience in response to changed environmental conditions: biotic interactions, functional adaptations, and community structure. Interactions between organisms in microbial ecosystems are commonly described through the presence or absence of the exchange of signals or metabolites, including mutualistic, parasitic, or neutral relationships ( Tipton et al., 2019 ). Here, the focus is on broad-scale community interactions, disregarding any potential microalgal-bacterial cooperation apart from that that involves carbon. Broadly, the considered interactions may primarily be characterized by either coexistence, governed by resource partitioning ( Sörenson et al., 2020 ), or by competition for energy and nutrients, which may lead to competitive exclusion ( Schoener, 1974 ; Chesson, 2000 ). Both types of interactions influence biogeochemical cycles, e.g., that of carbon, through potential functional changes and variations in microbial community structure ( Lindh and Pinhassi, 2018 ; Sörenson et al., 2020 ). Functions relevant for studies of aquatic microbial ecosystems commonly relate to whether organisms are autotrophs, heterotrophs, or mixotrophs, which is defined by the type of carbon (inorganic, organic, or both) they have the capacity to acquire as a food source and to use for energy production. Temporal dynamics in the structure of a community relate to its species richness or diversity, in which a more diverse community is characterized by a more efficient use of resources compared to a less diverse community that likely have a more narrow functional range ( Cardinale et al., 2006 ; Ptacnik et al., 2008 ). In resilience theory, the term panarchy has been used together with adaptive cycles and cross-scale resilience theories to describe the sustainability of both social and ecological systems ( Holling, 1973 ; Peterson et al., 1998 ; Gunderson and Holling, 2002 ). Adaptive cycles postulate four phases that a system continuously pass through: birth – growth and accumulation of resources ( r ), maturation – conservation of established processes ( K ), death – the release upon changed conditions ( Ω ), and renewal – the creative phase of reorganization and adaptation to new conditions ( α ; Holling, 1973 ). Panarchy describes how separate levels within an ecosystem, each with their own adaptive cycle, interact in order to accommodate and adapt to changed conditions. Where lower levels, primarily when entering the Ω-phase, influence the level above (termed revolt) while the upper levels, primarily during the K-phase, are able to buffer the impact (termed remember), and thereby the levels together affect the community resilience ( Gunderson and Holling, 2002 ). Cross-scale resilience describes how ecosystems may become resilient by balancing overlapping functional diversity within and functional redundancy across levels ( Peterson et al., 1998 ). In this study, levels are interpreted as trophic levels. Ecosystem resilience may be explained as the capacity to harbor, through internal fluctuations of function and structure, smaller or larger environmental changes ( Holling, 1973 ), while maintaining over-all function, structure, and identity ( Walker, 2004 ). The capacity of aquatic microbial ecosystems to respond in a resilient manner to the regime shifts in, e.g., temperature that might be the result of present and future climate change is difficult, by important, to assess ( O’Gorman et al., 2012 ). Currently, few studies have empirically investigated resilience within aquatic microbial ecosystems (e.g., Shade et al., 2012b ; Lindh and Pinhassi, 2018 ). For the coastal regions of Scandinavia projected environmental changes are increasing temperature, precipitation, land run-off, and ocean acidification ( Collins and Knutti, 2013 ). Coupling analyses of the responses in controlled and simplified ecosystems to environmental change, in terms of structural and functional dynamics together with analyses of the impact on community interactions, with established ecological theories, models of aquatic ecosystem responses to climate change may be improved ( Prosser and Martiny, 2020 ). Using model systems with only a few species and controlled conditions in a laboratory help to gain a regulatory mechanistic insight of microbial interactions at the detailed level ( Segev et al., 2016 ; Bolch et al., 2017 ). It is, however, important to study more complex, yet simplified systems, with several interacting levels, as ecosystem responses to environmental change, depend on the response at each contained level ( Gunderson and Holling, 2002 ). Thus, systems of medium complexity, with several interacting functional groups (auto-, hetero-, and mixotrophs), kept under controlled nutrient conditions and influenced by a few environmental parameters, will help in predicting the consequences of environmental change on microbial communities and the impact of this on larger scale biogeochemical cycles ( Otwell et al., 2018 ). In the present study, an algal polyculture kept in an outdoor photobioreactor (PBR), with a capacity to produce up to 0.88gl −1 biomass per day ( Supplementary Figure S1 ), was investigated. The PBR community, composed of a few naturally selected microalgae species, dominated by a mixotrophic green microalgae (with the ability to utilize both inorganic and organic carbon), and a mixed, naturally established, bacterial community, was provided with inorganic carbon, and studied under two different temperature conditions (warm/cold). As the availability of light influence the efficiency of photosynthesis and uptake of carbon, this was also studied in addition to temperature as a potential structuring factor. The aim of the study was to elucidate the effect that changes in temperature regimes have on microalgae-bacteria interactions, by focusing on the functional regulation in the acquisition of carbon (organic and/or inorganic) and on the impact of this regulation on the dynamics of community structure. Further, we wanted to investigate the influence of interlevel interactions on the resilience of the community, in terms of maintained production of microalgal biomass. Analyses of community structural dynamics were made by generating amplicon sequencing data and using co-occurrence network analysis. Analyses of the functional regulation in the acquisition of carbon by the PBR community were made using a metatranscriptomic approach. The capacity of the microbial community for resilience was investigated using adaptive cycles, panarchy, and cross-scale resilience theories.",
"discussion": "Discussion It is of importance to increase our understanding of how microbial communities respond to environmental change. This can be achieved by revealing the mechanisms these communities use either to maintain their function, structure, and identity through internal adaptations or use to reform into a new type of system with new functions, structure, and identity. For aquatic microbes, this is relevant both with regard to the ecosystem services that they provide and to the impact that these changes might have on the biogeochemical cycling of nutrients in aquatic ecosystems ( Daufresne and Loreau, 2001 ; Zell and Hubbart, 2013 ). The results from the present study illustrate how a PBR microbial community regain its ability to produce biomass at high capacity after having been exposed to temperature stress (during the exceptionally hot summer of 2018, 3.5°C above normal; Swedish Meteorological and Hydrological Institute), i.e., is able to respond in a resilient manner ( Figure 9 ; Levin and Lubchenco, 2008 ; Feng et al., 2017 ). The underlying mechanisms behind this behavior are suggested to be regulated by dynamic interlevel shifts in both community structure and function, ultimately leading to interactions between eukaryotes (microalgae) and prokaryotes (bacteria) going from coexistence to competition, as seen in the regulation of uptake and utilization of organic carbon. Despite being exposed to shifts both in temperature and light, the shift in temperature was found to be the most influential structuring factor of both community structure and function ( Figures 2 , 8 ). Figure 9 Conceptual model of the impact in microalgae-bacteria interactions induced by temperature stress (A) . Less CO 2 got incorporated (1) while microalgal excretion of organic C (OC) was utilized by bacteria (2), leading to a higher diversity of the microalgae (3) and a lower diversity of the bacterial community (4). Resulting in coexistence (5), due to the partitioning of carbon resources (6). The release from temperature stress (B) introduced more CO 2 to the system resulting in a higher accumulation of microalgal biomass (7), while less OC got excreted (8), leading to a lower diversity of the microalgae, being dominated by one species (9), while the bacterial diversity became higher (10). This resulted in competition between microalgae and bacteria for organic carbon (11), due to mixotrophic microalgal uptake of both CO 2 and OC (12). The partitioning of carbon resources is indicated by CO 2 (flue gas) and OC (autochthonously produced carbon; see Discussion for details). During the initial, warmer period, the microalgal growth was repressed, likely by heat stress, resulting in less introduced inorganic carbon through photosynthesis to the system and a significantly ( p < 7.9e-05) lower production of biomass, and excretion of organic carbon by the microalgae ( Figure 9A ). Microalgal responses to abiotic stress, such as heat, include reduction in photosynthesis, as a mechanism to balance cellular energy levels necessary for metabolism ( Biswal et al., 2011 ). Other modifications involve alterations of the cellular membrane, changes in protein and carbohydrate production, increase of cellular antioxidant and scavenge mechanisms, increased DNA-repair, as well as the induction of cell death (reviewed by Barati et al., 2019 ). Microalgae exposed to heat stress for a limited time have been shown to be retarded in growth, both in direct connection to the stress and up to 6h afterwards ( Béchet et al., 2017 ). Thus, the heat stress likely induced both a lower level of photosynthesis and repressed the microalgal growth rate. The heat stress is here suspected to have opened up niches for more microalgal species, thus leading to a significantly higher diversity, p = 0.007, and richness, p = 4.4e-06, of the microalgae population ( Supplementary Figure S5A ). A higher diversity and richness, both in community structure and function, have been suggested to act as stabilizing factors and increase the ability of communities to be resilient to temporary disturbances ( Steiner et al., 2006 ; Downing and Leibold, 2010 ; Loreau and de Mazancourt, 2013 ). Having a broad response diversity, a community could respond rapidly upon an environmental challenge, which could lead to the domination of one or a few species ( Steiner et al., 2006 ). This can be exemplified by the bacterial population in the PBR where a few taxa significantly ( p = 0.001) dominate the community during the warmer period ( Figure 4 and Supplementary Figure S5B ). Thus, the high availability of organic carbon appeared to have led to a lower diversity of the bacterial population structure. During the warm period, there was a partitioning of the carbon resources between microalgae and bacteria, where the microalgae primarily utilized inorganic carbon and the bacteria a range of organic carbon sources, benefitting a few groups such as Phycisphaerales and Cellvibrionales ( Figure 3 ). Representatives from these bacterial groups have previously been found to be associated with algae. Planctomycetal Phycisphaerales was first isolated from the surface of a macroalgae ( Fukunaga et al., 2009 ), and planctomycetal organisms have been found associated with phytoplankton biomass in the Baltic Sea ( Bunse et al., 2016 ). Gammaproteobacterial Cellvibrionales has been shown to assimilate specific organic carbon sources, such as amino acids, glucose, and starch in coastal surface waters ( Bryson et al., 2017 ). Indicating that a close association with an organic carbon-producing microalgae such as Mychonastes could be beneficial to these bacterial groups. During the colder period, the ratio of CO 2 flow to total DW was below two, indicative of carbon limitation ( Herzog and Golomb, 2004 ; Figure 1 , Supplementary Table S7 ), suggesting high uptake of inorganic carbon together with a significantly higher production of microalgal biomass ( p = 7.8e-05), which seemed to reduce the availability of organic carbon for the bacteria ( Figures 6 , 9B ). This is indicated by a lower diversity of the microalgal population structure, favoring Mychonastes ( Figure 7D ), while there was a significant increase in bacterial diversity ( p = 0.03; Supplementary Figure S5B ). The suggested carbon limitation, leading to competition between the two levels could have been influenced by the ability of the dominant microalgal species Mychonastes for both C-fixation and uptake of organic carbon ( Figure 5 ). This dual carbon utilization provides the microalgae with a competitive advantage over the bacteria, which are left to rely on respiration in order to maintain cellular processes ( Figure 6 ). During bacterial respiration, O 2 will be utilized and CO 2 produced, thereby facilitating microalgal photosynthesis. This phenomenon has previously been demonstrated in laboratory co-cultures of microalgae and bacteria ( Mouget et al., 1995 ; Danger et al., 2007 ). Thus, the response of the PBR microbial community upon the two different temperature conditions may have been regulated at two interconnected levels, through function (auto-, hetero-, or mixotrophy) and population structure (increased or reduced diversity), which together affect microalgae-bacteria interactions, going from coexistence to competition ( Figure 9 ). These results suggest that the PBR microbial community, with lower complexity than natural systems, but more complex than 2-3 species model systems, has the ability to respond in a manner to temperature stress, by structural and functional modulations that span across levels, which could be considered as resilient ( Supplementary Figure S1 ). To be resilient a community must not lose its over-all function (production of biomass), deviate from its original level of diversity, or become too different in taxonomic identity ( Walker, 2004 ). As the interactions shift from coexistence to competition the functional guild ( Vanwonterghem et al., 2014 ; Bryson et al., 2017 ) with an organic carbon preference, initially represented by bacteria become represented by both microalgae and bacteria during the colder period. This suggests that the function of organic carbon acquisition is not limited to one level, or taxonomic entity (bacteria), but may cross the inter-level boundary. Thus, the decoupling previously seen in strictly bacterial experimental systems between function and taxonomy ( Fernandez et al., 2000 ; Wang et al., 2011 ; Vanwonterghem et al., 2014 ; Louca and Doebeli, 2016 ; Louca et al., 2020 ) is seen also in our system consisting of two levels. This underlines the importance of interlevel interactions for the ability of a community to maintain its over-all functional capacity, structure, and identity, in order to be able to respond in a resilient manner when faced with the environmental challenge ( Holling, 1973 ). Theoretical Models to Describe Resilience Mechanisms In order to study how interlevel interactions influence community resilience, the adaptive cycle model might be used ( Gunderson and Holling, 2002 ; Walker, 2004 ). This cycle describes four stages that a community are thought to pass at shorter or longer intervals: birth ( r ), maturation ( K ), death ( Ω ), and renewal ( α ; Figure 10 ). Adaptive cycles have been used to describe the seasonal successions of algal blooms in the Baltic Sea ( Angeler et al., 2015 ), but are more commonly applied for describing resilience in socio-ecological systems consisting of nested levels ( Berkes and Ross, 2016 ) within the panarchy theory ( Gunderson and Holling, 2002 ). The rationale behind the panarchy theory is that as ecosystems are made up of multiple and interconnected levels (e.g., autotrophs and heterotrophs), and each level have their own adaptive cycle, adaptations occurring at one level will influence the cycling of surrounding levels. This primarily occurs as a lower level is passing through its death/release phase, Ω , the “window of opportunity” during which it may collapse or start to adjust to changed conditions. If this collapse occurs when an upper level is in its least resilient phase, between birth or maturation, r or K , or in the maturation phase, it will be affected by the impact from below. When this occurs, the upper level may harbor or absorb the impact posed from below. In turn, this absorption affects the renewal/adaptation phase, α , of the lower level impacting the readjustments that are made to face the new conditions ( Figure 10 ; Gunderson and Holling, 2002 ; Walker, 2004 ; Allen et al., 2014 ). When combining these theories with that of cross-scale resilience, that describes ecosystem resilience by functional overlaps and redundancy within and across levels ( Peterson et al., 1998 ; Sundstrom et al., 2018 ), the mechanisms behind the ability of the PBR microbial community to increase its production of biomass after having been exposed to temperature stress may be explained. The cross-scale resilience model has previously been tested to describe resilience for natural ecosystems consisting of avian and mammalian populations ( Wardwell et al., 2008 ) and of lake algae exposed to chemical waste and vertical mixing ( Baho et al., 2019 ). Our study is the first to apply these three theories to explain the resilience of a PBR community. Here, the microalgae, representing the upper level, would – while adapting to the temperature stress during the warmer period – be somewhere in between the death/release, Ω , and renewal/reorganization, α , phases, as indicated by the more diverse microalgal population. While the bacteria – during the warmer period – would be in the steadily growing maturation/conservation, K , phase, as indicated by a lower population diversity ( Figure 10 ). This suggests that the levels during this period are not posing an immediate influence on each other and are coexisting through acquiring different types of carbon ( Figures 5 , 6 ). While, during the colder period, the microalgae would have entered into r phase, becoming more structurally homogenous and starting to express new functions, and the bacteria into Ω phase, becoming less structurally homogenous and functionally less diverse. Leading to that the levels thus are able to have more influence on each other, according to panarchy theory. This is here ultimately represented by the evidence of competition for organic carbon manifested by microalgal expression of hydrolases for acquiring organic carbon and by a higher level of expression of bacterial transcripts associated with respiration than during the warmer period ( Figures 5 , 6 ). These expression patterns are connected to similar but opposing structural and functional adjustments among the two levels ( Figure 9 ), where a significantly more diverse microalgal population is matched by a significantly less diverse bacterial population during the warmer period, and vice versa for the colder period ( Figures 4 , 9 ). Beyter et al. (2016) present a similar pattern in a reactor community of primarily green algae (ITS2) and bacteria (16S), where higher diversity of one coincides with lower diversity of the other during a 1year study. The combined panarchy and cross-scale resilience theories could help explain these opposing responses, saying that the response at one level help balance the response at the other level through functional overlap and redundancy ( Peterson et al., 1998 ; Sundstrom et al., 2018 ). This mechanism would thus enable the maintenance of both the total structural diversity, by balancing the population diversity across the levels, and of the functional overlap in the ability for the acquisition of organic carbon found both among the bacteria and the microalgae ( Figure 10 ; Gunderson and Holling, 2002 ). Thus, by applying these theoretical models, not previously used for this type of system, the regulatory mechanism by which the community responds to temperature stress may be explained ( Figure 9 ). Figure 10 An illustration of adaptive cycles and the concept of panarchy, used to describe the interactions between the microalgae (upper level) and bacteria (lower level), going from coexistence during the warmer (orange) temperature regime to competition during the colder (blue) temperature regime. r – growth phase, K – conservation phase, Ω – release phase, and α – adaptation phase. Remember – impact on lower level by upper level, revolt – influence from lower level on upper level. Biotic Interactions of Importance for Microbial Community Resilience An important aspect of the concept of panarchy is the influence of interactions between levels on the resilience of a community ( Gunderson and Holling, 2002 ). In this study, either of the two modes of interaction, coexistence or competition, dominate during a specific temperature regime linking the dynamics of interactions with the resilience of the system. When microalgae were stressed by warmer temperature, bacterial growth was promoted, leading to microalgal-bacteria coexistence, while when relieved from temperature stress the microalgal growth was promoted, and the community was governed by the competition between the levels ( Figure 10 ). The shift in community interactions follows dynamics as proposed by Chesson (2000) in the modern coexistence theory. In which stabilizing effects of increasing niche differentiation (the use of different resources), in combination with the equalizing effects of decreasing fitness (more evenly distributed abundances) describe a situation favoring coexistence, while the opposite conditions favor competition and competitive exclusion. Examples of niche differentiation, of either light or nutrient preferences, and coexistence of different microalgal groups have been suggested by previous studies both in laboratory experiments and nature ( Alexander et al., 2015 ; Burson et al., 2019 ). Studies of interactions among microbial communities often focus on niche overlaps/differentiations between similar organisms. For instance, Hunt et al. (2008) describe how members of a bacterial family in a coastal environment may coexist through resource partitioning. Previous works performed in large scale reactors with microalgae and bacteria commonly explored community stability ( Stockenreiter et al., 2012 ; Beyter et al., 2016 ; Fulbright et al., 2018 ) rather than interlevel interactions. Interlevel interaction analysis have mostly been performed in well-designed co-cultures ( Durham et al., 2014 ; Seyedsayamdost et al., 2014 ; Amin et al., 2015 ; Segev et al., 2016 ; Landa et al., 2017 ) or in association with natural algal blooms ( Mayali et al., 2011 ; Teeling et al., 2012 , 2016 ; Zhou et al., 2018 ), but rarely with regards to competition or coexistence ( Sörenson et al., 2020 ). However, Le Chevanton et al. (2016) suggest that nitrogen limitation may have caused competition between algae and bacteria in a laboratory co-culture. The results from the present study suggest that interlevel interactions, in relation to functional and structural dynamics, are of importance for microbial community resilience. Considerations Related to our PBR Experimental Setup This study was performed under replete nutrient conditions, enabling the focus of the study on carbon and the transfer of energy between the microalgal and bacterial populations in the PBR community. The availability of inorganic carbon was likely pushing the PBR community towards carbon limitation during the colder, more productive period, with a ratio of supplied CO 2 to biomass at just below two ( Figure 1 ). This is suggested by our data to have lead to the upregulation of organic carbon uptake pathways expressed by the mixotrophic microalgae ( Figure 4 ), thus forcing the community into competition for organic carbon. In the PBR, the shift from coexistence to competition did not impact the carbon cycle flux per se , as the resilience of the system maintained the over-all system function, but the magnitude of cycled carbon increased as more inorganic carbon was introduced through photosynthesis during the colder period, as significantly more biomass was produced. The limited complexity in terms of community structure and influential environmental parameters of the system facilitated the analysis and allowed for the application of established ecological theories. The relatively short time scale in which the study was conducted (6weeks in total), was enough time to capture the shifts seen in response to significantly changed temperature conditions, nonetheless, more extensive sampling before and after the perturbation would have been beneficial but are not considered to limit the conclusions of this study. Models of climate change and projected environmental disturbances are based on changes seen over long periods of time ( Collins and Knutti, 2013 ). Short-scale studies, with controlled conditions, are however important in order to reveal short-term mechanisms in microbial ecosystems, such as those seen in this study."
} | 8,024 |
35479804 | PMC9036573 | pmc | 494 | {
"abstract": "In nature, wetting by water droplets on superhydrophobic materials is governed by the Cassie–Baxter or Wenzel models. Moreover, sticky properties, derived from these types of wettings, are required for a wide range of applications involving superhydrophobic materials. As a facile new strategy, a method employing a gaseous fluorine precursor to fabricate core–shell particles, comprising perfectly shaped fluorine shells with adjustable adhesive strength, is described in this paper. Silica was used as the hydrophilic core, while polyvinylidene fluoride (PVDF) was used for the hydrophobic shell coating, forming a raspberry-like shape. In addition, controlling the amount of PVDF coated on the silica surface enabled the water droplets to come into contact with both the PVDF of the shell and the silica of the core, thereby controlling both the superhydrophobicity and the adhesive strength. Thus, the synthesized particles formed a structured coating with controllable stickiness and contact angles of 131–165°. Furthermore, on surfaces with high adhesivity, the water droplets remained stable at tilt angles of 90° and 180° even under a strong centrifugal force, whereas on surfaces with low adhesivity, the water droplets slid off when the substrate was tilted at 4°.",
"conclusion": "4. Conclusion To obtain controllable superhydrophobic structures with strong adhesivity, a new facile strategy was developed for fabricating colloidal structured coatings using SPRCSPs. Raspberry-shaped particles composed of PVDF were specifically synthesized using VDF, a gaseous precursor instead of the conventional liquid precursors. Unlike the conventional method, which requires surface modifications using a fluorine monomer, the current approach resulted in a perfect core–shell form. In addition, the degree of coating of the PVDF shell was controlled to enable interactions between some of the silica, constituting the hydrophilic core, and water molecules. As a result, the superhydrophobicity and stickiness of the structured coating were controllable. The controlled superhydrophobicity led to CAs of 131–165°. For particles with high-strength adhesivity (15 g VDF), the water droplets remained adhered to the substrate even at tilt angles of 90° and 180°. Moreover, when a high centrifugal force was applied, some water droplets maintained their shape. However, for particles with low-strength adhesivity (VDF 18 g), none of the water droplets maintained their shape, and they slid off the surface after bouncing several times on the coating at a tilting angle 4°. Based on these results, we believe that improving the durability of the coated structures will increase the potential for using the controllable stickiness in a variety of applications, such as real water/fog collection systems and self-cleaning surfaces.",
"introduction": "1. Introduction In recent years, biomimetic superhydrophobic surfaces have garnered considerable attention in the field of liquid/solid interfaces. A superhydrophobic surface has a water droplet contact angle (CA) greater than 150°, while the water droplets roll or slide off the surface at an inclination angle of less than 5°. 1 These surfaces exhibit the characteristic lotus effect, corresponding to the Cassie–Baxter wetting state. 2 Typically, superhydrophobic surfaces that exhibit the lotus effect have been used for oil/water separation, reducing friction, increasing thermal stability, increasing transparency, and in vivo drug release, and in self-cleaning, antibacterial, anti-fogging, anti-icing, and anti-contamination surfaces. 3–15 However, in applications like microdrop manipulation, water harvesting, microfluidic transportation, chemical/biological separation, microanalysis, and in situ detection, a sticky surface is necessary. 16–22 This is generally achieved using the rose petal effect, which corresponds to the Wenzel wetting state. 23 Such surfaces are fabricated by chemical etching, plasma treatment, photolithography, chemical vapor deposition, chemical coating, and using colloidal particles. 24–30 The structured colloidal coatings have high CAs because the area of solids and gases in the contact region between the droplet and the structured surface can be easily controlled. 31,32 Furthermore, the CA can be adjusted according to the empty space between the particles. 33,34 In particular, raspberry-shaped particles have a larger volume of empty space than normal particles, which results in a larger water CA. 35,36 Additionally, the surface modification of colloidal particles with fluorine groups have significantly low free energy, thus yielding a structured coating with a larger CA. 37,38 Although this strategy provides a large CA, it does not produce a sticky surface. In contrast, certain raspberry-shaped particles provide adhesivity owing to the van der Waals forces or the large amount of space between them. However, this adhesivity is difficult to control. 39,40 To obtain a structured coating with a high CA and controllable high-strength adhesivity, we describe the fabrication of SiO 2 @PVDF raspberry core–shell particles (SPRCSPs) by coating polyvinylidene fluoride (PVDF) onto SiO 2 particles. Unlike silica particles that have been surface-modified using fluorine compounds, the current approach offers perfect control over the SPRCSPs shell thickness. Previously, a liquid precursor has been used to prepare the shell of the raspberry-shaped core–shell particles. 41 However, to the best of our knowledge, this is the first study to report perfect raspberry-shaped inorganic@organic core–shell particles directly prepared using a gaseous precursor, viz. 1,1-difluoroethylene (VDF). Our SPRCSPs exhibit high CAs ranging from 131 to 165°, depending on the degree of PVDF coating. In addition, they have controllable, strong adhesive properties owing to the high electronegativity of fluorine and hydrophilicity of the silica surface. In addition, the adhesivity of the coating was controllable, which ensured that the water droplets remained on the coating surface even under a strong centrifugal force at tilt angles of 90° and 180° (vertical and upside down, respectively), while they slid off at a tilt angle of 4°.",
"discussion": "3. Results and discussion 3.1 Preparation of SiO 2 @PVDF raspberry core–shell particles To create a structured coating with controllable sticky superhydrophobicity with respect to water, the SPRCSPs were fabricated using hydrophilic silica particles as the core, and coating them with the hydrophobic PVDF raspberry-shaped shell ( Fig. 1a ). First, the SiO 2 spherical particles were prepared as uniform-sized cores ( Fig. 1b ) using a sol–gel method. Owing to the presence of OH groups on their surface, PVDF cannot be directly coated onto the silica particles as a shell. Thus, the particle surfaces were first functionalized with vinyl groups and the resulting vinyl-functionalized silica particles (V-SiO 2 ) were dispersed in ethanol to prevent agglomeration. 42,43 However, unlike other polymers, PVDF particles must be synthesized from a gaseous precursor rather than a liquid. Therefore, the presence of even a small amount of ethanol may interfere with the reaction and prevent the synthesis. Hence, the silica surface must be further modified to facilitate the mixing of the OH and vinyl groups. Thus, 3-(trimethoxysilyl)propyl methacrylate (TPM) was added prior to fully growing the silica particles from tetraethyl orthosilicate (TEOS). As a result, the desired surface modification was obtained using a facile one-pot approach instead of the conventional method, where the synthesized silica particles are first cleaned, followed by the surface modification with TPM. Based on the FT-IR peaks at 1740 and 3040 cm −1 ( Fig. 1c ), the prepared particles contained C \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"13.200000pt\" height=\"16.000000pt\" viewBox=\"0 0 13.200000 16.000000\" preserveAspectRatio=\"xMidYMid meet\"><metadata>\nCreated by potrace 1.16, written by Peter Selinger 2001-2019\n</metadata><g transform=\"translate(1.000000,15.000000) scale(0.017500,-0.017500)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z\"/></g></svg>\n\n O and CH CH 2 bonds. This indicates that the PVDF coating formed a raspberry-like surface ( Fig. 1d and e ) because the OH and vinyl groups were mixed on the surface of the silica. Furthermore, the EDS elemental mapping images showed that the core had a uniform distribution of Si and O, while the PVDF shell coating had a uniform distribution of F ( Fig. 1f–h ). Fig. 1 Preparation and analysis of SPRCSPs. (a) Schematic of SPRCSP preparation, including the synthetic route to produce SPRCSPs from SiO 2 spheres functionalized by vinyl groups through emulsion polymerization of VDF precursors into PVDF. (b) SEM images of V-SiO 2 particles. (c) FT-IR spectra of SiO 2 and V-SiO 2 particles. (d) SEM and (e) TEM images of SPRCSPs. (f–h) EDS elemental mappings of F, Si, and O of SPRCSPs. The stickiness of the raspberry particles can be attributed to the large gaps between the particles 44 and the van der Waals forces. 40 To obtain controllable and strong adhesivity, we exploited the advantageous properties of the conventional raspberry-like particles. Accordingly, we controlled the degree of the shell coating to enable the interaction of water with the core, thereby enabling Wenzel wetting. Thus, water-dispersible (instead of ethanol) V-SiO 2 particles were first prepared, which explains the presence of some OH groups on the surface of the silica core. Further, by controlling the amount of the VDF precursor, which polymerizes to produce the PVDF coating, the interactions between the core surface and water molecules can be controlled ( Fig. 2a–c ). In other words, the silica areas that can react with water remain exposed, depending on the degree of PVDF coating. As the reaction progressed, the PVDF coating further grew from the vinyl-modified areas on the silica particles ( Fig. 2a ; S1a † ) eventually connecting with each other ( Fig. 2b, c , S1b and c † ). Consequently, the area of the unmodified silica with OH groups that can react with water gradually disappeared. Moreover, the amount of F increased with increasing VDF amount, whereas the amount of O and Si concurrently decreased ( Fig. 2d ). In addition, all the PVDF sub-particles connected with each other as the amount of VDF increased, thereby completely coating the silica surface (Fig. S2 † ). Fig. 2 Preparation and analysis of SPRCSPs according to the amount of VDF gas. SEM and high magnification SEM (insert) images of SPRCSPs prepared using VDF gas: (a) 10 g, (b) 15 g, and (c) 18 g. (d) XPS spectra of SPRCSPs. (red area: core silica area). 3.2 Controllable sticky superhydrophobic structures by SiO2@PVDF raspberry core–shell particles A spray coating method is generally used to evenly distribute colloidal particles on a substrate and offers the advantages of easy control, low cost, and high efficiency. In addition, it can attain ordered structures. 45,46 Therefore, we spray-coated SPRCSPs onto a substrate using an air spray nozzle ( Fig. 3a ), which resulted in an ordered arrangement ( Fig. 3b ). This uniform and ordered coating is suitable for confirming the behavior of water according to the raspberry particle morphology. This is because the changes in the CA can be attributed to the degree and shape of the PVDF shell-coated silica core, and not the overall surface roughness of the structured coating ( Fig. 3c ). Fig. 3 (a) Schematic of spray coating SPRCSPs onto the substrate. (b) SEM images of the ordered structure of the resulting SPRCSP coating (VDF 18 g). (c) Schematic of a water droplet on the structured coating composed of SPRCSPs. Optical image of the CA between the water droplet and the structured coating composed of SPRCSPs prepared using VDF gas: (d) 10 g, (e) 15 g, and (f) 18 g. (g) Change in the water CA corresponding to the amount of VDF gas used for SPRCSP preparation. Insets: photographs of SPRCSPs corresponding to the different amounts of VDF gas and pure PVDF. To that end, the water CAs were measured to confirm the wetting behavior of the structured SPRCSPs coatings synthesized with varying amounts (10–25 g) of the VDF precursor. In general, a smooth surface modified by –CF 3 groups ( i.e. , without any structural ordering) has a CA of 120°, despite its low free energy. 47 PVDF particles with a smooth surface that are not raspberry-shaped exhibit a CA of 117°, owing to the –CF 3 functional groups and the colloidal nature of the resultant coating (Fig. S3a and b † ). In contrast, the coatings made from SPRCSPs exhibited a larger CA because of the larger empty space between these rough particles than those between conventional smooth particles ( Fig. 3d–f ). However, in the structured coatings composed of particles with a mitigated raspberry morphology, where the PVDF shell grew smoother and more uniform, the empty space between the particles was reduced, causing a decrease in the CA (Fig. S3c † ). In addition, the SPRCSPs have silica cores that influence the wetting properties of the PVDF shell and the resultant structured coating. Furthermore, the amount of the corresponding hydrophilic areas on the water-contacting silica core surface decreased with increasing VDF amount (10–18 g) despite the same raspberry shape; this caused an increase in the CA from 142° to 165° ( Fig. 3g ). However, when the amount of VDF was increased to 25 g, the raspberry-like structure of the particles weakened, and the CA decreased to 131°. While superhydrophobic structures typically have a CA of 150° or greater without stickiness, a structured coating made of raspberry-shaped particles is sticky even at a CA of 150° or greater. The SPRCSPs (VDF 15 g) prepared by controlling the area of core silica parts exhibited strong stickiness with a high CA of 150° or greater, owing to the hydrophilicity of the core silica ( Fig. 4a , Video S1 † ). The water droplets remained adhered to the coating even at tilting angles of 90° (vertical) and 180° (upside down) ( Fig. 4b ). Fig. 4 (a) Dropping water droplet and its movement on a sticky structured coating using SPRCSPs (15 g VDF). (b) Water droplet behavior at 90° and 180° inclination of the sticky SPRCSP coating (VDF 15 g). (c) Water droplet behavior on the sticky SPRCSP coating (VDF 15 g) after applying a rotational force while gradually increasing the rotational speed (500–2000 rpm) for 10 s. (d) Dropping water droplet and its and movement on a low-strength sticky SPRCSP coating (VDF 18 g). (e) Images of the moving water droplet on a SPRCSP coating (VDF 18 g) with a tilting angle of 4°. We also measured the effect of applying a centrifugal force to the coated substrate on the CA. The centrifugal force was applied using a spin coater while gradually increasing the rotational speed (500–2000 rpm). Although the CA decreased, a significant portion of the water droplets remained adhered to the structured coating ( Fig. 4c ). Such strong adhesion can be beneficial for potential applications in water/fog collection systems 48 and microdroplet manipulation. 49 In contrast, SPRCSPs (VDF 18 g) with almost no core silica exposed exhibited weak adhesivity and the water droplets detached from the coated substrate ( Fig. 4d , Video S2 † ). In addition, when a water droplet was dropped onto the SPRCSP (VDF 18 g) structured coating at a tilting angle of 4°, it bounced on the coating several times and rolled to the bottom (Fig. S4, 4e and Video S3 † ). This behavior may be beneficial for self-cleaning surfaces. 50"
} | 3,925 |
27617746 | PMC5019393 | pmc | 496 | {
"abstract": "Multi-species microbial communities play a critical role in human health, industry, and waste remediation. Recently, the evolution of synthetic consortia in the laboratory has enabled adaptation to be addressed in the context of interacting species. Using an engineered bacterial consortium, we repeatedly evolved cooperative genotypes and examined both the predictability of evolution and the phenotypes that determine community dynamics. Eight Salmonella enterica serovar Typhimurium strains evolved methionine excretion sufficient to support growth of an Escherichia coli methionine auxotroph, from whom they required excreted growth substrates. Non-synonymous mutations in metA , encoding homoserine trans-succinylase (HTS), were detected in each evolved S . enterica methionine cooperator and were shown to be necessary for cooperative consortia growth. Molecular modeling was used to predict that most of the non-synonymous mutations slightly increase the binding affinity for HTS homodimer formation. Despite this genetic parallelism and trend of increasing protein binding stability, these metA alleles gave rise to a wide range of phenotypic diversity in terms of individual versus group benefit. The cooperators with the highest methionine excretion permitted nearly two-fold faster consortia growth and supported the highest fraction of E . coli , yet also had the slowest individual growth rates compared to less cooperative strains. Thus, although the genetic basis of adaptation was quite similar across independent origins of cooperative phenotypes, quantitative measurements of metabolite production were required to predict either the individual-level growth consequences or how these propagate to community-level behavior.",
"introduction": "Introduction Multi-species microbial communities critical to human health [ 1 , 2 ], industrial production [ 3 ], and waste remediation [ 4 , 5 ] are governed by complex social dynamics. Predicting how these communities might respond to environmental changes requires an understanding of how species interactions change the evolution of new traits. Interspecies interactions, like competition or cooperation, complicate environmental selective pressures and decrease the predictive power of monoculture behavior [ 6 , 7 ]. For example, a cooperative species that excretes a beneficial cellular product, or “public good,” may alter the environment and thus alter the selective pressure on other species [ 8 ]. When this cooperation is costly to individual growth, but rewarded by cooperative behavior, a complex tension arises in which the relative costs and benefits and the structure of the interactions determine whether the cooperation is driven to fixation, coexists, or is eliminated. From the point of view of adaptation, it is unclear whether evolution in communities should be highly variable between instances, owing to the presence of multiple partners, or highly similar, because there are often few phenotypes that primarily govern the interaction [ 7 , 9 , 10 ]. Parallelism at various levels is common in experimental evolution. Replicate microbial populations under selection for growth in liquid medium have often evolved parallel phenotypes [ 7 , 8 ] or genotypes [ 11 – 14 ]. For within-species interactions, examples have suggested that genetic parallelism can be common, despite a resulting range in phenotypic consequences from these mutants [ 15 , 16 ]. Here, we address the spectrum of mutations that gave rise to metabolic cooperation between species, and how the resulting metabolite production phenotypes translated to individual costs or community dynamics. Our model system is a synthetic community, or consortium, comprised of two-members: an Escherichia coli methionine auxotroph (Δ metB ) and Salmonella enterica serovar Typhimurium [ 17 ]. Both members have a reciprocal requirement for the other to grow in lactose minimal media ( Fig 1 ). E . coli excretes carbon byproducts that are consumed by S . enterica , as S . enterica cannot metabolize lactose. S . enterica must, in turn, evolve sufficient methionine excretion to support growth of the E . coli methionine auxotroph. By selecting in a spatially-structured environment, a cooperative methionine-producing S . enterica genotype was selected for, which could then sustain community growth either on petri dishes or in liquid media. In this initial instance of the evolution of cooperation methionine excretion by S . enterica was found to be individually costly, substantially decreasing the cooperator’s own growth rate compared to its ancestor [ 17 ]. 10.1371/journal.pone.0161837.g001 Fig 1 S . enterica cooperators evolved from ethionine resistant ancestors feature mutations in metA . a) S . enterica ethionine resistant strains (R strains) were co-cultured with E . coli methionine auxotrophs on lactose minimal media. Adaptive methionine excretion by evolved cooperators enabled growth of E . coli ΔmetB , which in turn excretes usable carbon for S . enterica . Non-synonymous substitutions in the metA gene, encoding homoserine trans-succinylase (HTS), are listed next to producer strain name. b) A homology model of residues 2–297 of S . enterica HTS was created using Bacillus cereus HTS. Each evolved cooperator features a mutation at one highlighted residue. The active site is shown with its cognate substrate homoserine. Here we uncover the genetic basis of methionine excretion in the previously evolved cooperative S . enterica , as well as repeat the use of a spatially-structured environment to select for a series of new cooperative strains from different, closely-related S . enterica backgrounds. We first examined the underlying molecular changes in our S . enterica mutants and found that all evolved strains featured mutations in the metA gene, which encodes the first step of methionine biosynthesis, homoserine trans-succinylase (HTS) [ 18 ]. We used molecular modeling to predict how these mutations modified HTS folding and homodimer formation and found that most mutations increased the binding affinity for dimerization. We determined the effect of the metA mutations upon growth of consortia in well-mixed, liquid medium. While growth in liquid is distinct from the spatially structured environment in which the S . enterica evolved, it represents an informative phenotype and is more experimentally tractable. Although we found genetic parallelism, the various metA alleles gave rise to a wide range of methionine excretion levels. We found that methionine excretion correlated negatively with individual growth of S . enterica , but positively with the growth rate of the consortia and with the proportion of E . coli partner that the given strains could maintain. The extent a given allele commits the cell to cooperation thus simultaneously determines the individual-level growth costs, the community growth rate and the species ratio in the consortium.",
"discussion": "Discussion By selecting for cooperative phenotypes multiple times with a synthetic, two-species consortium, we determined the degree of both the genetic and phenotypic parallelism for mutations that allowed between-species cooperation to emerge. Within the context of a multi-species, spatially-structured community, the environmental interactions are presumed to be more complex than those faced by individual, planktonic cells in well-mixed media. It might be expected that the range of adaptive mutations similarly increase just as biotic interactions often increase diversification. Because the initiation of cooperation was immediately observable on petri dishes due to the co-metabolic conversion of X-gal caused by lactose growth, our experimental design allowed us to obtain first step beneficial mutations as they occurred, before they competed with other possible mutations for fixation. By avoiding clonal interference [ 24 ] with other consortia-inducing mutations that generated separate colonies, it seems more likely that the genetic parallelism we observed was due to what mutations can occur, not just those whose selective coefficients are sufficiently large to outcompete others occurring in a given population size [ 25 , 26 ]. We also avoid the possible confounding effects of long-term positive interactions between genotypes such as cross-feeding that can lead to clonal reinforcement [ 27 , 28 ]. We found that the genetic basis for S . enterica to initiate a novel cooperative interaction was quite narrow, similar to previous work on within-species cooperation [ 29 ]. Why might natural selection have acted so narrowly? In this case, a relatively dramatic change in phenotype was required for consortia growth, and the ability of consortia to grow was limited by a single factor: methionine production. Given that regulation of methionine production (like many amino acid biosynthetic pathways) is strongly controlled at the first step of the five enzymes involved, HTS encoded by metA exerts a high degree of metabolic control [ 30 ] over this phenotype under these conditions. This suggests that when a single metabolic compound is the currency of exchange, changes at one critical point in central metabolism of one organism might be sufficient for large-scale ecological changes. An additional factor that may have contributed to the observed genetic parallelism was that the evolved phenotype was likely a loss of function of an active negative regulation mechanism upon HTS levels. Previous work has demonstrated that the N-terminus region of the E . coli HTS destabilizes the protein via energy-dependent proteolysis [ 31 ]. Deletion or replacement of the first 68 amino acids of HTS in E . coli dramatically increased HTS half-life. Several of our P mutations were in HTS in S . enterica; the homologs in the two species share 95% amino acid identity. This suggests that the mutations we observed in S . enterica acted to increase protein stability. Consistent with this hypothesis, two independent mutations that stabilized the E . coli HTS and increased heat tolerance, S61T and I229T [ 32 ], neighbor amino acid substitutions in metA P4 , metA P5 , and metA P8 . The R228C substitutions in metA P4 and metA P8 occur in the C-terminus region where other mutations have been found to desensitize HTS to allosteric inhibition of methionine [ 33 ]. Collectively, these prior experiments are consistent with the idea that any of a potentially wide range of mutations that increase HTS stability or reduce its sensitivity to end-product inhibition could lead to cooperation, and thus parallelism at the level of the gene. While the probability and/or the magnitude of beneficial mutations in metA led to genetic parallelism, the resulting alleles conferred a wide spectrum of phenotypes. Genetic parallelism has been a fairly common finding from evolution of microbes in the laboratory, as well as environments such as chronic infections [ 34 ], but there are relatively few examples where consistency of phenotype between alleles has been tested [ 12 , 35 – 37 ]. Similar to work for within-species cooperation enabled by mutations affecting siderophore production [ 29 ] or a transcriptional regulator [ 15 ], we found that parallel mutations in metA resulted in variation in methionine production over three orders of magnitude. Furthermore, methionine production explained most of the variation in decreased growth rate on galactose caused by these alleles. Though the link between parallel genetic changes and the resulting diversity in methionine excretion is unclear, this is one of few examples where the phenotypes of an individual microbial species have been shown to quantitatively correlate with community phenotypes in a complex assemblage [ 7 , 9 , 10 , 38 ]. The correlations between production, individual growth and community performance suggest that, at least in the absence of refinement through additional mutations, metabolite production itself governs the benefits and costs of cooperation: the more methionine you make, the slower you grow alone. It is also apparent, however, that methionine production was only substantially costly at high levels. The four isolates producing below 5 μM/OD 600 grew alone with less than a 10% defect, whereas the four isolates producing 8 μM/OD 600 had growth defects of 20–40%. A second general trend we found was that the more cooperative a strain is, the lower its observed equilibrium frequency in the community. This result parallels findings by Momeni et al 2013 in a single species [ 39 ]. In our work the most cooperative S . enterica strain (R1P2) is illustrative. Its consortia grows at a rate indistinguishable from its individual maximal growth rate, suggesting that it is not limited by growth substrate coming from E . coli , but that the E . coli is solely dependent upon a small, slow population of R1P2 for support. For all other S . enterica cooperators, they are capable of faster growth than their corresponding consortia with E . coli , suggesting that they are limited by growth substrate from E . coli . Since the same E . coli partner strain was present in all consortia, this in turn suggests that E . coli was limited in their own growth by the insufficient provisioning of methionine from their respective S . enterica partner strains. Simply by measuring the methionine output by the S . enterica partner, predictions about higher-level community characteristics within this community can be made with some accuracy. The ability to correlate community composition with future behavior grows increasingly important as we come to rely on genetic sequencing of complex, multi-species microbial systems as a first pass estimator of community characteristics. Accurate inference of ecological behavior becomes more difficult as interactions between shifting selective pressures, genetic changes, and phenotypic expression within an organism are further complicated by species interactions. Metagenomic investigation of known interactions such as adaptive diversification have suggested that even time-series sequence data offer surprisingly few obvious clues of the underlying ecology [ 27 , 40 ]. We have shown here that even highly repeatable genetic adaptations in a dynamic community may translate into an unexpected array of phenotypes; our sequence data alone did not reflect the range of observed methionine excretion. Yet consistent evidence of physiological trade-offs and repeatable community dynamics gives some credence to the idea that the trajectory of even a species-rich evolving community might become predictable once the relevant phenotypes are characterized."
} | 3,686 |
19897353 | null | s2 | 498 | {
"abstract": "Syntrophy is an essential intermediary step in the anaerobic conversion of organic matter to methane where metabolically distinct microorganisms are tightly linked by the need to maintain the exchanged metabolites at very low concentrations. Anaerobic syntrophy is thermodynamically constrained, and is probably a prime reason why it is difficult to culture microbes as these approaches disrupt consortia. Reconstruction of artificial syntrophic consortia has allowed uncultured syntrophic metabolizers and methanogens to be optimally grown and studied biochemically. The pathways for syntrophic acetate, propionate and longer chain fatty acid metabolism are mostly understood, but key steps involved in benzoate breakdown and cyclohexane carboxylate formation are unclear. Syntrophic metabolism requires reverse electron transfer, close physical contact, and metabolic synchronization of the syntrophic partners. Genomic analyses reveal that multiple mechanisms exist for reverse electron transfer. Surprisingly, the flagellum functions were implicated in ensuring close physical proximity and synchronization of the syntrophic partners."
} | 284 |
37799812 | PMC10548143 | pmc | 499 | {
"abstract": "The burgeoning human population has resulted in an augmented demand for raw materials and energy sources, which in turn has led to a deleterious environmental impact marked by elevated greenhouse gas (GHG) emissions, acidification of water bodies, and escalating global temperatures. Therefore, it is imperative that modern society develop sustainable technologies to avert future environmental degradation and generate alternative bioproduct-producing technologies. A promising approach to tackling this challenge involves utilizing natural microbial consortia or designing synthetic communities of microorganisms as a foundation to develop diverse and sustainable applications for bioproduct production, wastewater treatment, GHG emission reduction, energy crisis alleviation, and soil fertility enhancement. Microalgae, which are photosynthetic microorganisms that inhabit aquatic environments and exhibit a high capacity for CO 2 fixation, are particularly appealing in this context. They can convert light energy and atmospheric CO 2 or industrial flue gases into valuable biomass and organic chemicals, thereby contributing to GHG emission reduction. To date, most microalgae cultivation studies have focused on monoculture systems. However, maintaining a microalgae monoculture system can be challenging due to contamination by other microorganisms (e.g., yeasts, fungi, bacteria, and other microalgae species), which can lead to low productivity, culture collapse, and low-quality biomass. Co-culture systems, which produce robust microorganism consortia or communities, present a compelling strategy for addressing contamination problems. In recent years, research and development of innovative co-cultivation techniques have substantially increased. Nevertheless, many microalgae co-culturing technologies remain in the developmental phase and have yet to be scaled and commercialized. Accordingly, this review presents a thorough literature review of research conducted in the last few decades, exploring the advantages and disadvantages of microalgae co-cultivation systems that involve microalgae-bacteria, microalgae-fungi, and microalgae-microalgae/algae systems. The manuscript also addresses diverse uses of co-culture systems, and growing methods, and includes one of the most exciting research areas in co-culturing systems, which are omic studies that elucidate different interaction mechanisms among microbial communities. Finally, the manuscript discusses the economic viability, future challenges, and prospects of microalgal co-cultivation methods.",
"conclusion": "7 Conclusion Co-cultivation systems incorporating microalgae have risen to prominence as a resource platform for an array of biotechnological applications, which span the spectrum from biodiesel production to wastewater treatment. These systems have a remarkable ability to thrive on inexpensive feedstocks such as cellulosic biomass, thereby reducing operational costs substantially. Yet, to translate microalgae cultivation into a large-scale operation, we need a more profound understanding of cultivation methods, metabolite recovery and purification from co-culture systems, and strategies to bolster culture resilience. Resilience, in this context, refers to the ability of the co-culture system to withstand changes in environmental conditions and recover from disturbances. The resilience of co-cultivation systems is essential for sustainable large-scale operations. Future research should focus on optimizing the selection of cost-effective feedstock and harvesting methods, and on the evaluation of varying operating conditions. These include factors such as pH, temperature, light intensity, nutrients, and carbon availability that govern the successful operation of these systems. These steps will be crucial to achieve the economic feasibility of large-scale production of high-value microalgae products. While the significance of microalgae cannot be understated, it is equally important to acknowledge the critical role played by the associated organisms in the co-culture system. The success of these co-cultivation systems hinges not only on the algal components but also on the symbiotic relationship established with other microbial members. Their contributions are fundamental in enhancing the productivity and overall functionality of these systems. Advancements in the field of omics have opened up new horizons for microalgae cell analysis and present promising prospects for co-cultivation systems. Continuous research efforts in this domain can potentially unveil intricate details about the interactions within these co-cultures, contributing substantially to our comprehensive understanding of these systems. To conclude, the sustainable synthesis of diverse products at an industrial scale lies in the successful implementation of microalgae co-cultivation. This vision, however, can only be realized through an in-depth understanding of the co-culture system as a whole, focused optimization of operating conditions, and leveraging the advancements in omics technology.",
"introduction": "1 Introduction The escalating human population’s increased demand for raw materials and energy sources is predicted to have a deleterious environmental impact characterized by elevated greenhouse gas (GHG) emissions. This trend is expected to continue in the near future ( Barati et al., 2021a ), given the ongoing process of industrialization, economic growth, and energy consumption ( Masson-Delmotte et al., 2021 ). Consequently, to counterbalance these environmental threats and generate alternative bioproduct-producing technologies, sustainable methodologies are no longer an option but a necessity. One promising approach involves utilizing microbial consortiums or communities as a platform for developing diverse, sustainable applications that can outperform current wastewater treatment technologies, reduce GHG emissions, alleviate the energy crisis, and improve soil fertility ( Das et al., 2021 ). The inclusion of microalgae species in such consortia addresses an essential aspect of circular economy and bioeconomy strategies: the generation of high-value compounds derived from the photosynthetic metabolism of oxygenic microalgae species. Microalgae are photosynthetic microorganisms inhabiting marine and/or freshwater ecosystems. They exhibit a remarkably high CO 2 fixation capacity compared to any other land plant, while also producing oxygen ( Barati et al., 2021b ). They can convert light energy into biomass and organic chemicals ( Moreno-Garcia et al., 2017 ) and can consume atmospheric CO 2 or industrial flue gases under specific circumstances, thereby reducing GHG emissions while producing biomass. Furthermore, microalgae can consume nutrients available in wastewater and collaborate with bioremediation ( Barati et al., 2021b ). Culturing domestic strains is typically straightforward, easy to maintain, and does not compete for arable lands ( Lakshmikandan et al., 2020 ). Moreover, several species can exhibit an extraordinary capacity to adapt to different environmental niches, facilitating the bioprospecting of a microalgae species suitable for a particular environmental condition or its adaptation to a cultivation process ( Lam and Lee, 2012 ). The potential for biotechnological and commercial applications of microalgae biomass is vast. It has been used in animal and human nutrition, cosmetics, biofertilization, the dyes industry, and antioxidant and pharmaceutical compounds ( Rizwan et al., 2018 ). Additionally, bio-oil from microalgae can be used for biofuel production, in agricultural applications, controlling ammonia and balancing pH drops caused by nitrifying bacteria in an aquaponic system ( Addy et al., 2017 ). They can also benefit plant growth in hydroponic systems by providing oxygen for the plant and utilizing the CO 2 produced by respiration and exudation of crop roots for their growth, thereby inhibiting anaerobiosis in the crop’s root system ( Huo et al., 2020 ). Most microalgae cultivation studies have been focused on monoculture systems. However, monoculture open cultivation systems pose significant challenges due to contamination by other microorganisms, such as yeasts, fungi, bacteria, and other microalgae species. These instances of contamination can lead to low productivity, culture collapse, low-quality biomass, and nutrient loss. Accordingly, recent attention has been drawn to co-culture systems and the potential advantages of developing specific, robust microorganism consortia. Due to microalgae’s metabolic adaptability and capacity for survival in diverse environmental conditions, co-culturing microalgae with other microorganisms may circumvent the constraints of monoculture in open systems ( Rashid et al., 2019 ). Co-culturing microalgae in consortia, at both small and large scales, has been developed and is utilized in biomanufacturing, with proposed applications in the food, agronomic, pharmaceutical, nutraceutical, chemical, biofuel sectors, and other industries associated with bioremediation and nutrient recycling strategies ( Figure 1 ). FIGURE 1 Advantages of microalgae co-culture systems and their potential applications. This review presents a thorough examination of research conducted in the last few decades, exploring the advantages and disadvantages of microalgae co-cultivation systems, including microalgae-bacteria, microalgae-fungi, and microalgae-microalgae/algae systems. The manuscript also addresses diverse uses of co-culture systems and growing methodologies and includes one of the most exciting research areas in co-cultivation systems, specifically, omics analysis, capable of elucidating different interaction mechanisms among microbial communities. Finally, the manuscript discusses the economic viability, future challenges, and prospects of microalgal co-cultivation methods."
} | 2,470 |
38351312 | PMC10864392 | pmc | 502 | {
"abstract": "The distribution of symbiotic scleractinian corals is driven, in part, by light availability, as host energy demands are partially met through translocation of photosynthate. Physiological plasticity in response to environmental conditions, such as light, enables the expansion of resilient phenotypes in the face of changing environmental conditions. Here we compared the physiology, morphology, and taxonomy of the host and endosymbionts of individual Madracis pharensis corals exposed to dramatically different light conditions based on colony orientation on the surface of a shipwreck at 30 m depth in the Bay of Haifa, Israel. We found significant differences in symbiont species consortia, photophysiology, and stable isotopes, suggesting that these corals can adjust multiple aspects of host and symbiont physiology in response to light availability. These results highlight the potential of corals to switch to a predominantly heterotrophic diet when light availability and/or symbiont densities are too low to sustain sufficient photosynthesis, which may provide resilience for corals in the face of climate change.",
"introduction": "Introduction In contemporary tropical and subtropical oceans, symbiotic corals provide a literal and figurative ecological framework that retains nutrients, supports high rates of primary production, and permits extensive biological diversity. However, these fragile ecosystems are threatened with extinction in the coming century 1 – 3 . One of the dominant threats to reefs is the loss of autotrophic symbionts or dysbiosis as a result of increasing seawater temperatures 4 , 5 . Prolonged periods of dysbiosis may ultimately lead to coral mortality as many species are reliant on translocated photosynthate from endosymbiotic dinoflagellates in the family Symbiodiniacaea to meet the majority of their metabolic demands 5 – 8 . Yet, corals are known to exhibit adaptations to environmental gradients, such as alterations in Symbiodiniacaea species association and/or density 9 – 11 , feeding strategy 12 , and metabolism 13 . Conspecific corals, for example, typically exhibit an increase in symbiont density and chlorophyll concentration with increasing depth to maintain stable rates of photosynthesis under decreasing light availability 14 – 19 . On the other hand, some symbiotic corals living in extremely low light environments, such as caves and overhangs, maintain very few photosymbionts, appearing white in color 20 . In fact, not all calcifying corals have an obligate symbiosis, with several species expressing a facultative relationship where autotrophic symbionts may be absent or maintained at low densities in a non-stressed state 21 . These species often have sub-tropical distributions and are frequently found with reduced symbiont densities in low light conditions related to latitude, seasonality, or physical environmental parameters 22 – 24 . Facultative symbiosis might be advantageous for coral survival under future climate change scenarios, as higher densities of symbionts were found to result in increased bleaching severity 25 . Thus, understanding how facultative species function with variable symbiont densities will provide insight into the mechanisms that corals may employ during extended periods of dysbiosis 24 . The genus Madracis is ubiquitous across coral reef habitats from shallow to deep regions, and includes obligate and facultative symbiotic species, as well as non-symbiotic species. The species M. pharensis has a broad distribution ranging from the Caribbean to the Atlantic, and into the Mediterranean Sea, at depths from 0 to 80 m 20 . In the Mediterranean, M. pharensis forms small, knobby colonies that have a facultative symbiosis with Breviolum psygmophilum 26 . The species is characterized by high morphological plasticity and wide environmental tolerance 27 , forming massive and encrusting colonies in cryptic sites compared to nodular colonies in the light and is commonly found without symbionts in caves 20 . However, while M. pharensis is documented to associate with B. psygmophilum in the Mediterranean, Frade et al. 11 found differences in symbiont species associations across depth among congenerics. The species is therefore an excellent candidate to examine the connection between phenotype and physiology, and to explore mechanisms of adaptation by a facultative symbiotic coral to variable environmental conditions. Examining coral photophysiology in situ primarily relies on instantaneous measurements such as maximum quantum yield which is commonly used as a proxy for photosynthetic efficiency 28 . However, the coral host itself can also attain nutrients via heterotrophy and thus the photosynthate produced by endosymbiotic Symbiodiniacaea is not the only available source of nutrients 29 . Importantly, previous studies have shown plasticity in reliance on autotrophy versus heterotrophy by facultative symbiotic corals, where conspecific individuals that typically rely on autotrophy switch to heterotrophy in less-favorable conditions 12 , 29 , 30 . One technique used to examine changes in photophysiology is chlorophyll variable fluorescence, which provides a wide range of measurements for fluorescent and photosynthetic parameters of an organism 31 . The diving-Fluorescence Induction and Relaxation (Diving-FIRe) fluorometer developed by Gorbunov & Falkowski 32 uses a two-phase approach of both strong short pulses (induction phase) and weak modulated light (relaxation phase) to measure the steady state quantum yield of photochemistry in PSII (F v ’/F m ’), the functional absorption cross-section of PSII (σ PSII ’; A 2 ), the connectivity parameter (p) that determines the probability of excitation energy transfer between individual photosynthetic units, and the maximum photosynthetic rate (P max : electron·s −1 ·PSII −1 ). In contrast to other fluorescence techniques that are amplitude-based 33 – 35 , the FIRe combines both classic amplitude-based analysis and a new kinetic-based approach to directly measure the absolute value of light-driven electron flux in PSII (electron transport rates (ETR)). The amplitude-based model does not measure ETR directly but is based on the change in amplitude of chlorophyll fluorescence (ΔF v ’/F m ’) as a proxy of quantum yield of photochemistry in PSII under ambient irradiance 33 . On the other hand, kinetic analysis is based on monitoring the kinetics of the quinone reoxidation in PSII to quantify the photosynthetic ETR 36 . The ETR can then be converted to the rates of carbon fixation using the electron yield of carbon fixation 37 . Gorbunov and Falkowski 38 revealed that the kinetic analysis offers more accurate ETR measurements, as evidenced by stronger correlation with growth rates (and thus net production), at least in high-light environments (e.g., shallow coral reefs). In corals, such measurements of ETRs have additional advantages, as the kinetic analysis is not affected by the “pigment packaging” effect, which may be very strong in densely pigmented coral and can therefore be used to understand photochemistry under different environmental conditions in situ. To examine changes in food source and trophic level, the compound-specific Stable Isotope Analysis of Amino Acids (CSIA-AA) provides a powerful tool. Essential AA can be used to trace changes in the carbon sources and diet as they can only be synthesized by primary producers, and therefore do not change in carbon isotope value with trophic transfers 39 . Examining the source AA, phenylalanine, for which the nitrogen isotope does not change between trophic levels, and the trophic AA, glutamic-acid, which increases its nitrogen isotope value with every trophic transfer, can be used to calculate the trophic position (TP) as an indicator of an autotrophic (TP = 1), mixotrophic (TP = 1.5–2), or heterotrophic (TP > 2) diet 40 . Thus, combining photophysiology with stable isotope analyses provides a comprehensive picture of coral nutrient cycling. In the Bay of Haifa, Israel, M. pharensis colonies inhabit the surface of the Leonid shipwreck at 30 m depth. These colonies can be found in two contrasting orientations that express different phenotypes, where colonies facing the light are generally brown in color, while those facing downwards in overhanging shaded compartments are pale pink or white (Figs. 1 and 2 ). The aim of this study was to compare these divergent coral phenotypes using a combination of photophysiology, nutrient acquisition, and skeletal morphology, to describe how these corals function under different light conditions. As light is a determinant in symbiotic-coral distribution and plays a critical role in coral bleaching, changes in photosynthetic efficiency, or potential photo acclimatization, are critical to understand how these organisms survive with reduced symbiont densities and thus how corals may function under future climate change scenarios. Figure 1 Image of divers on the top side of the Leonid shipwreck with an overview of the site (left). Image of the benthos on the side-facing surface of the wreck showing various species of algae, invertebrates, and stony corals (right). Figure 2 (Left) Representative corals found on the surface of the wreck oriented upwards towards the light (top left; light-adapted) and downwards towards the dark (bottom left; shade-adapted), with corresponding SEM images of individual polyps used for skeletal analyses. Scale bar 200 µm. (Right) Analyses of ( A ) calyx width, ( B ) center (columella) width, and ( C ) septa width based on SEM images. Horizontal black lines within boxes are median values and box limits represent first and third quartiles. Whiskers represent 1.5 times the interquartile range. Round black points are individual sample data.",
"discussion": "Discussion Madracis pharensis is a ubiquitous coral found across a broad geographic and vertical distribution 20 . In the Mediterranean, the species is documented from shallow reefs to the lower mesophotic (0–80 m). In the Bay of Haifa, we found individuals of M. pharensis on the surface of a small shipwreck at 30 m depth that showed visible differences in coloration (brown vs. white/pink) based on differential light conditions experienced due to orientation/location on the shipwreck (170PAR vs 106PAR). Based on molecular identification, we found that despite strong differences in phenotypes, these individuals were the same species of coral ( M. pharensis ). We also found that skeletal morphology, using basic polyp features, did not differ between corals living in the shaded overhang compared to those exposed to light. Thus, although the visual coloration of colonies in each orientation was in strong contrast, their molecular and morphological fingerprints were not. Similar skeletal morphologies under different light conditions contrast previous findings for other coral species, where skeletal morphology was shown to differ intraspecifically across light gradients 41 – 43 . These previous studies speculated that changes in skeletal morphology in response to light were an adaptive response that increased light harvesting capabilities to ensure sustained photosynthetic efficiency 8 , 44 . The lack of skeletal differences found in this study might suggest that these corals are less dependent on light-driven photosynthesis than the previously studied species, and thus modifications to skeletal morphology are not required. Variations in light exposure are also known to affect the species of Symbiodiniacaea hosted by a coral, as certain symbiont species are documented to be more or less photosynthetically efficient under different light exposure scenarios either within 42 or between different coral hosts 45 . In fact, several studies have documented shifts in associated symbionts across depth gradients for a variety of coral species, including M. pharensis 10 , 11 , 17 , 46 – 49 . Here, we found that individuals exposed to light primarily hosted Breviolum psygmophilum (clade B2), while those in the dark hosted a range of species dominated by Symbiodinium microadriaticum (clade A1), with B. psygmophilum and Cladocopium spp . in lower densities. Previous studies have found a higher diversity of symbiont species hosted by coral colonies that have experienced dysbiosis 6 , 50 , 51 . It is hypothesized that maintaining a diverse assemblage of symbiont species may assist in sustaining photosynthesis under variable conditions 52 . Shade-adapted M. pharensis colonies also had significantly reduced densities of photosymbionts and significantly lower concentrations of chlorophyll a compared to those oriented towards the light. While these differences in symbiont density and chlorophyll concentration would suggest reduced available photosynthate for the shade-adapted corals than the light-adapted counterparts, the total protein content for the shade-adapted corals was equal to that of the light-adapted corals, even slightly higher, suggesting that these corals sustain equivalent levels of protein. These seemingly contradictory results might be explained by differences in light use efficiency. Here we found significant differences based on orientation for one photophysiology metric, the connectivity parameter (p), which was significantly higher in the light-adapted corals. The connectivity parameter defines the probability of the excitation energy transfer between individual photosynthetic units, photosystems I and II (PSI and PSII) 38 . Under natural diel light cycles, an increase in energy transfer (connectivity) is directly proportional to the functional absorption cross section of PSII, which is a measure of the probability that an absorbed photon will drive a photochemical reaction 53 , suggesting a common biophysical mechanism 54 . Gorbunov et al. 54 propose that in the dark, such as the shade-adapted M. pharensis colonies in this study, connectivity between photosystem units is low and the thylakoid membrane units are energetically segregated 54 . The low connectivity parameter for the shade-adapted corals might suggest, therefore, that the photosynthetic pathway is uncoupled and does not result in the production of photosynthate. In high light environments, higher connectivity (energy transfer) between photosystems is expected as it promotes redistribution of excitons, thus providing protection from excess energy flux. However, we did not find a similar significant increase in the functional absorption cross section in response to orientation towards the light, which may suggest that, despite receiving 40% of available surface light, the amount of light reaching the surface of the wreck is below saturation. All hermatypic corals are mixotrophic and, under laboratory conditions, some can survive on a purely autotrophic diet or when fed can be voracious predators, yet the extent to which corals rely on heterotrophy in the wild remains poorly understood 15 , 30 , 55 , 56 . Corals with a TP of 1 are assumed to rely only on autotrophy while corals with a TP > 1.5 are considered more heterotrophic. All individuals examined here were found to have a TP higher than 2, regardless of orientation, indicating that corals in both orientations are primarily heterotrophic, and again suggesting that overall light availability at the site is limited and may be insufficient to support a predominantly autotrophic diet. Importantly, these results highlight the capacity of this facultative symbiotic species to primarily utilize heterotrophy in low light environments. The facultative symbiotic coral Oculina patagonica has also been found to grow and survive for long periods of time under dark conditions in the laboratory and naturally in caves by relying on an exclusively heterotrophic diet 30 , 57 , 58 . Similarly, the relative contribution of autotrophy and heterotrophy were found to range among Pocillopora meandrina individuals living close to one another, further supporting trophic plasticity in corals 55 . Interestingly, the shade-adapted corals in this study had a significantly higher TP than the light-adapted individuals, indicating that they are even more reliant on heterotrophy and may be sustaining their symbionts. Many studies suggest that the host can regulate the transfer of photosynthate from the symbiont to the host, as well as nitrogen from the host to the symbiont 59 – 61 . When exploring the coral carbon source signature from the essential amino acids in this study, we found that in the light-adapted corals, the carbon source was the same for the host and the symbionts suggesting tight nutrient cycling. However, in the shade-adapted corals, the carbon signature differed between the host and the symbionts, indicating different sources of carbon. Likewise, the amino acid nitrogen isotope analysis showed a different nitrogen signature for symbionts based on orientation. Previous studies have shown that under short laboratory conditions, the symbionts are the first site of heterotrophic assimilation 62 . Since the coral host and symbiont are also known to share nutrients in both directions 62 it would be expected that the hosts and symbionts will reach a steady state where they have a similar signature, which was not the case for the shade-adapted corals. Hence, the different signatures between the host and symbionts for the shade-adapted corals suggest unequal sharing of resources, where the symbiont is receiving nutrients obtained from ingested prey by the host 63 , but also has an alternative nutrient source that is not being shared. One potential alternative nutritional source for the shade-adapted symbionts could be amino acid resynthesis, which may alter the carbon signature of these symbionts compared to the host leading to the results found here 55 . Regardless of the alternative nutrient source, our results suggest that the host-symbiont relationship in the shade-adapted corals is more likely parasitic rather than mutualistic 64 . Another explanation might be that the symbionts are responsible for a metabolic pathway that we are unaware of, like that in the pea aphids-Buchnera symbiosis where the symbiont sustains itself but also produces byproducts that are beneficial for the host 65 , 66 . Overall, we found that conspecific individuals living at the same location and depth but experiencing dramatically different light conditions had different consortia of symbiont species, differences in the probability of excitation energy transfer between photosynthetic units, and different amino acid isotopic signatures. These results all suggest that M. pharensis is highly plastic in its physiological response to light through a facultative symbiotic relationship. Thus, our findings highlight the capacity of corals to potentially switch to a predominantly heterotrophic diet when light availability and/or symbiont densities are too low to sustain sufficient photosynthesis. This potential metabolic plasticity may provide resilience for corals in the face of climate change, either through diet supplementation during periods of heat stress induced dysbiosis or through survival in low-light regions that may serve as thermal refuges."
} | 4,807 |
33674723 | PMC7935968 | pmc | 503 | {
"abstract": "Bioprocesses converting carbon dioxide with molecular hydrogen to methane (CH 4 ) are currently being developed to enable a transition to a renewable energy production system. In this study, we present a comprehensive physiological and biotechnological examination of 80 methanogenic archaea (methanogens) quantifying growth and CH 4 production kinetics at hyperbaric pressures up to 50 bar with regard to media, macro-, and micro-nutrient supply, specific genomic features, and cell envelope architecture. Our analysis aimed to systematically prioritize high-pressure and high-performance methanogens. We found that the hyperthermophilic methanococci Methanotorris igneus and Methanocaldococcoccus jannaschii are high-pressure CH 4 cell factories. Furthermore, our analysis revealed that high-performance methanogens are covered with an S-layer, and that they harbour the amino acid motif Tyr α444 Gly α445 Tyr α446 in the alpha subunit of the methyl-coenzyme M reductase. Thus, high-pressure biological CH 4 production in pure culture could provide a purposeful route for the transition to a carbon-neutral bioenergy sector.",
"introduction": "Introduction Methane (CH 4 ) is an energy carrier of worldwide importance. It can be produced through biogenic, thermogenic, and pyrogenic processes 1 . Most biogenic CH 4 is emitted by methanogenic archaea (methanogens) 2 , with minor amounts originating from cyanobacteria 3 and marine microorganisms 4 . Methanogens are a phylogenetically diverse group of microorganisms, which can be found in various anoxic environments 5 . Among other substrates, methanogens convert short chain organic acids and one-carbon compounds to CH 4 through their energy and carbon metabolism 2 , 5 , 6 . Their metabolic capability is important for anaerobic organic matter degradation in environments with low concentrations of sulfate, nitrate, manganese, or iron 5 . Moreover, methanogens are of biotechnological relevance due to their ability to produce isoprenoid-containing lipids 7 , 8 or polyphosphate 9 , and were recently described to excrete proteinogenic amino acids 8 . Methanogens are central to biofuels production, as they can be employed as autobiocatalysts for carbon dioxide (CO 2 ) and molecular hydrogen (H 2 ) conversion in the CO 2 -based biological CH 4 production (CO 2 -BMP) process. The CO 2 -BMP process can be employed in multiple applications such as biogas upgrading, power-to-gas applications, decentralized energy production, and for the conversion of H 2 /CO 2 of process flue gasses in waste to value concepts from, e.g., ethanol, petroleum, steel, and chemical industries 10 . There are two main approaches for CO 2 -BMP 11 : ex situ biomethanation using pure cultures 12 , 13 or enriched mixed cultures 14 – 16 , and in situ biomethanation 17 , 18 . In situ biomethanation is examined for upgrading the CH 4 content of biogas by adding H 2 to anaerobic digesters. Ex situ pure culture biomethanation exhibits high volumetric CH 4 productivity and offers a straightforward bioprocess control by utilizing biochemically and biotechnologically well-characterized microorganisms in pure culture 12 . Among the most studied organisms in this regard is Methanothermobacter marburgensis 12 , 19 , exhibiting several advantageous traits such as flexibility with regard to substrate gas impurities 10 and high CH 4 productivity 20 . In addition, M. marburgensis can be used for CO 2 -BMP when short-term transitions in the order of minutes are demanded between stand-by to full load biomethanation. Furthermore, downtime periods above 500 h did not reduce CH 4 productivity after a process restart 21 . Compared to CO 2 -BMP, chemical methanation or the “Sabatier reaction” should not be operated intermittently due to various catalytic constraints 22 and the fast bulk-like oxidation of the nickel catalyst in the CO 2 atmosphere 23 . Furthermore, activity loss of the chemical catalyst after a certain lifespan necessitates the exchange of the catalyst and the carrier material leading to periodic downtimes in production. Thus, applying methanogens, which are autobiocatalysts, offers numerous advantages compared to a chemically catalyzed CO 2 methanation. The lower power demand and the stable selectivity observed in CO 2 -BMP compared to chemical methanation 22 strongly suggest that CO 2 -BMP is a viable biotechnological alternative to chemical methanation. However, the autobiocatalytic characteristics of methanogens require further investigation. The CO 2 -BMP bioprocess can be operated as a gas transfer limited process 12 when a proper feeding strategy is applied 24 . In this case, the kinetic limiting step is the mass transfer of H 2 to the liquid phase. In biochemical engineering, gas to liquid mass transfer can be enhanced by several technical measures 20 . Besides reactor geometry and agitation, which influence the specific mass transfer coefficient (k L a), pressure increases the solubility of H 2 in the liquid phase. The influence of pressure on substrate uptake, growth, and production kinetics of methanogens is therefore an important parameter in CO 2 -BMP. Some experiments with Methanocaldococcus jannaschii have already been performed at high pressure in order to investigate transcription profiles 25 or growth and CH 4 production 26 . The effect of pressure on CH 4 production has also been examined in bioreactors 20 , 27 , while media for cultivation of methanogens have been developed and their growth assessed 28 – 31 . However, a systematic biotechnological survey with regard to nutritional demands of methanogens across different temperature regimes in the same cultivation conditions and at different pressure levels has not yet been the focus of any study. On the way to develop a high-pressure pure culture CH 4 production bioprocess, we systematically and quantitatively investigated the productivity of methanogens at pressures up to 50 bar. Growth, conversion, and CH 4 productivity were first examined in order to identify cell factories with the highest CH 4 productivity among 80 methanogens, in a range of different media (in terms of composition and medium amendments) and in conditions ranging from psychrophilic to hyperthermophilic. Secondly, the 14 prioritized fastest growing and with the highest productivity methanogens were investigated using a high frequency gassing (HFG) experiment and by using 10 bar H 2 /CO 2 to CH 4 conversion experiments. Among these 14 methanogens, four strains were chosen for the third step, consisting of 50 bar H 2 /CO 2 to CH 4 conversion experiments. Finally, we analyzed these results in the context of their natural habitat, temperature optima, specific genomic features, and their cell envelope architecture.",
"discussion": "Discussion Pure culture CO 2 -BMP is regarded as a key technology combining chemical energy storage, CO 2 utilization and biofuel production. Within CO 2 -BMP, methanogens are employed as autobiocatalytic CH 4 cell factories. Thus, we aimed to identify and characterize the highest performing CH 4 cell factories. This up to now unprecedented quantitative comparative physiological, bioinformatic, and biotechnological analysis provides a comprehensive view on growth and CH 4 production kinetics, essential nutritional components and barotolerance of 80 methanogens. The quantitative analysis of axenic methanogenic cultures enabled the identification of high performing cell factories for CH 4 production (high qCH 4 ) with a high maximum specific growth rate ( µ max ), straightforward cultivation methods (in terms of sterility, media demand, reproducibility), and tolerance to hyperbaric cultivation conditions. Psychrophilic methanogens reached a rather low OD max < 0.2 in this study. This could be explained by the fact that psychrophilic microbes have in general a slower metabolism or a longer doubling time compared to microorganisms that grow at higher temperatures 43 – 45 . The heterogeneous growth pattern of mesophilic methanogens on complex and defined media could be explained by their ecological and phylogenetic heterogeneity. Although high biomass concentrations are often linked to growth on complex medium, highest productive methanogens do not necessarily require complex medium to reach a high OD. The highest CH 4 productivities were achieved by Methanococci, and especially by Methanocaldococcus spp. and Methanotorris sp. which exhibited higher conversions and CH 4 production kinetics (Fig. 2 and Supplementary Fig. S 7 ) than thermophilic methanogens belonging to Methanobacteria. Methanococci were shown to possess a faster metabolism, indicated by higher CH 4 production kinetics, possibly due to the usage of [NiFeSe]-hydrogenases for H 2 oxidation. Instead of using [NiFe]-hydrogenases for the oxidation of H 2 as Methanothermobacter spp. (F 420 -reducing hydrogenase Frh and F 420 -nonreducing hydrogenase Mvh), Methanococcus spp. and Methanocaldococcus spp. use [NiFeSe]-hydrogenases (F 420 -reducing hydrogenase Fru and F 420 -nonreducing hydrogenase Vhu) that display much higher catalytic activities 46 , 47 . Additionally, Methanocaldococcus spp. do not harbor selenium-free hydrogenases 46 , 48 , 49 . The catalytic activity of [NiFeSe]-hydrogenases is greatly increased compared to [NiFe]-hydrogenases. Vhu of M. voltae showed a catalytic activity of 43,540 U mg −1 47 , whereas Mvh of M. marburgensis indicated a catalytic activity of 1600 U mg −1 50 . Our results reveal that methanogens, which showed the highest turnover rates and MERs, were covered with an S-layer. S-layer proteins can be positively or negatively charged, and it has been shown that charged S-layers enhance diffusion through the membrane 51 . The cell envelope of M. kandleri is known to be covered with an S-layer 36 , although no S-layer motif was found during our UniProtKB search. Therefore, one could hypothesize that the S-layer proteins present on M. kandleri are characteristic for this phylogenetic group. Furthermore, our bioinformatic analysis of MCRα revealed that all highly productive prioritized methanogens harbor the Tyr α444 Gly α445 Tyr α446 amino acid motif and belong to Class I methanogens (Supplementary Fig. S 8 ). Among the amino acids, especially cysteine is a required media supplement for certain methanogens (Supplementary Table S 2 ). Compared to the prioritized Methanobacteria, hyperthermophilic Methanococci have a necessity of cysteine in the cultivation media, although Class I methanogens (Methanobacteriales, Methanococcales, and Methanopyrales) use primarily sulfide and not cysteine as sulfur source, such as Class II methanogens 52 . The cysteine requirement of hyperthermophilic Methanococci in the medium could be linked to the usage of cysteine via cysteine desulphidase (CDD) for H 2 S, NH 4 + , H + , and pyruvate production 53 , the production of cysteine via the t-RNA dependent pathway (SepRS/SepCysS) 54 , 55 , and absence of cysteine desulphurase (CSD) 52 , 53 , 55 (Supplementary Table S 2 ). Besides that, CDD seems to be associated with the sulfur transfer for Fe-S cluster biosynthesis 55 – 57 . In case of M. fervidus , where CSD was found to be expressed and CDD had not been (Supplementary Table S 2 ), cysteine might have a key function in tolerating elevated temperatures 58 . Besides the nutritional demand of methanogens regarding cysteine, the TES that is used in a medium plays an important role in the biocatalytic activity. The trace element composition of a medium should mimic the heavy metal composition and respective concentrations present at the isolation spot, but might need to be optimized for meeting a biotechnological purpose. Based on our findings during the multivariate comparative analyses, methanogens that were cultivated on a medium with a rich-TES composition (TES1, TES2, TES4, and TES5) require additional cysteine or vitamins in the growth medium. Growth on a defined medium including a minimal/optimized TES (TES3), without cysteine or vitamins, was just possible for certain groups of methanogens, such as some Methanobacteria and M. maripaludis (cluster 3 in Supplementary Data 1 , Table S1 , and Supplementary Fig. S 6 ). Vice versa, strains that grow best on media with a rich-TES composition, cysteine or vitamin addition indicated poor growth and CH 4 productivity on a medium with a minimal TES (TES3, MM medium), even with cysteine and/or vitamins also added. This leads to the conclusion that the combination of a rich TES and the addition of cysteine and/or vitamins is essential for the tested hyperthermophilic methanogens to exhibit high MERs. We obtained the highest conversion and CH 4 production kinetics under hyperthermophilic and hyperbaric conditions. H 2 solubility at hyperbaric pressure of 10 or 50 bar leads to a 5- or 25-times higher substrate availability in the medium, compared to a cultivation at 2 bar. Therefore, adaptations to hyperbaric conditions, liquid limitation, and the suitability of the cultivation medium for high-pressure bioreactor cultivations can be studied if the experimental set-up is designed accordingly 12 , 20 , 24 , 30 . Instead of achieving a 5- and 25-fold productivity increase at 10 and 50 bar RCB experiments, an average of two- and threefold productivity increase was achieved, respectively. This might be due to cell envelope characteristics of the investigated methanogens and/or corresponding low pH 40 , lipid composition, limitation of conversion kinetics by a liquid nutrient, or not enough available catalytically active biomass (biomass limitation) to instantly convert the additionally available gas, which could also be a result from a liquid limitation or natural borders if the culture is growing at µ max . The CH 4 productivity pattern between RCB1 and RCB2 at 10 and 50 bar (Fig. 3 and M. marburgensis in Fig. 4 ) could be an adaptation response to hyperbaric cultivation conditions. However, the tested thermophilic and hyperthermophilic Methanococci have a different core lipid composition (archaeol, macrocyclic archaeol, and tetraether lipids) than Methanobacteria (archaeol and tetraether lipids). Strains from both orders increase the percentage of tetraethers under challenging growth conditions (Supplementary Table S 3 ). M. jannaschii decreases archaeol and increases the percentage of tetraether lipids with increasing temperature 59 , or temperature and pressure 60 , while M. marburgensis increases tetraether lipids (GDGT-0), when growing with detergents 61 . Moreover, M. okinawensis increases tetraether lipids (GMGT-0, GMGT-0′, and GDGT-0) and decreases archaeol upon addition of high amounts of inhibitors, such as ammonium chloride and/or methanol, except for formaldehyde, which leads to an increase of archaeol 7 , 8 . At 10 bar, putative liquid limitation or biomass limitation occurred during RCB3 and RCB4 ( M. marburgensis, M. thermautotrophicus, M. jannaschii, M. vulcanius, M. villosus, M. igneus , and M. kandleri ) (Fig. 3 and Supplementary Fig. S 9 ). However, at 50 bar putative liquid limitations arose right after RCB1 for Methanocaldococcus spp. and during RCB3 for M. marburgensis . Our findings indicate that just M. marburgensis is growing on a well-optimized medium (MM medium) 30 . The growth media (282c 18 or 282c 30) for Methanocaldococcus spp. would need to be adapted for hyperbaric applications. Although 282-based media were not yet designed for cultivations at 50 bar, the time for full conversion of H 2 /CO 2 was not affected in the cases of M. igneus and M. jannaschii , which did not show any retardation in CH 4 production during 50 bar cultivations. Perhaps these strains could be tested at higher pressure conditions, such as M. okinawensis , which showed CH 4 production up to 90 bar 40 . Methanocaldococcus spp. exhibited higher specific growth rates than M. marburgensis (Supplementary Data 1 and Table S1 ), and thus liquid limitation occurs faster. Besides that, the metabolism of M. marburgensis is slower compared to Methanocaldococcus spp., indicated by the lower k min values of Methanocaldococcus spp. (Supplementary Fig. S 11 ). Therefore, the liquid limitation in our setup might not have had a strong effect. This study on high-pressure biological CH 4 production in pure culture is a cornerstone of the emerging research and development field of Archaea Biotechnology 19 . The systematic assessment indicated that the high-performance strains belong to Class I methanogens. Hyperthermophilic Methanococci are high-pressure CH 4 production cell factories and the addition of cysteine and a rich TES in the media are essential for efficient growth of these Methanococci. Therefore, we propose to perform bioprocess development utilizing M. igneus and M. jannaschii to develop these organisms into high-pressure CH 4 cell factories. Moreover, methanogens that exhibited the highest turnover rates and MERs are covered with S-layers, and the amino acid motif Tyr α444 Gly α445 Tyr α446 in the alpha subunit of MCR is present in all high-performance methanogens. This analysis sets the foundation for a future high-pressure bioprocess optimization endeavor with the identified hyperthermophilic CH 4 cell factories. The autobiocatalytic activity of hyperthermophlic, autotrophic, hydrogenotrophic methanogens could therefore be employed for balancing the power grid system (energy storage) or to biologically depressurize H 2 and/or CO 2 containing emission flue gasses to CH 4 via the CO 2 -BMP process. High-pressure biological CH 4 production in pure culture could provide a purposeful route for the transition to an independent carbon-free or low-carbon energy bioeconomy."
} | 4,452 |
21434644 | null | s2 | 504 | {
"abstract": "This manuscript describes the fabrication of arrays of spatially confined chambers embossed in a layer of poly(ethylene glycol) diacrylate (PEGDA) and their application to studying quorum sensing between communities of Pseudomonas aeruginosa. We hypothesized that biofilms may produce stable chemical signaling gradients in close proximity to surfaces, which influence the growth and development of nearby microcolonies into biofilms. To test this hypothesis, we embossed a layer of PEGDA with 1.5-mm wide chambers in which P. aeruginosa biofilms grew, secreted homoserine lactones (HSLs, small molecule regulators of quorum sensing), and formed spatial and temporal gradients of these compounds. In static growth conditions (i.e., no flow), nascent biofilms secreted N-(3-oxododecanoyl) HSL that formed a gradient in the hydrogel and was detected by P. aeruginosa cells that were ≤8 mm away. Diffusing HSLs increased the growth rate of cells in communities that were <3 mm away from the biofilm, where the concentration of HSL was >1 μM, and had little effect on communities farther away. The HSL gradient had no observable influence on biofilm structure. Surprisingly, 0.1-10 μM of N-(3-oxododecanoyl) HSL had no effect on cell growth in liquid culture. The results suggest that the secretion of HSLs from a biofilm enhances the growth of neighboring cells in contact with surfaces into communities and may influence their composition, organization, and diversity."
} | 366 |
36780524 | PMC9974440 | pmc | 505 | {
"abstract": "Significance Global warming is causing the loss of coral reefs worldwide, as a result of heat-induced coral bleaching and mortality. Here, we examined the potential mechanisms that have increased the heat resistance of dominant framework-building coral taxa ( Pocillopora spp.) on reefs in the eastern tropical Pacific. We propose that increasing abundance of a thermotolerant symbiotic alga ( Durusdinium glynnii ) hosted by these corals has facilitated the maintenance of high coral cover after three mass coral bleaching events. This study reveals a mechanism by which some reefs may be more resilient than previously thought and illustrates how future reefs might still maintain high cover for several decades, albeit with low diversity, provided other stressors are minimized.",
"conclusion": "Concluding Remarks. The future of coral reefs in the ETP largely depends on the capacity of Pocillopora populations to persist and sustain reef accretion under rapid climate change. Until now, most ETP reefs have been resilient to strong El Niño disturbances, exhibiting recovery of coral cover after massive mortality, as well as higher resistance to heat stress during the last warming events ( 9 , 11 ). This pattern contrasts with regional declining trends in coral cover in the Caribbean ( 57 ), the Indo-Pacific ( 58 ), and the Great Barrier Reef ( 59 ). Although multiple mechanisms likely contribute to resilience in ETP reefs (e.g., healthy herbivore populations, dominance of fast-growing coral species, isolation from other anthropogenic disturbances, selection of resistant host genotypes) ( 11 , 41 ), our results suggest that the acquisition of thermotolerant algal symbionts by Pocillopora type 1, as well as the selection of colonies hosting this symbiont, have likely played a dominant role in increasing tolerance to temperature disturbances and allowing these framework-builders to maintain reef structures that have retained stable coral cover over the latest El Niño-related marine heatwaves. Moreover, based on relatively modest bleaching and mortality following the last two heatwaves, these changes appear to have primed these reefs to be more thermotolerant to future heat disturbance. Even under worst-case emissions scenarios (SSP5-8.5), pocilloporid ETP reefs hosting thermotolerant D. glynnii may be able to persist with high levels of coral cover well into the second half of the current century, indicating that some reef systems may be more resilient to warming than previously thought, and suggesting that the winnowing of reef communities to a few resilient species might be a common fate for some reefs. However, although the low-diversity, high-cover reefs of the ETP may illustrate a potential ecological state for some future reefs, this state may only be temporary unless global greenhouse gas emissions and resultant global warming are curtailed.",
"discussion": "Discussion Global climate models predict that most reefs in the world will experience annual heat stress levels >8 DHW before 2070 ( 34 ). Despite growing evidence of increased heat tolerance in some coral species, much about coral’s adaptive capacities and the underlying mechanisms remains unknown. ETP reefs, in general, have been resilient to three mass bleaching events and have either recovered from or tolerated high heat stress levels associated with El Niño ( 9 ). Although the 1982 to 1983 El Niño caused devastating coral mortality across the ETP (>85% cover loss), with the exception of the central and southern Galápagos ( 35 ), most reefs recovered 10 to 15 y following massive mortality and experienced less bleaching and mortality in subsequent heatwaves ( 9 , 11 , 23 , 25 , 36 , 37 ). Understanding the mechanisms by which these reefs have acclimatized or adapted to warmer temperatures will help elucidate the potential limits of increased thermal resistance, as well as the physiological and ecological trade-offs that may result from it (e.g., loss of biodiversity, slower coral growth, or higher susceptibility to other stressors). Based on the comparative responses of different coral taxa to repeated warming, and on the dynamic changes in the algal symbiont communities described during the last heat event, we propose that ETP reefs have increased their heat resistance through mechanisms that include 1) the selection of Pocillopora over other coral genera that do not associate with thermotolerant symbionts in the region; 2) the selection of Pocillopora type 1 over type 3 [since the former associates with D. glynnii more frequently than other Pocillopora types in the region ( 38 , 39 )] , and 3) the potential acquisition of D. glynnii during or after heat stress in Pocillopora colonies initially hosting Cladocopium ( 10 ). It is likely that these resistant corals repopulated ETP reefs after mass coral bleaching and mortality through a combination of tissue resheeting ( 40 ), asexual proliferation of surviving clones ( 41 ), and the recruitment of local, sexually produced larvae from these surviving corals ( 42 , 43 ), which would result in a gradual increase in the heat-resistance of the overall coral community, but could reduce coral diversity and potential resilience to other stressors. The ETP harbors one of the least diverse coral faunas in the tropics, with reefs commonly dominated by a few species in the genera Pocillopora and Porites , complemented in lower abundance by Pavona , Gardineroseris and Psammocora ( 44 ). After multiple mass bleaching events in the ETP, Pocillopora has exhibited higher recovery capacity compared to other scleractinians, likely related to its higher growth rate and capacity for asexual reproduction through fragmentation, as well as increased heat resistance, related to its capacity to host thermotolerant D. glynnii . Contrastingly, nonpocilloporid corals in the ETP either continued to be highly susceptible to heat (e.g., P. varians and P. clavus ) or showed moderate heat susceptibility under medium levels of stress that increased as bleaching conditions became more extreme (e.g., G. planulata and P. lobata ). The algal symbiont communities in these nonpocilloporid species in the ETP are dominated by Cladocopium ( 23 , 45 ) with the occasional detection of other symbiont genera, including Durusdinium, at only background densities ( 46 ). Under the ocean temperatures anticipated to occur in the next two decades in the region (expected annual maximum DHW = 3 to 10), ETP reefs are likely to maintain high coral cover composed of dense Pocillopora frameworks and positive reef accretion ( 47 ). However, these anticipated heat levels could be enough to further reduce coral diversity, with nonpocilloporid species becoming progressively less abundant. The loss of the ecological functions of massive, plating, and nodular species could lead to the loss of ecological redundancy and resilience to other stressors. More frequent heatwave events in the next decades could also alter the relative abundance of Pocillopora types in the ETP and might lead to a further loss of diversity. However, these changes would be difficult to quantify given the overlapping morphologies of the Pocillopora lineages ( 39 , 48 ). Differential responses to heat stress have been detected among cryptic Pocillopora lineages at Moorean reefs (French Polynesia), and resulted in lower relative abundances of a sensitive type in locations that experienced more severe heat stress ( 15 ). In our study, the response of the Pocillopora lineages suggested that type 3 colonies were less likely to host and acquire thermotolerant D. glynnii compared with type 1 , and that the type 3 lineage was more susceptible to bleach and die under heat stress. This hypothesis requires further testing given that the number of type 3 colonies sampled was low and none of them hosted D. glynnii, thus the effects of the host and symbiont identity cannot be separated . Although other studies have found that D. glynnii association with type 3 colonies is less common compared to type 1 ( 39 ), this symbiont–host combination has been reported in the ETP ( 38 ). Controlled experiments that compare the performance and physiological response to environmental stressors among the different Pocillopora lineages, in combination with their multiple algal symbionts, are necessary to anticipate future changes in these host–symbiont distributions. The wider distribution of Pocillopora type 1 in the ETP compared with type 2 (only reported for Clipperton Island) and type 3 (reported in the southern locations such as Panama and The Galapagos), suggests a higher competitive capacity of type 1 in the region ( 49 ). Although the higher success of type 1 could be associated with higher heat resistance, it is also possible that this lineage responds better to other common ETP disturbances such as the upwelling of cold and nutrient-rich water, high turbidity, or depressed aragonite saturation states ( 50 ). During the 2015 to 2016 El Niño, two different processes resulted in increasing frequency of Pocillopora colonies dominated by D. glynnii: higher mortality of colonies that predominantly hosted Cladocopium in 2014 (in Pocillopora type 1 and 3) and increasing abundance of D. glynnii in colonies initially dominated by Cladocopium (in Pocillopora type 1). Increases in the proportion of Pocillopora hosting D. glynnii have been also reported for the Gulf of California after a low-temperature bleaching event ( 17 ). However, during this event, the proportion of Pocillopora type 1 hosting D. glynnii mainly increased through the mortality of Cladocopium- colonies ( 17 ), while changes in the composition of the symbiont communities of the surviving colonies (symbiont shuffling) were minimal ( 28 ). These differences between Pocillopora type 1 symbiont dynamics in these studies could reflect the differential effect of cold bleaching vs. heat bleaching ( 17 , 28 , 51 ), variations in the severity of the bleaching stress ( 27 ), or warmer recovering temperatures during the 2015 to 2016 event, which could favor the proliferation of thermotolerant symbionts in the surviving colonies over more heat-sensitive symbionts ( 52 ). Contrasting with Pocillopora type 1, we did not observe symbiont changes in type 3 colonies during the 2015 to 2016 heatwave. Since Pocillopora type 3 is known to associate with both Cladocopium and D. glynnii in the ETP ( 38 ), it is possible that the acquisition of novel algal types in this coral is limited to early-life stages ( 53 ), or that different disturbances or recovery conditions are required to induce changes in the composition of Pocillopora type 3 symbiont communities ( 52 ). Based on the algal symbiont dynamics observed in tagged colonies during 2014 to 2016, we also estimated that selective mortality alone is unlikely to fully explain the changes in Pocillopora algal communities recorded during the 1997 to 1998 heatwave ( 10 ). Indeed, our results from 2014 to 2016 and estimations of the symbiont dynamics during 1997 to 1998 support that acquisition of D. glynnii by the surviving colonies is an important driver of the increased thermotolerance of these reefs. However, our ability to extend these projections further back to 1982 to 1983 is constrained by a lack of data on algal symbiont community structure before or after this event. Nevertheless, the loss of >90% of pocilloporid cover on these reefs following the 1982 to 1983 event points to a naive coral assemblage dominated by Cladocopium, especially since the magnitude of the 1982 to 1983 event was similar or less than the subsequent two events (7.6 DHW compared with 10.3 and 8.0 DHW) and the fact that D. glynnii imparted high thermotolerance to pocilloporids during the latter two events. The balance of evidence suggests that the relative abundance of D. glynnii on these reefs prior to 1982 to 1983 was extremely low, likely accompanied (and perhaps driven by) a relatively high abundance of type 3 Pocillopora , which together rendered these reefs vulnerable to high-temperature stress. Given the increasing prevalence of D. glynnii colonies after bleaching, we anticipate that this symbiont would become predominant in pocilloporid reefs as heat stress events grow in frequency and intensity. These symbiont changes would likely increase the bleaching threshold of the reef and could delay the time at which unsustainable heat stress levels will be reached ( 32 , 33 , 54 ). To explore this scenario, we incorporated two additional thresholds for DHW accumulation based on the higher thermotolerance expected for D. glynnii [e.g., maximum monthly mean (MMM) + 2.5 °C and MMM + 2.25 °C compared with MMM + 1 °C], rather than defining different DHW thresholds at which resistant vs. sensitive taxa are affected (e.g., >15 DHW vs. >5 DHW). Indeed, because reef communities comprise diverse taxa with varied thermal tolerance, characterizing the magnitude of thermal stress at a particular site in terms of a single DHW value can be problematic for rapidly changing reefs because it assumes a single bleaching threshold. We therefore avoided treating our site of interest as being characterized by a certain DHW and instead used the DHW metric to refer to the amount of thermal stress experienced by the organism(s) of interest (in this case holobiont combinations). Such considerations may improve our ability to detect or model adaptive or compensatory responses into future conditions. It is important to note, however, that exact present and future bleaching thresholds for the Pocillopora + D. glynnii holobionts remain uncertain, and caution should be taken when projecting trajectories of future coral cover. Because of the high dominance of Pocillopora spp. in the ETP, the mechanisms for increased heat tolerance described here likely apply to other coral reefs throughout this region. The milder bleaching responses after the 1997 to 1998 heatwave in reefs from Ecuador, Costa Rica, and Colombia, which had previously experienced widespread bleaching and mortality in 1982 to 1983 ( 23 – 25 ), support the generality of increased heat tolerance in the ETP. However, different hydrographic conditions are likely to lead to differential persistence of acquired heat tolerance following a bleaching event. For example, ETP locations with colder water temperatures (e.g., Baja California and The Galapagos Islands) or that experience seasonal upwelling (e.g., Gulf of Panama and Bahia Salinas) ( 50 ) might be more likely to reverse to algal communities that are dominated by Cladocopium ( 52 , 55 ) , and have sometimes escaped exposure to previous heatwaves ( 56 ). Although the resulting differences in the current prevalence of D. glynnii among pocilloporid ETP reefs could lead to differences in the present-day heat tolerance in different locations, under future warmer ocean temperatures an increasing number of reefs are likely to experience conditions favorable for D. glynnii proliferation. Concluding Remarks. The future of coral reefs in the ETP largely depends on the capacity of Pocillopora populations to persist and sustain reef accretion under rapid climate change. Until now, most ETP reefs have been resilient to strong El Niño disturbances, exhibiting recovery of coral cover after massive mortality, as well as higher resistance to heat stress during the last warming events ( 9 , 11 ). This pattern contrasts with regional declining trends in coral cover in the Caribbean ( 57 ), the Indo-Pacific ( 58 ), and the Great Barrier Reef ( 59 ). Although multiple mechanisms likely contribute to resilience in ETP reefs (e.g., healthy herbivore populations, dominance of fast-growing coral species, isolation from other anthropogenic disturbances, selection of resistant host genotypes) ( 11 , 41 ), our results suggest that the acquisition of thermotolerant algal symbionts by Pocillopora type 1, as well as the selection of colonies hosting this symbiont, have likely played a dominant role in increasing tolerance to temperature disturbances and allowing these framework-builders to maintain reef structures that have retained stable coral cover over the latest El Niño-related marine heatwaves. Moreover, based on relatively modest bleaching and mortality following the last two heatwaves, these changes appear to have primed these reefs to be more thermotolerant to future heat disturbance. Even under worst-case emissions scenarios (SSP5-8.5), pocilloporid ETP reefs hosting thermotolerant D. glynnii may be able to persist with high levels of coral cover well into the second half of the current century, indicating that some reef systems may be more resilient to warming than previously thought, and suggesting that the winnowing of reef communities to a few resilient species might be a common fate for some reefs. However, although the low-diversity, high-cover reefs of the ETP may illustrate a potential ecological state for some future reefs, this state may only be temporary unless global greenhouse gas emissions and resultant global warming are curtailed."
} | 4,299 |
35529205 | PMC9070864 | pmc | 508 | {
"abstract": "Compared with superhydrophobic surfaces, superamphiphobic surfaces have a wider commercially availability. However, the initially fragile nature of micro or nano-structures hinders the large-scale applications of superamphiphobic surfaces. In this work, we report free-standing monoliths with durable superamphiphobic properties not only in the outer layer surfaces but also extending throughout the whole volume, which will demonstrate permanent superamphiphobicity. The monolith surface can repel a series of organic solutions with a surface tension as low as 36.4 mN m −1 , and display good self-cleaning effect toward to blood or viscous mud. In addition, the monolith can maintain the superhydrophobicity no matter whether facing corrosive solution attack or mechanical abrasion, indicating the excellent chemical and mechanical properties. The monolith surfaces also display delayed-icing property and easy de-icing process.",
"conclusion": "4. Conclusions In conclusion, a simple and easy method was developed to make free-standing monoliths with super-robust superamphiphobic properties. The monoliths surface can repel the different organic solution with the surface tension as low as 36.5 mN m −1 and resists adhesion by blood and mud. The monolith shows a remarkable robustness and can maintain the superhydrophobic and superoleophobic properties to harsh environments such as strong corrosion, UV irradiation, high temperature, and long-term water droplet impact. In addition, the superamphiphobic monolith surface demonstrates the performance of delayed icing, and the frozen ice droplets can be removed under a minimal force, showing the characteristics of easy de-icing. This method provides a new thinking for the fabrication of super-robust superamphiphobic surface and yield a prospective candidate for various practical applications in real world.",
"introduction": "1. Introduction Superamphiphobicity is an effect where surface roughness and surface chemistry combine to generate surfaces which are both superhydrophobic and superoleophobic, i.e. , contact angles greater than 150° along with low contact angle hysteresis not only towards probing water but also for low surface tension ‘oils’. 1 Superamphiphobic surfaces that exhibit both water-repellent and oil-repellent properties, especially with a contact angle for both water and oil at 150° or above, have attracted much attention in practical applications of water repellency, self-cleaning, 2,3 anti-freezing, 4–6 biological and organic contamination prevention, 7,8 and fingerprint-resistant touch-screen devices. 9 The preparation of superamphophobic surfaces presents a greater challenge since lipophilic liquids such as cooking oils have a lower surface tension compared to that for water. 10–12 Meanwhile, textured surface required for superamphiphobic surface exhibits roughness on both the nanometer and the micrometer scales. 13,14 So far, many methods have been developed to meet this demand for hierarchical roughness in the quest for superamphiphobic surface. 15–17 However, the fragile nature of the textured surface is easily destroyed, which hinders their large-scale applications such as for structural materials. It is still a challenge to fabricate a robust superamphiphobic surface. 18–20 Superhydrophobic and superoleophobic bulk materials with features in the nanoscale are proposed as a new concept in designing damage-tolerant superhydrophobic and superoleophobic materials. 21 Bulk monoliths possessing low surface-energy microstructures extending throughout its whole volume are regarded as good candidate for damage tolerant superamphiphobicity. When the uppermost layer is damaged or removed upon scrape abrasion, the newly exposed rough surface with low surface-energy is also water-repellent, thereby making the superamphiphobic property permanent. 22 In our previous work, 23 we presented a general method to fabricate super-robust superhydrophobic blocks through compressing nanoparticles and a series of polymers. These free-standing blocks are independent of substrates and show high abrasion-resistant properties. In this work, we demonstrate the simple compressing method to fabricate free-standing superamphiphobic monoliths with excellent mechanical and chemical stability. The obtained monoliths show a series of liquid repellency and self-cleaning properties. The ice accretion and deicing process on the obtained water-repellency surfaces also have been investigated, and it is found that the melt icing droplet can easily slide away from the surface. Not that a slight external force can make the ice droplet leave the monolith surface, which means that the deicing process is much easier compared with the hydrophilic surface. 24",
"discussion": "3. Results and discussions 3.1 Liquid repellency of the monoliths The obtained monoliths can repel a series of organic solution with different surface tension, and the relationship between the contact angle, sliding angle and the solution surface tension is shown in Fig. 1 . It can be seen that contact angle increases with the solution surface tension, while the sliding angle decreases with the surface tension. The monoliths show superoleophobicity to the organic solutions with surface tension as low as 36.4 mN m −1 , and the liquid contact angle is about 155° and sliding angle is about 32°. Fig. 1 Contact angle (a) and sliding angle (b) as a function of different solution surface tension. 3.2 Characterization and durability of the superhydrophobic monoliths \n Fig. 2(a) and (b) shows the water and ethylene glycol droplets sitting on the monolith surface with a static water contact angle (CA) of 171 ± 1° and 167 ± 1°, respectively. The monolith is so amphiphobic that the water or ethylene glycol droplets readily slide away from the surface therefore showing excellent self-cleaning properties (Video S1 and S2 † ). Since the low surface energy and microstructures extending throughout the whole volume of the monoliths, the monolith internal parts also demonstrate superoleophobicity and it is clear that the ethylene glycol droplets on the interior area retain spherical shapes with oil contact angle about 166 ± 1° ( Fig. 2(c) ). The wettability of the monoliths under oil (liquid paraffin) was also investigated, and it is found that the water contact angle is 174 ± 1° (Fig. S1 and Video S3 † ), which demonstrates that the monolith is also superhydrophobic under oil. In combination with FT-IR (Fig. S2 † ) analysis, the hydrophobic –CH 3 was successfully modified on the surface of the SiO 2 nanoparticles. From SEM image ( Fig. 2(d) ), it is clear that the surface is rough, which is a requirement for superamphiphobicity. From element distribution maps of the superamphiphobic monolith (Fig. S3 † ), it can be clearly seen that SiO 2 and FC-70 are well-distributed in the monoliths. Thermogravimetric (TG) analysis shows that the superamphiphobic monolith loss 4% of its weight before 200 °C, indicating the thermal stability under 200 °C ( Fig. 2(e) ); this would meet most of the required in our daily life. Fig. 2 Optical image of water (a) and (b) ethylene glycol droplets sitting on the obtained monolith surface with contact angle about 171 ± 1° and 167 ± 1°. (c) Water (upper) and ethylene glycol (lower) test at the cross-sectional crack to show superamphiphobicity inside the monoliths. SEM (d) and TG (e) analysis of the obtained monoliths. (f) Radar diagrams of the superamphiphobic monoliths. Herein, “WCA initial” and “WSA initial” refer to the water contact angles and water sliding angles of the samples without any mechanical and chemical tests. “WCA after abrasion” and “WSA after abrasion” refer to the water contact angles and water sliding angles that were measured after the sample being abraded for 1500 cm. “EGCA initial” and “EGSA initial” refer to the ethylene glycol contact angles and ethylene glycol sliding angles of the sample before any mechanical and chemical tests. “EGCA after abrasion” and “EGSA after abrasion” refer to the ethylene glycol contact angles and ethylene glycol sliding angles that were measured after the sample being abraded for 1500 cm (32 g loads, SiC, 220 Cw sandpaper). “WCA pH = 1 and EGCA pH = 1” refer to the water and ethylene glycol contact angles measured after 50 min acid attack. “WCA pH = 14 and EGCA pH = 14” refer to the water and ethylene glycol contact angles measured after 50 min alkali attack. Radar diagram was used to evaluate the mechanical and chemical durability of the monoliths, and the experimental data as shown in Fig. 1(f) . In the radar diagrams, we included contact angle and sliding angle of water and ethylene glycol droplet before and after sandpaper abrasion (32 g loads, SiC, 220 Cw sandpaper) about 1500 cm; water and ethylene glycol contact angle was also measured after the “strong corrosive soak test” for 50 min; Table S1 in ESI † shows the rating system of the radar diagram according to the performance of the samples, and their data sheets were as shown in Table S2 (ESI † ). The larger overall points that a sample achieved, the larger area on the radar diagram will be obtained, which indicates better performance. To further quantify the abrasion-resistance of the superamphiphobicity, we investigated the functional of water and EG contact angle as the abrasive distance and the detailed information was shown in Fig. S5(a and b). † For water droplet, the abrasion shows no obvious influence on the contact angle and sliding angle; the contact angle maintains about 170° and the sliding angle less than 4° as the abrasive distance goes. As far as the lower surface tension liquid, such as ethylene glycol, the contact angle decreases and the sliding angle increases as the abrasive distance goes. However, the monolith keeps the superamphiphobic properties after 1500 cm abrasive distance, demonstrating the excellent abrasion resistance. Chemical stability is very important for superamphiphobic surface facing the real world application. Two independent methods were employed to further investigate the anti-corrosion properties of the surface, and the detailed information was shown in Fig. 3 . In the “droplet test”, strong acid and alkali droplets were dropped on superamphiphobic monolith surface, respectively, as shown in Fig. 3(a) and (b) . It is noted that the water droplets became smaller due to the evaporation, and as acid/alkali contact time increased, the CAs of acid/alkali droplets slightly decreased. Despite slight decreases in the contact angle with contact, the monolith can maintain its superhydrophobicity under 100 min corrosive attack. In a more aggressive test, the monoliths were immersed into acid (pH = 1) and alkali (pH = 14) baths, as shown in Fig. 3(c–f) . Although the water/ethylene glycol contact angle decreases and sliding angle increases with the increasing soak time, the monoliths can retain the superamphiphobicity in a strong corrosive solution for more than 40 min, and this is very important for superamphiphobic monoliths in our daily life use. Fig. 3 (a and b) Water contact angle as a function of time in acidic/alkali droplet contact tests. (c and d) Water contact angle as a function of time in acidic/alkali soak tests. (e and f) Ethylene glycol contact angle as a function of time in acidic/alkali soak tests. In consideration of outdoor applications, the monoliths surface is expected to be UV resistant. The obtained monoliths were placed in an UV accelerated weathering tester (wavelength: 313 nm) for 7 days to evaluate their UV resistance. The samples were taken out at a specific time each day for the WCA and WSA measurements. As shown in Fig. 4(a) and (b) , under UV irradiation for 7 days, the water CA value and ethylene glycol CA value are larger than 150°. The water SA value maintains below than 10° during the process, indicating excellent resistance to UV light. However, the ethylene glycol SA value increases to about 32° after being exposed under UV irradiation for 7 days, which indicates the UV irradiation has a deep effect on the oil adhesion property. Fig. 4 Stability tests. (a and b) Contact angle and sliding angle in UV accelerated aging tests at 40 °C; (c and d) contact angle and sliding angle as a function of time in water washout tests. (e and f) Water/EG contact angle and sliding angle on superamphiphobic monoliths surface after being calcined at different temperature. Superamphiphobic surfaces are usually subject to rainwater impact because the poor surface mechanical stability. In consideration of outdoor applications, we investigated the superamphiphobic durability of the monoliths surface facing the rainwater impact. As shown in Fig. 4(c) and (d) , after exposure at rain washout for 6 h, the monolith surface still maintain superhydrophobic and superoleophobic properties, and the SA value for water and ethylene glycol increases with washout time. It is notice that ethylene glycol sliding angle increases to 180°, indicating the high adhesion of the monolith surface. In order to restore the original superhydrophobicity, the water impact layer just needs to be removed by mechanical abrasion to expose the interior of the superamphiphobic monolith material. To further indicate the thermal stability, the monolith was calcined at different temperatures for 1 h under an air atmosphere. As shown in Fig. 4(e) and (f) , the superamphiphobicity has hardly changed before 400 °C, and the monolith surface retained the water and ethylene glycol CA value of >150°; water and ethylene glycol SA value of <10° during the process. It's worth noticing that when the temperature continues to rise to 500 °C, the surface wettability turns from superhydrophobic to superhydrophilic, which due to the volatilization of hydrophobic FC-70 and the breakage of long-chain alkane bonds modified on the surface of silica at a high temperature. 3.3 Self-cleaning and anti-icing properties The superhydrophobic surfaces, like a lotus leaf, have excellent self-cleaning properties. Herein, the obtained superamphiphobic monoliths also show dirty resistance, as shown in Fig. 5 and Video S4–S6, † after vigorous stirring blood and mud for several times, the monolith remained dry and clean. Water or ethylene glycol droplets could easily take away the soil and leave the monolith surface clean ( Fig. 5(k–o) ). Fig. 5 Self-cleaning properties of the obtained monoliths. No adhesion after dipping and stirring in blood (a–e) and mud (f–j) for several times. (k–o) water or ethylene glycol droplet can bounce and take away the dirt. Numerous studies have shown that surface wettability affects the nucleation time of undercooled water, 26 Hydrophilic surface is easy to be wetted with water droplets and the large contact areas will increase the possibility of nucleation, which is crucial to the icing rate. Whereas superhydrophobic surface with Cassie–Baxter state always exhibits the longest freezing delay time, due to the multitudinous air pockets between the liquid/ice and solid. The pockets sharply reduced the actual contact area and the adhesion on the surface accordingly and effectively restrained thermal conduction at low temperature. As shown in the Fig. 6(a–c) and Video S7, † after the low temperature icing-thawing process, the superhydrophobic monolith surface remains contact angle about 158°. From Fig. 6(d) , it is clear that the superhydrophobic monolith surface can delay the icing time compared with the hydrophilic glass surface. For example, water droplet icing time is only 49 s on hydrophilic glass surface at −10 °C. However, on superhydrophobic monolith surface, it needs 302 s. Note that the icing can be get rid of from the monolith surface under a slight external force which means the easier deicing process. As shown in Video S8, † it is clear that the water droplets froze on a large area and adhered firmly to the hydrophilic glass surface after freezing at −10 °C for 1 h, and it is not easy to get rid of the ice beads. Conversely, spherical ice beads can be removed easily from superhydrophobic monolith surface under a slight external tap. Fig. 6(e) shows the detailed processes of icing and thawing on superhydrophobic monoliths and hydrophilic glass surfaces. After having been frozen for 1 h, the sample was put into ambient environment to melt at room temperature. The melting process need longer time on superamphiphobic monolith surfaces than on the hydrophilic glass surfaces, which indicates that the air pockets are a disadvantage of melting. However, the melted droplet has a high contact angle value (more than 150°) on the monolith surface and a lower sliding angle (lower than 20°), and the melted droplet can slide away easily from the monolith surface and no wet was left. Whilst, the melted water droplet adheres on glass surface even being turned upside down, which also means it is not easy of the de-icing process on hydrophilic glass surface. Fig. 6 Anti-icing process at different low temperatures (a) at initial time; (b) freezing; (c) after being melted at room temperature. (d) The total icing time for a droplet at different low temperatures. (e) Detailed ice formation and thawing process on superhydrophobic monolith and hydrophilic glass surface."
} | 4,315 |
36130968 | PMC9492681 | pmc | 509 | {
"abstract": "Engineered living materials (ELMs) embed living cells in a biopolymer matrix to create materials with tailored functions. While bottom-up assembly of macroscopic ELMs with a de novo matrix would offer the greatest control over material properties, we lack the ability to genetically encode a protein matrix that leads to collective self-organization. Here we report growth of ELMs from Caulobacter crescentus cells that display and secrete a self-interacting protein. This protein formed a de novo matrix and assembled cells into centimeter-scale ELMs. Discovery of design and assembly principles allowed us to tune the composition, mechanical properties, and catalytic function of these ELMs. This work provides genetic tools, design and assembly rules, and a platform for growing ELMs with control over both matrix and cellular structure and function.",
"introduction": "Introduction Naturally occurring living biomaterials, such as bones or wood, grow bottom-up from a small number of progenitor cells into macroscale structures 1 . Engineered living materials (ELMs) 2 – 4 are inspired by naturally occurring living materials, but use synthetic biology to introduce tailored, non-natural functions. By incorporating engineered cells into a biopolymer matrix, these materials can function as living sensors 5 , therapeutics 6 , biomanufacturing platforms 7 , electronics 8 , energy converters 9 , and structural materials 10 . While cells confer functionality to ELMs, the matrix assembles the material and controls the bulk material composition, structure, and function 11 . Since the matrix plays such a key role in generating material properties, one primary goal of the field is to create ELMs that both have a synthetic biomolecular matrix—that can control these properties—and grow autonomously into macroscopic structures. However, such bottom-up, de novo ELMs are considered well beyond the current state-of- art 11 because secreting recombinant biopolymers at concentrations that gelate is challenging 12 and because the assembly of micrometer-sized cells into centimeter-scale materials requires self-organization across length scales spanning four orders of magnitude. Engineering principles to achieve this assembly are unknown 11 , 12 . Therefore, most macroscopic ELMs have been produced by adopting a top-down approach (such as 3D printing) to incorporate living cells into an exogenous matrix 6 , 13 , 14 or by processing microscopic ELMs that grow a synthetic biomolecular matrix into macroscopic materials 15 – 19 . The few autonomously produced, macroscopic ELMs have been created by genetically modifying existing nanocellulose matrices 20 or genetically manipulating the mineralization of silica matrices. However, these two approaches to autonomously produced, macroscopic ELMs have afforded little genetic control over the mechanical properties, e.g., ~1.2–1.4-fold change in the storage modulus 20 , 21 . This tunability is much more limited than the tunability of naturally occurring materials, chemically synthesized materials, or macroscopic ELMs produced by processing 22 , 23 . We posit that new strategies for developing synthetic biomolecular matrices to self-assemble bacteria into macroscopic ELMs can be informed by prior work on surface-engineered bacteria and surface-modified colloidal particles. The surface of Escherichia coli has been engineered to display interacting proteins, such as leucine zippers 24 or antigen-nanobody pairs 25 , via outer membrane proteins. Engineered strains that display interacting pairs will self-assemble into cell–cell aggregates that flocculate 24 , 25 ; however, these aggregates are microscopic and must be processed to form larger materials 18 . In contrast, micron-sized colloidal particles (typically polystyrene) that display DNA have been programmed to self-assemble into both microscopic 21 and macroscopic crystalline solids 26 . Over two decades of work on these systems has established central principles that underlie their self-assembly 27 . One of these central principles is that the interactions between particles must be mediated by high-density surface modifications, e.g., 1 DNA molecule per 27 nm 2 , 26 . Since the outer membrane proteins used for bacterial adhesins are displayed at ~5% of this density, i.e., 1 nanobody per 640 nm 2 , 28 , we hypothesized that a matrix composed of self-interacting proteins displayed on bacteria at high density could lead to the formation of macroscopic solid materials. We have previously engineered the surface layer (S-layer) of the oligotrophic bacterium Caulobacter crescentus for high-density peptide display 29 and biopolymer secretion 23 . The S-layer forms a 2D crystalline layer on the extracellular surface of C. crescentus , opening the possibility of displaying proteins at a density of up to 1 protein per 70 nm 2 , 30 . Leveraging this prior work, here we describe the autonomous formation of macroscopic living material from C. crescentus engineered to display a synthetic, self-interacting, protein matrix based on the S-layer scaffold. We demonstrate that the mechanical properties of this material can be genetically controlled over a factor of ~25x. We also describe unexpected findings indicating that the protein matrix plays a multifaceted role in the material formation and that material assembly occurs through a multi-step process mediated by the air–water interface.",
"discussion": "Discussion In summary, we developed macroscopic living materials that autonomously grow from engineered bacteria and that can be genetically encoded to have a wide range of mechanical properties. Specifically, we show that the expression of a self-interacting protein—the BUD protein—enables macroscopic material formation (Fig. 1 and Supplementary Fig. 1 ). When displayed on the cell surface, the BUD protein mediates drives cell–cell aggregation; when secreted into the media, the BUD protein forms an extracellular matrix that binds these aggregates into a centimeter-scale structure (Fig. 2 ). Assembly of these ELMs starts with the growth of the engineered strain as a predominately planktonic culture, followed by the formation of a pellicle and its ultimate collapse into a final material (Fig. 3 ). Importantly, understanding of these design and assembly rules enabled us to alter the mechanical properties of these ELMs by ~25-fold and to imbue them with catalytic properties (Fig. 4 ). Our work identifies design rules that lead to the autonomous formation of BUD-ELMs and suggests other design rules to be tested. We identify a secreted matrix as a design constraint for this class of centimeter-scale, autonomously forming BUD-ELMs. Our work also indicates that a surface-anchored protein matrix is necessary for these materials to be cell-rich. Our data also suggests that this surface-anchored protein matrix may need to be present at high-density for cell-rich materials. This suggestion is supported by previous literature that shows that E. coli with self-interacting proteins displayed at ~10% the density of our engineered C. crescentus strains lead to small cell–cell aggregates 25 . However, additional studies that systematically vary the surface density are needed to test this hypothesis. Another design rule that will be critical to understand and explore in future work is the nature and strength of the self-interactions in the BUD protein. We selected the RsaA 690-1026 and ELP 60 domains because prior reports demonstrate they can self-aggregate 39 , 44 . However, additional studies are needed to identify the nature of self-interactions and their strengths in the existing BUD protein and the range of self-interactions that permit the assembly of macroscopic materials. This work also identified assembly principles for the autonomous formation of macroscopic materials. We have demonstrated that nucleation of a pellicle at the liquid-air interface and hydrodynamically-driven coalescence and collapse of the pellicle are required to form macroscopic ELMs. Since pellicle formation is also a key step in nanocellulose-based living materials 18 , we suggest that the use of the air–water interface to locally concentrate and order hydrophobic biomolecules into a matrix may represent a general assembly principle for macroscopic ELMs. The genetic tools and C. crescentus platform developed here will permit systematic exploration of design and assembly rules for programming the growth of centimeter-scale structures using living cells as building blocks. By creating BUD-ELMs with a de novo, modular protein matrix, this work greatly expands the ability to tailor macroscopic ELMs for specific applications. One of the key advantages of the C. crescentus BUD-ELM platform developed herein is the highly reproducible, autonomous formation of engineered living materials. Growing BUD-ELMs from an engineered strain of C. crescentus requires only control of the temperature, media composition, flask and culture volume, shaking speed, and shaking orbit. We envision this simplicity will enable the ready adoption of this platform by other researchers. The second advantage of this platform is that the modularity of the BUD protein and the ease of engineering protein biopolymers offer much greater opportunities for introducing desirable properties into the matrix 11 . The BUD-ELM variants described herein have storage moduli that ranges between 13 kPa, comparable to nanocellulose-based materials, and 0.5 kPa, comparable to printed curli fiber-based materials. Introducing sites for chemical crosslinking into the ELP domain could allow the BUD-ELMs to be developed into elastomers 45 . More broadly, this work enables leveraging known polypeptides and proteins that exhibit desirable optical, electrical, mechanical, thermal, transport, and catalytic properties 46 . We envision specific matrix properties that can be combined synergistically with existing cellular functions such as sensing, biomolecule production, and information processing. Thus, this work multiplies the opportunities to program ELMs tailored for applications in human health, energy, and the environment."
} | 2,538 |
36271072 | PMC9587027 | pmc | 510 | {
"abstract": "Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory-augmented neural networks have been proposed to achieve the goal, but the memory module must be stored in off-chip memory, heavily limiting the practical use. In this work, we experimentally validated that all different structures in the memory-augmented neural network can be implemented in a fully integrated memristive crossbar platform with an accuracy that closely matches digital hardware. The successful demonstration is supported by implementing new functions in crossbars, including the crossbar-based content-addressable memory and locality sensitive hashing exploiting the intrinsic stochasticity of memristor devices. Simulations show that such an implementation can be efficiently scaled up for one-shot learning on more complex tasks. The successful demonstration paves the way for practical on-device lifelong learning and opens possibilities for novel attention-based algorithms that were not possible in conventional hardware.",
"introduction": "Introduction Deep neural networks (DNNs) have achieved massive success in data-intensive applications but fail to tackle tasks with a limited number of examples. On the other hand, our biological brain can learn patterns from rare classes at a rapid pace, which could relate to the fact that we can recall information from an associative, or content-based addressable, memory. Inspired by our brain, recent machine learning models such as memory-augmented neural networks (MANN) 1 have adopted a similar concept, where explicit external memories are applied to store and retrieve learned knowledge. While those models have shown the ability to generalize from rare cases, they have struggled to “scale up” 2 , 3 . This is because the entire external memory module needs to be accessed from the memory to recall the learned knowledge, which greatly increases the memory overhead. The performance in a traditional von Neumann computing architecture 4 is thus bottlenecked in hardware by memory bandwidth and capacity issues 5 – 7 , especially when they are deployed in edge devices, where energy sources are limited. Emerging non-volatile memories, e.g., memristors 8 , have been proposed and demonstrated to solve the bandwidth and memory capacity issues in various computing workloads, including DNNs 9 – 14 , signal processing 15 , 16 , scientific computing 17 , 18 , solving optimization problems 19 , 20 , and more. Those solutions are based on the memristor’s ability to directly process analog signals at the location where the information is stored. Most existing demonstrations mentioned above, however, mainly focus on executing matrix multiplications for accelerating DNNs with crossbar structures 8 – 12 , 17 , 18 , 21 , whose experience cannot be directly applied to the models with explicit external memories in MANNs. Recently, several pioneering works aim to solve the problem with memristor-based hardware. One promising solution is to exploit the hyperdimensional computing paradigm 22 , 23 . A recent prototype of this framework showcased few-shot image classification using more than 256k phase change memristors in mixed software-hardware experiments 23 , and more recently another prototype demonstrated consecutive programming in-memory realization of continual learning 24 . Another solution is to use ternary content-addressable memories for distance functions in mature attention-based models 25 – 27 . Ferroelectric device based ternary content-addressable memories (TCAMs) have been proposed to be used as the hardware to calculate the similarity directly in the memory 28 , 29 , but it is only suitable for the degree of mismatch up to a few bits. Besides, the locality sensitive hashing (LSH) function that enables the estimation of cosine function was implemented in software, and the experimental demonstration was limited to a 2 × 2 TCAM array. More recently, a 2T-2R TCAM associative memory was used to demonstrate few-shot learning by calculating L 1 distance 30 . In this work, a 2-bit readout scheme is employed (requiring 64 cycles per row) which incurs high energy and latency overheads, and feature extraction is again relegated to a digital processor. The key challenge in this concept is the imperfections in the analog hardware, such as device variation, fluctuation, state drift, and readout noise during the massively parallel operations in physical crossbar or TCAM arrays; all of the above represent obstacles to viable, deployable, and efficient hardware realizations of MANNs. In this work, we experimentally demonstrate that different structures in MANNs, including the CNN controller, hashing function, and the degree of mismatch calculation in TCAM, can be implemented in our integrated memristor hardware for one- and few-shot learning. To achieve this goal, we implement those different functionalities in crossbars in addition to the widely reported matrix multiplication operations, and design the peripheral circuit to support those functionalities accordingly. One enabler is the locality sensitive hashing (LSH) function in crossbars, where we exploit the intrinsic stochasticity of memristor devices. This is different from crossbars for matrix multiplications, where stochasticity needs to be minimized. Another innovation is implementing search functions by using the crossbar as a TCAM. In addition to what is possible with conventional TCAMs, the proposed scheme can also measure the degree of mismatch reliably, which is crucial for few-shot learning implementation. Since the requirements for those functions are different from conventional matrix multiplications, here we introduce several hardware-software co-optimization methods, including the introduction of the wildcard ‘X’ bit in the crossbar-based LSH, and the careful choice of conductance range according to the device statistics. Finally, we are able to experimentally demonstrate the few-shot learning with a complete MANN model for few-shot image classification tasks with the standard Omniglot dataset. The model includes a five-layer convolutional neural network (CNN), the hashing function, and the similarity search. Given that the CNN has more parameters (265,696) than what can be fit in our hardware (24,576 memristors in six 64 × 64 arrays), the crossbars for CNNs are re-programmed when needed. Taking into consideration all imperfections in the emerging system, our hardware achieves 94.9% ± 3.7% accuracy in the 5-way 1-shot task with the Omniglot dataset 31 , a popular benchmark for few-shot image classification, and 74.9% ± 2.4% accuracy in the 25-way 1-shot task, which is close to the software baseline (95.2% ± 2.6% for 5-way 1-shot and 76.0% ± 2.7% for 25-way 1-shot). Our experimentally-validated model also shows that the proposed method is scalable with a 58.7 % accuracy to recognize new classes (5-way 1-shot) for the Mini-ImageNet dataset 32 , where each image is a color (RGB) image of size 84 × 84 pixels—nine times larger than the size of images in the Omniglot dataset (28 × 28 pixels). This accuracy is only 1.3% below the software baseline. We estimate about 5.36 μJ of energy consumption per inference for the 5-way 1-shot on the Omniglot dataset with the entire system, including the peripheral circuitry. One major portion was consumed during the conductance iterative read-and-verify re-programming. Still, the energy consumption is 257 × lower than that (1.38 mJ) with a general-purpose graphic processing unit (GPGPU) (Nvidia Tesla P100). Future systems with the capability to accommodate the weights of the entire MANN are expected to have much higher energy efficiency and scalability compared to the conventional von Neumann processors.",
"discussion": "Discussion Compared to conventional von Neumann based implementations, the key advantage of crossbar-based MANNs is lower latency and higher energy efficiency through co-located computing and memory, energy-efficient analog operations, and intrinsic stochasticity. To evaluate the strength of the approach, we run the same 5-way 1-shot problem with Omniglot and Mini-ImageNet datasets on a digital graphic processing unit (GPU) (Nvidia Tesla P100). The time required to classify a single image increases dramatically after the size of the MANN’s external memory capacity reaches a certain threshold (only several MB) because of the repeated off-chip data movement (see Fig. 5 c). This problem on conventional hardware has been the major bottleneck preventing the widespread adoption of few-shot learning. The approach of directly computing in the memory, or crossbar, provides a plausible solution to address this bottleneck. In the crossbar-based MANNs, the matrix multiplication in the convolutional layer, the hashing in TLSH, and the searching operation in TCAM are all computed with single-step current readout operations. With our current proof-of-concept experimental system, readouts take about 100 ns, but in a future system with more crossbar tiles that are fabricated with a more advanced technology node, the readout time can be reduced to 10 ns. We also considered the time latency for the peripherals that include but are not limited to the digital-to-analog converters (DACs), TLSH sensing block, and the analog adder for summing up the voltages in different tiled crossbar arrays. With these forecasts, we compared the latency of the nearest neighbor search operation on a GPU with our analog in-memory hardware. The results shown in Fig. 5 d indicate latency improvements of (10,466 × for Omniglot and 26,002 × for Mini-ImageNet) when the memory size (number of entries) is 8192. Additionally, our approach also offers high energy efficiency of the nearest neighbor search operation compared with the conventional GPU (2857 × for Omniglot and 50,970 × for Mini-ImageNet) in the forecasted system. Detailed analysis of the energy and latency estimations can be found in Supplementary Note 4 . In summary, we have experimentally demonstrated the viability of a complete MANN architecture, from the controller to distance calculation, in an analog in-memory platform with proven high robustness and scalability. We utilize the analog behavior of memristor devices to perform convolution operations for CNNs and exploit the inherent stochasticity of devices to perform hashing functions. A novel hardware-friendly hashing function (TLSH) is developed to provide better analog computing error tolerance and lower power consumption. In addition, a differential encoding method for a crossbar-based TCAM is applied to adapt to the ternary Hamming distance calculation requirements. In our experiments, all dot-product operations are performed in physical crossbars, which exhibit experimental imperfections, such as device state fluctuations, device nonlinearities, voltage drops due to wire resistance, and peripheral circuits. The hardware-implemented CNN, hashing and similarity search functionalities for MANN delivered similar accuracy compared to software on few-shot learning with the widely used Omniglot dataset. Simulation results on Mini-ImageNet show the ability of crossbar-based MANNs to execute real-world tasks, with much-improved latency and energy consumption. We demonstrate that analog in-memory computing with memristive crossbars efficiently supports many different tasks, including convolution, hashing, and content-based searching. The successful demonstration of these functions opens possibilities with other machine learning algorithms, such as attention-based algorithms, or reaching scales that are currently prohibited by conventional hardware (e.g., Fig. 5 c). Additionally, there are many opportunities for future software-hardware co-optimization to improve the accuracy and efficiency results further."
} | 2,959 |
36271605 | PMC10092215 | pmc | 512 | {
"abstract": "Abstract Cold seeps in the deep sea harbor various animals that have adapted to utilize seepage chemicals with the aid of chemosynthetic microbes that serve as primary producers. Corals are among the animals that live near seep habitats and yet, there is a lack of evidence that corals gain benefits and/or incur costs from cold seeps. Here, we focused on Callogorgia delta and Paramuricea sp. type B3 that live near and far from visual signs of currently active seepage at five sites in the deep Gulf of Mexico. We tested whether these corals rely on chemosynthetically‐derived food in seep habitats and how the proximity to cold seeps may influence; (i) coral colony traits (i.e., health status, growth rate, regrowth after sampling, and branch loss) and associated epifauna, (ii) associated microbiome, and (iii) host transcriptomes. Stable isotope data showed that many coral colonies utilized chemosynthetically derived food, but the feeding strategy differed by coral species. The microbiome composition of C. delta , unlike Paramuricea sp., varied significantly between seep and non‐seep colonies and both coral species were associated with various sulfur‐oxidizing bacteria (SUP05). Interestingly, the relative abundances of SUP05 varied among seep and non‐seep colonies and were strongly correlated with carbon and nitrogen stable isotope values. In contrast, the proximity to cold seeps did not have a measurable effect on gene expression, colony traits, or associated epifauna in coral species. Our work provides the first evidence that some corals may gain benefits from living near cold seeps with apparently limited costs to the colonies. Cold seeps provide not only hard substrate but also food to cold‐water corals. Furthermore, restructuring of the microbiome communities (particularly SUP05) is likely the key adaptive process to aid corals in utilizing seepage‐derived carbon. This highlights that those deep‐sea corals may upregulate particular microbial symbiont communities to cope with environmental gradients.",
"conclusion": "5 CONCLUSION This study showed that C. delta and Paramuricea sp. type B3 populations living near signs of active cold seeps gain benefits from seepage including input of chemosynthetic‐derived nutrition, but this pattern was species specific. Each coral species may have used a different mixotrophic strategy to obtain chemosynthetically produced food, either via direct uptake from the environment or through a symbiotic relationship with sulfur‐oxidizing bacteria as suggested by Goffredi et al. ( 2021 ) and Vohsen et al. ( 2020 ). Therefore, we propose that these coral populations do not simply benefit from the substrate at seeps as previously hypothesized but may also benefit from additional sources of nutrition in seep habitats. Interestingly, the proximity to cold seeps significantly affected the microbiome communities in C. delta and the relative abundance of various SUP05 phylotypes in both coral species were upregulated that likely facilitated corals to utilize or adapt to cold seeps. In contrast, fitness traits of coral colonies or host genes related to detoxification/sulfur pathways were not affected suggesting that living near cold seeps does not impose a cost we could detect in these coral species. Our study provides the first evidence that corals utilize available chemosynthetically derived food in cold seep habitats with aid of their associated microbiome communities.",
"introduction": "1 INTRODUCTION Cold‐water coral communities are diverse and abundant in the deep sea. Corals increase habitat heterogeneity and provide a three‐dimensional framework for many invertebrate and fish species (Cho & Shank, 2010 ; Cordes et al., 2008 ). Cold‐water corals are long‐lived and slow growing species, and thus, they are vulnerable to natural and anthropogenic threats (Clark et al., 2016 ; Thresher et al., 2015 ; Weinnig et al., 2020 ). For example, hydrocarbon pollution has long‐term impacts on coral health and the associated fauna (Girard et al., 2018 ; Guzman et al., 2020 ; McClain et al., 2019 ). Exposure to hydrocarbons and related toxic chemicals may cause colony mortality or have sublethal consequences such as; (i) a decline in health and growth of coral colonies (Girard & Fisher, 2018 ; Girard et al., 2019 ), (ii) a change of the associated epifauna (Demopoulos et al., 2016 ; Lewis et al., 2020 ), (iii) a shift in the associated microbial community (Luter et al., 2019 ; Turner & Renegar, 2017 ), and (iv) an influence on gene expression of the coral host (DeLeo et al., 2018 , 2021 ). Yet, some cold‐water corals grow near active cold seeps (Quattrini et al., 2013 ) that are a source of various chemicals including hydrogen sulfide, methane, and other hydrocarbon‐rich fluids in the environment. The presence of these chemicals raises the question of whether coral species utilize seepage effluents and/or acclimatize to seepage exposure in some way. At cold seeps, hydrocarbons naturally leak from the sea floor over a few to hundreds of square meters. Microbial processing of the leaked hydrocarbons results in authigenic carbonates that serve as reef‐like habitats and are settled by many invertebrates (see Joye, 2020 ). Chemosynthetic microbes also use seeping chemicals to produce organic carbon and so act as primary producers for cold seep communities (Joye, 2020 ). Generally, cold seep community members benefit from the higher food availability near seeps relative to background habitats, but detoxification of seep effluent may be necessary and energetically costly. Some animals use metabolic detoxification pathways in their tissue to convert hydrocarbons to simpler compounds (e.g., alcohols and ketones—see Kennicutt, 2017 ). Other animals associate with chemosynthetic microbes (Fisher et al., 2007 ) that convert toxic to non‐toxic substances and fix carbon (Laso‐Pérez et al., 2019 ; Niemann et al., 2013 ; Sogin et al., 2020 ). Most cold seep fauna (e.g., siboglind tubeworm, bathymodiolus mussels) have chemosymbiotic microbes and/or upregulate expression of genes related to the innate immune system, heavy metal detoxification, and metabolic pathways involving sulfide when toxic chemicals are present in the environment (Cheng et al., 2019 ; Osman & Weinnig, 2022 ; Sogin et al., 2020 ; Wong et al., 2015 ). A number of coral species live near cold seeps (e.g., Lophelia pertusa , Balanophyllia sp.) in different biogeographic regions, but it is not clear what benefits corals gain from living near cold seep habitats (Deng et al., 2019 ; Hovland & Thomsen, 1997 ). In a single laboratory study, the holobiont of the deep‐sea scleractinian coral L. pertusa was shown to be capable of chemoautotrophy and nitrogen fixation (Middelburg et al., 2015 ), but the microbes responsible for these processes and the degree to which they supply nutrition in situ remains unresolved. So far, the influence of cold seeps on these corals appears limited. In fact, most deep‐sea organisms rely on photosynthetic detritus sunk from photic/shallow water or other organisms that feed on surface‐derived food (McClain‐Counts et al., 2017 ). Previous stable isotope studies showed that photosynthetic‐derived food is the major food source in deep‐sea corals, even near cold seeps (e.g., Becker et al., 2009 ). A signature of chemosynthetically derived food in their tissue or a mechanism to adapt to cold seeps has not been found (Rincón‐Tomás et al., 2019 ; Xu et al., 2019 ). Therefore, it was proposed that coral species mainly occupy seep habitats because the authigenic carbonates provide suitable substrate but only after the seepage has largely faded and hydrocarbons/oil are no longer released. Nevertheless, the octocorals Callogorgia delta (200–1000 m) and Paramuricea spp. (835–1090 m) occasionally grow in very close proximity (within a few meters) to areas of active seepage in the Gulf of Mexico (Doughty et al., 2014 ; Quattrini et al., 2013 ). Indeed, C. delta and Paramuricea sp. type B3 have been observed living amid bacterial mats and near dead mussel beds that relied on active seepage (Figure 1 ). This observation suggests that certain coral populations may be exposed to active seepage and be able to utilize and/or tolerate cold seeps like other seep fauna. FIGURE 1 Map shows sampling sites of Callogorgia delta and Paramuricea sp. collected from cold seep and non‐seep from five sites in the Gulf of Mexico (a). Colonies of C. delta (b) and Paramuricea sp. (c) were found near active and far from active cold seep. Coral colonies were sampled and photographed to assess the impact of cold seeps on corals health, growth rate, regrowth after sampling, branch loss, and associated epifauna. Colonies were also sampled to assess the microbiome community and host gene expression. Boxplots and point clouds represent the range of carbon δ 13 C (d) and nitrogen δ 15 N (e) stable isotope values (‰) in C. delta , Paramuricea sp. and sediment in cold seep (teal) and non‐seep (orange) samples collected from five sites in the Gulf of Mexico. Stable isotope values demonstrate significant differences between seep and non‐seep samples in C. delta and sediment samples (δ 13 C value <‐23 indicates chemosysnthesis), but not in Paramuricea sp., highlighting chemosynthetic signatures. Here, we focus on these coral populations and use proximity to signs of active seepage as an indicator of exposure to seep effluents. We assessed to what extent these corals gain benefits from living near cold seeps and whether they rely on food with a chemosynthetic and/or photosynthetic origin. We compared colony and holobiont traits as an indicator of metabolic benefits or cost imposed by exposure to seepage. C. delta and Paramuricea sp. type B3 were imaged and collected from five sites in the Gulf of Mexico near and far from signs of active seepage. Bulk carbon and nitrogen stable isotope composition of tissue was analyzed to differentiate between chemo‐ and photosynthetic origin. We assessed coral health status, growth rates, branch loss, and how well coral colonies recovered from sampling (cutting injury) as a proxy for energetic reserves. Also, we characterized the associated epifauna and microbiome composition and analyzed host gene expression relative to signs of active seepage. We report that these corals obtain some of their nutrition from a chemosynthetically derived food source whose origin appears to vary between coral species. While the composition of the microbial communities of C. delta and sediment samples changed significantly between seep and non‐seep sites, we could not detect a measurable impact of cold seeps on coral fitness traits, associated epifauna, or the host transcriptome. Our study provides the first evidence that corals living near cold seeps not only use seep habitats for substrate, but also feed on chemosynthetic food similar to other seep fauna, without experiencing negative impacts on the measured coral colony traits or on the composition of associated epifauna communities. A shift in microbial community composition, particularly the SUP05 group, is likely a key adaptive mechanism that enabled those coral colonies to utilize and/or tolerate cold seeps.",
"discussion": "4 DISCUSSION Cold‐water corals are a diverse group that can be found from shallow water to over 2000 m depth. Some of the habitats they occupy represent unique challenges such as cold seeps that release hydrocarbons‐rich fluid which can be toxic to many organisms. However, seepage effluents can fuel food chains via chemosynthesis when chemical concentrations are high enough (Åström et al., 2018 ; Childress et al., 1986 ). Previous stable isotope analyses showed that corals living near seeps feed mostly on photosynthetically derived suspended organic matter and plankton and failed to detect significant input from chemosynthetic sources (Becker et al., 2009 ). It was thought that corals may be a later successional stage that colonize carbonate outcrops after most surface expression of seepage has subsided, and thus, corals occupy seep habitats primarily to take advantage of the available carbonate substrate (Cordes et al., 2008 ; Fisher et al., 2007 ; Xu et al., 2019 ). Here, colonies of two coral species, Callogorgia delta and Paramuricea sp. type B3, were observed living close to and on top of chemosynthetic organisms that rely primarily on active seepage. Hence, we investigated whether colonies of these two coral species gained benefits or, possibly, incurred a cost when living near cold seeps in the deep Gulf of Mexico. We provide the first evidence that both coral species obtain some nutrition in situ from chemosynthetic primary production at active seeps, but in a species‐specific manner. Proximity to active signs of seepage was accompanied by shifts in microbiome community composition in C. delta and an increase in the relative abundance of SUP05 phylotypes in both coral species. We thus suggest that changes in their microbial symbiont communities provide a mechanism for corals to survive and grow near active cold seeps. 4.1 Proximity to cold seeps affects diet of C. delta and Paramuricea sp. in different ways We report here that stable isotopes from C. delta colonies sampled at cold seeps were significantly lower in δ 13 C and δ 15 N than colonies sampled far from seeps indicating a component of chemosynthetically derived food in their diet (see Figure 1 ). Furthermore, the proportion of live tissue on C. delta colonies was correlated with chemosynthetic‐derived δ 13 C and δ 15 N values suggesting that C. delta colonies benefited from ingesting chemosynthetically fixed carbon (Figure S1 ). This contrasts with most previous studies where stable isotope values did not indicate chemosynthetic food input for coral species collected in situ, including fossil coral samples (e.g., Deng et al., 2019 ; Xu et al., 2019 ). Here, we suggest that C. delta likely obtained chemosynthetic food primarily via heterotrophic filter feeding, as previously proposed (Becker et al., 2009 ). This is because the majority of investigated C. delta colonies near seeps had both chemosynthetic and photosynthetic stable isotope values (Figure 1 ) highlighting the flexibility of C. delta to obtain food from different sources. The alternative explanation, that C. delta was supplemented with chemosynthetically fixed carbon from a bacterial symbiont living within its tissue, was less likely for three reasons. (i) The assembled whole genome of Mollicutes (i.e., the dominant bacterial phylotype, see Figure 3 ) collected from some of the C. delta colonies used in the current study showed lack of chemosynthetic pathways (Vohsen et al., 2022 ). (ii) The relative abundances of C. delta dominant symbiotic bacteria (i.e., Mollicutes—Figure 3 ) were not significantly different between seep and non‐seep samples and did not correlate with stable isotope values (Figure S6 ). (iii) Few colonies had low relative abundance of SUP05 in GC249 despite a strong chemosynthetic signature in their tissue (Figure 4 ). Thus, it is unlikely that SUP05 is the primary source of the pronounced chemosynthetic signature in most of C. delta colonies near active seeps. SUP05, instead, may partially supplement the diet for some colonies or contribute to detoxifying seep effluents (see below). In contrast, stable isotope values in Paramuricea sp. colonies were similar in seep and non‐seep colonies. The difference in stable isotope values between C. delta and Paramuricea sp. may be attributed to their; (i) feeding strategies and prey size, (ii) assimilation/storage efficiencies, and/or (iii) metabolic pathways of coral hosts and/or associated bacteria to digest chemosynthetically derived food. Vohsen et al. ( 2020 ) proposed that the dominant endosymbiotic sulfur‐oxidizing bacteria (SUP05) found in some of our samples (see Figure 3a ) supplement the diet of Paramuricea sp. despite slightly similar stable isotope values between seep and non‐seep colonies (see also Figure 1 ). They inferred this from; (i) the negative correlation between the relative abundance of SUP05 and isotopic composition of carbon and nitrogen, and (ii) active transcription of genes related to chemosynthetic pathways. Here, we also reported that the relative proportion of SUP05 was significantly higher in cold seep Paramuricea sp. colonies than in non‐seep colonies (Figure S5 ). This suggests that Paramuricea sp., similar to C. delta , may upregulate abundances of SUP05 symbionts near active cold seeps that provide some chemosynthetically derived nutrition for the host. As such, Paramuricea sp. likely obtains part of its nutrition chemoautotrophically when living near active cold seeps while otherwise relying on heterotrophic filter feeding. However, C. delta appears to primarily feed heterotrophically where chemosynthetic food is available to be directly consumed and thus, it has lower (chemosynthetic) isotopic values near seeps compared to Paramuricea sp. Thus, both species behave like a mixotroph but to a different extent. We concluded that both coral species opportunistically use available food and substrate near cold seeps, however, each species obtains their chemosynthetic food using a different approach. 4.2 Variability of microbiome communities between seep and non‐seep coral colonies Changes in microbiome communities have been observed in shallow and deep‐sea corals as a response to environmental gradients (Osman et al., 2020 ; van de Water et al., 2017 ). Here, we found significant, but subtle (2%), variation in the composition of microbial communities between seep and non‐seep colonies of C. delta . The difference is more pronounced when the most active seep site was compared to the non‐seep site (variance explained 37%). Hence, there might be a link between seepage chemical composition and concentration on the microbial communities of C. delta . Furthermore, the influence of seepage on the corals' microbiome might be also due to having to digest a chemosynthetically derived diet near active seeps rather than photosynthetic‐derived organic carbon far from active seeps (Figure 1 ). Starved deep‐sea corals, Lophelia pertusa and Madrepora oculata , fed on various types of diets exhibited diet specific changes in their microbiome (Galand et al., 2020 ), suggesting that diet may drive, or at least contribute, to the change in microbial composition. However, the microbiome of L. pertusa has also been shown to be far more variable than that of M. oculata , suggesting that it has more flexibility in the potential niches it could occupy (Meistertzheim et al., 2016 ). Overall, shifts in microbial composition might be a key adaptive mechanism of corals that facilitate survival of colonies near cold seeps and other habitats. Microbiome‐host specificity and composition in corals relative to surrounding seawater and sediment are well‐documented patterns for shallow and deep‐sea coral species (La Rivière et al., 2015 ; Osman et al., 2020 ). This specificity may be linked to several factors such as the chemical composition of coral mucus, the transmission mode of the microbiome, or host‐bacteria recognition mechanisms (Osman & Weinnig, 2022 ; van de Water et al., 2018 ). However, biogeographical variation between sampling sites may be also attributed to host specificity where Paramuricea sp. was exclusively sampled at AT357 at 1140–1160 m, while C. delta was sampled at the remaining four sites at 450–800 m. Our data support this notion that depth was significantly correlated with microbiome composition (e.g., Franco et al., 2020 ), unlike other environmental variables (temperature, salinity, and oxygen) that did not change microbiome composition of either species. Notably, depth was confounded with sites which was likely driving the difference in microbiome composition (see Table S7 ). Interestingly, SUP05 phylotypes were associated with both coral species, had a strong correlation with carbon and nitrogen stable isotope values, and their relative abundance varied with exposure to active seepage. SUP05 are common sulfur‐oxidizing endosymbionts associated with a broad range of fauna in cold seep habitats (see Morris & Spietz, 2022 ). Previously, it was proposed that the lack of specialized respiratory structures or oxygen‐transport mechanisms in cnidarians would preclude corals from harboring sufficient chemosynthetic symbionts to supply the majority of their nutrition because they would not able to satisfy the high oxygen demand of chemosymbionts (Childress & Girguis, 2011 ). However, SUP05 was recently found associated with Paramuricea sp. in cold seep habitats, transcribing genes related to carbon fixation and sulfur oxidation processes (Vohsen et al., 2020 ). Similar symbiosis between SUP05 and sea anemones ( Ostiactis pearseae ) living near active hydrothermal vents (3700 m) was also discovered in the Gulf of California (Goffredi et al., 2021 ). This suggests that there is a mechanism to deliver adequate oxygen to maintain a symbiotic relationship between SUP05 populations and cnidarian hosts sufficient to contribute to the host nutritional needs. It is worth noting that even a relatively small contribution to bulk nutrition can be critical to the hosts in nutrient limited habitats or if the contribution includes essential nutrients not otherwise available. Our work supports the hypothesis that SUP05 phylotypes are functional chemosymbionts in corals near active cold seeps, and may provide supplemental nutrition to the host. Furthermore, several Endozoicomonas phylotypes were dominant in both coral species. Endozoicomonas is a wide‐spread coral associate enriched in genes related to carbon sugar transport and utilization (Neave et al., 2016 ). Endozoicomonas also were reported in two cold‐water coral species that live below 1000 m emphasizing the potential role of Endozoicomonas as endosymbionts of deep‐sea corals (Kellogg & Pratte, 2021 ). 4.3 Proximity to cold seeps does not change colony phenotypes or host transcriptome We measured a comprehensive set of holobiont phenotypes in relation to proximity to cold seeps to explore whether living near cold seep provide benefits or incurs a fitness cost for the coral colonies. This study assessed 685 images of 384 colonies (to quantify colony traits) and 184 biological samples of coral tissue, surrounding sediment and seawater collected at five sites over 3 years, which represent a substantial sampling effort with the power to detect the effects of cold seeps on coral colonies. 4.3.1 Colony phenotypes Our data showed a correlation between apparent healthy tissue of C. delta colonies and chemosynthetic isotope values of carbon and nitrogen highlighting that colonies gain benefits from feeding on chemosynthetic‐derived food. However, annual growth and recovery from sampling rates were relatively low in both C. delta and Paramuricea sp. despite the proximity to cold seeps. This was in line with Girard et al. ( 2019 ) who estimated the growth rate of Paramuricea biscaya and Paramuricea sp. B3 in the Gulf of Mexico to be only 0.14–2.5 cm/year/colony. These corals grow very slowly indeed, and it is therefore unsurprising that we could not detect a temporal effect of seepage on these traits over the course of 3 years. Notably, the proportion of branch loss in C. delta was higher than Paramuricea sp., while the proportion of non‐healthy branches was higher in Paramuricea sp. colonies at seeps, highlighting the susceptibility of both coral species to environmental impact or biotic interactions (predations and physical damage). Epifauna of C. delta and Paramuricea sp. species were host specific. Cordes et al. ( 2008 ) similarly reported distinct epifaunal communities associated with Lophelia pertusa relative to those associated with vestimentiferan tubeworm aggregations that occur nearby at the same sites. They suggested that habitat heterogeneity, the specific niche provided by each host, and different interactions with the host species contributed to this variation. The host specificity in our study may also be related to biogeographic distance between C. delta and Paramuricea sp. sampling sites which do not overlap in depth. Proximity to cold seeps had a significant, but limited, effect on associated fauna as it explained only 1% of the variation. This may be attributed to (i) the mobile nature of epifauna associated with both coral species that were dominated by ophiuroids, gastropods, crabs, and other crustaceans (Figure S2 ), (ii) the higher diversity of fauna associated with cold seep habitats (due to food availability) relative to background sediment. Ophiuroids were the dominant epifaunal group on both coral species (brittle stars—Figure S2 ), a general pattern for cold‐water coral species world‐wide (Mosher & Watling, 2009 ). In our study, proximity to cold seeps did not have a significant effect on relative abundance of brittle stars in Paramuricea sp. (glm, F = 0.5, p = .4), but their relative abundances were significantly higher in cold seep than non‐seep colonies of C. delta (glm, F = 28, p < .001). Although the presence of ophiuroids has been shown to limit impacts of acute exposure to hydrocarbons (Girard et al., 2016 ), it seems unlikely that brittle stars offer significant physical protection of colonies from diffuse seep effluents. The variation is more likely attributed to food availability in seep habitats or colony sizes as larger colonies provide space for higher numbers of brittle stars. 4.3.2 Host gene expression \n Callogorgia delta has a documented affinity for living around areas of active hydrocarbon seepage in the Gulf of Mexico (Quattrini et al., 2013 ). However, we could not detect a noteworthy difference in the global gene expression patterns between seep and non‐seep colonies (Figure 5 ). In contrast, the comparison between sites (MC751 and MC885) showed a larger number of DEGs (Table S12 ), but the subsequent GO identifications indicated that these genes were related to typical housekeeping processes (Table S13 ). As such, C. delta does not primarily rely on regulation of a suite of specific genes within the coral transcriptome to augment tolerating active cold seep exposure in the Gulf of Mexico. The absence of detectable differences in host gene expression between seep and non‐seep colonies may be attributed to lack of power to detect a variation in gene expression. We used three colonies from seep and non‐seep markers at each site to detect the variation in gene expression. More sampling effort is needed, particularly between active seep and non‐seep sites, to understand the response of host to seep exposure. The reported slight differences in gene expression profiles in our study could be explained, at least partially, by temporal cycles that the corals experience. Research on shallow‐water corals has suggested the expression patterns of corals can change throughout tidal and lunar cycles (Oldach et al., 2017 ; Ruiz‐Jones & Palumbi, 2017 ). Since the corals for this study were sampled across three different years and seasons, it is possible that natural rhythms influenced gene expression patterns."
} | 6,853 |
36793323 | PMC9922812 | pmc | 515 | {
"abstract": "The development of engineered living materials (ELMs) has recently attracted significant attention from researchers across multiple disciplines. Fungi-derived ELMs represent a new type of macroscale, cost-effective, environmentally sustainable materials. However, current fungi-based ELMs either have to undergo a final process to heat-kill the living cells or rely on the co-culture with a model organism for functional modification, which hinders the engineerability and versatility of these materials. In this study, we report a new type of ELMs – grown from programmable Aspergillus niger mycelial pellets – by a simple filtration step under ambient conditions. We demonstrate that A. Niger pellets can provide sufficient cohesion to maintain large-area self-supporting structures even under low pH conditions. Subsequently, by tuning the inducible expression of genes involved in melanin biosynthesis, we verified the fabrication of self-supporting living membrane materials with tunable colors in response to xylose concentration in the surroundings, which can be further explored as a potential biosensor for detecting xylose level in industrial wastewater. Notably, the living materials remain alive, self-regenerative, and functional even after 3-month storage. Thus, beyond reporting a new engineerable fungi chassis for constructing ELMs, our study provides new opportunities for developing bulk living materials for real-world applications such as the production of fabrics, packaging materials, and biosensors.",
"conclusion": "4 Conclusion Mycelium composites are an emerging class of cheap and environmentally sustainable materials experiencing increasing research interest for diverse applications, such as bovine leather and its substitutes, synthetic foams for packaging, insulation, and textiles, as well as high-performance paper-like materials [ 20 , 36 ]. Previous studies showed considerable promise in the fabrication of various materials using fungal mycelium. Nonetheless, some of these approaches face major obstacles, including the high-temperature processing steps and heavy reliance on strong chemical treatments during processing [ 20 , 36 ], leading to the death of living cells. Moreover, cells were required to be killed under certain circumstances, such as in food preservation. Living organisms exhibit remarkable environmental responsiveness to a variety of external stimuli and thereby provide an attractive opportunity for the development of dynamic functional materials. Hence, as a conceptual design, we constructed bulk ELMs with tunable colors and envisioned that this material could be developed with many other applications in further studies. By leveraging the power of genetic engineering and the intrinsic advantage of the environmental tolerance of A. niger , we introduced a new type of engineered living material that enables tunable melanin production. Our self-supporting living membrane material made of genetically programmable living cells for environmental responsiveness has opened the door to creating fungi-derived materials with adaptive self-regeneration, as well as other previously unattainable material properties such as low pH resistance and accurate signal response. Beyond the tunable properties that we demonstrated for our living membrane material, A. niger may be genetically engineered further for optimization using directed evolution methods. Thus, researchers can introduce various fused domains in conjunction with other materials to form composite materials with an even wider range of properties, such as responses to different inputs, including chemical inducers, pH changes, or temperature. However, as conidia eventually shed from the conidiophore and disperse into the air to maintain the colors of ELM, it is desirable to develop effective solutions to prevent the uncontrollable release of conidia in the environment. For instance, A. niger can be engineered with specific genetic circuits, which regulate the growth and survival of the conidia under specific nutrient contents [ 37 ]. Alternatively, a transparent, robust polymer-based coating can be developed and applied to maintain the colors of materials, preventing the conidia from escaping into the surroundings [ 38 ]. These efforts, in addition to advances in biomanufacturing technology and synthetic biology, will facilitate the production of fabric, packaging, and biosensors.",
"introduction": "1 Introduction For billions of years, living systems have constantly been evolving to produce diverse, complex materials under ambient conditions (from biofilms to skeletal tissues) that have acquired remarkable properties such as hierarchical assembly from simple raw materials, self-repairing, and the ability to sense and respond to environmental stimuli [ [1] , [2] , [3] , [4] , [5] ]. There is tremendous potential for exploiting different forms of engineered living materials (ELMs) to recapitulate the unique “living” attributes of natural living systems. Existing ELMs reported thus far include two basic categories, hybrid living materials and self-organizing ones [ 6 , 7 ]. The former generally requires the use of an extra matrix such as hydrogel, organic coating, and inorganic material in which living cells are embedded. The introduction of these exogenous scaffolds often affects the ability of cells to perceive the external environment, which restricts their living abilities [ [8] , [9] , [10] , [11] ]. Moreover, these ELMs face several major obstacles, including the complex processing steps and associated high manufacturing costs [ 12 , 13 ]. Alternatively, self-organizing living materials are produced by harnessing engineered cells to simultaneously make the matrix and incorporate novel functionalities into it [ 4 , 6 , 9 , 10 , 14 ]. Compared with existing self-organizing living materials that often use model bacterial strains, such as Escherichia coli and Bacillus subtilis , as the main chassis, the use of other microorganisms, such as fungi, is relatively less explored even though they have exhibited great potential to produce self-organizing bulk materials without supplementation of extra scaffolding materials [ [15] , [16] , [17] , [18] ]. The relatively high aspect ratios of the filaments in fungi render them particularly suitable as prime candidates for fabricating sheet-like structures or membranes, or even for application as a reinforcing phase in composite materials [ 15 ]. Indeed, researchers have already reported the generation of fungal-based materials as scalable biocomposites, including packaging materials, bio-bricks, and leathers [ 16 , 17 , 19 ]. These studies showed considerable promise in the fabrication of living materials using fungal mycelium. However, these materials either have to undergo a final process to heat-kill the living cells or reply on the co-culture with a model organism for functional modification [ [19] , [20] , [21] , [22] ], severely limiting the engineerability and versatility of fungi-based ELMs. Here, we report the fabrication of new fungal ELMs that leverage Aspergillus niger as an engineerable chassis. A. niger is a filamentous fungus that primarily comprises a vast three-dimensional network of web-like hyphae, forming a complex and well-ordered structure. Moreover, it is genetically tractable, thus rendering it an ideal chassis for use in ELMs [ 23 , 24 ]. In this study, using a simple filtration step, we first produced self-supporting living membrane materials composed of A. niger mycelial pellets. We further reconstructed the intrinsic melanin pigment synthesis pathway of A. niger to fine-tune the expression levels of melanin, leading to living membrane materials with variable colors. Our results not only provide a new viable chassis for constructing bulk ELMs with tunable colors but also open a new avenue for realizing many real-world applications, such as the production of fabrics, packaging materials, and biosensors."
} | 1,993 |
28191922 | null | s2 | 516 | {
"abstract": "Tailored biomaterials with tunable functional properties are crucial for a variety of task-specific applications ranging from healthcare to sustainable, novel bio-nanodevices. To generate polymeric materials with predictive functional outcomes, exploiting designs from nature while morphing them toward non-natural systems offers an important strategy. Silks are Nature's building blocks and are produced by arthropods for a variety of uses that are essential for their survival. Due to the genetic control of encoded protein sequence, mechanical properties, biocompatibility, and biodegradability, silk proteins have been selected as prototype models to emulate for the tunable designs of biomaterial systems. The bottom up strategy of material design opens important opportunities to create predictive functional outcomes, following the exquisite polymeric templates inspired by silks. Recombinant DNA technology provides a systematic approach to recapitulate, vary, and evaluate the core structure peptide motifs in silks and then biosynthesize silk-based polymers by design. Post-biosynthesis processing allows for another dimension of material design by controlled or assisted assembly. Multiscale modeling, from the theoretical prospective, provides strategies to explore interactions at different length scales, leading to selective material properties. Synergy among experimental and modeling approaches can provide new and more rapid insights into the most appropriate structure-function relationships to pursue while also furthering our understanding in terms of the range of silk-based systems that can be generated. This approach utilizes nature as a blueprint for initial polymer designs with useful functions (e.g., silk fibers) but also employs modeling-guided experiments to expand the initial polymer designs into new domains of functional materials that do not exist in nature. The overall path to these new functional outcomes is greatly accelerated via the integration of modeling with experiment. In this Account, we summarize recent advances in understanding and functionalization of silk-based protein systems, with a focus on the integration of simulation and experiment for biopolymer design. Spider silk was selected as an exemplary protein to address the fundamental challenges in polymer designs, including specific insights into the role of molecular weight, hydrophobic/hydrophilic partitioning, and shear stress for silk fiber formation. To expand current silk designs toward biointerfaces and stimuli responsive materials, peptide modules from other natural proteins were added to silk designs to introduce new functions, exploiting the modular nature of silk proteins and fibrous proteins in general. The integrated approaches explored suggest that protein folding, silk volume fraction, and protein amino acid sequence changes (e.g., mutations) are critical factors for functional biomaterial designs. In summary, the integrated modeling-experimental approach described in this Account suggests a more rationally directed and more rapid method for the design of polymeric materials. It is expected that this combined use of experimental and computational approaches has a broad applicability not only for silk-based systems, but also for other polymer and composite materials."
} | 827 |
33815083 | PMC8010134 | pmc | 518 | {
"conclusion": "Conclusion Machine learning and neuromorphic computing are two modeling paradigms on the road toward AGI. ANNs have achieved tremendous breakthroughs in many intelligent applications benefitting from big data, high-performance processors, effective learning algorithms, and easy-to-use programming tools; in contrast, SNNs are still in its infant stage and there is a dire need for more neuromorphic benchmarks. Through cross-discipline research, we expect to understand and bridge the gap between neuromorphic computing and machine learning. This Research Topic is just a small step in this direction, and we look forward to more innovations that can achieve brain-like intelligence.",
"introduction": "Introduction On the road toward artificial general intelligence (AGI), two solution paths have been explored: neuroscience-driven neuromorphic computing such as spiking neural networks (SNNs) and computer-science-driven machine learning such as artificial neural networks (ANNs). Owing to availability of data, high-performance processors, effective learning algorithms, and easy-to-use programming tools, ANNs have achieved tremendous breakthroughs in many intelligent applications. Recently, SNNs also attracted a lot of attention due to its biological plausibility and the possibility of achieving energy-efficiency (Roy et al., 2019 ). However, they suffer from ongoing debates and skepticisms due to worse accuracy compared to “standard” ANNs. The performance gap comes from a variety of factors, including learning techniques, benchmarks, programming tools and execution hardware, all of which in SNNs are not as developed as those in the ANN domain. To this end, we propose a Research Topic, named “Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning,” in Frontiers in Neuroscience and Frontiers in Computational Neuroscience to collect recent researches on neuromorphic computing and machine learning to help understand and bridge the aforementioned gap. We received 18 submissions in total and accepted 14 of them in the end. The scope of these accepted papers covers learning algorithms, applications, and efficient hardware."
} | 538 |
38004871 | PMC10673497 | pmc | 519 | {
"abstract": "Memristors, known for their adjustable and non-volatile resistance, offer a promising avenue for emulating synapses. However, achieving pulse frequency-dependent synaptic plasticity in memristors or memristive systems necessitates further exploration. In this study, we present a novel approach to modulate the conductance of a memristor in a capacitor–memristor circuit by finely tuning the frequency of input pulses. Our experimental results demonstrate that these phenomena align with the long-term depression (LTD) and long-term potentiation (LTP) observed in synapses, which are induced by the frequency of action potentials. Additionally, we successfully implement a Hebbian-like learning mechanism in a simple circuit that connects a pair of memristors to a capacitor, resulting in observed associative memory formation and forgetting processes. Our findings highlight the potential of capacitor–memristor circuits in faithfully replicating the frequency-dependent behavior of synapses, thereby offering a valuable contribution to the development of brain-inspired neural networks.",
"conclusion": "6. Conclusions The capacitor–memristor circuit was simulated using COMSOL. With the well-designed action pulses, it was shown that the capacitor–memristor circuit can emulate the frequency-dependent behavior of synapses, in which high- and low-frequency pulses induce LTP and LTD, respectively. In addition, a Hebbian-like learning mechanism was realized by connecting two memristors to the same capacitor, which naturally shows the process of associative memory and forgetting.",
"introduction": "1. Introduction While the brain lags behind computers in terms of computational speed and accuracy, it possesses a remarkable advantage in its ability to perform massively parallel processing [ 1 , 2 , 3 ]. This advantage stems from the brain’s vast number of neurons and synapses, as well as its ability to process analog signals [ 1 , 3 , 4 ]. As the connections between neurons, synapses play a crucial role in this process [ 5 , 6 , 7 ]. The plasticity of synapses, including long-term potentiation (LTP) and long-term depression (LTD) [ 8 , 9 ], is considered to be the biological basis of learning and memory [ 4 , 6 ]. The development of electronic devices capable of emulating the behavior of biological synapses is considered fundamental to the construction of brain-inspired neural networks [ 10 ]. The concept of the memristor was first proposed by Chua in 1971 to describe the relationship between flux-linkage and charge. It was subsequently developed and produced by HP in 2008 [ 11 , 12 ]. The adjustable conductance of the memristor bears similarity to the weight of synapses [ 5 , 6 , 13 ]. However, the conventional method of modifying memristor conductance using positive and negative voltages fails to account for the influence of action potential frequency on synaptic weight changes [ 5 ]. In biological synapses, high-frequency stimulation leads to LTP, while low-frequency stimulation induces LTD [ 14 ]. Previous attempts to introduce frequency dependence in conductance modification, such as using diffusion memristors and second-order drift memristors, have yielded results that deviate from synaptic behavior [ 15 , 16 ]. Therefore, the challenge of accurately mimicking the frequency-dependent behavior of synapses remains unresolved. In this study, we propose a capacitor–memristor circuit as a means of emulating the frequency-dependent behavior of biological synapses. We first investigate the mechanism of the capacitor–memristor circuit by applying rectangular pulses as an input. Our findings reveal the emergence of positive or negative voltage spikes across the memristor on the rising or falling edges of the input rectangular pulse, respectively. We then introduce specialized pulses, called action pulses, into the capacitor–memristor circuit to emulate synaptic LTP and LTD. High-frequency pulses result in an increase in memristor conductance, akin to LTP, while low-frequency pulses lead to a decrease in conductance, similar to LTD in synapses. Finally, by connecting two memristors to the same capacitor, we achieve a Hebbian-like learning mechanism, where the increase and decrease in conductance signify the processes of associative memory formation and forgetting, respectively."
} | 1,072 |
39418284 | PMC11521316 | pmc | 520 | {
"abstract": "The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.",
"conclusion": "Conclusions This work highlights the significant role of metabolic cross-feeding in shaping microbial composition and MFC performance under varying OLRs. Through functional-based lumped compartmentalized modeling, the mmGEM facilitated a comprehensive study of the intricate dynamic metabolic interactions within microbial communities in MFCs, ultimately enhancing our understanding of microbial ecology and facilitating the optimization of MFC performance for various biotechnological applications. Here, it presents a detailed description of the transition from an SOB-dominant community at L-OLRs to a MET-dominant community at H-OLRs, as the MFC performance declines. This transition can be attributed to dynamic interactions among microbial groups in the system. Our findings unveiled various cross-feeding mechanisms. Specifically, we observed a deterioration in SRB-SOB interactions, including S cycling and acetate cross-feeding. Additionally, we identified efficient acetate consumption by MET. These interactions collectively led to changes in the composition and structure of the microbial community within the MFC system, finally impacting performance. We propose a manipulation strategy to enhance MFC performance by sustaining the dominance of SOB under H-OLR conditions. By increasing the amount of proton (H + ) in the MFC environment, we simulate the growth of SOB, enhance ATP production, strengthen SRB-SOB interactions, and boost MFC performance. The anticipated outcomes offered a hopeful prospect for the long-term functioning of MFCs, illuminating potential future applications within the industrial domain.",
"introduction": "Introduction Microbial fuel cells (MFCs) are an ideal technological solution to support zero-waste practices within the concept of a circular economy [ 1 ]. This technology exploits microbial activities to extract electrons from the breakdown of waste biomass. These electrons can be released to anode electrode and then flow to cathode electrode for completing the circuit and generating electricity [ 2 ]. MFCs offer distinct advantages over existing green technologies such as anaerobic digestion, including the ability to directly convert organic wastes into electricity, the capability to remove toxic residues, and the production of less-polluting effluents [ 3 ]. Thus, MFCs can complement the anaerobic digestion process or serve as an alternative green technology for waste treatment across diverse industries [ 4 ]. A recent study on the application of MFCs in wastewater treatment systems within the canned-pineapple industry underlines the practicality of this technology in real-world scenarios. The MFCs system showed great performances in chemical oxygen demand (COD) removal, sulfide removal, and in current generation with organic- and sulfide-containing wastewater [ 5 ]. Nonetheless, the performance of MFCs is often hindered by microbial activity destabilization when exposed to fluctuating conditions of real-world industrial operations [ 6 ]. The variable nature of organic substrates in wastewater profoundly influences the microbial dynamics within MFCs, including the composition, metabolic functions, and interrelationships among microbial guilds, thereby ultimately impacting their performance. Different waste characteristics favor the growth of specific microbial species, causing a shift in microbial profiles and composition [ 7 ]. Moreover, microorganisms do not live in isolation but interact through substrate-product exchanges. This exchange of metabolites known as “ metabolic cross-feeding ” drives intricate interactions between microbes, which finally modulate the convolution of the microbial community [ 8 – 10 ]. While there is extensive research on how microbial species influence each other and the sensitivity of microbial communities to environmental conditions, our current understanding of the fundamental mechanisms underlying these interactions and variations remains limited. Microbial species in MFCs typically cooperate through mutualistic interactions (i.e., syntrophic relationship), where they rely on each other for survival and perform complementary roles in converting organic matter into electricity. Complex carbon compounds are primarily digested into smaller carbon molecules, such as volatile fatty acids (VFAs), ethanol, acetate, and hydrogen (H 2 ), by organic-hydrolyzing and organic-fermenting microbes. These molecules are subsequently oxidized by exoelectrogenic bacteria (EB), which are capable of performing extracellular electron transfer to produce electricity [ 11 ]. In sulfur-rich environments, sulfate-reducing bacteria (SRB) play a major role in metabolizing sulfate and larger carbon molecules. This metabolism results in the production of smaller carbon e.g. VFAs, which support the growth of various bacteria, including sulfide-oxidizing bacteria (SOB), which utilize VFAs and, in turn, provide sulfate as a substrate for SRB [ 12 , 13 ]. Additionally, prevalence of specific substrates such as acetate, formate, and H 2 in MFCs tend to favor methanogenic bacteria (MET) [ 14 , 15 ]. Growth of MET helps scavenge the remaining substrates, thus maintaining COD removal efficiency [ 16 ]. The interactions among microbial guilds are highly dynamic and largely affected by the surrounding environment, including substrate availability and concentration [ 17 , 18 ]. These conditions pose an even higher challenge in pursuing a comprehensive study and manipulation of MFCs performance. Wastewater characteristics, including substrate type, concentration, and pH vastly influence microbial interactions and composition within MFCs. Different types of organic substrates can selectively support the growth and activity of distinct microbial species. For example, acetate-fed MFCs exhibited a high abundance of Geobacter species, while glucose-fed MFCs favored the prevalence of Clostridium and Bacillus bacteria [ 10 , 19 ]. Besides substrate species, the composition of each individual organic compounds in the industrial wastewater varies with the production seasons, and these fluctuations alter the organic loading rate (OLR) to MFCs. Studies have shown that an increase in OLR in MFCs can lead to a decline in electricity generation. This deterioration is likely due to the invasion of MET, which compete against EB in MFCs [ 15 , 16 ]. Changes in substrate types and concentration, coupled with the MFC system operation, impact the pH levels in the reactor chamber. This pH alteration critically disrupts the equilibrium of the microbial community and the performance of the system. An acidic environment can decrease the overall growth rate of the microbial community, impacting electricity generation. In addition, it can alter the overall activity and structure of the microbial community as different species may respond variably to pH changes, leading to alterations in their behavior and organization within the community [ 20 , 21 ]. In real-world situations, the equilibrium of microbial interactions faces challenges from multiple influential factors. These combined factors increase the complexity of the interrelationships between species. The intricate and diverse relationships among microbial species and their surrounding environments prevent in-depth investigation of metabolic cross-feeding in MFCs. While metabolic flux analysis using 13 C labeling can precisely track microbial metabolism [ 22 ], it has limitations in capturing the multitude of metabolite exchanges within microbial populations. Moreover, metabolic flux analysis is primarily applicable in synthetic laboratory settings [ 23 – 25 ]. To address these challenges, mathematical modeling has been introduced to study microbe-microbe interactions in MFCs [ 25 – 28 ]. Artificial intelligence (AI)-based modeling is a powerful approach to accommodate a large number of microbial species that are involved in the scope of study [ 26 ], whereas metabolic modeling is a niche approach to gain comprehensive insights underlying the association of microbial species in a community [ 25 , 28 ]. In particular, the genome-scale metabolic models (GEMs) of microbial communities created using Flux Balance Analysis (FBA) framework [ 29 ] allow the observation of mechanistic scenarios of microbial interactions under any studied conditions that are not restricted to the realm of empirical experiments [ 30 – 33 ]. The GEMs approach is typically conceived as an integrated compartmental model to represent the fundamental metabolic process generated by the entire microbial community, or as a compartmentalized model to mimic the complementation and restriction inter-relationships between individual metabolic processes provided by the community members [ 34 ]. Previous studies of the compartmentalized GEMs demonstrate the ideality of the conceptual framework for examining how the microbial structure as a whole may adapt to changing conditions due to modified interactions between microorganisms. The multi-species GEM was initially adopted to deepen understanding of the commensal relationship between SRB and MET in a co-culture experiment, where hydrogen exchange was found to be a key metabolic cross-feeding that supports their syntrophic growth [ 35 , 36 ]. The approach has been improved in a number of ways to enable the simulation of the complex connections between microbial species in the real-world situation, for examples incorporation of microbial ecology constraints [ 37 ], contextualization of community dynamics through integration of omics data [ 38 ], and inclusion of spatial and temporal effects on microbial interactions and composition under dynamic environments [ 39 – 41 ]. Despite significant progress, the compartmentalized GEMs model still faces challenges in maximizing the number of community members in model coverage and minimizing computational load resulting from the large number of complexly associated components in order to arrive at a mathematically plausible solution. Addressing these challenges would improve the model’s representation of community-level scenarios. In this work, we aimed to address a critical gap in the practical implementation of industrial MFCs by focusing on understanding the dynamics of microbial communities under changing operating conditions, particularly an increase in the OLR. We postulated that microbial interactions, specifically metabolic cross-feeding through substrate-product exchange, would be disrupted during elevated OLR conditions. This disruption might favor different microbial guilds, resulting in alternative equilibria of cross-feeding, where different microbial species interact with each other in new ways to exchange nutrients and metabolites. Consequently, these shifts could lead to changes in the overall composition of the microbial community, with some species becoming more dominant while others decline. To this end, we developed an effective modeling approach of microbe-microbe interaction GEM, or mmGEM, based on a functional-based lumped compartmentalized structural model design of the main microbial guilds that hold essential metabolic capabilities required for MCF substrate conversion under context of study. Our approach fundamentally intends to minimize the computational complexity of modeling while preserving the highest level of complexity present in real-world scenarios. This design compromises ideas between the compartmentalized and lumped metabolic model by representing microbial compartment of a metabolism-specific function by assuming that each essential metabolic function is not driven by only a single species but by a cohort sharing functional characteristics. This approach allows an inclusion of microbial species while limiting the number of compartments in the model. Expanding the community model also improves its representativeness by accounting for a larger proportion of the nutrients available in the real condition. In particular, the mmGEM was employed to represent the metabolic cross-feeding interactions among SRB, MET, and SOB microbial guilds in MFCs under varying OLRs. The model demonstrated a transition of a SOB-dominant community in low production seasons (L-OLR) to a MET-dominant community in high production seasons (H-OLR) from previous experiments, with the simulated cross-feeding dynamics. Despite of simplified microbial guilds, the model displayed mechanism of metabolic cross-feeding transition influencing the change of microbial community composition. Interestingly, enhancing SRB-SOB interactions could save SOB-dominant communities from collapsing under increasing OLR conditions. Leveraging our understanding, we proposed scenario-based mmGEM simulations incorporating elevated environmental H + concentrations to improve the growth of SOB, which expected to improve MFC performance under high OLR condition. Indeed, we demonstrated the potential of leveraging GEMs in MFC applications. These models facilitate the study of MFCs and the implementation of eco-friendly technology in real industrial settings.",
"discussion": "Discussion The performance of MFCs in sulfide and organic-rich wastewater from the canned-pineapple industry, including electricity production and the removal of COD and sulfide, was significantly influenced by the composition and activity of the microorganisms. To investigate microbial complex interaction in MFC system, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) using functional-based lumped compartmentalized model design to unravel the intricate metabolic cross-feeding among important microbial guilds. Since fundamental microbial functions in MFCs are driven by groups of microbial species rather than by single species, a microbial compartment in the mmGEM was modeled as a microbial guild, ideally aggregating multiple species. The mmGEM conceptual design provides effective computational simulation and interpretation with strategically retaining sufficient biological complexity to accurately represent the studied system, enabling a comprehensive understanding of the circumstances. Under this design, we demonstrated that the mmGEM could simulate an accurate microbial transition from SOB-dominant to MET-dominant during increasing OLR conditions. The approach also revealed metabolic cross-feeding mechanisms that influenced changes in microbial composition. Additionally, the function-based metabolic model effectively responded to dynamic organic substrate conditions and captured the capacity for microbial interactions. This not only provided insights into the microbial ecosystem in MFCs but also suggested a manipulative strategy for improving MFC performance. Our approach achieves a well-balanced combination of computational feasibility and realistic contextual representation with minimal data requirements. Unlike many GEM studies that rely on species-level or synthetic communities, this study offers an alternative approach that is practical for real and complex microbial systems. The mmGEM precisely simulated microbial abundances under both L-OLR and H-OLR conditions ( Fig 2A ) highlighting the pivotal roles of SRB, MET, and SOB in shaping the microbial community composition in MFCs across varying OLRs. In addition, the observed microbial community adaptation is linked to metabolic cross-feeding among microbial guilds, influencing MFC performance. The persistent COD removal in both conditions can be attributed to increased acetate utilization by MET (Figs 2B and S1 ). This finding aligns with previous studies highlighting MET’s crucial role in COD removal, particularly in higher OLR conditions [ 15 , 16 ]. However, reports indicate a negative influence on electricity production in MFCs [ 16 , 52 , 53 ]. Considering the correlation between SOB and electricity generation [ 5 , 49 , 50 ], competition between MET and SOB would impact electricity generation. Simulated acetate cross-feeding highlighted this competition and the consequent dominant role of MET, resulting in lower current density in H-OLR conditions ( S1 Table ). Our findings suggest that metabolic cross-feedings arise from SRB engaging in incomplete metabolism due to sulfate limitations ( Fig 2 ). This aligns with prior reports highlighting how sulfate constraints disrupt redox balance when the organic substrate exceeds the capacity of electron acceptors, leading to the secretion of metabolic byproducts instead of completing metabolism [ 54 , 55 ]. The secreted metabolites, including acetate, sulfide, and H 2 ( Fig 2 ), probably create niches suitable for the coexistence of MET and SOB [ 14 , 56 ]. This coexistence leads to constant scavenging of these metabolites ( Fig 2 ), preventing thermodynamic constraints such as the accumulation of acetate and sulfide, which could negatively impact the metabolic processes of SRB [ 57 – 59 ]. Under higher organic loading conditions, a smaller ratio of carbon to sulfate substrates in SRB leads to the secretion of more diverse metabolites, notably acetate, H 2 and formate ( Fig 3 ). These incremental secretions agree with an increased abundance of diverse methanogens (e.g., Methanosaeta , Methanolinea , and Methanoregula ) at H-OLRs [ 5 ]. This could be explained by the necessity for greater diversity and selective function to effectively disseminate the higher free energy [ 60 , 61 ]. In H-OLR conditions, MET outperformed SOB in efficiently utilizing acetate ( Fig 3 ), likely as a result of their metabolic efficiency and lower growth-dependency compared to SOB [ 43 , 62 ]. Although SOB can derive substantial benefits from acetate assimilation, this process relies on sulfide as an energy source [ 63 ], in line with simulation results demonstrating a correlation between the decline in S cycling efficiency and reductions in both acetate assimilation and biomass biosynthesis (Figs 2 and 3 ). The simple exchange of catabolic metabolites, involving S cycling and acetate assimilation between SRB and SOB, significantly determined metabolic cross-feeding and subsequent shifts in community composition ( Fig 4 ). The original equilibrium state of the microbial community demonstrated resilience against an organic increment of approximately 28% before transitioning into a community structure more conducive to SOB growth and activity ( Fig 5 ). These findings align with those of Sriwichai et al. (2024), who noted sustained high current generation in another MFC operating condition even when the rate of organic loading into the MFC chambers remained constant. This sustained high current generation at a constant OLR is likely due to the sustained growth and metabolic activity of SOB over time [ 5 ]. Our findings demonstrate a strong SRB-SOB interaction, which facilitated consistent acetate cross-feeding, preventing the development of a niche for MET. Similarly to Dolinšek et al. (2022), our study underscores how the initial microbial composition can gradually shape metabolic cross-feeding and community structure by gradually influencing environmental conditions over time [ 64 ]. This suggests that augmenting the initial abundance of SOB can strengthen their bonds with SRB ( Fig 6 ), thereby enhancing the community’s resilience against fluctuations in organic concentrations. While some studies have suggested the detrimental effect of extreme acidity on MFC performance, others have reported improved electrical production in an acidified anodic environment [ 65 , 66 ]. Our simulations highlighted the differential responses of microbial guilds to acidity within the MFC environment, with SOB showing a favorable response in terms of growth and ATP generation compared to SRB and MET ( Fig 7 ). Previous research has highlighted the species-specific nature of microbial responses to acidity. For example, it was observed that Pseudomonas putida showed an increase in cellular ATP under high environmental H + conditions, while Staphylococcus epidermidis stabilized ATP synthesis in the same acidic conditions [ 21 ]. The increased growth of SOB in response to H + supplementation in the MFC system is associated with an improvement in cross-feeding interactions with SRB, which led to a reduction in substrates typically utilized by MET, thereby limiting their growth and activity ( Fig 7B ). The fact that acetoclastic methanogens do not thrive in acidic environments provides additional evidence to support the idea that the perturbation caused by the increase in H + ions can significantly impact the microbial dynamics within the system [ 51 ]. Here, we propose that a temporal H + perturbation could be used to exploit the SRB-SOB interactions to suppress the competitiveness of MET. The MFC microbiome can then recover from a collapse caused by low pH conditions once a neutral pH is restored [ 20 ]. Further investigation is, however, required to determine effective conditions for inducing H + perturbation in MFCs. This study highlights the potential of MFC technology to operate sustainably in industrial settings, even amidst fluctuating conditions. By addressing critical challenges in its implementation, our simulated results indicate that controlled perturbations introduced to the microbial community within MFC systems can enhance microbial structure and improve MFC performance under high organic loading conditions. Furthermore, the mmGEM proves to be an effective tool for studying complex interactions within MFC systems. Despite requiring minimal data for its construction and simulations, the model provides detailed insights into metabolic processes and composition changes within the microbial community. In essence, mmGEM validity is leveled by the extent of comprehensive knowledge about the metabolic properties of microorganisms (i.e. as to define: a choice selection of metabolic-specific microbial guilds) and the key metabolic processes of the system (i.e. as to define a choice selection of essential metabolic functions to achieve the system’s outcome) that are taken into account for modeling. In this case, the mmGEM has demonstrated its reliability by effectively responding to the increasing OLR covering the scope of the studied conditions ( Fig 4 ). Additionally, it remains robust across various ranges of parameterized cross-feeding reactions ( S2 Text ). Further expanding the model’s scope to cover a wider range of substrates and microbial species, including fermentative bacteria, would better simulate processes influencing electricity generation in MFCs and provide a more comprehensive representation of chemical balances within the system [ 11 , 67 ]. While the mmGEM relies on a constant cell yield assumption ( Y cell mass / substrate = 0.15 mg O 2 ‐ cell mg O 2 ‐ substrate ‐ 1 ) derived from a previous MFC study using acetate as a substrate [ 68 ], we recommend reevaluating this assumption to account for potential variations in cell yield under different substrate conditions. Despite falling within the typical range observed in anaerobic wastewater treatments and MFCs (0.04–0.25 mg O 2 ‐ cell mg O 2 ‐ substrate ‐ 1 ) [ 69 , 70 ], such reevaluation could improve the accuracy of MFC modeling."
} | 6,240 |
39468081 | PMC11519575 | pmc | 523 | {
"abstract": "Exploring microorganisms with downstream synthetic advantages in lignin valorization is an effective strategy to increase target product diversity and yield. This study ingeniously engineers the non-lignin-degrading bacterium Ralstonia eutropha H16 (also known as Cupriavidus necator H16) to convert lignin, a typically underutilized by-product of biorefinery, into valuable bioplastic polyhydroxybutyrate (PHB). The aromatic metabolism capacities of R. eutropha H16 for different lignin-derived aromatics (LDAs) are systematically characterized and complemented by integrating robust functional modules including O- demethylation, aromatic aldehyde metabolism and the mitigation of by-product inhibition. A pivotal discovery is the regulatory element PcaQ, which is highly responsive to the aromatic hub metabolite protocatechuic acid during lignin degradation. Based on the computer-aided design of PcaQ, we develop a hub metabolite-based autoregulation (HMA) system. This system can control the functional genes expression in response to heterologous LDAs and enhance metabolism efficiency. Multi-module genome integration and directed evolution further fortify the strain’s stability and lignin conversion capacities, leading to PHB production titer of 2.38 g/L using heterologous LDAs as sole carbon source. This work not only marks a leap in bioplastic production from lignin components but also provides a strategy to redesign the non-LDAs-degrading microbes for efficient lignin valorization.",
"introduction": "Introduction The valorization of biomass to serve as a source of energy and diverse chemicals and polymers has been extensively investigated and is gradually being industrialized for its advantages in carbon emission reduction and environmental sustainability 1 , 2 . The rapid development of biomass biorefining had led to the development of large quantities of lignin as a byproduct, with up to 1.5 kg of lignin produced per liter of cellulosic ethanol 3 . However, only a very limited fraction of the lignin produced is commercially utilized, and the majority of the lignin stream is used only for heat or electricity generation by combustion, and some even is directly discarded 4 . The valorization of lignin to high-value products, while crucial for the economic viability and sustainability of lignocellulosic biorefineries, is also challenging due to the heterogeneity of lignin 5 . Although some microbes, such as wood-rot fungi, can degrade lignin in nature, their lignin mineralization process is slow, greatly hindering their applicability in industrial lignin valorization 6 . Recently, bacterial metabolic engineering through integration of robust biological elements from different microbial systems to systematically produce high-value products from lignin waste streams has been demonstrated to be a good potential strategy for lignin valorization 7 . Meanwhile, developing biodegradable plastics with economic efficiency and sustainability has become a global strategy to address the issues caused by white pollution 8 , 9 . There is an urgent need to develop an efficient system to utilize waste biomass resources as cost-effective carbon substrates for sustainable bioplastic synthesis in competitive large-scale commercial applications 10 , 11 . The development of technologies for converting lignin to biopolyesters, such as polyhydroxybutyrate (PHB), which is a completely biodegradable bioplastic, has garnered wide interest due to its great significance for both the biomass biorefining and bioplastics industries 5 , 12 . The conversion of lignin to biopolyesters requires microorganisms that possess the ability to metabolize aromatic compounds upstream and synthesize biopolyesters downstream 13 . Some microorganisms in nature, such as Pseudomonas putida KT2440, have been shown to have the capacity to convert lignin-enriched biorefinery waste and LDAs compounds to PHB 12 . However, the biopolyester accumulation ability of chassis organisms developed to date is insufficient, leading to lower-than-expected polyester production, which is a critical hurdle in the commercialization of biopolyesters 14 . Exploring microorganisms with superior capacity in biopolyester accumulation for lignin valorization by further integrating multiple efficient aromatic metabolism pathways is one of the most effective and attractive strategies for cost-effective biopolyester production from lignin waste streams 13 , 15 . R. eutropha H16 is a resilient biopolyester production system due to its remarkable metabolic versatility, high-cell-density fermentation capacity and elevated polyester accumulation 16 . However, R. eutropha H16 lacks an aromatic demethylation system, which is the key and rate-limiting step for lignin-derived methoxy-containing aromatics metabolism 5 , 17 . Recent studies have discovered and characterized many critical enzymes for LDAs demethylation, such as VanAB, GcoAB, and LigM, which provide essential biocatalytic elements for upgrading PHB-producing strains for lignin degradation and valorization 17 – 19 . Nonetheless, integration of the synthesized aromatic demethylation modules into chassis cells for efficient LDAs conversion remains a great challenge. The development of cells with superior compatibility requires not only the introduction of adaptive catalytic elements into the cells but also a series of auxiliary genes that are coordinately expressed. An intelligent gene regulatory system capable of coordinately regulating the expression of related genes responding to substrates is essential for developing an efficient aromatic demethylation system for lignin conversion 20 . Some inducible circuits have been developed for gene regulation in R. eutropha H16, while most use additional compounds such as IPTG or arabinose as inducers 21 , 22 . External inducers usually exacerbate the metabolic burden and are even toxic to cells, which could lead to an additional increase in production cost 23 . In recent years, autoregulatory systems, using substrates as inducers to trigger the expression of genes needed for substrate metabolism, have received extensive attention for their advantages in eliminating the cost of external inducers and reducing the unnecessary metabolic burden of cells by more intelligently and precisely regulating the expression of the responding genes 20 , 24 . Nevertheless, there is still no report showing any robust autoregulatory system developed for lignin metabolism due to the great challenge of overcoming the complexity and heterogeneity of LDAs. It would be cumbersome and inefficient to develop an exclusive autoregulatory system for each LDAs substrates. Hence, relying on the metabolic characteristics of microbial biological funnels, the regulatory elements that respond to central metabolites can be used as the core to construct the regulatory hub, which can realize the automatic regulation of the metabolism of various LDAs. This is an ideal and sufficient strategy to address the aforementioned challenge for metabolic autoregulation of lignin substrates. In this work, a self-enhanced autoregulation system is established in R. eutropha H16 to regulate the heterologous expression of genes for efficient conversion of lignin-derived aromatics (LDAs) to PHB. The metabolic capacities of R. eutropha H16 for different types of LDAs are systematically analysed. The potential and main limiting factors for the use of R. eutropha H16 for lignin valorization are identified. To overcome the limitation of R. eutropha H16 in lignin valorization by conversion to PHB, suitable genes with aromatic demethylation functions are screened and expressed in R. eutropha H16 for the efficient metabolism of lignin-derived methoxy-containing aromatics. The native gene-regulating elements responding to the core intermediates of aromatic metabolism in R. eutropha H16 are identified and characterized for their regulatory patterns in the presence of different LDAs. The identified gene-regulating element PcaQ is used to construct a lignin-derived substrate autoregulatory system, and its intelligence in responding to various LDAs is improved via rational design. The autoregulatory system is applied to regulate the multiple genes encoding demethylase and acetaldehyde dehydrogenase to enable R. eutropha H16 to achieve much more efficient conversion of LDAs to biopolyesters. The utilization of a cost-effective and efficient cell factory system shows potential for the conversion and valorization of lignin.",
"discussion": "Discussion Despite having the advantages in LDAs metabolism, lignin-degrading bacteria usually have the limitation with unsatisfying capacity in biopolyester synthesis 52 , 53 . Those bacteria with stronger biopolyester accumulation capacity have not been studied for their potential in lignin valorization to produce biopolyesters. For example, the PHB producer R. eutropha H16, displaying many advantageous metabolic traits for PHB biosynthesis, such as high-density fermentation, singular and stable polyester components and exclusive polyester accumulation capacity 54 – 56 , has never been systematically analysed and developed for lignin bioconversion to produce PHB. To successfully improve the metabolic capabilities and address the limitations of R. eutropha H16 when grown on LDAs, certain factors need to be considered, including appropriate substrate concentrations, sufficient initial cell inoculum and stable culture system, due to the inhibitory effect of aromatic compounds on microorganisms and their disruptive effects on culture system’s pH 57 . Compared with other inexpensive carbon sources such as glycerol, R. eutropha H16 showed a more prominent rate of conversion and utilization of protocatechuic acid as substrate and could accumulate higher biomass rapidly 58 , 59 . Protocatechuic acid is an important intermediate monomer in the process of lignin biodegradation. It can be produced through the metabolic funnel of the microbes from various aromatic compounds depolymerized from lignin. The R. eutropha H16 engineered strain with enhanced upstream metabolic capabilities shows immeasurable potential in lignin valorization. This strategy provides a one stop solution for the introduction of more non-lignin-degrading microorganisms with downstream metabolic advantages for lignin valorization. The goal of biorefinery integration was the efficient conversion of the full biomass components, of which the efficient conversion of lignin has been the rate-limiting step in biorefinery integration. In contrast to carbohydrates, lignin conversion entails a variety of challenges such as the toxicity of high concentrations of aromatic compounds to cells and the heterogeneity of aromatic compounds. This study provided a strategy to overcome these challenges for biological lignin valorization. The efficiency of the HMA system was demonstrated by the record 2.38 g/L PHA yield with lignin-derived aromatics as sole carbon source which is higher than that reported (Supplementary Data 1 ). The strategy developed in this study can also be applied to convert lignin to other valuable products. The continuous increase in lignin conversion efficiency will effectively contribute to the realization of the ultimate objective of complete biomass biorefinery integration. It has been shown that demethylation is one of the major barriers to restrict many microorganisms from participating in lignin valorization 60 . Horizontal transfer of demethylation function requires consideration of multiple factors to achieve. Cai et al. showed that overexpression of ligM in R. opacus PD630 could significantly enhance its demethylation ability 7 . While the enzyme LigM, as well as DesA, need tetrahydrofolate as a cofactor to conduct demethylation catalysis, which most likely depend on the special system obtained by the evolution of Sphingomonas sp. SYK-6 61 . When we heterologously expressed LigM as well as the tetrahydrofolate regeneration system for R. eutropha H16, even though it endowed the strain with demethylation ability, the efficiency was much lower than that by VanAB, which is another type of demethylase with two-enzyme system containing iron-sulfur cluster 62 . It suggests that the adaptability of the demethylation catalytic system to the chassis is important to make the genetic components to be functional effectively. R. eutropha H16 harbors many enzymes with iron-sulfur clusters, and has impeccable NAD(P)H regeneration system 63 , thereby the VanAB-based demethylation system could be adaptable well to the host to achieve highly efficient demethylation of the vanillic acid. As an important cofactor involved in LigM and DesA catalysis, the content of tetrahydrofolate and the regeneration system will have an impact on demethylation efficiency. Fully understanding the supply mechanism of this cofactor in Sphingomonas sp. SYK-6 will effectively improve the application of this type of demethylation genes 64 . To achieve efficient degradation of the lignin-derived methoxy-containing aromatics, timely removing of the demethylation byproduct formaldehyde is required. The introduction of formaldehyde dehydrogenase relieves the accumulation of toxic formaldehyde and regenerates the cofactor NADPH needed for the demethylase. The lack of demethylation ability of R. eutropha H16 can be effectively filled by complementing suitable demethylation genes and removing the inhibitory effect caused by catalytic by-products. Incorporating the metabolism of methoxy-containing aromatics such as vanillic acid into the substrate metabolism map of R. eutropha H16 improves the conversion efficiency of lignin components to PHB. The broadening of the substrate range of the catalytic element better addresses the challenge of the complex heterogeneity of lignin 19 , 65 . However, the regulatory elements currently mined for major LDAs show certain regulatory specificity 66 . Definitely, it is unfeasible to design regulation system one by one for each lignin-derived substrate. To overcome the barrier, a more efficient system needs to be developed to intelligently respond to various LDAs. The importance of regulatory elements in metabolic engineering has received more attention, and mining switches with different properties is crucial for complex metabolic regulation 67 , 68 . The six regulatory elements from R. eutropha H16 displayed three different types of traits: fast opening and fast closing, slow opening and fast closing, and fast opening and slow closing. These different types of regulatory properties are necessary for the development of complex pathways in metabolic engineering 69 . The regulatory differences among different regulatory elements also provide more possibilities for gradient expression or rhythmic regulation 70 . Different from the purpose of a comprehensive screening of regulatory elements in the genome 37 , the present study developed regulatory elements to enhance the metabolism of aromatic compounds by R. eutropha H16 with the explicit purpose of constructing regulatory hubs that were effective in regulating the metabolism of complex substrates. By modifying PcaQ, the mutant PcaQ R145k enhanced the response ability to low concentrations of effectors. Compared with the regulatory elements developed for monomers such as vanillin 20 , the regulatory hub constructed in this study can effectively realize the metabolic regulation of complex substrates and was more in line with the needs of lignin depolymerization product metabolism. The concept and process of constructing the regulatory hub were equally insightful. This study also demonstrated that the PcaQ of R. eutropha H16 was able to use the metabolic core intermediate protocatechuic acid as an effector with different regulatory properties comparing with the previously reported PcaQ of S. meliloti 39 . Compared with the regulation of lac operator by IPTG or P BAD by arabinose, the HMA system constructed based on PcaQ R145K /P PCA in this study can achieve the regulation of substrate metabolism without the addition of inducers, highlighting the autoregulatory property. This avoids the cellular toxicity and additional cost of inducers such as IPTG. Even though there were some regulatory similarities to the system in response to vanillin constructed by Wu et al. 20 , the HMA system constructed in this study shows the advantage in simultaneously responding to different aromatics, which is essential for efficient lignin valorization. The engineered strain with HMA system could efficiently convert the mixed aromatics or the LDAs to produce PHB, suggesting the advantage of the designed HMA system in autoregulation of multiple genes responding to lignin depolymerized aromatics. In addition, the HMA system could also be further used to reduce the metabolic burden to promote the utilization of biomass hydrolyzate by reducing the expression of related genes after removing the inhibition of aromatic compounds 71 . Besides the single strain harboring multiple enzymes to convert different aromatics to PHB, which is the strategy applied in this study, engineered R. eutropha consortium, where each strain is responsible for specific substrates conversion, could be designed in future to further promote the lignin conversion efficiency and the precision of targeting gene regulation. In addition, other hub metabolites (i.e., catechol) during the degradation of the heterologous lignin depolymerized aromatics could be used to design the similar autoregulatory system with same concept approved by this study."
} | 4,409 |
32440262 | null | s2 | 524 | {
"abstract": "Animal silks are built from pure protein components and their mechanical performance, such as strength and toughness, often exceed most engineered materials. The secret to this success is their unique nanoarchitectures that are formed through the hierarchical self-assembly of silk proteins. This natural material fabrication process in sharp contrast to the production of artificial silk materials, which usually are directly constructed as bulk structures from silk fibroin (SF) molecular. In recent years, with the aim of understanding and building better silk materials, a variety of fabrication strategies have been designed to control nanostructures of silks or to create functional materials from silk nanoscale building blocks. These emerging fabrication strategies offer an opportunity to tailor the structure of SF at the nanoscale and provide a promising route to produce structurally and functionally optimized silk nanomaterials. Here, we review the critical roles of silk nanoarchitectures on property and function of natural silk fibers, outline the strategies of utilization of these silk nanobuilding blocks, and we provide a critical summary of state of the art in the field to create silk nanoarchitectures and to generate silk-based nanocomponents. Further, such insights suggest templates to consider for other materials systems."
} | 337 |
39689915 | PMC11756291 | pmc | 525 | {
"abstract": "Abstract Microbial functional ecology is expanding as we can now measure the traits of wild microbes that affect ecosystem functioning. Here, we review techniques and advances that could be the bedrock for a unified framework to study microbial functions. These include our newfound access to environmental microbial genomes, collections of microbial traits, but also our ability to study microbes’ distribution and expression. We then explore the technical, ecological, and evolutionary processes that could explain environmental patterns of microbial functional diversity and redundancy. Next, we suggest reconciling microbiology with biodiversity–ecosystem functioning studies by experimentally testing the significance of microbial functional diversity and redundancy for the efficiency, resistance, and resilience of ecosystem processes. Such advances will aid in identifying state shifts and tipping points in microbiomes, enhancing our understanding of how and where will microbes guide Earth's biomes in the context of a changing planet.",
"conclusion": "Concluding remarks Even though microbial functional ecology is still lagging behind the knowledge acquired for animals and plants, the field is growing rapidly due to manifold technical advances. Importantly, a census of microbial effect traits exists and is growing. Furthermore, new genome-centric methods allow the study of the distribution of these traits across microbial taxa and ecosystems. This opens the door to robust comparisons of microbial functional diversity and redundancy. Working at the level of population genomes will also help quantify the contribution of various ecological and evolutionary drivers to changes in microbial functions. This outlines a framework in which testing the importance of microbial functional diversity and redundancy for ecosystem processes, their resistance, and resilience is within our reach. Nevertheless, further cross-disciplinary research including biogeochemists and modelers, is required to fully comprehend microbial functional diversity. Results from these joint efforts will expand our understanding of ecosystem functioning and could inform decision-makers in the context of global change. Glossary \n Ecosystem functioning: The sum of properties or processes measured at the ecosystem level, e.g. energy flow or chemical cycling (Violle et al. 2007 , Krause et al. 2014 ). While a function represents a single process, e.g. denitrification affects the nitrogen cycle. \n Trait: Morphological, physiological or phenological feature measurable at the individual, population or community level, e.g. the nirK gene (Violle et al. 2007 ), encoding for nitrite reductase that performs denitrification, part of the nitrogen cycle. \n Effect trait: Any trait that affects ecosystems. They reflect, or can be used as a proxy of, the function a microbe performs in the ecosystem, e.g. the presence of the nirK gene in a genome. \n Response traits: Traits that vary in response to changes in environmental conditions (Violle et al. 2007 ). Response traits are used as proxies of the performance of an individual along an environmental gradient, e.g. cell size and morphology usually correlate with diverse environmental conditions (Litchman and Klausmeier 2008 ). Confusion can arise from traits that can be used both as effect and response traits. For example, size in phytoplankton is related to nutrient uptake efficiency. Thus, it is a response trait , as it predicts the success of larger phytoplankton cells in resource-replete conditions. But it is also an effect trait , as it predicts the rate at which nutrient uptake will be performed. \n Functional group: A group of taxa that affect the ecosystem in the same manner, perform the same function, or harbor similar traits. Groups can be defined at different levels, e.g. all denitrifiers or only the individuals possessing the nirK gene. \n Functional redundancy: The fact that different taxa harbor the same effect trait(s) and can thus play the same role in ecosystem functioning. Using this definition, microbial taxa can share some traits but can differ in their rate, the presence of other traits, or ecological preferences (Nico et al. 2023 ). \n Gene: Here, we use gene as a synonym for ORF. An ORF is a sequence delimited by a start and stop codon and holds the potential to be translated into a protein.",
"introduction": "Introduction A widely accepted principle in ecology is that biodiversity enhances ecosystem functioning. More diverse ecological communities indeed tend to positively influence ecosystem processes such as resource uptake efficiency, biomass production, decomposition, and nutrient cycling (Cardinale et al. 2012 ). Nevertheless, determining how environmental change, diversity, and ecosystem processes interact remains a great challenge (Loreau 2001 ). Microbiology has gradually adopted this perspective (Naeem et al. 2000 , Cardinale 2011 , Delgado-Baquerizo et al. 2016a , b ). Microbes have colonized all habitats on Earth where they drive major ecosystem processes and represent an important part of the standing biomass (Bar-On et al. 2018 , Bar-On and Milo 2019 ). Bacteria and Archaea (prokaryotes) have developed a vast array of metabolisms that directly affect the cycles of hydrogen, carbon, nitrogen, oxygen, sulfur, or iron (Falkowski et al. 2008 ). In turn, protists and fungi (microbial eukaryotes) influence Earth's biogeochemistry through primary and secondary production (Massana and Logares 2013 ), participating in many microbial interactions, affecting the availability of organic matter, and its transfer to the rest of the trophic food web (Worden et al. 2015 , Keeling and del Campo 2017 ). Recent findings show that we have yet to discover many pathways within those general functions, such as the complete oxidation of ammonia to nitrate (comammox) discovered in Nitrospira (Daims et al. 2015 ), complex predatory behaviors in Bacteroidetes (Lien et al. 2024 ), or new microbial actors such as the fungus Gjaerumia minor that appears to be a key degrader of recalcitrant organic matter in the Pacific bathypelagic ocean (Pernice et al. 2024 ). Microbes have also recently been shown to be key players in underexplored environments (Ruiz-González et al. 2021 , Shu and Huang 2022 ), and to be involved in novel functional cooperations, such as the symbioses between N 2 -fixing bacteria and haptophytes or diatoms (Cornejo-Castillo et al. 2024 , Tschitschko et al. 2024 ). The functioning of ecosystems is, therefore, closely linked to the production, storage, decomposition, or remineralization performed by microbial communities (Cavicchioli et al. 2019 ). In a changing planet, where processes such as warming, acidification, deoxygenation, disruption of dispersal chains, land use, aridity, or pollution alter microbiomes and their activity (Hutchins et al. 2019 ), it is crucial to understand the relationship between microbial biodiversity and ecosystems. Part of this challenge is to integrate the multidimensional nature of biodiversity. This includes the taxonomic, phylogenetic (the evolutionary history and relatedness between organisms), and functional dimensions (the range of things that organisms do that affect ecosystems) (Petchey and Gaston 2006 , Diaz et al. 2013 ), which show high variability across ecological scales (Ladau and Eloe-Fadrosh 2019 ). Functional ecology relies on the study of traits (Streit and Bellwood 2023 ). These are any genetic, morphological, or physiological features that can be measured at the individual, species, or community levels. Ecologists study and explain (i) the response of organisms to environmental change using response traits and (ii) how organisms affect ecosystem functioning using effect traits (Lavorel and Garnier 2002 , Violle et al. 2007 ). Many traits can be considered as both response and effect traits , notably resource utilization traits (Lavorel and Garnier 2002 , Diaz et al. 2013 ), as they predict the performance of an organism according to the availability of a resource, but also its uptake and potential transformation by an organism in a given ecosystem (Litchman et al. 2015 , Martiny et al. 2015 ). Community composition is shaped by biotic and abiotic selection, dispersal, speciation, or ecological drift (Vellend and Agrawal 2010 ), processes that sort organisms based on their response traits . The emergent communities resulting from the interplay of these processes express effect traits that impact ecosystem functioning (Diaz et al. 2013 ). As this piece is mainly concerned with biodiversity–ecosystem functioning studies, we focus on effect traits and refer to them as traits. Relevant traits among microbiomes have been identified (Fierer et al. 2014 , Litchman et al. 2015 , Escalas et al. 2019 ), such as metabolic traits, which are direct indicators of the processes (resource uptake, decomposition, and nutrient cycling) that microbes can perform (Martiny et al. 2015 ). By studying effect traits ecologists have progressively unveiled the mechanistic link between ecosystem processes and a wide range of communities (Petchey and Gaston 2006 , Mouillot et al. 2013 , van der Plas 2019 ). Functional diversity is the breadth of functions that the species are able to perform within an ecosystem (Díaz and Cabido 2001 ). It can be estimated by identifying the traits harbored by the species of a community and measuring their relative abundance (Violle et al. 2012 ). Functional diversity is generally better at predicting ecosystem processes than taxonomic diversity (van der Plas 2019 ). For instance, in dryland plant ecosystems, the diversity in specific leaf areas and maximum plant height were shown to be better predictors of multifunctionality (here, plant productivity, soil enzymatic activity, ammonification, and N transformation rate) than species richness (Gross et al. 2017 ). In addition, the traits harbored by the most abundant organisms are often driving these processes (Grime 1998 , Garnier et al. 2004 ). In turn, functional redundancy , or functional similarity (Loreau 2004 , Nico et al. 2023 ), is the coexistence of species with similar traits and functional roles within an ecosystem. Functional redundancy ensures ecosystem functions against disturbance and species loss, maintaining stable ecosystem functioning over time (Yachi and Loreau 1999 , Díaz and Cabido 2001 , Biggs et al. 2020 ). By accumulating species with the same effects traits, but different ecological strategies ( response traits ), functional redundancy also leads to more efficient resource uptake (Loreau 2001 , Loreau and Hector 2001 ), and increases the provision of multiple ecosystem functions simultaneously (multifunctionality) (Le Bagousse-Pinguet et al. 2019 ). However, it is evident that much of this knowledge has been derived from studies on macro-organisms, underscoring a significant research gap in the field of microbiology. Testing the significance of microbial functional diversity and redundancy within ecosystems has indeed been a complex task because of the lack of a unified framework to study microbiomes and their traits (Escalas et al. 2019 , Lajoie and Kembel 2019 ). In parallel, several studies indicated independence between taxonomy and function, with different taxa performing the same functions across ecosystems, which suggests that microbiomes are functionally redundant. For example, a human gut survey showed minimal similarities in the taxonomy of microbiomes among patients, while many microbial genes were common and considered essential or core across patients (Turnbaugh et al. 2009 ). In the ocean microbiome, it was found that metabolic functions and taxonomy were driven by different processes (Louca et al. 2016 ). Other studies showed high taxonomic variability across spatial scales despite stable patterns of microbial functions (Sunagawa et al. 2015 , Haggerty and Dinsdale 2017 ). Nevertheless, other studies found contrasting results. For example, taxonomic and gene compositions displayed a high covariation in the microbiomes of North America's prairie soils (Fierer et al. 2013 ). Similarly, the taxonomy and gene content of the marine microbiome in the northwestern Mediterranean Sea showed high covariance over time (Galand et al. 2018 ). For marine protists, the variability in taxonomic composition altered the proportion of protistan functional groups across North Atlantic coastal ecosystems (Ramond et al. 2019 ). Altogether, the discrepancy between these results sparked a debate over the extent of functional redundancy in microbiomes, with important implications for the stability and resilience of ecosystems (Scheffer et al. 2015 , Biggs et al. 2020 ). Functional redundancy indeed suggests that the functions performed by microbiomes could be maintained under disturbances, as new taxa with similar effect traits but different response traits could be selected (Beauvais et al. 2023 ). In turn, a lack of redundancy implies that microbiomes facing disturbances could more easily lose functions, through the loss of the taxa solely responsible for a specific function (Allison and Martiny 2008 , Philippot et al. 2021 ). In fact, results highlight that microbiomes respond variably to disturbances (Shade et al. 2012 , Jurburg et al. 2024 ). This suggests that functional redundancy may exist more as a gradient across microbiomes, from microbiomes with low redundancy, more prone to shift under disturbances, to microbiomes with high redundancy, potentially more stable and resilient when facing disturbances. Understanding how microbial diversity and ecosystem functioning interact is a major goal, especially in the face of global change, which is expected to increase disturbances for microbes across all biomes (Cavicchioli et al. 2019 ). Here, we argue that studying the patterns of functional diversity and redundancy across microbiomes is now feasible, timely, and will make for a great leap forward for the field. We begin by examining recent advancements that could serve as the foundation for a unified framework of microbial functional ecology. We then focus on the potential drivers of microbial functional diversity and redundancy across biomes. In a final perspective section, we focus on how quantifying microbial functional diversity and redundancy will allow us to test their significance for the functioning of present and future ecosystems."
} | 3,633 |
40013791 | PMC11915838 | pmc | 526 | {
"abstract": "ABSTRACT Industrial anaerobic digestion (AD) represents a relevant energy source beyond today’s fossil fuels, wherein organic matter is recycled to methane gas via an intricate and complex microbial food web. Despite its potential, anaerobic reactors often undergo process instability over time, which is frequently caused by substrate composition perturbations, making the system unreliable for stable energy production. To ensure the reliability of AD technologies, it is crucial to identify microbial and system responses to better understand the effect of such perturbations and ultimately detect signatures indicative of process failure. Here, we investigate the effect of the microalgal organic loading rate (OLR) on the fermentation product profile, microbiome dynamics, and disruption/recovery of major microbial metabolisms. Reactors subjected to low- and high-OLR disturbances were operated and monitored for fermentation products and biogas production over time, while microbial responses were investigated via 16S rRNA gene amplicon data, shotgun metagenomics, and metagenome-centric metaproteomics. Both low- and high-ORL fed systems encountered a sudden decline in methane production during OLR disturbances, followed by a recovery of the methanogenic activity within the microbiome. In the high-OLR disturbances, system failure triggered an upregulation of hydrolytic enzymes, an accumulation of fermentation products, and a shift in the methanogenic population from hydrogenotrophic to acetoclastic methanogens, with the latter being essential for recovery of the system after collapse. IMPORTANCE Anaerobic digestion (AD) with microalgae holds great potential for sustainable energy production, but process instability caused by substrate disturbances remains a significant barrier. This study highlights the importance of understanding the microbial dynamics and system responses during organic loading rate perturbations. By identifying key shifts in microbial populations and enzyme activity, particularly the transition from hydrogenotrophic to acetoclastic methanogens during recovery, this research provides critical insights for improving AD system stability and can contribute to optimizing microalgae-based AD processes for more reliable and efficient methane production.",
"conclusion": "Conclusion Wastewater-cultured microalgae generate a nutrition-rich biomass, presenting a promising substrate for methane production. OLR disturbances are well known to cause imbalances to reactors’ performance with implications for methane production and system failure, potentially necessitating an extended and costly process recovery time. In this study, we examined the microbiome responses to OLR shocks when using microalgae as substrate. Consistent with previous research involving different feedstocks, high overloading led to a significant loss in reactor methane yield. However, despite the extensive shocks and reduction in methane yield, both CSTRs presented returning points and were able to recover the methane yield completely within weeks by introducing a shift in the microbial community, albeit the recovery from starvation taking a longer time than from high OLR. The presence of multiple methanogenic populations was key to AD recovery. It is well recognized that more diverse microbial communities provide a wider range of parallel pathways, contributing to functional redundancy, as observed here, and contribute to the resilience and recovery potential of a methane-producing CSTR. Anaerobic digestion of microalgal biomass presents a promising conversion method for sustainable energy and waste management. When compared to traditional AD feedstocks, microalgal biomass offers multiple potential advantages, including non-competition for arable lands, the contribution to CO 2 mitigation, and the ability to be cultivated in waste streams (e.g., wastewater). In this regard, microalgae culturing has been claimed as a sustainable, low-cost technology to recover nutrients from wastewater in the form of biomass, representing a promising process to replace conventional wastewater treatments (i.e., activated sludge). The absence of aeration in microalgae technology considerably reduces the energy demand of the wastewater treatment plant, as well as the emission of pollutants, such as NOx and volatile organic compounds. By employing microalgae as feedstock of AD, the biomass can be converted into a clean energy source (biogas) while simultaneously treating residual streams. This process does not only contribute to a circular bioeconomy but also minimizes reliance on fossil fuels, aligning with global sustainability goals. Therefore, the main importance of this study is to provide insights to fully comprehend the AD of microalgae biomass, supporting the process scalability.",
"introduction": "INTRODUCTION Global energy crises, geopolitical instabilities, and the imminent impact of climate change raise concerns for energy security. Biogas can play an important role in meeting the greenhouse gas reduction targets in the years to come and support the transition to circular economies and improved waste recycling ( 1 , 2 ). Biogas, which is a mixture of mainly methane (CH 4 ) and carbon dioxide (CO 2 ), is naturally produced through anaerobic digestion (AD) of organic material enabled by complex and intertwined processes carried out by a network of diverse microorganisms. The microbial and metabolic complexities of AD and its performance to cope with oscillations in organic loading rates (OLRs) impose challenges for maintaining a stable biogas flow ( 3 – 5 ), as well as implications for potential industrial automation, warranting in-depth studies of the state and function of systems under perturbations or near tipping points. Industrial biogas production necessitates defined deviation measures and identification of potential warning signals for process failure. Parameters used for monitoring AD systems include the type of feedstock, hydraulic retention time (HRT), OLR, temperature, pH, alkalinity, concentration of ammonium/ammonia (NH 4 + /NH 3 ), volatile fatty acids (VFAs), alcohols, hydrogen (H 2 ), CO 2 , and CH 4 ( 6 ). These parameters intricately intertwine with the dynamics of the inherent microbiome, which drives hydrolysis, acidogenesis, acetogenesis, and methanogenesis either from acetate (acetoclastic pathway), H 2 /formate, and CO 2 (hydrogenotrophic pathway) or from methylated compounds (methylotrophic pathway). Earlier studies have highlighted the importance of interrogating AD microbiomes and their metabolic interactions within industrial reactors ( 7 , 8 ). Disturbances in OLR applied to biogas systems are recognized for molding the bacterial and archaeal communities involved in the AD processes ( 2 , 9 ) and are a common cause of the decline in methane production ( 3 , 4 ). Organic material commonly used in large-scale AD includes food waste, industrial waste, agricultural residues, sewage sludge, and manure-based substrates ( 10 , 11 ). While these traditional substrates are widely utilized, microalgae-based feedstocks stand out for their exceptionally high bioenergy potential ( 12 , 13 ), despite the lack of industrial-scale implementation. Many microalgae strains can achieve high biomass yields using readily available resources, such as wastewater effluents ( 14 , 15 ), while simultaneously consuming CO 2 and nutrients. This makes them promising candidates for methane production in bioreactors. Additionally, with an annual sequestration of approximately 100 Gt of CO 2 into biomass ( 16 , 17 ), microalgae cultivation supports efforts to reduce carbon footprints. Unlike traditional lignocellulosic biomass, such as crop residues, microalgae also lack lignin in their cell walls ( 18 ), simplifying the fermentation process. Here, we intentionally induced imbalanced levels of microalgae biomass in bioreactors and evaluated whether such OLR disturbances caused AD process failure and investigated how they influenced the biogas production and shaped the microbiome structure over time. Based on previous observations with OLR trials using the traditional feedstocks grass, sludge, and sugar beet ( 3 , 4 , 19 ), we hypothesized that abrupt changes in OLR using microalgae would show similar negative impacts on methane production, but with unclear effects on VFA accumulation and bioreactor acidification (acidosis) due to the potential release of pH-stabilizing ammonium (NH 4 + ) from microalgae. To test this, we monitored the effect of low- and high-OLR disturbances on (i) intermediary product accumulation, (ii) methane production yield, (iii) bacterial and archaeal temporal dynamics, and (iv) overall microbiome function. We interpreted the data by linking and integrating reactor performance data with metagenome-centric metaproteomics analyses to highlight each microbial member’s response and metabolic activity during the OLR shocks and recovery phases.",
"discussion": "DISCUSSION Optimizing performance and stability of AD using new feedstocks is central for meeting current and future energy demands. Microalgae biomass is a proven favorable substrate for methane production; however, disturbances in the AD process can adversely affect reactor performance and methane yield. It is, therefore, essential to assess how substrate loading affects microbiome dynamics, as done previously using high loading of pig manure ( 59 ), intermittent feeding with mixed VFAs ( 9 ), and for starvation studies with cattle manure ( 60 ), and relate changes to biomethanation process performance with the aim of identifying signatures indicative of process failure and/or recovery of the system. The green algae used in this study, Scenedesmus , possess a trilaminar cell wall structure with an inner layer composed of cellulose and an outer layer of highly recalcitrant algaeanans, contributing to the rigidity of the cell wall. Furthermore, pectin, glucosamine-containing biopolymers, and glycoproteins are present ( 61 , 62 ), in addition to starch (amylose or amylopectin) within the chloroplast. Enzymatic pretreatment was used to reduce the recalcitrance of the cell wall, thus exposing the complex polysaccharides in the algal extracellular polymeric substances for microbial degradation. Our metaproteomics analyses indeed showed that a large repertoire of (hemi-)cellulases, amylases, proteases, and lipases was expressed for effective hydrolysis of the green algae, and that these were more abundant during the high organic loading shock. The large number of GH109s might stress the importance of degrading galactomannans, releasing the initial GalNAc residue from the glycoproteins ( 63 ) in the Scenedesmus cell wall; high levels of galactose have been observed in extracellular polymeric substances of many microalgae species, including Scenedesmus ( 64 ). The hydrolysis process generates soluble, monomeric, or dimeric substrates to be used as nutrients for other microorganisms within the system. Fermentative bacteria utilize these monomers during acidogenesis to convert them into short-chain fatty acids (VFAs, e.g., acetate, propionate, and butyrate), alcohols, ketones, CO 2 , NH 3 , H 2 S, and H 2 . The removal of VFAs is dependent on the removal of substrate compounds often through a syntrophic relation between syntrophic fatty acid oxidizers and methanogens ( 65 ). As reported in the current study, high organic overload (7 g COD L −1 d −1 ) led to increased hydrolysis and accumulated fermentation products, including VFAs and EtOH. This in turn affected the methanogenic populations, causing their decline in relative abundance, inhibition of methanogenesis, and subsequent large reduction in reactor methane yield. Abrupt changes in organic loading, such as those used here, and with a following cease in methane production, have also been observed previously with grasses ( 3 ), municipal sludge ( 4 ), and sugar beet ( 19 ), typically leading to acidosis (low pH, high VFAs). While accumulation of fermentation products was observed, this did not result in a notable decrease in pH; however, acidosis can occur without drops in pH ( 66 ). According to the metaproteomics results, several members of the microbiome carried out beta-oxidation to degrade butyrate and longer-chain FAs. Metaproteomic results further suggested that the accumulation of propionate is linked to an inhibition of Pelotomaculaceae members, a family that includes known SPOBs ( 67 , 68 ). This could have been triggered by direct ammonia inhibition of the population ( 69 ) or inhibition of syntrophic propionate oxidation through elevated levels of fermentation products ( 70 , 71 ). While the NH 4 concentration during the second shock in the high-OLR systems approached a level (3 g/L, see Table S5 ), which has previously been associated with potentially inducing a shift from the ammonium-sensitive acetolactic methanogenesis to syntrophic acetate oxidation ( 72 , 73 ), our metaproteomic results indicated that acetoclastic methanogens, rather than SOABs, were crucial for recovering stable acetate levels after the VFA accumulation resulting from the perturbations conducted. Notably, the high-OLR shocks led to a shift in the methanogenic population, where Methanothrix and Methanospirillum declined, while the acetate-tolerating Methanosarcina thrived. At high acetate levels, Methanosarcina may outcompete Methanothrix populations due to their different kinetic features and ability to transform acetate ( 74 ). Furthermore, the presence of Methanothrix has been shown to be negatively correlated with the total VFA content ( 19 ); however, at low acetate concentrations with stable performance, Methanothrix has been the dominant acetoclastic methanogen ( 75 , 76 ). The presence and increase in abundance of Methanosarcina , as seen here, have previously been proposed as an early indicator of acidification in overloaded biogas reactors digesting maize silage ( 77 ). However, as a signature for reactor failure, it is important to remember that the methanogens are last in the reactor food web, and the detection of signature microbes upstream of these would likely be more advantageous. During starvation (low-OLR) conditions, several key metabolic functions stagnated likely due to limited substrate access rather than inhibition. However, the AD functions recovered relatively rapidly once organic loading resumed. Notably, while Methanothrix initially dominated, the hydrogenotrophic methanogen Methanospirillum rebounded faster after the shock, pushing the overall system back toward status quo. Of note, our findings on microbial responses to the two induced shocks in low- and high-OLR systems are based on analyses of single bioreactors, as detailed in Section 2.2. While two replicate reactors were used for each condition, microbiome analysis was performed on one replicate per condition due to the stability and reproducibility observed between the replicates. This approach effectively captures microbial community shifts from steady state to shocks within the same reactor. However, we recognize that the study’s design does not provide the statistical power required for detecting significant changes, and this limitation is acknowledged. Conclusion Wastewater-cultured microalgae generate a nutrition-rich biomass, presenting a promising substrate for methane production. OLR disturbances are well known to cause imbalances to reactors’ performance with implications for methane production and system failure, potentially necessitating an extended and costly process recovery time. In this study, we examined the microbiome responses to OLR shocks when using microalgae as substrate. Consistent with previous research involving different feedstocks, high overloading led to a significant loss in reactor methane yield. However, despite the extensive shocks and reduction in methane yield, both CSTRs presented returning points and were able to recover the methane yield completely within weeks by introducing a shift in the microbial community, albeit the recovery from starvation taking a longer time than from high OLR. The presence of multiple methanogenic populations was key to AD recovery. It is well recognized that more diverse microbial communities provide a wider range of parallel pathways, contributing to functional redundancy, as observed here, and contribute to the resilience and recovery potential of a methane-producing CSTR. Anaerobic digestion of microalgal biomass presents a promising conversion method for sustainable energy and waste management. When compared to traditional AD feedstocks, microalgal biomass offers multiple potential advantages, including non-competition for arable lands, the contribution to CO 2 mitigation, and the ability to be cultivated in waste streams (e.g., wastewater). In this regard, microalgae culturing has been claimed as a sustainable, low-cost technology to recover nutrients from wastewater in the form of biomass, representing a promising process to replace conventional wastewater treatments (i.e., activated sludge). The absence of aeration in microalgae technology considerably reduces the energy demand of the wastewater treatment plant, as well as the emission of pollutants, such as NOx and volatile organic compounds. By employing microalgae as feedstock of AD, the biomass can be converted into a clean energy source (biogas) while simultaneously treating residual streams. This process does not only contribute to a circular bioeconomy but also minimizes reliance on fossil fuels, aligning with global sustainability goals. Therefore, the main importance of this study is to provide insights to fully comprehend the AD of microalgae biomass, supporting the process scalability."
} | 4,448 |
36355038 | PMC9648965 | pmc | 527 | {
"abstract": "The dichotomy that separates prokaryotic from eukaryotic cells runs deep. The transition from pro- to eukaryote evolution is poorly understood due to a lack of reliable intermediate forms and definitions regarding the nature of the first host that could no longer be considered a prokaryote, the first eukaryotic common ancestor, FECA. The last eukaryotic common ancestor, LECA, was a complex cell that united all traits characterising eukaryotic biology including a mitochondrion. The role of the endosymbiotic organelle in this radical transition towards complex life forms is, however, sometimes questioned. In particular the discovery of the asgard archaea has stimulated discussions regarding the pre-endosymbiotic complexity of FECA. Here we review differences and similarities among models that view eukaryotic traits as isolated coincidental events in asgard archaeal evolution or, on the contrary, as a result of and in response to endosymbiosis. Inspecting eukaryotic traits from the perspective of the endosymbiont uncovers that eukaryotic cell biology can be explained as having evolved as a solution to housing a semi-autonomous organelle and why the addition of another endosymbiont, the plastid, added no extra compartments. Mitochondria provided the selective pressures for the origin (and continued maintenance) of eukaryotic cell complexity. Moreover, they also provided the energetic benefit throughout eukaryogenesis for evolving thousands of gene families unique to eukaryotes. Hence, a synthesis of the current data lets us conclude that traits such as the Golgi apparatus, the nucleus, autophagosomes, and meiosis and sex evolved as a response to the selective pressures an endosymbiont imposes.",
"conclusion": "Conclusion Morphologically complex life on Earth has a singular origin: eukaryogenesis. The LECA had evolved all canonical traits that we understand separates prokaryotic from eukaryotic life. Closing the gap between a simple FECA and a complex LECA by presupposing a complex FECA opens an equally wide gap between a simple and a complex archaeal host. Picturing FECA without an endosymbiont offers little explanation for the existence or emergence of eukaryotic traits and the lack thereof in prokaryotes (including asgard archaea), apart from the inevitable that all eukaryogenesis models face: the need to script the blueprint of a eukaryotic cell. And should then not all (asgard) archaea with a syntrophic partner be considered FECA in the sense that in principle they have the potential to become eukaryotic? For 4 billion years, prokaryotes have overall remained the same in terms of cellular complexity with some rare exceptions having evolved a single compartment type, but nothing vaguely similar to the conserved nature of the eukaryotic endomembrane system. Reflecting on eukaryotic traits and their cell biological connection to mitochondrial origin lets us conclude they are better understood as being selected for to service an endosymbiont and less so as means of acquiring one. Phylogeny guided models should connect interpretations to a physiological and cell biological rationale, while facing the challenge of resolving the fluid nature of the pangenomes of both host and endosymbiont genome throughout eukaryogenesis – we need to talk to phylogenetic trees, not only about them. Physiology ( Imachi et al., 2020 ; Martin and Müller, 1998 ; Moreira, 1998 ; Spang et al., 2019 ), geochemistry ( Mills et al., 2022 ), phylogenetics ( Wu et al., 2022 ), and culturing and imaging ( Imachi et al., 2020 ) all point to a syntrophic origin of eukaryotes involving two prokaryotic partners. The data suggests that first steps towards endosymbiosis in eukaryogenesis were of prokaryotic nature, that eukaryogenesis likely only solidified upon endosymbiosis, and that hence the definition of FECA should include an endosymbiont.",
"introduction": "Introduction ‘ A scientist in his laboratory is not a mere technician: he is also a child confronting natural phenomena that impress him [her] as though they were fairy tales ’ (Marie Curie). In evolutionary biology, the transition from prokaryotic to eukaryotic life was a true game changer. Eukaryogenesis involves the origin of new cell biology, genetics, and an unprecedented emergence of morphological diversity. Historically, the prokaryote-eukaryote divide was based on observed differences in morphology and in turn defined this aboriginal branch in the tree of life by their lack of traits that eukaryotes posses ( Stanier and Van Niel, 1962 ). Phylogeny and biochemistry separate prokaryotes into bacteria and archaea ( Fox et al., 1980 ; Koga et al., 1998 ; Woese et al., 1990 ) and document the dichotomy of pro- and eukaryotes, which is further evident in the number of protein families ( Rebeaud et al., 2021 ), average protein length ( Brocchieri and Karlin, 2005 ), cellular and morphological complexity ( Stanier et al., 1963 ), and the overall prevalent contribution to the planet’s biomass ( Bar et al., 2018 ). For decades the field of eukaryogenesis speculated on the existence of a eukaryotic lineage with intermediate biology bridging the prokaryote-eukaryote divide, an elusive grade known as archezoa ( Cavalier-Smith, 1987 ). For curious reasons (see Martin et al., 2017a , for details) this search focused on a eukaryotic phylum lacking a mitochondrion ( Cavalier-Smith, 1987 ; Speijer, 2020 ), but not necessarily one lacking a nucleus or endoplasmic reticulum (ER). Varying models, but with the common theme of promoting an autogenous origin of a last eukaryotic common ancestor (LECA) independent of a bacterial partner, were proposed (reviewed in Martin et al., 2015 ). Through the identification of hydrogenosomes and mitosomes (reduced mitochondria; Tovar et al., 1999 ; Williams et al., 2002 ) and modern phylogenomics ( Burki et al., 2020 ), we now understand that the biology of LECA matched that of extant garden variety protists. This might appear trivial from todays’ perspective, but reaching this consensus and settling on a mitochondrion-bearing LECA took decades. LECA evolved from an archaeal host cell and its endosymbiotic alphaproteobacterial partner ( Imachi et al., 2020 ; Wu et al., 2022 ; Zaremba-Niedzwiedzka et al., 2017 ) and could have been syncytial and fungus-like, with the first gametes budding off as a selectable unit, in what one could describe to be a flagellated protist ( Garg and Martin, 2016 ; Skejo et al., 2021 ). The field exploring eukaryogenesis has moved on to studying the nature and origin of the first eukaryotic common ancestor (FECA). This subtle change in terminology has far-reaching consequences. The term FECA only puts a label on the first lineage that we would no longer define as prokaryotic, but which had not yet evolved all traits characterizing the LECA. But at what point did prokaryotic evolution transition into eukaryotic origin? Was it upon the emergence of meiosis and sex? Or the ER and its specialized compartment the nucleus? Or after the transition from archaeal to eukaryotic (bacterial-type) membrane lipids? The transition between prokaryotes and eukaryotes was fluid in nature, with the emergence of the new traits occurring in a currently unknown order ( Gould et al., 2016 ; López-García and Moreira, 2020 ; Vosseberg et al., 2021 ). The critical question is: what drove the emergence of eukaryotic traits and what fixed them in evolution? Here, we discuss the scenarios of a morphologically simple FECA versus a complex one on the basis of reviewing models and data that emerged after the report of the asgard archaeal superphylum from which the eukaryotic host lineage stems. We explore key eukaryotic traits and the phylogenetic distribution of protein associated families, in light of housing an endosymbiont that differs by all other traits in that it represents a semi-autonomous living entity. This imposed unique challenges onto the host throughout eukaryogenesis and whose solution, we argue, is witnessed in the form of compartmentalization, meiosis, and sex. FECA and theories of eukaryogenesis in light of the asgard archaea ‘ It can be considered a relatively harmless habit, like eating peanuts, unless it assumes the form of an obsession; then it becomes a vice ’ (Roger Y Stanier). The relatively harmless habit of tracing the origins of the eukaryotic cell has occupied scientists across several generations, a historical account of which is beyond the scope of this review but has been summarized elsewhere ( Martin, 2017 ). Current models of eukaryogenesis differ above all in the relative placement and contribution of the endosymbiont and consequently the cellular complexity of the host archaeon prior to endosymbiosis. Briefly, mitochondria-early theories place endosymbiosis closer to or at FECA ( Figure 1 ), suggesting that the traits that characterize LECA evolved after endosymbiosis from a prokaryotic-like host cell. On the contrary, mitochondria-late scenarios view endosymbiosis and mitochondrial origin as a finishing touch to the LECA ( Figure 1 ). Intermediate models are gaining popularity, but are often vague on which traits evolved prior or after endosymbiosis. Figure 1. Eukaryogenesis scenarios with respect to the origin and nature of the first eukaryotic common ancestor (FECA). Scenarios summarized in ( a ) were based on the assumption that eukaryotic groups existed that lack mitochondria and which originated independently of endosymbiosis. Such models are no longer supported and we understand that the last eukaryotic common ancestor (LECA) was a mitochondrion-bearing lineage. Models in ( b ) speculate on a (semi-) autogenous origin of complexity. By their definition, the FECA was free of vertically transmittable endosymbionts. Genomic and cellular complexity of FECA grew through horizontally donated genes from diverse prokaryotes part of its habitat; to a degree FECA was eukaryote-like prior to endosymbiosis. Models falling under ( c ) differ from ( b ) in that both partners at the onset of eukaryogenesis were prokaryotic in nature and that complexity only began to evolve after endosymbiosis of the mitochondrial ancestor. By this definition, the FECA is characterized by the endosymbiotic event and describes the lineage that transitioned from prokaryotic to eukaryotic evolution. Note that the prokaryotic cells on the left and the LECA on the right are shown to scale on the basis of average cell diameters. The notion that eukaryote-like complexity was a prerequisite to phagocytosis for promoting mitochondrial origin appears mandatory to some, but the idea remains unsubstantiated ( Leão et al., 2018 ; Martin et al., 2017b ; Mills, 2020 ; Shiratori et al., 2019 ). Mitochondria-lacking but phagocytosing LECA models – such as the archezoa hypothesis ( Cavalier-Smith, 1987 ) – lost support due to the now known universal presence of mitochondria across the diversity of all eukaryotic super groups ( Hjort et al., 2010 ; Stairs et al., 2015 ), but variations of the archezoa hypothesis populate the literature, rekindled on the basis of inferred proteomes from asgard archaea. Reports of a patchy distribution of homologues of the eukaryotic ESCRT-III, a ubiquitin modifying system, and eukaryote-like actin in the TACK superphylum, triggered thoughts about phagocytosing archaea ( Guy and Ettema, 2011 ; Yutin et al., 2009 ). The identification of proteins with homology to ESCRT I and II, longin domains, sec23 and sec24 ( Zaremba-Niedzwiedzka et al., 2017 ), Rab-like GTPase ( Akıl and Robinson, 2018 ; Klinger et al., 2016 ; Surkont and Pereira-Leal, 2016 ), or profilin that can inhibit (in vitro) rabbit actin polymerization ( Akıl and Robinson, 2018 ; Survery et al., 2021 ) quickly channelled into speculations that asgard archaea might have a dynamic cytoskeleton, intracellular membrane trafficking, and are morphologically complex ( Klinger et al., 2016 ; Neveu et al., 2020 ; Zachar et al., 2018 ; Zaremba-Niedzwiedzka et al., 2017 ). These interpretations mirror a FECA that is reminiscent of the host lineage at the centre of the archezoa hypothesis (discussed in Martin et al., 2017b ), culminating in the depiction of a mitochondrion-lacking eukaryote on the cover of Nature ( Pittis and Gabaldón, 2016 ). When transmission electron microscopy revealed images of asgard archaea, that of Prometheoarchaeon syntrophicum , they uncovered tiny prokaryotes with no intracellular eukaryotic traits living in syntrophy with bacteria ( Imachi et al., 2020 ). Such images contradict the narrative of complex asgard archaea, but resonate well with early warnings regarding overinterpretations of metagenome data ( Dey et al., 2016 ). Eukaryogenesis models rapidly adapted to the discovery of asgard archaea. They now focus on FECA with various speculations regarding the roles of the discovered genes in host biology prior to endosymbiosis. While the level of cellular complexity is not always explicitly declared, several cases can be made out that depict FECA without an endosymbiont ( Baum and Baum, 2020 ; Dacks et al., 2016 ; Eme et al., 2017 ; Pittis and Gabaldón, 2016 ; Vosseberg et al., 2021 ). Some models can be interpreted one way or the other ( Imachi et al., 2020 ), while some explicitly state that the host cell was a bona fide prokaryote and that eukaryotic traits and biology evolved after endosymbiosis ( Gould et al., 2016 ; López-García and Moreira, 2020 ; Wu et al., 2022 ). Notably, the differences among these models rest upon a few dozen genes from the pan-asgard archaeal genome repertoire, whose overall unique contribution to the roots of eukaryotes was 0.3% or less ( Knopp et al., 2021 ; Liu et al., 2021 ). Sources and timing of gene acquisition in the FECA to LECA transition are equally essential to correctly quantify as they remain hard to predict. Entangled branches connecting kingdoms Among eukaryotic genomes there are more genes of bacterial than of archaeal origin ( Alvarez-Ponce et al., 2013 ; Brueckner and Martin, 2020 ; Makarova et al., 2005 ). An autogenous origin of cellular complexity on the basis of an archaeal (host) source alone would predict the opposite but prokaryotes are characterized by mosaic genomes due to horizontal gene transfer (HGT) whose contribution to cellular complexity prior to endosymbiosis is debated ( Martin et al., 2017b ; Pittis and Gabaldón, 2016 ). Claims concerning differential loss of genes in extant archaea ( Koonin and Yutin, 2014 ; Eme et al., 2017 ) are at odds with pangenomes that support a pan-asgard concept ( Knopp et al., 2021 ; Liu et al., 2021 ). Dynamic genomes and the time passed since eukaryote origin challenge phylogenomic approaches and can skew interpretations including the timing of compartment origin. The estimated timing of gene duplications that depend on molecular clock techniques that are error-prone ( Graur and Martin, 2004 ; Rodríguez-Trelles et al., 2001 ; Tiley et al., 2020 ) with respect to the origins of cellular complexity are also debated ( Tria et al., 2021 ; Vosseberg et al., 2021 ). A reliance purely on relative branch lengths concluded that mitochondrial metabolism and the ER in eukaryogenesis ensued the origin of the nucleus ( Pittis and Gabaldón, 2016 ). The method used has been questioned ( Martin et al., 2017a ), and the use of unspecific COG (cluster of orthologous genes) annotations in the study is problematic. The few universal proteins listed might operate in the present-day nucleus, but provide little to no evidence for the presence of one prior to endosymbiosis. Proteins of the nuclear pore complex, of which there are about three dozen ( Raices and D’Angelo, 2012 ), were not identified or discussed, nor was the fact that the nucleus is a specialized compartment of the ER from which it forms during cell division ( Anderson and Hetzer, 2008 ). Substitution rates that challenge molecular clock studies vary substantially across species ( Baer et al., 2007 ; Halligan and Keightley, 2009 ) and the functional unit a protein is associated with ( Hartwell et al., 1999 ). Considering that thousands of new protein families emerged at eukaryote origin that fall into such categories ( Preisner et al., 2018 ) further highlights the caution with which we need to digest molecular clock studies on eukaryogenesis. The distribution of protein families associated with eukaryotic traits across the domains of life is always telling. Seventy percent or more protein families involved in major eukaryotic traits (such as cell cycle, meiosis, autophagy, nucleus) are specific to eukaryotes, 10–15% (e.g. kinases) are universal across all domains of life, 10–15% are bacterial (e.g. aminopeptidases, mTOR interacting proteins, glycosyltransferases), and a small fraction appear to originate from archaea (DNA licensing proteins of cell cycle, ARG GTPases, N -glycan biosynthesis). The distribution of protein families across prokaryotes and eukaryotes ( Figure 2 ) confirms that eukaryotes acquired genes from bacterial or archaeal sources and co-opted them to suit new eukaryotic traits evolving in the FECA to LECA transition, but the majority of protein families involved in eukaryotic cellular complexity are absent across the entire realm of prokaryotic diversity ( Brunk and Martin, 2019 ; Dell et al., 2010 ; Knopp et al., 2021 ; Liu et al., 2021 ; Lombard, 2016 ). Hence, HGT falls short at explaining the pro- to eukaryote transition with respect to the origin of thousands of eukaryote-unique gene families and a reason for their positive selection in the absence of an endosymbiont. Beyond question, HGT fed into eukaryogenesis – after all, the eukaryotic cell is the product of two prokaryotes – but endosymbiotic partners bring along thousands of genes and many were integrated into the host genome ( Timmis et al., 2004 ). They can explain the pronounced non-alphaproteobacterial signal among proteins supporting eukaryotic traits, especially if we place mitochondrial origin at the root of the FECA and accept HGT to be prevalent. Figure 2. Distribution of protein families involved in eukaryotic traits. All eukaryotic protein sequences associated with different functions in KEGG (Kyoto Encyclopedia of Genes and Genomes, as of November 2021) were used to build representative hidden Markov models (HMMER/3.1), which were then searched against all prokaryotic genomes available on KEGG. Based on hits found across bacterial phyla, the following categories were assigned: Universal (shared with >50% bacterial phyla and archaea), Eu|Ar|Ba (shared with archaea and <50% bacterial phyla), Eu|Ar|Pr (shared with archaea and proteobacteria only), Eu|Ar (shared with Ar), Eu|Asgard (shared only with asgard archaea), Eu|non-Asgard (shared only with non-asgard archaea), Eu|Ba (shared with bacteria), Eu|Proteo (shared only with proteobacteria), Eu (not shared with any prokaryotes). Each box shows the percentage of protein families across these categories (x-axis) for the pathways analysed. For example, a substantial percentage of ribosomal proteins (bottom right) are shared between Eukaryote-Archaea or Eukaryote-Archaea-Bacteria, highlighting the host’s ribosomal contribution to eukaryotes. Proteins for other categories, however, are either predominantly eukaryote-specific or shared between eukaryotes and bacteria (e.g. glycotransferases or some proteins of autophagy). CMA, chaperone-mediated autophagy; CVT, cytoplasm to vacuolar targeting pathway. Phylogenetic trees built using concatenated gene sequences boost phylogenetic signals, but under the premise that the individual genes used recapitulate the evolutionary history of the species ( Robinson and Foulds, 1981 ). For incomplete and contaminated metagenomes (including early releases of asgard archaeal ones), the individual ribosomal gene trees were incongruent ( Garg et al., 2021 ). Similar to simulated chimeric genomes containing genes from different species, metagenome assembled genomes are prone to assembly and binning artefacts. The frequent use of automated pipelines and poorly fitting phylogenetic models exacerbates the risk of drawing false conclusions from metagenome data ( Williams and Embley, 2014 ). For instance, the presence of glycerol-3-phosphate lipids in asgard archaea was claimed (with far-reaching implications on the lipid transition during eukaryogenesis) based on the predicted presence of enzymes involved in the synthesis of ester-linked fatty acids ( Villanueva et al., 2017 ). No evidence of such lipids, however, was found in the biochemical analysis of a cultured asgard archaeon ( Imachi et al., 2020 ) and the presence of the set of required enzymes in asgard archaea has yet to be identified. Better assembly methods result in more complete circular genomes from both axenic culture and metagenomic approaches that mitigate issues of tree congruence ( Garg et al., 2021 ), albeit leaving the same room for interpretations. Underpinning studies of evolutionary history are phylogenetic trees and theories behind constructing and interpreting them. While it is well beyond the scope of this manuscript to discuss all the vagaries of the field of cladistics and modern phylogenies, it is increasingly evident that many phylogenetic studies have moved from a field that requires expertise in biology to a field that requires expertise in computation ( Fitzhugh, 2016 ) – hypotheses generated from DNA sequences run the risk of taking precedence over morphological evidence ( Wheeler et al., 2013 ). This is less of a critique than a realization. Although sequencing and computational techniques have made significant progress over the years, for the timescales dealt with in early evolution, most issues and challenges remain. It is critical to remember that phylogenetic trees are hypotheses on the evolutionary relationship between organisms and not an observation on itself ( Hennig, 2000 ). No phylogenetic tree is perfect, few are for eternity, and no tree alone will ever satisfy the need for empirical evidence. Eukaryotic traits in light of accommodating a prokaryotic endosymbiont ‘ In the case of living machinery, the ‘designer’ is unconscious natural selection, the blind watchmaker ’ (Richard Dawkins). Evolution is typically understood to progress gradually and randomly through mutations and the selection of beneficial traits vertically across generations ( Darwin and Murray, 1859 ; Futuyma, 1986 ). Endosymbiosis adds a massive horizontal component to evolution that is, however, still subject to the basic selection and fixation process. In other words, while the emergence of eukaryotic traits was gradual, the selective pressure that demanded their emergence was more radical. It is this duality that stands between eukaryogenesis theories like a firewall. Any hypothesis that pictures an archaeal lineage transitioning from prokaryotic to eukaryotic cell biology – even of an intermediate type – in the absence of an endosymbiont needs to explain why it was a singularity. Microbial syntrophy is the norm and so is the selective pressure to optimize it. Why are intermediate cell types not observed among the many syntrophic prokaryotes studied, if it was not for the lack of an endosymbiotic event? A mitochondrion-lacking but complex FECA explains eukaryotic traits solely from a host perspective and misses to provide a plausible reason for selection and the emergence and fixation of traits we here discuss in more detail ( Figure 3 ). Figure 3. Eukaryotic traits in light of housing an endosymbiont. Each segment highlights an eukaryotic (Eu) trait and the comparable, if present, situation in bacteria (Ba) and archaea (Ar). ( i ) Prokaryotes secrete outer membrane vesicles (OMVs) and an endosymbiont (mitochondrion) secreting OMVs could have (ii) given rise to a dynamic endomembrane system within the archaeal host and explaining the transition from archaeal to bacterial lipids. N -glycosylation has been identified in all domains of life, but the eukaryotic N -glycosylation pathway is homologous to that of archaea. (iii) A specialized extension of the endoplasmic reticulum (ER), the nucleus prevents co-transcriptional translation of proteins – as is the rule in prokaryotes – to allow for the splicing of introns. (iv) Prokaryotes constantly shed and acquire DNA from the environment, and often promiscuously by transformation, transduction, and conjugation. In the absence of such dedicated mechanisms, eukaryotes avoid Muller’s ratchet through sex and meiosis whose origin might be linked to coordinating the merging of two genomes and synchronizing nuclear and mitochondrial division. ( v ) Peroxisomes also form through mitochondria-derived vesicles and house enzymes of alphaproteobacterial origin. (vi) Eukaryotes perform autophagy using membranes and proteins of the ESCRT machinery to surround and digest internal membrane compartments including the mitochondrion. (vii) While bacteria use homologs of tubulin to perform fission, eukaryotic fission utilizes actin and components of the ESCRT machinery similar to archaea, whereas the tubulin in eukaryotes is for instance used to separate chromatin and intracellular compartments. The ER and Golgi apparatus Glycosyltransferases are promiscuous enzymes and it has been suggested they are separated through ER-Golgi compartmentalization for that reason ( Biswas and Thattai, 2020 ). N - and O -glycosylation are ubiquitous in eukaryotic cells, but not so in prokaryotes. Eukaryotic N -glycosylation is likely derived from the archaeal ancestor, while O -glycosylation is more prevalent among bacteria ( Abu-Qarn et al., 2008 ; Dell et al., 2010 ; Jarrell et al., 2014 ). Hence, if each pathway stems from one of the prokaryotic partners, natural selection would foster a spatial separation only upon and not prior to endosymbiosis. The ER lumen and mitochondrial intramembrane space (the former bacterial periplasm) share notable homologies. This includes calcium storage ( Dominguez, 2004 ; Raffaello et al., 2016 ), disulfide relay systems ( Backes et al., 2019 ), and redox balance ( Cardenas-Rodriguez and Tokatlidis, 2017 ). The contact sites of the ER and mitochondrion are cornerstones for the synthesis and regulation of lipids and a plethora of cellular roles ( Booth et al., 2016 ; Flis and Daum, 2013 ; Friedman et al., 2011 ; Hamasaki et al., 2013 ). This could be a consequence of the ER stemming from mitochondrial-derived vesicles (MDVs) ( Gould et al., 2016 ). MDVs could have provided the necessary endomembrane material for compartmentalization and remain the most plausible source for the lipid transition from ether-linked, archaeal head groups to ester-linked (bacterial) eukaryotic head groups. Much on the origin of the endomembrane system remains a speculation, but not so the existence of MDVs, their role in eukaryotic biology, and how they induce compartment formation ( Schuler et al., 2021 ; Sugiura et al., 2017 ; Sugiura et al., 2014 ; Yamashita et al., 2016 ). Eukaryogenesis models failing to acknowledge their existence miss a biological fact with significant explanatory power. Vesicle trafficking Vesicle secretion from the plasma membrane into the environment is a common trait of all cells. Unique to eukaryotes are the many ways with which they can internalize membrane vesicles of various sizes, ranging from clathrin-mediated endocytosis (~100 nm) to phagocytosis (>750 nm), using different molecular machineries. Intracellular vesicle trafficking connects the plasma membrane with the endomembrane system and the compartments thereof among each other. All compartments that define the endomembrane system – with the ER at its core – as well as the majority of regulatory and structural proteins are conserved across eukaryotes and absent in prokaryotes ( Klinger et al., 2016 ; Kontou et al., 2022 ). The nucleus The nucleus is a distinctive extension of the ER and forms from the latter after mitosis ( Anderson and Hetzer, 2008 ) using homologs of ESRCT complex ( Olmos et al., 2015 ). It separates transcription from translation and is the site of pre-ribosome assembly ( Peña et al., 2017 ). As with any trait, a selective reason for its presence must outweigh the costs for its maintenance; consider, for example, the exchange of mRNA and effectors with the cytoplasm ( Nerurkar et al., 2015 ; Warner, 1999 ). A plausible selection could have been imposed by the transfer of group II introns from the endosymbiont that drove the origin of eukaryotic splicing and need for separating transcription slowed by the spliceosome from fast translation ( Martin and Koonin, 2006 ). Mitochondria-early scenarios provide both the problem (group II introns that need to be spliced) and the solution (MDVs that might have given rise to the ER) ( Gould et al., 2016 ). Sex and meiosis The prokaryotic solution to prevent mutational overload through Muller’s ratchet is HGT ( Muller, 1964 ). The nucleus renders the eukaryotic cytoplasm almost sterile of DNA (preventing HGT), wherein it plays a regulatory immune function ( Abe et al., 2019 ; Paludan and Bowie, 2013 ). The eukaryotic solution was ploidy, multinucleated cells and reciprocal recombination through meiosis ( Garg and Martin, 2016 ). A multinucleated state is readily achieved by decoupling nuclear from cell division, a mechanism commonly observed in prokaryotes wherein the DNA replicates independently of the cell before portioning into daughter cells ( Haeusser and Levin, 2008 ). The syncytial theory for eukaryotic origin ( Garg and Martin, 2016 ) posits that by virtue of multinucleated cells within a singular archaeal host, multiplying bacterial symbionts are free to lose genes via endosymbiotic gene transfer to the multiple copies of the host nucleus/nuclear material in the cytoplasm to explore various configurations under the constant onslaught of group II introns, yet retaining fitness by compensating viable mRNA in-trans within the same shared cytoplasm. The explanatory power of this model is twofold: (i) it explains how homologous recombination – which subsequently evolved to meiosis as we understand it today – was necessary to maintain viable copies of undisrupted genes, while simultaneously maintaining the presence of bacterial transferred genes, and (ii), it explains the monophyly of eukaryotes. As long as the FECA to LECA transition continued, the multitude of host nuclei remained within a single confined cytoplasm until the fittest version was optimized via various rounds of endo-meiosis and homologous recombination. Any origin of cell division and/or cell cycle might have given rise to gamete-like spores that separated off the original syncytium. In cases where a successful combination was released through ESCRT-driven scission (see below), a similar process applies for further optimization. In scenarios in which the budded off cell (gamete) was fitter than the syncytium, it would outcompete the original syncytium or alternatively would be outcompeted when it contained aberrant genomes. In either case, the singularity of LECA is well explained by the syncytial model of the FECA to LECA transition ( Garg and Martin, 2016 ; Skejo et al., 2021 ). Meiosis in itself is ancient, ubiquitous, and the central process that imparts an advantage to sex in eukaryotes ( Colnaghi et al., 2022 ; Colnaghi et al., 2020 ; Malik et al., 2008 ; Speijer et al., 2015 ). Several theories place mito-nuclear interactions, heteroplasmy, and mitochondrial ROS as drivers of eukaryotic sex ( Colnaghi et al., 2020 ; Hörandl and Speijer, 2018 ; Radzvilavicius and Blackstone, 2015 ). HGT alone was insufficient for LECA to escape Mullers ratchet in the absence of homologous recombination ( Colnaghi et al., 2022 ), when considering expanding genome size and repeat sequence frequency. Everything points to an origin of sex and meiosis necessitated by the presence of mitochondria. Moreover, sex, as a trait, restricts the number of potential mating partners (by 1/number of sex types), and it is hence less surprising that it did not evolve in groups outside of eukaryotes, but had to in the FECA. Peroxisomes The majority of enzymes of peroxisomal beta-oxidation are of alphaproteobacterial origin ( Bolte et al., 2015 ) and peroxisomes might have evolved to compartmentalize ROS-producing beta-oxidation and protect the mitochondrial genome ( Speijer, 2017 ). De novo biosynthesis of peroxisomes involves MDVs with integrated Pex3 and Pex14 that fuse with ER-derived vesicles containing Pex16 ( Sugiura et al., 2017 ), and the compartment for beta-oxidation appears absent in species lacking respiring mitochondria ( Le et al., 2020 ). Peroxisomes not only make sense in the presence of a mitochondrion, they are also partly a product thereof ( Mohanty and McBride, 2013 ; Sugiura et al., 2017 ). Autophagy Cytosolic protein homeostasis in prokaryotes is performed by proteases and proteasomes, which are common to both archaea and bacteria. Defective membrane proteins and membranes are shed by mechanisms similar to bacterial outer membrane vesicle secretion ( Schwechheimer and Kuehn, 2015 ). Eukaryotes utilize membrane-bound compartments in the form of autophagosomes, also for recycling membranes including their proteins ( Nakatogawa, 2020 ). Mitophagy removes damaged mitochondria and is initiated by ER-mitochondrial contact sites ( Hamasaki et al., 2013 ). It is a trait needed in the presence of large intracellular compartments and the occasional yet inevitable breakdown of organelles that require immediate containment ( Anding and Baehrecke, 2017 ). Cell division The eukaryotic cell cycle is a series of choreographed steps that leads to the correct portioning of genetic material, endomembrane, and organelles to both daughter cells ( Harashima et al., 2013 ). The presence of a nuclear compartment and an endosymbiont are incompatible with binary fission in the absence of orchestrated replication and organelle and compartment division, and cytokinesis. As mentioned in the previous section, in prokaryotes the nuclear material (genome) replicates independently of cell division, which would have facilitated the formation of syncytial populations ( Haeusser and Levin, 2008 ). During this time, however, we speculate that mitochondrial metabolism started playing a more significant role in controlling cell division, given the role of nutrient availability in coordination of cell division. The G1 phase of the eukaryotic cell cycle results in the mitochondria as the master regulator for S/G2 progression ( Antico Arciuch et al., 2012 ; Mitra et al., 2009 ), suggestive of a deep link between mitochondria and the cell cycle and one that would have been difficult to integrate into a pre-existing one. Eukaryotic cell division employs the use of ESCRT homologs and actin in contrast to bacterial division mechanisms involving FtsZs from which also tubulin evolved ( Christ et al., 2016 ; Goliand et al., 2014 ; Stoten and Carlton, 2018 ). This suggests the evolution of an independent pathway for cell division involving ESCRT proteins consistent with their role in archaeal cell division ( Tarrason Risa et al., 2020 ), one that was based on outer membrane vesicle secretion, but this time packaging mitochondria and the nucleus/genetic material, the latter similar to a role of prokaryotic OMVs ( Schwechheimer and Kuehn, 2015 ). Understanding how these pre-existing mechanisms were leveraged in an elaborate checkpoint system of the eukaryotic cell cycle remains to be elucidated. One might consider the cytoskeleton another eukaryotic trait, but this is more involved. The eukaryotic cytoskeleton rests on three main pillars: (i) actin and associated proteins, (ii) tubulin and associated proteins, and (iii) the utterly diverse intermediate filament (IF) proteins. Components of each pillar, sometimes also in combination, can be found in archaea and bacteria alike ( Duggin et al., 2015 ; Larsen et al., 2007 ; Preisner et al., 2018 ; van den Ent et al., 2001 ; Wickstead and Gull, 2011 ; Zaremba-Niedzwiedzka et al., 2017 ). As with many things in eukaryogenesis, it is the intricate combination and universal presence of all three cytoskeletal pillars and the dynamic nature which they are used in eukaryotes that is characteristic. The latter is best demonstrated by the rapid switch in motility between actin-based gliding and tubulin-based flagella-driven swimming in many protists, likely also a feature of the LECA ( Fritz-Laylin et al., 2010 ; Kusdian et al., 2013 ; Preisner et al., 2018 ). Basic components were derived from the host cell, such as gelsolin-regulated actin filaments ( Akıl et al., 2020 ) and evolution co-opted such mechanisms en route to LECA. It is conceivable that with expanding cell size, increased intracellular complexity and the need of an orchestrated cell cycle, the selection for a dynamic but simultaneously in parts rigid cytoskeleton increased, which triggered the expansion of the IF protein family required for mechanical support, and the origin of additional accessory proteins and regulatory mechanisms that are absent in prokaryotes. The identification and subsequent characterizations of asgard archaea have done the following for eukaryogenesis: (i) They underpin the syntrophic origin of eukaryotes involving two prokaryotic partners and (ii) provide support for a 2D tree of life (i.e. two domains of life, bacteria and archaea, evolved from the origin of life and eukaryotes emerged from within archaea after endosymbiosis of an alphaproteobacterial partner). (iii) They provide no evidence for the presence of bacterial-type ester-linked lipids in asgard archaea, (iv) reject a complex archaeal ancestor necessary to explain the patchy distribution of eukaryogenesis-relevant gene families ( Wu et al., 2022 ), and (v) show that the asgard archaeal set of genes before unique to eukaryotes closes the gap to the number of gene families encoded by eukaryotes by only 0.3% ( Knopp et al., 2021 ) or less ( Liu et al., 2021 ). Hence, with respect to explaining the origin of eukaryotic traits and a rationale for their universal presence in eukaryotes, the asgard archaea and their syntrophic bacterial partners support and place us at scenarios that were submitted some 25 years ago ( Martin and Müller, 1998 ; Moreira, 1998 ; Vellai and Vida, 1999 ). Considering syntrophy as a key ecological parameter in eukaryogenesis was an early notion that has stood the test of time ( Imachi et al., 2020 ; López-García and Moreira, 2020 ; López-García and Moreira, 2019 ; Sousa et al., 2016 ; Spang et al., 2019 ; Wu et al., 2022 ). Ever since, observations from the field of microbial ecology, genomics, and geology continue to encourage us to picture eukaryogenesis to have occurred within a microbial mat, where multiple species thrive in close proximity and ample syntrophies exchanging substrates such as H 2 /electrons under limited or no oxygen ( López-García and Moreira, 2020 ). Recent advances in geochemistry added new support to the proposal that eukaryogenesis occurred in anoxic niches with a preferred shift towards aerobic metabolism being a secondarily derived state ( Mills et al., 2022 ) and so do the culturing conditions of Prometheoarchaeon ( Imachi et al., 2020 ). Such prokaryotic consortia can source genes through HGT from pangenomes of other bacteria and (asgard) archaea and the virosphere that also contributed to the birth of the eukaryotic genome ( Spang et al., 2022 ; Wu et al., 2022 ). Evidently, however, most eukaryotic protein families evolved during the FECA to LECA transition and selective pressures due to endosymbiosis were likely key. To conclude, physiological and phylogenomic studies support a mitochondria-early scenario and so does cell biology ( Figure 3 ). Claiming mitochondria were of little importance in eukaryogenesis contradicts the simultaneous claim that intermediates lacking mitochondria all went extinct – an oxymoron that suggests that the mitochondrial endosymbiont contributed little in the FECA to LECA transition, while its presence was vital for the survival of FECA during eukaryogenesis. On being the right size in eukaryogenesis ‘ The most obvious differences between different animals are differences of size, but for some reason the zoologists have paid singularly little attention to them ’ (John BS Haldane). Haldane began his influential essay by addressing a lack of scale bars in zoology books. One can point to a similar issue regarding eukaryogenesis, in which models often depict cells not changing in size up to scale in the course of the FECA to LECA transition ( Baum and Baum, 2020 ; Gould et al., 2016 ; Spang et al., 2019 ). Though not intentional, this is important. In eukaryogenesis we are dealing with at least a 10 times increase in cell diameter, with known consequences regarding cell volume, morphology, and molecular diffusion limits among other factors ( Young, 2006 ). Engulfing a proteobacterium with a surface area of 10 µm 2 requires 10 times the surface area of the asgard archaeon Prometheoarchaeon . For a typical protist it is only 1% of its surface area. Putting scales on a recent model, the entangle-engulf-endogenize mode ( Imachi et al., 2020 ) brings forth details worthy considering: an observed tubular protrusion requires 12% of cytoplasm for a 50% increase in surface area interacting with syntrophic partners ( Figure 4 ). Four to six protrusions approximately result in the doubling of cytosolic volume, maybe explaining why Prometheoarchaeon has not been observed to produce more than six protrusion per cell. Figure 4. Extracellular membrane protrusions from asgard archaea. Potential trade-off between cytosol investment between cell and protrusion. Each 1 µm increase in protrusion takes 12% cytoplasm, likely limiting total protrusion and reducing cell volume. Values in the table for the asgard archaea were calculated based on Eme et al., 2017 : average asgard cell radius: 0.25 µm, average protrusion radius 50 nm, and length 1 µm. Average alphaproteobacterium radius and length were taken to be 0.5 and 3 µm, respectively. Such protrusions might have been relevant for the uptake of the symbiont, but the surface area of such a protrusion is 3% that of a proteobacterium. Entangling a proteobacterium entirely would take at minimum 50 protrusions, the cost of which is six times the cytoplasmic volume and not considering the multitude of proteins needed for recognition, surface attachment, and the processes thereof. We also note that we know neither of a case in nature where tubular-like extensions (allowing nutrient exchange) fuse to sheets (allowing cell engulfment), nor can we imagine how this would work on a molecular level, respecting membrane biology, and in 3D. Considering scale bars or tubular versus sheet-like membrane biology is not intended to disprove any model, but it highlights potential issues and also reminds us of the question of when and how did the size increase in the FECA to LECA transition. A feed forward loop supporting an increase in cell size ‘ The higher animals are not larger than the lower because they are more complicated. They are more complicated because they are larger ’ (John BS Haldane). Haldane noted that an elephant has to be as complicated as an elephant, because it is as large as an elephant. Across eukaryotes, an increase in cell size (1000–10,000 times) and morphological complexity is common, matched by a comparable increase in genome size. The upper end of bacterial genomes is 15 Mb ( Land et al., 2015 ), that of (haploid) eukaryote is around 130 Bb ( Pellicer et al., 2010 ). What drove this increase in cell and genome size during eukaryogenesis ( Figure 5 )? Figure 5. Evolution of last eukaryotic common ancestor (LECA) after the symbiotic event. Vesicles from the proteobacterium accumulated in cytosol giving rise to permeative barrier around the host DNA Brueckner and Martin, 2020 , selected positively due to parallel bombardment of genes ( Land et al., 2015 ). As a result, the genome of bacterium shrunk, whereas that of the host expanded, along with increase in cell size. New combinations of genes, powered by energy from the endosymbiont, led to emergence of novel trait and LECA with eukaryotic cell biology. In prokaryotes ATP production is limited by the available cell surface. This limits the replication rate and imposes negative selection on genome expansion ( Lane, 2007 ). Conversely, in mitochondria powered eukaryotes, energetic efficiency increases with cell size ( Hochachka et al., 2003 ; Lane, 2007 ), imposing a positive selection. Increased cell size in eukaryotes means increased DNA content to maintain the karyoplasmic ratio ( Cavalier-Smith, 2005 ; Cavalier-Smith, 1985 ), which is positively selected for ( Lane, 2007 ). Through this, and remembering that the endosymbiont provided the host cell with both problems and solutions (main text), one can speculate on mechanisms and a selective pressures for the emergence of eukaryotic cell biology, cell and genome size during the FECA to LECA trajectory. Endosymbiosis provided an influx of endosymbiotic genes and membrane material. An early endomembrane system with minimal protein content, for example, proteins that are likely to be packed for secretion via OMVs, etc., emerged and the nucleus formed for reasons discussed in the main text. Constant fusion of endosymbiont-derived vesicles with the archaeal host provided a mechanism for the lipid shift and compartment origin ( Gould et al., 2016 ), which might have fostered an increase in cell size. Integration of endosymbiotic DNA provided one early mechanism for why the genome size increased, together with duplication events ( Kelly, 2021 ; Tria et al., 2021 ; Van de Peer et al., 2009 ). The ongoing concentration of ATP synthesis to mitochondria imposed positive selection on cell size, which allowed for a further increase in genome size. A feed-forward process of increased cell size stipulating increased genome size and vice versa commenced that was supported by an emerging endomembrane system and intracellular transport that counteracted the molecular diffusion limit ( Figure 5 ). It provided ground for new combinations of genes to emerge and expressed ( Lane and Martin, 2010 ) and increased cell size accommodated experimental expression of new proteins in the cytosol ( Dill et al., 2011 ). This presented an opportunity – almost unlimited in theory – for the origin of new protein families and complex traits. Considering that most of these new inventions revolve around the endosymbiont ( Figure 3 ), suggests it drove the selection for their emergence and fixation. Haldane might have put it this way: the LECA got larger because it was complex and it became complex because it was larger. Did eukaryogenesis come with a price tag and if so, who paid? ‘ I have not failed. I’ve just found 10,000 ways that won’t work ’ (Thomas Edison). The cost of innovation is significantly higher than the manufacturing of the final product – the COVID vaccine serves as a topical example ( Light and Lexchin, 2021 ). Eukaryotes have innovated several folds higher number of protein families than archaea as evident from genome ( Brueckner and Martin, 2020 ) and proteome data ( Müller et al., 2020 ) alike, underpinning the complexity across the eukaryotic tree of life. Since mutations lack foresight and are more likely to be deleterious than advantageous ( Eyre-Walker and Keightley, 2007 ), inventing new proteins takes a considerable amount of trial and error. Ribosome production and protein biosynthesis consumes the majority of a cell’s energy budget ( Harold, 1986 ; Kafri et al., 2016 ) and the energy budget of trial and error would be orders of magnitude higher. Mitochondria were key by providing eukaryogenesis with an energetic freedom that supported this unparalleled level of innovative protein evolution and expression ( Lane and Martin, 2010 ). While challenged ( Lynch and Marinov, 2017 ; Schavemaker and Muñoz-Gómez, 2022 ), we do not see it disproven ( Gerlitz et al., 2018 ; Lane, 2020 ). Calculations questioning the bioenergetic contribution of mitochondria do not account for the cost of evolving novel proteins. The acquisition of a respiratory electron transport chain through excessive HGT does not make a cell complex ( Nelson-Sathi et al., 2012 ), because the location of the bioenergetic membrane matters. The ratio of bioenergetic membrane (=energy generation) to genome size is high when harbouring an endosymbiont with internalized energetic membranes and a reduced genome ( Lane and Martin, 2010 ). Also, a bioenergetic plasma membrane is incompatible with phagocytosis and the internalization of the bioenergetic membrane was a prerequisite to evolve phagocytosis ( Martin et al., 2017b ). A physiological observation that puts a timing on events in the FECA to LECA transition. Eukaryotes that maintain complexity in the absence of respiring mitochondria has prompted some to question the importance of mitochondria and a surplus of ATP at eukaryote origin ( Hampl et al., 2019 ), while missing a critical detail: the examples listed stem from species that are either parasites or commensals of eukaryotes and who are energetically dependent on canonical mitochondria. The same holds true for the only eukaryotic taxon not possessing mitochondria, Monocercomonoides. They secondarily lost mitochondria and can only thrive in the gut of some animals ( Karnkowska et al., 2016 ). Such parasites or commensals have engaged an evolutionary path characterized by energetic dependency. Their complexity might diminish over evolutionary timescale, should they not go extinct with their hosts first. The issue is the origin of eukaryote complexity from prokaryotic ancestors, not the maintenance of eukaryotic complexity from eukaryotic ancestors. An alternative to the energetics argument in explaining the ubiquity of mitochondria and its role in eukaryogenesis is missing, and the papers that question it are no exception. The internalization of energetic membrane – energy production from only 10% of cell volume – decoupled from the genome, as is the case in mitochondria ( Fenchel, 2014 ), provides an optimum for protein innovation and a selection towards a complexity that can maintain a 200 ton blue whale. A successful endosymbiosis and origin of a new domain: chance or necessity? ‘ Everything existing in the universe is the fruit of chance and of necessity ’ (Democritus). Evolution is random and selects for fitness. Extinction is the rule and so is the common principle of use it or lose it ( Lahti et al., 2009 ). Traits that remain unchanged across various organisms and through a billion years of evolution are indicative of the fact they are fundamental. Endosymbiosis is absent among prokaryotes (apart from isolated exceptions) and so is morphological complexity comparable to that of eukaryotes (reviewed in Martin et al., 2017b ). Is it by chance or necessity? It is unlikely that the endosymbiosis leading into the origin of the eukaryotic domain was the first and only attempt throughout now 4 billion years of prokaryotic evolution. The set of challenges posed by an endosymbiont are generic in nature: (i) there is a constant influx of endosymbiotic DNA which, after integration into the host genome ( Allen, 2015 ; Portugez et al., 2018 ), is also exposed to an increased accumulation of deleterious mutations ( Eyre-Walker and Keightley, 2007 ). The secretion of outer membrane vesicles by the endosymbiont is inevitable ( Deatherage and Cookson, 2012 ), as well as the removal of irreversibly damaged organelles or the need to supply the endosymbiont with substrate from ions to peptides. Dividing endosymbionts need to be integrated into the division cycle of an archaeal host itself relying on simple binary fission. A lot of solutions are associated with compartmentalization and this is a good time to remember that the mitochondrial endosymbiont not only provided the challenges, but maybe also the material to solve some ( Gould et al., 2016 ). Any attempts at eukaryogenesis are prone to fail, if such challenges are not met by solutions that furthermore require correct timing ( Barbrook et al., 2006 ). An influx of genes via HGT alone does not translate into complexity. Despite the metabolic transformation of haloarchaea via a chunk of some 1000 genes of bacterial origin ( Nelson-Sathi et al., 2012 ) – maybe through a syntrophic partner and failed endosymbiosis – haloarchaea show no intracellular complexity. So while the encounter of the mitochondrial ancestor with an archaeal host occurred by chance, the emergence of a complex cell biology upon endosymbiosis was a necessity. Once a cell biology that can chaperone an endosymbiont is established, however, additional endosymbionts may follow without noticeable changes to the host. The subsequent acquisition of the plastid added no extra compartments to the heterotrophic host that gave rise to the Archaeplastida, despite adding thousands of cyanobacterial genes to the host genome ( Timmis et al., 2004 ). The same is true for an independent plastid acquisition by a rhizarian protist ( Lhee et al., 2019 ) and likely many other endosymbiont-bearing protists ( Husnik et al., 2021 ). Ever since eukaryogenesis, the cellular framework required for housing another prokaryote was in place. Some compartments have experienced physiological remodelling, such as the peroxisome ( Islinger et al., 2010 ), but many components that evolved to service mitochondria during eukaryogenesis were recycled for the plastid: dynamins for fission ( Fujimoto and Tsutsumi, 2014 ), redox balance through thioredoxins ( Thormählen et al., 2017 ), and organelle digestion and recycling through the autophagosome ( Ishida et al., 2014 ). One could add secondary endosymbioses, in which the acquisition of algae by eukaryotic hosts can lead to the stripping of all eukaryotic compartments of the endosymbionts (including their mitochondria), but that otherwise add no additional compartment or complexity to the host. Conclusion Morphologically complex life on Earth has a singular origin: eukaryogenesis. The LECA had evolved all canonical traits that we understand separates prokaryotic from eukaryotic life. Closing the gap between a simple FECA and a complex LECA by presupposing a complex FECA opens an equally wide gap between a simple and a complex archaeal host. Picturing FECA without an endosymbiont offers little explanation for the existence or emergence of eukaryotic traits and the lack thereof in prokaryotes (including asgard archaea), apart from the inevitable that all eukaryogenesis models face: the need to script the blueprint of a eukaryotic cell. And should then not all (asgard) archaea with a syntrophic partner be considered FECA in the sense that in principle they have the potential to become eukaryotic? For 4 billion years, prokaryotes have overall remained the same in terms of cellular complexity with some rare exceptions having evolved a single compartment type, but nothing vaguely similar to the conserved nature of the eukaryotic endomembrane system. Reflecting on eukaryotic traits and their cell biological connection to mitochondrial origin lets us conclude they are better understood as being selected for to service an endosymbiont and less so as means of acquiring one. Phylogeny guided models should connect interpretations to a physiological and cell biological rationale, while facing the challenge of resolving the fluid nature of the pangenomes of both host and endosymbiont genome throughout eukaryogenesis – we need to talk to phylogenetic trees, not only about them. Physiology ( Imachi et al., 2020 ; Martin and Müller, 1998 ; Moreira, 1998 ; Spang et al., 2019 ), geochemistry ( Mills et al., 2022 ), phylogenetics ( Wu et al., 2022 ), and culturing and imaging ( Imachi et al., 2020 ) all point to a syntrophic origin of eukaryotes involving two prokaryotic partners. The data suggests that first steps towards endosymbiosis in eukaryogenesis were of prokaryotic nature, that eukaryogenesis likely only solidified upon endosymbiosis, and that hence the definition of FECA should include an endosymbiont."
} | 14,100 |
27392086 | PMC5335547 | pmc | 528 | {
"abstract": "Reef-building corals are well regarded not only for their obligate association with endosymbiotic algae, but also with prokaryotic symbionts, the specificity of which remains elusive. To identify the central microbial symbionts of corals, their specificity across species and conservation over geographic regions, we sequenced partial SSU ribosomal RNA genes of Bacteria and Archaea from the common corals Stylophora pistillata and Pocillopora verrucosa across 28 reefs within seven major geographical regions. We demonstrate that both corals harbor Endozoicomonas bacteria as their prevalent symbiont. Importantly, catalyzed reporter deposition–fluorescence in situ hybridization (CARD–FISH) with Endozoicomonas -specific probes confirmed their residence as large aggregations deep within coral tissues. Using fine-scale genotyping techniques and single-cell genomics, we demonstrate that P. verrucosa harbors the same Endozoicomonas , whereas S. pistillata associates with geographically distinct genotypes. This specificity may be shaped by the different reproductive strategies of the hosts, potentially uncovering a pattern of symbiont selection that is linked to life history. Spawning corals such as P. verrucosa acquire prokaryotes from the environment. In contrast, brooding corals such as S. pistillata release symbiont-packed planula larvae, which may explain a strong regional signature in their microbiome. Our work contributes to the factors underlying microbiome specificity and adds detail to coral holobiont functioning.",
"conclusion": "Conclusions Global profiling of microbial community structure in a multi-species framework revealed that Endozoicomonas bacteria are prevalent and often the most abundant prokaryotic symbionts residing intimately within the tissues of two common corals across their global distribution. These differences in Endozoicomonas specificity map onto the different reproductive strategies of the coral hosts. S. pistillata is a brooder and may control the evolution and specificity of its microbiome through the vertical transfer of symbionts, while P. verrucosa is a spawner and acquires its prokaryotic symbionts from the water column, thus allowing for greater variability within and less spatial structure across Endozoicomonas genotypes. Our study introduces a coral–bacterial relationship that features a consistent, cosmopolitan, highly aggregated endosymbiont that exhibits some taxonomic variability, and is an important component of the endosymbiotic relationships of the coral holobiont that should be examined with attention to evolutionary and ecological interactions and constraints.",
"introduction": "Introduction Coral reefs are declining globally at unsustainable rates ( Descombes et al. , 2015 ), driven by stressors including increasing sea surface temperatures, overfishing and anthropogenic inputs ( De'ath et al. , 2012 ). Devising strategies for mitigating future reef loss is challenging because corals are ‘metaorganisms', comprised not only of the host itself, but also symbiotic algae of the genus Symbiodinium , viruses, bacteria, archaea and fungi. This complex consortium is referred to as the coral holobiont ( Rohwer et al. , 2002 ; Knowlton and Rohwer, 2003 ). The best-studied members of the coral holobiont are the coral animal host and the Symbiodinium algae, which provide most of the host's energy requirements through photosynthates. In return, the symbiotic algae receive a safe refuge, consistent sunlight and nutrients ( Goodson et al. , 2001 ). Although coral-prokaryotic associations have been recognized since the late 1970s ( Ducklow and Mitchell, 1979 ; Williams et al. , 1987 ), recent advancements in sequencing technology have revealed that microbes associated with corals are distinct from those in the seawater and are diverse, totaling several hundred species ( Rohwer et al. , 2001 ; Kellogg, 2004 ; Wegley et al. , 2004 ; Sunagawa et al. , 2009 ; Roder et al. , 2013 , 2014 ; Bayer et al. , 2013b ; Pantos et al. , 2015 ). The high diversity of these microorganisms, with many belonging to uncultured genera, makes untangling the complicated interactions between microbes, the coral host and the algal dinoflagellate partners challenging. However, certain microbial species in the coral holobiont can fix nitrogen ( Lesser et al. , 2004 ; Lema et al. , 2014 ), metabolize sulfur ( Raina et al. , 2009 ), provide antibiotic compounds ( Reshef et al. , 2006 ; Ritchie, 2006 ) and probably have roles in various other biogeochemical cycles ( Kimes et al. , 2010 ). Moreover, particular microbes appear to be ‘core' symbionts of the holobiont, always associating with a specific coral host, and likely providing important functional benefits to the coral holobiont ( Rohwer et al. , 2001 ; Speck and Donachie, 2012 ; Jessen et al. , 2013 ; Rodriguez-Lanetty et al. , 2013 ; Bayer et al. , 2013b ; Ainsworth et al. , 2015 ). One of the potential ‘core' symbionts of many corals are the Endozoicomonas , members of the Gammaproteobacteria's Oceanospirillaceae family ( Morrow et al. , 2012 ; Speck and Donachie, 2012 ; Apprill et al. , 2013 ; Jessen et al. , 2013 ; Rodriguez-Lanetty et al. , 2013 ; Bayer et al. , 2013b ; Lesser and Jarett, 2014 ; Morrow et al. , 2014 ; Pantos et al. , 2015 ). These bacteria also frequently associate with other marine organisms, including gorgonians ( Correa et al. , 2013 ; La Rivière et al. , 2013 ; Vezzulli et al. , 2013 ; Bayer et al. , 2013a ; Ransome et al. , 2014 ), ascidians ( Dishaw et al. , 2014 ), tube worms ( Forget and Juniper, 2013 ), mollusks ( Jensen et al. , 2010 ; Hyun et al. , 2014 ) and fish ( Mendoza et al. , 2013 ). Although the sequence-based evidence for Endozoicomonas as a core microbiome member of certain corals is compelling, most studies have used corals from only one geographic location and often methods across studies are not standardized, making comparative analyses difficult. A comprehensive study of global reefs, with fine-scale attention to Endozoicomonas genotypes, is required to understand worldwide patterns of association between Endozoicomonas symbionts and corals. Here, we examine the microbiomes of Stylophora pistillata and Pocillopora verrucosa across their global distribution to better understand the bacterial and archaeal community composition and governing principles, as well as the fine-scale specificity of their core symbionts. We predict that if microorganisms are important to healthy coral functioning and have co-evolved with corals, then coral microbiomes should be similar worldwide. Indeed, both coral species harbor Endozoicomonas as their abundant symbiont, which was found to reside in aggregates within coral tissues. Furthermore, S. pistillata displayed a highly geographically structured microbiome and was associated with Endozoicomonas genotypes unique to each geographic region. In contrast, P. verrucosa exhibited a weakly geographically structured microbiome and contained similar Endozoicomonas symbionts across large spatial scales. This suggests that microbial structure in corals may be linked to life history traits, and advances our understanding of coral holobiont acquisition and functioning.",
"discussion": "Discussion Different microbiome structuring for S. pistillata and P. verrucosa S. pistillata and P. verrucosa are closely related coral species both belonging to the family Pocilloporidae. They probably diverged relatively recently, near the beginning of the Neogene period around 23 million years ago ( Park et al. , 2012 ). Despite this phylogenetic similarity, we found significant differences in the microbiomes of the two corals and in the apparent forces structuring the microbiome. Specifically, S. pistillata microbiomes were strongly clustered according to geographic location, whereas in P. verrucosa , the groupings were much weaker. These differences may have arisen because of the distinctive reproductive strategies of the corals; S. pistillata is a brooder ( Hall and Hughes, 1996 ; Shlesinger et al. , 1998 ) and P. verrucosa is a broadcast spawner ( Pinzón et al. , 2013 ). In fact, some authors have suggested that the divergence between Stylophora and Pocillopora was established by the evolution of these different reproductive modes ( Schmidt-Roach et al. , 2014 ). Previous studies of the broadcast spawning coral, P. meandrina , found that larvae less than 3 days old were sterile of prokaryotes ( Apprill et al. , 2009 ) and some of their microbiome originates from the surrounding seawater ( Apprill et al. , 2012 ). The gametes of other broadcast spawning corals are also sterile of prokaryotic cells, which are acquired later from the water column ( Sharp et al. , 2010 ; Ceh et al. , 2013 ). In contrast, the brooding coral, Porites astreoides , passed microbes vertically from parent to offspring ( Sharp et al. , 2012 ). Assuming these patterns hold true for the corals studied here, it could explain the observed geographic structuring patterns. Namely, the brooding coral S. pistillata may vertically transfer microbes to their offspring, thereby tightly controlling the microbiome and its evolution, and resulting in strong geographic structuring. Conversely, if P. verrucosa larvae were sterile and their microbial associates were acquired from seawater, symbiont mixing between distinct reefs is likely, resulting in weaker geographic structuring. Moreover, Pocillopora species have unusually large areas of genetic connectivity and low species diversity compared with other cosmopolitan coral species, such as S. pistillata ( Pinzón et al. , 2013 ; Robitzch et al. , 2015 ). This potentially high gene flow between Pocillopora species is probably facilitated by exceptionally long planktonic stages. For example, P. damicornis , which is closely related to P. verrucosa , has a planktonic stage of more than 100 days in aquaria ( Richmond, 1987 ), which would allow mixing of populations from relatively distant oceanic basins. High connectivity between hosts would almost certainly provide opportunities for symbiont sharing, potentially explaining the weaker geographic signature in P. verrucosa microbiomes. The geographic structuring pattern for the overall microbial communities was even more pronounced for Endozoicomonas , the most abundant symbiont in both corals. For S. pistillata , each large geographic region contained unique Endozoicomonas OTUs. This was not seen in P. verrucosa , which often harbored the same Endozoicomonas OTUs across large geographic ranges. Reproductive differences in the corals may again account for this pattern. If Endozoicomonas are passed vertically in the brooder S. pistillata, this could produce strong regional clustering, whereas if Endozoicomonas are acquired from the seawater in P. verrucosa , the endosymbiont types may be shared. The higher genetic connectivity of Pocillopora ( Pinzón et al. , 2013 ; Robitzch et al. , 2015 ) would also allow for greater sharing of Endozoicomonas genotypes. Interestingly, Hester et al. (2015) studied three spawning coral species ( Acropora rosaria, A. hyacinthus and Porites lutea ) and did not find stable symbionts that were universally associated with one host, similar to P. verrucosa here. Our study was able to extend this analysis by including the brooder, S. pistillata , allowing us to consider the mode of symbiont transmission as an influence on microbiome structure, and also used a deeper sequencing depth to more thoroughly characterize the microbiome. Endozoicomonas fine-scale specificity resembles coral–algae and other symbiotic systems Endozoicomonas appear to be ‘core' microbiome members of both S. pistillata and P. verrucosa, and were detected in 79% and 85% of the coral samples, respectively. Although these percentages provide support that Endozoicomonas are core microbiome members, these calculations are dependent on the sequence identity used in the formation of OTUs, which can alter the results (for discussion, see Shade and Handelsman (2012) ). Although colonies of S. pistillata and P. verrucosa both generally associated with a single dominant Endozoicomonas genotype, often several Endozoicomonas OTUs were present, frequently from distinct lineages ( Figure 3 ). This reflects the population structure of the most conspicuous coral endosymbiont, the Symbiodinium algae. Although most coral colonies are dominated by a single Symbiodinium type, several genotypes are generally present ( Arif et al. , 2014 ; Quigley et al. , 2014 ). Moreover, in times of heat stress, some corals can undergo a process of ‘symbiont shuffling', whereby the current symbionts are replaced with Symbiodinium clades or types that have a higher thermotolerance ( Silverstein et al. , 2014 ). It is possible that different Endozoicomonas types are also adapted to different conditions and become abundant at certain times. To test the stability of Endozoicomonas –host associations, we compared our Red Sea MED OTUs with Endozoicomonas sequences from a previous study of S. pistillata from nearby Red Sea reefs collected in June 2009 ( Bayer et al. , 2013b ) ( Figure 3 ). All of the most abundant sequences from the earlier study had correspondingly abundant MED OTUs ( Figure 3 ), suggesting that the most prevalent Endozoicomonas types maintained their dominance for at least the last 4.5 years. Nevertheless, future studies should explore whether rare Endozoicomonas genotypes become abundant depending on time of day and season, or whether their abundance correlates with the fluctuations of different Symbiodinium clades. This differential host specificity in Endozoicomonas parallels the specificity seen between Apidae bees and their symbionts. The honeybee, Apis mellifera , and the bumblebee, Bombus spp., both contain two abundant symbionts, Snodgrassella alvi and Gilliamella apicola ( Kwong et al. , 2014 ). Despite this similarity, strong host specificity exists for different symbiont strains; for example, S. alvi strains isolated from honeybees could not colonize bumblebees ( Kwong et al. , 2014 ). The specificity was driven by host–microbe and microbe–microbe interactions and transmission differences, which may also structure the Endozoicomonas specificity seen in S. pistillata and P. verrucosa . Potential functional roles of Endozoicomonas endosymbionts Despite the abundance and worldwide distribution of Endozoicomonas in multiple marine organisms, their function is unknown. A suggested role is DMSP breakdown ( Raina et al. , 2009 ; Lema et al. , 2013 ; Pike et al. , 2013 ; Vezzulli et al. , 2013 ; Ransome et al. , 2014 ), however, sequenced Endozoicomonas genomes do not contain known DMSP breakdown pathways ( Neave et al. , 2014 ). Others have suggested a nutritional symbiosis ( La Rivière et al. , 2013 ), interactions with the algal symbiont Symbiodinium ( Morrow et al. , 2012 ; Pantos et al. , 2015 ) or roles in producing antimicrobial compounds ( Bourne et al. , 2008 ). Although most studies propose that Endozoicomonas are beneficial symbionts, Mendoza et al. (2013) found that cyst-like aggregations of Endozoicomonas were responsible for epitheliocystis in fish and caused mass mortalities. This suggests that different Endozoicomonas genotypes may have disparate roles in their diverse hosts. Genomic sequencing revealed that Endozoicomonas genomes are large (>5 Mbs) and are not streamlined for an obligate endosymbiotic lifestyle, implying they have free-living stages ( Neave et al. , 2014 ). The genomes are suggestive of an aerobic heterotrophic lifestyle with the ability to metabolize diverse carbon sources, including amino acids and nucleic acids. The genomes also code for a large number of transport molecules, which they may use to interact with compounds produced by the host or by the Symbiodinium algae. CARD–FISH revealed the spatial location of Endozoicomonas in the tentacles of S. pistillata and in the gastrodermis of P. verrucosa , which underlines that different host species show distinct association patterns further supporting that it is (i) a highly intimate relationship and (ii) a highly specialized relationship. Further, the aggregation patterns observed here suggest that Endozoicomonas may utilize metabolic products from other cells within the aggregation, or a component of their metabolism may rely on quorum sensing ( Waters and Bassler, 2005 ). Further, the presence of intratentacular Endozoicomonas aggregates in S. pistillata suggests that they may have a role in prey acquisition. In line with this, Schuett et al. (2007) found comparable Endozoicomonas aggregations in the tentacles of a sea anemone. This potentially suggests similar functional roles for some Endozoicomonas genotypes across host species, although further microscopy studies examining the location and arrangement of Endozoicomonas cells in a variety of hosts are required. It should be noted that the rod-shaped Endozoicomonas cells imaged here are distinct from those previously reported for S. pistillata ( Bayer et al. , 2013b ), and the use of CARD–FISH has greatly aided our ability to localize specific cells within highly autofluorescent coral tissues. Altogether, we provide further evidence that Endozoicomonas have an important role in the coral holobiont, however, their precise function remains to be determined."
} | 4,405 |
37343101 | PMC10284536 | pmc | 529 | {
"abstract": "Memristor-enabled in-memory computing provides an unconventional computing paradigm to surpass the energy efficiency of von Neumann computers. Owing to the limitation of the computing mechanism, while the crossbar structure is desirable for dense computation, the system’s energy and area efficiency degrade substantially in performing sparse computation tasks, such as scientific computing. In this work, we report a high-efficiency in-memory sparse computing system based on a self-rectifying memristor array. This system originates from an analog computing mechanism that is motivated by the device’s self-rectifying nature, which can achieve an overall performance of ~97 to ~11 TOPS/W for 2- to 8-bit sparse computation when processing practical scientific computing tasks. Compared to previous in-memory computing system, this work provides over 85 times improvement in energy efficiency with an approximately 340 times reduction in hardware overhead. This work can pave the road toward a highly efficient in-memory computing platform for high-performance computing.",
"introduction": "INTRODUCTION Scientific computing technology has allowed scientists and engineers to describe the natural and technological processes or phenomena in both scientific research and engineering ( 1 , 2 ). This technology has widely been used to investigate various processes, such as fluid flow and turbulence in physics, molecular structure and reactivity in chemistry, and structure-function relationships in biology, with ever-increasing fidelity ( 3 , 4 ). Nevertheless, scientific computing tasks are commonly computationally intensive and involve massive data. Therefore, the development of scientific computing tasks in the past two decades progressed with the performance improvement of the modern digital computer. Unfortunately, digital computers are ultimately constrained by the scaling limit of complementary metal-oxide semiconductor technology and by the power-hungry data movement in von Neumann computers ( 5 , 6 ). Herein, constructing a high-performance computing system comes with the enormous requirement of hardware resources and large energy consumption ( 7 , 8 ). Memristor-enabled in-memory computing (mIMC) provides promising non–von Neumann computing solutions for addressing these issues ( 9 – 12 ). By performing matrix-vector multiplication using Ohm’s law and Kirchhoff’s law in situ in the memristor array ( 13 ), the mIMC paradigm has been adopted to accelerate various data-intensive tasks, such as machine learning ( 14 – 17 ), image processing ( 18 , 19 ), and solving matrix equations ( 20 – 22 ), whereas the computational speed and energy efficiency have been substantially improved. Recent studies have demonstrated processing scientific computing tasks, such as solving partial differential equations (PDEs) within the crossbar array ( 22 – 24 ). However, constructing an mIMC system for scientific computing tasks is still challenging ( 25 ). This is due to the difficulty in realizing efficient sparse matrix-vector multiplication (SpMV) in the crossbar array, which lies at the heart of scientific computing tasks ( Fig. 1A ) ( 22 ). The challenge arises because of the following reasons: (i) Owing to the limitation of the analog computing mechanism, specifically Ohm’s law and Kirchhoff’s law, matrix elements must be mapped to array cells corresponding to their positions in the matrix for correct calculation. However, the elements whose values are zero notably outnumber nonzero elements in a sparse matrix. As a result, mapping the sparse matrix is inefficient because only a few devices are used to store the nonzero elements ( Fig. 1B ). (ii) Because the zero values are commonly presented by the nonzero high resistance value, the computation accuracy is severely degraded by the resistance variation and other device imperfections. (iii) The sparse matrix to describe a practical system is remarkably large, whereas the high-sneak current of the prior fabricated memristors potentially limits the array scalability ( 26 ). This results in massive hardware overhead to realize large-scale computing using multiple arrays, such as the matrix-slicing techniques ( 22 , 24 ). Eventually, addressing these issues requires a revolutionary innovation in computing mechanisms and the joint efforts of devices and algorithms. Fig. 1. Motivation for this work. ( A ) The main computational expensive operation in modern scientific computing problems is massive SpMV. ( B ) However, deploying the scientific computing tasks to prior mIMC hardware suffers substantial performance degradation in area/energy efficiency because of the inevitable necessity to map the zero element. For instance, the activated devices to process 2D PDEs 19× outnumbered the matrix nonzero elements ( 22 ). ( C ) Our work is dedicated to developing a high-efficiency in-memory sparse computation system through the rectifying nature of SRMs. In this work, by using a self-rectifying memristor (SRM), we demonstrated a highly efficient in-memory sparse computing system ( Fig. 1C ). The fabricated SRM demonstrates superior inhibition of array sneak currents and a record-low switching/read power, which, in turn, supported large-scale computation with extremely low energy consumption. Meanwhile, an analog computing mechanism based on the device’s self-rectifying nature is purposed to realize efficient SpMV with zero compression in the crossbar array. The effectiveness of our approach is validated by solving a static problem in situ in the SRM array. This approach is further used in a mixed-precision solver to solve the large-scale problem in simulation. The system is confirmed to achieve software-comparable solving precision with a substantial reduction in processing complexity. By substantially improving the energy efficiency for sparse matrix multiplication, our study provides a suitable option for high-performance computing hardware enabled by emerging devices.",
"discussion": "DISCUSSION In summary, we demonstrated a highly efficient hardware solution for in-memory sparse linear algebra problems using an SRM array. The fabricated SRM array demonstrated superior performance in terms of low leakage current and record-low switching/read power, which is of great importance in achieving high computational energy efficiency. Moreover, our devices also exhibit large RR and NL, and the passive array is expected to be scaled up to 95 Mb, enabling high-density integration for memory applications. To realize highly efficient in-memory SpMV, we proposed an analog computing mechanism inspired by the device’s self-rectifying nature. This mechanism enables area-efficient SpMV with zero compression in the crossbar array, whereas our SpMV framework has been evaluated to achieve one or two orders of magnitude reduction in hardware consumption compared to the previous in-memory computing systems. Meanwhile, this system was verified to substantially offset the impact of resistance variation by solving a time-evolving problem in situ in the fabricated SRM array. Furthermore, a hybrid mIMC system was designed to solve large-scale sparse matrix equations in high-precision scientific computing tasks. This hybrid system can substantially boost processing speed and achieve software-comparable accuracy. Further performance benchmark shows that the hybrid system can work properly with defective crossbar arrays. In addition, compared to prior mIMC implementation, our system demonstrated a superior energy efficiency of nearly two orders of magnitude improvement. By substantially improving the processing efficiency, precision, and computational robustness of the in-memory mixed-precision solver, we believe that this work is a solid step forward in exploring a high-energy efficiency mIMC hardware solution for high-performance computing. As the performance of the parallel in-memory multiplication scheme for large-scale applications was validated, the next step is to generalize this computing scheme to other computationally intensive tasks and further improve the processing speed using the 3D integrated circuits, specifically, the deep conventional neural network ( 33 , 49 , 50 )."
} | 2,046 |
36500566 | PMC9739919 | pmc | 530 | {
"abstract": "Spider dragline silk has unique characteristics of strength and extensibility, including supercontraction. When we use it as a biomaterial or material for textiles, it is important to suppress the effect of water on the fiber by as much as possible in order to maintain dimensional stability. In order to produce spider silk with a highly hydrophobic character, based on the sequence of ADF-3 silk, we produced recombinant silk (RSSP(VLI)) where all QQ sequences were replaced by VL, while single Q was replaced by I. The artificial RSSP(VLI) fiber was prepared using formic acid as the spinning solvent and methanol as the coagulant solvent. The dimensional stability and water absorption experiments of the fiber were performed for eight kinds of silk fiber. RSSP(VLI) fiber showed high dimensional stability, which is suitable for textiles. A remarkable decrease in the motion of the fiber in water was made evident by 13 C solid-state NMR. This study using 13 C solid-state NMR is the first trial to put spider silk to practical use and provide information regarding the molecular design of new recombinant spider silk materials with high dimensional stability in water, allowing recombinant spider silk proteins to be used in next-generation biomaterials and materials for textiles.",
"conclusion": "4. Conclusions A new recombinant spider silk protein, RSSP(VLI) based on the RSSP(QQQ), where all QQ sequences are replaced by VL, while single Q is replaced by I, is produced with E. coli to overcome a critical defect: the low dimensional stability of RSSP(QQQ) fiber in water, preventing its practical use. The fiber was prepared from powder using formic acid as the spinning solvent and methanol as the coagulant solvent during the wet spinning process. A higher dimensional stability of RSSP(VLI) fiber than other silk fibers was obtained. A remarkable decrease in the motion of the fiber in water was evident by 13 C solid-state NMR studies. This study, including 13 C solid-state NMR is the first trial to put spider silk to practical use, providing information for the molecular design of new recombinant spider silk materials with high dimensional stability in water, allowing the use of recombinant spider silk proteins in next-generation biomaterials and materials for textiles.",
"introduction": "1. Introduction Spider dragline silks have attracted much attention as a potential source of next-generation biomaterials and textiles because of their outstanding mechanical properties and biocompatibility [ 1 , 2 , 3 , 4 , 5 , 6 ]. The spider silks have a unique property, supercontraction, which occurs in the hydration process, i.e., interaction with water causes the spider dragline silk fiber to contract by up to 50% in length and to transition from glassy to rubbery phases [ 7 , 8 , 9 , 10 , 11 ]. However, this latter characteristic is not suitable for use in biomaterials, because biomaterials are generally used in water, and therefore, the maintenance of the dimensional stability of biomaterials in water is required [ 3 , 4 , 5 ]. In addition, for textiles, it is generally required to overcome some inferior characteristics of the fiber, i.e., low water repellency, vulnerability to friction, low dimensional stability, being prone to wrinkles, and so on, and therefore, dimensional stability in water is also necessary [ 12 , 13 , 14 ]. All spider silks are mainly composed of spidroins. The best characterized silk is the major ampullate silk. It is known that the assembly of major ampullate spidroin 1 (MaSp1) and spidroin 2 (MaSp2) into a fiber demonstrates outstanding mechanical properties [ 1 , 2 , 3 , 4 , 5 , 15 , 16 , 17 ]. The repetitive domains of MaSpl protein are mainly composed of polyalanine (poly-Ala), which forms antiparallel β-sheets (AP-β) in a crystalline region, and Gly-rich regions, which form an amorphous region. The former region has been considered as the origin of high fiber strength, and the latter as the source of the high elasticity of spider dragline silk fiber [ 1 , 2 , 3 , 4 , 5 ]. Because of their cannibalistic behavior, we cannot farm spiders. Furthermore, collecting the silk fiber from spiders is time consuming. Therefore, silk genes have been transferred from spiders to other host organisms to construct recombinant spider silk proteins [ 18 ]. In our previous work [ 19 , 20 , 21 , 22 ], recombinant spider silk protein (RSP) based on the sequence of ADF( A. diadematus fibroin)-3 silk from the European garden spider Araneus diadematus was produced with E. coli. The two major ampullate silk components of A. diadematus are historically referred to as ADF-3 and -4 [ 18 ]. The primary structure and amino acid composition of RSP are shown in Figure S1 [ 19 ]. In addition, another spider silk protein named RSSP(QQQ), with a slightly modified primary structure of RSP was also produced using E. coli [ 23 ]. We used the abbreviation RSSP(QQQ) in reference to the focus on three types of Q. The primary structure and amino acid composition of RSSP(QQQ) are shown in Figure S2 [ 23 ]. Then, the regenerated fibers of these recombinant spider silk proteins were prepared by the wet spinning process, and the secondary structures of the fibers were investigated using 13 C solid-state NMR methods. In this paper, a new recombinant spider silk protein, RSSP(VLI) ( Figure 1 ), based on RSSP(QQQ) [ 23 ], where all QQ sequences are replaced by VL while single Q is replaced by I, is produced with E. coli to overcome a critical defect: the low dimensional stability of RSSP(QQQ) fiber in water. In addition, to clarify the structural and dynamical change in the fiber formation process of the recombinant silk protein at the molecular level, solid-state NMR was used. Because the recombinant spider silk proteins are generally obtained in a powder state, a spinning process to prepare the fibers is required in order to use them widely, and the wet spinning method in particular is frequently used [ 24 ]. The dissolution of the powders is a critical step in producing the fibers. The spinning solvents used previously for native spider silks [ 25 ] and recombinant spider silks [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ] were mainly hexafluoroisopropanol (HFIP) and water. More recently, by learning the spinning system of native spiders, water and aqueous buffers were used for preparing recombinant spider silk fibers with both N-terminal and C-terminal domains [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. The relatively small molecular weight makes the recombinant spider silk soluble in water, but the strength of the fiber is quite low at present. The RSSP(VLI) powder was insoluble in water, as well as RSSP(QQQ) powder. In addition, there were many voids in the fiber prepared from the HFIP solution of RSSP(QQQ) [ 23 ], which was not suitable for textiles. On the other hand, formic acid (FA) [ 42 , 43 , 44 , 45 , 46 , 47 ] and CaCl 2 -FA [ 48 , 49 , 50 , 51 ] have been used as the spinning solvents for regenerated B. mori silk fibroin (RSF), and excellent physical properties have been obtained. Thus, we select FA as the spinning solvent [ 21 , 22 , 23 , 42 , 52 , 53 ]. FA induces chemical modifications, namely, the formylation of proteins [ 21 , 22 , 23 , 54 , 55 , 56 , 57 ]. In addition, the selection of the coagulation solvents is also important. Thus far, alcohols such as methanol (MeOH), ethanol, and isopropanol have been used [ 24 ]. We select MeOH as the coagulation solvent [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Previous reports have shown the esterification of Ser residue in recombinant spider silk proteins incubated in FA [ 21 , 22 , 23 ]. Thus, first, the formylation of Ser residue in RSSP(VLI) was studied using the 13 C solution NMR method. The mechanical properties of the RSSP(VLI) fibers were examined before and after hydration treatments. Then, dimensional stability was determined for the RSSP(VLI) and RSSP(QQQ) fibers to examine changes in the stability when all QQ sequences were replaced by VL, while single Q was replaced by I. The dimensional stability experiments were expanded by adding the water absorption experiments to more samples, i.e., acetylated RSSP(VLI) and acetylated RSSP(QQQ) fibers, RSP and acetylated RSP fibers, and regenerated B. mori silk fibroin and regenerated acetylated B. mori silk fibroin fibers. The dimensional stability of the latter four samples was reported previously [ 22 , 58 ]. Finally, in order to clarify what happened in the wet spinning process of RSSP(VLI) from FA solution to fiber formation, we observed three kinds of 13 C solid-state NMR, i.e., 13 C refocused insensitive nuclei enhanced by polarization transfer (r-INEPT), 13 C dipolar decoupled-magic angle spinning (DD/MAS), and 13 C cross polarization-magic angle spinning (CP/MAS) NMR experiments are performed in the dry and hydrated states [ 19 , 22 , 23 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ]. This study, including 13 C solid-state NMR is the first trial to put spider silk to practical use, providing information about the molecular design of new recombinant spider silk materials with high dimensional stability in water, allowing the use of recombinant spider silk proteins for next-generation biomaterials and materials for textiles.",
"discussion": "2. Results and Discussion 2.1. 13 C Solution NMR Spectra of RSSP(VLI) Samples Prepared by Changing the Storage Times in FA FA is known as a formylating reagent. We previously reported the conformations of RSP and RSSP(QQQ) samples in FA and the formylation of its amino acid side chains [ 21 , 23 ]. These samples consist of Ser and Tyr residues, which are candidates to form formyl groups, but only the Ser side chain was found to be formylated. Thus, we evaluated the formylation of RSSP(VLI) dissolved in FA by 13 C solution NMR and confirmed the formylation of the Ser side chain. First, we assigned the 13 C solution NMR peaks of most of the amino acid residues of RSSP(VLI). Figure 2 shows the 13 C solution NMR spectrum of the RSSP(VLI) sample in FA after dissolving it for 15 h, together with the peak assignments. All CH n (n = 1–3) groups for most amino acid residues of RSSP(VLI), i.e., Gly, Ala, Pro, Ser, Tyr, Val, Leu, and Ile, were assigned by using 13 C HSQC and 1D 13 C NMR spectra. The 13 C NMR chemical shifts are listed in Table S1 . The 13 C HSQC spectra of RSSP(VLI) were measured several times continuously to characterize the formylation of the Ser side chain in real time after the dissolution of RSSP(VLI) in FA. The 13 C chemical shifts of Ser CαH and Ser CβH 2 peaks changed after the dissolution of RSSP(VLI) in FA. As shown in Figure 3 , unformylated Ser CαH and Ser CβH 2 peaks were observed in the first 13 C HSQC spectrum at 0.7 h after dissolution in FA. The 1 H and 13 C chemical shifts were 4.73 ppm and 57.5 ppm for the CαH and 4.08 ppm and 63.3 ppm and 4.17 ppm and 63.3 ppm for the CβH 2 , respectively. The intensity of these peaks gradually decreased with time and became negligible at 7 h after dissolution. On the other hand, formylated Ser peaks were observed at 4.95 ppm and 54.6 ppm for the CαH and 4.62 ppm and 64.6 ppm and 4.66 ppm and 64.6 ppm for the CβH 2, and the intensity of these peaks increased with time. The time-dependent changes in intensity for the CαH and CβH 2 peaks of unformylated and formylated Ser residues are plotted in Figure 4 . These measurements show that most of the Ser residues in RSSP (VLI) were formylated within 2 h after dissolution in FA. The formylation rate of RSSP (VLI) is faster than that of RSP and RSSP (QQQ) reported previously [ 21 , 23 ]. This may be due to the fact that the reaction temperature of RSSP(VLI) is higher than that of RSP and RSSP (QQQ), being 313 K for RSSP (VLI) but 293 K for RSP and 298 K for RSP (QQQ). It was necessary to dissolve RSSP (VLI) in FA at a higher temperature because the solubility of RSSP (VLI) was lower than that of RSP and RSSP(QQQ). 2.2. Stress–Strain Curves of RSSP(VLI) Fiber after Repeated Immersion in Water and Drying of the Fiber The calculated tensile strength (MPa) and elongation-at-break (%) calculated from the stress–strain curves of RSSP(VLI) fiber prepared by the wet-spinning method from the FA solutions of RSSP(VLI) powder (storage time in FA was 20 h at 40 °C) are shown in Figure 5 a. Stress–strain curves of RSSP(VLI) fiber after hydration treatment, i.e., repeated immersion of the fiber in water and drying at room temperature, are also shown in Figure 5 b. The mechanical properties of RSSP(VLI) fiber were compared before and after hydration treatments. The tensile strength (MPa) and elongation-at-break (%) were 134 ± 10 and 19 ± 4 for RSSP(VLI) fiber, respectively, before hydration treatment. The value of the tensile strength (MPa) was lower than the previous value of RSSP(QQQ) fiber, i.e., 191 ± 4 (storage time in FA, 40 h) and 238 ± 5 (storage time in FA, 4 h) [ 23 ]. This is potentially due to the difference in the Mw of the samples, i.e., the Mw of RSSP(QQQ) fiber is 210 kDa, which is remarkably higher than the value of Mw 51.6 kDa for RSSP(VLI) fiber. By hydration treatment, the tensile strength (MPa) of RSSP(VLI) fiber decreased from 134 ± 10 to 97 ± 3, but the elongation-at-break (%) increased remarkably from 19 ± 4 to 62 ± 3. These changes are a general matter due to the hydration effect of the silk fibers. Namely, in general, because of the presence of water molecules, the inter-molecular hydrogen bonding of protein fiber such as silk fiber weakens; the molecules are separated from each other, and the space for the movement of the molecules becomes greater, namely, the fiber becomes more plasticized by water. As a result, the tensile strength of silk protein fiber decreases and the elongation-at-break increases by hydration [ 22 , 23 , 24 ]. 2.3. Dimensional Stability Experiments of Eight Kinds of Silk Fiber Samples At first, we compared the dimensional stabilities of RSSP(QQQ) and RSSP(VLI) fibers, determined by repeated immersion in water and drying, as shown in Figure 6 . Because changes in the P ratio largely depend on the condition of the fiber formation, we consider only the S ratio for dimensional stability. Here, the definitions of the P and S ratios are given in the Materials and Methods section. The change is remarkable in the S ratio, i.e., the S ratio of RSSP(VLI) fiber becomes almost half that of the RSSP(QQQ) fiber. This is due to the hydrophobic effect as a result of replacing the QQQ amino acid residues with VLI in the recombinant spider silk. Next, the dimensional stability experiments were expanded for eight kinds of silk fiber, as shown in the histogram in Figure 7 . We added the S ratio of RSP fibers reported previously [ 22 ] for recombinant spider silk fibers other than RSSP(QQQ) and RSSP(VLI) fibers. During the acetylation of the silk fiber, it is known that the fiber becomes more hydrophobic due to the acetylation of Ser and Tyr residues in the silk molecules, and therefore, the acetylated RSSP(QQQ), RSSP(VLI), and RSP fibers were selected for dimensional stability experiments. For this purpose, we prepared acetylated RSSP(QQQ) and RSSP(VLI) fibers, as described in the Materials and Methods section. The regenerated B. mori silk fiber and acetylated regenerated B. mori silk fiber reported previously [ 58 ] were also compared with the corresponding data of the recombinant spider silk fibers. The S ratios of both non-acetylated and acetylated recombinant spider silk fibers decreased in the order of RSP, RSSP(QQQ), and RSSP(VLI). The amino acid composition (%) of RSP (Mw = 47.5 kDa) is G:37, Q:19, A:16, P:15, S:6, and Y:4 and that of RSSP(QQQ) (Mw = 210 kDa) is G:31, Q:17, A:20, P:14, S:9, and Y:7. Because the amino acid composition does not change significantly between two samples, the cause of the decrease in the S ratio of the RSSP(QQQ) sample is likely due to the remarkably larger molecular weight compared with that of the RSP sample. The smallest S ratio of the RSSP(VLI) (Mw = 51.6 kDa and G:31, A:20, P:14, S:10, Y:7, V:6, L:6, and I:6) sample is clearly due to the hydrophobic effect as a result of substituting QQQ with VLI, although the Mw is smaller than that of the RSSP(QQQ) sample. In addition, all of the S ratios of the acetylated fibers were smaller than those of the non-acetylated fibers. This is due to the acetylation of the Ser and Tyr residues mentioned above. For B. mori silk fibroin fiber, the decrease in the S ratio was relatively larger than the S ratios of recombinant spider silk fibers. Because both Ser and Tyr residues (amino acid composition (%) of S:12 and Y:5.3) are acetylated in B. mori silk fibroin fiber [ 58 ], the hydrophobic effect by acetylation seems greater than that of recombinant spider silks. 2.4. Water Absorption Experiments of Eight Kinds Using Silk Fiber Samples Eight kinds of silk fiber samples were used for the water absorption experiment, as shown in Figure 8 a. The water absorption is generally a reflection of the hydrophilic character of the fiber samples. To obtain a more detailed insight into the interaction of silk fiber and water molecules, the 2 H solution NMR relaxation and exchange measurements of water molecules interacting with silk fibers are very useful, as reported previously by us [ 59 , 61 ]. Namely, (a) bulk water outside the fiber, (b) water molecules trapped weakly on the surface of the fiber, (c) bound water molecules located in the inner surface of the fiber, and (d) bound water molecules located in the inner part of the fiber were distinguishable in the experiments. We would like to focus on these experiments in future work. The water adsorption is far greater for RSP fiber, including acetylated RSP fiber, compared with that of other fibers. Figure 8 b shows the plot of the S ratio against the value of water absorption. It can be noticed that there is a correlation between the water absorption and S ratio, with only regenerated B. mori silk fibroin fiber deviating from the trend of the whole plot. Thus, both the RSSP(VLI) fiber and acetylated RSSP(VLI) fiber show the low water absorption and high dimensional stability of recombinant spider silk fiber. 2.5. 13 C Solid-State NMR Spectra of RSSP(VLI) Powders and Fibers in the Dry and Hydrated States A variety of techniques, such as X-ray diffraction, Fourier transform infrared/Raman spectroscopy, transmission electron microscopy, and so on, were used to elucidate the structure of spider dragline silk, from its secondary structure, to its molecular arrangement, to its hierarchical structure [ 1 , 2 , 3 , 4 , 5 , 6 ]. However, the most detailed picture of the structure and dynamics of spider silk at the molecular level was obtained from NMR spectroscopy. The conformation-dependent 13 C chemical shifts coupled with selective 13 C labeling can be used effectively to determine the secondary structure of spider silks in an amino-acid-specific manner. Moreover, many kinds of advanced solid-state NMR techniques have been used to obtain the local structure and dynamics of spider silk at atomic resolution [ 19 , 20 , 21 , 22 , 23 , 52 , 53 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ]. In this paper, three kinds of 13 C solid-state NMR, i.e., 13 C r-INEPT, 13 C DD/MAS, and 13 C CP/MAS NMR spectroscopy [ 19 , 22 , 23 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ], were used to clarify the secondary structure and dynamics of RSSP(VLI) powder and fiber in the dry and hydrated states. The 13 C solid-state NMR spectra of RSSP(VLI) powder before MeOH treatment were first observed in the dry and hydrated states. Figure 9 shows (a) 13 C r-INEPT, (b) 13 C DD/MAS, and (c) 13 C CP/MAS NMR spectra in the hydrated state, together with (d) the 13 C CP/MAS NMR spectrum in the dry state. Four kinds of spectra are considerably changed and the spectra look sharper gradually from Figure 9 (d) to 9(a). The intensity of the 13 C CP/MAS NMR signal is sensitive to the components of very slow motion (< 10 4 Hz). In the 13 C CP/MAS NMR spectrum of RSSP(VLI) in the dry state, the main 13 C Ala Cβ and Ala Cα peaks were observed at 20.2 (f) and 48.9 ppm (o), respectively (see Table 1 ). This means that the conformation of the poly-Ala region has an AP-β structure [ 73 , 74 ]. The 13 C chemical shifts of the Ser Cα and Ser Cβ peaks were 55.0 (r) and 63.2 ppm (v), respectively, indicating that the Ser residue also has an AP-β structure [ 73 , 74 ]. Other peaks from the Gly-rich region were also observed. This means that there are amino acid residues forming inter-molecular hydrogen bonds and/or a spatially densely packed region. These amino acid residues are in a restricted state, even if the conformation of the amino acid residue takes the form of random coil. When the powder is hydrated in water, water molecules cause a significant increase in the motion of the amino acid residues in the powder. In the 13 C CP/MAS NMR spectrum Figure 9 c, a loss in CP signal occurs because a mobile domain cannot be observed [ 63 , 64 , 70 ]. Thus, spectrum (c) became sharper than spectrum (d), and the β-sheet peaks of Ala Cα, Cβ, and CO carbons became the main peaks of RSSP(VLI) powder. On the other hand, the 13 C r-INEPT was sensitive to the component of fast motion (> 10 5 Hz) in hydrated powder. Therefore, only the peaks that were very mobile in solution NMR could be observed in the 13 C r-INEPT spectrum (a). Only random coil and sharp peaks induced by water absorption were observed for the Cα and Cβ carbons of Ala and Ser residues. All carbons of other residues in the Gly-rich region were also observed as sharp peaks. Thus, mobile components of RSSP(VLI) powder were observed in water. All of the 1 H attached carbons in the amino acid residues were observed, although the carbonyl and aromatic peaks with no protons were not observed in the 13 C r-INEPT spectrum. In the 13 C DD/MAS spectrum (b), both mobile and immobile components in the hydrated state can be observed, namely, the β-sheet peaks of the Cα and Cβ carbons of Ala and Ser residues were observed together with the mobile peaks observed in the 13 C r-INEPT spectrum. The carbonyl peaks were also a mixture of both sharp and broad peaks. These results indicate that both rigid β-sheet and flexible random coil structures can be observed in the hydrated state. Figure 10 shows the (a) 13 C r-INEPT, (b) 13 C DD/MAS, and (c) 13 C CP/MAS NMR spectra of RSSP(VLI) powder after MeOH treatment in the hydrated state, together with (d) the 13 C CP/MAS NMR spectrum in the dry state. The β-sheet peaks of Ala Cα, Ala Cβ, Ser Cα, and Ser Cβ carbons were observed together with other peaks from the Gly-rich region in spectrum (d). With hydration, the β-sheet peaks of Ala residues were also relatively larger in spectrum (c) compared with spectrum (d). The 13 C DD/MAS NMR spectrum of RSSP(VLI) powder in water became slightly sharper compared with spectrum (c), but the change was as that shown in Figure 9 . The 13 C r-INEPT spectrum (a) in Figure 10 shows only a small Ala Cβ peak, and other peaks disappeared completely, which is remarkably different from spectrum (a) in Figure 9 . Thus, by MeOH treatment, the mobility of RSSP(VLI) powder does not increase significantly, even in the hydrated state. Figure 11 shows the (a) 13 C r-INEPT, (b) 13 C DD/MAS, and (c) 13 C CP/MAS NMR spectra of RSSP(VLI) fiber in the hydrated state, together with (d) the 13 C CP/MAS NMR spectrum in the dry state. The spectra are very similar between Figure 10 and Figure 11 because both samples were obtained after MeOH treatment. molecules-27-08479-t001_Table 1 Table 1 13 C solid-state NMR chemical shifts (ppm) of RSSP(VLI) powder and fiber. \n Cα Cβ Cγ Cδ Gly 42.0, 42.8 \n \n \n \n m m \n \n \n Ala 50.0 (r.c.) 16.6 (r.c) \n \n \n p c \n \n \n 48.9 (α) 20.2 (β) \n \n \n o f \n \n Pro 61.4 31.3 27.0 47.1 \n u k i n Ser 53.3 (r.c.), 52.4 (a) 61.4 (r.c) \n \n \n q q* u \n \n \n 55.0 (α) 63.2 (β) \n \n \n r v \n \n Tyr 56.1 36.4 \n \n \n s l \n \n Val 61.4 29.3 17.8, 18.5 \n \n u j d e \n Leu 53.3 42.8 27.0 22, 24.6 \n q m i g h Ile 59.2 36.4 13.9, 24.6 11.4 \n t l b h a (a). The 52.4 (a) ppm peak (q*) was assigned to Cα peak of formylated Ser residue. r.c.: random coil and β: β-sheet. However, there are two different points in Figure 11 compared with Figure 10 . Namely, as mentioned above, the Ser residue of RSSP(VLI) is formylated after FA treatment. Therefore, the Ser Cα peak (q*) of formylated RSSP(VLI) was observed at approximately 1 ppm higher than the Ser Cα peak (q) of unformylated RSSP(VLI) (see Table 1 ). In addition, the formyl peak was newly observed at 161 ppm in Figure 11 ."
} | 6,166 |
26116678 | PMC4551263 | pmc | 532 | {
"abstract": "In marine sediments cathodic oxygen reduction at the sediment surface can be coupled to anodic sulfide oxidation in deeper anoxic layers through electrical currents mediated by filamentous, multicellular bacteria of the Desulfobulbaceae family, the so-called cable bacteria. Until now, cable bacteria have only been reported from marine environments. In this study, we demonstrate that cable bacteria also occur in freshwater sediments. In a first step, homogenized sediment collected from the freshwater stream Giber Å, Denmark, was incubated in the laboratory. After 2 weeks, pH signatures and electric fields indicated electron transfer between vertically separated anodic and cathodic half-reactions. Fluorescence in situ hybridization revealed the presence of Desulfobulbaceae filaments. In addition, in situ measurements of oxygen, pH, and electric potential distributions in the waterlogged banks of Giber Å demonstrated the presence of distant electric redox coupling in naturally occurring freshwater sediment. At the same site, filamentous Desulfobulbaceae with cable bacterium morphology were found to be present. Their 16S rRNA gene sequence placed them as a distinct sister group to the known marine cable bacteria, with the genus Desulfobulbus as the closest cultured lineage. The results of the present study indicate that electric currents mediated by cable bacteria could be important for the biogeochemistry in many more environments than anticipated thus far and suggest a common evolutionary origin of the cable phenotype within Desulfobulbaceae with subsequent diversification into a freshwater and a marine lineage.",
"conclusion": "Conclusions. In the present study, we demonstrated the presence of cable bacteria and associated electrogenic processes in freshwater sediments. Our findings extend the known habitats of cable bacteria and suggest a widespread occurrence at oxic-anoxic interfaces of rivers, lakes, wetlands, aquifers and soils. Our findings also suggest a common evolutionary origin of the cable phenotype within Desulfobulbaceae with subsequent diversification into a freshwater and a marine lineage. It is possible that the freshwater lineage, like the marine lineage, is associated with the e-SOx reaction. Cable bacteria in freshwater sediments may then provide a mechanism for recycling scarce resources of sulfate, stimulating sulfate reduction. The exact nature of the interactions between cable bacteria and the cycling of sulfur in freshwater sediment warrants further investigations.",
"introduction": "INTRODUCTION Filamentous Deltaproteobacteria of the Desulfobulbaceae family—so-called cable bacteria ( 1 )—mediate an electron current that links spatially separated cathodic and anodic redox reactions in marine sediments ( 2 ). This process, which invokes electron transfer across centimeter distances, was first discovered in laboratory experiments with marine sediment ( 3 ). In these experiments, long-distance electron transfer was demonstrated from (i) an otherwise unexplainable formation of a sulfide- and oxygen-free zone, (ii) the presence of a distinct pH signature consistent with proton consumption by cathodic oxygen reduction (i.e., O 2 + 4H + + 4e − → 2H 2 O) and proton production by anodic sulfide oxidation (i.e., H 2 S + 4H 2 O → SO 4 2− + 10H + + 8e − ), and (iii) rapid interaction between spatially separated H 2 S oxidation and O 2 reduction ( 3 ). Subsequent studies demonstrated that an electric field, manifested as an electric potential gradient is associated with the microbially mediated long-distance electron transport between the anodic and cathodic reactions ( 4 – 6 ), as predicted by geobattery and biogeobattery theories ( 7 , 8 ). The electric field drives an ionic current in the pore water of the same magnitude but opposite in direction to the electron current in the cable bacteria, thus closing the electric circuit. On the basis of the initial ( 3 ) and subsequent studies, it has been found that the major anodic process in marine sediments is the oxidation of sulfides ( 1 , 6 , 9 , 10 ), whereas the cathodic process might be the reduction of either oxygen ( 2 , 5 , 9 , 11 ) or nitrate ( 5 , 12 ). The overall electrically coupled process has been termed electrogenic sulfur oxidation (e-SOx) ( 9 ). At present, cable bacterial filaments, 16S rRNA gene sequences similar to cable bacterial sequences, and/or geochemical hallmarks of e-SOx have been found in a variety of marine environments. These include sulfide-rich coastal sediments ( 1 , 2 , 9 , 12 ) and salt marsh sediments ( 9 , 13 ), as well as seasonally hypoxic basins, subtidal coastal mud plains, hydrothermal vent areas, cold seep flats, and mangrove sediments ( 9 ). The common traits of these environments are high rates of sulfide generation and limited bioturbation ( 9 ) in combination with fairly high porewater conductivity (salinities > 20‰). Thus far, cable bacteria have not been reported from outside the marine environment, but e-SOx has previously been suggested as a mechanism that contributes to the rapid recycling of sulfate often observed in freshwater sediments ( 14 ), while patterns of P-mobilization and pH distribution in sulfide-rich sediments from a volcanic freshwater lake have been suggested to indicate the presence of the process ( 15 ). The presence of electric fields around contaminated groundwater plumes has also been ascribed to processes involving microbial long-distance electron transport ( 7 , 16 ), although the underlying biogeochemical processes, the microorganisms, rates, and spatial scales have not been investigated. In the present study, we directly tested for the presence of cable bacteria and electrogenic oxidation processes involving microbially mediated long-distance electron transport in freshwater sediments. We applied microscale measurements of electric fields, oxygen, pH, and sulfide to demonstrate electrogenic activity in laboratory incubations of homogenized freshwater sediments. In addition, we measured the same parameters directly in the field to demonstrate the presence of microbial electrogenic activity in situ . Although this approach allows for the detection of general electrogenic oxidation activity, it does not allow for specification of the anodic reaction involved. To which extent this includes sulfide oxidation will be discussed. Fluorescence in situ hybridization (FISH), scanning electron microscopy (SEM), and 16S rRNA gene sequencing of single filaments were used to determine the abundance, morphology, and identity of freshwater cable bacteria.",
"discussion": "DISCUSSION Cable bacteria and electrogenic oxidation in freshwater sediment. The results of our study demonstrate that cable bacteria and electrogenic processes are present in natural freshwater sediment environments. The Desulfobulbaceae filaments found in sediment from Giber Å are closely affiliated and monophyletic with the marine cable bacteria ( Fig. 6 ) and also, morphologically, the freshwater filaments show similarities to their marine counterpart, most notably a ring of ridges that runs along the length of the filament ( Fig. 5c ) ( 2 , 9 ). Since the ridges of the marine cable bacteria show enhanced charge mobility and consist of fibers inside a continuous periplasmic space ( 2 ), they have been proposed to function as wires, enabling electron transport along the length of the bacterial filament and thereby facilitating the electrogenic oxidation process ( 2 , 10 ). This hypothesis is further strengthened by the presence of similar ridges in freshwater stains. The phylogeny of the cable bacteria suggests that this mode of life has evolved once within the Desulfobulbaceae ; subsequently, the cable bacteria diversified into a freshwater and a marine group. Based on 16S rRNA sequence identities of ca. 92%, the freshwater and the marine cable bacterial lineages are distinct genera ( 34 ) and are clearly distinct from the closest cultured genus, Desulfobulbus . Signatures of electrogenic oxidation processes were found in both laboratory-incubated sediment and in situ at the exposed water-saturated banks of Giber Å. The pH peak observed in the oxic zone of laboratory incubated sediment ( Fig. 2b ) and in situ ( Fig. 4a ) in the absence of net photosynthesis is indicative for proton consumption by cathodic oxygen reduction ( 3 ), while the electric fields seen in both the laboratory cores and in situ ( Fig. 2b and 4a ) suggest electron transfer between vertically separated anodic and cathodic half-reactions ( 5 – 7 ). That the electric field was primarily associated with electrogenic processes driven by cathodic oxygen consumption via a contiguous electron conductor was confirmed by two lines of evidence: more than 90% of the electric field disappeared when (i) oxygen was removed in the laboratory incubated sediment ( Fig. 3a ) and (ii) when the sediment was cut below the oxic zone in situ and in the laboratory ( Fig. 3b and 4b ). The small residual electric fields observed were possibly contributed by diffusion potentials or streaming potentials ( 35 , 36 ). Activity and niche of freshwater cable bacteria. The density of the cable bacteria and the electrogenic oxidation activity at the Giber Å site were very similar to the findings reported from natural marine sediments: Malkin et al. ( 9 ) reported cable bacterial length densities of 82 to 122 m cm −3 in coastal marine sediments at the North Sea, which is only 2 to 3 times greater than the ∼40 m cm −3 observed at the Giber Å site ( Fig. 5a ). The electron current density at the anoxic-oxic interface, a measure of electrogenic oxidation ( 3 , 5 ), at the Giber Å site was 14.4 ± 2 mA m −2 when estimated as described previously ( 4 , 5 ) from the electric fields ( Fig. 4a ) and the conductivity of the sediment (0.02 S m −1 , as estimated from the sediment porosity [0.6] and the conductivity of the Giber Å water [0.1 S m −1 ] according to Ullman and Aller [ 21 ]). This lies well within the 5- to 30-mA m −2 range reported from the North Sea sites ( 9 ). Assuming a vertical orientation of the filaments and no curls, the filament length density at the Giber Å site corresponds to a maximum abundance of 4 × 10 7 filaments m −2 in the upper part of the sediment. With the given current density at the anoxic oxic interface, each filament then supports an average current of ca 0.35 nA. Filaments at the marine sites support a current in the range 0.20 to 0.36 nA according to the data reported by Malkin et al. ( 9 ) and Meysman et al. ( 10 ). These similarities suggest that freshwater sediments represent niches that are favorable for cable bacteria and electrogenic processes as the marine environment and, as a consequence, suggest that the much lower sulfate concentrations in freshwater systems and the possible higher dissipative loss of energy due to a higher ohmic resistance of the porewater are not major limitations. That low sulfate concentration is not a major limitation for freshwater cable bacteria might at first suggest that the electrogenic oxidation process does not rely on sulfide but is based on other electron donors such as short-chain fatty acids, Fe 2+ , or methane. Traditionally, iron reduction and methanogenesis, and not sulfate reduction, have been supposed to prevail in freshwater sediments due to low concentrations of sulfate (e.g., <1 mM in Giber Å compared to >28 mM in seawater at salinities of 35‰), implying an excess of reduced iron and carbon compared to sulfide. However, compilations of actual measurements have shown sulfate reduction to be a major pathway for anaerobic mineralization in many types of freshwater sediments ( 14 , 37 ), and rates reported from both oligotrophic and eutrophic freshwater sediments ( 37 ) seem to be sufficient to support electrogenic oxidation of sulfide at rates higher than those that can be inferred from the current density measurements at Giber Å. The current density of 14.4 ± 2 mA m −2 estimated for the Giber Å site would correspond to an oxidation of 1.6 mmol of H 2 S m −2 day −1 if the anodic reaction was based solely on sulfide oxidation. Sulfate reduction rates in freshwater sediments with concentrations below 0.3 mM can reach 20 mmol m −2 day −1 ( 14 , 37 ). The high rates of sulfate reduction in freshwater sediments are associated with rapid reoxidation of sulfide, with turnover times in the range of hours for the sulfate pool. Typically, sulfide oxidation mediated by iron reduction has been suggested as an important route in the anoxic zone ( 14 ). In principle, an electrogenic oxidation process involving sulfide oxidation (i.e., e-SOx) could substitute this route through its inherent generation of sulfate ( 6 ), and e-SOx could thereby be a component in an iron-independent, carbon-fueled cryptic sulfur cycle, where sulfate is reduced to sulfide by sulfate-reducing bacteria and regenerated by colocalized cable bacteria. Such an interaction would allow the occurrence of highly active, sulfide-oxidizing cable bacterium communities in low sulfate environments. Sulfur mass balance studies ( 6 ) and further studies of the metabolic capability of freshwater cable bacteria are, however, required to test this hypothesis. That the dissipative loss of energy due to ohmic resistance of the pore water is not a major restriction for freshwater cable bacteria and electrogenic processes is evident from the electric potentials measured in the sediments. The cable bacterial sediment can be considered a “biogeobattery” ( 7 ) where cable bacteria act as both electron conductor and catalyzer of the associated anodic and cathodic reactions, while the surrounding water-saturated sediment acts as a passive electrolytic conductor with a given electric resistance. The measured electric potential differences in the sediment are then direct measures of the energy dissipation per charge unit transported in the sediment porewater. For a given current density the electric potential scales as the inverse of the electrical conductivity ( 7 ), and therefore energy dissipation should be much higher in freshwater than in marine environments. However, while the highest recorded difference of 32 mV ( Fig. 2b ) is indeed high compared to the ∼1 mV recorded in marine sediment with similar current densities ( 6 ), it only represents ca. 3.2% of the ∼1,000 mV available from the sulfide oxidation process ( 38 ), leaving a considerable surplus for microbial activity. Conclusions. In the present study, we demonstrated the presence of cable bacteria and associated electrogenic processes in freshwater sediments. Our findings extend the known habitats of cable bacteria and suggest a widespread occurrence at oxic-anoxic interfaces of rivers, lakes, wetlands, aquifers and soils. Our findings also suggest a common evolutionary origin of the cable phenotype within Desulfobulbaceae with subsequent diversification into a freshwater and a marine lineage. It is possible that the freshwater lineage, like the marine lineage, is associated with the e-SOx reaction. Cable bacteria in freshwater sediments may then provide a mechanism for recycling scarce resources of sulfate, stimulating sulfate reduction. The exact nature of the interactions between cable bacteria and the cycling of sulfur in freshwater sediment warrants further investigations."
} | 3,846 |
23657259 | PMC4109411 | pmc | 533 | {
"abstract": "Bacterial biofilms are surface-associated, multicellular, morphologically complex microbial communities 1 - 7 . Biofilm-forming bacteria such as the opportunistic pathogen 7 - 10 \n Pseudomonas aeruginosa are phenotypically distinct from their free-swimming, planktonic counterparts. Much work has focused on factors impacting surface adhesion and it is known that P. aeruginosa secretes the Psl exopolysaccharide, which promotes surface attachment by acting as a ‘molecular glue’ 11 - 15 . However, how individual surface-attached bacteria self-organize into microcolonies, the first step in communal biofilm organization, is not well understood. Here, we identify a new role for Psl in early biofilm development using a massively parallel cell-tracking algorithm to extract the motility history of every cell on a newly colonized surface via a search-engine based approach 16 . By combining these techniques with fluorescent Psl staining and computer simulations, we show that P. aeruginosa deposits a trail of Psl as it moves on a surface, which influences the surface motility of subsequent cells that encounter these trails and thus generate positive feedback. Both experiments and simulations indicate that the web of secreted Psl controls the distribution of surface visit frequencies, which can be approximated by a power law. This Zipf's Law 17 indicates that the bacterial community self-organizes in a manner analogous to a capitalist economic system 18 , a ‘rich-get-richer’ mechanism of Psl accumulation that results in a small number of ‘elite’ cells extremely enriched in communally produced Psl. Using engineered strains with inducible Psl production, we show that local Psl levels determine post-division cell fates and that high local Psl levels ultimately allow ‘elite’ cells to serve as the founding population for initial microcolony development."
} | 468 |
38869927 | PMC11215770 | pmc | 534 | {
"abstract": "With the increasing\ndemands and complexity of the neuromorphic\ncomputing schemes utilizing highly efficient analog resistive switching\ndevices, understanding the apparent capacitive and inductive effects\nin device operation is of paramount importance. Here, we present a\nsystematic array of characterization methods that unravel two distinct\nvoltage-dependent regimes demonstrating the complex interplay between\nthe dynamic capacitive and inductive effects in volatile perovskite-based\nmemristors: (1) a low voltage capacitance-dominant and (2) an inductance-dominant\nregime evidenced by the highly correlated hysteresis type with nonzero\ncrossing, the impedance responses, and the transient current characteristics.\nThese dynamic capacitance- and inductance-dominant regimes provide\nfundamental insight into the resistive switching of memristors governing\nthe synaptic depression and potentiation functions, respectively.\nMore importantly, the pulse width-dependent and long-term transient\ncurrent measurements further demonstrate a dynamic transition from\na fast capacitive to a slow inductive response, allowing for the tailored\nstimulus programming of memristor devices to mimic synaptic functionality.",
"introduction": "Introduction Hardware implementation of artificially\nintelligent devices in\nbioinspired computing has been gaining significant attention, due\nto the increasing computation demands of neural network configurations\nbased on traditional von Neumann architecture. 1 − 3 Among the novel\nemerging technologies, resistive random access memories (ReRAMs),\nalso known as memristors, have been widely considered as one of the\nmost promising candidates that can emulate biological neural functions\nby device physics. 4 − 6 Memristors can retain information as the device conductivity,\nwhich can be dynamically reconfigured when stimulated by electrical\ninputs. This unique nature of having both the memory and the processing\nunit colocated in the same device establishes memristors to be ideally\nsuitable in realizing highly efficient bioinspired neural networks\nin hardware. 7 , 8 The distinctive property\nof ideal memristive devices is the pinched\nhysteresis loop with a zero crossing point in their current–voltage\n( I – V ) characteristic curves\narising from the nonlinear dynamics when subjected to a periodic input. 9 − 11 However, experimental results from a wide range of memristor device\nconfigurations exhibit a nonzero crossing pinched hysteresis loop,\nwhich have been attributed to capacitive and inductive contributions. 12 − 15 The capacitive effect is observed from the nonzero crossing point, 16 − 18 while the inductive effect is observed from the inverse hysteresis\nloop, 19 − 21 typically observed in perovskite-based devices. Notably,\nthese capacitive and inductive characteristics in the I – V curves of memristor devices have been\ncorrelated with the observed impedance spectroscopy measurements, 21 , 22 and transient current responses. 23 , 24 This indicates\nthat the nonlinear dynamics of the resistive switching is more appropriately\nrelated as a change in the overall device impedance. 25 This complex nonlinear interplay among the resistive, capacitive,\nand inductive effects is manifested as a frequency-dependent impedance\nmodulation resulting in distinct resistive switching specific to the\ndevice configuration. 26 From classical\ncircuit theory, the current through an ideal capacitor\nwith a capacitance C is given by I = C d V /d t , while\nthe voltage through an ideal inductor with an inductance L is given by V = L d I /d t . Upon the application of a periodic voltage\ninput, the I – V curves ( Figure 1 a) of the capacitor\nand the inductor exhibit normal hysteresis (higher current levels\nin the forward scan than in the reverse scan) and inverse hysteresis\n(higher current levels in the reverse scan than in the forward scan),\nrespectively. On the other hand, the impedance of the capacitor at\nan angular frequency ω is given by Z = ( i ω C ) −1 , while the\ninductor is given by Z = i ω L . With the application of a sinusoidal perturbation with\nvarying ω, the impedance spectra ( Figure 1 b) of a resistor connected in parallel with\nan ideal capacitor and an ideal inductor exhibit a semicircular arc\nin the first quadrant and the fourth quadrant of the complex plane\nimpedance plot, respectively. Furthermore, when connected in series\nwith a resistor with a resistance R , the transient\ncurrent response of the capacitor is given by while the inductor\nis given by Upon the application of a voltage\npulse, the\ntransient current response ( Figure 1 c) of the capacitor exhibits an exponential decay,\nwhile the inductor exhibits an exponential rise. Notably, these capacitive\nand inductive features have been observed in memristor devices. 13 Numerous numerical models have been proposed\nin order to provide a deeper understanding of the underlying mechanisms\ngoverning the resistive switching of memristor devices. 11 , 25 , 27 − 29 However, most\nof the proposed models describe only the pertinent parameters of\nthe resistive switching of the characteristic I – V response to simulate neural network algorithms. More importantly,\nthese models do not sufficiently account for the corresponding impedance\nand transient response of the memristive response providing valuable\ninformation on the physical and chemical processes governing switching\nmechanisms. Figure 1 (a) Current–voltage ( I – V ) curves of an ideal capacitor and an ideal inductor exhibiting normal\nand inverted hysteresis, respectively. (b) The impedance spectra of\nan ideal capacitor and an ideal inductor connected in parallel with\na resistor exhibiting a semicircular arc in the first quadrant and\nthe fourth quadrant of the complex plane impedance plot, respectively.\n(c) The transient current responses of an ideal capacitor and an ideal\ninductor connected in series with a resistor exhibiting a current\ndecay and an exponential current increase, respectively. The applied\nvoltage stimuli for each characteristic response are indicated by\nthe schematic diagrams. Here, we present the\nextensive characterization of methylammonium\nlead bromide (MAPbBr 3 ) memristors demonstrating the intimate\ncorrelation among the characteristic I – V response, voltage-dependent IS spectral evolution, and\nthe transient current response with both the capacitive and inductive\nresponses. The MAPbBr 3 perovskite formulation is chosen\nin order to have a simple and well-established understanding of the\nmixed electronic and ionic dynamics with low activation energy and\nhigh operational stability. Two voltage-dependent regimes are demonstrated:\n(1) a low-voltage capacitive regime associated with synaptic depression\nand (2) a high-voltage inductive regime associated with synaptic potentiation.\nFurthermore, transient measurements with varying pulse durations further\ndemonstrate the interplay between the fast capacitive response and\nthe slow inductive response as observed in the highly correlated IS\nand current transients. These results provide a more complete picture\nof the dynamic resistive switching of memristor devices crucial for\nthe development and integration of these artificially intelligent\nhardware with more complex novel analog neural network algorithms. The characteristic I – V response of the perovskite memristor, measured at a scan rate of\n1 V s –1 , on the semilogarithmic scale, is shown\nin Figure 2 a with the\ninset illustrating the device and measurement configuration. The perovskite\nmemristor exhibits a gradual threshold resistive switching in the\npositive polarity with an ON/OFF ratio of ∼2 orders of magnitude. 30 − 32 At low applied voltages, the device is at its initial high resistance\nstate (HRS) or the OFF state. As the positive voltage scan reaches\nand goes beyond the initial threshold voltage V th1 ≈ 0.65 V, the device current gradually transitions\nfrom the HRS to the low resistance state (LRS) or ON state promoting\nthe SET process. 33 On the other hand, in\nthe reverse scan direction, the ON state is maintained until the applied\nvoltage reaches a lower threshold voltage V th2 ≈ 0.3 V, where the device current transitions from the LRS\nback to the HRS, promoting the RESET process. The OFF state is then\nmaintained throughout the negative polarity scan. This characteristic\nresistive switching is considered to have a volatile memory where\nthe ON state relaxes back to the OFF state upon the removal or sufficient\nreduction of the applied voltage. 5 , 34 , 35 Moreover, by varying the upper vertex voltage of\nthe I – V measurements ( Figure 2 b), the device displays\na multilevel/multistate resistive switching suitable for analog volatile\nmemory applications in neuromorphic systems. 36 − 41 The memristive response of 20 devices is shown in Figure 2 c, indicating the robustness\nand reproducibility of the gradual threshold resistive switching of\nthe device configuration. Endurance measurements of a representative\ndevice via cycling for 1000 times demonstrates excellent device operational\nstability with a sustained ON/OFF ratio of ≥1 order of magnitude\nat a read voltage of V read = 0.6 V ( Figure 2 d). Figure 2 (a) Characteristic I – V response for 5 cycles at a scan\nrate of 1 V s –1 in the semilogarithmic scale of\nthe FTO/PEDOT:PSS/MAPbBr 3 /Au memristor device exhibiting\na threshold resistive switching with\nthe arrows and numbers indicating the scan direction and the inset\nillustrating the schematic diagram of the device configuration, (b)\nthe upper vertex-dependent multilevel/multistate analog resistive\nswitching of the memristor device, (c) the characteristic I – V response of 20 distinct devices\nexhibiting highly reproducible threshold switching, and (d) the endurance\nmeasurements for 1000 cycles of the LRS (ON state) and HRS (OFF state)\nat a read voltage ( V read ) of 0.6 V. A closer look at the individual characteristic\nresponse of the\nupper vertex-dependent I – V curves exhibiting the multistate/multilevel analog memory of the\ndevice reveals a transition from a normal to an inverted hysteresis.\nIn the linear scale with an upper vertex of 0.25 V ( Figure 3 a), the characteristic I – V response exhibits high current\nlevels in the forward scan direction and low current levels in the\nreverse scan direction, commonly referred as normal hysteresis, attributed\nto a fully capacitive response. 13 , 21 , 42 The corresponding characteristic I – V response in the semilogarithmic scale is shown in Figure 3 d, indicating the\nvoltage range in the fully capacitive regime. Increasing the upper\nvertex up to 0.75 V ( Figure 3 b), the I – V response\nexhibits a pinched hysteresis with a crossing point at ∼0.38\nV. This crossing point varies depending on the scan rate of the I – V measurement, as well as the\ndevice operational stability ( Figure S1 ), indicating a dynamic response of the state transitions from capacitive\nto inductive regimes. 26 , 43 Notably, the device consistently\nexhibits a normal hysteresis for voltages below the crossing point,\nwhich transitions to an inverted hysteresis for voltages above the\ncrossing point. This inverted hysteresis loop in the I – V response is attributed to an inductive\ntime domain response where the forward scan has lower current levels\nthan the reverse scan, typically observed in MAPbBr 3 -based\nsolar cells. 13 , 21 , 42 The corresponding response in the semilogarithmic scale is shown\nin Figure 3 e, indicating\nthe transition from a fully capacitive to an inductive regime. At\nan even higher upper vertex of 1.25 V ( Figure 3 c), the device exhibits a predominantly inverted\nhysteresis, due to the higher current levels. However, from the corresponding\nsemilogarithmic I – V response\n( Figure 3 f), the fully\ncapacitive region still persists at voltages below the transition\nvoltage, while the strong inductive region is observed at voltages\nabove the transition voltage. Figure 3 Characteristic I – V response\nin the linear scale of the FTO/PEDOT:PSS/MAPbBr 3 /Au memristor\ndevice with varying upper vertex voltages of (a) 0.25, (b) 0.75, and\n(c), 1.25 V, with the arrows indicating the scan direction. Corresponding I – V response in the semilogarithmic\nscale for upper vertex voltages of (d) 0.25, (e) 0.75, and (f) 1.25\nV, with the arrows and numbers indicating the scan direction and sequence,\nrespectively. [Reproduced with permission from Bisquert, J. Inductive\nand capacitive hysteresis of current–voltage curves. A unified\nstructural dynamics in solar energy devices, memristors, ionic transistors\nand bioelectronics, PRX Energy 2023 , 3 , 011001, licensed under a Creative Commons Attribution\n(CC BY 4.0) license.] This inductive response,\nmanifested as the inverted hysteresis,\nin the time domain I – V curves,\nis further corroborated in the frequency domain by tracking the voltage-dependent\nimpedance spectral (IS) evolution. The IS spectral evolution of the\nperovskite memristor is shown in Figure 4 . Notably, the implemented IS measurement\nprotocol corresponds to a very slow I – V scan close to the steady state. 21 , 42 , 44 At low voltages ( V app < 0.3 V), the device in the OFF state exhibits a fully\ncapacitive IS response ( Figures 4 a and 4 b). As the voltage approaches\n0.3 V ( Figure 4 c),\nthe low-frequency (LF) capacitive arc begins to decrease and eventually\ntransforms to an LF inductive arc at V app = 0.4 V ( Figure 4 d). Noticeably, a midfrequency noise is observed, which can be attributed\nto the device state instability, due to the voltage-dependent ion\nmigration dynamics of the mobile Br – species. 45 At these transition voltages, the current gradually\nstarts to increase, indicating a reduction in the overall device resistance. 32 , 44 Beyond these threshold voltages ( V app ≥ 0.4 V), the high-frequency (HF) capacitance continues to\ndecrease while the LF inductive response is sustained at higher voltages\nuntil the device completely switches to the ON state ( Figures 4 e and 4 f). This LF inductive response of the memristor device is not electromagnetic\nin nature but rather due to the chemical inductor. 24 , 46 − 48 Figure 4 IS spectral evolution at representative applied voltages\n( V app ) under dark controlled conditions,\nexhibiting\n(a, b) a fully capacitive regime at low voltages ( V app < 0.3 V), (c, d) a transition region at intermediate\nvoltages (0.3 V ≤ V app ≤\n0.4 V), and (e, f) a low-frequency inductive regime at high voltages\n( V app > 0.4 V). Not only is the inductive response observed in the characteristic I – V (strong inverted hysteresis)\nand IS (LF inductive arc), but it is also observed in the transient\ncurrent response. The voltage-dependent transient response of the\nperovskite memristor using a train of 20 identical voltage pulses\nwith a pulse width ( t pulse ) of 10 ms and\na period ( T pulse ) of 20 ms is shown in Figure 5 a. At low applied\nvoltages ( V app < 1 V), the current\nlevel is maintained throughout the full voltage train. However, at\nhigher applied voltages ( V app ≥\n1 V), the current begins to gradually increase with every succeeding\nvoltage pulse. This gradual increase in current response with the\nsubsequent application of voltage pulses is the distinctive phenomenon\nof synaptic potentiation. 41 , 49 , 50 A closer look at the transient response of a single voltage pulse\n( Figure 5 b) at applied\nvoltages lower than the threshold voltage, a sharp initial transient\npeak is observed, subsequently followed by a gradual decay attributed\nto a capacitive charging of the device. 24 , 51 In contrast,\nat higher applied voltages, a gradual increase in current is observed\nafter the gradual decay, indicating the slow inductive contribution\nonce the capacitor has been charged, further reducing the total resistance. 51 Finally, a sharp negative current peak is observed\nupon the removal of the voltage pulse due to the internal voltage\nand the series resistance of the device. 51 , 52 This correlation of the impedance characteristics and transient\nbehavior has been well-established by neuron-style models in halide\nperovskite solar cells and memory devices. 53 − 55 Moreover, this\ntransient response of the voltage-dependent potentiation has been\nobserved in voltage-gated potassium channels abundantly expressed\nin the human brain. 56 Figure 5 (a) Voltage-dependent\ntransient current response of the perovskite\nmemristor by applying 20 identical voltage pulses with an amplitude\nof V app and a pulse width of 10 ms (schematic\ndiagram shown in inset), (b) the magnified view of the transient current\nresponse of a single voltage pulse at representative V app levels, (c) the pulse width-dependent transient current\nresponse by applying 20 identical voltages pulses with a period T pulse of 100 ms, (d) the corresponding magnified\nview of the first and last transient responses, and (e) the synaptic\npotentiation and depression characteristic response of the memristor\nmeasured at a read voltage V read = 0.6\nV (pulse width t read = 10 ms), SET voltage V SET = 1.75 V (pulse width = 100 ms), and a RESET\nvoltage V RESET = 0.2 V (pulse width =\n100 ms) with the inset illustrating the schematic diagram of the pulsed\nmeasurement sequence. In addition to the voltage-dependent\ntransient current response\nof the memristor device, the synaptic potentiation behavior can also\nbe realized via pulse width-dependent measurements shown in Figure 5 c. With a V app = 1 V and a pulse period of 100 ms, the\nvoltage trains with short pulse widths of ≤30 ms exhibit no\npotentiation after 20 pulses ( Figure 5 d). This indicates that the interval between the two\npulses is long enough that the device relaxes back to the initial\nHRS before the next pulse arrives. 50 On\nthe other hand, the longer pulse widths of >30 ms demonstrate the\ninductive effect of the gradual current increase leading to the synaptic\npotentiation of the device upon the application of 20 identical pulses.\nThis potentiation due to the pulse width implies that the memory effect\nis closely related to the slow time scales of the voltage-dependent\ninductive response being retained with the subsequent application\nof voltage pulses. From the voltage- and pulse width-dependent\ntransient current response\nof the memristor device, the synaptic potentiation and depression\nof the resistance state via a read voltage ( V read ) for operational applications is demonstrated as shown\nin Figure 5 e. 57 , 58 By applying a V read < V th1 with a short pulse width of t read = 10 ms and a relatively higher SET voltage V SET = 1.75 V with a longer pulse width of t SET = 100 ms, the memristor potentiates from the OFF state\nto the ON state after the subsequent application of 20 identical voltage\npulses. The short t read ensures that the\ndevice does not exhibit potentiation at low V read values while the longer t SET allows the memristor to probe its inductive response. In contrast,\nby applying a lower positive RESET voltage V RESET = 0.2 V with t RESET = 100\nms, the device exhibits synaptic depression, making the device transition\nfrom the ON state back to the OFF state. This sufficiently low but\nstill positive V RESET further confirms\nthe volatile memory characteristics of the perovskite memristor device.\nThe full transient response of the device at representative SET and\nRESET processes during the synaptic potentiation and depression measurements,\nrespectively, is shown in Figure S2 . As the synaptic potentiation is demonstrated to be intimately correlated\nwith the adequate voltage applied and response time of the inductive\neffect of the memristor, the long-term transient current profile provides\nessential information on the suitable conditions for promoting this\npotentiation. 24 The comparison of the voltage-dependent\ntransient current response of the perovskite memristor between a single\nlong voltage pulse (5 s) and a voltage train of 20 shorter pulses\n(pulse width of 200 ms and pulse period of 250 ms) is shown in Figure 6 , in which three\ndifferent domains can be distinguished. (i) For an applied voltage\nlower than\nthe SET threshold voltage T th1 ( Figure 6 a), the long-term\ntransient profile exhibits a small initial current peak, followed\nby a slight current decay with no potentiation and the device stays\nin the OFF state ( Figure 6 b). This indicates that, at this applied potential, the response\nis fully capacitive and the inductive effect of the memristor is not\npromoted, even for longer durations. 51 Consequently,\nfor the short pulses, no synaptic potentiation is observed. (ii) For an applied voltage\nabove V th1 ( Figure 6 c), the small initial peak with a slight\ncurrent decay\nis again observed, but this time, the current gradually increases,\nswitching the device to the ON state. This potentiation of the single\nlong pulse implies that, at applied voltages higher than V th1 , the inductive response of the device is activated\nfurther reducing the device resistance, consistent with the LF IS\nresponse. 23 , 44 Consistently, the first pulse of the voltage\ntrain exhibit the same sharp transient peak and current decay; however,\nthe inductive response is activated within the duration of the pulse\n( Figure 6 d) and potentiation\nis observed with 20 pulses. Notably, the ON state current of the single\npulse is not obtained by the voltage train due to the volatile memory\nof the device, which gradually promotes the short RESET process when\nthe applied voltage is at 0 V between the pulses. (iii) Finally, for applied voltages further\nbeyond the V th1 ( Figure 6 e), the rate of current increase after the\ninitial transient peak is faster, reaching the ON state current within\na shorter period. This higher potentiation rate indicates that, at\nhigher applied voltages, the contribution of the inductive effect\nis more dominant. Correspondingly, the first pulse of the voltage\ntrain exhibits the same faster current increase promoting synaptic\npotentiation with ON state current closer to that of the single long\npulse ( Figure 6 f).\nThis indicates that (a) the inductive contribution is high during\nthe 200 ms pulse width and (b) the 50 ms duration at 0 V only promotes\na slight synaptic depression, resulting in current levels closer to\nthat of the long single voltage pulse. The higher ON state current\nlevels of the pulsed stimulus indicate that the duration of the RESET\nprocess between the pulses is substantially short, maintaining the\ndevice closer to the LRS. The long-term transient current profile\nof the device response allows for the proper identification of the\nvoltage, pulse width, and period of the applied stimulus, promoting\nsynaptic potentiation, which is crucial to the device implementation\nin more-complex neural network configurations. Figure 6 Transient\ncurrent response of the volatile perovskite memristor\nwith a single long pulse (5 s) and a train of 20 identical short pulses\n(pulse width of 200 ms; pulse period of 250 ms) at applied voltages\nof (a) 0.4 V, (c) 0.8 V, and (e) 1.2 V with the inset showing the\nschematic diagram of the applied voltage stimuli. Panels (b), (d),\nand (f) correspond to the magnified view of the first voltage pulse\nof panels (a), (c), and (e), respectively."
} | 5,828 |
22798614 | null | s2 | 536 | {
"abstract": "In their natural environment, microbes organize into communities held together by an extracellular matrix composed of polysaccharides and proteins. We developed an in vivo labeling strategy to allow the extracellular matrix of developing biofilms to be visualized with conventional and superresolution light microscopy. Vibrio cholerae biofilms displayed three distinct levels of spatial organization: cells, clusters of cells, and collections of clusters. Multiresolution imaging of living V. cholerae biofilms revealed the complementary architectural roles of the four essential matrix constituents: RbmA provided cell-cell adhesion; Bap1 allowed the developing biofilm to adhere to surfaces; and heterogeneous mixtures of Vibrio polysaccharide, RbmC, and Bap1 formed dynamic, flexible, and ordered envelopes that encased the cell clusters."
} | 210 |
38410727 | PMC10895028 | pmc | 538 | {
"abstract": "Current work in photosynthetic engineering is progressing along the lines of cyanobacterial, microalgal, and plant research. These are interconnected through the fundamental mechanisms of photosynthesis and advances in one field can often be leveraged to improve another. It is worthwhile for researchers specializing in one or more of these systems to be aware of the work being done across the entire research space as parallel advances of techniques and experimental approaches can often be applied across the field of photosynthesis research. This review focuses on research published in recent years related to the light reactions of photosynthesis in cyanobacteria, eukaryotic algae, and plants. Highlighted are attempts to improve photosynthetic efficiency, and subsequent biomass production. Also discussed are studies on cross-field heterologous expression, and related work on augmented and novel light capture systems. This is reviewed in the context of translatability in research across diverse photosynthetic organisms.",
"conclusion": "6 Conclusions Despite major differences in physiology and large-scale growth, plants and algae share many parallels and research into the photosynthetic efficiency of one class of organisms often leverages ideas and results in another. In the context of improving photosynthetic efficiency, many fundamental studies are conducted on single celled cultures, whereas more applied studies are often conducted on plants. Due to natural photosynthetic evolution and physiology, these plant studies are often not backwards compatible when considering large scale microalgal or cyanobacterial cultivation. \n Table 1 \n presents a summary of recent developments in photosynthetic research in several plant, microalgal, and cyanobacterial groups. Table 1 Summary of genes and proteins reviewed in this paper. Gene/Enzyme Function Citations Cyano-bacteria Micro-algae Plants Antiox Antioxidant peptides capable of fusion directly to PSII D1 subunit ( Antonacci et al., 2021 ) P + P \n atpA \n ATP synthase with reduced redox regulation ( Ungerer et al., 2018b ) + P P \n bkt \n Conversion of beta-carotene to astaxanthin or canthaxanthin ( Lin et al., 2019 ; Cazzaniga et al., 2022 ) P + P \n cpc \n Phycocyanin synthesis ( Kirst et al., 2014 ; Sengupta et al., 2023 ) + – – COX Cytochrome c oxidase that acts as an alternative electron acceptor to PSI ( Torrado et al., 2022 ) + – – CpSRP Various chaperone enzymes for assembling light harvesting complexes (includes ALB and LTD) ( Baek et al., 2016 ; Jeong et al., 2018 ; Nymark et al., 2019 ; Rotasperti et al., 2022 ; Caddell et al., 2023 ; Krishnan et al., 2023 ) – + + \n crtr-B \n Conversion of beta-carotene to astaxanthin or canthaxanthin ( Lin et al., 2019 ) + + + \n cyn 28 Formation of protease FtsH complexes. Photodamage repair ( Fu et al., 2023 ) P + P Cyt P450 Cytochrome P450 that acts as an electron acceptor from PSI ( Santos-Merino et al., 2021 ; Torrado et al., 2022 ) + P P \n dschyb \n Conversion of β-carotene to zeaxanthin ( Hu et al., 2021 ) P + P \n flv \n Excess electron sink from PSI ( Tula et al., 2020 ; Smolinski et al., 2022 ) + P + \n hpe1 \n RNA signal recognition peptide related to nucleus-plastid communication ( Jin et al., 2016 ) – P + LHCSR Excess excitation energy dissipation ( Barera et al., 2021 ) – + P LHC Light capture and transfer to reaction centers (includes Lhcx) ( Buck et al., 2019 ; Krishnan et al., 2023 ) – + + \n myb \n Transcription factor involved in LHC complex formation ( Agarwal et al., 2022 ) – + P \n nblA \n Phycobilisome disassembly ( Carrieri et al., 2021 ) + – – \n osbHLHqq11 \n Transcription factor stimulating chlorophyll synthesis ( Jang et al., 2022 ) – – + \n ppnK \n NAD + /ATP kinase with favorable kinetics ( Ungerer et al., 2018b ) + P P PsbS, VDS, ZEP PH dependent and VAZ cycle related NPQ ( Kromdijk et al., 2016 ) – +P + \n rrl \n Phycobilisome rod-rod linker ( Sengupta et al., 2023 ) + – – \n tpp \n Oxidative stress relief ( Rathod et al., 2023 ) P + P \n ygl1 \n Metabolite control in chlorophyll synthesis ( Mao et al., 2023 ) P P + \n zmCRD 1 Chlorophyll Synthesis ( Xue et al., 2022 ) P P + + indicates that a gene/enzyme was used in some organisms of that category, P represents that a gene/protein could be used in a category but was not represented in the literature presented in this paper, - indicates that use in that category is not feasible. Included are recent publications exploring ideas and approaches to improving photosynthetic efficiency. Many of which are applicable across all three types of organisms, in particular the concept of heterologous pigment and transcription factor expression, and gene swapping. Several effective ideas relate to adding additional electron sinks or improving resistance to oxidative stress. Astaxanthin and canthaxanthin expression in particular has repeatedly been shown to improve photoprotection under high light, even in a foreign system. Another idea that proved successful in cyanobacteria is overexpressing a disassembly gene for phycobilisomes. This is a creative alternative approach to directed knockouts and could avoid the issue of unintended side effects due to multifunctional genes. It would be interesting to see similar work carried out in eukaryotic strains. An area of this research that continues to produce contrasting results is the modification of pigmentation, reaction centers and NPQ mechanisms. Often modified strains with lower pigmentation show reduced growth, and several strains showing improved short term photosynthetic efficiency (F v /F m ) do not achieve improved biomass productivities. An important idea that arises from this work is that changes in light harvesting pigments must be offset by an appropriate change in energy and electron flow, such as changes in NPQ or AEF. Similarly, energy transfer between both photosystems should be balanced by appropriate pigmentation and reaction center levels. This idea will become more relevant as research continues into understanding far red and green light absorption and integration into ‘traditional’ photosystems. Much work remains to be done integrating novel pigments and dyes in vivo , however the absorption of additional yellow-green wavelengths and the utilization of the lower infrared wavelengths presents the balance of energy input and transport across the light systems as a double-sided issue. Overcoming this challenge is an important part of designing novel photosystems that allow biomass production from previously under used light. One other point of comparison between plants and microalgae is in the context of industrial scale cultivation. Plant photosynthetic engineering focuses heavily on relevant staple plants that are currently used in industry. Much of microalgal engineering is focused on algal species that may not be viable for large scale cultivation, specifically because of a lack of robust growth under diel conditions which are often argued as advantages for large scale algal growth. Focusing on strains that grow robustly outdoors will become critical as demands on limited resources continue to increase. State-of-the-art agriculture will not be sufficient to supply the biomass and agricultural needs of the world’s population. To meet this challenge effectively researchers must be aware of the variety of photosystems already present, their advantages, and the tools that can be used to integrate and improve them.",
"introduction": "1 Introduction In 2022 the world population reached 8 billion. The United Nations Population Division projects it will grow to 9 billion by 2037. Although the rate of population growth is slowing, it will likely exceed 10 billion by 2100 ( United Nations ). This growth represents a staggering increase in demand for food, fuels, and useful materials, yet there already exist considerable populations across the globe with limited resources in one or more of these categories ( U.N. Environment, 2019 ). Pressures produced by increased resource demands are exacerbated by the growing consumption of limited materials such as fossil fuels and the increased production of pollutants and greenhouse gases ( Daly and Farley, 2011 ). In addition, sources of freshwater are simultaneously being used and polluted at an unsustainable rate, driven by contemporary agriculture ( Hoekstra and Mekonnen, 2012 ; Kummu et al., 2016 ; Shi and Tanaka, 2020 ). Biomass from phototrophic growth is a promising component of the solution to these issues through its use as a source of food ( Chacón-Lee and González-Mariño, 2010 ; Tilman et al., 2011 ; Ort et al., 2015 ), fuel ( Ort et al., 2015 ; Bhat et al., 2022 ) and materials ( Rehman et al., 2017 , p. 20; Toker-Bayraktar et al., 2023 ). Utilizing biomass has several environmental advantages as it is a sunlight driven process, and fixes the largest greenhouse gas contributor, CO 2 . However, there are limitations to how much biomass is currently produced through photosynthesis. Some current bottlenecks include the photosynthetic process itself, and technological limitations in harvesting and processing biomass, as well as extracting useful materials. Most commercial biomass sources are from terrestrial plants, so the availability of conventionally arable land and freshwater is another important consideration ( OECD, 2011 ). In addition, plant evolution has led to limited genetic diversity when compared to cyanobacteria or microalgae ( Häder, 2022 ). On the other hand, much work is focused on making cyanobacterial and microalgal cultivation practical at the large scales needed to effectively complement traditional agriculture ( Novoveská et al., 2023 ). It is possible that cultivation of microalgae can bypass or mitigate these concerns by production in non-arable environments using non-freshwater sources (e.g., saltwater, wastewater) ( Ahmad et al., 2022 ; Energy.gov ). For the purposes of this paper phototrophic biomass is delineated into three sources: cyanobacteria, eukaryotic microalgae, and terrestrial plants. Each system has a practical advantage in terms of sustainability. Plants are the current industrial standard for bulk bioproducts (e.g., palm oil) and staple food production. The rapid growth times and relatively simple metabolic systems of these microorganisms are promising for both fundamental and applied studies to improve photosynthetic efficiency and rationally engineer productive strains. When considering photosystems, eukaryotic plants and algae are more similar to each other than cyanobacteria ( \n Figure 1 \n ). However, in terms of physiology, eukaryotic microalgae share more in common with cyanobacteria. The combination of phototrophic bacteria, eukaryotic microalgae, and terrestrial plants represents an impressively diverse set of organisms and expands the possible resources and environments which can be utilized in making biomass. In this manuscript, we present a wide range of organisms and genera by describing recent publications related to engineering their photosystems. For the sake of brevity only a brief introduction to each organism is included, and the reader is encouraged to read the presented papers for more detailed descriptions. Figure 1 The fate of light energy in photosystems. Orange arrows represent electron flow. (A) Representative cyanobacterial photosystem. (B) Representative eukaryotic photosystem. Note that only those system components relevant for this review are shown. Abbreviations are as follows: PSII-photosystem II, PQH 2 /PQ-plastoquinol/plastoquinone, Cyt b 6 f -cytochrome b 6 f complex, PC-plastocyanin, PSI-photosystem I, FLV-flavodiiron protein, ATP SYN-ATP synthase, COX-cytochrome c oxidase, CYD-cytochrome bd -type quinol oxidase. Figure made with bioRender software, based on Nikkanen et al., ( Nikkanen et al., 2021 ). The diversity between cyanobacteria, microalgae, and plants is best looked at through the evolutionary lineage of photosynthetic organisms, which began in bacteria, and continued to eukaryotic microalgae, and then to plants ( Häder, 2022 ). Delineating along these three lines provides a convenient way to categorize phototrophs. In terms of light reactions, recent work has focused around a relatively narrow set of organisms ( \n Figure 2 \n ). Plant related work has focused on the model systems Arabidopsis thaliana and Nicotiana tabacum (tobacco), and staple crops such as soy ( Glycine max ), rice ( Oryza sativa ), barley ( Hordeum vulgare ) and maize ( Zea mays ). Microalgal work centers around Chlamydomonas , Chlorella , Dunaliella , and Phaeodactylum , with additional work being done in emerging systems of interest such as Picochlorum, Desmodesmus , and Parachlorella . While most work in cyanobacteria has been done with strains of Synechococcus and Synechocystis . The study of each of these organisms presents a broad range of physical and genetic diversity that serves to push our understanding of genetic engineering in oxygenic photosystems. Figure 2 Delineation of organisms that have recently been studied in context of photosynthetic light reactions. Taxonomy for cyanobacteria and microalgae obtained from algaebase.org. Taxonomy for plants obtained from the USDA Plant Database. Several detailed reviews of all three fields, cyanobacteria ( Vijay et al., 2019 ; Luan et al., 2020 ; Mehdizadeh Allaf and Peerhossaini, 2022 ), microalgae ( Vecchi et al., 2020 ; Hu et al., 2023 ), and plants ( Nowicka, 2019 ; Miglani et al., 2021 ; Theeuwen et al., 2022 ; Kumar et al., 2023 ; Leister, 2023 ; Li et al., 2023 ), were published within the past five years. Many authors focus on the parallels between plants and eukaryotic algae ( Kirst and Melis, 2014 ; Guardini et al., 2022 ; Sharma et al., 2023 ), with relatively little focus on understanding parallels between microalgae and cyanobacteria ( Wijffels et al., 2013 ). There is an apparent lack of work comparing all three systems simultaneously, despite similar ideas for how to improve photosynthetic efficiency, particularly depigmentation, improved oxidative stress resistance and enhanced electron sinks. The respective benefits of modifying these systems are controlling the initial photon flux into the system, reducing uncontrolled electron transport, and providing regulated dissipation of excess energy absorption. However, as several of the above reviews point out, techniques used for one species to improve photosynthesis can yield contrasting results in different species. Much of the work related to expanding and augmenting photosystems to increase the photosynthetic action spectrum centers around cyanobacteria. This is an emerging field, with most work being done within the last decade. Chlorophyll d is a cyanobacterial pigment that can replace chlorophyll a in the photosystem I (PSI) reaction center, driving photosynthesis at infrared wavelengths ( Chen and Blankenship, 2011 ; Allakhverdiev et al., 2016 ). Similarly, several studies have shown that pigments absorbing in ‘unused’ wavelengths (e.g., green light) can be integrated as additional photosynthetic pigments into light systems ( Leister, 2023 ). In terms of application of these systems, Hitchcock et al. recently published an overview of a consortium project called PhotoRedesign , which is attempting to expand the working light spectrum in photosynthetic organisms ( Hitchcock et al., 2022 ). This is an appropriate addition in this review because these novel light systems are being integrated de novo into photosystems of all three organisms."
} | 3,889 |
39288161 | PMC11407623 | pmc | 539 | {
"abstract": "Considering biological systems as information processing entities and analyzing their organizational structure via information-theoretic measures has become an established approach in life sciences. We transfer this framework to a field of broad general interest, the human gut microbiome. We use BacArena, a software combining agent-based modelling and flux-balance analysis, to simulate a simplified human intestinal microbiome (SIHUMI). In a first step, we derive information theoretic measures from the simulated abundance data, and, in a second step, relate them to the metabolic processes underlying the abundance data. Our study provides further evidence on the role of active information storage as an indicator of unexpected structural change in the observed system. Besides, we show that information transfer reflects coherent behavior in the microbial community, both as a reaction to environmental changes and as a result of direct effective interaction. In this sense, purely abundance-based information theoretic measures can provide meaningful insight on metabolic interactions within bacterial communities. Furthermore, we shed light on the important however little noticed technical aspect of distinguishing immediate and delayed effects in the interpretation of local information theoretical measures.",
"conclusion": "Conclusion We took first steps into the study of the human gut microbiome as computing entity. First steps only, since we restricted ourselves to a simplified and simulated community. That allowed us to directly intervene in the system to confirm two hypotheses: Information transfer among the species reflects coherent behavior both as a reaction to environmental changes and in form of direct effective interactions. In particular, the latter will serve as a valuable tool when being applied to real data. It could provide insight into little understood or so far unknown relationships and dependencies between bacterial species. Such knowledge can be crucial when it comes to the design of dietary measures or medical intervention strategies. Hence, the transfer of our work to real data provides a natural next step. Albeit rare, suitable long-term time series of the composition of the human gut microbiome do exist. Using a rather constrained simulation for the basis of our analysis allowed us to decipher the artefacts of entropy estimation which must be taken into account when interpreting courses of local AIS and CTE. Currently, we chose to manually distinguish immediate and delayed effects in the signals. However, for wider applicability, an automatized process would be desirable. Such a process might for example be developed via objective decision criteria based on k -variants of the signals.",
"introduction": "Introduction One way to approach the question of what constitutes biological systems is to understand them as information-processing entities [ 1 – 3 ]. Living systems sense their environment and make use of this information to react. Next to exchange of mass and energy, transfer of information is one of the key characteristics underlying biological systems (see [ 1 , 4 ]). Information theory [ 5 ] provides a framework to measure and analyze these operations in physical systems. Information processing can be understood as “computing” in the sense of transferring information, transforming, and storing it. Following this paradigm, the interaction of a set of biological agents can be considered as “computing system” (see, e.g., [ 2 , 6 ]). Hereby, agents can make use of their own past states ( information storage ), or information gained from interaction with other agents ( information transfer ). Whatever information cannot be captured as storage or transfer will be subsumed as intrinsic uncertainty [ 7 ]. This comprises aspects of environmental conditions, randomness, or information being actively modified. Identifying the evolving degree to which agents make use of information sources, i.e. studying their information decomposition , can yield insights into the systems’ internal organizational structure and allows to examine interactions of living organisms even without the precise observation of biochemical processes (see, e.g. [ 8 , 9 ]). Understanding life as a “computing” system goes back to Erwin Schrödinger [ 4 ]. Various computational characteristics of living systems have been recently studied. Using information theory, Flack identifies hierarchies in living systems [ 8 ]. Similarly, Krakauer et al. discuss the information theory of individuality [ 9 ]. Lizier, Prokopenko, Zomaya and others have proposed information-theoretic measures that allow analyzing information processing in systems of interacting agents [ 7 , 10 – 12 ]. In addition to the exemplary application to swarm algorithms [ 6 ], such analyses are primarily used in the field of neuroscience [ 13 , 14 ]. In the present paper we explore the idea of using information processing as a means to infer biological interactions without assuming any further prior knowledge about the nature of these interactions. Various measures for different aspects of information in information processing systems have been proposed (see, e.g. [ 15 ]). In particular, Schreiber’s transfer entropy [ 16 ] is used as a measure of information transfer, while active information storage [ 11 ] serves as a measure of the actively used memory of an agent. In accordance with the basic idea of information decomposition, Schreiber’s definitions are based on time series of agents’ states. Heuristically, (active) information storage of agent A is defined as the amount of uncertainty about A’s next state that can be reduced by the knowledge of its past states. Analogously, information transfer from agent B to agent A is defined as the amount of uncertainty about agent A’s next state that can be reduced by the additional knowledge of agent B’s past states. The estimation of these quantities depends on choosing a fixed number of of past states of an agent to be used for the estimation, the so-called history length . Intuitively, history length should exactly cover the length of the agent’s “memory”. The exact interpretations of these quantities of information processing depend on the specific system and its environment. On a certain level of abstraction, information storage measures the independence of an agent’s development from its surrounding, while information transfer measures development driven by interaction. If an interaction between two agents A and B is being reflected in their states (we call this an effective interaction ), a transfer of information will be measurable on the basis of two corresponding time series covering the agent’s ‘state’. It is in this sense that dynamic information decomposition captures the underlying organizational structure of a complex system. The aim of this work is to explore the extent to which microbial communities can be understood as “computational systems”, and what insight this perspective provides on the biology of interactions both within a community and between a community and its environment. The latter is of particular relevance in host-microbiome interaction. An essential component of human interaction with its environment is given by the host-microbe system of the gastrointestinal tract [ 17 – 19 ]. However, a better understanding of this system is still a challenging research question due to the complexity and dynamics of the human microbiome. Uncovering interactions among species of the gut microbial community and examining corresponding effects on human health is subject of ongoing research [ 20 – 22 ]. New tools and concepts towards a better understanding of this system within its environment in a holistic sense are therefore of great interest for future studies on diagnosis and modulation. The central research claim we address in this paper therefore is the following: If indeed, microbial communities can be understood as “computing” entities, it should be possible to infer meaningful relations between the community’s information-theoretic structure and microbiome(-host) interactions. Using the purely abundance-based measures of information transfer and active information storage, this would allow us to gain insight on microbial interaction patterns without the knowledge of metabolic processes . To make this hypothesis testable, we focus on a reduced representation of the intestinal microbial community, the Simplified Human Intestinal Microbiota (SIHUMI). This highly standardized community, which has been introduced by Becker et al. [ 23 ], consists of seven species, which represent the key functional capabilities of the human intestinal microbiome in both composition and fermentation capacities [ 24 ]. In our framework, we consider each species as an agent, all of which together form a joint complex system, i.e. a system of interacting agents. The SIHUMI community consists of specific strains of Anaerostipes, Bacteroides, Bifidobacterium, Blautia, Clostridium, Escherichia , and Lactobacillus . Genome sequences are available for all seven organisms, such that the metabolic potential of the community is well understood. Instead of using data from fermentation systems, we base our analysis on modeled data. We use BacArena, a software combining agent-based modelling and flux-balance analysis, to simulate the SIHUMI community [ 25 ]. Using simulated abundance data enables us to compare our information theoretic measures to the metabolic processes underlying the abundance data. To keep our approach viable also for in vitro analysis, we need to define the state of an agents in terms of a quantity which can be measured also in in vivo systems. Therefore, we define the “state” of an agent at a given time in terms of the number of individuals of the respective species, i.e. its abundance at this time. The ability to realistically model microbial interactions based on metabolic modeling has been proven for BacArena. In particular, intestinal microbial communities, such as SIHUMI, have been shown to be well reflected by corresponding BacArena simulations [ 25 ]. The set-up of our base simulation follows the set-up for the simulation of the seven SIHUMI strains in [ 25 ]. In particular, we use the same growth stimulating initial base medium . In order to enforce dynamics of collapse and recovery throughout the simulation, we intervene with the system by adding the base medium several times in so-called feeding events . There are several reasons why it is beneficial to base this first systematic assessment of information processing in a microbial community in in silico data. Primarily, it enables interventions that would be difficult to carry out in in vitro experiments. Nevertheless, the simulation comprises all essential interactions, such as growth and occupation of space, as well as metabolic degradation within a fully controlled set-up. In addition, the actual state of the system as a whole, including all metabolic processes, is known at all times, such that methodological artefacts can be easily separated from signals of community interaction to be studied during the analysis. In this study, we infer signals of transfer entropy and active information storage from simulated time series of microbial abundance data and explore the potential of these quantities of information decomposition with regard to their potential for biological interpretation. Our results support the usefulness of active information storage as an indicator of sudden internal change. Concerning the biological interpretation of information transfer, our in silico experiments support two hypotheses: Hypothesis 1 . Information transfer captures coherence of the community in reaction to environmental changes. Hypothesis 2 . Information transfer captures coherence resulting from effective interaction among members in the community. Note that the notion of coherence has appeared in similar contexts before. For example, Wang et al. [ 6 ] use it to characterize phases of high information transfer and storage in the distributed computation of swarms. In order to test for stability, we repeat all estimations for varying history length. In doing so, we observe the occurrence of delayed effects . These technical artefacts are signals in information transfer/active information storage that point to past phenomena in the abundance time series rather rather than to concurrent ones. They can occur in any kind of history-based information theoretical measure and, if kept unnoticed, may lead to severe misinterpretations. We contrast them with so-called immediate effects , characterized by being stable with respect to changing the parameter of history length. Commonly, immediate effects are of higher interpretive relevance. Eventually, we discuss our results in the context of general principles that Wang et al. suggest in their information-theoretical analysis of swarm dynamics [ 6 ].",
"discussion": "Discussion Interpreting information storage and transfer Note that from an experimental perspective, the data resulting from our simulation appears exceptional to some extent. Longer periods of constant abundances are quite unlikely to be observed in natural systems due to various sources of uncertainty. However, following our reductionist approach using BacArena, the data still captures the essentials of bacterial community interaction we need to focus on. At the same time, the special structure of our data allowed us to identify artefacts of entropy estimation which would have been difficult to identify in analog estimates based on noisy observational data. However, filtering out such estimation artefacts is substantial for the interpretation of the information theoretical signals. We have observed that active information storage is a mere reflection of sudden changes in the underlying abundances (recall Fig 1B ). This relation between abundance data and AIS is not surprising. Active information storage quantifies the degree of predictability of a species’ abundance from its past –which is surely low whenever abundance suddenly increases or decreases after a period of constancy. The fact that AIS can serve as an indicator of changes in composition or behavior of a complex system has been discussed before, see e.g. [ 6 ] for experiments on swarm dynamics, or [ 13 ] for neural information processing. In our case, in which AIS of an agent is estimated on basis of time series of abundance data, only, it can be seen as a pure indicator of sudden changes in the underlying time series, and therefore cannot be expected to contain further information about the system. In contrast, we have seen that (collective) information transfer captures “coherent” development in species’ abundances, both as a result of environmental changes and in reaction to crossfeeding. It can be assumed that further types of interaction, like competition over nutrients or the exposure to detrimental by-products of other species, may lead to an increased information transfer as well. In general, effective synergistic or competitive interactions are difficult to enforce in the SIHUMI community. The species in this community rather seem to coherently react to environmental changes. Indeed, there is growing evidence that the gut microbiome is shaped by habitat filtering rather than direct synergistic interactions (see e.g. [ 27 , 28 ]), meaning that community assembly is dominated by the availability of nutrients rather than by the direct metabolic interaction among the bacteria being present. Our simulation experiments following realistic scenarios also support this claim. The main drivers of abundances and thereby also of the course of transfer entropy obviously are the feeding events. Coherent reactions to environmental changes follows the availability of nutrients. The sheer abundance of nutrients in combination with the mostly generalist metabolic repertoire of the chosen species seems to render competition less important. Furthermore, abundant resources being available appear to reduce the pressure on the bacteria to directly interact. One might hypothesize that this would change for a community in a rather restricted environment. The impact of history length in measuring information storage and transfer—Distinguishing immediate and delayed effects In order to understand the origin of the “shifted patterns” observable in the information theoretic signals across history lengths ( Fig 1B and 1C ), compare the definition of active information storage for history length k at time step x n +1 ( Eq (3) , below). One conditions on a certain past state x t in the estimation of AIS at time steps x t +1 , …, x t + k . The same holds for collective transfer entropy ( Eq (5) , below). Therefore, if increasing the history length by s leads to a shift of some feature in AIS/CTE from x n +1 to x n +1+ s , this suggests that the observed feature is related to omitting x n − k from the conditioned past rather than to the transition from x n to x n +1 . We call such a feature a delayed effect , since it reflects a past development. Unrecognized delayed effect bear the risk of strong misinterpretations. In contrast, if some feature in AIS/CTE is temporally stable at x n +1 across history lengths, we may confidently relate it to the development in abundances between x n and x n +1 . We call such a feature an immediate effect . We will further illustrate this phenomenon in the information theoretical signals of Clostridium . Fig 4A displays its abundance and active information storage for history lengths 10, 15, and 20. Fig 4B allows a closer look at an apparent shifted pattern after the third feeding event. The strong increase in AIS for k = 10 between time steps 72 and 73 repeats itself five time steps later for k = 15 and ten time steps later for k = 20. This hints towards the fact that the cause for this pattern in AIS is not Clostridium’s abundance between time steps 72 and 73 (nor its abundance between time steps 77 and 78 or time steps 82 and 83, respectively). Instead, we have to relate this pattern to the omission of time step 62 from the conditioned past. In this case, the shift of the conditioned past onto the plateau of constant abundance naturally increases the informative value of the past on the next value of abundance. Following this procedure, we identify all shifted patterns in the signal. Fig 5 illustrates the result of our identification process, with dotted lines indicating the periods where a clear identification of the signal is difficult due to superposition of shifted and non-shifted features. As expected, the strong downward outliers are all immediate effects, being clearly related to the sudden increases or decreases in abundance. In contrast, several increases of AIS are delayed effects, which are not primarily related to the concurrent development of abundance, but to the fact that the conditioned past is being successively shifted on plateaus of constant abundance. 10.1371/journal.pcbi.1012359.g004 Fig 4 Identifying immediate and delayed effects in the active information storage of Clostridium . A: Abundance and active information storage (AIS) of Clostridium with varying history lengths 10, 15, and 20 for a run of the base simulation. Dashed vertical lines mark the feeding events after time steps 35, 62, and 121. B: Illustration of a delayed effect in the AIS of Clostridium . The highlighted sections of the k-variants of AIS are shifted by exactly five time points, indicating that they capture an effect lagging behind by the respective history length. 10.1371/journal.pcbi.1012359.g005 Fig 5 Filtered active information storage of Clostridium . Filtered active information storage (AIS) of Clostridium for a run of the base simulation with delayed effects marked by dotted lines. Dashed vertical lines mark the feeding events after time steps 35, 62, and 121. The procedure is analog in the case of collective transfer entropy. Fig 6A displays the abundance and collective transfer entropy of Clostridium for history lengths 10, 15, and 20. As already implied by the average CTE in Fig 1C , CTE tends to build up during periods of longer constancy. From time to time, the signal shows strong upward and downward outliers. Again, indicating ambiguous parts by dashed lines results in a “filtered” version of the CTE of Clostridium (see Fig 6B ). As we have already seen for AIS, it is mainly increases of CTE during times of constant abundance, which are identified as delayed effects. 10.1371/journal.pcbi.1012359.g006 Fig 6 Filtering the collective transfer entropy of Clostridium . A: Abundance and collective transfer entropy (CTE) of Clostridium with varying history lengths 10, 15, and 20 for a run of the base simulation. Dashed vertical lines mark the feeding events after time steps 35, 62, and 121. B: Filtered collective transfer entropy (CTE) of Clostridium with delayed effects marked by dotted lines. The relation of information storage and transfer Wang et al. [ 6 ] studied collective communication and memory in the spatiotemporal dynamics of simulated swarms, suggesting general principles for distributed computation in social and biological systems. The authors observe that average maximal information transfer tends to follow maximal information storage. We observe this pattern as well in the sense that with compositional changes around feeding events, which lead to declines in both information theoretic measures, AIS tends to build up and reach its maximal plateau quicker than CTE (compare Fig 7A and 7B for community-wide and species-specific signals). This phenomenon can easily be explained: In our simulation, a drop in AIS of a species is caused by either a sudden increase in abundance (after a feeding event) or a sudden decrease in abundance (with essential nutrients missing). Those disturbances are followed by either constant abundance (which renders the next state of abundance highly predictable from its past), or by a steady increase (which is, as well, a stable and thereby predictable trend). In contrast, high CTE requires uniformity in preferably many species or effective direct interactions among them. Both is not given during the phases of disturbance. Species react differently, both with respect to shortage of nutrients, as well as to the subsequent feeding events. However, it is not interactions driving their abundances. Only after abundances have been simultaneously constant over some period, CTE reaches maximal values. 10.1371/journal.pcbi.1012359.g007 Fig 7 Comparing active information storage and collective transfer entropy. A: Filtered active information storage (AIS) and collective transfer entropy (CTE) for a run of the base simulation averaged over all living species in the SIHUMI community. Dashed vertical lines mark the feeding events after time steps 35, 62, and 121. B: Filtered active information storage (AIS) and collective transfer entropy (CTE) of Clostridium . Additionally, the results presented in [ 6 ] suggest that information transfer alternates with information storage. While this may be valid for a wide range of disturbed complex systems, it does not hold true in our specific scenario. Recall that an individual in the swarm simulated in [ 6 ] has effectively two possible sources of information: it’s own past, which dominates in phases of “coordinated” collective behavior, and other individuals which become more important in phases of disturbance. However, in our setting there is a third crucial source of information which is the system’s environment. One could think of the environment as constituting an additional agent governing the availability of nutrients. Lack of, or a sudden refill of nutrients clearly shapes this other agent’s “behavior”. Neither AIS nor CTE will be able to capture such interaction directly since the environment is not considered as an agent in the system. And indeed, both measures drop after the feeding event (compare Fig 7A and 7B ), indicating the past to be misinformative about the future. In such times of disturbance, the only measure that we would expect to increase is intrinsic uncertainty (compare the Methods Section). Nevertheless, we have seen earlier that there are situations in which species’ reactions to the environment lead to an increase in (collective) transfer entropy. It will be an interesting future task to find ways to distinguish environmentally-induced from interaction-driven transfer signals."
} | 6,149 |
37738343 | PMC10516491 | pmc | 540 | {
"abstract": "Nanorobots powered by designed DNA molecular motors on DNA origami platforms are vigorously pursued but still short of fully autonomous and sustainable operation, as the reported systems rely on manually operated or autonomous but bridge-burning molecular motors. Expanding DNA nanorobotics requires origami-based autonomous non–bridge-burning motors, but such advanced artificial molecular motors are rare, and their integration with DNA origami remains a challenge. Here, we report an autonomous non–bridge-burning DNA motor tailor-designed for a triangle DNA origami substrate. This is a translational bipedal molecular motor but demonstrates effective translocation on both straight and curved segments of a self-closed circular track on the origami, including sharp ~90° turns by a single hand-over-hand step. The motor is highly directional and attains a record-high speed among the autonomous artificial molecular motors reported to date. The resultant DNA motor-origami system, with its complex translational-rotational motion and big nanorobotic capacity, potentially offers a self-contained “seed” nanorobotic platform to automate or scale up many applications.",
"introduction": "INTRODUCTION Nanorobotics based on DNA nanotechnology ( 1 ) is a flourishing field thanks to rich designability and addressability of DNA molecules and structures. In particular, DNA nanorobots powered and controlled by rationally designed DNA molecular motors ( 2 ) are vigorously pursued by integrating these small motors with larger DNA origami platforms ( 3 ) that have virtually arbitrary designed shapes and a large capacity for site-specific functionalization. These DNA motor-origami robotic systems already demonstrate many functions, such as navigation of prescriptive landscapes ( 4 – 12 ), nanoscale assembly lines ( 13 ), cargo sorting ( 14 ), muscle-like sliding nanosystems ( 15 , 16 ), and linear or rotary actuating systems ( 17 , 18 ) for subdiffraction control of nanophotonic elements. However, these reported DNA motor-origami robotic systems are either manually operated [e.g., by orderly administration of operational DNA strands ( 15 – 18 ) or light irradiations ( 12 )] or autonomous but incapable of repeated or sustainable directional operation [with bridge-burning DNA walkers ( 4 , 5 , 7 ) or even directionless DNA walkers ( 11 , 14 )]. Still missing are the advanced DNA motor-origami robotic systems capable of fully autonomous and sustainable directional operation, which is often necessary for scaling up a nanorobotic application. Expansion toward the advanced DNA nanorobotics requires origami-based autonomous molecular motors that are beyond the simplistic bridge-burning design for sustainable self-directed motion. However, non–bridge-burning autonomous artificial molecular motors are still rare in the first place, with only a few reported ( 19 – 21 ) to date regardless of their building molecules [DNA ( 19 , 20 ) or synthetic molecules ( 21 )] and types [translational motors ( 19 , 20 ) or rotational ones ( 21 )]. It remains a challenge to adapt these advanced molecular motors to DNA origami due to incompatible molecular building blocks or system constructions [e.g., interlocked rings ( 21 ) or continuous single-stranded DNA track ( 19 ) difficult to fit into DNA origami], and due to delicate rectification mechanisms ( 20 ). Non–bridge-burning but fully autonomous molecular motors rely on size-sensitive and entirely automatic molecular mechanical effects ( 2 ) for direction rectification. It is highly nontrivial to engineer such sophisticated molecular mechanics on a highly restrictive two-dimensional DNA origami surface (e.g., serious lattice restriction for arranging straight periodic tracks, high charge density for strong electrostatic effects, blocked half space, and reduced entropy as compared to simpler one-dimensional tracks) ( 12 ). In this study, we design an autonomous non–bridge-burning DNA molecular motor specifically for walking on a triangle DNA origami surface and demonstrate the motor’s self-directed translocation on both straight and curved segments of a self-closed circular track on the origami. This is made possible by combining a special type of local DNA migration (cartwheeling) proven ( 11 ) to be robust and fast even on DNA origami and a mechanics-based rectification mechanism for translational bipedal motors tolerant of unstraight tracks. This motor is highly directional and attains a record-high speed among the reported autonomous artificial molecular motors (including both translational and rotational ones). The resultant DNA motor-origami system thus achieves autonomous translational-rotational motion on a self-closed circular track, potentially offering a self-contained “seed” robotic platform to encode sophisticated functions and autonomously execute them for repeated/sustainable nanorobotic applications. Many previously reported origami-based nanorobotic applications may be automated or scaled up on this DNA motor-origami platform, as it has a rather large surface area (~130 nm across) to host these nanorobotic applications and also has a potential for chip-based high-throughput applications thanks to established methods ( 22 , 23 ) for arranging the same triangle origami on patterned solid substrates.",
"discussion": "DISCUSSION Fully autonomous and rationally designed non–bridge-burning molecular motors are rarely available, and this study develops such a DNA molecular motor capable of mixed translational-rotational motion on a triangle DNA origami surface—by a high directional fidelity and record-breaking speed among autonomous artificial molecular motors reported to date. The resultant DNA motor-origami system has a big nanorobotic capacity: Its autonomous motility on a self-closed ~290-nm-long circular track plus a large DNA origami substrate potentially allows site-specific encoding of sophisticated nanorobotic functions and their execution in an autonomous, sustainable, and self-contained manner. Thus, this DNA motor-origami system may serve as a seed nanorobotic platform to automate or scale up many previously reported origami-based nanorobotic applications that are limited by manually operated or bridge-burning molecular motors. Suitable nanorobotic applications for this motor-origami platform include nanoscale assembly lines ( 13 ), cargo sorting ( 14 ), and linear or rotary actuation ( 17 , 18 ). Other applications are possible too, especially in the area of positioning-encoded nanorobotic applications like molecular walker–automated chemical synthesis ( 51 – 53 ). Such nanorobotic synthesizers ( 51 – 53 ) are previously demonstrated using bridge-burning molecular walkers on short linear tracks, with the walker-track system not reusable and the synthesis difficult to be scaled up. The non–bridge-burning motor-origami system with a self-closed track has a potential for scaling up synthesis and other positioning-encoded applications and even for microchip-based high-throughput applications thanks to the established method ( 22 , 23 ) for orientation-controlled arrangement of the same triangle origami into dense nanoarrays on lithography-patterned solid substrates."
} | 1,797 |
30480098 | PMC6244237 | pmc | 542 | {
"abstract": "Plants influence their soil environment, which affects the next generation of seedlings that can be established. While research has shown that such plant–soil feedbacks occur in the presence of mycorrhizal fungi, it remains unclear when and how mycorrhizal fungi mediate the direction and strength of feedbacks in tree communities. Here we show that arbuscular mycorrhizal and ectomycorrhizal fungal guilds mediate plant–soil feedbacks differently to influence large-scale patterns such as tree species coexistence and succession. When seedlings are grown under the same mycorrhizal type forest, arbuscular mycorrhizal plant species exhibit negative or neutral feedbacks and ectomycorrhizal plant species do neutral or positive feedbacks. In contrast, positive and neutral feedbacks dominate when seedlings are grown in associations within the same versus different mycorrhizal types. Thus, ectomycorrhizal communities show more positive feedbacks than arbuscular mycorrhizal communities, potentially explaining why most temperate forests are ectomycorrhizal.",
"introduction": "Introduction Feedbacks between plant community assembly and soil biota are critical to understanding the dynamics of forest ecosystems such as coexistence and succession 1 – 4 . Plant–soil feedbacks influence seedling community assembly when the effects of soil biota that reside in association with a given plant species are expressed more strongly on conspecific than on heterospecific seedlings 1 , 5 – 7 . Recent studies have emphasized that the direction and strength of plant–soil feedbacks can be explained by the mycorrhizal fungal types (or guilds) of plant species 8 , 9 . Negative feedbacks are generally limited to arbuscular mycorrhizal plant species, and positive feedbacks are typically observed in ectomycorrhizal plant species 8 , 9 . The negative feedbacks of arbuscular mycorrhizal plant species increase the abundance of soil biota that make the soil less suitable for conspecific seedlings relative to heterospecifics, thereby promoting the coexistence of different arbuscular mycorrhizal plant species at the community level 7 , 9 – 12 . In contrast, the positive feedbacks of ectomycorrhizal plant species increase the abundance of soil biota that favor conspecific seedlings over heterospecifics, thereby promoting the dominance of the ectomycorrhizal plant species within a community 8 , 13 , 14 . While many studies have assessed the direction and strength of plant–soil feedbacks within the same mycorrhizal type (i.e., growth responses of arbuscular mycorrhizal seedlings under arbuscular mycorrhizal resident trees, or those of ectomycorrhizal seedlings under ectomycorrhizal resident trees), few studies have quantified feedbacks when resident plants and colonizing seedlings have mismatched mycorrhizal types. Bennett et al. 9 measured feedbacks using the growth responses of seedlings in soils conditioned by conspecifics and heterospecifics. They concluded that the direction and strength of feedbacks were generally species-specific, and hence plant species identity (i.e., whether the resident species is conspecific or heterospecific) could be a more important predictor of plant–soil feedbacks than mycorrhizal type match/mismatch. Nevertheless, given that arbuscular mycorrhizal and ectomycorrhizal plants co-occur across broad climatic ranges and that ectomycorrhizal forests frequently have an arbuscular mycorrhizal understory in temperate zones 14 , matching/mismatching of mycorrhizal type between resident plants and recruited seedlings may play a prominent role in driving plant community dynamics 9 , 15 , 16 . For example, ectomycorrhizal seedlings may grow faster than arbuscular mycorrhizal seedlings when colonizing forests dominated by ectomycorrhizal trees, generating positive feedbacks as a consequence of mycorrhizal type matching. Alternatively, negative feedbacks may allow for species with contrasting mycorrhizal types to coexist in mixed communities as a result of the suppression of dominant species 5 , 17 . Given that trees may attempt to recruit into areas that are more homogeneously dominated by either arbuscular mycorrhizal or ectomycorrhizal plants (not mixed compositions as in Bennett et al. 9 ), mycorrhizal type matching could govern the outcome of plant–soil feedbacks with possible consequences for seedling community assembly 17 , 18 . To better understand how plant–soil feedbacks affect seedling community assembly in natural communities, emphasis should shift from the feedback effects/responses of one plant species on another (e.g., home-versus-away experiments) to the feedbacks in multi-species community contexts. In such contexts, plant–soil feedbacks may emerge as a general community-scale process, where multiple seedlings and residents collectively form common mycelial networks belowground via mycorrhizal fungi 19 – 22 . These networks have the potential to modulate how resident species modify the soil biotic properties and how seedling species respond to these changes through a broad set of mechanisms (e.g., transfers of nutrients among connected resident and seedling species 18 , 20 , 23 and modifying biogeochemical cycling 24 , 25 ). While there is some evidence that plant–soil feedbacks have a greater impact in mixed-species communities 10 , few studies have investigated feedbacks in the presence of microbiota potentially connecting neighboring resident trees and seedlings via mycelial networks. Such community-scale feedbacks may influence seedling community assembly differently from the commonly studied species-pairwise feedbacks (possibly through different belowground mechanisms). Here we examine whether community-scale plant–soil feedbacks affect seedling community assembly and, if so, how these effects may be linked with mycorrhizal type match/mismatch. Using experimental mesocosms simulating mixed-species forest stands, we established resident sapling communities carrying mycorrhizal inocula (the conditioning phase) and then introduced uninoculated seedling communities into the mesocosms and followed the subsequent growth of the seedlings (the feedback phase). By implementing this experiment in a fully factorial design—that is, by varying mycorrhizal type of resident forest types (arbuscular mycorrhizal, ectomycorrhizal, and control [mimic trees]) and seedling community types (arbuscular mycorrhizal, ectomycorrhizal, and control [no seedlings])—we are able to make a direct inference regarding how mycorrhizal type match/mismatch mediates plant–soil feedbacks in the presence of soil microbiota at the community level. Specifically, by adopting a spatially hierarchical design where sapling species treatments are nested within a mesocosm, we address two-layered predictions about how seedling species respond to the soil conditions modified by conspecific/heterospecific saplings, and/or by matching/mismatching mycorrhizal types. We used seedlings as phytometers to measure the properties of neighboring saplings (i.e., conspecific versus heterospecific sapling microenvironments) and mesocosm (i.e., mycorrhizal type). Our findings indicate that seedlings generally exhibited negative to positive feedbacks depending on sapling-seedling species combinations. When comparing plant–soil feedbacks associated with conspecific versus heterospecific saplings within mesocosms with same mycorrhizal type (i.e., arbuscular mycorrhizal seedlings grown in arbuscular mycorrhizal resident forests, or ectomycorrhizal seedlings grown with ectomycorrhizal resident forests), we tended to detect negative or neutral feedbacks for arbuscular mycorrhizal plant species and neutral or positive feedbacks for ectomycorrhizal plant species. In contrast, when comparing plant–soil feedbacks associated with heterospecific saplings in matching versus mismatching mesocosms (i.e., arbuscular mycorrhizal seedlings grown in ectomycorrhizal resident forests, and ectomycorrhizal seedlings grown in arbuscular mycorrhizal resident forests), we found positive to neutral feedbacks at the mesocosm-scale. We conclude that the assembly of a temperate tree community may be determined by a combination of species-specific plant–soil feedbacks within the same mycorrhizal fungal guild and positive plant–soil feedbacks driven by the match/mismatch of mycorrhizal type between resident plants and seedlings. By accounting for community-scale plant–soil feedbacks, we will be able to consider how tree species of the same mycorrhizal type can coexist, and why ectomycorrhizal trees, but not arbuscular mycorrhizal trees, can become dominant in late-successional temperate forest communities.",
"discussion": "Discussion Based on a mesocosm experiment of arbuscular mycorrhizal and ectomycorrhizal artificial plant communities, we examined how mycorrhizal types determine plant–soil microbiota feedbacks. Previous studies have generally reported negative feedbacks in arbuscular mycorrhizal plant species and positive feedbacks in ectomycorrhizal species 1 , 8 , 9 , and Bennett et al. 9 emphasized that species-specific feedbacks could play a more important role than mycorrhizal type match/mismatch. We found that, in a multi-species community context, the direction and strength of feedbacks depend critically on mycorrhizal type match/mismatch. When seedlings colonize forests dominated by the matching mycorrhizal type, arbuscular mycorrhizal plant species tend to exhibit negative or neutral feedbacks and ectomycorrhizal plant species do neutral or positive feedbacks (Fig. 3 ). In contrast, when seedlings colonize forests dominated by the matching versus mismatching mycorrhizal type, both arbuscular mycorrhizal and ectomycorrhizal species exhibit neutral or positive feedbacks as a consequence of mycorrhizal type matching (Fig. 4 ). Our results suggest, when these within- and across-mycorrhizal type feedbacks occur simultaneously in natural forest, ectomycorrhizal plant species may show more positive feedbacks than arbuscular mycorrhizal plant species do. Consequently, the assembly of a temperate tree seedling community may be shaped by a combination of variable feedbacks within the same mycorrhizal guilds and positive feedbacks across different mycorrhizal guilds. This study also revealed that the root-associated fungal community shared between saplings and seedlings may be associated with the observed patterns of plant–soil feedbacks. Specifically, for the ectomycorrhizal plant community, seedlings’ fungal symbiont acquisition and the spatial structuring of belowground fungal communities may account for the pattern that ectomycorrhizal seedlings generally performed better under the matching resident forests than under the mismatching forests (Figs. 5 and 6 ). We propose that the observed effects of mycorrhizal type matching on resident–seedling feedbacks may have resulted from four non-mutually exclusive mechanisms: first, more spatially extended and temporally prolonged infection of seedling roots by matching fungal communities than by mismatching ones; second, detrimental effects of incompatible mycorrhizal fungi for seedlings when grown with mismatched saplings; third, access to a larger soil nutrient pool made available by compatible fungal networks than by incompatible networks 19 – 21 ; and fourth, more active transport of nutrients from resource-rich regions of mycelial networks to resource-poor areas 22 via more structured hyphal networks provided by matching symbioses. The analyses of fungal communities within the mesocosms (Figs. 5 and 6 ) are consistent with all these possibilities. Nevertheless, the use of the internal transcribed spacer (ITS) region in the molecular analysis might have resulted in a low detection rate of arbuscular mycorrhizal fungi (see ref. 26 for more), and it is likely that arbuscular mycorrhizal fungal communities would have displayed spatial structuring when analyzed using DNA markers specific to arbuscular mycorrhizal fungi. While our results suggest the roles of mycorrhizal fungal communities and their belowground networks as a potential driver of plant–soil feedbacks, detailed mechanisms underlying the link between fungal symbiont acquisition and plant−soil feedbacks require further investigation. Our approach to determining plant–soil feedbacks is different from previous research in two important ways. First, previous studies either focused on the effects of live soil inocula associated with a single resident plant species on conspecific or heterospecific seedlings (i.e., plant competition-free conditions 9 , 11 , 27 ) or on the effects of one resident plant species on another in the presence of resident-seedling competition 20 , 28 – 30 . Our study assembled plant−soil communities on identical substrates of a set area, naturally and in situ at a field site, and for a known period of time, and then measured seedling growth responses under conditions in which both plant competition and mycorrhizal networks were allowed to develop. These features made it possible to examine feedbacks in more realistic, multi-species plant communities. Second, most field studies have used fungicide to test for the potential effects of soil pathogens 2 , 3 , 7 , 9 , but such chemical applications were necessarily confounded with possible reductions of soil mycorrhizal fungi, which could also act as key agents of feedbacks 18 , 25 . By combining a factorial experiment with follow-up sequencing, we were able to quantify the similarities in root-associated fungal community composition between resident trees and seedlings as a potential key agent of plant−soil feedbacks. Our findings show that the biological effect of plant−soil interactions can be placed into a broader community context, whereas it has typically been only observed in pairwise interactions. It is necessary to note, however, some caveats of our study. First, there are some methodological limitations in our soil handling, which might potentially bias the estimation of feedbacks (see Methods for mode detail). Second, at the conditioning phase of the experiment, we chose to use mycorrhizal saplings (collected from natural forests by a local nursery) over using cultured mycorrhizal inocula. Therefore, it is possible that diverse soil biota other than mycorrhizal fungi might have driven the observed feedbacks. For instance, bacteria, soil micro-arthropods, and nematodes are also known as potential key agents of plant−soil feedbacks 2 , 4 , 10 , 31 , so whether they could explain the direction and strength of feedbacks observed in our study is still an open question. Third, we did not account for variation in plant species composition and richness in the mesocosm designs. For the sake of experimental feasibility and tractability, we assembled two artificial tree communities from each type to test for matching/mismatching feedbacks. If we are to confirm the generality of the findings, further studies must be undertaken to assess how the direction and strength of feedbacks differ depending on the compositions of the arbuscular mycorrhizal and ectomycorrhizal plant species used to build the experimental mesocosms. This is important because research has shown that plant–soil feedbacks can not only affect plant community structure 4 , 7 , 10 (but see ref. 29 ) but also be affected by plant community structure 32 , 33 . Fourth, seedlings used in this study might have exceeded the stage susceptible to soil pathogens which might have caused underestimation of negative soil biota effects 34 , and this possibility cannot be ruled out given the scarcity of pathogens detected in our study (Supplementary Figure 4 ). A related issue is that such pathogens are known to deactivate under high light intensity, potentially reducing the efficacy of pathogen-mediated negative feedbacks. Despite our efforts to simulate the natural forest environment by controlling for light availability experienced by seedlings (see Experimental design described in Methods), our field experiment might have been performed in environments with higher light intensity compared to previous studies. Previous experiments and ours may thus differ in several aspects potentially influencing the functioning of plant–soil microbiota feedbacks (e.g., light intensity, soil fertility 24 , 35 – 37 ), and hence the results of feedbacks should be interpreted with caution (see Supplementary Table 5 for the soil chemical profiles for our experiment). Understanding plant–soil feedbacks in mixed-species communities and evaluating how contrasting plant mycorrhizal types shape plant−soil feedbacks are critical to predicting plant community dynamics and succession. Our findings show that the effects of plant–soil feedbacks on seedling community assembly can be modulated by mycorrhizal type match/mismatch, and such matching effects may emerge as a community-scale process in which the networks of interactions formed by soil microbiota influence the outcome of seedling community assembly. Plant–soil feedback theory predicts that stronger negative feedbacks are more likely to stabilize species coexistence within the same mycorrhizal fungal guilds (i.e., within an arbuscular mycorrhizal plant community or an ectomycorrhizal plant community), whereas more positive feedbacks observed across different mycorrhizal types are more likely to allow specific mycorrhizal plant guilds to become dominant in a forest (in our case, arbuscular mycorrhizal plant dominated forest or ectomycorrhizal plant dominated forest). Our findings may provide clues to simultaneously explain why different tree species of the same mycorrhizal type can coexist in a natural temperate forest, and why ectomycorrhizal plant communities (but not arbuscular mycorrhizal plant communities) often dominate in late-successional temperate forest. Although the idea that mycorrhizal type is a significant predictor of plant community succession is not new 8 , 38 , it has not been tested experimentally at the community level. This study is a first step toward contrasting arbuscular mycorrhizal and ectomycorrhizal plant–soil feedbacks in a multi-species context and highlights the importance of simultaneously examining arbuscular mycorrhizal and ectomycorrhizal plant communities. As mycorrhizal types have been linked to plant nutritional acquisition strategies, soil properties, and nutrient cycling 24 , 25 , they provide a useful approach for an understanding of feedbacks at the ecosystem level. To develop a more comprehensive understanding of plant community assembly, future studies need to quantitatively evaluate the roles of both arbuscular mycorrhizal and ectomycorrhizal fungi 15 , 38 , 39 as well as the diversity and biomass of mycorrhizal, endophytic, and pathogenic fungi in plant root systems 16 , 40 in association with various abiotic factors 41 . Incorporating such complexities of real belowground plant–soil interactions will be an avenue for better predicting plant community dynamics."
} | 4,750 |
34471855 | PMC8390849 | pmc | 543 | {
"abstract": "Summary Microbial electrosynthesis (MES) represents a sustainable platform that converts waste into resources, using microorganisms within an electrochemical cell. Traditionally, MES refers to the oxidation/reduction of a reactant at the electrode surface with externally applied potential bias. However, microbial fuel cells (MFCs) generate electrons that can drive electrochemical reactions at otherwise unbiased electrodes. Electrosynthesis in MFCs is driven by microbial oxidation of organic matter releasing electrons that force the migration of cationic species to the cathode. Here, we explore how electrosynthesis can coexist within electricity-producing MFCs thanks to electro-separation of cations, electroosmotic drag, and oxygen reduction within appropriately designed systems. More importantly, we report on a novel method of in situ modulation for electrosynthesis, through additional “pin” electrodes. Several MFC electrosynthesis modulating methods that adjust the electrode potential of each half-cell through the pin electrodes are presented. The innovative concept of electrosynthesis within the electricity producing MFCs provides a multidisciplinary platform converting waste-to-resources in a self-sustainable way.",
"conclusion": "Conclusions In this perspective, we present an innovative concept of electrosynthesis that can coexist within the electricity-producing MFC bioreactors thanks to electrochemical processes such as electroseparation of cationic species, electroosmotic drag, and ORR. This can provide a platform for microbially assisted electrosynthesis of target compounds within the cathode half-cell. Moreover, in situ signal modulation and control can be achieved using a novel design engineering method, through the introduction of additional “pin” electrodes. Through this modulation method we can adjust the electrode potential of each of the half-cells through the pin electrodes transferring incoming charge from another MFC source. In this way, the potential modulation could become self-sustainable."
} | 507 |
33274469 | PMC8049059 | pmc | 544 | {
"abstract": "Abstract The plant‐associated microbial community (microbiome) has an important role in plant–plant communications. Plants decipher their complex habitat situations by sensing the environmental stimuli and molecular patterns and associated with microbes, herbivores and dangers. Perception of these cues generates inter/intracellular signals that induce modifications of plant metabolism and physiology. Signals can also be transferred between plants via different mechanisms, which we classify as wired‐ and wireless communications. Wired communications involve direct signal transfers between plants mediated by mycorrhizal hyphae and parasitic plant stems. Wireless communications involve plant volatile emissions and root exudates elicited by microbes/insects, which enable inter‐plant signalling without physical contact. These producer‐plant signals induce microbiome adaptation in receiver plants via facilitative or competitive mechanisms. Receiver plants eavesdrop to anticipate responses to improve fitness against stresses. An emerging body of information in plant–plant communication can be leveraged to improve integrated crop management under field conditions.",
"conclusion": "4 CONCLUDING REMARKS Plants have adapted to growth in complex and competitive ecosystems during their evolutionary history. They need to compete with interspecific and conspecific plants for light, water and nutrients, and communicate with neighbouring plants to anticipate upcoming biotic/abiotic challenges (Ballaré & Austin, 2019 ; Effah, Holopainen, & McCormick, 2019 ; Hodge, Fitter, & Robinson, 2013 ; Hortal et al., 2017 ). However, only some of the competition and communication mechanisms rely on the plant genome. Plant microbiota have pivotal roles in nutrient solubility and uptake, especially nitrogen, phosphorus and iron (Adesemoye, Torbert, & Kloepper, 2008 ; Sharifi, Ahmadzadeh, Sharifi‐Tehrani, & Talebi‐Jahromi, 2010 ). Microbiota also improve water use efficiency and osmotic stress response (Fan et al., 2015 ; Sharifi & Ryu, 2018c ). The plant holobiome leverages the collection of its member genes to optimize performance and survival. Plants have spatiotemporal layers of defence consisting of rhizosphere microbes, endophytes, pattern‐triggered immunity, effector‐triggered immunity and recruited natural enemies; each of these can efficiently suppress specific groups of attackers (Carrión et al., 2019 ; Sharifi & Ryu, 2017 ). Because of these advantages conferred by microbiota, plants donate 10–30% of their carbon and nitrogen to the rhizosphere to organize their microbiota. Information and signal transferring systems play important roles in plant growth and survival. Plants decipher their complex habitat situations by perceiving physical and chemical cues and signals, either directly from neighbouring plants or indirectly from their symbionts such as mycorrhiza, endophytic fungi and dodder. Previous research has characterized the signal types and signal mediators involved in plant interactions with other members of the ecosystem and revealed that deaf and mute mutants of plants have reduced ecological competence. However, plants may have lost some of their communication abilities during domestication and plant breeding programs. Therefore, plant breeders and genetic engineers should have a holistic view of plants as members of the holobiome. Otherwise, small changes in genes related to inter‐plant and inter‐kingdom signals may substantially affect plant performance, a phenomenon called the butterfly effect. Agricultural practices also affect plant–plant communication. As we mentioned above, no‐tillage and minimum tillage systems preserve common mycorrhizal networks and endophytic fungi as mediators of wired plant–plant communication. Moreover, Information can transfer between plants during intercropping or from crop to crop in the next season. Thus, recent advances in our understanding of plant–plant communication can help manage agricultural practices so that they utilize the abilities of plants to exploit ecological interactions, and survive in competitive environments.",
"introduction": "1 INTRODUCTION Plants adapt to stress via sensor elicitation, signalling cascade activation, gene expression and phenotype modification (Glazebrook, 2005 ; Jung, Tschaplinski, Wang, Glazebrook, & Greenberg, 2009 ). Signal processing time is critical for success or failure in stress responses. Plants acquired the ability to anticipate and respond to imminent dangers, which conferred ecological competence in highly dynamic ecosystems. This system is known as defence priming (Conrath, Beckers, Langenbach, & Jaskiewicz, 2015 ; Jung et al., 2009 ). Plants can acquire early warning information through their microbiome and plant–plant signals (Gilbert & Johnson, 2017 ; Vahabi et al., 2018 ; Yi, Heil, Adame‐Alvarez, Ballhorn, & Ryu, 2009 ). The plant can exploit the unique capabilities of its symbionts including the mycorrhizal network, dodder ( Cuscuta spp.) and endophytic fungi, which directly prime plant defence responses or transfer inter‐plant signals (da Trindade et al., 2019 ; Hettenhausen et al., 2017 ; Vahabi et al., 2018 ). The plant microbiome and macrobiome can modify plant‐derived inter‐plant signals such as volatiles and root exudates (Figure 1 ; Sharifi, Lee, & Ryu, 2018 ; Song, Sim, Kim, & Ryu, 2016 ). Herbivore‐induced plant volatiles (HIPVs) and microbe‐induced plant volatiles (MIPVs) are good examples of inter‐plant signals (Heil & Bueno, 2014 ; Sharifi et al., 2018 ). FIGURE 1 Wired and wireless phytobiome communication. Clonal plants (right) communicate via physical connections (e.g. stolons and rhizomes) or VOCs. Plants also communicate via dodder and mycorrhiza (left). Receiver plants can act as nodes to transfer defence signals against pests and pathogens to neighbouring conspecific and heterospecific plants. Volatiles and root exudates received by neighbouring plants modulate receiver plant defence systems, attract parasitoids and entemopathogens and induce plant microbiome remodelling to protect plants against imminent stress conditions [Colour figure can be viewed at wileyonlinelibrary.com ] BOX 1 Definitions: holobiont, holobiome, microbiome Recent reports proposed that multicellular organisms (e.g. animals and plants) and their associated unicellular organisms (e.g. microbes) could be considered as super‐organisms, or holobionts (in ancient Greek, holos means whole and biont means unit of life) (Gilbert, 2019 ; Suarez & Stencel, 2020 ). However, the definition and concept of a holobiont is still debated. We consider a holobiont as an ecological unit (assembly) of a group of organisms that gather together based on their evolutionary capability to achieve a common purpose, which is the survival of the holobiont. The holobiome includes all living organisms, their genetic materials and their primary and secondary metabolites, as well as the molecules produced within a particular habitat (Berg et al., 2020 ; Sharifi & Ryu, 2017 ). The microbiome includes the microbial community living in a particular habitat and their metabolites, mobile genetic elements and relic DNA (Berg et al., 2020 ). The microbiome helps the holobiont survive during biotic and abiotic stresses. The presence and abundance of specific microbial species in the microbiome change during successive phases of plant ontogeny and during biotic/abiotic stresses (Carrión et al., 2019 ; Cotton et al., 2019 ; Edwards et al., 2018 ; Gu et al., 2016 ). Here, we review plant–plant communications that improve plant defence against pathogenic microbes. From multiple layers of plant–plant communications, we distinguished two distinct types: wired and wireless communications. Wired communication involves one plant sending a signal to another plant though direct contact via microbial structures and hyper‐parasitic plant organs. This can be considered as an information highway mediating plant–plant communication. Wireless communication involves signal transfer across the space separating two plants. We investigated how wired and wireless communications affect plant defence responses. We determined that these signal transduction pathways proceeded via the following three steps: signal input (extracellular signal perception generates an endogenous signal cascade); transferring signal (direct connection from signal producer to receiver through mycorrhizal network and parasitic plants, and indirect signal translocation via plant volatile compounds and exudates) and signal output (receiver plant responses to biotic and abiotic stresses)."
} | 2,156 |
25683239 | PMC4329568 | pmc | 545 | {
"abstract": "Anaerobic digestion is a widely used technology for waste stabilization and generation of biogas, and has recently emerged as a potentially important process for the production of high value volatile fatty acids (VFAs) and alcohols. Here, three reactors were seeded with inoculum from a stably performing methanogenic digester, and selective operating conditions (37°C and 55°C; 12 day and 4 day solids retention time) were applied to restrict methanogenesis while maintaining hydrolysis and fermentation. Replicated experiments performed at each set of operating conditions led to reproducible VFA production profiles which could be correlated with specific changes in microbial community composition. The mesophilic reactor at short solids retention time showed accumulation of propionate and acetate (42 ± 2% and 15 ± 6% of COD hydrolyzed , respectively), and dominance of Fibrobacter and Bacteroidales . Acetate accumulation (>50% of COD hydrolyzed ) was also observed in the thermophilic reactors, which were dominated by Clostridium . Under all tested conditions, there was a shift from acetoclastic to hydrogenotrophic methanogenesis, and a reduction in methane production by >50% of COD hydrolyzed . Our results demonstrate that shortening the SRT and increasing the temperature are effective strategies for driving microbial communities towards controlled production of high levels of specific volatile fatty acids.",
"discussion": "Discussion In this study, reactors with varying SRT and temperature were operated using inoculum from a stably performing methanogenic digester to develop a strategy for restricting methanogenesis while maintaining efficient hydrolysis and fermentation. Using these operational parameters, we aimed to selectively drive mixed microbial communities towards increased accumulation of specific intermediate volatile fatty acids. Reducing the SRT from 12 to 4 days at 37°C (M4) decreased the cellulose utilization by ~13% of the feed COD relative to the parent reactor (M12) ( Supplementary Figure S1 ), likely due to the lower contact time between the cellulosic substrate and hydrolytic bacteria, and to biomass washout. The shorter SRT led to accumulation of VFAs, primarily propionate (75%) and a smaller amount of acetate (25%) ( Supplementary Figure S1 ), consistent with prior findings on overloading of ADs at reduced SRT 25 . Although the richness did not decrease significantly between M12 and M4, there were changes in the microbial community composition. Propionate accumulation in M4 was correlated with an increased abundance of a member of the genus Alkaliflexus , known to be capable of propionate production 6 26 27 . Washout of syntrophic propionate oxidizers at low SRT could also potentially explain propionate accumulation, and populations within this functional guild were not detected in both M12 and M4. There was also a shift in the dominant hydrolytic populations from Ruminococcus OTU1 to a member of the genus Fibrobacter in M4. Ruminococcus is known to have a competitive advantage under cellulose- and cellobiose-limited conditions 28 , which may explain its higher abundance when residual cellulose concentrations were low in M12. At higher loading rates, resulting from a shorter SRT in M4, Fibrobacter was able to outcompete Ruminococcus , which may be due to their difference in cellulose attachment strategies and high cellulose hydrolysis efficiency of Fibrobacter 29 30 . Our results contradict prior studies showing more rapid attachment of some Ruminococcus populations to cellulose compared to Fibrobacter , which suggest Ruminococcus would have a competitive advantage at lower contact times resulting from the shorter SRT in M4 31 . These conflicting results again show the complexity of the interactions between cellulolytic populations which can be influenced by multiple factors 30 31 . In M4, methane production still occurred during VFA accumulation, although yields decreased by 54% (based on hydrolyzed COD) compared to M12 ( Supplementary Figure S1 ). This lower amount of methane was produced by a population belonging to the family Methanoregulaceae through hydrogenotrophic methanogenesis from formate and/or H 2 /CO 2 derived from fermentation. Limited conversion of acetate to H 2 /CO 2 by syntrophic acetate oxidizers (SAO), washout of the slow-growing acetoclastic Methanosaeta at reduced SRT, and partial inhibition of the dominant hydrogenotrophic methanogens at high VFA concentrations are possible explanations for the observed lower methane production in M4. Shortening the SRT to less than 4 days would likely further increase selective pressure against methanogenesis, however this may also result in less stable performance, reduced substrate utilization and potentially lower VFA yields. Experiments and model-based predictions are potential additional strategies to determine the optimum SRT to maximize cellulose hydrolysis and VFA accumulation with minimal methane production. Cellulose hydrolysis at 55°C and a 12 day SRT (T12) was highly efficient, and similar to the level measured in the parent reactor (M12) ( Supplementary Figure S1 ). The hydrolysis efficiency was expected to be higher at increased temperature 14 , however this was not observed and may be due to the already high efficiency of the parent reactor compared to other studies 9 14 . Another possible explanation is the lower diversity of the inoculum and the microbial communities established in these reactors compared to full-scale mesophilic and thermophilic ADs ( Supplementary Table S1 ). Substrate complexity also has a large influence on the community composition 15 20 with a lower number of potential substrates typically leading to low diversity communities that are more susceptible to changes in operating conditions 4 . The higher sensitivity of these low diversity communities to changes in operating conditions, biomass washout at reduced SRT and growth suppression at excess carbon may have caused the decrease in hydrolysis efficiency observed under thermophilic conditions and at a 4 day SRT (T4). Increasing the temperature led to a significant decrease in microbial community richness and evenness, indicating that only a limited number of populations in the mesophilic inoculum were capable of responding to temperature increase. There was a substantial shift in community profile at the higher temperature in both T12 and T4, resulting in communities dominated by populations belonging to the genus Clostridium that were not detected in the inoculum (<0.0005%). This highlights the strong selective pressure of temperature, which allowed populations with a competitive advantage to become dominant at a higher temperature. Clostridia has previously been found as the dominant population in thermophilic ADs 20 32 33 , which is consistent with the thermophilic growth properties of members within this class. The dominant populations were closely related to C. stercorarium (OTU1) and C. clarivlavum (OTU2 and OTU3), which are known anaerobic thermophilic bacteria capable of hydrolyzing cellulose to produce acetate and ethanol 34 35 36 37 . The relative distribution of these Clostridium populations was influenced by the difference in SRT leading to dominance of OTU1 and OTU2 in T12, and OTU1 and OTU3 in T4, which correlated with differences in ethanol and (iso-)butyrate production. The main VFA product in both T12 and T4 was acetate, which accounted for >50% of the hydrolyzed COD in both reactors ( Supplementary Figure S1 ). Acetate accumulation was significantly correlated with an increased abundance of the Clostridium populations (OTU1, OTU2 and OTU3), suggesting these populations played a large role in acetate production at high temperatures. Due to the lack of functional information, the specific mechanism for acetate accumulation in T12 and T4 could not be identified; however common pathways for acetate production that may have been stimulated at high temperatures are direct fermentation of glucose to acetate and conversion of higher chain VFAs to acetate via acetogenesis. While propionate accumulation has been observed at elevated temperatures 11 , concentrations remained relatively low in T12 and T4 compared to the acetate accumulation in these reactors and the propionate concentration in M4 ( Supplementary Figure S1 ). This was likely due to a combination of lower propionate production and effective conversion to acetate at low partial H 2 pressures. Acetate consumption generally occurs through SAO or direct cleavage by acetoclastic methanogens. As acetate consuming methanogens were not detected in the thermophilic reactors, direct cleavage was likely to be very limited. Recent studies have identified syntrophic acetate oxidation linked to hydrogenotrophic methanogenesis in thermophilic AD 14 38 , however the observed high acetate concentrations in T12 and T4 suggest that populations performing SAO were not present in the reactors or only present at very low abundance. H 2 -consuming methanogens belonging to the genera Methanothermobacter and Methanobacterium were the dominant methanogens in T12 and T4. Although methane production was lower under thermophilic conditions, 30%–40% of hydrolyzed COD in these reactors was still being converted to methane through hydrogenotrophic methanogenesis. Our findings are consistent with previous research showing that consumption of H 2 by Methanothermobacter and Methanobacterium during glucose fermentation at high temperatures (70°C) assists in selective and stable production of acetate 13 . Replication of the experiment clearly demonstrated high reproducibility in terms of both changes in VFA production profiles and microbial community composition under each set of operating conditions. Previously, both niche and neutral ecological theories have been applied to describe the factors driving changes in microbial community function and structure 18 22 23 . The reproducible results from this study highlight the importance of niche differentiation allowing more competitive populations to become dominant when conditions change, and underline the predominant role of deterministic processes such as operating conditions, substrate availability and microbial interactions in anaerobic digestion 16 . In this study, we demonstrate that VFA accumulation can be achieved at relatively high concentrations with reduced levels of methane production, while maintaining a stable microbial community. This outcome demonstrates the potential for novel carboxylate processes to produce both high value products and renewable energy in a single reactor. However, it should be emphasized that the use of a different substrate would likely result in a different microbial community and product profile. The process conditions should therefore be optimized for biotechnological applications depending on the substrate-product combination required. In terms of controlling product formation, increasing the temperature and shortening the SRT both resulted in VFA accumulation, however the type of VFA produced was predominantly driven by temperature. Additional methods could be explored to further enhance VFA production, such as lowering the pH to increase product yield and specificity, and extracting products to eliminate thermodynamic constraints and release toxicity pressures on hydrolysis and fermentation 8 39 . Furthermore, meta-omic analyses and substrate labelling methods would allow us to identify the mechanisms responsible for the accumulation of specific VFAs under the different operating conditions 40 , which in turn will lead to further process optimization. The outcomes from this study demonstrate that microbial communities performing AD can be driven towards enhanced production of specific high value VFAs in a controlled and replicated manner by using selective operating conditions."
} | 2,981 |
37222619 | PMC10401116 | pmc | 546 | {
"abstract": "Abstract Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In‐memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first‐order dynamics. Here, a second‐order memristor is experimentally demonstrated using yttria‐stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second‐order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL‐based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second‐order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high‐efficiency, compact footprint, and hardware‐encoded plasticity.",
"conclusion": "3 Conclusion In summary, inspired by the neuronal excitability modulation of biological neurons, we demonstrated an STL neuron using second‐order memristors for self‐adaptive spatial attention. We first showed the existence of the second state variable in the YSZ:Ag‐based memristor and explored its physical origin via high‐resolution TEM and EDS analysis. Then, we simulated the SCNN made of YSZ:Ag memristor‐based STL neurons for multiobject detection, which leverages the STL to form spatial attention on the area of interest. The resultant temporal separation of features of different objects improves classification accuracy on multiple objects in the receptive field as well as the system energy efficiency and speed. Such second‐order memristors not only overcome the scaling and von Neumann bottlenecks of CMOS digital hardware but also possess rich and bio‐plausible dynamics for future machine intelligence.",
"introduction": "1 Introduction The brain features different plasticity of both synapses and neurons. Although the former is commonly regarded as the dominant form of neuroplasticity relevant to learning and memory, nonsynaptic, for example, intrinsic plasticity through modification of neuronal excitability also plays an important role. One of such mechanisms is the spontaneous threshold lowering (STL), where the threshold potential at which an action potential is triggered can be lowered by the regulation of voltage‐gated channels on the initial segment of axons, thus it is easier for the neuron to fire, influencing all incoming synaptic inputs. The intrinsic STL plasticity plays an important role in a number of learning protocols like spatial attention, fear conditioning and odor conditioning. [ \n \n 1 \n , \n 2 \n \n ] For example, a hyperpolarized shift of voltage‐gated sodium (Nav) channel activation lowers the spiking threshold and increases intrinsic excitability of hippocampal CA1 pyramidal neurons to speed up learning. [ \n \n 3 \n \n ] Similarly, due to the slow activation kinetics of voltage‐gated potassium channel Kv7.2, the downregulation of another voltage‐gated potassium channel Kv1 reduces the spiking threshold and effectively raises attention to the auditory neurons losing auditory inputs. [ \n \n 4 \n \n ] Another representative example is the formation of spatial attention in vision system as illustrated in Figure \n 1 a . The frequently‐firing STL neurons of the receptive field define the area of interest, where the threshold of the neurons in the area of interest decreases more and forms spatial attention in a self‐adaptive way. The widely evidenced excitability of STL‐regulated neurons can greatly benefit the adaptation of biological neural systems to complex environments. Figure 1 Spontaneous threshold lowering neuron using YSZ:Ag‐based second‐order diffusive memristor for self‐adaptive spatial attention. a) In vision system, the frequently firing STL neurons of the receptive field define the area of interest, where the threshold of the neurons in the area of interest decreases more and forms spatial attention in a self‐adaptive way. b) Schematic of the metal‐insulator‐metal (MIM) structure of a Au/YSZ:Ag/Au/Ti memristor with second‐order switching dynamics, which is employed to model STL neurons. The evolution of temporal dynamics of the YSZ:Ag memristor shows a gradually decreasing switching delay (electric STL behavior) that reduces from ≈100 µs down to ≈30 µs over 30 DC sweeps cycles (i.e., 10 DC cycles per pulse test). c) Consecutive 50 DC voltage sweeps cycles with positive bias from 0 to 1.1 V followed by 50 DC sweeps cycles with negative bias from 0 to −1 V, showing repeatable bidirectional threshold switching behavior with an ON/OFF ratio over 10 6 . d) Fitting curve of the switching delay as a function of the accumulated pulse width extracted from (b). The switching delay reduced with the accumulated pulse width, indicating spontaneous threshold lowering (STL) and the existence of the second state variable. Hardware neuromorphic computing may leverage such neuron plasticity of the human brain for artificial intelligent systems in the era of big data and Internet of Things. In‐memory computing is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers. The Moore's law [ \n \n 5 \n \n ] and Dennard scaling [ \n \n 6 \n \n ] that fueled the past development of complementary metal oxide semiconductor (CMOS) for decades cannot sustain their pace as the transistor size is close to its physical limit, rendering technology node shrinking less effective. [ \n \n 7 \n , \n 8 \n \n ] This makes the complexity of CMOS computers difficult to parallel that of the brain, where the latter consists of 10 12 neurons and 10 15 associated synapses. [ \n \n 9 \n \n ] Meanwhile, traditional digital computers are based on the von Neumann architecture with physically separated processing and memory units. The frequent and massive data shuttling between these units incurs large time and energy overheads. In contrast, the neurons and synapses of the brain collocate information storage and processing, which makes the human brain consume only 20 W. [ \n \n 10 \n \n ] Thus, a brand new computing hardware is in demand to implement STL neurons to unleash the power of the brain‐inspired computing paradigm. Emerging memristors [ \n \n 11 \n , \n 12 \n , \n 13 \n , \n 14 \n , \n 15 \n , \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n , \n 21 \n , \n 22 \n , \n 23 \n , \n 24 \n , \n 25 \n , \n 26 \n \n ] are two terminal circuit elements that could change their resistance in response to electrical stimulation, regarded as one of the most promising contenders for hardware neuromorphic computing. [ \n \n 27 \n , \n 28 \n , \n 29 \n , \n 30 \n , \n 31 \n , \n 32 \n , \n 33 \n , \n 34 \n , \n 35 \n , \n 36 \n , \n 37 \n , \n 38 \n , \n 39 \n , \n 40 \n \n ] The memristors are not only scalable and 3D stackable thanks to their simple device structure, but also process information right at where it is stored through “compute‐in‐physics” that can emulate synaptic plasticity and neural integrate‐and‐fire in an energy efficient manner. [ \n \n 41 \n , \n 42 \n \n ] Such memristor‐based synapses and neurons can work together, like how the synapses and neurons interact in the brain, to physically implement spiking neural networks. [ \n \n 43 \n , \n 44 \n , \n 45 \n , \n 46 \n , \n 47 \n \n ] However, how to endow the memristor neurons with plasticity remains challenging for brain‐inspired learning, since majority memristors are first‐order dynamic systems governed by a single state variable. [ \n \n 48 \n , \n 49 \n \n ] \n A second‐order memristor may naturally fulfil the requirement. [ \n \n 50 \n , \n 51 \n , \n 52 \n \n ] Unlike the first‐order memristor, the second order dynamic system possess two distinct and interdependent state variables, governed by two first‐order (or an equivalent second‐order) differential equations to describe their respective dynamics, [ \n \n 53 \n , \n 54 \n , \n 55 \n \n ] which can be mathematically written as\n \n (1) \n I t = G w , s , V , t V t \n \n \n (2) \n d x d t = f w , s , V , t \n where w and s are the two state variables that are physically encoded to the memristor (such as filament length and temperature). The interplay between the two dynamic state variables w and s equip the memristor with concurrent long‐term and short‐term dynamic behaviors, offering the capability to model complex dynamic behaviors of biological neurons such as periodic action potential, spiking number adaption as well as the STL. [ \n \n 52 \n , \n 56 \n \n ] \n In this work, we experimentally demonstrated a second‐order volatile memristor using yttria‐stabilized zirconia with Ag doping (YSZ:Ag) for hardware implementing STL neurons (Figure 1b ) at a small hardware overhead, which mimicked neural intrinsic plasticity and boosted the performance of spiking neural networks (see Table S3 in the Supporting Information). The physical origin of the STL dynamics, the size evolution of Ag nanoclusters, was investigated using transmission electron microscopy (TEM). [ \n \n 57 \n , \n 58 \n , \n 59 \n \n ] Biomimicking self‐adaptive spatial attention mechanism in a spiking convolutional neural network (SCNN) was proposed using this YSZ:Ag second‐order memristor‐based STL neuron, which improved the accuracy of multiobject detection of handwritten digits from 70% to 90% for the object within the area of interest (first spike) and 20% to 80% for the object outside the area of interest (second spike). [ \n \n 60 \n \n ] In addition to the advantages in speed and energy efficiency, such second‐order memristor STL neurons may pave the way for future machine intelligence based on the emerging neuromorphic device and algorithm."
} | 2,516 |
33500004 | PMC7807759 | pmc | 547 | {
"abstract": "Background Coral-associated microbial communities are sensitive to multiple environmental and biotic stressors that can lead to dysbiosis and mortality. Although the processes contributing to these microbial shifts remain inadequately understood, a number of potential mechanisms have been identified. For example, predation by various corallivore species, including ecologically-important taxa such as parrotfishes, may disrupt coral microbiomes via bite-induced transmission and/or enrichment of potentially opportunistic bacteria. Here, we used a combination of mesocosm experiments and field-based observations to investigate whether parrotfish corallivory can alter coral microbial assemblages directly and to identify the potentially relevant pathways (e.g. direct transmission) that may contribute to these changes. Results Our mesocosm experiment demonstrated that predation by the parrotfish Chlorurus spilurus on Porites lobata corals resulted in a 2-4x increase in bacterial alpha diversity of the coral microbiome and a shift in bacterial community composition after 48 h. These changes corresponded with greater abundance of both potentially beneficial (i.e. Oceanospirillum ) and opportunistic bacteria (i.e. Flammeovirgaceae, Rhodobacteraceae) in predated compared to mechanically wounded corals. Importantly, many of these taxa were detectable in C. spilurus mouths, but not in corals prior to predation. When we sampled bitten and unbitten corals in the field, corals bitten by parrotfishes exhibited 3x greater microbial richness and a shift in community composition towards greater abundance of both potential beneficial symbionts (i.e. Ruegeria ) and bacterial opportunists (i.e. Rhodospiralles, Glaciecola ). Moreover, we observed 4x greater community variability in naturally bitten vs. unbitten corals, a potential indicator of dysbiosis. Interestingly, some of the microbial taxa detected in naturally bitten corals, but not unbitten colonies, were also detected in parrotfish mouths. Conclusions Our findings suggest that parrotfish corallivory may represent an unrecognized route of bacterial transmission and/or enrichment of rare and distinct bacterial taxa, both of which could impact coral microbiomes and health. More broadly, we highlight how underappreciated pathways, such as corallivory, may contribute to dysbiosis within reef corals, which will be critical for understanding and predicting coral disease dynamics as reefs further degrade.",
"conclusion": "Conclusion Our findings provide evidence that parrotfish corallivory can have important effects on coral microbiomes, with the potential to impact coral health. C. spilurus predation both in the laboratory and field induced an increase in alpha diversity and a compositional shift in the microbial assemblages of P. lobata corals, which coincided with a greater abundance of potential beneficial bacteria (i.e. Ruegeria , Phaeobacter ) as well as opportunistic taxa (i.e Flammeovirgaceae, Rhodospirillaleceae, Glaciecola). Importantly, several taxa were undetectable on mechanically wounded and naturally unbitten corals but present in predated, naturally bitten corals and in parrotfish mouths, suggesting parrotfish vector new bacteria to corals during predation. However, the ability of C. spilurus to vector and/or facilitate the enrichment of microbial opportunists, as well as increase microbiome variability, in naturally bitten P. lobata corals is consistent with recent findings linking nutrient pollution and parrotfish predation to coral mortality [ 13 ]. This suggests that common trophic interactions may increase coral susceptibility to dysbiosis, especially when corals are already stressed from other factors such as nutrient pollution, temperature, or sedimentation. Together, our results shed light on underappreciated pathways linking parrotfishes to microbial enrichment and dysbiosis within reef corals. Future work should investigate the interactive effects of parrotfish corallivory and abiotic stressors (e.g. nutrient pollution and ocean warming) to evaluate their consequences for coral microbiomes and fitness.",
"discussion": "Discussion A number of corallivores are suspected to facilitate the enrichment and/or transmission of microbes within reef-building corals [ 14 , 16 ], including consumers such as parrotfishes that play key roles in regulating reef ecosystem processes [ 13 ]. Using a combination of mesocosm- and field-based approaches, we demonstrated that corallivory by the parrotfish species Chlorurus spilurus leads to significant changes in bacterial community composition of Porites lobata . In particular, these changes included greater abundances of potential beneficial bacterial taxa and opportunists, some of which were naturally occurring in parrotfish mouths. Our findings indicate that parrotfishes may play an important role in driving the structure of coral microbial communities, either by acting as vectors and/or by facilitating the enrichment of bacteria in reef corals via corallivory. Parrotfish-induced P. lobata microbiome changes in mesocosm Patterns of alpha and beta diversity in our mesocosm experiment were similar at T i for mechanically wounded and predated corals. However, five taxa were already observed in greater abundance in predated corals compared to those that were mechanically wounded. All were present at relatively low abundances (< 10%) in predated corals, but may have the potential to affect coral microbiomes and health. For instance, cyanobacteria from the Nostocales order (sOTU_18) are often found in fish guts [ 25 ] and were associated with diseased corals [ 26 ]. Members of the clade SGUS912 (sOTU_195) are commonly present in corals exposed to sewage and wastewater outfalls [ 27 ]. Taxa from the orders Oscillatoriales (sOTU_12) and Rhizobiales (sOTU_697), and filamentous Cyanobacteria from the genus Rivularia (sOTU_15), were associated with stressed and diseased corals and sponges [ 9 , 28 – 30 ]. Whether and how these changes affect coral health and fitness, especially when coupled with other stressors, should be investigated further. At the end of the experiment (T f ), we observed greater bacterial richness and diversity in predated corals compared to mechanically wounded corals. Patterns of increased alpha diversity are often associated with numerous physical and biotic stressors including water pollution [ 31 , 32 ], elevated temperature [ 33 , 34 ], ocean acidification [ 35 ], algal competition [ 36 , 37 ], mechanical wounding, and snail corallivory [ 38 , 39 ]. However, other studies demonstrated no changes or a significant decrease in microbial diversity and/or richness following mechanical injury [ 38 , 40 ]. These differences among studies may indicate that responses of coral microbiomes differ due to biological vs. mechanical wounding, or that stressor-induced impacts may be variable depending on coral species or genotypes, local environmental conditions, and/or exposure time. In the present study, increases in bacterial richness and diversity coincided with a compositional shift in bacterial assemblages in predated corals compared to mechanically wounded ones. In addition, microbiomes of predated corals were characterized by moderate abundance (59.9% at T f ) of the putative beneficial symbiont Hahellaceae at 48 h when compared to Ti (83.9%). Lower abundance of Hahellaceae bacterial taxa is a pattern previously reported in stressed, mechanical injured, and predated corals [ 9 , 38 , 39 , 41 ]. In addition, bacterial communities of corals exposed to predation were dominated by members of the families Rhodobacteraceae, Pseudoalteromonadaceae, Alteromonadaceae, Verrucomicrobiaceae and Flavobacteriaceae – taxa that are often associated with both stressed and healthy coral colonies [ 32 , 42 ], and were also found in relatively high abundance in parrotfish mouths. Four sOTUs were present in greater abundance among predated corals compared to mechanically wounded ones, including taxa from the genera Phaeobacter (sOTU_771) and Oceanospirillum (sOTU_467), as well as sequences from the Lentisphaerae (sOTU_39) and Rhodospirillales (sOTU_480) orders. Their potential influences on corals may be diverse – ranging from beneficial to opportunistic. Members of the genus Phaeobacter were previously found in corals and jellyfish [ 43 – 45 ] and were linked to the production of antibacterial compounds in fishes [ 46 , 47 ]. Bacteria from the genus Oceanospirillum are frequently observed in healthy coral colonies [ 48 , 49 ], while members of the phylum Lentisphaerae are common in the fish gut [ 50 ] and healthy corals [ 48 ]. Sequences from the order Rhodospirillales are commonly found in high abundance in stressed and diseased coral colonies [ 9 , 51 – 53 ], indicating an opportunistic character. Given that our experiment lasted for 48 h, the persistence of potential beneficial symbionts and opportunistic bacterial taxa and their consequences on coral microbiomes and health will have to be further investigated over longer time period. Microbiomes of naturally bitten vs. unbitten P. lobata in the field Microbiomes of P. lobata corals found in the field reinforced findings from our mesocosm, as naturally bitten corals exhibited greater bacterial richness compared to unbitten corals. It is worth noting that corals of all treatments, from both the mesocosm experiment and field survey, exhibited relatively low bacterial richness compared to previous work [ 54 ]. However, lower richness has consistently been observed among corals inhabiting reefs in Mo’orea [ 39 , 55 ] and may be related to greater community dominance by members of the Hahellaceae family. In our study, increased bacterial richness in naturally bitten was associated with a shift in bacterial community composition compared to unbitten corals. Bitten corals were mainly populated by potential opportunistic bacterial taxa, including sequences from the families Rhodobacteraceae, Paenibacillaceae, Flavobacteriaceae, Rhodospirillaceae, Moraxellaceae, Alteromonadaceae, and Flammeovirgaceae [ 9 , 56 – 58 ], all of which are taxa that were also present in relatively high abundance in parrotfish mouths. Significant changes in community composition were associated with greater abundance of six taxa in naturally bitten vs. unbitten corals. Among them, three represented the Flammeovirgaceae (genus JTB248; sOTU_1051), and Alteromonadaceae (sOTU_2321; sOTU_3182;) families, that were previously associated with stressed, aged, and/or diseased corals [ 59 – 61 ]. Three other taxa were assigned to the Rhodobacteraceae family, taxa commonly associated with both healthy and stressed corals [ 56 , 62 ]. In particular, a strain from the genus Ruegeria was found in lesioned and diseased corals [ 41 , 56 ] and is known to inhibit growth of the coral pathogen Vibrio coralliilyticus [ 63 ]. As with our mesocosm experiment, our field survey identified taxa with potential beneficial and/or deleterious implications for coral microbiomes, health, and fitness. Further studies are needed to understand the functional roles of these microbes and their interplay with coral stressors. Finally, we observed greater bacterial compositional variability in naturally bitten compared to unbitten corals. Increased microbiome variability is consistent with previous studies showing that numerous animals, including corals, exhibit elevated community variability when exposed to stressors (i.e. the Anna Karenina Principle [ 64 ];), such as mechanical wounding [ 39 ]. This further indicates the potential for parrotfish to increase dysbiosis susceptibility in corals via corallivory . Potential parrotfish-mediated bacterial transmission and enrichment pathways in P. lobata Parrotfish predation could alter the microbiomes of P. lobata via several pathways, including i) direct transmission of bacteria from fish mouths to the coral mucus/tissue layer, ii) indirectly facilitating bacterial invasion from the surrounding environment following wounding, iii) indirectly facilitating growth of bacterial taxa already present within the coral microbiomes or from the surrounding environment, and iv) a combination of these three pathways. We observed evidence for each of these possible pathways in our experiments. For example, evidence that parrotfish may directly transmit bacteria to P. lobata was observed both in the mesocosm experiment and field survey. In the former case, a taxon from the order Nostocales (sOTU_18) at Ti, as well as two taxa from the genera Phaeobacter (sOTU_771) and Oceanospirillum (sOTU_467) at T f , were both found in predated corals and fish mouths, but not in mechanically wounded corals. This indicates that mechanical wounding was insufficient to introduce these taxa and that they were likely vectored via parrotfish predation. Similar patterns were observed in the field, with sequences from the families Flammeovirgaceae (sOTU_1051; genus JTB248 ), Rhodobacteraceae (sOTU_2451; genus Glaciecola ), and Alteromonadaceae (sOTU_3182) present only in bitten corals and fish mouths – not unbitten corals. We also observed evidence that predation may facilitate the invasion of bacterial taxa from the surrounding environment. At T i in our mesocosm experiment, three potential opportunistic bacterial coral taxa were found in predated corals, but not in mechanically wounded corals or parrotfish mouths (sOTU_12, sOTU_15 and sOTU_697). Similarly, in the field, sequences from the family Alteromonadaceae (sOTU_2321) were only found in naturally bitten corals, indicating enrichment from the surrounding environment. We also observed potential enrichment from microbes preexisting on corals and/or from the external environment, such as members from the clade SGUS912 (sOTU_195) and the Rhodospirillales order (sOTU_480), which were identified in predated and mechanically wounded corals – but not fish mouths – at T i and T f , respectively. Finally, evidence from both experiments suggested a combination of different pathways including transmission and/or enrichment. In the manipulative experiment, taxon sOTU_39 from the Lentisphaerae order was present in moderate abundance in predated corals, as well as in low abundance in mechanically wounded corals and parrotfish mouths at T f . In the field, two taxa from the Rhodobacteraceae family (sOTU_3439, sOTU_3450) were present in fish mouths and bitten corals, as well as in relatively low abundance in unbitten corals. Collectively, our findings suggest that parrotfish corallivory may be an important driver structuring coral-associated bacterial communities. Evidence that parrotfish vector and/or facilitate the enrichment of bacteria within corals, both in our mesocosm experiment and field surveys, was surprisingly consistent – especially given that sampling of corals and parrotfish mouths was conducted haphazardly in the back reef during our field surveys. This suggests that parrotfish mouths may harbor a consistent microbial signature in the studied reef area that allows C. spilurus to vector rare taxa via corallivory. Our findings add to growing body of evidence demonstrating the potential for corallivores, such as snails ( Drupella spp ., Coralliophila spp. ), crown-of-thorn sea stars ( Acanthaster spp. ) and worms ( Hermodice caniculata ), to vector and/or facilitate the enrichment of microbes in corals [ 38 , 65 , 66 ]. Our study is the first to document such potential in parrotfishes, adding to their key roles as corallivores, bioeroders, and herbivores on coral reefs. Previous work suggests that other candidate species, such as butterflyfishes, are unlikely to vector microbes [ 16 , 67 ] – potentially due to their distinct “browser” feeding mode (but see [ 17 ]). In contrast, the “scraper” and “excavator” feeding modes of many parrotfishes may make them ideal candidates to transmit microbes to corals. The interplay between these abilities and the other critical roles of parrotfishes on coral reefs will be of considerable interest for reefs of the future."
} | 4,034 |
21862629 | PMC3157894 | pmc | 548 | {
"abstract": "ABSTRACT Mechanisms for electron transfer within microbial aggregates derived from an upflow anaerobic sludge blanket reactor converting brewery waste to methane were investigated in order to better understand the function of methanogenic consortia. The aggregates were electrically conductive, with conductivities 3-fold higher than the conductivities previously reported for dual-species aggregates of Geobacter species in which the two species appeared to exchange electrons via interspecies electron transfer. The temperature dependence response of the aggregate conductance was characteristic of the organic metallic-like conductance previously described for the conductive pili of Geobacter sulfurreducens and was inconsistent with electron conduction through minerals. Studies in which aggregates were incubated with high concentrations of potential electron donors demonstrated that the aggregates had no significant capacity for conversion of hydrogen to methane. The aggregates converted formate to methane but at rates too low to account for the rates at which that the aggregates syntrophically metabolized ethanol, an important component of the reactor influent. Geobacter species comprised 25% of 16S rRNA gene sequences recovered from the aggregates, suggesting that Geobacter species may have contributed to some but probably not all of the aggregate conductivity. Microorganisms most closely related to the acetate-utilizing Methanosaeta concilii accounted for more than 90% of the sequences that could be assigned to methane producers, consistent with the poor capacity for hydrogen and formate utilization. These results demonstrate for the first time that methanogenic wastewater aggregates can be electrically conductive and suggest that direct interspecies electron transfer could be an important mechanism for electron exchange in some methanogenic systems.",
"introduction": "Introduction Effective interspecies electron exchange is essential in methanogenic ecosystems ( 1 – 3 ). In order for complex organic matter to be converted to methane and carbon dioxide, the microbial community that metabolizes multicarbon organic compounds other than acetate requires an electron sink. Methanogenic microorganisms can function as this sink, consuming electrons in the reduction of carbon dioxide to methane. Understanding the mechanisms of this electron exchange is key to modeling and/or manipulating methane production in natural methanogenic environments, such as anaerobic soils and sediments, the gastrointestinal systems of diverse animals, and anaerobic wastewater treatment systems. The first mechanism described for electron exchange in methanogenic systems was interspecies hydrogen transfer, in which microorganisms that require an electron sink reduce protons to produce hydrogen and the methanogens utilize hydrogen as an electron donor ( 1 , 2 , 4 , 5 ). Formate may also act as an electron carrier ( 1 , 5 – 7 ). A potential alternative is direct electron transfer between cells. Connections between cells via electrically conductive filaments were suggested as a mechanism for microbes to exchange electrons ( 8 – 10 ). However, only one specific example of direct electron transfer to a methanogen was proposed ( 9 ), and subsequent studies demonstrated that the filament connecting the fermentative microorganism and the methanogen was a flagellum, which is not expected to be conductive ( 2 , 11 ). Therefore, direct electron transfer to the methanogen was unlikely ( 2 ). However, studies in which two Geobacter species were cocultured under conditions that required interspecies electron exchange indicated that direct electron transfer is feasible ( 12 ). Adaptation of Geobacter metallireducens and Geobacter sulfurreducens to cooperatively metabolize ethanol resulted in the formation of large (1 to 2 mm in diameter) aggregates. Analysis of a mutation selected over the course of the laboratory evolution of the coculture, as well as studies in which specific mutations were introduced into G. sulfurreducens , suggested that the cells within the aggregates were directly exchanging electrons via a mechanism that involved the multiheme c -type cytochrome OmcS ( 12 ). Previous studies ( 13 ) demonstrated that OmcS aligns along the conductive pili of G. sulfurreducens , and a similar localization was observed in the aggregates, suggesting that pili were also involved in the electron exchange. Furthermore, the aggregates were electrically conductive, demonstrating that electrons that G. metallireducens released into the extracellular matrix might readily flow to G. sulfurreducens ( 12 ). Studies of the capacity for hydrogen and formate metabolism suggested that they were not important electron carriers between the two Geobacter species. The aggregates that the Geobacter species formed had a morphology similar to that of the aggregates that are found in upflow methanogenic wastewater digesters ( 14 ). Methanogenic digester aggregates are comprised of a diverse community of hydrolytic-fermentative bacteria, hydrogen-producing acetogenic bacteria, and methanogens, which cooperate to degrade complex organic compounds to methane and carbon dioxide ( 1 , 2 , 15 , 16 ). One factor favoring aggregation is that it prevents cells from being washed out of the system ( 1 , 14 , 17 – 19 ). Furthermore, aggregation could enhance the exchange of hydrogen and formate between syntrophic partners ( 20 , 21 ). An additional potential benefit of aggregation is that it may make it feasible for cells to directly exchange electrons ( 12 , 22 ). Here we report on conductive properties, community composition, and metabolism of wastewater aggregates consistent with direct exchange of electrons within the aggregates.",
"discussion": "RESULTS AND DISCUSSION General characteristics of the aggregates. The aggregates obtained from the industrial-scale reactor and the aggregates propagated in the laboratory reactor had similar appearances. They were black and roughly spherical (diameter, 0.5 to 2 mm), comprised primarily of tightly packed rods ( Fig. 1 ). FIG 1 Scanning electron micrographs of an entire aggregate (A) and higher magnification of the aggregate surface (B). Aggregate conductance. The possibility that the aggregates might be electrically conductive, in a manner similar to that described for the aggregates formed by a coculture of Geobacter species ( 12 ), was investigated. Aggregates spanning a nonconductive gap between two gold electrodes exhibited a linear current-voltage response when a DC voltage was applied, consistent with ohmic conductivity ( Fig. 2A ). The conductivity of the aggregates from the industrial-scale reactor (6.1 ± 0.3 µS/cm [mean ± standard deviation]; n = 3) and that of the aggregates propagated in the laboratory reactor (7.2 ± 3.0 µS/cm) were similar. They were more conductive than the previously described conductive aggregates comprised of a coculture of two Geobacter species (1.4 ± 0.3 µS/cm). Conductive aluminum beads treated in a similar manner had a high conductivity (11.4 ± 0.1 µS/cm), whereas artificial wastewater alone ( Fig. 2A ) or porous alginate beads in wastewater ( Fig. 2B ) did not exhibit an ohmic response. FIG 2 Current-voltage response of reactor aggregates (A) and aluminum and alginate control beads serving as positive and negative controls, respectively (B), in artificial wastewater. In order to further evaluate the nature of the conductance, aggregate conductance was measured as a function of temperature ( Fig. 3 ). Upon cooling from room temperature, aggregate conductivity increased exponentially by more than an order of magnitude. An exponential increase in conductivity upon cooling is a characteristic signature of organic metals, such as polyaniline, whereas inorganic metals and minerals exhibit only linear increases in conductivity ( 23 ). Therefore, even though the aggregates contain inorganic iron that could conceivably contribute to their conductivity, the temperature dependence response rules this out as a major source of conductivity and suggests an organic metal and hence a biological response. This temperature response of the aggregates is similar to that of conductive biofilms and purified pilus preparations of Geobacter species ( 24 ). The continued exponential decrease in conductivity with further cooling ( Fig. 3 ) can be attributed to intrinsic disorder present in the aggregate samples ( 23 , 24 ). FIG 3 Temperature dependence of aggregate conductivity measured using a four-probe setup. Experimental evaluation of electron sources for methane production. To further evaluate possible mechanisms for electron exchange within the aggregates, the potential for methane production from ethanol and potential intermediates of ethanol metabolism were evaluated ( Fig. 4 ). High concentrations of potential substrates were added in order to estimate the maximum rates at which the compounds might be metabolized. Methane production rates from ethanol exceeded those from other substrates ( Fig. 4 ). There was substantial production of methane from acetate, consistent with the necessity of the community to metabolize the acetate expected to be produced from the ethanol and propionate in the reactor feed and the fact that acetate was directly provided in the reactor feed. The addition of hydrogen did not stimulate methane production, indicating that the aggregates were not adapted to convert hydrogen to methane. FIG 4 Methane production from the aggregates with different potential electron donors. Controls received no electron donor additions. Results are the means and standard deviations of data for six replicates for each treatment. The aggregates had some potential to convert formate to methane. However, the combined maximum capacity for the aggregates to convert acetate and formate to methane was less than the capacity for conversion of ethanol to methane ( Fig. 4 ). This was the case even though the concentrations of acetate and formate provided to the aggregates were much higher than those the methanogens were likely to experience during syntrophic metabolism of the added ethanol. Thus, if formate was the dominant electron shuttle for ethanol metabolism, combined rates of acetate and formate conversion to methane would be expected to be higher than the rates of methane production from added ethanol. The finding that the combined rates of methane production from added formate and acetate were, in fact, lower that from ethanol suggested that a mechanism other than interspecies hydrogen and formate transfer was required to account for the rapid metabolism of ethanol observed in the aggregates. Microbial community composition. The 16S rRNA gene sequences in the aggregates were evaluated in order to gain further insight into aggregate metabolism. Rarefaction curves for the 16S rRNA gene libraries constructed with universal primers or primers specific to the archaea reached an asymptote, suggesting adequate coverage for assessing the dominant members of the microbial community ( Fig. 5 ). The microbial community of the aggregates propagated in the laboratory reactor ( Table 1 ) was similar to that of the aggregates directly harvested from the industrial-scale reactor (data not shown). The microbial community was phylogenetically diverse, as expected from previous studies ( 25 – 27 ). Bacteria accounted for 79% of the 16S rRNA gene sequences recovered and included members of the Proteobacteria (33.9%), the Chloroflexi (15.8%), the Firmicutes (6.4%), and the Synergistetes (4.4%) ( Table 1 ). FIG 5 Rarefaction curves demonstrating high diversity coverage values for the 16S rRNA gene sequences recovered with the two primer sets employed. TABLE 1 Sequence abundances in 338F/907R and archaeal clone libraries with closest representative in GenBank a Library Phylum Closest relative b No. of clones Relative abundance (%) 338F/907R primers \n Actinobacteria \n Bacterium Ellin 6510 (92) 2 1.0 \n Bacteroidetes \n \n Prolixibacter bellariivorans (89) 9 4.4 \n Chloroflexi \n \n Longilinea arvoryzae (90%) 16 7.8 Anaerobic bacterium MO-CFX2 (89) 2 1.0 Bacterium JN18_A7_F* (96) 13 6.4 \n Chloroflexi bacterium BL-DC-9 (86) 2 1.0 Deltaproteobacteria Geobacter daltonii c (97) 50 24.5 \n Syntrophobacter sulfatireducens (95) 11 5.4 \n Syntrophorhabdus aromaticivorans (94) 3 1.5 \n Syntrophobacter fumaroxidans MPOB (92) 3 1.5 \n Syntrophus aciditrophicus (95) 2 1.0 \n Euryarchaeota \n \n Methanobacterium petrolearium (97) 3 1.5 \n Methanosaeta concilii (96) 40 19.6 \n Firmicutes \n \n Clostridiales sp. SM4/1 (94) 2 1.0 \n Oryza sativa Indica (97) 1 0.5 \n Syntrophomonas sp. TB-6 (97) 10 4.9 \n Spirochaetes \n \n Spirochaeta sp. isolate TM-3 (93) 5 2.5 \n Spirochaetes bacterium SA-10 (93) 3 1.5 \n Spirochaeta sp. isolate TM-3 (93) 1 0.5 \n Synergistetes \n \n Synergistetes bacterium 7WAY-8-7 (98) 1 0.5 \n Aminomonas paucivorans (89) 8 3.9 \n Thermotogae \n \n Thermotogales bacterium MesG1Ag4.2.16S.B (98) 1 0.5 Unclassified bacteria 16 7.8 Arch21F/Arch958R primers Euryarchaeota Methanosaeta concilii (99) 102 87.8 \n Methanobacterium petrolearium (99) 7 6.1 \n Crenarchaeota \n Unclassified Desulfurococcales 7 6.1 a The aggregates from the UASB reactor were used for analyses. Representative sequences comprising more than 10% of the sequences of their respective library are highlighted in grey. b Percentages of nucleotide similarity are shown in parentheses. c Formerly strain Geobacter FRC-32. The most abundant sequence recovered from the aggregates belonged to Geobacter species most closely related to Geobacter daltonii ( Table 1 ). The finding that Geobacter sequences accounted for 25% of the sequences recovered is consistent with the finding that, in the absence of reactor disturbance, 20 to 30% of the 16S rRNA gene sequences recovered from aggregates treating brewery wastewater were in the family Desulfuromonadales , which includes Geobacter species ( 28 ). Although the most intensively studied physiological characteristic of Geobacter species is their capacity for extracellular electron transfer to metals and electrodes ( 22 , 29 , 30 ), they are also capable of syntrophic growth ( 12 , 31 – 33 ). Syntrophic oxidation of ethanol and organic acids is their most likely role in brewery digester aggregates. Also abundant were sequences most closely related to Methanosaeta concilii ( Table 1 ). The importance of this organism was further confirmed with the archaeal domain-specific primer set ( Table 1 ). This is consistent with the known role of Methanosaeta species in initiating aggregate formation in digesters and their usual role as the primary methanogens converting acetate to methane in these systems ( 34 , 35 ). Sequences that could be assigned to methanogens capable of utilizing hydrogen or formate were much less abundant than those of the acetate utilizers ( Table 1 ), consistent with the low potential of the aggregates to convert these substrates to methane. The only 16S rRNA gene sequences recovered that were closely related to hydrogen/formate-utilizing methanogens were those related to Methanobacterium petrolearium , a hydrogen-utilizing methanogen ( 36 ). These sequences accounted for only 1.5% of the sequences recovered with the primers designed to recover sequences from all microorganisms. The studies with both primer sets indicated that Methanobacterium sequences were less than 10% as abundant as Methanosaeta sequences. These results further suggest that that neither hydrogen nor formate was an important electron shuttle for methane production. Implications. These results suggest that direct electron transfer could be an important mechanism for electron exchange within methanogenic wastewater treatment aggregates. The aggregates were even more conductive than the previously described aggregates of G. metallireducens and G. sulfurreducens , which multiple lines of evidence suggested exchanged electrons via direct cell-to-cell electron transfer ( 12 ). Geobacter species were present in high abundance in the aggregates and could be expected to facilitate long-range electron transfer via their conductive pili ( 8 , 10 , 37 ). However, the higher conductivity of the methanogenic aggregates suggests that aggregate constituents other than Geobacter species could have been contributing to aggregate conductivity. Methane production via direct interspecies electron transfer, rather than via interspecies transfer of hydrogen or formate, would require that methanogens have the capacity to directly accept electrons as an electron donor for methane production. The apparent ability of a Methanobacterium -like isolate ( 38 ) and a strain of Methanococcus maripaludis ( 39 ) to accept electrons directly from metallic iron suggests that this is possible. Therefore, it seems likely that the Methanobacterium species in the methanogenic aggregates could have the capacity for directly accepting electrons from the conductive aggregate matrix. It has also been proposed that the archaea within anaerobic methane-oxidizing aggregates, which are related to Methanosaeta ( 40 , 41 ), are also capable direct accepting electrons ( 1 , 42 ) because physiological and modeling studies have suggested that interspecies hydrogen and formate transfer is unlikely within those aggregates ( 5 , 43 – 47 ). Although Methanosaeta has been considered to comprise exclusively acetate-utilizing methanogens based on physiological studies, the genome sequences of Methanosaeta species contain genes encoding the enzymes required for the carbon dioxide reduction pathway ( 41 ). Therefore, it cannot be ruled out that the highly abundant Methanosaeta species might also have the ability to directly accept electrons within aggregates. The formation of large, visible aggregates may be only the most extreme instance of cells forming electrical contacts. The possibility of cell-to-cell electron transfer in smaller methanogenic associations and in aggregates carrying out other types of synthrophic processes warrants further investigation."
} | 4,541 |
36303112 | PMC9615231 | pmc | 549 | {
"abstract": "Background Crustose coralline algae (CCA) are calcifying red macroalgae that play important ecological roles including stabilisation of reef frameworks and provision of settlement cues for a range of marine invertebrates. Previous research into the responses of CCA to ocean warming (OW) and ocean acidification (OA) have found magnitude of effect to be species-specific. Response to OW and OA could be linked to divergent underlying molecular processes across species. Results Here we show Sporolithon durum , a species that exhibits low sensitivity to climate stressors, had little change in metabolic performance and did not significantly alter the expression of any genes when exposed to temperature and pH perturbations. In contrast, Porolithon onkodes , a major coral reef builder, reduced photosynthetic rates and had a labile transcriptomic response with over 400 significantly differentially expressed genes, with differential regulation of genes relating to physiological processes such as carbon acquisition and metabolism. The differential gene expression detected in P. onkodes implicates possible key metabolic pathways, including the pentose phosphate pathway, in the stress response of this species. Conclusions We suggest S. durum is more resistant to OW and OA than P. onkodes , which demonstrated a high sensitivity to climate stressors and may have limited ability for acclimatisation. Understanding changes in gene expression in relation to physiological processes of CCA could help us understand and predict how different species will respond to, and persist in, future ocean conditions predicted for 2100. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08931-9.",
"conclusion": "Conclusions This study is the first to reveal differentially expressed genes and pathways that underpin physiological responses of CCA to stressors, and to implicate genes involved in crucial chemical and physical processes (i.e., PPP, glycolysis, Calvin-cycle, and photorespiration). We propose that the differing transcriptional responses of CCA to global change drivers provides an explanation into the species-specific responses of CCA observed in previous studies. We suggest transcriptomic plasticity or lability, as seen in P. onkodes , is indicative of susceptibility to global change drivers, whereas transcriptomic stability, as seen in S. durum , is indicative of resistance in CCA taxa. Although it may be argued that plasticity is an expression of adaptation, that is not always the case [ 7 ] and at times plasticity can be maladaptive or not contribute to increased resistance in an organism [ 3 , 7 ]. The findings from our study have implications for coral reef ecology worldwide. Our results indicate that P. onkodes , an abundant and significant reef-building species, may be negatively affected by predicted anthropogenic global change, with consequences for the distribution of the species and its contribution to reef cementation and resilience. In contrast, other tropical CCA species such as S. durum , although not currently major reef builders, may have the potential to thrive under predicted OW and OA scenarios.",
"discussion": "Discussion Researching molecular responses of CCA and how these responses relate to physiological measurements (e.g. photosynthesis and respiration) is central to understanding the impact of environmental stressors on CCA and more broadly coral reefs. The present study on two species of reef-building coralline algae, S. durum and P. onkodes, provides insight into biological processes that are likely to be altered in response to environmental stressors through measurements of gene expression and metabolic rates. In P. onkodes , transcripts found to be uniquely differentially expressed in T + pH were present in processes involving carbohydrates and lipids. Carbohydrates, specifically polysaccharides, have been suggested to play a role in the calcification process of CCA by acting as a matrix for biomineralization in their primary cell wall [ 28 ]. Changes in calcification rates observed in previous studies [ 19 , 25 , 29 ] could potentially be explained by alterations of expression of these transcripts, with negative implications for CCA biomineralization, reef cementation, and reef growth. Transcripts that were found commonly expressed across comparisons were related to functions such as carbon acquisition and metabolism, suggesting the combination of elevated temperature and reduced pH results in changes to crucial primary physical and chemical processes in P. onkodes . When investigating the differential expression of certain transcripts, it was found that the 99 downregulated transcripts were involved in biological functional groups relating to some mitochondrial processes. Mitochondria are the powerhouses of eukaryotic cells and a growing area of plant research involves linking mitochondrial function and composition to environmental stress response [ 30 , 31 ]. Our finding of overrepresentation of mitochondrial-related processes in downregulated genes supports a role for mitochondria in the stress response and physiological processes of P. onkodes . Downregulation could be indicative of a negative effect on physiological processes, as was suggested in Antarctic algae in response to heat stress [ 32 ]. Two proposed proteins that play a role in photosynthesis were significantly upregulated in P. onkodes , while chaperone protein dnaJ was significantly downregulated. Generally, upregulation of genes involved in protective stress responses (dnaJ) can facilitate a faster and more efficient response [ 5 ]. Collectively, these results indicate that global climate change drivers have a significant impact on the energy cycle of P. onkodes . O 2 production in P. onkodes decreased in response to T + pH, simultaneously we found enzymes involved in photorespiration were upregulated. We propose that reallocation of energy to photorespiration may have resulted in a decrease in the efficiency of photosynthesis, which was observed in the decrease in the rate of net photosynthesis/average O 2 production (Additional file 1 , Figure S1). Interestingly, in the present study, S. durum did not share a similar response to P. onkodes and instead had no DEGs after three months in treatment, possibly indicating some level of resistance in this species. The nature of the molecular response to OW and OA was unknown in CCA, but, as shown here, likely underlies potential resistance or susceptibility. Our study demonstrates that transcriptional response differs between species, with CCA species showing differences in resistance and susceptibility to global change stressors, supporting our hypothesis that transcriptomic response and physiological responses are not uniform across CCA. To our knowledge, there are currently no published systematic studies that have specifically investigated differences in transcriptomic responses to stressors in multiple species of coralline algae, however physiological studies that are available suggest that response to stressors is species-specific (Fig. 1 a,b). There are several factors that may explain the varying responses across species, including differences in anatomical and mineralogical features [ 33 ], variability in ecological niches (e.g., light and nutrient requirements, depth, hydrodynamics), and/or different evolutionary histories. One possibility is the increased tolerance and a muted transcriptomic response in S. durum may be related to its more ancient evolutionary origin (the genus originated ~ 70 mya and has undergone little recent diversification [ 17 ]), where Sporolithon spp. persisted through periods of elevated ocean temperature and p CO 2 /reduced pH in the geological past (e.g. Paleocene-Eocene Thermal Maximum) that equalled or surpassed levels projected for the year 2100 [ 16 – 18 ]. In contrast, the labile transcriptomic response in P. onkodes may be more related to its recent evolutionary origin (the genus originated ~ 20 mya and has exhibited considerable recent diversification [ 17 , 34 ]). A review discussing temperature tolerance in terrestrial plants indicated a more tolerant species, Arabidopsis thaliana , had a muted transcriptional response compared with a less tolerant species, Sorghum bicolor [ 2 ]. Interestingly, these two species have very different evolutionary histories, with the Arabidopsis genus having diverged ~ 43 mya [ 35 ] and the lineage containing S. bicolor estimated to have diverged between 3.9 – 2.4 mya [ 36 ]. This suggests that increased sensitivity in more recently derived groups may be a feature of Archaeplastida. A stable or dampened transcriptional response in a tropical reef coral to high variable temperature environment has been found to be indicative of thermal tolerance [ 11 ], however, a muted response or lack of expression response could also be indicative of a stressor being mild [ 4 ]. In the current study, S. durum may have been unable to respond because the stressor (i.e. elevated temperature and/or reduced pH) was not strong, or perhaps not long, enough to induce a response in this species. This possibility still indicates a level of resistance in S. durum , however. Although we observed a muted or lack of apparent responsiveness in S. durum , there could be other significant posttranslational modifications occurring that we did not measure or analyse that could be indicative of stress in this species [ 37 , 38 ], and future studies should consider measuring changes across multiple functional levels (e.g. transcriptome, proteome, metabolome). Furthermore, future studies should continue to investigate the responses of additional species of tropical CCA, across multiple clades and groups (e.g. Peña et al. 2021 [ 26 ] for temperate corallines) to investigate further species-specific response to climate stressors by systematically testing other possible contributing factors such as evolutionary history, acclimatisation history, and environmental history."
} | 2,522 |
36032533 | PMC9400054 | pmc | 550 | {
"abstract": "The sustainable production of chemicals from renewable,\nnonedible\nbiomass has emerged as an essential alternative to address pressing\nenvironmental issues arising from our heavy dependence on fossil resources.\nMicrobial cell factories are engineered microorganisms harboring biosynthetic\npathways streamlined to produce chemicals of interests from renewable\ncarbon sources. The biosynthetic pathways for the production of chemicals\ncan be defined into three categories with reference to the microbial\nhost selected for engineering: native-existing pathways, nonnative-existing\npathways, and nonnative-created pathways. Recent trends in leveraging\nnative-existing pathways, discovering nonnative-existing pathways,\nand designing de novo pathways (as nonnative-created\npathways) are discussed in this Perspective. We highlight key approaches\nand successful case studies that exemplify these concepts. Once these\npathways are designed and constructed in the microbial cell factory,\nsystems metabolic engineering strategies can be used to improve the\nperformance of the strain to meet industrial production standards.\nIn the second part of the Perspective, current trends in design tools\nand strategies for systems metabolic engineering are discussed with\nan eye toward the future. Finally, we survey current and future challenges\nthat need to be addressed to advance microbial cell factories for\nthe sustainable production of chemicals.",
"introduction": "Introduction Commercial chemicals produced from fossil\nresources through petrochemical\nrefinery processes have played an integral part in human society over\nthe past century. However, our overdependence on fossil resources\nand derived products have led to serious problems such as environmental\npollution, extreme weather, and the depletion of fossil resources,\nwhich threatens not only humanity but also the entire planet as a\nwhole. It is crucial to address these pressing issues by transforming\nthe current petrochemical processes into sustainable, environmentally\nfriendly processes for the production of chemicals. Microbial cell\nfactories are engineered microorganisms designed and optimized to\nproduce chemicals of interest from renewable resources such as nonedible\nbiomass or even carbon dioxide. The fermentative production of chemicals\nusing engineered microbial cell factories has been demonstrated to\nbe a viable alternative to the production of chemicals. In addition\nto the advantage of using renewable resources as carbon sources, microbial\ncell factories use a relatively lower temperature and pressure and\ndo not use toxic solvents and catalysts for the production of chemicals,\nunlike conventional chemical processes. With the increasing international\neffort of research groups and companies around the world, a large\nportfolio of chemicals can now be produced using microbial cell factories. 1 When microorganisms are first isolated\nfrom nature, they are not\noptimized to the desired function to readily uptake carbon sources\nderived from renewable biomass and produce a target chemical of interest\nwith a high enough efficiency. Thus, metabolic engineering—the\npurposeful modification of cellular and metabolic networks to achieve\ndefined objectives 2 —is performed\non the microorganism to convert it into an efficient microbial cell\nfactory. Through metabolic engineering, a microorganism can be engineered\nto utilize an inexpensive renewable carbon source as a substrate to\nproduce a chemical of interest, even those nonnative to its metabolism. In 1991, the term “metabolic engineering” was first\nofficially suggested, 3 and the first generation\nof metabolic engineering began with the development of molecular tools\nthat enabled the deletion, insertion, or replacement of genetic components\nin the microbial chromosome or through the use of plasmids. Typically,\nmetabolic pathways were streamlined by the manipulation of one to\nseveral genes to direct the metabolic flux toward a desired chemical\nproduct. At the turn of the millennium and with the advent of omics\n(genomics, transcriptomics, and proteomics) data, metabolic engineers\nwere able to view microorganisms as systems made up of complex networks.\nThe “1.5th generation” of metabolic engineering, therefore,\nleveraged such new omics data to perform local metabolic engineering\non the basis of local information selected from global omics information—genomics,\ntranscriptomics, and proteomics data were used to identify gene targets\nto enhance the production of target chemicals or proteins. 4 The second generation of metabolic engineering\nis characterized\nby combining metabolic engineering with synthetic biology, systems\nbiology, and evolutionary engineering. This generation is also widely\nknown as “systems metabolic engineering.” Tools and\nstrategies of systems metabolic engineering to develop microbial cell\nfactories have previously been reviewed. 5 − 7 High-throughput\ntechnologies are widely employed to generate bio-big\ndata, which comprises vast omics data. As bio-big data became available,\ndata-driven approaches, such as the rapidly expanding field of artificial\nintelligence, are now being used to solve various biotechnology problems.\nWith breakthroughs in artificial intelligence and the use of bio-big\ndata in designing microbial cell factories, the third generation of\nmetabolic engineering has begun. Coupled with automated systems to\nconstruct hundreds of thousands of molecular systems and bacterial\nstrains, the speed at which microbial cell factories are being developed\nis unparalleled in the history of metabolic engineering, thereby opening\nup endless possibilities to produce chemicals. Metabolic pathway\ndesigns can be broadly classified into three\ncategories on the basis of whether the pathways are native to the\nmicrobial host (native or nonnative) or whether they are reported\nor found in nature (existing) or are completely synthetic (created; Figure 1 ). Native-existing\npathways are biosynthetic pathways existing in an isolated microbial\nhost capable of producing the target chemical endogenously without\nthe need to introduce any foreign biosynthetic pathways. Nonnative-existing\npathways are reconstructed biosynthetic pathways that utilize existing\nor reported pathways in nature but are nonnative to the microbial\nhost. Nonnative-created pathways are reconstructed pathways that do\nnot exist in nature but have been purposefully designed and created\nusing synthetic enzymes and pathways with new functions. With an increasing\nnumber of tools and strategies available to discover and design biosynthetic\npathways, the production of a target chemical may not necessarily\nrequire a single category of synthetic pathway. For example, some\nchemicals, such as glutaric acid (which will be discussed more in\ndetail below), can be produced by both nonnative-existing pathways\nand nonnative-created pathways. Figure 1 Overall design process to construct a\nmicrobial cell factory for\nthe production of a target chemical. First, an appropriate microorganism\nis selected as the microbial host. Next, biosynthetic pathways toward\ntarget chemical production are examined, and the optimal pathway is\nintroduced to the microbial host, accordingly. The microbial host\nharboring the biosynthetic pathway is subject to systems metabolic\nengineering to improve strain performance. Model organisms Escherichia coli and Saccharomyces\ncerevisiae are two of the most widely used hosts for microbial\ncell factories because their metabolisms are best understood and molecular\ntools to engineer these hosts are well established. 1 However, the production of a target chemical may not necessarily\nbe optimal with E. coli and S. cerevisiae as hosts due to their metabolic and physiological properties. With\nthe expanding number of genetic and computational tools available,\nother microorganisms with superior metabolic and cellular properties\nsuch as Corynebacterium glutamicum , Pichia\npastoris , Pseudomonas putida , and Yarrowia lipolytica are increasingly being explored for\nthe production of chemicals, with a notable increase in efforts toward\nthe production of chemicals in the past five years ( Table 1 ). Table 1 Properties of Platform Hosts for Microbial\nCell Factories host type characteristics available GEM references Escherichia coli Gram-negative bacteria -model organism iML1515 refs ( 190 , 191 ) -well-established\ngenome engineering tools -well-studied metabolism -weak cell wall -endotoxins Corynebacterium glutamicum facultative anaerobe, Gram-positive bacteria -robust, powerful metabolism iCW773 refs ( 11 , 192 ) -well-studied fed-batch fermentation -chemical-produced labeled GRAS Pseudomonas putida Gram-negative\nbacteria -robust, suitable for production of natural\nchemicals iJN1462 refs ( 13 , 193 ) Saccharomyces cerevisiae eukaryote -GRAS Yeast8 refs ( 12 , 194 ) -well-established\ngenome engineering tools -advantageous for\nexpressing eukaryotic genes (e.g.: P450s) Yarrowia lipolytica eukaryote -GRAS iYLI647 refs ( 10 , 195 ) -oleaginous\nmicroorganism -TAG storage Bacillus subtilis Gram-positive, catalase-positive bacteria -model Gram-positive strain iYO844 refs ( 8 , 196 ) -spore-forming Pichia pastoris eukaryote -methylotrophic iMT1026v3.0 refs ( 14 , 197 ) Previous reviews on designing microbial cell factories\nfor chemical\nproduction have focused on individual microbial cell hosts, 8 − 14 systematic methods to develop strains on the basis of specific examples, 5 or the summary of tools and strategies used in\nsystems metabolic engineering. 6 , 7 This Perspective focuses\non designing biosynthetic pathways for microbial cell factories, various\nmethods to construct completely novel pathways, and on the design\nstrategies to optimize microbial cell factories for the production\nof chemicals. Readers are encouraged to refer to the flowchart ( Figure 2 ) containing the\nguiding principles on how to design the pathways. We also use recent\nexamples of successful engineering of microbial cell factories to\nshowcase various methods for engineering a microbial host for chemical\nproduction. Looking into the future, we discuss various obstacles\nthat need to be overcome in this field and offer our vision for the\nsustainable production of chemicals using microbial cell factories. Figure 2 Flowchart\nillustrating the guiding principles in designing three\ncategories of biosynthetic pathways. Blue and red colored boxes indicate\nsteps for constructing nonnative-existing pathways and nonnative-created\npathways, respectively.\n\nMolecular Tools for the Introduction of Biosynthetic Pathways Recent developments in genetic engineering tools have remarkably\nadvanced the introduction to and optimization of pathways in host\nstrains ( Figure 8 A).\nPlasmids still play an important role to this end because of their\neasy manipulation and introduction to microbial host cells. The nonnative\npathways designed can be rapidly constructed and tested in microbial\ncell factories using plasmids. Tools and strategies for fine-tuning\nthe gene expression levels with plasmids are still being actively\nstudied. 75 − 79 An increasing number of synthetic promoters and ribosome binding\nsite (RBS) sequences are being developed to fine-tune the expression\nof biosynthetic genes at plasmid levels. While there have been several\npromoter libraries previously designed on the basis of random sequences,\nnovel synthetic promoter libraries have recently been constructed\nfor yeast using model-guided design strategies 34 and for E. coli using a deep generative\nnetwork. 80 Additionally, synthetic RBS\nsequences with diverse translation initiation levels can be designed\nfor the control of gene expression using computational tools such\nas RBS calculator, UTR designer, and RBS designer. 81 For the strains used in industrial applications,\nthe chromosomal\nexpression of biosynthetic genes is often favored over plasmid-based\nexpression because of the plasmid maintenance and instability problems.\nRecombinase systems such as the Lambda Red or RecET from E.\ncoli have widely been employed to delete or introduce target\ngenes in various microbial hosts. However, as such conventional methods\nrely on laborious and time-consuming processes, much effort has been\nexerted to improve the recombination-based methods 82 or develop efficient alternative chromosomal engineering\nmethods to further expedite the speed of strain development. CRISPR-Cas\nsystems, an adaptive immune system of microorganisms, have recently\nattracted much interest as genome engineering tools because they enable\nsimple and rapid engineering compared with the conventional tools.\nThe Type II and class 2 CRISPR/Cas9 system derived from Streptococcus\npyogenes has most widely been employed for genome engineering\napplications. Many variations of the CRISPR/Cas9 system were developed\nin combination with existing molecular tools for the genome engineering\nof microorganisms by leveraging the capability of the CRISPR/Cas9\nsystem to introduce double-stranded breaks at a precise DNA sequence\nin the genome. More recently, Tn7-like transposons associated with\nCRISPR-Cas systems have been developed as powerful tools for inserting\nDNA into genomes. 83 The CRISPR-associated\ntransposase (CAST) system 84 derived from\ncyanobacteria Scytonema hofmanni and Anabaena\ncylindrica and the INTEGRATE (insert transposable elements\nby guide RNA-assisted targeting) system 85 , 86 derived from Vibrio cholerae have enabled the insertion of large gene\nclusters into targeted sites in the chromosome. For engineering\nnonmodel organisms, much effort has been made to\ndevelop broad-host-range genetic manipulation tools such as mobile-CRISPRi, 87 chassis-independent recombinase-assisted genome\nengineering (CRAGE), 88 and XPORT. 89 Mobile-CRISPRi was developed to characterize\ndiverse bacterial species, with large guide-RNA libraries to rapidly\nscreen essential pathways and desired phenotypes. 87 CRAGE uses transposon and conjugation systems for introducing\nbiosynthetic gene clusters into diverse bacteria, 88 while XPORT is an engineered donor strain constructed to\ntransfer miniaturized integrative and conjugative elements to undomesticated\norganisms. 89 With the increasing availability\nof molecular tools to engineer nonmodel organisms, those nonmodel\norganisms potentially possessing higher capability to overproduce\ncertain chemicals can be metabolically engineered to become the industrial\nproduction strains in the future."
} | 3,621 |
39123962 | PMC11314768 | pmc | 553 | {
"abstract": "Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human–machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.",
"introduction": "1. Introduction The sensory system integrates various modalities, including visual, auditory, tactile, gustatory, and olfactory, which serve as the neural foundations for the reception and processing of sensory stimuli within the nervous system [ 1 , 2 , 3 , 4 , 5 ]. This system, consisting of sensory receptors, neural pathways, and cortical regions, governs perceptual phenomena. In humans, the complex nervous network enables bidirectional interaction between organisms and their external environment. This interaction imparts remarkable efficiency and intelligence in sensing, processing, and responding to stimuli [ 6 , 7 , 8 , 9 ]. Engineers and scientists have long been devoted to the investigation and development of bionic neuromorphic architectures and intelligent neuromorphic systems that emulate the functionality of biological systems [ 10 , 11 , 12 ]. Classical computing systems based on the von Neumann architecture, operating on software simulations of biological neural networks, have traditionally utilized centralized, sequential processing and a store-and-compute separation manner [ 13 ]. However, biological neural systems inherently adopt a distributed, parallel, and event-driven approach, processing information through synapses and neurons [ 13 , 14 ]. In comparison, the latter are significantly more compact and efficient when handling complex real-world scenarios [ 15 ]. Consequently, investigating the complex cognitive aspects of information interaction and processing mechanisms in biological neural systems, especially at the neuromorphic device level, remains a paramount research focus in the field. To create artificial neuromorphic devices and systems capable of performing tasks with human-like efficiency and intelligence, initial efforts utilized traditional complementary metal oxide semiconductor (CMOS) technology to emulate various brain-like functions [ 16 , 17 , 18 ]. However, due to intrinsic limitations in CMOS technology, such as integration density and power consumption, which increasingly fail to meet performance and energy efficiency requirements, the focus has shifted towards novel electronics that better mimic neuro functions [ 19 , 20 ]. Among these, two-terminal devices, including memristor [ 21 , 22 , 23 , 24 ], phase-change memory (PCM) [ 25 , 26 , 27 , 28 ], and magnetic tunnel junctions (MTJ) [ 29 , 30 , 31 , 32 , 33 ], have been extensively explored and implemented in neuromorphic designs. Despite addressing several challenges, some issues for bioinspired neuromorphic applications remain unresolved. For instance, the stochastic nature of crystallization and device variability in PCM can hinder precise parameter control, introduce noise, and complicate the development of efficient algorithms, necessitating additional circuitry [ 27 , 34 ]. MTJ and memristors offer significant advantages for neuromorphic computing due to their nonvolatility and energy efficiency [ 35 ]. However, they still face substantial challenges related to scaling, performance variability, and integration with existing technologies [ 36 , 37 ]. These issues underscore the necessity for further optimization in device mechanisms and fabrication processes. As a consequence, tri-terminal-based transistors have emerged as promising candidates for advancing neuromorphic device development [ 13 , 14 , 17 , 38 , 39 , 40 ]. The inherent capacity of transistors to be effectively modulated via manipulation of the gate electrode bears resemblance to biological processes, thus garnering significant attention for their integration into neuromorphic systems [ 41 , 42 , 43 ]. Notably, electrolyte-gated transistors (EGTs), reliant on mixed ionic-electronic coupling and transport mechanisms, offer biocompatibility [ 14 , 44 ], low operating voltage [ 42 , 45 , 46 ], reliable mechanical flexibility [ 6 , 47 , 48 ], and simple fabrication processes [ 7 , 46 ]. Such attributes have facilitated the emulation of biological neural network behavior, enabling the realization of sophisticated computational architectures and the exploration of pioneering bioinspired neuromorphic applications. However, the majority of current reported reviews on EGT primarily focus on the design of p-type and/or n-type materials and device structures, the detection of specific biomarkers, and the study of synaptic electronics [ 25 , 49 , 50 , 51 ]. Overviews related to the construction of synapse, neuro, and more complicated bioinspired neural systems are still lacking. In this review, we present a meticulous analysis of recent advancements and prospective avenues within the domain of bio-inspired electronics utilizing electrolyte gated transistors. Initially, we expound upon the foundational principles underlying the utilization of electrolyte gate transistors for the realization of neuromorphic devices, encompassing synaptic and neuros functionalities. Subsequently, we furnish an exhaustive exposition on bio-inspired neuromorphic electronics, with a particular emphasis on the evolution of pioneering bionic systems endowed with sensing, information processing, and cognitive modulation capabilities. Furthermore, we delineate advancements in interactive systems characterized by perceptual, storage, and computational functionalities, notably encompassing tactile, visual, chemical, and other multimode neuromorphic systems. Finally, we conclude with a perspective on development and research focal points, alongside the challenges associated with translating these innovations into practical applications."
} | 1,712 |
25558867 | PMC4867547 | pmc | 554 | {
"abstract": "Metabolic engineering of microorganisms such as Escherichia coli and Saccharomyces cerevisiae to produce high-value natural metabolites is often done through functional reconstitution of long metabolic pathways. Problems arise when parts of pathways require specialized environments or compartments for optimal function. Here we solve this problem through co-culture of engineered organisms, each of which contains the part of the pathway that it is best suited to hosting. In one example, we divided the synthetic pathway for the acetylated diol paclitaxel precursor into two modules, expressed in either S. cerevisiae or E. coli , neither of which can produce the paclitaxel precursor on their own. Stable co-culture in the same bioreactor was achieved by designing a mutualistic relationship between the two species in which a metabolic intermediate produced by E. coli was used and functionalized by yeast. This synthetic consortium produced 33 mg/L oxygenated taxanes, including a monoacetylated dioxygenated taxane. The same method was also used to produce tanshinone precursors and functionalized sesquiterpenes.",
"introduction": "Introduction Plants synthesize numerous structurally complex compounds that have important therapeutic properties 1 – 6 , e.g. paclitaxel, a potent antitumor agent 1 . Heterologous production of these molecules in industrial microbes—mainly bacteria and yeasts—could provide a robust and sustainable production process. However, in bacteria it has been challenging to functionally express sophisticated eukaryotic enzymes that are often required in the synthesis of complex compounds 7 ; on the other hand, it has been equally difficult to engineer yeasts for high-yield production of building blocks of natural products, e.g. the isoprenoid biosynthetic pathway of bacteria has higher theoretical yield than that of yeasts 1 . In nature, microbes can form interacting communities to accomplish chemically difficult tasks through division of labor among different species 8 . These natural microbial consortia have been used in food and other industries for decades 9 . Furthermore, interactions of microbial species in mixed microbial cultures were studied extensively in the 60s and 70s 10 , 11 , aiming to establish operating diagrams for maintaining synthetic co-culture, which has been challenging due to difference in their doubling time and secretion of toxic metabolites 11 . Recently, a few synthetic consortia comprising genetically engineered microbes have been reported for production of biofuels and chemicals 12 – 14 . However, these prior studies were mostly concerned with the stability of microbial consortia while the more recent work focused on utilizing non-conventional biomass, e.g. cellulose 12 , 13 . In these examples, which both involved two different species, the first species only provided the carbon source for the second, which harbored the essential pathway for the final product in its entirety and was able to make the final product on its own. Strictly speaking, none of this prior work examined the potential to use more than one species for the purpose of constructing a long synthetic pathway, which enables production of structurally complex compounds. In this study, we demonstrate the concept of reconstituting a heterologous metabolic pathway in a microbial partnership in which one microbe is engineered to synthesize a metabolic intermediate that is translocated to another microbe, in which it is further functionalized. In principle, it could be attractive to use synthetic microbial consortia for production of valuable metabolites, especially those with complex structures. One major advantage of this design is that each expression system and pathway module can be constructed and optimized in parallel, so that the time required would be significantly reduced. Other advantages of using synthetic consortia include, (i) taking advantage of unique properties and functions of different microbes, (ii) exploring beneficial interactions among consortium members to enhance productivity, and, (iii) minimizing problems arising from feedback inhibition through spatial pathway module segregation. We report the use of two model laboratory and industrial microbes, E. coli and S. cerevisiae in a consortium to produce precursors of the anti-cancer drug paclitaxel. E. coli is a fast growing bacterium that can be engineered to overproduce taxadiene, the scaffold molecule of paclitaxel 1 . S. cerevisiae , having advanced protein expression machinery and abundant intracellular membranes, has been suggested as a preferable host for expressing cytochrome P450s (CYPs), which functionalize taxadiene by catalyzing multiple oxygenation reactions 15 – 17 . We find that integration of parts of the whole pathway in separate species cultured together combines dual properties of rapid production of taxadiene in E. coli with efficient oxygenation of taxadiene by S. cerevisiae . This novel approach has overcome the challenges of using E. coli alone—perturbation of the fine-tuned taxadiene production by introducing CYPs and functional expression of these enzymes in E. coli 1 .",
"discussion": "DISCUSSION Our major motivation for using a stable co-culture is the introduction of modularity to the design of pathways for microbial metabolite production by assigning a different part of the metabolic pathway to each member of a partnership or synthetic consortium. In such an experimental set-up pathway modules can be separately optimized and assembled to enable optimal functioning of the complete pathway. The examples in this report demonstrate this modularity: the screening of a better promoter for CYP expression in yeast could be carried out independent of E. coli ( Figure 3 ), and producing the acetylated diol in the co-culture also only required modification of one of its modules ( Figure 5 ). Such modularity should significantly expedite the reconstruction of long biosynthetic pathways in microorganisms as the construction of the cells carrying the pathway modules can be carried out in parallel and the number of genetic modifications per cell is substantially reduced. To achieve this modularity, pathway modules in different cells should not directly interact with each other to minimize possible regulation. For example, CYPs and their reductase involved in taxane oxygenation generate reactive oxygen species 31 , 32 , which inhibit two enzymes (ISPG and ISPH) in the taxadiene biosynthetic pathway containing iron-sulfur clusters that are hyper-sensitive to ROS 33 . Spatial segregation, in two different microbes, of the pathway of taxadiene production from its oxygenation pathway prevents inactivation of ISPG/ISPH by ROS generated by CYPs. Because of modularity of a co-culture approach, we were able to exploit advantages of the different species. Before this study, taxadiene could only be overproduced in E. coli 1 while most biochemical characterizations of the taxadiene-functionalizing enzymes were carried out in S. cerevisiae 16 , 26 , 34 . By using E. coli to synthesize taxadiene and S. cerevisiae to functionalize it, we combined the advantages of the two species for taxane production (fast growth of E. coli and complete protein expression system of S. cerevisiae ). Using co-culture, we were able to synthesize a complex taxane (putative taxadiene-5α-acetate-10β-ol) ( Figure 5 ) that has never been produced by microorganisms growing on a simple carbon source in the past, and achieve higher titers of isoprenoid production than has been reported previously ( Figure 6b ). As most synthetic microbial consortia are competitive 12 , 13 ( Supplementary Fig. 7 ), a primary challenge in their design is to avoid the dominance of one species over another, due to a shorter doubling time 11 , 12 or production of substances that are inhibitory to the other species 13 . Conventionally, titration of the inoculum ratio 13 and optimization of growth conditions (such as pH and temperature 11 ) can be exploited to maintain coexistence. However, these strategies require time-consuming experimental trials or construction of sophisticated mathematical models 13 , whose parameters also need to be estimated experimentally. In addition, batch-to-batch variability can be high in these competitive co-cultures (data not shown). In this study, we avoided these complications by building a mutualistic co-culture in which S. cerevisiae used as its sole carbon source acetate, which was provided by and inhibitory to E. coli , which in turn grew better in the presence of yeast compared to without the yeast ( Supplementary Fig. 8 ). We applied additional genetic and growth constraints to enforce this cooperation, for instance, the respiration-deficient E. coli was forced to produce acetate as this was its primary way to generate cellular ATP ( Figure 4a ), and the yeast also had to consume acetate because it cannot utilize xylose ( Figure 2a ). Under such interdependency, the inoculum ratio of our co-culture can be simply set to over-inoculation of yeast (the inoculum ratio of yeast to E. coli was approximately 40:1, online methods, bioreactor experiments for the E. coli – S. cerevisiae co-culture ). This eliminated the inhibitory acetate levels, but did not result in yeast overpopulation, because yeast growth was strictly limited by the concentration of acetate produced by E. coli , leading to a balanced ratio of the two species (the ratio of yeast to E. coli was 1:2 at 41 h, Figure 4b ). Furthermore, this ratio was controllable through altering the specific acetate productivity ( Figure 4b ). Because of this ability to alter the consortium composition by increasing the relative yeast population, we managed to minimize accumulation of the pathway intermediate (taxadiene) and increase the titer of oxygenated taxanes ( Figure 4c and Supplementary Fig. 9 ). In addition to the mutualistic design, we also explored other strategies to avoid microbial competition. The first was a two-stage culture, in which E. coli was cultured separately for a few days before mixing with an active S. cerevisiae culture. This approach allowed both microbes to grow at their preferred conditions and taxadiene to be efficiently oxygenated ( Supplementary Fig. 10 ). However, this process required a longer cultivation time (180 h) and, additionally, it is more complicated than that of the mutualistic co-culture. We also explored a two-carbon-source strategy, in which xylose can only be utilized by E. coli and ethanol (manually added at low concentration, <2 g/L) was exclusively used by yeast ( Supplementary Fig. 11 ). A stable co-culture could be maintained under these conditions by controlling the ethanol addition, and oxygenated taxanes were also produced at a relatively high titer (8 mg/L in 130 h, Supplementary Fig. 12 ). However, both E. coli and S. cerevisiae produced acetate under this scheme leading to microbial inhibition ( Supplementary Fig. 12 ), which was eliminated in the mutualistic design. The co-culture concept is not restricted to the pairing E. coli with S. cerevisiae . We have briefly explored the use of two different E. coli strains for production of oxygenated taxanes ( Supplementary Fig. 13 ), which worked, although the titer was low, mainly due to lack of the mutualistic interactions present in the E. coli - S. cerevisiae co-culture. As a general guideline, a target pathway should be divided into modules, each of which should be assigned to a specific host strain so that the combined genetic traits of the consortium strains are favorable for pathway completion. These microorganisms should rely on each other for supply of an essential nutrient or detoxification of an inhibitory substance, ensuring a stable and controllable microbial composition. A necessary condition for co-culture is that the pathway intermediate (taxadiene) can cross cell membranes and is secreted to the extracellular medium. This property was first confirmed for taxadiene in prior studies where organic solvent mixed with E. coli cell culture was found to efficiently extract taxadiene (C20) from the cells in a bioreactor 1 . We also measured distribution of taxadiene in E. coli , medium and yeast in this study, which confirmed that taxadiene can cross cell membranes efficiently even in absence of an organic solvent ( Supplementary Fig. 14 ). This physiochemical property is shared by many isoprenoids ranging from C5 to C40, including isoprene 35 , limonene 3 , amorphadiene 36 and canthaxanthin 37 . Hence, the co-culture concept should be generally applicable to the production of most isoprenoids (in this study, we have experimentally validated production of sesquiterpene and diterpene, Figure 6 ). The experiments reported here provide evidence that a secondary metabolite pathway can be reconstructed in a microbial consortium, paving the way for engineering the microbial synthesis of natural compounds with complex structures that currently cannot be efficiently synthesized in a single microbe such as alkaloids and flavonoids (including >10,000 molecules), which all derived from aromatic amino acids that can be high-titer produced and excreted by E. coli 38 and functionalized by S. cerevisiae 39 . The co-culture can also benefit producing short chain dicarboxylic acids (C6-C10), whose precursors are short chain fatty acids that can be easily produced in engineered E. coli 40 , 41 and efficiently oxidized in the yeast expressing CYPs 42 ."
} | 3,388 |
33281549 | PMC7689062 | pmc | 555 | {
"abstract": "To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online adaptive weight pruning method that dynamically removes non-critical weights from a spiking neural network (SNN) to reduce network complexity and improve energy efficiency. The adaptive pruning method explores neural dynamics and firing activity of SNNs and adapts the pruning threshold over time and neurons during training. The proposed adaptation scheme allows the network to effectively identify critical weights associated with each neuron by changing the pruning threshold dynamically over time and neurons. It balances the connection strength of neurons with the previous layer with adaptive thresholds and prevents weak neurons from failure after pruning. We also evaluated improvement in the energy efficiency of SNNs with our method by computing synaptic operations (SOPs). Simulation results and detailed analyses have revealed that applying adaptation in the pruning threshold can significantly improve network performance and reduce the number of SOPs. The pruned SNN with 800 excitatory neurons can achieve a 30% reduction in SOPs during training and a 55% reduction during inference, with only 0.44% accuracy loss on MNIST dataset. Compared with a previously reported online soft pruning method, the proposed adaptive pruning method shows 3.33% higher classification accuracy and 67% more reduction in SOPs. The effectiveness of our method was confirmed on different datasets and for different network sizes. Our evaluation showed that the implementation overhead of the adaptive method regarding speed, area, and energy is negligible in the network. Therefore, this work offers a promising solution for effective network compression and building highly energy-efficient neuromorphic systems in real-time applications.",
"conclusion": "Conclusion In this work, we proposed an online adaptive weight pruning method that adapts the pruning threshold over time and neurons during training in an unsupervised SNN. The effects of the threshold adaptation over time and neurons were studied individually. Different functions used to adapt the threshold were applied and compared. It is demonstrated that both adaptation over time and neurons can improve the network performance against an online constant weight pruning method. The adaptation enables the network to reserve critical weights when the network is not trained enough at the early phase of training and balance the connection strength of excitatory neurons in the network to avoid largely deteriorating the performance of weak neurons. So, combining the two adaptation schemes can further improve network performance. The online adaptive pruning method provides better performance than the post-training pruning method, suggesting that it can not only improve training energy efficiency but also achieve higher accuracy. Regarding the computational cost, the number of SOPs was analyzed, which shows that the proposed online adaptive pruning method can significantly reduce the SOPs/image during both training and inference. Furthermore, comparisons with the previous works reveal that our method can lead to better accuracy and a more significant reduction in SOPs. The implementation overhead of the proposed method was evaluated in terms of processing speed, area, and energy, which is proven to be negligible in the network. Therefore, the proposed online adaptive pruning method provides a promising approach for reducing network complexity and improving energy efficiency with good performance in SNNs for real-time applications.",
"introduction": "Introduction In recent years, as the prediction of Moore’s law slows down prominently, neuromorphic computing has been widely regarded as a promising approach for large-scale computing. Neuromorphic systems are constructed following biological principles existing in our central nervous systems, which features in massive parallelism, collocated memory, and processors, and asynchronous event-driven computation ( Mead, 1990 ; Furber et al., 2014 ; Davies et al., 2018 ). Generally considered as the third generation of neural network models, spiking neural networks (SNNs) have started a paradigm shift in the brain-inspired research exploration. Different from artificial neural networks (ANNs), SNNs are well-known for its capability of accurately capturing neural dynamics and biological behaviors of the central nervous system and processing spatio-temporal information. With energy-efficient computation and parallel information processing features, SNNs are widely adopted for building neuromorphic hardware systems ( Thakur et al., 2018 ). In such systems, information is transmitted through synapses from a presynaptic neuron to a postsynaptic neuron on the occurrence of an event (or a spike). Neural networks require deep and complex structures to tackle real-world tasks, like pattern recognition, object detection, and motor controls ( Shrestha and Mahmood, 2019 ). The complexity leads to large synaptic memories and high energy consumption, which poses a big challenge in hardware implementation. Therefore, it is necessary to search for practical solutions to reduce network complexity and improve the energy efficiency of SNNs. During early brain development, creations of synaptic connections between neurons exponentially increase with the numerous stimuli coming from environments every day ( Zillmer and Spiers, 2001 ). The rapid synapse creation is vital for learning and memory formation. Between early childhood and adulthood, weight pruning occurs as a natural process during which our brain eliminates unnecessary synaptic connections. It is regarded as a purposeful process of maintaining a more efficient brain function. This biological process has been extensively studied in current ANNs for its attractive memory and energy reduction benefits. Han et al. (2015) introduced a training-pruning-retraining approach that can reduce the number of synaptic connections by 12x and computational operations by 5x for the VGG-16 network. Weight pruning was also proved to be an effective means of alleviating the overfitting problem in ANNs ( Paupamah et al., 2020 ). Moreover, to avoid irregular structure of pruned weight matrices and aid in the leverage of sparse matric-vector multiplication, a variety of structured weight pruning techniques were proposed where the entire rows and columns in the weight matrices are removed by imposing certain constraints during the pruning process ( Anwar et al., 2017 ; Sredojevic et al., 2017 ). While weight pruning has been widely applied in different ANNs, the benefits that weight pruning could provide for SNNs have yet to be explored. Limited works have reported applying weight pruning in SNNs so far ( Iglesias et al., 2005 ; Rathi et al., 2019 ; Shi et al., 2019 ). Rathi et al. proposed a spike-timing-dependent plasticity (STDP) based online synaptic pruning method, which sets non-critical weights to zero during the training phase and removes the weights below a certain threshold at the end of training ( Rathi et al., 2019 ). This method only sets the weights to zero without removing them. It allows them to be updated during training, which is not an effective approach to improve the energy efficiency for online learning systems. Shi et al. presented an online soft-pruning method by setting the weights below a constant threshold to a constant value instead of removing them during training. While this method could reduce the number of STDP updates during training, it does not induce any sparsity in the network, leading to little benefit for hardware implementation. Moreover, these pruning methods use a constant weight threshold throughout the whole pruning process. With a constant threshold, the network can not effectively select the non-critical weights to be pruned. In the early phase of training, weights are not completely learned, and a large threshold can mistakenly remove important weights. If a small threshold is used, some non-critical weights can not be pruned at the end of training since these weights could grow. On the other hand, the connection strength of neurons in one layer with the previous layer varies. A large threshold could remove most of the critical weights from the neurons with weak connection and hence severely affect the neurons’ function, which could lead to substantial performance degradation of the network. Therefore, it is crucial to adapt the weight threshold over time and all the neurons during training to improve network performance. In this work, we propose an online adaptive weight pruning method that adapts the pruning threshold over time and neurons during training and completely remove the weights below the threshold from the network. It is demonstrated to be an effective approach for reducing network complexity and improving energy efficiency during both training and inference operations. The main contributions of this work are summarized as follows. • A simple online adaptation scheme for the pruning threshold is presented, which can change the threshold dynamically over time during training. It also considers the spatial difference of the connection strength of neurons in one layer with the previous layer and adapts the threshold over the neurons based on their firing activity. • The proposed method is demonstrated to be more effective in retaining classification accuracy after pruning than the constant threshold weight pruning and neuron pruning methods. It resulted in a 67% reduction in synaptic operations (SOPs) while outperforming the previously reported soft weight pruning method by 3.33%. The advantage of the proposed method was confirmed in the SNN on different datasets with different network sizes. • In terms of training, the proposed online adaptive pruning method outperformed post-training pruning methods by providing more than 30% reduction in training SOPs when the pruning percentage is larger than 90% while providing more than 3% higher classification accuracy, which shows significant potential for developing high-performance and energy-efficient online neuromorphic learning system. • The overhead of implementing the proposed method in a neuromorphic system is demonstrated to be insignificant in terms of processing speed, area, and energy. This paper is organized as follows: section “Methods and Results” introduces different neural models used in this work and the SNN architecture. It then presents an overview of our methods, algorithmic implementation details, and pruning results for each method. In section “Comparisons and Discussions,” different pruning methods are discussed and compared. Section “Conclusion” concludes this work.",
"discussion": "Comparisons and Discussion Comparison Among the Proposed Weight Pruning Methods Firstly, we will compare the three different adaptation functions and select the best spike count interval. Figure 7A shows the comparison among the three adaptation functions with the optimized adaptation factors for the APT method. Clearly, the function f 1 gives the best performance improvement over the constant pruning method, i.e., the highest accuracy when connectivity is smaller than 15% and similar accuracy to other functions otherwise. This can be attributed to the fact that f 1 allows the pruning threshold to grow slowly at the early phase of the pruning process and hence more weights to be trained. It increases the threshold rapidly at the end, which guarantees largely reduced network connectivity. Figure 7B shows the comparison among the three adaptation functions with the optimized adaptation factors for the APN method. The same conclusion can be drawn that the function f 1 gives the best performance. Moreover, after selecting the adaptation function as f 1 , we studied the effect of the spike count interval on the performance. The results are shown in Figure 8 . The spike count interval is used to identify how similar the firing activities of neurons in the same group are. In Figure 8A , with a smaller interval, a smaller initial threshold is needed to reach a certain threshold. A small interval results in a large number of groups and hence creates a large difference in pruning threshold among the groups. This can cause a very high threshold to be applied in the group with weak neurons and deteriorate their performance significantly. So, it is not an effective grouping. A large interval can gather the neurons with very different firing activity into one group where the same pruning threshold is shared. This way is also not effective because a large threshold can significantly deteriorate the performance of weak neurons and a small threshold is not able to remove enough non-critical weights from strong neurons. From the results in Figure 8B , SI = 30 shows the best performance. FIGURE 7 Performance comparison among different adaptation functions in the SNN trained on MNIST dataset with 100 neurons. (A) APT: Online adaptive pruning over time, and (B) APN: Online adaptive pruning over neurons. The spike count interval is set as 30. FIGURE 8 Simulation results of online adaptive pruning over neurons for different spike count intervals (SI) in the SNN trained on MNIST dataset with 100 neurons. (A) Connectivity vs. initial threshold, and (B) Accuracy vs. connectivity. SI = Infinity (Inf) means that there is only one group and hence no adaptation over neurons. To apply adaptation over both time and neurons, we combined the proposed adaptive pruning methods with the selected adaptation functions and adaptation factors. In this approach, the pruning threshold is increased over time and adapted across all the excitatory neurons. The comparisons among the proposed adaptive pruning methods for MNIST dataset and Fashion-MNIST dataset are shown in Figures 9A,C obtained from the SNN with 100 excitatory neurons, respectively. The same adaptation function and parameters were used to obtain the pruning results on Fashion-MNIST dataset. For all the pruning methods, up to 80% of weights trained on MNIST dataset can be pruned with less than 1% accuracy loss. It is because the trained weight maps on MNIST dataset are very sparse, as shown in Figure 10A . Whereas, only up to 50% of weights trained on Fashion-MNIST dataset can be pruned with less than 1% accuracy loss since the input patterns from Fashion-MNIST dataset are more complex, as shown in Figure 10B . Clearly, applying adaptation over both time and neurons can further improve the network performance, especially when the network becomes very sparse (connectivity < 10%). When the sparsity of the network increases, the performance of each excitatory neuron is very sensitive to critical weights, so it is very important for a pruning method to effectively identify critical weights and prevent the neurons from failure. The effect of the threshold adaptation lies in two different aspects. The first one is to allow the network to reserve critical weights when the network is not trained enough in the early phase of training. The second aspect is to balance the connection strength of excitatory neurons in the network so that more weights can be pruned from strong neurons and less from weak neurons to avoid causing substantial performance degradation of some neurons since neurons are critical processing units in the network. Moreover, a post-training pruning method is included for comparison ( Rathi et al., 2019 ). Instead of pruning weight while training, this method prunes weights after the training process is done. It shows slightly better performance than the online constant pruning method but much worse performance than the proposed online APTN method when the connectivity is smaller than 10%. This is because, during the online constant pruning process, some critical weights can be mistakenly removed, whereas the adaptive method can effectively reserve the critical weights and provide more chances for them to be trained. The proposed pruning methods were also studied in the SNN with 800 excitatory neurons trained on both datasets. The adaptation function f 1 was used. The time adaptation factor, the neurons adaptation factor, and the spike count interval were optimized and selected as 1.2, 1.15, and 30, respectively. The comparison among the proposed pruning methods is shown in Figures 9B,D . The pruning results show similar comparisons, and the same analysis can be applied. The post-training pruning shows slightly better performance than the online constant pruning method but worse performance than the proposed online adaptive pruning methods. The pruning results further confirm that the proposed APTN method outperforms the other weight pruning methods, especially when the network becomes very sparse (connectivity < 10%). We can draw a conclusion that the proposed APTN method is the most effective pruning method that can significantly reduce network connectivity and maintain high accuracy. FIGURE 9 Comparison among different weight pruning methods in the SNN trained on different datasets. MNIST dataset: (A) 100 excitatory neurons and (B) 800 excitatory neurons. Fashion-MNIST dataset: (C) 100 excitatory neurons and (D) 800 excitatory neurons. FIGURE 10 Trained weight maps on (A) MNIST dataset and (B) Fashion-MNIST dataset in the SNN with 100 neurons without pruning. Each pattern in the maps is formed by arranging the weights associated with each neuron to a 28 × 28 matrix. For the proposed online pruning method, it is crucial to find the right starting point for pruning during training. If pruning starts too early, weights are not learned enough, and hence some critical weights can be mistakenly removed. While the weights are learned for enough time after 30,000 training images, the remaining training process will further fine-tune the unpruned critical weights as they get more chance for STDP updates when more weights are pruned. This is due to the dynamics of the STDP learning rule that the weights with more contribution to the neural firing are strengthened more often. So if pruning starts during the last stage of training, the unpruned weights will not have enough chance to be fine-tuned to preserve good network performance. The adaptation will not be carried out effectively, and there will be less improvement in accuracy and training energy efficiency. In section “Methods and results,” we decided to start pruning after training over 30,000 images based on the change of network dynamics. To further investigate the impact of the number of pre-pruning training images, we have obtained pruning results for the various number of pre-pruning training images which are presented in Figure 11 . It confirms that pruning too early causes more performance loss. If the pruning happens at the later stage of training (50,000 pre-pruning training images), the performance loss is also observed, as there is almost no adaptation effect. 30,000 is proven to be the optimal point where neurons start to fire stably, and weight updates start to stabilize. Different from the post-training pruning method (60,000 pre-pruning training images), the online APTN method requires a crucial starting point during training in order to achieve the best network performance, and it also provides more chance for the unpruned critical weights to be trained during the training process. Starting at the 30,000 point, the online APTN method outperforms the post-training pruning method, especially when the network becomes very sparse, as demonstrated before. FIGURE 11 MNIST accuracy results at different connectivity values in the SNN with 100 neurons after applying APTN method. The number of pre-pruning training images was changed from 1,000, 10,000, 20,000, 30,000, 50,000, to 60,000. Additionally, the selection of an adaptation function and the corresponding adaptation factors can be further optimized with more choices of functions and a finer grid of factor values. However, this is not in the scope of this work that aims to demonstrate the effectiveness of the proposed adaptive pruning method. Computational Cost Reduction In general, for neuromorphic hardware systems, like TrueNorth, SpiNNaker, and Loihi, the fundamental operation is the synaptic event that occurs when a spike is transmitted from a source neuron to a target neuron. So the computational energy of an SNN is proportional to the synaptic activity ( Merolla et al., 2014 ). Pruning leads to a reduced number of synapses in the network and hence less synaptic events. To evaluate the energy improvement benefit of our proposed adaptive pruning method, we computed the number of SOPs per image (SOPs/image) during both training and inference. The training SOPs include weight accumulations and STDP updates, while the inference SOPs only count weight accumulations. The results for the SNNs trained on MNIST dataset with 100 and 800 excitatory neurons are shown in Figure 12A . The SOPs/image is normalized to the value obtained from the SNN without pruning. Clearly, the SOPs/image during both training and inference decreases almost linearly with connectivity, as the number of synaptic events is proportional to the number of unpruned synapses. The inference SOPs/image is reduced more significantly than the training SOPs/image. Moreover, the online pruning method can effectively reduce the number of training SOPs and hence improve training energy efficiency, making it promising for improving online learning systems. To help choose the network connectivity to reach the best overall performance, we define a figure of merit by considering accuracy loss and the total SOPs/image (training + inference) as below. FIGURE 12 (A) Normalized SOPs/image and (B) a figure of merit (FOM) for different connectivity values are obtained in the SNNs with 100 and 800 excitatory neurons using the online adaptive pruning over time and neuron method. \n F O M = A c c u r a c y l o s s × N o m a l i z e d t o t a l S O P s / i m a g e The defined FOM is used on a per-network basis to help identify the best network connectivity for that specific network, as demonstrated in Figure 12B . As a result, the best choices of the connectivity are 14.5% and 17% for 100-neuron and 800-neuron networks, respectively. Specifically, at 14.5% connectivity, the adaptive pruning method leads to a 27% reduction in SOPs/image during training and a 60% reduction during inference with 2.85% accuracy loss in the SNN with 100 excitatory neurons. In the case of 800 excitatory neurons, at 17% connectivity, the method leads to a 30% reduction during training and a 55% reduction during inference with only 0.44% accuracy loss. It should be noted that the proposed FOM provides one way to determine the best network connectivity, and other factors or definitions could also be applied depending on the requirements of specific applications. Comparison With Prior Works Neuron pruning is one of the structured weight pruning strategies, which eliminates all the weights associated with the pruned neurons and reduces the network complexity proportionally. However, directly removing neurons from the network could cause severe deterioration of network performance. We compared the proposed online weight pruning methods with an online adaptive neuron pruning method presented in our previous work ( Guo et al., 2020 ). The comparison is shown in Figure 13 . In Figure 13A , the online adaptive neuron pruning method shows worse accuracy than the weight pruning methods, which proves that weight pruning is more effective in preserving network performance. Despite the severe accuracy drop, the neuron pruning method requires fewer training SOPs/image than the adaptive weight pruning method and can reduce the inference SOPs/image much more significantly. Moreover, an additional benefit of the neuron pruning method is the elimination of state memory and processing power of the pruned neurons. FIGURE 13 Comparison between online weight pruning methods and an online adaptive neuron pruning method in the SNN trained on MNIST dataset with 100 excitatory neurons. (A) Accuracy and (B) normalized SOPs/image change with connectivity. CWP, AWP, and ANP are short for constant weight pruning, adaptive weight pruning, and adaptive neuron pruning, respectively. An online soft weight pruning method for unsupervised SNNs was reported in Shi et al. (2019) . Unlike conventional pruning methods, instead of removing the pruned weights, this method sets the pruned weights constant at the lowest possible weight value or the current value and stops updating them for the rest of the training process. By setting the pruned weights to the lowest possible value, the soft pruning method is equivalent to the constant pruning method in our case since the lowest value is 0. In this comparison, we refer to the soft pruning method as the case where the pruned weights are kept constant at their current values. Since the soft pruning method does not induce the sparsity in the network, the connectivity remains 100% and hence is not applicable in the comparison. Instead, we use the unpruned percentage that is the percentage of the unpruned weights in the total weights before pruning. In Figure 14A , it can be seen that the soft pruning method starts to have performance improvement over the constant pruning method after the unpruned percentage drops below 10%. Our proposed adaptive pruning method gives better performance when the unpruned percentage is between 5% and 20%, but worse performance after the unpruned percentage drops below 5%. When most of the weights are pruned, the soft pruning method is still able to retain high accuracy by keeping the pruned weights that were trained for some time in the network. However, the soft pruning method brings less benefit to the computational cost compared with the adaptive pruning method. Figure 14B shows that it contributes to less reduction in training SOPs/image and no reduction in inference SOPs/image. In comparison, our proposed adaptive pruning method can lead to more reduction in SOPs/image, especially during inference. The constant pruning method gives the most improvement in decreasing the training SOPs/image when a large number of weights are pruned at the cost of severe accuracy loss, because it applies a large constant threshold throughout the whole pruning process. FIGURE 14 Comparison with the online soft weight pruning method adopted from Shi et al. (2019) in the SNN trained on MNIST dataset with 100 excitatory neurons. (A) Accuracy and (B) normalized SOPs/image change with unpruned weights percentage. Since the soft pruning method does not remove the pruned weights, the connectivity is not applicable as the x axis here. Instead, the unpruned percentage is used, which is defined as the percentage of the unpruned weights in the total weights before pruning. Comprehensive comparisons among different pruning methods in terms of accuracy loss and SOPs are provided in Algorithm 1 , Table 3 , including results for two network sizes and two datasets. The reduction is defined as the reduced percentage of the SOPs/image by pruning against the SOPs/image in the SNN without pruning. Network connectivity is selected as 10%. The neuron pruning method achieves the highest reduction in inference SOPs but the worst accuracy loss on both datasets. The post-training weight pruning method is able to produce small accuracy loss but no reduction in training SOPs. The soft online pruning method leads to the least accuracy loss on Fashion-MNIST dataset, because classifying more complex patterns in the dataset is more sensitive to the weights loss and this pruning method keeps the pruned weights in the network at their current values instead of removing them. However, this method leads to no benefits in reducing inference operations. The constant online pruning method can reduce both training and inference operations effectively at the cost of high accuracy loss. Our method achieves the least accuracy loss on MNIST dataset and slightly higher accuracy loss on Fashion-MNIST dataset than the soft online pruning method. Our method can lead to a large reduction in SOPs comparable to the constant online weight pruning and adaptive online neuron pruning methods during both training and inference. The network size has no substantial impact on the comparisons. In conclusion, our proposed adaptive pruning method can significantly reduce computational operations during both training and inference and maintain high accuracy at the same time. TABLE 3 Comparison among different pruning methods in the SNN trained on Fashion-MNIST dataset. Pruning methods Accuracy loss 100/800 Training SOPs reduction 100/800 Inference SOPs reduction 100/800 Online adaptive neuron pruning Guo et al., 2020 42.14%/40.88% 23%/39% 90%/90% Post-training weight pruning Rathi et al., 2019 16.35%/19.98% 0%/0% 82%/85% Online soft weight pruning Shi et al., 2019 12.23%/9.63% 27%/29% 0%/0% Online constant weight pruning Shi et al., 2019 20.43%/19.24% 45%/49% 88%/87% Online adaptive weight pruning (Our work) 14.09%/13.67% 28%/31% 85%/84% Accuracy loss and SOPs reduction for two network sizes (100 and 800 neurons) are shown. The connectivity is selected as 10%. Implementation Overhead The proposed adaptive pruning algorithm can be implemented in hardware systems without adding significant overhead. To investigate the overhead, we chose three metrics: processing speed, area, and energy. Figure 15A shows the software simulation runtime of the whole network during training, including the time used for executing the pruning algorithm. The software simulation is programmed in Python language and runs sequentially in a single process. The runtime decreases with the increasing pruning percentage (decreasing connectivity), which proves that the proposed online pruning method is able to shorten the network runtime as it reduces the number of SOPs, including weight accumulations and STDP updates. Besides, the APTN pruning runtime is negligible compared to the total runtime (SNN runtime plus pruning runtime). For example, in Figure 15B , the pruning runtime percentage is around 0.001% at the batch size of 5,000 and less than 0.04% even when the batch size is decreased to 100. For hardware runtime, we estimated the number of clock cycles required to run the pruning algorithm in a general synchronous digital system, as shown in Table 4 . At each batch, the proposed pruning process requires three essential phases, including dividing the neuron groups (grouping phase), adapting pruning thresholds over neurons (adapting phase), and writing 0 s to weight memory (weight pruning phase) operations. The grouping method with sorting in the proposed algorithm can be replaced by simply searching for the minimum and maximum values of firing activities of neurons and dividing the whole range of firing activity (max – min) according to the spike interval without performance loss. The adapting phase is simply to position each neuron in the right group according to its firing activity and assign the corresponding pruning threshold. Both grouping and adapting phases depend on the number of groups that varies over time but is smaller than 20. We used 20 for the estimation. For both phases, we assume that no parallelism is applied for estimating the upper limit. Moreover, we assume that all the weights are stored in one memory, and the weight pruning operations can only access one weight at a time. However, it should be noted that multiple accesses to weight memory are available in practice. So the estimation is at the upper limit of the running cycles of the pruning algorithm. The estimated number of different phases in the table is for single-batch pruning. The number of clock cycles for the SNN training phase is much larger than the number of training SOPs/image, 96,099, since each SOP includes many processes, such as searching for destination addresses, reading out synaptic weights, routing spikes to the destination, and weight addition or STDP update, which takes multiple cycles to finish. It can be seen that the average number of clock cycles per image for pruning with a small batch size of 100 is far smaller than the number of training SOPs. Therefore, the hardware runtime of the pruning algorithm is negligible. FIGURE 15 Simulation runtime. (A) Total network simulation runtime during training at different network connectivity values after applying the proposed adaptive pruning method APTN. (B) Pruning algorithm runtime percentage over the total network simulation time at different network connectivity values. Different batch sizes were used as 100, 1,000, and 5,000. TABLE 4 Estimated number of clock cycles and computational operations (Ops) for the pruning algorithm and SNN training phase in the network with 100 neurons. Phase Pruning (single batch) Pruning (average per image) Batch: 100/5,000 SNN Training (average per image) Grouping Adapting Weight pruning # Cycles 120 2,000 78,400 404/9 ≫96,099 # Ops 400 1,100 156,800 794/18 96,099 Two batch sizes (100 and 5,000) were used for estimating the average per image. The number of operations for SNN training only includes synaptic operations obtained at the connectivity of 10%. For energy overhead, the number of basic operations, such as addition, comparison, and memory access, was estimated in Table 4 for different pruning phases. For the estimation, 16 bits and 8 bits were used to represent the integer part and fractional part, respectively. The multiplication operation involved in the algorithm can be approximated by shift and addition operations. The grouping phase requires addition, comparison, and memory access operations, while the adapting phase only needs comparison and memory access operations. The operations in the weight pruning phase involve memory access and comparison between weights and a pruning threshold. The average number of operations per image is 794 and 18 for the batch size of 100 and 5,000, respectively, which are very small compared to the number of training SOPs/image. For energy comparison, we take an example of SNN implementation on Loihi neuromorphic hardware ( Davies et al., 2018 ). The reported minimum energy/SOP on this hardware is 23.6 pJ. So the minimum SOP energy per image is around 2.3 uJ. Since memory access consumes more energy than addition and comparison operations, we used the energy of memory access for all the operations for the comparison. The memory access (read and write) to an SRAM cell under the same technology consumes around 0.5 pJ ( Yang et al., 2016 ). So, the estimated energy for pruning operations per image is 3.4 nJ at the batch size of 100, which is around 0.1% of the SOP energy. Besides, the network also spends energy on updating neural states in neural cores, which makes the percentage even smaller. Thus, we can claim that the energy overhead is negligible. As for area overhead, the number of essential digital gates and memories required to implement the pruning algorithm and equivalent NAND gates was estimated in Table 5 . Each weight needs a flag bit to indicate if it has been pruned. This bit can be simply attached to the weight bits in the memory with very little overhead. The number of NAND gates for an SNN with 100 neurons was estimated according to the proposed digital implementation from Guo et al. (2020) . Clearly, the number of equivalent NAND gates for the pruning algorithm is much smaller than that for the SNN. For example, the number of equivalent NAND gates in the pruning unit is only around 0.3% of that in the SNN. For memory comparison, in the pruning unit, firing activity and pruning threshold of neurons are assumed to be stored in block RAMs (BRAMs). Two 18 Kb BRAMs are totally enough, which is much smaller than the memory size required in the SNN. Therefore, the area overhead is very small. TABLE 5 Estimated number of essential digital gates and memories required for the pruning algorithm and equivalent NAND gates. Pruning unit SNN Sub/Add (16 bits) Comparator (16 bits) Register NAND BRAM (18 Kb) NAND BRAM (18 Kb) 4 18 620 8284 2 3.0 × 10 6 50 The number of NAND gates and BRAMs for an SNN were obtained with 100 neurons according to the proposed digital implementation from ( Guo et al., 2020 ). Impact and Future Work The proposed adaptive method would be effective in improving the compression rate and preserving good network performance in other neural networks, as different threshold adaptation techniques have also been applied to improve the pruning performance in other neural networks. The iterative pruning method has been the most successful and popular pruning technique in ANNs, which relies on numerous cycles of training and pruning in order to induce sparsity in weight matrices and preserve network performance ( Han et al., 2015 ). This method iteratively sets the weights below a certain threshold to zero and retrains the network to regain its performance. The main limitation is the need to manually tweak the thresholds for neurons in different layers to achieve the best results by iterative tuning. While this iterative method can effectively compress networks, it requires a large amount of time and resources in order to find the optimized sparse networks, which hinders its use in large-scale applications. In order to eliminate the need for iterative threshold tuning, many works have explored to adapt threshold values for neurons in different layers by training the thresholds together with weights ( Manessi et al., 2018 ; Ye et al., 2019 ; Azarian et al., 2020 ). These methods use the same concept of adapting threshold spatially as in our method based on the fact that neurons in different layers have different sensitivity to pruning thresholds, but in a different adaptation process. In our method, we used the firing activity of neurons to determine their pruning thresholds, while these methods adapt the thresholds based on the network loss in a supervised fashion. These methods were able to find the optimal thresholds for each layer and do not require pruning-retaining cycles. The results have shown that with the threshold adaptation, their methods can achieve a much larger compression rate with higher classification accuracy than the method without adaptation. Moreover, threshold adaptation over time during training was demonstrated to be beneficial in accelerating the pruning process and achieving a higher compression rate. Narang et al. (2017) proposed to adapt the pruning threshold over time using a monotonically increasing function during training. A heuristic function was presented to calculate the threshold at different iteration steps, which requires many hyper-parameters. They tested the method in different types of recurrent neural networks (RNNs) and demonstrated that this adaptive method could achieve better network performance and a higher compression rate without pruning-retraining cycles than a hard pruning method that simply prunes the weights with a constant threshold. Therefore, we believe that the proposed adaptive pruning method can be useful in improving the compression rate and preserving good network performance in ANNs. To test the versatility of our method, we will investigate the impact of the proposed adaptive method in deep SNNs in our future works."
} | 9,930 |
26097503 | PMC4475311 | pmc | 557 | {
"abstract": "Background Production of fuels from the abundant and wasteful CO 2 is a promising approach to reduce carbon emission and consumption of fossil fuels. Autotrophic microbes naturally assimilate CO 2 using energy from light, hydrogen, and/or sulfur. However, their slow growth rates call for investigation of the possibility of heterotrophic CO 2 fixation. Although preliminary research has suggested that CO 2 fixation in heterotrophic microbes is feasible after incorporation of a CO 2 -fixing bypass into the central carbon metabolic pathway, it remains unclear how much and how efficient that CO 2 can be fixed by a heterotrophic microbe. Results A simple metabolic flux index was developed to indicate the relative strength of the CO 2 -fixation flux. When two sequential enzymes of the cyanobacterial Calvin cycle were incorporated into an E. coli strain, the flux of the CO 2 -fixing bypass pathway accounts for 13 % of that of the central carbon metabolic pathway. The value was increased to 17 % when the carbonic anhydrase involved in the cyanobacterial carbon concentrating mechanism was introduced, indicating that low intracellular CO 2 concentration is one limiting factor for CO 2 fixation in E. coli . The engineered CO 2 -fixing E. coli with carbonic anhydrase was able to fix CO 2 at a rate of 19.6 mg CO 2 L −1 h −1 or the specific rate of 22.5 mg CO 2 g DCW −1 h −1 . This CO 2 -fixation rate is comparable with the reported rates of 14 autotrophic cyanobacteria and algae (10.5–147.0 mg CO 2 L −1 h −1 or the specific rates of 3.5–23.7 mg CO 2 g DCW −1 h −1 ). Conclusions The ability of CO 2 fixation was created and improved in E. coli by incorporating partial cyanobacterial Calvin cycle and carbon concentrating mechanism, respectively. Quantitative analysis revealed that the CO 2 -fixation rate of this strain is comparable with that of the autotrophic cyanobacteria and algae, demonstrating great potential of heterotrophic CO 2 fixation. Electronic supplementary material The online version of this article (doi:10.1186/s13068-015-0268-1) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions In this study, quantitative analysis approaches have been developed for CO 2 fixation in heterotrophic microbes. The difficulty in access to CO 2 was found to be a limiting factor for heterotrophic CO 2 fixation. An E. coli strain capable of fixing CO 2 at a rate of 19.6 mg CO 2 L −1 h −1 or 22.5 mg CO 2 g DCW −1 h −1 was constructed by incorporation of partial cyanobacterial Calvin cycle and carbon concentrating mechanism. This work demonstrated that CO 2 fixation by the engineered heterotrophic E. coli can be as effective as the natural autotrophic cyanobacteria and algae, showing great potential of heterotrophic CO 2 fixation.",
"discussion": "Discussion Recycling CO 2 directly into fuels or chemicals is a potential approach to reduce carbon emission as well as to resolve energy crisis [ 6 , 7 ]. The past 5 years have witnessed great success in production of CO 2 -derived molecules that have potential to be used as fuels and chemicals by autotrophic microbes. Quantitative analysis in this study revealed that an engineered heterotrophic E. coli could assimilate CO 2 at a rate comparable to that of the autotrophic cyanobacteria and algae. It is noteworthy that the specific CO 2 -fixation rates of the E. coli strains were superior to most of the autotrophic microbes listed in Table 2 . Since E. coli can easily grow to a high density in fermentors under well-controlled conditions, we believe that heterotrophic microbes might be an alternative candidate for CO 2 fixation with great potential. The most striking advantage of using heterotrophic microbes for CO 2 fixation is their fast growth rates. The doubling times for E. coli and yeast are only 20 min [ 36 ] and 2 h [ 37 ], respectively, whereas those for common cyanobacteria and algae are in the range of 8–44 h [ 38 , 39 ]. Most autotrophic microbes use photosynthesis to provide energy for CO 2 assimilation and ultimately biomass accumulation. The theoretical maximum of solar energy conversion efficiency in photosynthesis is only 8–10 % [ 40 ], whereas the actual values for several species of cyanobacteria, microalgae, and plants do not exceed 3 % [ 41 ]. The low efficiency of photosynthesis can be ascribed to many inherent factors including insufficient absorption of all light wavelengths during light-dependent reactions and low carboxylation activity of Rubisco and existence of energy-consuming photorespiration during light-independent reactions [ 42 ]. Although many efforts have been made [ 43 , 44 ], dramatic increases in photosynthetic efficiency as well as growth rate are still big challenges for autotrophic microbes [ 44 ]. However, billions of years of evolution have enabled the heterotrophic microbes to efficiently assimilate the high-energy sugars to generate both carbon backbone and energy at the same time. Therefore, heterotrophic microbes might be a better choice for CO 2 fixation, since the fixed CO 2 can be easily joined into the central metabolism and then be efficiently metabolized. For the current version of the CO 2 -fixing E. coli strain constructed in this study, CO 2 was fixed at the expense of sugar consumption because all energy required for CO 2 fixation comes from sugar. However, it is not unbelievable that CO 2 fixation can occur without sugar consumption in heterotrophic microbes once energy can be supplied from other sources. The pioneer work by Liao’s group has demonstrated that electricity can be used as the sole energy to convert CO 2 to higher alcohols in Ralstonia eutropha [ 8 ], opening the door of employing other energy forms for CO 2 fixation. There is no doubt that improving the carboxylation activity of Rubisco is the ultimate way to increase the efficiency of CO 2 fixation in both autotrophic and heterotrophic microbes. However, decades of Rubisco engineering gained limited success [ 24 , 45 ]. In this work, the difficulty of Rubisco in access to CO 2 was found to be another limiting factor of heterotrophic CO 2 fixation. Expression of the CA from Synechococcus sp. PCC7002 under a weak constitutive promoter increased the E. coli CO 2 -fixation rate by 47.4 %. It is thus suggested that screening of the CA gene and optimization of its expression might be feasible ways to further improve the heterotrophic CO 2 -fixation rate. CA, which catalyzes the reversible interconversion of CO 2 and HCO 3 − , is widely existed in animals, plants, archaebacteria, and eubacteria, and plays an important role in many physiological functions [ 46 ]. Although some CAs prefer the direction of CO 2 hydration, the carboxysomal CAs in cyanobacteria and some chemoautotrophic bacteria favor the direction of HCO 3 − dehydration. To date, two forms of carboxysomal CAs (α and β), which are encoded by three types of genes with distinct sequences and structures ( CsoSCA for α-CA and CcaA and CcmM for β-CA), were reported [ 47 , 48 ]. The selected CA-encoding gene from Synechococcus sp. PCC7002 in this study was the CcaA gene. Whether the other two types of CA-encoding genes can be expressed in E. coli and whether their expression can increase the heterotrophic CO 2 -fixation rate are now under investigation by our group. Moreover, a stronger inducible promoter might be employed to enhance the CA expression in a controllable way to further improve the CO 2 supply. As a compensation for the low carboxylation activity of Rubisco, some autotrophic microbes have evolved some physical barriers (e.g., the semi-permeable caboxysome in cyanobacteria and the bundle sheath cells in C4 plants) to concentrate CO 2 around Rubisco. Inspired by these, we suppose that constraining CO 2 and the CO 2 -fixing enzyme in a microcompartment (e.g., reconstruction of the caboxysome in E. coli [ 49 ]) or recruiting the CO 2 -producing and CO 2 -fixing enzymes in a protein/RNA scaffold in E. coli might be an alternative way to further improve its CO 2 -fixation rate."
} | 2,035 |
34466784 | PMC8384924 | pmc | 558 | {
"abstract": "Summary We investigated the short-term dynamics of microbial composition and function in bioreactors with inocula collected from full-scale and laboratory-based anaerobic digestion (AD) systems. The Bray-Curtis dissimilarity of both inocula was approximately 10% of the predicted Kyoto Encyclopedia of Genes and Genomes pathway and 40% of the taxonomic composition and yet resulted in a similar performance in methane production, implying that the variation of community composition may be decoupled from performance. However, the significant correlation of volatile fatty acids with taxonomic variation suggested that the pathways of AD could be different because of the varying genus. The predicted function of the significantly varying genus was mostly related to fermentation, which strengthened the conclusion that most microbial variation occurred within the fermentative species and led to alternative routes to result in similar methane production in methanogenic bioreactors. This finding sheds some light on the understanding of AD community regulation, which depends on the aims to recover intermediates or methane.",
"introduction": "Introduction Microorganisms and their surrounding environments are the basis for a range of bioreactors, e.g. aerobic activated sludge, anaerobic digestion (AD), enabling the treatment of carbon or nutrient-enriched wastewaters; recently, increasing research attention has been paid to advancing the understanding of the functional role of microorganisms in bioreactors ( Rittmann and McCarty, 2012 ). Culture-dependent characterization was commonly used to understand species functionality and how it influenced reactor performance ( Roest, 2007 ; Vilela et al., 2020 ; Wagner et al., 1993 ). However, the core community of a bioreactor represents an artificial ecosystem consisting of multiple-syntrophic microbial communities, and it remains a significant challenge to understand the hidden mechanisms underlying the microbial ecology of bioreactors. Despite the ongoing controversy of the “1% culturability paradigm,” a majority of microorganisms in bioreactors, including specific functional species, may still be unculturable ( Martiny, 2019 , 2020 ; Steen et al., 2019 ). The rapid development of sequencing technology in the last 20 years has revealed an enormous microbial diversity; this has emerged as a culture-independent method to explore the ecological mechanisms underlying bioreactor microbial communities, advancing the understanding of hidden microbial ecological systems underpinning reactor performance ( Rittmann et al., 2006 ). The research questions about ecosystem function versus community composition in bioreactors opened up a Pandora's box: how do the microbial communities evolve, and how can the productivity and functional stability of a reactor be achieved or sustained ( Fernández et al., 1999 ). Next-generation sequencing accelerated the discoveries in this area by generating new knowledge and understating at a molecular biology level. Advanced metagenomic sequencing (targeted 16s rRNA amplicons and/or shotgun sequencing) offers insights into taxonomic classification, microbiome composition, and powerful tools to monitor the AD process performances and inform operators how to optimize AD pathways and performance by regulating the community composition in bioreactors. This could lead to sophisticated bioaugmentation strategies and enhanced performance and stability. Despite the microbial ecology focus on coupling the community composition variance to their function in natural ecosystems ( Waldrop et al., 2000 ; Waldrop and Firestone, 2006 ), a “decoupling” phenomenon existed. Notably, a long-term experimental study with amplicon and metagenomic sequencing showed that the microbial assembly relied on functional genes rather than species in accordance with the differences in Bray-Curtis distance ( Burke et al., 2011 ). This perspective has been enhanced in further studies which considered the variation of community assemblages and associated functions ( Louca et al, 2016 , 2018 ). Generally, the community composition and functions are always interlinked because of the presence of functional species, which are often regarded as a performance index, or an indicator to predict the physiological dynamics of sludge, e.g. bulking and foaming ( Wagner et al., 2002 ; Wagner and Loy, 2002 ; Yang et al., 2011 ). Several previous studies used the 16S rRNA or metagenomic sequencing technology to show the decoupling relationship between the community composition and the performance stability in artificial bioreactors in the presence of functional redundancy ( Fernández et al., 1999 ; Fernandez-Gonzalez et al., 2016 ; Vanwonterghem et al., 2016 ; Wang et al., 2011 ; Wittebolle et al., 2008 ). Recent studies showed that the functional redundancy of Fe(II) metabolism impacted the functional stability under a wide range of pH and Fe(II) concentrations ( Ayala-Muñoz et al., n.d. ). Although functional redundancy was considered as the driver for this decoupling, recent research has provided a different perspective showing that strong links exist between community composition and function, which disagrees with the redundancy widely observed in marine environments ( Galand et al., 2018 ). Interestingly, strong correlations between community composition and function have been also demonstrated in previous research where the microbial community was applied as a training database to predict bioreactor performance ( Günther et al., 2012 ; Lesnik and Liu, 2017 ). Overall, the relationship between community composition and function in bioreactors remains as a research frontier worthy of more in-depth exploration. Notably, limited studies have been published in this field investigating AD with industrial wastewater, with a notable gap on food-fermentation wastewater. In this study, experiments were performed in continuous stirred-tank reactors (CSTRs) to investigate changing and community composition during reactor start-up with a carbon-rich wastewater generated from the fermentation industry. Quorn Foods was selected to represent the advanced fermentation technology, and wastewater was collected from a mycoprotein production process at Quorn which are currently aerobically treated on-site. We have investigated different inocula originating from a full-scale reactor (inocula-F) and a laboratory-based system (inocula-L). The former was obtained from a centralized full-scale AD plant codigesting wastewater and organic solid waste, while the latter was obtained from a laboratory-scale anaerobic membrane bioreactor described in a previous study ( Tao et al., 2020 ). We selected the classical CSTR as the AD reactor in this study; a preadaptation was used to acclimate the inocula to adapt to the unique food-fermentation industrial wastewater from a Quorn mycoprotein production process. The same environmental stressor (ecological factor) was applied on different inocula that represent the distinct microbial sources; the similar performance observed from a CSTR offers evidence to elucidate that the community variation could be decoupled from the reactor performance over a short-term response period. Thus, the parameters for all CSTR were the same, which were also the key to compare the reactor performance with different inocula. Specifically, the two different inocula were preacclimated in batch reactors for 42 days (d) followed by a start-up of CSTRs inoculated with these acclimated sludges. All samples were collected daily during the preacclimation experiment and the start-up period of the CSTRs (23 d). The water samples were characterized by analytical methods to determine chemical oxygen demand (COD), volatile fatty acids (VFAs), total suspended solids (TSS), and volatile suspended solids (VSS). Biosamples were prepared for amplicon sequencing analysis following the DNA extraction and sequencing protocol detailed in the STAR Methods section. Overall, this study tested the hypothesis that the functional stability of AD over a short-term response period in anaerobic bioreactors fed with food-fermentation industry wastewater could be decoupled from the community composition variance because of the presence of functional redundancy, while the community composition variance may have an impact on the intermediate's generation. Two aspects enabled us to investigate the relationship between community variation and reactor performance, i.e. different inocula and the variation of specific community in a short term in anaerobic bioreactors. The former reflected the variance in microbial sources and initial composition, whereas the latter focused on the stability of reactor performance over a short-term community variation.",
"discussion": "Results and discussion Performance of reactors: preacclimation The initial inocula collected from the wastewater treatment systems (inocula-F and inocula-L) were introduced into Automated Methane Potential Test System (AMPTS) batch reactors to characterize the initial activity of these inocula under preacclimation experiments. As shown in Figure S1 and Table S1 , similar daily methane production trends were found across samples with varying hydraulic retention times (HRTs). The methane production of each cycle duration (4 d for inocula-F and 6 d for inocula-L) was 241.35 ± 15.23 NmL CH 4 for inocula-L and 217.74 ± 40.48 NmL CH 4 for inocula-F, indicating significantly higher performance of inocula-L than that of inocula-F (t test, p < 0.05). The inocula-F was originally collected from a wastewater treatment plant with inert and nonbiodegradable organics, which may reduce the activity of microorganisms per mass of culture. Considering the removal of COD each day, the average methane yield of inocula-L was 7.55 ± 2.05 NmL CH 4 /g COD [removed] , which was lower than the inocula-F average (9.41 ± 2.24 NmL CH 4 /g COD), but these yields were not statistically different (p > 0.05). Performance of reactors: start-up and operation The preacclimated sludges were inoculated into the CSTRs to obtain stable methane production. As shown in Figure 1 , the average methane production was 161.96 ± 76.11 NmL CH 4 /d and 179.71 ± 80.04 NmL CH 4 /d for inocula-F and inocula-L, respectively (p > 0.05). The COD removal efficiency was in the range of 30%–50% over the 23 days of operation, averaging 37.64 ± 7.01% for inocula-F, which was close to the performance of inocula-L (36.42 ± 5.85% COD removal, p > 0.05). As a similar amount of organics had been removed during methane generation, there was no significant difference in the observed methane yield (p > 0.05), and the CSTR inoculated with preacclimated inocula-F produced 58.51 ± 31.87 NmL CH 4 /g of COD daily, whereas inocula-L sludge generated 62.33 ± 30.75 NmL CH 4 /g COD. Although the original seed sludge was different in the two CSTRs, there was no statistical difference in their performance in terms of methane production and COD removal as the different inocula had evolved and improved over fed batch experiments, especially inocula-F. Figure 1 Operational performance of inocula-F and inocula-L CSTR; the rectangle represents the results of inocula-F, the triangle represents the results of inocula-L, the purple color represents the methane yield, and the black color represents the methane accumulation volume A further specific methanogenic activity (SMA) test confirmed the similar capacities of inocula-F and inocula-L sludge. As shown in Figure S2 , the cultured inocula-F sludge showed a similar performance (0.30 ± 0.08 g CH 4 /g VSS/d) to inocula-L (0.28 ± 0.11 g CH 4 /g VSS/d, p > 0.05). A pH increase was observed in both CSTR reactors over time – from 7.5 to 7.88 for inocula-F and from 7.5 to 7.73 for inocula-L ( Figure S3 ), although the decrease of pH in the first days could be caused by the accumulation of VFAs ( Figure S4 ). The fluctuation in pH was significantly different (p < 0.05), where inocula-F and inocula-L averaged 7.66 ± 0.25 and 7.56 ± 0.19, respectively. The variation in VFAs exhibited visible differences in Figure 2 . The propionate concentration in inocula-L was significantly higher than that of inocula-F (p < 0.05); in contrast, significantly lower iso-butyrate concentrations were found in inocula-L CSTR than in inocula-F (p < 0.05). As shown in Figure S4 , the change of VFAs in inocula-F and inocula-L CSTR was clearly different. In AD, VFA production from complex organics would eventually be converted into acetate and hydrogen which are the main substrates for methanogenesis, although the dynamics of VFA production are influenced by thermodynamic limitations caused by high-hydrogen partial pressure; therefore, the conversion pathways of VFAs could be diverse ( Łukajtis et al., 2018 ). Despite the similarity of performance in methane production, the variation in VFAs suggested that the fermentative pathway of the microbial community could be different for inocula-F and inocula-L CSTRs. Figure 2 Violin plot of VFA concentrations in inocula-F and inocula-L CSTR; the stars represent significance (p < 0.05), the red line is the mean value of each fatty acid Figure 3 A clustering results of all samples MDS plots of KEGG (A) and OTU (B) based on Bray-Curtis distance. Figure 4 Microbial taxonomy for CSTR stage Variation of microbial composition at genus level (A) and KEGG pathway 2 level (B). Figure 5 The variation of relative abundance of fermentative microorganisms over the operational period of the CSTR Variation of microbial composition and function profile The microbial taxonomy initially present in inocula-F and inocula-L sludge differed; Mesotoga accounts for nearly 80.70% and 31.20% in inocula-L and inocula-F sludge ( Figure S5 ), respectively, resulting in a clear difference of diversity (Shannon index, 1.40 vs 3.34, shown in Figure S6 ). The subsequent preacclimation eliminated the dominant advantage of Mesotoga , which declined to 53.80% ± 17.60% and 17.30 ± 6.60% at the end of the batch cycle. As shown in Figure S6 , the Shannon index increased in the batch mode of both inoculated reactors, considering the microbial evolution, while the phylogenetic diversity (PD) was steady over the batch operational period ( Figure S7 ). The PD of inocula-F and inocula-L was 9.84 ± 0.23 and 8.55 ± 0.44, respectively (p < 0.05). The PD value of inocula-F was still higher than that of inocula-L, which accorded with the alpha diversity index. There is a slight change of PD value in both inocula over the adaptation period, implying the change of microbial community was insignificant. In the continuous mode (shown in Figure S8 ), the phylogenetic diversity decreased over time in both the CSTRs inoculated with full-scale and laboratory sludge. The microbial community of inocula-F evolved in the CSTR with a significantly higher PD compared to inocula-L (p < 0.05). The PD value has been previously shown to be relevant to the stability of reactors, as the diversity provided a broader metabolic potential to adapt to environmental shocks in wastewater treatment bioreactors ( Yang et al., 2011 ; Zhang et al., 2019 ). From the viewpoint of microbial ecology, the species as phylogenetic relatives could compete for resources with a similar metabolism, and a lower PD value indicates that most species evolved from the same ancestor with a similar metabolism; however, in contrast, a higher PD may reflect complex metabolic patterns ( Cordero et al., 2012 ; David et al., 2014 ). The preacclimation enabled the cultures to maintain the PD values which would be beneficial for a community adapting to the environment. However, the washout and enrichment in the CSTRs indicated that a specifically functional unit would be enhanced with decreasing PD, which commonly occurred in our previous studies ( Cai et al., 2016 , Cai et al., 2019 , Cai et al., 2020 ). A variation in community composition was revealed by the change in PD; a clustering result is depicted in Figure 3 A where the nonmetric multidimensional scaling (NMDS) based on the Bray-Curtis distance demonstrated that the inocula-F batch was far away from the cluster of inocula-L batch, and the difference of operational taxonomic units (OTU) composition contributes to this distance. The corresponding Bray-Curtis dissimilarity between inocula-F and inocula-L batches was 75.96% ± 4.72%. The scatters representing inocula-F CSTR and inocula-L CSTR were clustered together; if considering time variation, the position of the time-dependent plots distributed from left to right on the main axis and from bottom to top in the second axis. The averaged OTU-based dissimilarity between the inocula-F and inocula-L CSTRs was 41.60% ± 5.40%, which was nearly half that in the batch mode. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (level 3) predicted by Tax4fun2 exhibited different clustering results of NMDS and Bray-Curtis dissimilarity in contrast to OTU-based analyses; as shown in Figure 3 , inocula-F and inocula-L batches were clustered together with lower dissimilarity (18.39 ± 4.57%). However, an average dissimilarity of 14.23 ± 3.69% was found throughout the operation of the CSTRs. The difference in distance between the OTU and KEGG matrices suggests that the function dynamic was different from OTU variation, i.e., a stable function might be obtained with varying composition; this research finding is in accordance with a previous study by Burke et al. Considering the time effect, as shown in Figure S9 , there is no observed trend in CSTR dissimilarity over time, but the stationary check augmented Dicky-Fuller test (ADF) verified that the change in OTU-based and KEGG-based Bray-Curtis dissimilarity is nonstationary, i.e., time relevant (both p > 0.05). The positive coefficient of linear regression indicated an invisible increased trend of dissimilarity for OTU-based and KEGG-based dissimilarity (0.0027 for OTU and 0.0003 for KEGG). As shown in Figure 4 , the variation of genus was clear, whereas the fluctuation in the KEGG pathway level 2 was invisible. These results were in accordance with the Bray-Curtis dissimilarity as the function profile was stable over the operational period. Environmentally driven changes in community assembly The contribution of environmental factors to community assembly was summarized as a deterministic theory, which is in contrast to stochastic or neutral processes ( Zhou et al., 2013 ). The null model can quantify the dominance of each process by adopting the index of normalized stochasticity ratio (NST). Clearly, the community assembly was more stochastic for inocula-F (72.17%) than the stochastic assembly of inocula-L CSTR (87.67%), indicating the shift in community could be mostly explained by stochastic processes rather than environmental determinism; in addition, a higher proportion of neutral processes was commonly found in previous studies ( Zhou et al., 2013 ). The redundancy of the microbial community was determined by redundancy analysis (RDA). The environmental variables explained 57.40% and 52.90% for inocula-F and inocula-L group, respectively. As mentioned earlier, functional redundancy could exist in a phylogenetically diverse community, while stochasticity could be dominant in modulating the redundancy section of the community without negative effects on performance. The partial Mantel test based on Bray-Curtis dissimilarity was used to detect the correlation between environmental variables and the microbial community. As shown in Table 1 , the pH significantly affected the variations in the inocula-F community at the OTU level (r = 0.72, p < 0.05), as well as the inocula-L CSTR system (r = 0.46, p < 0.05), while the iso-butyrate and iso-valerate were significantly correlated with community variation in inocula-F CSTR (r = 0.24, p < 0.05 and r = 0.40, p < 0.05). The iso-butyrate was also significantly correlated with community fluctuation (r = 0.28, p < 0.05) in the inocula-L CSTR. In addition, the acetate and propionate only demonstrated significant association (r = 0.19, p < 0.05 for acetate and r = 0.15, p < 0.05 for propionate) in inocula-L CSTR. Considering the variations in the KEGG pathway (level 3), pH was still significantly correlated with the inocula-F community in the CSTR, as the partial Mantel is 0.64 (p < 0.05). However, inocula-L CSTR showed an insignificant correlation with pH (p > 0.05), while the significant factors that affected the change of the KEGG pathway were propionate (r = 0.31, p < 0.05) and COD (r = 0.44, p < 0.05) in the inocula-L CSTR. The results of iso-butyrate and iso-valerate from the inocula-F CSTR were still significant, r = 0.31 (p = 0.003) and r = 0.31 (p < 0.05), respectively. Overall, performance was similar between the inocula-F and inocula-L CSTRs, which could be ascribed to their stable functional profiles even under the presence of taxonomic variation. Although the correlation pattern of VFAs with taxonomic variation were different between inocula-F and inocula-L CSTR, the community variation showed a strong correlation with metabolic products in both. The metabolic products could be affected by the microbial taxonomy, leading to a significant difference in VFA concentrations. Table 1 Partial Mantel test results of inocula-F and inocula-L in the CSTRs Environmental factors Inocula-F Inocula-L OTU level KEGG level OTU level KEGG level r p r p r p r p pH 0.7172∗ 0.001 0.641∗ 0.001 0.4637∗ 0.002 0.0824 0.139 Formic 0.1232 0.137 0.0225 0.372 0.091 0.124 −0.0828 0.789 Acetic −0.2862 1 −0.2166 0.986 0.1933 0.022 0.0015 0.447 Propionic −0.2292 0.993 −0.1542 0.958 0.1472∗ 0.034 0.3077∗ 0.001 Isobutyric 0.2357∗ 0.02 0.3063∗ 0.003 0.2757∗ 0.005 0.0428 0.269 Butyric −0.2424 0.999 −0.2325 1 −0.1597 1 −0.0485 0.701 Isovaleric 0.4048∗ 0.004 0.3069∗ 0.006 0.1109 0.071 0.0162 0.455 Valeric −0.2462 0.999 −0.1665 0.973 −0.0852 0.837 −0.0985 0.803 COD 0.0515 0.304 0.0413 0.311 0.1271 0.1 0.4429∗ 0.004 Methane −0.0278 0.547 −0.0185 0.529 −0.0748 0.816 −0.1535 0.942 ∗represent the significant result. The functional profile of the microbial communities was constructed by referring to Midas, as well as to FAPROTAX and previous literature, and the results can be found in Table S2 and Figure 5 . A significantly varying genus was defined as those which showed significant variance using the one sample t test, and the sum of relative abundance of varying species reached more than 80% in inocula-F or inocula-L group. A shown in Figure S9 , the most significantly varying genus had a metabolism which underpinned the fermentation process, accounting for 52.48 ± 5.91% for inocula-F and 46.42 ± 4.05% for inocula-L. In AD, the initial compounds are catabolized through a range of biochemical pathways including hydrolysis, fermentation, acetogenesis, and methanogenesis to achieve energy recovery and organics removal. The species-related VFAs always show high diversity in the phyla of Bacteriodates , Chloroflexi , Firmicute , etc., as their functional redundancy provided alternative routes for VFA production ( De Vrieze et al., 2015 ; Zamanzadeh et al., 2016 ). Thermodynamic feasibility favors diversity as the free energy associated with the endpoint of linear catabolic reactions was nonsensitive to taxonomic variation with the same electron donor and acceptor ( Rittmann and McCarty, 2012 ). Detailed modeling of a methanogenic bioreactor reported in previous research confirmed that the functional redundancy is related to taxonomic variation and increases the functional stability in response to phage invasion ( Louca and Doebeli, 2017 ). In addition to fermenters, the relative abundance of methanogens was relatively stable without significant variation except Methanobacterium in inocula-L CSTR. The stable methanogenic population could be due to the fact that methanogens only utilize a few substrates (hydrogen, formate, acetate, or methyl) to generate methane and are categorized into three groups of hydrogenotrophs, acetotrophs, and methylotrophs, which are comprised of several classes ( Methanobacteria , Methanococci , Methanomicrobia , Methanonatronarchaeia, and Methanopyri ) ( Baker et al., 2020 ). The functional redundancy was rarely observable by a significant variation in the methanogenic community in the anaerobic digester, i.e., the methanogens detected were stable as a few genera of methanogens over time in the methanogenic bioreactors ( Tao et al., 2020 ). The generally dominant methanogens were Methanosarcina , Methanosaeta , and Methanobacterium ( Demirel and Scherer, 2008 ). On the other hand, we believe that the ecological niches of methanogens may be phylogenetically diverse, as species from the order Methanomicrobiales fall in the pH range of 5.1–7.4 and generation time from 10 hrs to 144 hrs ( Browne et al., 2017 ); hence, the unique AD environment seems to lead to a specific methanogenic community. The correlation between VFAs and community variation also implies that the fermentation stage has broadened functions associated with a taxonomic shift. Iso-butyrate was found to be the driving factor which impacts the digestion process of inocula-F wastewater. Overall, an outline of the relationship between performance and composition variation gradually emerged from our work as the taxonomic fluctuation of fermenters could be decoupled from the variation in performance, which relied on the presence of redundancy, while coupled with the variation of metabolic products during the fermentation. In summary, the decoupling of taxonomic variation and function in methane production was observed, concluding that taxonomic variation may not be sufficient for predicting the function of a specific bioreactors. Further analysis indicated that the redundancy in the fermenters is likely to be the key to achieve such decoupling. Importantly, the VFAs varied substantially over the period of operation (time), which was coupled to a high proportion of fermentative species varying significantly, implying that taxonomic variation changed the pathway of fermentation while resulting in similar functional profiles. Overall, our research contributed insights into microbial ecology in AD, for both research communities and industry practitioners. In particular, our study highlighted research directions needed on functional variation rather than taxonomic changes in microbial communities when analyzing AD reactors. Specifically, our research suggested that the redundancy of fermenters diversified the metabolic pathway to a similar methane production in anaerobic bioreactors; our observation implied that a high-throughout functional genes variation could be a more insightful indicator to underpin the reactor performance, in comparison with 16S rRNA gene sequencing with highly diverse taxonomy. Limitations of the study Although the experimental analysis showed the function variation could be decoupled from microbial community changing, a long-term monitor of functional genes will provide solid proof on the stable function in specific metabolism flow. A further study focused on the specific metabolism variation in AD will provide advanced understanding in the coupling of function and microbial community."
} | 6,823 |
39934126 | PMC11814226 | pmc | 559 | {
"abstract": "Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website ( neurobench.ai ).",
"introduction": "Introduction In recent years, the rapid growth of artificial intelligence (AI) and machine learning (ML) has resulted in increasingly complex and large models in pursuit of higher accuracy and range of use cases 1 . The substantial growth rate of model computation exceeds efficiency gains realized through Moore and Dennard technology scaling 2 , indicating a looming limit to continued advancements with existing techniques. This issue is compounded by the open challenges of adapting such methods for resource-constrained edge devices (tinyML) in order to enable pervasive and decentralized intelligence through the Internet of Things (IoT) 3 . As such, the urgency for exploring new resource-efficient and scalable computing architectures has intensified. Neuromorphic computing has emerged as a promising area in addressing these challenges, aiming to unlock key hallmarks of biological intelligence by porting primitives and computational strategies employed in the brain into engineered computing devices and algorithms 4 – 6 . Neuromorphic systems hold a critical position in the investigation of novel architectures, as the brain exemplifies an exceptional model for accomplishing scalable, energy-efficient, and real-time embodied computation. Initially, the term “neuromorphic” referred specifically to approaches that aimed to emulate the biophysics of the brain by leveraging physical properties of silicon, as proposed by Mead in the 1980’s 7 . However, the field of neuromorphic computing research has since grown to encompass a wide range of brain-inspired computing techniques at the algorithmic, hardware, and system levels 4 . While the range of approaches is diverse, neuromorphic computing research generally utilizes mechanisms emulating or simulating biophysical properties more closely than conventional methods, aiming to reproduce high-level performance and efficiency characteristics of biological neural systems. Neuromorphic algorithms 8 encompass neuroscience-inspired methods which strive towards goals of expanded learning capabilities, such as predictive intelligence, data efficiency, and adaptation, and include approaches such as spiking neural networks (SNNs) and primitives of neuron dynamics, plastic synapses, and heterogeneous network architectures. Algorithm exploration often makes use of simulated execution on readily-available conventional hardware such as CPUs and GPUs, with the goal of driving design requirements for next-generation neuromorphic hardware. Neuromorphic systems 9 are composed of algorithms deployed to hardware, which seek greater energy efficiency, real-time processing capabilities, and resilience compared to conventional systems. Neuromorphic hardware utilizes a variety of biologically-inspired hardware approaches, including analog neuron emulation, event-based computation, non-von-Neumann architectures, and in-memory processing. Neuromorphic systems target a wide range of applications, from neuroscientific exploration, to low-power edge intelligence and datacenter-scale acceleration. Despite its promises, progress in the field of neuromorphic research is impeded due to the absence of fair and widely-adopted objective metrics and benchmarks 8 , 10 . Without such benchmarks, the validity of neuromorphic solutions cannot be directly quantified, hindering the research community from measuring technological advancement. Standard and rigorous benchmarking is necessary for the neuromorphic community to objectively assess and compare the achievements of novel approaches, and make evidence-based decisions on which directions show promise for achieving breakthrough efficiency, speed, and intelligence, thereby helping to focus research and commercialization efforts on techniques that concretely improve on prior work and conventional computing. Neuromorphic benchmarks have been previously proposed for classical vision 11 , 12 and audition tasks 13 , open-loop 14 and closed-loop 15 tasks, and for SNN simulator performance assessment 16 . While prior works have made valuable contributions, there are opportunities to further advance the field by addressing three outstanding challenges: Lack of a formal definition – The variety of approaches to exploring brain-inspired principles creates difficulties in defining a set of criteria for what should be benchmarked as a “neuromorphic” solution. Closed definitions can impose narrow assumptions and thus risk unfairly excluding promising methods. This challenge necessitates inclusive benchmarks that can be applied generally across the spectrum of potential approaches, allowing for flexible implementation while focusing on task capabilities and metrics of interest such as temporal processing and efficiency. Furthermore, the benchmarks should ideally allow for direct comparison of neuromorphic and conventional approaches. Implementation diversity – A wide array of different frameworks targeting different goals, such as neuroscientific exploration 17 and automatic SNN training 18 , are used in neuromorphic research. This diversity, which has been instrumental in exploring the landscape of bio-inspired techniques following different methodologies and abstraction levels, comes at the cost of portability and standardization, which in turn limits the ease of benchmark implementation. Benchmarks require common infrastructure that unites tooling to enable actionable implementation and comparison of new methods. Rapid research evolution – Neuromorphic approaches are continually and rapidly evolving as part of an emerging field. As the research community continues to make technological progress, so too should benchmark suites and methodology expand to foster inclusion and capture salient performance metrics. An iterative benchmark framework with structured versioning will facilitate productive foundational and evolving performance evaluation. To tackle these challenges, this article presents NeuroBench, a dual-track, multi-task benchmark framework. NeuroBench addresses the existing neuromorphic benchmark challenges by advancing prior work in three distinct ways. Firstly, the benchmark framework reduces assumptions regarding the specific solution being assessed, encouraging inclusive participation of neuromorphic and non-neuromorphic approaches by utilizing general, task-level benchmarking and hierarchical metric definitions which capture key performance indicators of interest. Secondly, the NeuroBench benchmarks are associated with a common open-source benchmark harness tool which facilitates actionable benchmark implementation and offers structure for further expansion to neuromorphic algorithm frameworks and systems. Finally, NeuroBench establishes an iterative, community-driven initiative designed to evolve over time to ensure representation and relevance to neuromorphic research, analogous to the well-established MLPerf benchmark framework for machine learning 19 , 20 . As a whole, NeuroBench intends to align the neuromorphic research community on standard benchmarking, providing a dynamically evolving platform to ensure ongoing relevance and facilitate advancements through workshops, competitions, and a centralized leaderboard. As Fig. 1 shows, the NeuroBench framework involves two tracks to enable agile algorithm and system development. As an emerging technology, neuromorphic hardware has not converged to a single platform which is commercially available, thus a large fraction of neuromorphic research explores algorithmic advancement on conventional systems which may not be optimal for performance. Thus, NeuroBench consists of an algorithm track for hardware-independent evaluation and a system track for fully deployed solutions. The algorithm track defines four novel benchmarks for neuromorphic methods across diverse domains, namely few-shot continual learning, computer vision, motor cortical decoding, and chaotic forecasting, and utilizes complexity metrics to analyze solution costs. Such hardware-independent benchmarking enables algorithmic exploration and prototyping, especially when simulating algorithm execution on non-neuromorphic platforms. Meanwhile, the system track defines standard protocols to measure the real-world speed and efficiency of neuromorphic hardware on benchmarks ranging from standard machine learning tasks to promising fields for neuromorphic systems, such as optimization. Up-to-date information on the latest benchmarks and official results can be found on the NeuroBench website ( https://neurobench.ai/ ). Fig. 1 The two NeuroBench tracks: algorithms and systems. Grey boxes designate what is defined by the benchmark, and orange boxes indicate what is unique to each solution. Connecting arrows between the two tracks denote the co-innovation between the tracks and the cross-stack innovation enabled by this approach. Between algorithm and system solutions, best-performing results from each track can motivate future solutions to the other. In addition, system metrics and results can inform hardware-independent algorithmic complexity metrics. Each NeuroBench track includes defined datasets, metric and measurement methodology, and modular evaluation components to enable flexible development. Promising methods identified from the algorithm track will inform system design by highlighting target algorithms for optimization and relevant system workloads for benchmarking. The system track in turn enables optimization and evaluation of performant implementations, providing feedback to refine algorithmic complexity modeling and analysis. The interplay between the tracks creates a virtuous cycle: algorithm innovations guide system implementation, while system-level insights accelerate further algorithmic progress. This approach allows NeuroBench to advance neuromorphic algorithm-system co-design. Both the algorithm and system track will be extended and co-developed as NeuroBench continues to expand. In the next few sections, we describe the algorithm track, including general complexity metric definitions, benchmark tasks, and common infrastructure tooling. We apply the framework to report baseline results for each algorithm benchmark, which outline unexplored research opportunities in optimizing algorithmic architectures and training of sparse, stateful models to achieve greater performance and resource efficiency. Then, we show baseline results established in the system track to assess neuromorphic performance across promising application workloads. By outlining both tracks, we provide a roadmap towards standardizing benchmark procedures in both hardware-independent and hardware dependent settings.",
"discussion": "Discussion and opportunities for further research Baseline results for the four v1.0 algorithm track tasks compare the correctness and complexities of various solution types. Compared to ANNs, SNNs and ESNs demonstrate complexity advantages such as smaller footprints, high sparsity, and accumulate rather than multiply-and-accumulate operations. Especially on the motor prediction and chaotic function prediction regression tasks, the SNN and ESN baselines already achieve competitive correctness at lower complexity than the ANN and LSTM counterparts. Further research opportunities in model architectures, data pre-processing and buffering, and training paradigms to achieve greater performance is enabled by the standard framework and tooling provided by NeuroBench.\n\nDiscussion and future work The initial baselines for the v1.0 system track compare correctness, timing, and efficiency of neuromorphic systems against conventional CPU systems in domains of both audio classification and optimization. Against mature, commercially-developed CPU systems, for both edge and server use cases, the neuromorphic systems show strong advantages in general efficiency, as well as further promises in terms of timing and correctness. In future NeuroBench iterations, the system track benchmarks can be unified under common tooling, similar to the algorithm track. Software toolchains such as Lava 36 , Fugu 37 , SPyNNaker 81 , and Samna 82 , among others, have been developed to interface with specific hardware platforms. Many of the stacks are built with general paradigms to support extension to any backend, and the community is actively moving towards developing standards for deployment tools. The current v1.0 benchmark specifications allow for open algorithm and software design in order to demonstrate fully optimized performance for neuromorphic systems. As standards mature in the future, a core focus of the NeuroBench system track is to introduce a closed-algorithm benchmarking category that leverages the recently proposed NIR model description framework 83 as a general, cross-platform tool for benchmarking key workloads of interest across many different platforms.\n\nDiscussion Benchmarking neuromorphic computing has faced challenges stemming from the diversity of neuromorphic approaches, the range of implementation and deployment tools, and rapid research evolution. NeuroBench addresses these challenges as a framework for the inclusive, actionable, and iterative benchmarking of neuromorphic solutions, by including novel tasks and metrics, open-source and extendable harness tooling, and facilitating systematic growth via community collaboration. NeuroBench is supported and developed by a broad community of neuromorphic researchers to be a standard, agreed-upon benchmarking framework for neuromorphic technology. Future directions of the NeuroBench initiative will build on the baselines outlined in this article to increase the scope of the benchmark framework. One important direction for NeuroBench is towards closed-loop benchmarks 15 , 84 . Biological systems excel in interacting with dynamic environments, demonstrating high energy efficiency, real-time reaction, and versatility. As such, embodied intelligence with adaptive sensory and action capabilities are of interest to neuromorphic research. In closed-loop scenarios, the objective is to sense and act within an environment to complete a task, rather than to statically process a frozen dataset, thus the benchmark harness infrastructure and measurement protocols will be extended to facilitate such benchmarks. Further important directions will be to increase the inclusivity of NeuroBench. While at present, the algorithm track harness supports PyTorch-based libraries, further coverage can be garnered by extending the interfaces to support other software libraries, potentially utilizing portable tooling such as NIR 83 as a standard for connecting to benchmark measurements. In addition, the system track guidelines can be extended to define benchmark protocols for continuous-time execution and exploratory hardware platforms in simulation stages, such as memristive hardware. All future NeuroBench expansion will be informed by the collected results and continue to be driven by the interests and development of the broader community."
} | 3,977 |
30936487 | PMC6588452 | pmc | 560 | {
"abstract": "Social interactions play an increasingly recognized key role in bacterial physiology 1 . One of the best studied is quorum sensing (QS), a mechanism by which bacteria sense and respond to the status of cell density 2 . While QS is generally deemed crucial for bacterial survival, QS-dysfunctional mutants frequently arise in in-vitro culture. This has been explained by the fitness cost an individual mutant, a “quorum cheater”, saves at the expense of the community 3 . QS mutants are also often isolated from biofilm-associated infections, including cystic fibrosis lung infection 4 , as well as medical device infection and associated bacteremia 5 – 7 . However, despite the frequently proposed use of QS blockers to control virulence 8 , the mechanisms underlying QS dysfunctionality during infection have remained poorly understood. Here we show that in the major human pathogen Staphylococcus aureus , QS-dysfunctional mutants arise exclusively in biofilm infection, while in non-biofilm-associated infection there is a high selective pressure to maintain QS control. We demonstrate that this infection-type dependence is due to QS-dysfunctional bacteria having a significant survival advantage in biofilm infection, because they form dense and enlarged biofilms that provide resistance to phagocyte attacks. Our results link the benefit of QS-dysfunctional mutants in vivo to biofilm-mediated immune evasion, thus to mechanisms that are specific to the in-vivo setting. Notably, our findings explain why QS mutants are frequently isolated from biofilm-associated infections and provide guidance for the therapeutic application of QS blockers."
} | 412 |
36336686 | PMC9639324 | pmc | 561 | {
"abstract": "Background Symbionts provide a variety of reproductive, nutritional, and defensive resources to their hosts, but those resources can vary depending on symbiont community composition. As genetic techniques open our eyes to the breadth of symbiont diversity within myriad microbiomes, symbiosis research has begun to consider what ecological mechanisms affect the identity and relative abundance of symbiont species and how this community structure impacts resource exchange among partners. Here, we manipulated the in hospite density and relative ratio of two species of coral endosymbionts ( Symbiodinium microadriaticum and Breviolum minutum ) and used stable isotope enrichment to trace nutrient exchange with the host, Briareum asbestinum . Results The patterns of uptake and translocation of carbon and nitrogen varied with both density and ratio of symbionts. Once a density threshold was reached, carbon acquisition decreased with increasing proportions of S. microadriaticum . In hosts dominated by B. minutum , nitrogen uptake was density independent and intermediate. Conversely, for those corals dominated by S. microadriaticum , nitrogen uptake decreased as densities increased, and as a result, these hosts had the overall highest (at low density) and lowest (at high density) nitrogen enrichment. Conclusions Our findings show that the uptake and sharing of nutrients was strongly dependent on both the density of symbionts within the host, as well as which symbiont species was dominant. Together, these complex interactive effects suggest that host regulation and the repression of in hospite symbiont competition can ultimately lead to a more productive mutualism. \n Video Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s40168-022-01382-0.",
"conclusion": "Conclusions This study provides insight into the complex nutrient dynamics that occur between corals and the Symbiodiniaceae that sustain them. While intraspecific competition seemed to be repressed in the host environment, rates of nitrogen assimilation showed dynamic and species-specific regulation. While theory has long suggested that hosts evolve strategies to stabilize mutualisms, these dynamics have been difficult to track. On the basis of our observations, we suggest that the regulation of endosymbiotic communities by coral hosts can lead to more productive mutualisms. As genetic techniques open our eyes to the breadth of symbiont diversity within myriad microbiomes, our understanding of the ecological mechanisms that restrict and maintain diversity in symbiosis must advance. Methodological approaches which provide a more holistic picture of potential conflict and synergy will continue to reveal critical aspects of the ecological and evolutionary dynamics of symbiosis.",
"discussion": "Discussion Recent research has highlighted the complex and principal role of nutrient exchange among coral hosts and their algal symbionts in maintaining a functional symbiosis [ 6 , 23 , 38 ]. In this study, we found that nutrients were largely affected by interactive effects of total symbiont density and the relative ratio of symbiont species. For example, only after a symbiont density threshold was achieved was carbon shown to scale with the relative ratio of symbionts, increasing with the relative ratio of B. minutum . Conversely, nitrogen dynamics were characterized by the dominant symbiont species. Those hosts dominated by B. minutum received consistent nitrogen resources independent of symbiont density, whereas increasing symbiont densities negatively impacted nitrogen assimilation in hosts dominated by S. microadriaticum . But rather than evidence for interspecific competition among symbionts, these species-specific patterns in nutrient cycling provide a mechanism by which nutrient dynamics may be involved in host regulation of symbiont community structure. Recent study of Symbiodiniaceae species in co-culture demonstrated that interspecific competition altered nutrient assimilation rates and subsequent compound production of Cladocopium goreaui and Durusdinium trenchii [ 18 ]. If these interactions are sustained within the host, where nitrogen is limited [ 39 ], they have the potential to destabilize the symbiosis through alterations in both nutrient acquisition and sharing. Instead, in hospite cohabitation of S. microadriaticum and B. minutum within the tissues of host B. asbestinum did not show a competitive shift in metabolism. After a threshold density of symbionts was reached, the production and sharing of photosynthetically derived carbon were well predicted by the ratio of each symbiont type with mixed communities providing intermediate levels of carbon resources (Fig. 3 A and C). An increased number of co-dominated ( S. m. : B. m. ~1:1) recruits may provide a more clear and potentially nonlinear fit to this relationship; however, the observed effect remains far smaller than that expected from co-cultured Symbiodiniaceae experiments (McIlroy et al. 2020). While multiple symbiont cells can exist within a single host cell [ 40 ], the symbiosome may serve to physically isolate individual symbionts [ 41 ]. The additive relationship observed here suggests that interactions among in hospite symbionts are indeed restricted. A similar pattern was seen in radiotracer experiments in the giant sea anemone where significant differences in carbon translocation ( 14 C) occurred between anemones harboring “type A” or “type B” Symbiodiniaceae, while mixed symbiont assemblages (A + B) translocated intermediate levels of carbon relative to the two monophyletic groups [ 9 ]. Both our study and Loram et al. (2007) assessed isotopic values of bulk tissues of mixed symbiont communities. It is therefore possible that antipodal responses of symbiont species would have gone undetected [ 18 ]. Ultimately however, interactions among codominant symbionts, if any, did not interfere with the balance of carbon in the symbiosis more generally. Carbon translocation (AP 13 C host ) mirrored that of symbiont tissues (AP 13 C sym ) with carbon enrichment values increasing linearly with increasing proportions of in hospite B. minutum to S. microadriaticum symbionts (Fig. 3 A and C). We found that S. microadriaticum was not only less productive but also more selfish with carbon resources (Fig. 5 A), as has been reported in other hosts [ 42 , 43 ]. In Briareum asbestinum , hosts that harbor B. minutum have been shown to have higher survival rates than those that harbour other symbiont species [ 28 ]. This link between variation in symbiont productivity and host survival provides a basis for natural selection in favour of hosts that can limit long-term associations with S. microadriaticum and/or promote associations with B. minutum . Indeed, in both the lab and field, B. asbestinum transition to Breviolum -dominated symbiont communities [ 26 , 28 ]. This occurs in spite of the fact that S. microadriaticum can infect corals sooner and at higher densities than other symbiont species [ 30 ]. While other studies have suggested that increasing symbiont densities can cause self-shading, and limit photosynthetically driven carbon assimilation [ 36 ], this was not seen in our study. Instead, symbiont densities did not affect AP 13 C sym and led to slight but significant increases in AP 13 C host (Fig. 3 ). Symbiodiniaceae are capable of maintaining high rates of photosynthesis across light conditions by increasing the efficiency of light harvesting and utilization [ 44 , 45 ]. Hosts with higher symbiont densities may also represent later stages in the onset of the symbiosis wherein symbiont cells drastically reduce cell replication and are able to generate more photosynthates in excess [ 6 , 36 ]. Although carbon fixation via photosynthesis is essential to the coral-algal symbiosis, in oligotrophic tropical waters, nitrogen availability often limits reef productivity (reviewed in [ 46 , 47 ]) and has a primary role in regulating symbiont abundance [ 38 , 39 ]. In our study, the dominant (i.e., the strain present at > 50% ratio) defined patterns of nitrogen assimilation which affected both AP 15 N sym and AP 15 N host . Where S. microadriaticum was the dominant strain, increasing cell densities resulted in decreasing AP 15 N host and AP 15 N sym (Fig. 4 ). This shows that S. microadriaticum are sensitive to nutrient dynamics with the ability to ramp up assimilation where nitrogen is in excess, a functional trait that may allow them to proliferate quickly in newly settled recruits [ 30 ]. Given a limited pool of nitrogen within the host habitat [ 39 ], density-dependent uptake may indicate that competition among symbionts (i.e., intraspecific competition among S. microadriaticum ) drives nitrogen assimilation rates. However, a closer look at the cumulative amount of newly assimilated nitrogen for an individual holobiont, accumulated across both host and symbiont tissues, shows that total amount of new nitrogen assimilated per recruit drops precipitously with increasing abundances of cells in S. microadriaticum -dominated hosts (Fig. 6 ). This suggests that less nitrogen overall was available to be assimilated as S. microadriaticum increased in abundance. Critically, this nitrogen limitation was not evident in hosts dominated by B. minutum (Fig. 4 B and D) where AP 15 N sym and AP 15 N host remained stable across densities. The cumulative effect of these limitations on nutrient dynamics of the symbiosis was ecologically complex. While AP 15 N in symbiont tissues was higher or equal in S. microadriaticum -dominated communities, relative to B. minutum -dominated communities, that functional difference did not consistently benefit the host. Instead, at low densities, S. microadriaticum communities were most beneficial to the host in terms of nitrogen (highest AP 15 N host ), but at higher symbiont densities, hosts dominated by S. microadriaticum received less nitrogen than those same densities of B. minutum -dominated communities (Fig. 4 D). This is not the first evidence of selfishness or parasitism of Symbiodinium spp. (see [ 30 , 43 , 48 ]). For long-lived hosts, sequential partnering with multiple symbiont species over time, including sub-optimal species, may have advantages. Acacia trees, for example, benefit from associations with a sterilizing ant species which increases survivorship in early ontogeny and is later replaced by ants that increase fecundity [ 49 ]. These accumulated effects on lifetime fitness, however, were dependent on the timing and efficiency of turnover between different partners. While poorly understood, a more drastic example of symbiont turnover occurs in B. asbestinum where, by 4 years of age, juveniles have switched between two species of Breviolum [ 29 ]; a similar phenomenon occurs in Acropora juveniles at 3 or more years old [ 27 ]. Initial uptake and winnowing may also be an important part of this sequence. Mortality is extremely high for newly settled coral recruits with rates that decrease as coral size increases [ 50 ]. Therefore, recruits may initially benefit from highly infectious, and quickly proliferating symbiont species that are ultimately sub-optimal, but only if they can later be replaced by more optimal symbionts. In this case, S. microadriaticum at low densities provided the highest nitrogen benefits to their hosts but became less beneficial relative to B. minutum as symbiont densities increased. Symbiont types within Breviolum ultimately provide the greatest growth and survivorship benefits to B. asbestinum hosts [ 28 ]. Ontogenetic flexibility in the regulation of symbiont densities [ 51 ] and identity [ 52 ] has been demonstrated. While a mechanistic understanding of symbiont turnover and the function of diversity in symbiosis is lacking, our stable isotope tracing experiments have provided a snapshot of nutrient transactions between host and symbionts across an array of symbiont community profiles. A host’s ability to regulate symbiont densities in hospite is critical for maintaining a stable symbiosis (Cunning & Baker 2014); here, we suggest that there is a species-specific mechanism for this. Technological advances in stable isotope tracing (e.g., NanoSIMS, compound specific stable isotope analyses) and further combinations with genetic techniques (e.g., qPCR, FISH probing, and flow cytometry sorting [ 18 , 53 ] can provide further insights into the maintenance and restriction of diversity in symbioses across systems."
} | 3,157 |
37444979 | PMC10342295 | pmc | 562 | {
"abstract": "Triboelectric nanogenerators (TENGs) possess significant attributes, such as a simple structure, high energy conversion efficiency, and ease of fabrication, rendering them crucial for powering mobile and distributed low-power electronic devices. In this study, a multilayer spring TENG with a cushion layer structure is proposed that enhances the output performance of the basic TENG structure. The fundamental topology of the energy harvesting circuit is chosen based on the electrical performance parameters of the generator and optimizes the selection of each electronic component in the actual circuit. This allows the small-size TENG (2 cm 3 ) to have a high storable power density (5.45 mW m −2 ). Finally, the fabrication method of the small-size TENG and how to choose suitable electronic components based on the intrinsic electrical parameters of the TENG were summarized. This work provides valuable guidance for designing and fabricating self-powered IoT node devices.",
"conclusion": "4. Conclusions In summary, this study investigates sensor self-power by employing an origami spring structure and Buck energy harvesting circuit topology. A multilayer stacked TENG structure is utilized, and the selection of circuit component parameters is optimized. The optimal output performance of the harvesting device is determined by considering the multilayer structure, external disturbance distance, external disturbance acceleration, device vibrator mass, and device operating frequency. Through these specific processes, the storable power density of the HS-TENG is significantly enhanced, offering valuable insights for micro-vibration energy harvesting units and TENG electric energy harvesting circuits. The key conclusions drawn from this study are as follows: (1) The manufacturing process of the TENG incorporates the use of Flexible Printed Circuit Board (FPCB) technology and includes an interlayer cushion layer. This design enhancement results in improved electrical performance of the HS-TENG compared to TENGs of the same volume size. (2) Engineering optimization approximation methods are employed, analyzing the workflow of the circuit and proposing a component selection approach for the energy harvesting circuit specifically applicable to the HS-TENG based on experimental results. This approach leads to an increased storable power density, aligning with the electrical consumption requirements of sensors and Internet of Things (IoT) nodes.",
"introduction": "1. Introduction With the rapid development of IoT devices [ 1 , 2 , 3 ], the need to use renewable and environmentally friendly new energy sources to power them is also growing [ 4 , 5 , 6 , 7 , 8 ]. Utilizing the abundant, random, and irregular mechanical energy existing in the environment to power IoT devices would be a highly promising prospect [ 9 ]. Triboelectric nanogenerators, first proposed by ZhongLin Wang in 2012, offer a powerful technology to address this need [ 10 , 11 ]. TENGs are based on the principles of triboelectrification and electrostatic induction to harvest micromechanical vibration energy that is difficult to reuse [ 12 ]. At the same time, TENGs have attracted considerable attention from the research community due to their simple structure [ 13 , 14 ], high energy conversion efficiency [ 15 ], wide range of applications [ 16 ], and ease of fabrication [ 17 ]. To promote the development and practical application of this promising new technology, researchers have proposed various techniques to improve the output performance of TENGs. Some works have focused on improving the electrical output performance by combining TENG with mechanical structure design through cascading multiple TENGs and increasing the effective contact area of the TENG’s dielectric layer [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Other works have aimed to improve the electrical output performance of TENGs by modifying the dielectric layer material through physical and chemical methods to obtain a higher surface charge density of the dielectric layer [ 28 , 29 , 30 , 31 , 32 ]. However, when using TENG as a power supply device today, it is common to store energy before supplying power to the back-end consumer device. Therefore, the storable power density of TENGs is extremely important. In this work, a Z-shaped origami spring structure [ 33 ] and a buffer layer structure were adopted in a high storable power density TENG (HS-TENG) to make the frictional contact between the dielectric layers more complete and tighter, thus improving the output performance of the generator. More importantly, the basic electrical performance parameters of the HS-TENG ports were optimized using engineering methods [ 10 , 17 , 32 ] to match the circuit with the HS-TENG and to obtain a higher storable power density. The experimental data demonstrate that the HS-TENG exhibits an open-circuit voltage output of 645.17 V, a peak-to-peak value of the short-circuit current of 14.11 μA, and an external instantaneous power of 0.8 mW at an external impedance of 100 MΩ. These results indicate the favorable external power supply capability of the HS-TENG. Furthermore, the storable power density was measured to be 5.45 mW m −2 when an external 1 mF capacitor was connected. The proposed approach provides a novel TENG fabrication method and guidance for circuit component selection, which will serve as a reference for self-powered IoT node devices.",
"discussion": "3. Results and Discussion This study proposes a high storable power density triboelectric nanogenerator. Figure 3 illustrates the fundamental constituent materials of the multilayer TENG and presents the 3D model of the experimental setup. The figure provides a succinct overview of the generator’s basic operating principle and showcases its potential distribution on the simulation surface. Moreover, it highlights the scientific advancements accomplished in this study. The HS-TENG full 3D view is shown in Figure 3 a. The diagram includes the test structure, the basic unit of the folded spring, and the multilayered structure of the HS-TENG. The test structure is assembled using acrylic plates, with two identical HS-TENG basic units placed in the upper and lower spatial locations to minimize experimental errors resulting from randomization. The basic unit of the folded spring is formed by bonding two small V-shaped structures, which allows for better utilization of both sides of the same physical location, thereby improving the spatial utilization efficiency of the HS-TENG. The small volume and modular design of the basic units offer considerable flexibility for assembling various configurations and cascades of the TENG. By combining different quantities of TENG basic units, a larger cascade unit can be constructed to yield enhanced output performance in diverse application scenarios. This fundamental interconnection approach enables remarkable scalability for the HS-TENG. The multilayered metal structure in the middle of the test structure functions as a weighting block to investigate the effect of different numbers of mass blocks on the output during the compression of HS-TENG. In the far-right part of Figure 3 a, a detailed display of the multilayered structure of the HS-TENG is provided, consisting of fluorinated ethylene propylene (FEP), copper, Propylamine (ABR), brass, Kapton, and FR-4 as the constituent materials of the HS-TENG. FEP and copper act as the dielectric layers, ABR serves as a cushion layer to ensure full contact between the dielectric layers, brass provides a large number of free electrons for the induction electrode, and FR-4 serves as a supportive structure for these relatively soft materials. By using the aforementioned structure and stacking multiple layers of materials, the goal of achieving good output performance in small volumes and space is achieved. Figure 3 b presents a simplified working principle of the HS-TENG’s V shape unit, which can be abstracted into four stages: pressed, releasing, released, and pressing. When there is a load between the two induction electrodes, during the releasing stage, the potential difference between the two electrodes drives electrons from the upper electrode to the lower electrode, generating an upward instantaneous current at the moment of release. As the distance between the two dielectric layers decreases during pressing, the potential of the upper electrode becomes higher than the lower electrode, causing electrons to flow back from the lower electrode to the upper electrode, reducing the amount of induced charge on the electrodes and generating a downward instantaneous current. When the dielectric layers come into contact again, all the induced charges are neutralized. Figure 3 c shows the spatial distribution of voltage potential corresponding to the above four operating modes through COMSOL simulation without a load. A multilayered model of the actual structure is added to the COMSOL simulation to better fit the real composition of HS-TENG. The specific simulation parameters and the detailed division of the simulation region are presented in Figure S1 . The animation of the simulation process is included in Supporting Information Video S1 . Figure 3 d presents, in a chart format, the works of seven previous studies in increasing order of storable power density. The first four studies mainly employed the combination of mechanical structures with TENG to improve the generator’s output performance. The fifth study modified the dielectric layer material by physical means to obtain a higher surface charge density and thus improve the generator’s output performance. The sixth study added oil as a medium between two dielectric layers to enhance the output performance. The seventh study used a method of ferromagnetic-based charge accumulation and, with the use of energy management circuits, improved the TENG’s output performance. To enable a comprehensive comparison with the output performance of the aforementioned seven references, a comparison dataset is introduced for the current application scenario, focusing on storable power density. Storable power density signifies the quantity of energy stored in the capacitor, available for powering the intended load. It is determined by calculating the actual energy stored in the capacitor, the time required to accumulate this energy, and the effective dielectric layer area of the TENG. Formula 1 in the supporting information provides the means to calculate the storable power density. Specific values and parameters of the mentioned references can be found in Supporting Information Table S1 . While previous studies have made significant strides in enhancing the electrical output performance of TENG for portable applications, they have not addressed the optimization of storable energy utilization in practical scenarios. This work combines previous research efforts and proposes a solution to address the issue of storable energy supply in practical applications, such as self-powered Internet of Things (IoT) nodes. Compared to the reference works, this study achieved a certain improvement in the parameter of storable power density. Figure 4 illustrates the fundamental test environment of the HS-TENG and the variable parameters employed in the experiment. The figure presents the electrical performance parameters, both with and without considering the output voltage difference originating from the cushion layer. Additionally, it investigates the influence of distance, acceleration, and mass parameters on electrical performance. To avoid the interference of the linear motor’s magnetic axis on the test device during the experimental process, a structure shown in Figure 4 a was employed to investigate the parameters affecting the output of the HS-TENG. During the testing phase, the two TENGs were positioned in a parallel orientation to the test bench to prevent any potential inconsistencies in their electrical performance due to gravitational influence. The acceleration depicted in the figure represents the acceleration of the linear motor applied to the test structure, rather than the acceleration acting on the HS-TENG itself. Similarly, the distance measurement refers to the displacement of the motor head relative to the fixed section of the testing platform, and not to the movement distance of the HS-TENG’s dielectric layer. Figure 4 b presents the open-circuit voltage characteristics of a spring structure TENG with and without a cushion layer. The average peak-to-peak output voltage of the origami TENG without a cushion layer was 85.37 V, while adding a cushion layer increased the average peak-to-peak output voltage to 638.25 V, resulting in a clear 7.5-fold increase in open-circuit voltage. The cushion layer softened the dielectric layer, making it more elastic and thicker, resulting in an increase in the effective dielectric contact area compared to the TENG without a cushion layer for the same displacement. Figure 4 c shows the average peak-to-peak open-circuit voltage values at intervals of 10 mm with the change in the moving distance of the testing structure at frequencies of 1 Hz and 2 Hz. It is noteworthy that the distance in the figure does not represent the change in the dielectric layer of the TENG, but rather the external vibration distance of the testing structure as an excitation and the output voltage of the TENG as a response, which is shown as a line graph. The maximum open-circuit voltage output point of the TENG without a cushion layer was 109.25 V at a moving distance of 50 mm under 1 Hz and 89.94 V at a moving distance of 30 mm under 2 Hz. However, the overall change was not significant under the influence of the displacement. For the TENG with a cushion layer, the maximum open-circuit voltage at frequencies of 1 Hz and 2 Hz occurred at a moving distance of 60 mm, reaching 618.55 V. The TENG with a cushion layer was more sensitive to external excitation movement. Nevertheless, the overall test results showed that the introduction of a cushion layer improved the open-circuit voltage output performance of the TENG. Figure 4 d displays the curves of open-circuit voltage and short-circuit current under different accelerations, where G represents the gravitational acceleration of 9.8 m/s 2 . When the acceleration of the linear motor was greater than 0.6 G, it could drive the vibration mass block to work the TENG normally. As the acceleration increased, the voltage reached 658.38 V at 1 G acceleration, with a fluctuation not exceeding ±10%. After reaching 1 G acceleration, the peak-to-peak voltage fluctuated only slightly with the acceleration change. The current value increased gradually with the increase in acceleration, but the rising trend gradually became slower. Hence, it is evident that the HS-TENG is capable of performing efficiently under external accelerations within the range of 0.7–2 G. However, it is noteworthy that a higher proportion of mechanical vibration energy is converted into electrical energy within the acceleration range of 0.7–1.6 G. The impact of the cushion layer on the output of TENG is significant. To investigate the effect of cushion layer thickness on the electrical output performance of a given TENG size, five sets of experiments were conducted, and the corresponding data are presented in Figure S2 . As depicted in Figure S2a , the cushion layer with a thickness of 0.5 mm exhibits better output performance under an acceleration of 1.5–2 G. Furthermore, the voltage is less affected by acceleration when compared to the current, but an increase in acceleration beyond 2 G results in a slowdown of the current’s raising rate of change. Figure S2b illustrates the electrical output performance of different accelerations and thicknesses. The overall trend of the voltage is less affected by frequency, and the TENG with the same thickness as the cushion layer generally maintains a stable voltage output. In contrast, the current increases significantly with the frequency, which is consistent with the output characteristics of a typical TENG. Figure S2c shows the variation in TENG output performance with different vibrating masses and cushion layer thicknesses. Notably, the current output is significantly higher for a cushion layer thickness of 0.5 mm than for the other two thicknesses. Moreover, the added cushion layer’s capacitance at different excitation frequencies is presented in Figure S2d , while Figure S2e summarizes the maximum output characteristics of three cushion thicknesses. Overall, the cushion layer with a thickness of 0.5 mm offers a significant advantage in electrical performance. Figure 4 e investigates the electrical output performance of the HS-TENG by changing the number of mass blocks under the condition of a moving distance of 60mm and an acceleration of 1.6 G (1 M mass block is equivalent to 25.2 g). The primary change in the electrical output performance of the HS-TENG occurred in the interval of 1 M to 2 M mass blocks. The subsequent increase in mass blocks had little effect on the current output performance of the generator. However, the voltage output performance gradually increased with an increase in the number of mass blocks until the voltage change could be almost negligible after adding 5 M mass blocks. When testing the electrical performance of the HS-TENG with a displacement of 60 mm, acceleration of 1.6 G, and a vibrating mass block of 4 M, Figure 5 a presents the voltage, current, and charge of the HS-TENG. The HS-TENG exhibited a peak-to-peak voltage of 645.17 V, a peak-to-peak current of 14.11 μA, and a transfer charge of 20.05 nC in a working cycle. Impedance matching is a crucial parameter for TENG to supply power to the load output. This process involves taking the conjugate of the circuit port characteristics of TENG, where the real part is equal and the imaginary part is opposite to each other. In practical engineering, it is often approximated that the real part is equal, and the influence of the imaginary part is neglected, which can still achieve a better power output effect [ 41 ]. Figure 5 b shows the test range of load resistance values from 1 MΩ to 10 GΩ to clearly observe the maximum power matching output point of 0.852 mW at 100 MΩ. In Figure 5 c, the investigation is centered on the optimal connection method for the two basic Z-shaped units of HS−TENG to achieve higher output. The graph clearly depicts that the parallel connection of the units provides better output performance than the series connection. This is because the phase and frequency of the two TENGs may not be entirely matched during the working process. This mismatch could cause the output voltage of one TENG to be at the positive half-peak while the output voltage of the other TENG is at the negative half-peak, leading to a cancellation of electric charges and a weakened output voltage. When TENGs are connected in parallel, the circuit port characteristic of TENG, which approximates the capacitance to an open circuit for low-frequency signals, ensures that the transferred charge will not be consumed by another generator, even if the phases do not match. Figure 5 d compares the energy collection effects of a full-bridge rectifier and a capacitor-collecting generator with energy management circuits. It is evident from the graph that the energy collection effect of the energy management circuit is significantly better than that of the full-bridge rectifier, as the 1 mF capacitor can be quickly charged to 2.19 V in only 330 s, compared to 0.31 V with the full-bridge rectifier. The charging speed of the energy management circuit is improved by seven times. Figure 6 presents an investigation into the performance of HS−TENG output and the comparison of the capacitor charging speed of HS−TENG at different operating frequencies. The testing structure and frequency testing schematic are shown in Figure 6 a, with testing conditions controlled at an acceleration of 1.6 G and a displacement of 30.625 mm. Figure 6 b reveals that the open-circuit voltage and short-circuit current of HS−TENG are relatively insensitive to frequency, indicating a good output response of HS−TENG to low-frequency mechanical signals in the testing structure. Figure 6 c shows that the energy stored in the capacitor generated by HS−TENG through the full-bridge rectifier is small, as most of the energy is consumed during transmission. To improve the efficiency of HS−TENG during the energy transmission stage, the basic topology structure of the BUCK step-down technology circuit is used in this work. On the basis of the original circuit topology, the electrical output signal of the HS−TENG port is analyzed. The method described in Materials and Methods is used to optimize the suitability of this circuit for HS−TENG and achieve higher energy conversion efficiency. Figure 6 d illustrates the four stages of circuit operation: in the first stage, HS−TENG charges the high-voltage capacitor through the full-bridge rectifier, completing the first stage of energy storage; in the second stage, when the voltage of the first-stage energy storage capacitor reaches the breakdown voltage threshold of the D1 voltage regulator diode, the voltage regulator diode breaks down, and the silicon-controlled rectifier (SCR) receives a passage signal, quickly releasing the energy in the first-stage energy storage capacitor to the downstream circuit; in the third stage, the controlled SCR is turned off, and the first-stage energy storage capacitor re-enters the energy storage stage, during which the diode can prevent large currents and over voltages from causing a breakdown of the downstream aluminum electrolytic capacitor, and the inductor can convert the energy into a magnetic field energy storage; in the fourth stage, the D2 diode conducts in the forward direction, transferring the stored energy from the inductor L to the storage capacitor Cout. At this stage, detailed component selection for the circuit is conducted, taking into account the performance of the TENG output and the requirement to maintain the high energy conversion efficiency of the BUCK circuit. Figure 6 e shows the relationship between the charging curve of the 1 mF capacitor with the energy management circuit and the frequency. The circuit can reach the maximum energy storage output point when the frequency of the testing structure reaches 5 Hz. The circuit structure described herein is integrated with the LTC3588 ultra-low-power energy harvesting power management module to provide a stable 3.3 V power supply voltage to the circuit load and manage the energy harvesting strategy. Specifically, the module enables the provision of power to the load circuit once the voltage stored in the storage capacitor reaches 5 V. Conversely, the module discontinues the power supply to the load circuit once the voltage stored in the storage capacitor drops to 4 V, thus allowing the energy storage capacitor to recharge. Figure 6 f shows the performance curve of the energy storage capacitor under load. This demonstrates the HS-TENG’s ability to drive sensors at a minute level in vibrating environments. Often, other similar-size TENG drive sensors take longer to operate once. Figure 6 g demonstrates the power supply capability of the circuit, which can support the normal operation of two temperature and humidity sensors for several seconds. A video of the testing process is in Supporting Information Video S2 . Moreover, in Figure 6 h, this system can drive the Xiaomi Mi (Beijing, China) Home wireless temperature, humidity, and pressure sensors to achieve wireless sensing every ten minutes or so. A video of the testing process is in Supporting Information Video S3 . Overall, Figure 6 presents the testing and results of HS−TENG output performance and the charging rate of the power management circuit. The proposed circuit structure shows efficient energy management and stable power supply voltage to the load circuit. The results also demonstrate the capability of the system to drive various sensors for wireless sensing applications."
} | 6,042 |
32156820 | PMC7064766 | pmc | 563 | {
"abstract": "The rise of antibiotic resistance requires the development of new strategies to combat bacterial infection and pathogenesis. A major direction has been the development of drugs that broadly target virulence. However, few targets have been identified due to the species-specific nature of many virulence regulators. The lack of a virulence regulator that is conserved across species has presented a further challenge to the development of therapeutics. Here, we identify that NADH activity has an important role in the induction of virulence in the pathogen P. aeruginosa . This finding, coupled with the ubiquity of NADH in bacterial pathogens, opens up the possibility of targeting enzymes that process NADH as a potential broad antivirulence approach.",
"introduction": "INTRODUCTION Pseudomonas aeruginosa is an opportunistic pathogen that is responsible for a range of illnesses, including lung infections in cystic fibrosis patients and hospital-acquired infections, sepsis, and disease in immunocompromised patients ( 1 ). The bacterium infects a broad range of hosts, including humans, animals, plants, insects, amoebae, and other bacteria using a multitude of virulence factors, including the type III secretion system, cyanide, pyocyanin, and proteases ( 2 – 5 ). Recent work has reported that the expression of virulence factors in P. aeruginosa is regulated by nutrient availability and central metabolic networks ( 6 – 8 ). In addition, tricarboxylic acid cycle (TCA) intermediates alter the activity of some virulence factor regulators in Gram-positive and intracellular pathogens ( 9 , 10 ). These studies provide a static snapshot of the involvement of metabolism in virulence regulation. The dynamics of central metabolic activity during the activation of virulence in P. aeruginosa are not known, and many questions remain about the regulatory link between central metabolism and virulence activation. What are the energetic requirements for the expression of virulence factors? Can central metabolism be tuned to inhibit virulence? We investigated these questions by measuring central metabolic activity during the transition from a low-virulence state to an activated virulence state in P. aeruginosa . Virulence factor production is induced in P. aeruginosa and other bacteria through the activation of surface sensing ( 11 – 15 ). The host-killing mechanism of surface-activated virulence in P. aeruginosa has not been attributed to a single virulence factor, including a type III secretion product, pyocyanin, or elastase, but has been attributed to the combinatorial nature of virulence factor production ( 2 , 12 ). A recent preprint reports that alkyl quinolones are a critical cytotoxic factor ( 16 ). Virulence induction by surface attachment is dependent on the protein PilY1, which is found on the outer surface of the cell membrane, contains homology to a mechanically active von Willebrand factor domain, mediates a c-di-GMP response to shear stress, and is required for the initiation of biofilm formation ( 17 – 21 ). Surface-induced virulence also requires coactivation of quorum sensing, which is triggered when cells reach a threshold density ( 22 , 23 ). Surface sensing and quorum sensing form a coincidence gate in which the activation of both pathways is required to induce virulence ( 12 ). Surface attachment regulates the levels of the metabolites cyclic AMP and cyclic di-GMP ( 13 , 20 , 21 , 24 ) and upregulates transcription of NADH-associated enzymes ( 12 ). Quorum sensing, which controls the expression of many virulence factors, produces a major shift in the production of a large fraction of metabolites ( 25 ). It is possible that surface sensing and quorum sensing induce virulence through changes in central metabolism. However, addressing this hypothesis has been challenging because surface sensing and quorum sensing are dynamic processes and monitoring their effects requires simultaneous measurements of both virulence and central metabolism. The phasor approach to fluorescence lifetime imaging microscopy (FLIM) measures the dynamics of central metabolism in live cells. This method reports the relative abundance of the free and bound forms of NADH by exploiting its autofluorescent properties ( 26 , 27 ). This method has been used extensively to track changes in NADH forms in live eukaryotic cells during critical cell processes, including duplication, proliferation, and differentiation ( 28 – 32 ). NADH is excited using two-photon excitation and decays to the ground state with distinct decay rates, or lifetimes, in the visible spectrum. The major advantage of this approach is the ability to track spatial and temporal changes in metabolic activity at subcellular resolution without the need to label molecules, introduce fluorescent reporters, or to stain, perturb, or harvest cells. Advances in optics, imaging, and analysis have enabled fluorescence lifetime measurements in a number of bacteria, including Lactobacillus acidophilus , Escherichia coli , and P. aeruginosa ( 33 – 37 ). However, independent measurements of NADH were not performed in these studies, which limited the interpretation of the FLIM measurements. In addition, the FLIM measurements were not performed during virulence activation. Here, we establish a metabolic trajectory in P. aeruginosa using FLIM and through independent in vitro measurements of NADH and NAD + concentrations. We measure metabolic states in P. aeruginosa during a critical growth transition in which virulence is activated in surface-attached cells. We show that compared to low-virulence (planktonic) cells, virulence-activated (surface-attached) cells exhibit FLIM lifetimes that are associated with decreased levels of enzyme-bound NADH and decreased NAD(H) production. Perturbation of central metabolism using citrate or pyruvate, which further decreases enzyme-bound NADH and total NAD(H) production, inhibits virulence, while treatment using an electron transport chain oxidase inhibitor induces virulence at an earlier time.",
"discussion": "DISCUSSION Bacterial virulence is regulated by a number of factors that ensure successful infection. How the metabolic state of the cell changes during virulence induction has been unknown. Our results indicate that a shift in central metabolism, in the form of changes in NADH and NAD + abundances and NADH binding to enzymes, accompanies the induction of virulence in P. aeruginosa . Using this finding, we perturb central metabolism to inhibit virulence or to induce virulence at an earlier time. As NADH is utilized as a central metabolic currency broadly across bacterial species, our results suggesting a role for NADH abundance in the regulation of virulence could have far-reaching significance. We have established a metabolic trajectory in P. aeruginosa using the phasor approach to fluorescence lifetime imaging microscopy. We observed that positions along the g axis of the fluorescence lifetime trajectory negatively correlated with total NAD(H) production. Greater FLIM g values, indicating decreased enzyme-bound NADH within the cell, correlated with decreases in NAD(H) production. In addition, analysis of the cumulative fluorescence lifetime data using a K-means entropy clustering algorithm identified five distinct metabolic states into which P. aeruginosa cells can be clustered (see Fig. S5 in the supplemental material). 10.1128/mBio.02730-18.5 FIG S5 Clustering analysis of P. aeruginosa fluorescence lifetimes identifies distinct metabolic states. (A) Five distinct metabolic clusters (cluster 1 [C1] to cluster 5 [C5]) were identified using a K-means clustering algorithm using composite fluorescence lifetime data of P. aeruginosa cells from the current study and from a previous study ( 37 ). (B) Silhouette analysis on K-means clustering was used to identify clusters. (C) The cluster score was highest for five clusters. Download FIG S5, PDF file, 1.8 MB . Copyright © 2020 Perinbam et al. 2020 Perinbam et al. This content is distributed under the terms of the Creative Commons Attribution 4.0 International license . By establishing fluorescence lifetime maps and performing NAD(H) concentration measurements and host-killing assays, we have measured the dynamics of central metabolism in P. aeruginosa during the activation of virulence. Our analysis revealed that P. aeruginosa undergoes a rapid and distinct metabolic rearrangement during the growth transition that differentiates cells into low-virulence or virulence-activated populations. At the beginning of the growth transition when P. aeruginosa entered a period of reduced growth rate, both planktonic and surface-attached populations were metabolically indistinguishable by FLIM and NAD(H) measurements. At the end of the transition, planktonic populations had an increased proportion of enzyme-bound NADH and increased the production of NAD(H) but did not activate host-killing factors. In contrast, surface-attached populations had comparatively less enzyme-bound NADH and decreased NAD(H) production, which resembled a state of metabolic dormancy, and transitioned to an activated virulence state. The observation that virulent (surface-attached) populations are metabolically distinct from low-virulence (planktonic) populations raises the possibility that altering central metabolism activity could affect virulence activation. Treatment of surface-attached P. aeruginosa with citrate and pyruvate decreased the enzyme-bound NADH pool, decreased the total NAD(H) production, and abolished host-killing activity. In contrast, glucose and glycerol had relatively small impacts on the level of enzyme-bound NADH and NAD(H) production and had no effect on host-killing activity. The impacts of individual carbon sources on host-killing activity may be interpreted in the context of the glyoxylate pathway, which bypasses the TCA cycle in favor of carbon preservation for gluconeogenesis and biomass production. The glyoxylate pathway activates the expression of type III secretion system and is important for lung infection models ( 53 , 54 ). Growth in citrate and pyruvate in P. aeruginosa biases metabolic activity in favor of the TCA pathway and away from the glyoxylate pathway ( 55 ). Thus, the inhibition of virulence observed here could be explained by the inhibition of the glyoxylate pathway by citrate or pyruvate. Treatment of surface-attached P. aeruginosa using an oxidase inhibitor induced virulence at an earlier time, which was also inhibited by treatment of citrate. Glucose does not appear to inhibit the glyoxylate pathway, and glycerol is not expected to inhibit the pathway ( 55 ). Consistent with our interpretation, supplementation with glucose or glycerol had no impact on host-killing activity. Together, our results suggest a model in which the glyoxylate pathway is activated in surface-attached populations, which results in the expression of host-killing factors. In this model, the activation of the pathway can be inhibited by citrate or pyruvate, but not by glucose or glycerol. The observed changes in NAD(H) production and the fraction of enzyme-bound NADH may be indicative of changes in TCA and glyoxylate pathway utilization. The decreased production of NAD(H) in surface-attached populations is consistent with inactivation of the TCA cycle in favor of the glyoxylate pathway. A recent preprint indicates that alkyl quinolines are responsible for cytotoxicity in surface-associated populations ( 16 ). Anthranilate is a metabolic precursor for quinolones ( 56 ), and its availability may have a significant impact on the production of these cytotoxic factors in surface-associated populations. The alteration of central metabolites could thus function as a regulator to rapidly coordinate virulence during the critical growth transition period. Virulence is observed in our experiments only in surface-attached cells. Low-virulence planktonic cells produce greater levels of NAD(H) and have greater enzyme-bound NADH. The mechanisms that give rise to the distinct metabolic states are unclear. The availability of electron acceptors and surface sensing in P. aeruginosa could have an impact on metabolism. These results thus suggest an important role for electron transfer activity in the activation of virulence mechanisms. Future experiments will need to address the extent to which NAD(H) production and free NADH in planktonic cells impact the production of host-killing factors. Fluorescence lifetime imaging microscopy provides spatial measurements of metabolism and may be a useful tool for measuring metabolic activity across multiple length scales from single cells to mature biofilms. We observed that fluorescence lifetimes were spatially heterogeneous in the cytoplasm of P. aeruginosa , which is consistent with the subcellular localization of metabolic activity ( 57 ). Future experiments will need to address the impact of changes in central metabolism on the spatial organization of NADH activity. In addition, metabolic dormancy in biofilms is associated with antibiotic resistance ( 58 ). The use of FLIM to map spatial changes in metabolism in biofilms may thus open new avenues for the investigation of antibiotic resistance in biofilms. Antivirulence therapy is a proposed strategy for combating pathogenesis as an alternative to conventional antibiotics, which typically target bacterial growth ( 59 ). The identification that NADH levels affect virulence induction highlights a potential target for virulence inhibition. Our results suggest metabolic manipulation as a strategy to inhibit virulence. Strategies such as targeting metabolic pathways involved in NAD(H) production or growth in the presence of bacteria that secrete metabolites that affect NAD(H) production could be effective at inhibiting virulence. Within microbiomes, complex microbial communities, and host environments, metabolite cross-feeding could have a significant impact on virulence activation in pathogens."
} | 3,507 |
21318462 | PMC3059824 | pmc | 565 | {
"abstract": "Several emerging technologies are aiming to meet renewable fuel standards, mitigate greenhouse gas emissions, and provide viable alternatives to fossil fuels. Direct conversion of solar energy into fungible liquid fuel is a particularly attractive option, though conversion of that energy on an industrial scale depends on the efficiency of its capture and conversion. Large-scale programs have been undertaken in the recent past that used solar energy to grow innately oil-producing algae for biomass processing to biodiesel fuel. These efforts were ultimately deemed to be uneconomical because the costs of culturing, harvesting, and processing of algal biomass were not balanced by the process efficiencies for solar photon capture and conversion. This analysis addresses solar capture and conversion efficiencies and introduces a unique systems approach, enabled by advances in strain engineering, photobioreactor design, and a process that contradicts prejudicial opinions about the viability of industrial photosynthesis. We calculate efficiencies for this direct, continuous solar process based on common boundary conditions, empirical measurements and validated assumptions wherein genetically engineered cyanobacteria convert industrially sourced, high-concentration CO 2 into secreted, fungible hydrocarbon products in a continuous process. These innovations are projected to operate at areal productivities far exceeding those based on accumulation and refining of plant or algal biomass or on prior assumptions of photosynthetic productivity. This concept, currently enabled for production of ethanol and alkane diesel fuel molecules, and operating at pilot scale, establishes a new paradigm for high productivity manufacturing of nonfossil-derived fuels and chemicals.",
"introduction": "Introduction The capture of solar energy to power industrial processes has been an inviting prospect for decades. The energy density of solar radiation and its potential as a source for production of fuels, if efficiently captured and converted, could support the goals of national energy independence. Analyses of photosynthetic conversion have been driven by this promise (Goldman 1978 ; Pirt 1983 ; Bolton and Hall 1991 ; Zhu et al. 2008 , 2010 ). The deployment of solar-based industries for fuels has, however, been limited by the lack of efficient cost-effective technologies. Projects funded between 1976 and 1996 under the US Department of Energy (DOE) aquatic species program explored phototrophic organisms and process technologies for the production of algal oils and their refinement into biodiesel. The results of these efforts were summarized in a report that delineated the technological barriers to industrial development (Sheehan et al. 1998 ). The traditional photosynthetic fuels process is one wherein triglyceride-producing algae are grown under illumination and stressed to induce the diversion of a fraction of carbon to oil production. The algal biomass is harvested, dewatered and lysed, and processed to yield a product that is chemically refined to an acyl ester biodiesel product. Many companies have been founded since the DOE final report that strive to make incremental improvements in this process to create viable solar energy-to-fuel technologies. However, many of the fundamental barriers to industrial photosynthetic efficiency remain and threaten to constrain this approach to one wherein only associated coproduct generation can salvage the process economics (Wijffels and Barbosa 2010 ). Here, we reassess industrial photosynthesis in light of the development of powerful tools for systems biology, metabolic engineering, reactor and process design that have enabled a direct-to-product, continuous photosynthetic process (direct process). Many of these innovations were presaged by DOE as well as academic and industrial sources (Gordon and Polle 2007 ; Rosenberg et al. 2008 ) who suggested that these types of technological advances could enable the success of industrial photosynthesis (see Table 1 for a list of innovations and advances inherent in the direct process). Table 1 Technological innovations leading to high-energy capture and conversion characteristics of a direct, continuous process for photosynthetic fuel production Process innovation System design Maximize energy capture and conversion by process organism • Metabolic engineering for recombinant pathway to directly synthesize final product • Gene regulation control to optimize carbon partitioning to product • Metabolic switching to control carbon flux during growth and production phases Minimize peripheral metabolism • Cyanobacterial system to obviate mitochondrial metabolism • Operation at high (>1%) CO 2 to minimize photorespiration Maximize yield and productivity • Decoupling of biomass formation from product synthesis • Engineering continuous secretion of product • Optimization of process cycle time via continuous production Enable economic, efficient reactor and process Photobioreactor that • minimizes solar reflection • optimizes photon capture and gas mass transfer at high culture density • optimizes thermal control \n The direct process uses a cyanobacterial platform organism engineered to produce a diesel-like alkane mixture, to maximally divert fixed CO 2 to the engineered pathway, and to secrete the alkane product under conditions of limited growth but continuous production. This creates a process analogous to those of engineered fermentative systems that use heterotrophic organisms, e.g., yeast, E coli , etc., whose phases of growth and production are separated and whose carbon partitioning is controlled to achieve very high maximal productivities (for example, see Ohta et al. 1991 ; Stephanopoulos et al. 1998 ). Such processes, where cells partition carbon and free energy almost exclusively to produce and secrete a desired product while minimizing energy conversion losses due to growth-associated metabolism, have much longer process cycle times and higher system productivities than those requiring organism growth and downstream biomass harvesting and processing. For purposes of energy conversion analysis, we compare the direct process to a conventional algal pond biomass-based process producing biodiesel esters. A simple comparative illustration of the algal biomass process and the direct photosynthetic concept is shown in Fig. 1 . Many analyses have been performed for the algal process (Benemann and Oswald 1994 ; Chisti 2007 ; Gordon and Polle 2007 ; Dismukes et al. 2008 ; Rosenberg et al. 2008 ; Schenk et al. 2008 ; Angermayr et al. 2009 ; Stephens et al. 2010 ; Weyer et al. 2009 ; Wijffels and Barbosa 2010 ; Zemke et al. 2010 ; Zijffers et al. 2010 ) and for photosynthetic efficiency associated with production of plant biomass (Zhu et al. 2008 , 2010 ) and we have incorporated the relevant aspects of these published reports to bound the current analysis. Our analysis of the algal process closely follows the assumptions of Weyer et al. ( 2009 ) with the exception that we use the more common open-pond scenario. Note that we also make a clear distinction between biodiesel esters derived from algal biomass and fungible alkane diesel synthesized directly. Fig. 1 Schematic comparison between algal biomass and direct photosynthetic processes. The direct process, developed by Joule and called Helioculture™, combines an engineered cyanobacterial organism supplemented with a product pathway and secretion system to produce and secrete a fungible alkane diesel product continuously in a SolarConverter™ designed to efficiently and economically collect and convert photonic energy. The process is closed and uses industrial waste CO 2 at concentrations 50–100× higher than atmospheric. The organism is further engineered to provide a switchable control between carbon partitioning for biomass or product. The algal process is based on growth of an oil-producing culture in an industrial pond on atmospheric CO 2 , biomass harvesting, oil extraction, and chemical esterification to produce a biodiesel ester"
} | 2,023 |
21915110 | PMC3195233 | pmc | 566 | {
"abstract": "Novel methods for information processing are highly desired in our information-driven society. Inspired by the brain's ability to process information, the recently introduced paradigm known as 'reservoir computing' shows that complex networks can efficiently perform computation. Here we introduce a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback. Through an electronic implementation, we experimentally and numerically demonstrate excellent performance in a speech recognition benchmark. Complementary numerical studies also show excellent performance for a time series prediction benchmark. These results prove that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing. This finding paves the way to feasible and resource-efficient technological implementations of reservoir computing.",
"discussion": "Discussion We have demonstrated, both in experiment and simulation, that a simple nonlinear dynamical system subject to delayed feedback can efficiently perform information processing. As a consequence, our simple scheme can replace the complex networks used in traditional RC. Moreover, to the best of our knowledge, this experiment represents the first hardware implementation of RC with results comparable to those obtained with state-of-the-art digital realizations ( Supplementary Discussion ). To get good performance with our system, a number of parameters need to be adjusted. These include the feedback gain η , the input gain γ , the delay time τ , the separation of virtual nodes in the delay line θ , the type of nonlinearity (in our case the exponent p of the MG system), and the choice of input mask. These parameters have analogues with similar parameters used in traditional RC: feedback gain and input gain have similar roles to spectral radius and input scaling; the delay time τ is related to the number of nodes and the separation of the virtual nodes to the sparsity of the interconnection matrix; the type of nonlinearity can also be varied in traditional RC and so on. As demonstrated here, some of the parameters can vary significantly around certain optimal values and still yield very good results. As experience with systems such as ours grows, we expect–as in traditional RC–that good heuristics on what parameter values to use will emerge. We expect that delay reservoirs can be realized that are, within some restrictions, versatile for different tasks. Moreover, owing to their much simpler hardware implementation, specifically optimized solutions for certain tasks could make sense. From a fundamental point of view, the simplicity of our architecture should facilitate gaining a deeper understanding of the interplay of dynamical properties and reservoir performance. Besides the fundamental aspect of understanding information processing capabilities of dynamical systems, our architecture also offers practical advantages. The reduction of a complex network to a single hardware node facilitates implementations enormously, because only few components are needed. Nevertheless, the use of delay dynamical systems implies certain constraints, because the feeding of the virtual nodes is carried out serially, in contrast to the parallel feeding of the nodes in traditional RC. This serial feeding procedure results in a slow-down of the information processing, compared with parallel feeding. This potential slow-down is compensated for by the much simpler hardware architecture of the reservoir, and by the fact that the read-out can be taken at a single point of the delay line. These simplifications will enable ultra-high-speed implementations, using high-speed components that would be too demanding or expensive to be used for many nodes. In particular, realizations based on electronics or photonics systems should be feasible using this simple scheme, including real-time processing capabilities. Moreover, we expect that compromises can be found concerning speed, performance and memory capacity by extending the concept to network motifs of delay-coupled elements. Ultimately, a novel information-processing paradigm might emerge."
} | 1,050 |
39747257 | PMC11696034 | pmc | 567 | {
"abstract": "Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited resolution and are affected by a high degree of variability. By developing a recurrent spiking neural network model of the retinocortical visual pathway, we show how such noisy and heterogeneous computing substrate can produce linear receptive fields tuned to visual stimuli with specific orientations and spatial frequencies. Compared to strictly feed-forward schemes, the model generates highly structured Gabor-like receptive fields of any phase symmetry, making optimal use of the hardware resources available in terms of synaptic connections and neuron numbers. Experimental results validate the approach, demonstrating how principles of neural computation can lead to robust sensory processing electronic systems, even when they are affected by high degree of heterogeneity, e.g., due to the use of analog circuits or memristive devices.",
"introduction": "Introduction The goal of an early visual processing system is to extract as much information as possible about the structural properties of the visual signal, efficiently and quickly. Such a system must provide reliable features of high informative content, with low latency, to best support subsequent processing stages, for example involved in navigation or visual scene interpretation. Recently developed asynchronous event-driven vision sensors combined with brain-inspired spiking neuromorphic processors represent a promising technological solution for implementing such systems. The properties of these sensors and processors include massively parallel operation with a degree of network reconfigurability that can support the definition of different types of real-time visual processing models. However, current prototypes have limited resources for programming arbitrary connectivity patterns among neurons. For this reason, neuromorphic vision front-ends have been restricted so far to implementing relatively simple edge and moving object detectors. For example, recently proposed neuromorphic visual processing for depth perception and stereo-vision operate exclusively on temporal contrast events, disregarding the local spatial structure of the visual signal 1 , 2 . Other examples implement simple (e.g., binary) feature matching, by composing local receptors outputs through receptive fields (RFs) with minimal and simple weighting profiles 3 . More sophisticated early visual processing systems would require highly structured RFs, e.g., with two-dimensional (2D) wavelet-like profiles to extract local amplitude, phase, and orientation information in a given frequency sub-band (cf. linear visual cortical cell responses, e.g., see ref. 4 ). Indeed, for many machine vision tasks, images are commonly analyzed by sets of oriented spatial-frequency channels in which some properties of the image are better represented than in image space. The spatio-temporal properties of the resulting harmonic components have been shown to be critically important for extracting primary early vision information. In general, as evidenced in several studies (e.g., see refs. 5 – 7 ), by using harmonic patterns for matching instead of image luminance measures, the resulting perception is more reliable, denser, and immune to changing lighting conditions. Since a direct implementation of such wavelet RFs on neuromorphic hardware is hampered by their limited routing resources, designing and validating efficient architectural solutions to obtain compact visual signal analyzers with minimal resource consumption is a challenge of critical importance. In this paper, we address this challenge, by demonstrating an economic way to implement spike-based early-vision detectors of oriented features in given spatial frequency bandwidths that reproduce the known properties of Gabor-like simple cells RFs in the primary visual cortex (V1) 4 , 8 . This work builds on previously proposed preliminary models 9 , 10 . The strength of this work lies in the presentation of a coherent framework that combines and integrates previous contributions and extends them with both theoretical contributions that demonstrate the validity of the approach proposed and additional experimental results that highlight the benefits of the neuromorphic setup used. Our experimental results demonstrate how sparse biologically plausible recurrent connectivity schemes lead to the emergence of realistic RFs that exhibit response properties very similar to those measured in cortical neurons. In addition to being a useful result that validates theoretical and modeling studies with a real physical computing substrate that has the same properties and limitations of the biological computing substrate, this work paves the way toward the construction of compact and low-power early vision processing front-end systems for complex vision processing systems.",
"discussion": "Discussion Todays’s neuromorphic systems represent a promising alternative to conventional von Neumann architectures for both understanding and reproducing the properties of biological sensory processing systems, as they are subject to similar constraints in terms of noise, variability, and parameter resolution 37 . Reproducing the dynamics of biological neural systems using sub-threshold analog circuits and asynchronous digital ones make these systems ideal computational substrate for testing and validating hypotheses about models of sensory processing for a wide range of application domains 14 , 38 . In addition, their real-time response properties allow us to test these models in closed-loop sensory-processing hardware setups and to get immediate feedback on the effect of different parameter settings. As the amount of data in visual processing is intrinsically high, providing sufficient resources for performing complex transformations – from pixels to features – and implementing corresponding computational models is particularly challenging. Indeed, front-end early vision modules have to construct high-dimensional quantitative representations of image properties, referable to local contrast variations across different orientations, and according to different spatial frequencies. Subsequent stages eventually combine these properties in various ways, to provide categorical qualitative descriptors, in which information is used in a non-local way to formulate more global spatial and temporal predictions (e.g., see ref. 39 ). However, it is only seldom that classical frame-based computational theories can be directly applied to event-based sensory data. Indeed, typically, object detection, pattern recognition, and scene reconstruction rely upon algorithms and computational procedures that well conform to the peculiar properties of the sensory data representation. Considering specifically image classification tasks 32 , 40 , 41 , intrinsically 1D properties, like edges and contours, are often sufficient to obtain a compact and complete feature description that enables a similarity measure to be applied to the different samples of popular image dataset. Other applications, like depth perception, optic flow, or simultaneous localization and mapping (SLAM), more decisively rely upon the timings of events 1 , 42 , 43 . Although fully exploiting the time coding of spikes trains can be extremely efficient, we cannot disregard extracting the information conveyed by the spatial structure (i.e., the texture) of the luminance pattern, which depends on precise relations among the phases of the various harmonics (e.g., see refs. 7 , 44 ). We must ensure that such information is not lost. The latter indeed plays a pivotal role in gaining dense feature maps potentially informative for several machine vision applications. Extracting stable spatial image structure requires local operations to regularize the information contained in spike trains. This can be done afterwards, on the result of the interpretation of the event stream (as mostly adopted by event-based machine vision algorithms, e.g., see ref. 45 ), or concurrently with picking-up sensory signal. Having such an early stage dedicated to the extraction of general-purpose regularized features brings about enormous advantages in terms of adaptability and versatility for compositionally building or learning a variety of higher-order visual descriptors. At a first level of abstraction, it is thus important that the rate coding model of network’s neuronal firing replicates the known encoding properties of the cells in the primary retinocortical pathway, according to a linear filtering model with appropriate kernels (i.e., receptive fields) 46 It is well acknowledged that Gabor wavelets are a powerful tool to gain an efficient regularized representation of the information contained in frame-based visual signal, in terms of local amplitude, phase and orientation maps of the transformed signal. In previous works 9 , 10 , we indicatively demonstrated that recurrent clustered inhibition can be successfully used in SNNs, both in simulation and on mixed-signal analog/digital neuromorphic hardware, to economically implement highly structured Gabor-like RFs. The results of this paper corroborate those preliminary findings, specifically extending the analysis of the linearity of the resulting RFs when using the net firing rate of the retina (ON firing rate minus OFF firing rate) instead of both as a whole, or separately. Such a push-pull combination of the complementary ON and OFF channels led to more reliable and unbiased representation of the harmonic content (see phase and energy in Fig. 7 e) which would eventually lead to steeper tuning curves of the V1 neurons, resulting in better selectivity to the local orientation, spatial frequency and phase of the visual input. Employing multiple banks of Gabor filters at the front-end of a bio-inspired vision system is not a novel concept per se 47 – 51 , and examples of hardware implementations can be found in the literature 24 , 52 – 56 . Yet, here we propose an economic way to implement them in hardware by a spiking neural network, which can be efficiently scaled with the kernel size. The resulting RFs are characterized by spatial profiles and by tuning curves that are typically sharper than the ones obtained using equivalent feed-forward schemes. Furthermore, RFs obtained through a recursive scheme use a lower number of interconnections than that required when using an exclusively feed-forward approach. The advantage of the recurrent network over strictly feed-forward schemes is up to more than 3 × for a five sub-region RF with a size of 21 × 21, and increases with the rescaling of the filter’s size. This is an important feature when dealing with the limitations in terms of available synaptic connections posed by neuromorphic processors. In summary, the solution proposed in this work demonstrates that an early vision filtering stage can be implemented in mixed-signal neuromorphic hardware in a relatively economic way, with adequate accuracy and stability. Particularly, exploiting both ON and OFF channels – through their push-pull combinations – shows to be an appropriate approach to remove the undesired effect of dc component sensitivity, and thus obtain highly informative phase-based features. The implemented units act as multiple oriented bandpass frequency channels, well supporting a compact and reliable representation of position, orientation and phase of local image patches. As a whole, the resulting harmonic signal description provided by the proposed neuromorphic circuit could be potentially used for a complete characterization of the 2D local structure of the visual signal in terms of phase relationships from all the available oriented channels. The amplitude (i.e., firing rate) information can be used as an indicator for the likelihood of the presence of a certain structure, while the orientation of contrast transitions and their spatial symmetry (i.e., phase, 7 , 57 ) can be used as an attribute of the visual descriptor."
} | 3,041 |
34195589 | PMC8237362 | pmc | 568 | {
"abstract": "Highlights • Engineered syntrophy of cyanobacteria and methanotrophs in photogranules • Novel syntrophy removed dissolved methane in batch and continuous reactor system • Phototrophic cyanobacteria produced oxygen for methane oxidation by methanotrophs • Methanotrophs provided carbon dioxide for photosynthesis by cyanobacteria",
"conclusion": "Conclusions • A methanotrophic-cyanobacterial syntrophy was established in the chassis of existing oxygenic photogranules. This syntrophy was maintained and propagated in a continuously operated reactor, proven by biomass growth and the removal of dissolved methane. We thus demonstrated the feasibility to ecologically engineer a novel photogranule community as potential biocatalyst for dissolved methane removal from anaerobic effluents. • Photogranule morphology could be controlled in part by adapting hydrodynamic shear in the system, demonstrating that morphology not only depended on the developmental state of the photogranules. • The established open community not only contained methanotrophic bacteria and phototrophs, but also non-methanotrophic methylotrophs, likely responsible for methanol conversion. Possibly, methanotrophs only incompletely oxidized methane to methanol, enabling the development of a methanol-degrading community, equally fueled by phototrophically generated oxygen. • Community composition may differ considerably between photogranules, hinting towards cross-feeding between individuals of the photogranule population. This variability needs to be considered in experimental and modeling studies. • The presence of non-methanotrophic methylotrophs is not problematic if the biotechnological aim was the removal of dissolved methane as post-treatment of anaerobic effluent. If simultaneous molecule recovery was the intention, e.g., methanol production, more specific ways for controlling the activity of the open microbial community would be needed. • Further research will focus on the treatment of real anaerobic wastewaters effluent in a long-term continuous mode. Nutrient recovery and an increased loading rate need to be studied as a function of temperature. Photogranules may be suitable to remove methane after psychrophilic anaerobic wastewater treatment with increased methane solubility and decreased biological kinetics.",
"introduction": "Introduction The use of anaerobic granulated biomass for biological wastewater treatment was introduced about 40 years ago ( Lettinga et al., 1980 ), and is now regarded an adequate methodology for municipal wastewater treatment ( Seghezzo et al., 1998 ) and energy recovery ( Gao et al., 2014 ). The development of anaerobic wastewater treatment since the end of the 1990s has been considered a more sustainable alternative to traditional aerobic processes, especially for high strength wastewater. Its primary purpose is the conversion of organic matter to methane as a renewable form of energy ( Vandevivere, 1999 ; Verstraete et al., 1996 ). An often-overlooked drawback of anaerobic wastewater treatment is the loss of dissolved methane as not all of it partitions to the gas phase inside the digesters. Estimations of the loss of dissolved methane from anaerobic wastewater treatment are typically calculated from methane concentrations in the gaseous headspace using Henry's Law, i.e., under equilibrium conditions ( Lobato et al., 2012 ). This idealized case does not always reflect the actual measured values of anaerobic treatment liquid effluent as mass-transfer limitations can lead to supersaturation of methane. In this case, an assumed equilibrium with headspace concentrations will thus underestimate the dissolved methane content. Souza et al. (2011) and Wu et al. (2017) found that dissolved methane was supersaturated in the liquid phase of an anaerobic bioreactor effluent (saturation factor of 1.03–1.67), increasing with the increased methane solubility at decreasing temperatures. Even at equilibrium, considerable amounts of methane are lost with the liquid effluent, especially when treating wastewater at low temperatures and/or in high-flow through situations (low hydraulic retention time) ( Brandt et al., 2019 ). Once the effluent is discharged and exposed to ambient methane partial pressures, methane degasses into the atmosphere. Theoretically, 0.38 liters of methane are produced per gram of chemical oxygen demand (COD) removed from the anaerobic wastewater treatment at standard ambient condition (25°C, 1 atm) ( Tchobanoglous et al., 2003 ). By assuming 80% COD removal efficiency for a typical high strength municipal wastewaters with an average soluble COD concentration of 450 g∙m −3 ( Henze et al., 2008 ), 137 l CH 4 ∙m −3 is produced, equivalent to 89.7 g CH 4 ∙m −3 . At a methane solubility of approximately 20 g∙m − 3 at 25°C ( Liu et al., 2014 ), approximately 22% of all produced methane would be in its dissolved form and likely leave the digester. At most about 107 l CH 4 ∙m −3 could be used for combustion, saving fossil CO 2 emissions of 107 l CO 2 ∙m −3 . Degassing of the dissolved methane to the atmosphere, however, would contribute approximately 500 g CO 2 equiv ⋅m −3 , equivalent to 278 l CO 2 ∙m −3 . Therefore, the greenhouse gas contribution in this example is about 2.5 times greater than the positive effects from generating a renewable energy (see Supplemental Materials). This methane loss is significantly reducing and, as in the given example, even offsetting the positive effect of energy recovery from anaerobic wastewater treatment. Therefore, a post-treatment process is required to remove dissolved methane, reducing the environmental impact of anaerobic wastewater treatment. Several methods have been proposed for removing or recovering dissolved methane from anaerobic effluents. These include air stripping oxidation ( Hatamoto et al., 2010 ; Matsuura et al., 2015 ) and degassing membrane-based recovery ( Bandara et al., 2011 ; Cookney et al., 2016 ). Dissolved methane can be biologically oxidized by methanotrophs. Methanotrophs are part of a larger group of bacteria called methylotrophs that typically utilize single-carbon compounds like methane, methanol, formic acid or even formaldehyde as carbon and energy source ( Chistoserdova et al., 2009 ). Methanotrophs may fully oxidize methane to CO 2 or partially to molecules like methanol. Molecular oxygen is required for the conversion. Through the coupled activities of eukaryotic algae or photosynthetic bacteria and methanotrophs in syntrophic bioaggregates, oxygen may be provided by direct, or at least local transfer. The produced oxygen is then immediately utilized by the methanotrophs to convert organic matter to CO 2 which is in turn used by phototrophs for autotrophic CO 2 fixation. These interactions are found in natural systems, for example, at the chemocline between anaerobic and aerobic water layers in freshwater lakes ( Milucka et al., 2015 ), and are also utilized in engineered systems, e.g., by van der Ha et al. (2012) for the production of lipids or polyhydroxy butyrate using co-cultured eukaryotic algae and methanotrophs. Rasoulie et al. (2018) also investigated a co-culture of green microalgae and methanotrophs for removing methane and recovering nutrients. They used industrial wastewater as media and synthetic biogas as methane source. In both studies, the authors used pure cultures of methanotrophs and microalgae ( Rasouli et al., 2018 ; van der Ha et al., 2012 ). In nature, methanotrophs often co-occur with non-methanotrophic methylotrophs that feed on partially oxidized methane intermediates like methanol ( Takeuchi et al., 2019 ; Yu et al., 2017 ). These more complex interactions are also relevant in our study using an environmental enrichment as basis for the construction of a new syntrophy. In contrast to studies using suspended phototrophic-methanotrophic consortia, we present here an aggregated biomass in the form of photogranules for the aeration-free removal of dissolved methane. The conversion relies on syntrophic interactions between phototrophic cyanobacteria and methanotrophic bacteria aggregated in oxygenic photogranules. Aggregation is particularly important in the bioprocesses as it allows efficient and fast removal of the biomass from the treated water and efficient intra-aggregate oxygen transfer. We established the presence of methanotrophic bacteria in the photogranule aggregates and propagated the newly developed syntrophy in an open community, challenged by invading microbes. The syntrophy was ecologically engineered from an enrichment culture of methanotrophs from activated sludge and oxygenic photogranules converting synthetic wastewater, as described in Milferstedt et al. (2017) . We discuss community assembly in the light of performance characteristics of a continuously operated reactor system for the removal of dissolved methane.",
"discussion": "Results and Discussions Successful establishment of a syntrophic, methane-degrading community Methanotrophs were enriched from activated sludge in gas-tight, stoppered serum bottles with a mixture of oxygen and methane in the headspace. We tested enrichments with and without mixing by magnetic stirring. After four transfers every five days into fresh media, all enrichments that were mixed during incubation removed methane and produced carbon dioxide according to the methane oxidation stoichiometry, closing the mass balance by more than 80%. We detected methane removal in approximately half of statically incubated cultures (i.e., without mixing). Mixing increases methane transfer across the gas-liquid interface, leading to more successful incubations. The enrichments from unstirred oxygenic photogranules mixed with activated sludge removed methane. However, unstirred and stirred activated sludge enrichments removed methane twice and four times faster, respectively. Fresh photogranules did not exhibit a measurable methanotrophic activity. We detected methane removal after adding 100% methane without externally provided oxygen to a mixture of fresh, non-methanotrophic photogranules and methanotrophic enrichments from activated sludge. Also, the production of oxygen and CO 2 was measured. This observation demonstrated the onset of engineered syntrophic interactions between methanotrophs and oxygenic photogranules. We were thus able to introduce a novel function into an existing, granulated microbial ecosystem, laying the basis for a potential future application in biotechnology in which biomass harvesting is feasible. Continuous reactor performance for removing dissolved methane The ecologically engineered methane-converting photogranules were then used as inoculum for the continuously operated reactor. Fig. 2 shows the dissolved COD removal efficiency as proxy for methane removal as well as effluent total suspended solids (TSS) over time. Dissolved methane removal efficiencies fluctuated over the first week of operation. Effluent concentrations stabilized over the following two weeks and the methane removal efficiency steadily increased. On day 16, biofilm on reactor surfaces and equipment was removed, resulting in a 5% decrease in methane removal. The rather moderate decrement indicates that the vast majority of methane oxidation was situated in photogranular biomass and not in the biofilm formed on the reactor surfaces. Approximately 10 mg TSS∙l −1 of suspended solids were washed out from the reactor. Photogranules became increasingly more filamentous at this time ( Fig. 3 a, middle). The change of photogranule morphology could have been influenced by the increase in biomass concentration and a local decrease in light availability in the reactor. The cyanobacteria might try to increase their surface area by forming filamentous outgrowth and therefore exposure to light ( Biddanda et al., 2015 ). Fig. 2 Removal efficiency of dissolved methane (filled circles) and concentrations of total suspended solids in the effluent (TSS, open circles) during continuous reactor operation. Mixing speed was increased on day 31. On day 40, the reactor effluent clogged. Severe biomass washout occurred on days 43 (deliberate) and 94 (accidental). Figure 2 Fig. 3 Photogranule development on macro and micro-scale. a) Full views of the reactor vessel. b) Examples of typical photogranule morphologies during continuous operation. Images were taken using white-light stereomicroscopy (scale bar for all images is 1 mm). Figure 3 After about three weeks of operation, effluent TSS increased due to the detachment of filaments from the photogranules. Application of higher mixing intensity on day 31 from 100 rpm to 125-128 rpm resulted in an increase in methane removal efficiency, now exceeding 90%. Mixing serves the purposes of minimizing the laminar boundary layer around the photogranules and keeping the photogranules in suspension ( Beun et al., 2000 ; Liu et al., 2003 ). We also changed mixing to increase detachment of filaments from the photogranule surfaces. This approach worked, resulting in temporarily increased effluent suspended solid concentrations from the reactor ( Fig. 2 , days 31 to 40) and less filamentous photogranules. However, the sudden detachment became problematic for reactor operation at day 40 when the effluent clogged, turning operation into safety mode for two days, i.e., without water in and outflow. The reactor was operational again after cleaning and wasting some photogranules at day 43. On day 43, biomass was purposely wasted to obtain approximately 1.5 g TSS∙l −1 . Introduction of a weekly cleaning and biomass wasting protocol, by removing approximately 0.5-0.7 g TSS∙l −1 , prevented further clogging and maintained a balanced biomass concentration in the reactor of approximately 1.2 g TSS∙l −1 . This weekly biomass removal represented about one third to half of the biomass in the reactor. The removal of granular biomass on day 43 caused a drop in methane removal efficiency from about 90% to 60%. The removal efficiency reached on average 84.8±7.4% (±standard deviation) between day 54-93. On day 94, a decreased methane removal efficiency was observed due to accidental wasting of a large numbers of photogranules. However, performance recovered over the next week to above 80%. The average effluent concentration of dissolved methane and the averaged methane removal rate during reactor operation was 4.9±3.7 mg CH 4 ∙l −1 and 26.3±2.6 mg CH 4 ∙l −1 ∙d −1 , respectively. In van der Ha et al. (2011) , an overall methane oxidation rate was reported to be 171 mg CH 4 ∙l −1 liquid phase∙d −1 which appears to be 6.6 times higher than in our experiments. A major factor leading to a higher removal rate is the organic loading. In van der Ha et al. (2011) , 235 ml of CH 4 were added over 72 hours, which corresponds to approximately 258 mg CH 4 ∙l −1 ∙d −1 at 22°C. Our OLR of 35.1±4.5 mg CH 4 ∙l −1 ∙d −1 was thus, 7.3 times lower. It is important to note that the rates are not immediately comparable as van der Ha et al. (2011) worked in a batch system over 90 h, while our results are obtained in a CSTR with an HRT of 12 h. The observed overall biomass yield was 0.7 g TSS∙g COD −1 , equivalent to 0.6 g VSS∙g COD -1 (assuming 15 % inorganic biomass fraction). Per mass substrate (CH 4 ) this is equivalent to 2.4 g VSS∙g CH 4 −1 . The observed yield represents the combination of cellular yield from methanotrophic and cyanobacterial growth. Literature values of methanotrophic yields relevant for our study have been reported by Leak & Dalton (1986) and Arcangeli & Arvin, (1999) . By theoretical analysis and experimental observations on suspended Methylococcus capsulatus, Leak & Dalton (1986) reported cellular yield of 0.6-0.7 g VSS∙g CH 4 −1 on cultivation conditions similar to this study. Arcangeli & Arvin (1999) studied a methanotrophic biofilm enriched from landfill soil and estimated the dry weight yield to be 0.56 g VSS∙g CH 4 −1 . As conditions and growth technique, granular aggregation is similar to biofilms, and our media (0.02 mg∙l −1 of CuSO 4 ∙5H 2 O) was comparable to the Cu limited experiment of Leak and Dalton (1986) , we estimate methanotrophic yields to be in the order of 0.5-0.6 g VSS∙g CH 4 −1 , which leaves the remaining observed 1.8 g VSS∙g CH 4 −1 to be the autotrophic contribution. Assuming all CO 2 from the mineralization of methane to be assimilated by the phototrophic bacteria, a combined methanotrophic and phototrophic yield of 1.54 g VSS∙g CH 4 −1 would be theoretically possible. The observed combined yield (2.4 g VSS∙g CH 4 −1 ) therefore indicates an additional autotrophic growth contribution of 0.9 g VSS∙g CH 4 −1 probably originating from the inlet bicarbonate. High biomass yields in this system highlight the potential for the recovery of chemical energy or the methane-based biorefinery using photogranules. The overall COD balance closed at 91% of the inlet COD. The unaccounted 9% COD could be explained the reactor system still not completely at steady state (positive bioaccumulation), and by the negative COD contribution by phototrophically produced oxygen consumed by the methanotrophs during methane mineralization. We can rule out leakages in the system and therefore potential methane loss or oxygen and CO 2 entering the system. The tightness of the reactor system was verified by frequently checking the gas composition in the headspace of the reactor using gas chromatography. The results showed that in the headspace, the dominant gas were nitrogen, oxygen, and methane by 89.3±3.3%, 4.4±2.6%, and 4.9±2.3% (v/v) (±standard deviation), respectively. The high presence of nitrogen gas resulted from the regular flushing of the headspace with nitrogen during reactor cleaning and maintenance. Only at most traces of CO 2 were detected in the gas phase at 0.01±0.02% (v/v). Cyanobacteria can use CO 2 , HCO 3 − and possibly also CO 3 2− as carbon source ( Schneider & Campion-Alsumard, 1999 ). In our study, methane was not the sole carbon sources, but HCO 3 − was contained as hardness in the tap water we used for media preparation. Based on the growth stoichiometry for methane oxidation coupled to photosynthesis, the theoretically produced oxygen from CO 2 assimilation during photosynthesis only provides roughly 20% of the oxygen needed for complete methane oxidation. The presence of HCO 3 − to the media would enhance the methane removal efficiency due to higher oxygen availability from bicarbonate photosynthesis. The 4 mM HCO 3 − contained in the tap water could theoretically supply an additional 2.58 mM O 2 , or an equivalent COD of 82 mg∙l −1 , upon autotrophic growth. Hence, methane oxidation is not stoichiometric oxygen limited by cyanobacterial growth. Similar findings were reported in the literature where in the absence of external oxygen supply, microalgal photosynthesis was not sufficient for methane oxidation ( Bahr et al., 2011 ). Only when bicarbonate was introduced, the methane removal efficiency increased. We suggest that the elevated methane removal in the absence of any external oxygen supply can only be explained by in-situ oxygen production and immediate uptake by methanotrophs. Our results therefore demonstrate the establishment of syntrophic interactions between phototrophs and methanotrophs. This syntrophy was stably maintained over seven weeks during continuous reactor operation. Photogranule development in continuously operated reactor During continuous reactor operation, new photogranules rapidly formed ( Fig. 3 ). After 16 days of continuous operation, the photogranules became dark green, roughly spherical, and filamentous ( Fig. 3 ). The presence of filamentous cyanobacteria was confirmed by white-light and fluorescence microscopy. Development of the filamentous morphology likely caused the increase in suspended solids in the reactor effluent ( Fig. 2 ). We successfully generated a less filamentous photogranule phenotype by increasing mixing from 100 rpm to 125-128 rpm starting on day 31. Immediately after the increase, the photogranules lost substantial amounts of filaments. Whitish areas on the photogranules surface became visible ( Fig. 3 b, middle). Using image analysis, we determined the number and the size of photogranules in the reactor. Over the first four weeks, the number of photogranules increased from initially 60 with an approximated total surface area of 0.08 cm 2 (day 0), to 888 and a total surface area of 240 cm 2 on day 16, and to about 2000 with a total surface area of 551 cm 2 on day 29 ( Fig. 4 ). After the major wasting and cleaning event on day 43, the number and surface area of photogranules decreased to 613 and 77.3 cm 2 , respectively. However, the biomass concentration increased in the following days and reached more than 3500 photogranules and 1105 cm 2 of surface area on day 79 ( Fig. 4 ). Fig. 4 The total number (empty diamonds) and surface area of photogranules (filled circles) in the reactor during continuous operation. Figure 4 The range of biomass diameters on day 16 was between 1 and 7 mm, with an average of 2.6±1.3 mm (±standard deviation). On day 43, because of increasing shear, the predominant diameter of photogranules was less than 2 mm. The largest size of photogranules in the reactor was approximately 7.5 mm. This size is larger than the typical size of aerobic granular sludge with sizes in the range of 4 to 6 mm ( Beun et al., 2000 ; Morgenroth et al., 1997 ). However, towards the end of reactor operation, more than 90% of the photogranules were between 1 and 3 mm with an average diameter of 2.6±1.0 mm. This value is greater than the size of photogranules (≤2 mm in diameter) reported from previous photogranule reactor studies ( Abouhend et al., 2018 ; Liu et al., 2017 ). Size specific analysis of methanotrophic activity Photogranule size may influence the specific phototrophic and methanotrophic activities as we assume phototrophic methane conversion to be a surface-depending process. Photogranule size affects the surface to volume ratios and diffusional lengths. We analyzed the specific metabolic activity in batch experiments for sets of on average six similar-sized photogranules in size classes between 1.3 and 5.5 mm in average diameter ( Fig. 5 ). Photogranules were sampled during stable reactor performance. Photogranules with diameters of approximately 1-2 mm gave the highest surface-specific methane removal rate of 0.53±0.02 mg CH 4 ∙d −1 ∙mm −2 (±standard deviation). The methane removal rate per photogranule surface area decreased with increasing diameter ( Fig. 5 a). The relation with the surface to volume ratio is presented in Fig. 5 b. An elevated surface to volume ratio is beneficial for methane removal. From a conversion perspective, it is favorable to engineer a size distribution within the reactor of minimal photogranule diameter. The surface dependent character of photogranule metabolism was also shown in a recent study by Abouhend et al. (2020) in which oxygenic photogranules of 0.5−1.7 mm in diameter showed the highest oxygen production rate compared to bigger photogranules ( Abouhend et al., 2020 ). Higher oxygen production rates influence the treatment potential of the biomass, in this case dissolved methane removal, due to higher oxygen availability from photosynthesis as electron acceptor. Bigger photogranules may also become less active because they lose their cyanobacteria from the core as the photo-layer appear to be limited to depth of about 700 µm ( Milferstedt et al., 2017 ). Fig. 5 Surface specific methane removal rates for individual photogranule sizes. Rates are plotted by (a) the average diameter of the photogranule batch, and (b) by the surface to volume ratio, derived from the average diameters of the tested photogranules. Each point represents an independent batch experiment conducted with on average six similar-sized photogranules. Figure 5 Community analysis related to methane removal performances Using MiSeq amplicon sequencing, we analyzed the microbial communities in various photogranules sampled from the sequencing batch reactor, as well as the enriched inoculum and background material before the enrichment. We detected the presence of sequences belonging to methylotrophic bacteria in our samples ( Fig. 6 a). The detected methylotrophs predominantly belong to methanotrophic bacteria, a subgroup of the methylotrophs, able to directly use methane as carbon and energy source. A frequent intermediate or even final product of methanotrophs is methanol ( Kalyuzhnaya & Xing, 2018 ). Also, non-methanotrophic methylotrophs were detected in elevated abundances ( Fig. 6 a), notably of the family Methylophilaceae , often involved in methanol conversion ( Yu et al., 2017 ). These groups of bacteria may participate in a communal metabolism of methane ( Beck et al., 2013 ; Oshkin et al., 2015 ). Fig. 6 Relative abundances of methylotrophic and phototrophic taxa in photogranules and background material. The background material before the enrichment is the original activated sludge (AS), and an oxygenic photogranules (OPG). The inoculum after the enrichment process is represented by four photogranules. In total eight photogranule communities during continuous reactor operation are shown for days 15, 28 and 44. a) Putative methylotrophic bacteria (Silva SSU 132) among the non-phototrophic bacteria, i.e., excluding cyanobacteria, in the 16S rRNA amplicons. The three samples with asterisks mark photogranules in which methanotrophs are present in low abundances compared to non-methanotrophic methylotrophs. b) Major (>5% total abundance) cyanobacterial and chloroplast OTUs (Silva LSU 132) among the phototrophic taxa of the 23S rRNA amplicons. Figure 6 In the background material before the enrichment process, only in raw activated sludge, we detected sequences of one methanotrophic type in the 16S rRNA amplicons at a relative abundance of 0.02% of all bacterial sequences (excluding cyanobacteria). This sequence type was unique to the activated sludge sample and undetected in the inoculum and during reactor operation. In oxygenic photogranules, no sequences affiliated with known methanotrophic bacteria were detected ( Fig. 6 a, “background”). The activated sludge and oxygenic photogranules used as starting material in this study contained fewer sequences of methanotrophic bacteria than previous observations ( Milferstedt et al., 2017 ). Non-methanotrophic methylotrophs were undetectable in background activated sludge and oxygenic photogranules. The enrichment process had a profound impact on the microbial community as at the end of it, 18.5±6.0% (±standard deviation) of all non-cyanobacterial bacterial 16S rRNA sequences were affiliated with known methylotrophic bacterial genera ( Fig. 6 a, “inoculum”). Of this methylotrophic fraction, 74.1±4.8% were known methanotrophs, notably of the family of Beijerinckiaceae . These bacteria are Alphaproteobacteria, frequently described as type II methanotrophs. Across all samples containing sequences of methanotrophic Beijerinckiaceae , 98.5±4.5% were of the genus Methylocystis. Methylocystis are often considered versatile in their oxygen and methane requirements ( Knief, 2015 ) allowing them to thrive in ecosystems with a varying methane supply ( Knief, 2015 ). This survival strategy is believed to be linked to the presence of two variants of particulate methane monooxygenase A (PmoA) ( Knief, 2015 ), the key enzyme in methane oxidation, converting methane to the intermediate methanol. One of them is especially adapted to low methane to oxygen ratios in the feed, as likely encountered during batch feeding cycles in the enrichment process. The batch supply of methane and oxygen may thus be a key environmental factor favoring growth of these methanotrophs whereas the continuous exposure to methane at low concentrations as in the continuously fed reactor may promote others. Also present at the end of the enrichment, however, at notably lower abundances of 21.6±5.5% (±standard deviation) of all methanotrophic bacteria, were members of the families of Methylococcaceae and Methylomonaceae . These subdominant families belong to the Gammaproteobacteria, also known as type I methanotrophs. Traditionally, the distinction in type I and type II methanotrophs allowed the differentiation of mutually exclusive physiological traits. Over the last years, however, it was realized that the distribution of these traits was less exclusive, and the distinction has become less meaningful ( Dedysh & Knief, 2018 ). During photoreactor operation, the overall relative abundance of methylotrophs dropped from 18.5% in the inoculum to, on average, 3.5±2.0% (±standard deviation), of which roughly half of all sequences were known methanotrophs (1.8±1.4%). Most analyzed photogranules had an approximate diameter of 2 mm and thus a comparable biovolume, with the exception of the third and fourth samples on day 28 that had a diameter of 5 mm. Assuming an approximately constant microbial community size for equally sized photogranules, the observed relative changes in the microbial community are likely translated into an absolute decrease in abundance per photogranule. Most of this loss is attributed to a significant decrease in the methanotroph Methylocystis of the Beijerinckiaceae (t-test, p-value = 0.006). Also, the abundance of the other two methanotrophic families Methylococcaceae and Methylomonaceae decreased significantly (t-test, p-value = 0.02) dropping from, on average, 2.9±1.0% to 1.3±0.9%. After the disappearance of the Methylocystis , these two families presented the majority of methanotroph-affiliated sequences during reactor operation (87.2±24.2%, Fig. 6 “continuous reactor operation”). Two photogranules, sampled at day 28, only contained about 0.1% of methanotrophic sequences, more than ten times fewer than the other samples taken during reactor operation. The overall loss of methanotrophs may be explained by a reduced substrate availability per photogranule during reactor operation with the increasing number of photogranules in the system. The comparably low number of methanotrophs may thus be a steady state concentration adapted to the prevailing environmental conditions. Even though the drop in methanotrophs is significant, the abundance of methylotrophic bacteria, including methanotrophs remains about 100 times above the background levels before the enrichment. We note that the overall methanotrophic performance of the reactor system was maintained even at comparably low sequence abundances of 1.8±1.4% of methanotrophs. We systematically detected sequences of non-methanotrophic methylotrophs in our amplicons, notably of the family Methylophilaceae . Their sequences represented on average 4.7±1.6% (±standard deviation) in the inoculum, and 1.7±1.5% during reactor operation. In natural systems like sediments, these organisms are frequently found to respire methanol produced by methanotrophic bacteria ( Yu et al., 2017 ). Yu et al. (2017) even suggested that among non-methanotrophic methylotrophs and methanotrophs, specific non-random pairings exist that seem to possess an environmental advantage over others. We did not detect specific pairings in our data, but the abundances of Methylophiliaceae sequences appears to be roughly one third of the counts of known methanotrophic sequences in photogranules (linear regression through origin with slope of 0.345 and adj-r 2 of 0.74) ( Fig. 7 ). The constant ratio in abundance between two distinct phylogenetic groups hints towards a stoichiometric relationship between the implied organisms, possibly through metabolite dependencies. In Fig. 7 , notable exceptions to an otherwise strong linear relationship are two for the three samples marked with an asterisk in Fig. 6 . In these samples, methanotrophs are only present at a comparably low number. The exceptions indicate that metabolic heterogeneity between photogranules existed in our reactor, with the coexistence of putatively methanotrophic and non-methanotrophic photogranules. The non-methanotrophic photogranules may consume substrates provided by other methanotrophic photogranules. These substrates could be for example methanol. A complete CH 4 to CO 2 conversion chain may therefore not be required to be present within each photogranule, but the entire population of photogranules participates in the methane conversion, cross-feeding beyond the boundaries of individual photogranules. Fig. 7 Ratio of non-methanotrophic methylotrophs vs. methanotrophs sequence types in the 16S rRNA amplicons. Figure 7 The enrichment process and the consequent transfer into the continuously operated reactor also shaped the non-methylotrophic and non-phototrophic bacteria in the community. During the enrichment, Sphingomonadaceae were enrich and reach abundances in the non-phototrophic 16S rRNA amplicons of more than 45% in one photogranule. This organism was affiliated with Porphyrobacter , an organism believed to be involved in the recycling of organic matter. During reactor operation this organism became less abundant in most cases while other recyclers increased in abundance, notably Chitinophagaceae . Some of the detected organisms are facultative anaerobes, suggesting that there are anaerobic microhabitats within the photogranules. No apparent correlation with the dynamics that we observed for the methylotrophs were detected. A graphical representation of the dominating families of non-methylotrophic and non-phototrophic bacteria is given in Figure S1. We were unable to detect sequences of nitrifiers in photogranules. Ammonium is therefore likely directly assimilated by the growing biomass. Archaea were only detected in traces in the activated sludge sample before enrichment and otherwise absent in the amplicons. The postulated trophic chain between the different methylotrophs in photogranules is coupled to the oxygen production by phototrophs, notably cyanobacteria. We analyzed in 23S rRNA amplicons the presence and abundance of cyanobacteria and microalgae. As expected, the total abundance of phototroph sequences in the background activated sludge sample was low compared to photogranule amplicons. Only 370 cyanobacterial sequences were found in activated sludge, whereas the mean cyanobacteria count in photogranules was 49900±6700 (±standard deviation). Microalgal sequences represented the biggest part of the phototrophic population in activated sludge (93%). In photogranules, microalgal sequences were significantly less abundant, accounting for on average 3.5±3.0%. The microalgal population in photogranules was dominated by one single taxonomically unclassified sequence type with a sequence identity over the entire amplicon of approximately 95% to various microalgae genera. This sequence type made up on average 83±26% (median 96%) of the microalgae we found in photogranules. In the activated sludge sample, this particular sequence type was virtually absent (1.2%) in a more diverse microalgal population. It appears that this microalga may be a member specific to the photogranule community, albeit low in abundance compared to cyanobacteria. In the background photogranule and the inocula at the end of the enrichment, three sequence types dominated the cyanobacterial counts were detected, two of which are affiliated with Leptolyngbya boryana and one with Phormidium tenue . These organisms are filamentous, motile cyanobacteria, as often found to constitute the phototrophic biomass of photogranules ( Milferstedt et al., 2017 ). After the enrichment, 90.0±4.3% of all phototrophic sequences were related to Leptolyngbya and 2.9±1.7% to Phormidium . During reactor operation, the distribution of the two cyanobacterial types was close to binary in the different photogranules, where either one of the two dominated the photogranule community, as indicated by the high standard deviations around their mean abundances (52±43% Leptolyngbya and 46±44% Phormidium ). A heterogeneous cyanobacterial composition between individual photogranules within the same reactor was observed not unlike our observations for methanotrophic bacteria. When assuming that the two cyanobacteria perform the same ecosystem function, dominance of one over the other may be the result of a random event at the “birth” of the photogranule, e.g., a photogranule developing from a detached Leptolyngbya -dominated aggregate that develops into a Leptolyngbya -dominated photogranule. Likewise, it may be possible that the dominance of either Leptolyngbya or Phormidium results from preferential interactions with other microbes, for example methanotrophs. Curiously, the samples that contain the lowest numbers of methanotrophs (marked with asterisk in Fig. 6 a) coincide with the photogranules in which the phototrophic community is dominated by Leptolyngbya- like sequences. Potential preferential pairing between microorganisms, as considered in this study within the methylotrophs, may need to be considered at larger phylogenetic scales than uniquely between methylotrophs. The differences between microbial communities of individual photogranules from the same environment emphasize the need to study these systems at the scale of individual photogranules, for example when formulating the conversion process in a mathematical model."
} | 9,367 |
33398106 | PMC7610595 | pmc | 569 | {
"abstract": "Resource competition and metabolic cross-feeding are among the main drivers of microbial community assembly. Yet, the degree to which these two conflicting forces are reflected in the composition of natural communities has not been systematically investigated. Here we use genome-scale metabolic modeling to assess resource competition and metabolic cooperation potential in large co-occurring groups (up to 40 members) across thousands of habitats. Our analysis revealed two distinct community types, clustering at opposite ends in a trade-off between competition and cooperation. On one end, lie highly cooperative communities, characterized by smaller genomes and multiple auxotrophies. At the other end, lie highly competitive communities, featuring larger genomes, overlapping nutritional requirements, and harboring more genes related to antimicrobial activity. While the latter are mainly present in soils, the former are found both in free-living and host-associated habitats. Community-scale flux simulations showed that, while the competitive communities can better resist species invasion but not nutrient shift, the cooperative communities are susceptible to species invasion but resilient to nutrient change. In accord, we show, through analyzing an additional dataset, that colonization by probiotic species is positively associated with the presence of cooperative species in the recipient microbiome. Together, our analysis highlights the bifurcation between competitive and cooperative metabolism in the assembly of natural communities and its implications for community modulation.",
"discussion": "Discussion In this work, we observe a competition-cooperation trade-off among microbial communities that, although intuitive, had not been reported before. This is most likely due to the limited scale of the previous studies in terms of the number of species, number of environments, and the degree of co-occurrence. For example, two early studies 13 , 14 considered circa 150 species. The latter study observed a positive correlation between co-occurrence and competition but not with cooperation, suggesting niche filtering as the main driver of species assembly. Further, as the polarization becomes more striking at higher-order co-occurrence ( Figure 1b ), it is not surprising that this pattern was not previously reported. The polarization of co-occurring microbial communities into competitive and cooperative groups and its spread across the phylogenetic tree indicates two different, habitat-driven, evolutionary paths in community assembly. Competitive communities retain diverse metabolic capabilities to exploit the available nutrients, which indirectly antagonizes competitors, and to reduce dependencies on other species. The members of these communities also harbor more potential for antimicrobial compound production. Cooperative communities, on the other hand, harbor complementary auxotrophies and exhibit stable proportions across habitats, in line with inter-species dependencies. The advantage of the interdependencies in this group is reflected in their high relative abundance ( Figure 2d ). Our phylogenetic analysis indicates adaptive gene loss in metabolic networks. Consistent with an adaptive process, auxotrophies for amino acids with high biosynthetic costs are more common ( Extended Data 7 ). Collectively, metabolic capabilities, antimicrobial production potential, phylogenetic analysis, and differences in habitat preference and relative abundances, highlight the evolutionary conflict and cooperation in the two co-occurring groups identified in our study. Our analysis suggests joint role of abiotic habitat and evolutionary gene loss in determining whether a competitive or cooperative community will be established. The competitive species are generally restricted to free-living habitats wherein the resources are likely to be more scarce making competition more prevalent. In contrast, the nutritional richness of the host-associated habitats seems to support the cooperative species, which exhibit complementary auxotrophies, in part resulting from gene loss. This adaptation not only confers a fitness advantage but is also likely to facilitate the survival of these species during migration between the hosts and the external environment as a highly self-sufficient group. The generally higher abundance and diverse habitat occupation of the cooperative highlight the advantages offered by the division of metabolic labor.. This dichotomy between competition and cooperation is in certain ways analogous to that between the red queen and the black queen hypotheses 51 , 52 . The former is reflected in competitive species as they tend to retain most biosynthetic capabilities and harbor genes useful for active antagonism; the latter is reflected in gene loss in cooperative species leading to dependencies on fellow community members. Our results provide evidence that both theories are operating in natura as two extremes in a metabolic trade-off between competition and cooperation. The existence of the two community types with contrasting metabolic make-up and habitat preference means that the strategies to modulate or re-engineer these communities also need to be separately tailored. Our in silico results, with support from previously published experimental data 49 , 50 , show that the competitive and cooperative communities are more malleable through, respectively, abiotic and biotic perturbations. However, our results are still limited and subject to biases due to the exclusion of species without a reference genome assembly, and due to variable quality of gene annotations – on which the models are based – across different species. These findings could, in future, be further refined to consider metagenome-assembled genomes 53 , improving coverage at species and strain levels, and by accounting for viral 54 and fungal 55 interactions. Altogether, we conclude that devising intervention strategies tailored to communities according to their position in the competition-cooperation landscape would be key to the modulation of complex microbial ecosystems."
} | 1,531 |
30361729 | PMC6390697 | pmc | 570 | {
"abstract": "This work addresses the methodology and implementation of a neuromorphic SNN system to compute the temporal information among neural spikes using ReRAM synapses capable of spike-timing dependent plasticity (STDP).",
"conclusion": "4 Conclusions ReRAM devices are among the most promising technologies for artificial synapses in neuromorphic computing systems. To construct an artificial neural network competing with the brain’s functionality and efficiency, a ReRAM based SNN that can replicate the temporal computing in the brain is critical. This work addresses the methodology and hardware implementation of a neuromorphic SNN system to compute the temporal information among neural spikes using ReRAM synapses capable of STDP. We first experimentally demonstrate the learning and recognition of spatiotemporal-coded spike sequences, enabling the wide application potential, e.g. , spell checking and DNA analysis. Cascade spatiotemporal computing within the multilayer networks of ReRAM synapses are also presented. Utilizing the temporal computing between spikes, it is possible to learn and detect the trace of a moving object. We then design and simulate a pattern recognition system mimicking the hierarchical structure of the biological visual cortex using ReRAM synapses and the proposed methodology. The results confirm the ability of the temporal computing of ReRAM synapses and the feasibility of ReRAM synapses for the hardware implementation of “brain-like” neuromorphic system with efficient spatiotemporal coding.",
"introduction": "1 Introduction The most relevant advances of artificial intelligence (AI) are currently in the area of deep neural networks (DNNs), 1 which enable the learning and recognition of images, sounds, and speech. Despite the broad success of DNNs, their supervised training requires a huge amount of computational resources, while their energy consumption is several orders of magnitude higher than the human brain. Since DNNs are implemented in conventional computers, such as the graphic processing unit (GPU) utilizing the complementary metal-oxide-semiconductor (CMOS) technology, the slowing down of Moore’s law may create a critical issue for the future progress of DNNs. Another possible roadblock for conventional computers is the memory bottleneck, arising from the large latency and energy consumption due to the physical separation between memory and computing circuits according to the von Neumann architecture. The memory bottleneck critically affects the implementation of DNNs, which are inherently data hungry. 2 On the other hand, the brain adopts an in-memory computing approach, where there is no distinction between memory and computing units. Neuromorphic computing takes inspiration from the brain to achieve a higher energy efficiency than software-based DNNs for solving AI tasks. 3 , 4 Resistive memory devices, 5 , 6 including resistive random-access memory (ReRAM) and phase change memory (PCM), can be used as artificial synapses with analog plasticity, similar to biological synapses. 7 – 13 \n Resistive switching synapses are used in neuromorphic systems according to two approaches. In the first approach, an artificial neural network (ANN) is trained by supervised learning, e.g. , the backpropagation (BP) algorithm, 14 – 18 to construct a hardware accelerator for DNNs. 19 – 21 The major issue for this approach is the non-linear weight update and the large variability of resistive switching devices. 20 , 22 On the other hand, brain-inspired spiking neural networks (SNNs) aim at replicating the brain structure and computation in hardware. Learning usually takes place via spike-timing dependent plasticity (STDP), 23 – 27 where synapses can update their weight according to the timing between spikes of the pre-synaptic neuron (PRE) and post-synaptic neuron (POST). This approach provides a more biologically plausible way to implement neuromorphic computing. Spikes convey information which can be coded in their rate, namely a high rate of the spikes represents a high intensity of the external/internal signal, 28 or by more efficient types of coding, 29 , 30 namely spatiotemporal coding 31 , 32 or precise spiking coding. 33 – 35 Spatiotemporal coding, in particular, contains information about space (which neuron is spiking) and time (when a neuron is spiking in relation to other neurons). The neuron corresponding to stimuli with the highest intensity spikes first, while neurons with lower intensity spike later. Spatiotemporal coding is a sparse coding method with high information capacity, and the neural systems based on such coding method theoretically show much larger solution space than the perceptron-based ANN. 36 , 37 \n Here we aim at addressing the methodology and implementation of neuromorphic computing based on spatiotemporal coding, also exploring possible applications of temporal computation using ReRAM synapses. We proposed a hybrid system combining CMOS neurons and ReRAM synapses. STDP is achieved in ReRAM synapses for both long-term potentiation (LTP) and long-term depression (LTD), while CMOS neurons provide the necessary spiking excitation for realizing plasticity. We first experimentally demonstrate the learning and recognition of spatiotemporal-coded spike sequences, enabling the wide application potential, e.g. , spell checking and DNA analysis. Multi-layer spatiotemporal computing within ReRAM synaptic arrays is also presented. We show that spatiotemporal computing can enable learning and detection of the trace of a moving object. We then design and simulate a pattern recognition system mimicking the hierarchy structure of the biological visual cortex. The results confirm the ability of the temporal computing of ReRAM synapses and the feasibility of ReRAM synapses for the implementation of a brain-like neuromorphic system with efficient spatiotemporal coding.",
"discussion": "2 Results and discussion 2.1 ReRAM device as an artificial synapse Despite the complexity of the human brain, which still defies understanding nowadays, the individual building blocks in the brain are relatively well known, as shown in Fig. 1a . Here, a PRE is connected to a POST via a synapse between the PRE axon and the POST dendrite. The synapse dictates the amount of signal passing from the PRE to the POST, according to the synaptic weight. The latter can be modified throughout the life of the synapse by plasticity, which is responsible for the learning process of the brain. The signal transmission, weighting and plasticity behavior can be mimicked by the two-terminal, non-volatile, and nanoscale ReRAM device shown in Fig. 1b . Fig. 1 (a) Illustration of a building block of the biological neural system, consisting of a pre-synaptic neuron (PRE), a post-synaptic neuron (POST), and a synapse between the PRE axon and POST dendrite. (b) Comparison between the biological synapse (left) and electronic synapse (right, resistive switching memory, ReRAM). The biological synapse weights the signal from the PRE by releasing a certain amount of neurotransmitters and activating the dendrite membrane with receptors. The synaptic plasticity derives from the regulation of the amount of neurotransmitters and the number and distributions of receptors. On the other hand, the ReRAM synapse can be viewed as a variable conductance which transforms a voltage signal into a current proportional to the synaptic conductance, which thus plays the role of the weight. The ReRAM conductance can be modified by a higher voltage excitation, thus mimicking the plasticity of the biological synapse. 8 , 38 \n 2.2 Computation of the temporal correlation of spikes The computation of the temporal information among spikes is illustrated by the computation of the temporal correlation between the two spikes in Fig. 2 . Assuming a simple network of 2 PREs, 2 synapses, and 1 POST ( Fig. 2a ), the temporal correlation between the PRE spikes can be denoted as their time delay t c . The CMOS PRE circuits convert the spikes into exponentially decaying pulses, mimicking the shape of the action potential reaching the axon ( Fig. 1a ). Each exponential pulse V G is applied to the gate of a one-transistor/one-ReRAM (1T1R) synapse, and is given by: 1 where t is time, t i is the spiking time of the i -th PRE, τ s is the decay time constant of the exponential pulse, V 0 is a parameter controlling the maximum value of the signal, and H ( t ) is the Heaviside function. The voltage applied on the gate terminal excites a synaptic current given by: 2 where V read is the read voltage applied to the top electrode of the ReRAM, w i is the weight (ReRAM conductance) of the i -th synapse, and k mos and V T are the parameters describing the transistor characteristics. The time-dependent total current entering the POST is determined by the temporal correlation of the two PRE spikes and the conductance of the two ReRAM devices. The CMOS POST circuit sums all the synaptic current by Kirchhoff’s law, and converts the total current into an internal potential by a trans-impedance amplifier (TIA) with a feedback resistance R TIA . Fig. 2 (a) Schematic view of the temporal computation between the two spikes using ReRAM synapses. To enable the interference of the two temporally separated PRE spikes, the PREs convert the spike signals into exponentially decaying voltage signals applied to the gate of the one-transistor/one-ReRAM (1T1R) synapses. Inset: the conductance of the two ReRAM devices. (b) The exponentially decaying signals of the output of the two PREs (upper panel) and the internal potential ( V int ) of the POST (lower panel). (c) Maximum V int as a function of the temporal correlation (the time difference of the two PRE spikes, t c ). \n Fig. 2b shows the exponentially decaying signals of the output of the two PREs (upper panel) and the resulting V int (lower panel), showing the incremental steps following each individual PRE spike, where each increment is proportional to the synaptic weight (the weights of the 2 ReRAM synapses for the demonstration are given in the inset of Fig. 2a ). The voltage decay between each increment contributes to the overall evolution of the V int , which is responsible for the temporal correlation among the PRE spikes. Fig. 2c gives the maximum V int as a function of t c . Note that similar results of the temporal computation can be obtained with regular spikes applied to the gate terminal of the synapse and with a leaky integrate & fire (LIF) POST neuron. Here we move the leakage function (decaying signal with time) to the PRE to mimic the shape of the action potential, as well as to enable the weight update algorithm related to the temporal information among the PRE spikes, as described in the following section. 2.3 Learning of the temporal correlation We adopt a Widrow–Hoff (WH) learning rule for the weight update of the ReRAM synapses in our SNN during the training process. In the WH rule, each synaptic weight is updated according to a weight change given by, 39 3 Δ w i = ηx i ( y d – y o ), where η is the learning rate, x i is the input variable, y o is the output, and y d is the expected output. Note that the output difference ( y d – y o ) can be viewed as the error, which generally drives the supervised training of a multi-layer perceptron by gradient descent techniques. 15 In the original WH rule, the variables ( x i , y d and y o ) in eqn (3) are real-valued vectors. In SNN, the input and output signals are described by the spike timing, thus a WH-like learning rule for the precise-timing learning algorithm can be obtained by modifying eqn (3) as, 33 , 40 \n 4 Δ w i ( t ) = ηV G i ( t )[ s d ( t ) – s o ( t )], where s d ( t ) = δ ( t – t d ) and s o ( t ) = δ ( t – t o ) are the teacher signals of the supervisor circuit and the actual output spike of the POST, with t d and t o denoting the timing of the teacher spike and the actual output spike, respectively, and δ (*) being the Dirac delta function. The value of s d ( t ) – s o ( t ) can only be 0, –1, or 1, denoting the true fire, false fire, or false silence situations of the POST, respectively, thus guiding the weight update. \n Fig. 3 shows the implementation of the temporal weight update algorithm in eqn (4). The inset in Fig. 3a gives the two typical I – V curves of the ReRAM device, demonstrating the synaptic plasticity by the set/reset processes. With a high positive voltage applied on the top electrode ( V TE ) of the ReRAM device, the device can switch from a high resistance state (HRS) to low resistance state (LRS), also called set transition. On the other hand, when a high negative voltage is applied, the device can switch from LRS to HRS, which is called reset transition. The LRS conductance after set transition can be regulated by a compliance current I C , which is controlled by the gate voltage applied to the series transistor. In the reset transition, varying the gate voltage also controls the HRS conductance (not shown here), since a lower gate voltage results in a higher voltage drop between the source-drain terminals of the transistor, thus lowering the voltage drop across the ReRAM device. Fig. 3 (a) The building block in a fully functional neuromorphic system implementing the weight update algorithm. Inset: The I – V characteristics of a single ReRAM device. (b–d) The weight updating rule implemented in the neuromorphic building block, showing: (b) the PRE signal applied to the gate terminal of 1T1R synapse; (c) the voltage applied to the top electrode of the ReRAM device, generated by the supervisor circuit; and (d) the weight update of the synapse. The direction of the weight update is decided by the polarity of the top electrode voltage, and the amount of weight change is related to the gate voltage of the transistor at the time of updating, which incorporates the temporal information of the PRE spikes. \n Fig. 3b–d illustrate the operation scheme of the potentiation/depression of the synaptic weight in the building block. When the internal voltage V int of POST exceeds a threshold V th , a spike is immediately generated and read by a supervisor circuit. The supervisor circuit compares the POST output signal with a teacher signal, which marks the presence of a ‘true’ sequence. There are three possible cases: (i) if the teacher signal and POST spike occur at the same time, this corresponds to a “true fire”, thus no weight update is needed; (ii) if the teacher signal occurs with no POST spike, this corresponds to a “false silence” case; (iii) if the POST spike occurs with no teacher signal, this corresponds to a “false fire” case. In the case of true fire (i), the top electrode voltage V TE remains at the low level read voltage V read ( Fig. 3c ). On the other hand, for the false silence (ii), a high positive voltage V TE+ is applied by the supervisor circuit to induce synaptic potentiation. Conversely, a high negative voltage V TE– is applied to induce synaptic depression for the false fire case (iii). False fire/silence cases are shown in Fig. 3d , indicating that, in the correspondence of the V TE+ or V TE– pulses, the exponentially decaying voltage on the transistor gate provides temporal information about the PRE spikes. As a result, the synaptic weight change is a function of the temporal information, allowing spatiotemporal learning. 2.4 Learning and recognition of the spike sequence To demonstrate learning and recognition of the spatiotemporal patterns, we considered spike sequences where neurons generate spikes sequentially with a fixed time interval. For instance, Fig. 4a shows the 4-spike sequences generated by 16 PREs, where the sequence [1, 4, 9, 16], namely the sequential spiking of the 1 st , 4 th , 9 th , and 16 th PREs (cycle i ), is considered as the ‘true’ sequence. Sequence learning was demonstrated by using a 16 × 1 spatiotemporal neuromorphic network, consisting of 16 PREs, 1 POST, and 16 ReRAM synapses. The goal of the training is that the POST spikes only in response to the true sequence [1, 4, 9, 16], while keeping silent in response to other sequences. During training, 4-spike sequences are submitted at each training cycle ( Fig. 4a left panel), while the teacher signal generates a spike in correspondence of the true sequence [1, 4, 9, 16]. The input spikes and the teacher signals are provided by a microcontroller, although all the learning functions took place locally and independently at the CMOS-neuron/ReRAM synapse network. Fig. 4b shows the measured evolution of the synaptic weights during training: after training, the 1 st , 4 th , 9 th , and 16 th synapses were potentiated to LRS, while other synapses were depressed to HRS. The four synapses in LRS show distinct conductance levels following the rule w 16 > w 9 > w 4 > w 1 , which evidences the learning of the temporal information in the true sequence [1, 4, 9, 16]. Fig. 4 (a) A schematic illustration of the input spiking patterns submitted to a 16 × 1 spatiotemporal network supervised by a teacher signal. (b) The experimentally measured evolution of the synaptic weights during training. After the training process, we measured V int in the POST in response to the submission of all the spike sequences ( Fig. 5 ). For the true sequence, the successive accumulation of V int reaches the threshold V th , thus leading to POST fire ( Fig. 5a ). The increments of V int in correspondence with each PRE spike can be clearly seen, each step increase being determined by the corresponding ReRAM synapse weight. On the other hand, V int remains below V th for false sequences. For instance, V int of the false sequence [16, 7, 4, 1] is far below the threshold ( Fig. 5b ), as a result of the 7 th synapse being in HRS. The permutations of the spiking pattern, e.g. , [9, 16, 1, 4], also lead to insufficient accumulation due to the time/weight mismatch. Fig. 5 Measured V int , indicating spike accumulation by the POST for (a) the true sequence and (b) false sequences. 2.5 Recognition of long sequences The recognition of spiking sequences requires that the ReRAM synapses are potentiated to distinct LRS levels, thus longer spiking sequence might face the issue of the limited number of LRS levels in the ReRAM. 41 , 42 \n A feasible solution to this issue is to introduce a multilayer neural network. For instance, Fig. 6a shows a neural network with 13 input neurons, 4 hidden neurons, and 1 output neuron, with 13 × 4 ReRAM synapses connecting the input neurons and hidden neurons in the first layer and 4 × 1 ReRAM synapses connecting the hidden neurons and output neuron. Fig. 6b and c shows the conductance map of the ReRAM synapses of the two-layer network, where the conductance states are assigned according to the result of the trained network in Fig. 4b . The network is designed to recognize the true spiking sequence of prime numbers from 1 to 13, i.e. , [1, 2, 3, 5, 7, 11, 13]. To this purpose, the first neuron in the hidden layer is designed to recognize the subsequence [1, 2, 3, 5], while the second neuron recognizes the subsequence [2, 3, 5, 7], and so on. The output neuron recognizes the successive spikes of the hidden neurons, thus leading to recognition of the long sequence [1, 2, 3, 5, 7, 11, 13]. Fig. 6 (a) Illustration of a two-layer spatiotemporal network to recognize a relatively long spiking sequence. (b and c) The conductance map of the synapses in the two-layer network. \n Fig. 7a shows the internal potential V int of the hidden layer neurons for the true sequence input, while Fig. 7b shows the response of the output neuron, where V int reaches the threshold thus demonstrating sequence recognition. On the other hand, submission of a false sequence [4, 2, 3, 5, 7, 11, 13] does not reach the threshold in Fig. 7c and d , as a result of substituting the first ‘1’ with a ‘4’ causing the silence of the first neuron in the hidden layer. Fig. 7 (a and b) The internal potential of the hidden layer neurons (a) and of the output neuron (b) under the submission of the true sequence. (c and d) The same, but under the submission of a false sequence. Note that the maximum value of V int ( Fig. 5b and 7d ) indicate the similarities of the test sequences with the true sequence, enabling even the toy neural network with wide application potential. For instance, with 26 PREs representing one letter each, the network can be used to check spelling errors of word. The V int similarities of sequences can be compared with the Damerau–Levenshtein (DL) distance 43 , 44 of sequences, which is widely used in spell checking, speech recognition, DNA analysis, etc. The calculation of the DL distance requires many steps of comparing each element of the sequences within at least two programming loops. 43 On the other hand, the POST V int can assess the similarity between the patterns with analog behavior, and only requires one inference step in a spatiotemporal SNN after training. 2.6 Learning of a spiking sequence In a multilayer spatiotemporal network, the output spikes of one layer must be spatiotemporally-coded to act as the input of the next layer. 45 Though a complete training algorithm of a multilayer spatiotemporal network is still missing, training the network to map a spatiotemporal input into a spatiotemporal output ( Fig. 8a ) is a critical step. 46 Fig. 8b shows a randomly generated spatiotemporal input pattern for 250 input neurons, and Fig. 8c shows a spatiotemporal pattern as the training target of the two output neurons. To associate these two spatiotemporal spiking patterns, a network consisting of 250 PREs, 2 POSTs, and 250 ReRAM synapses is needed. Following the same learning rule shown in Section 2.3, the 250 × 2 neural network is successfully trained to generate the target spiking pattern when the input spatiotemporal pattern was presented ( Fig. 8d ). Fig. 8 (a) Illustration of a neural network for the mapping of a spatiotemporal pattern. (b) Randomly generated spatiotemporal input pattern; (c) spatiotemporal output pattern; (d) the training of mapping a complex spatiotemporal pattern to a simple one. 2.7 Detection of a moving object The spatiotemporal sequence learning and recognition lies at the basis of the ability to interact with a dynamic environment, e.g. , for speech and gesture recognition. Fig. 9a illustrates the detection of moving objects by a spatiotemporal network, where the moving trace of pattern “X” can be represented by a spatiotemporal spiking pattern with each spiking neuron corresponding to the position of the pattern “X” at any given time. Fig. 9b shows the neural network for movement detection, where the first layer is a conventional spatial-pattern network for the recognition of the pattern ‘X’, 8 while the second layer is a spatiotemporal network to detect the trace of the pattern. Fig. 9 (a) Illustration of a dynamic “X” pattern moving horizontally (first row) or diagonally (second row). (b) Illustration of the two-layer neural network for the recognition of a moving object. The spatial network can be trained, for instance, by unsupervised learning method, 8 so that only the pattern “X” would induce a spike in the corresponding inter-layer neuron representing the position of “X”. The moving object, then, would result in a sequential spiking of the interlayer neurons. Output neurons can thus be trained to recognize spatiotemporal sequences representing the various directions of the pattern. 2.8 Spatiotemporal network for pattern recognition Vision in the mammalian brain follows hierarchical rules, 47 where signals from the retina are first projected to simple cells with orientation sensitivity to extract the basic features of the image, then more complex cells are used to increase the visual system’s invariant to the input image. Finally, the higher-level cortex participates in the detection of basic features in the image, enabling pattern recognition ( Fig. 10a ). 48 Fig. 10 (a) Illustration of the hierarchy structure of the biological visual system. (b) Schematic diagram of the artificial visual system for feature extraction and classification using spatiotemporal spike coding. Here, we propose an artificial visual system ( Fig. 10b ), where Gabor filters with various sizes and orientations are used to mimic the receptive fields of the simple cells. The output of the simple cells is converted into spatiotemporal spikes by amplitude–time conversion, i.e. , the neuron with the highest signal spikes first. Then max-pooling neurons acting as complex cells select the most salient features in nearby receptive fields. The spatiotemporal patterns from complex cells are finally used to train a fully-connected spatiotemporal network. To validate this artificial visual system, we used a simple pattern recognition task, i.e. , optical character recognition (OCR). The synapses of the fully connected layer are initially prepared in a random conductance map. The network is trained with the ideal character image ( Fig. 11a ), then inference was tested with a set of noisy patterns. Fig. 11b shows the results of the testing, in terms of the recognition rate of noise pattern as a function of the noise level. The recognition rate remains higher than 90% percentage with the noise level lower than 7%, thus demonstrating the accuracy of spatiotemporal coding for spatial pattern recognition. Fig. 11 (a) The optical digital character for training (clean pattern) and testing (noise pattern) of the artificial hierarchical visual neural network. (b) The recognition rate of the trained network as a function of the noise level of the optical digital character."
} | 6,449 |
27058505 | PMC4939269 | pmc | 571 | {
"abstract": "The biodegradation of organic pollutants in aquifers is often restricted to the fringes of contaminant plumes where steep countergradients of electron donors and acceptors are separated by limited dispersive mixing. However, long-distance electron transfer (LDET) by filamentous ‘cable bacteria' has recently been discovered in marine sediments to couple spatially separated redox half reactions over centimeter scales. Here we provide primary evidence that such sulfur-oxidizing cable bacteria can also be found at oxic–anoxic interfaces in aquifer sediments, where they provide a means for the direct recycling of sulfate by electron transfer over 1–2-cm distance. Sediments were taken from a hydrocarbon-contaminated aquifer, amended with iron sulfide and saturated with water, leaving the sediment surface exposed to air. Steep geochemical gradients developed in the upper 3 cm, showing a spatial separation of oxygen and sulfide by 9 mm together with a pH profile characteristic for sulfur oxidation by LDET. Bacterial filaments, which were highly abundant in the suboxic zone, were identified by sequencing of 16S rRNA genes and fluorescence in situ hybridization (FISH) as cable bacteria belonging to the Desulfobulbaceae . The detection of similar Desulfobulbaceae at the oxic–anoxic interface of fresh sediment cores taken at a contaminated aquifer suggests that LDET may indeed be active at the capillary fringe in situ .",
"introduction": "Introduction Excess carbon loads in hydrocarbon-polluted aquifers rapidly lead to the depletion of electron acceptors such as molecular oxygen, nitrate and sulfate in the core of contaminant plumes, which represents a major limitation for biodegradation ( Meckenstock et al. , 2015 ). This leads to steep geochemical countergradients of electron donors and dissolved electron acceptors at the plume fringes ( Anneser et al. , 2008 ; Winderl et al. , 2008 ) where biodegradation is sustained by the dispersive and diffusive transport of electron acceptors from groundwater outside of the plume ( Christensen et al. , 2000 ; Bauer et al. , 2008 ). The spatial separation of electron donors and acceptors represents a major limitation for microbial metabolism ( Meckenstock et al. , 2015 ) because of restricted transport across such interphases. Microbial long-distance electron transfer (LDET; Nielsen and Risgaard-Petersen, 2015 ) could perform direct electric coupling of microbial processes across such redox gradients and would allow for a unique ecological niche in hydrocarbon-contaminated aquifers. LDET is essentially based on the spatial segregation of redox half reactions and the presence of a conductive structure between the two locations. The concept of LDET within a geobattery was first proposed by Sato and Mooney (1960) , who presented a model for the generation of subsurface electric potentials and electric fields by coupling ferrous iron oxidation and oxygen reduction by electric currents through a conductive ore body. This was extended to a biogeobattery by Revil et al. (2010) , who suggested a microbial LDET by a conductive network of bacteria and minerals ( Bigalke and Grabner, 1997 ; Holmes et al. , 2004 ; Naudet et al. , 2004 ; Revil et al. , 2010 ). Recently, LDET was inferred from biogeochemical profiles in marine sediments ( Nielsen et al. , 2010 ; Risgaard-Petersen et al. , 2012 ). The spatial separation of the redox half reactions resulted in a characteristic pH profile ( Meysman et al. , 2015 ): a pH maximum by proton consumption in the oxic zone and a pH minimum by sulfide oxidation in the sulfidic zone ( Nielsen et al. , 2010 ; Risgaard-Petersen et al. , 2012 ). The LDET was shown to be mediated by long filamentous bacteria affiliated to the Desulfobulbaceae ( Pfeffer et al. , 2012 ), bridging a suboxic zone over 1–2-cm distances where neither oxygen nor sulfide was detectable. A single filament of these so-called ‘cable bacteria' can be composed of thousands of individual cells having a characteristic, shared envelope with 15–58 marked ridges, giving them a cable-like appearance ( Pfeffer et al. , 2012 ; Malkin et al. , 2014 ). Periplasmic strings underneath the ridges might serve as electric conductors with the common outer membrane as isolation ( Pfeffer et al. , 2012 ; Meysman et al. , 2015 ). Hitherto, LDET catalyzed by microorganisms has been observed for marine sediments ( Malkin et al. , 2014 ), seasonal hypoxic basins ( Seitaj et al. , 2015 ), salt marshes ( Larsen et al. , 2014 ; Malkin et al. , 2014 ) and a freshwater stream ( Risgaard-Petersen et al. , 2015 ), all representing saturated sediment environments rich in organic carbon. Inspired by recent reports on high abundances of not further classified Desulfobulbaceae at the fringes of a hydrocarbon contaminant plume ( Winderl et al. , 2008 ; Pilloni et al. , 2011 ; Larentis et al. , 2013 ), we hypothesize that LDET might occur in freshwater aquifers fulfilling important ecological functions. Evidence supporting this hypothesis is provided here by laboratory incubations of aquifer sediments and by screening for cable bacteria across a redox gradient in situ in a tar-oil-contaminated aquifer.",
"discussion": "Discussion Recently, filamentous cable bacteria were discovered in organic-rich marine sediments to spatially bridge the redox half reactions of sulfide oxidation and oxygen reduction via LDET over 1–2 cm ( Nielsen et al. , 2010 ; Pfeffer et al. , 2012 ; Malkin et al. , 2014 ). Here, we aimed to investigate whether microbially mediated LDET may also occur in freshwater sediments, specifically in hydrocarbon-contaminated groundwater, where it could recycle sulfate as electron acceptor and thus increase biodegradation rates. In FeS-amended laboratory incubations of sediments from the investigated site, a suboxic zone developed with no detectable oxygen or sulfide but with distinct cathodic pH maxima and anodic pH minima indicative of LDET ( Nielsen et al. , 2010 ). Oxygen only penetrated 8 mm into the sediment and yet served as a direct sink for electrons from oxidation of sulfide up to 19 mm below, congruent with the LDET hypothesis. The calculated current density between the oxic and the anoxic layers was higher than 1.5 mA m −2 corresponding to a cathodic oxygen consumption of 340 μmol m −2 per day and representing 40% of the total oxygen consumption. This calculation does not include calcite precipitation and ferrous iron oxidation and might therefore underestimate cathodic oxygen consumption ( Risgaard-Petersen et al. , 2014 ). Moreover, the coarse sediment did not allow for microsensor measurements of pH profiles, but only for a macroelectrode with a tip size of 3 mm at a resolution of 1 mm. This smoothened the pH profile, resulting in lower calculated alkalinity fluxes and cathodic oxygen consumption. Thus, the electron transfer rate inferred for groundwater cable bacteria was 1–2 orders of magnitude lower than the 4.6–92 mA m −2 reported for marine sediments ( Nielsen et al. , 2010 ; Nielsen and Risgaard-Petersen, 2015 ). This discrepancy could be also caused—besides the underestimation of fluxes—by up to 100-fold lower sulfide concentrations in the investigated sediment compared with marine sediments ( Rao et al. , 2015 ). 16S rRNA gene sequencing and FISH identified groundwater cable bacteria as members of the Desulfobulbaceae . The marine cable bacteria are only distantly related with 88% similarity on the 16S rRNA level. The closest cultivated relative with 91% 16S rRNA gene similarity was Desulfurivibrio alkaliphilus AHT 2, which can grow chemo–litho–autotrophically with H 2 as electron donor. The strain cannot perform sulfate reduction but utilizes elemental sulfur, thiosulfate, and nitrate as electron acceptor ( Sorokin et al. , 2008 ). The genome exhibits a complete aerobic respiratory chain with a terminal cytochrome c oxidase cbb3 type. Sox genes for sulfide oxidation are not present, but a reverse sulfate reduction pathway seems possible. The second-most closely related chemo–litho–autotrophic organism, the arsenate-reducing strain MLMS-1, is able to grow with sulfide as electron donor and shows 90% 16S rRNA gene similarity to the groundwater cable bacteria ( Hoeft et al. , 2004 ). The high abundance of cable bacteria at the oxic–anoxic interface of aquifers indicates an ecological competitiveness to other sulfide-oxidizing, chemo–litho–autotrophs such as, for example, Thiobacillus . A possible explanation is fluctuations of the groundwater table leading to shifting redox gradients at the capillary fringe ( Meckenstock et al. , 2015 ). Under such dynamic conditions, unicellular microorganisms will often not be located in the zone of overlapping countergradients and lack either electron donor or acceptor. Such unfavorable conditions can be overcome by spatially decoupling of the oxidation and reduction half reactions by cable bacteria. Only some cells at both ends of the filaments need to have access to electron donors or acceptors, respectively. Thus, cable bacteria could still be active as long as the spatial shifts of redox gradients do not exceed the lengths of the filaments. Even though groundwater table fluctuations of ~20 cm within 1 year have been reported from this site ( Einsiedl et al. , 2015 ), cable bacteria could still have a competitive advantage by buffering short-term fluctuations in a smaller scale. Cable bacteria can also adapt rapidly to changing conditions, as shown for laboratory incubations ( Nielsen et al. , 2010 ) and seasonal hypoxic basins ( Seitaj et al. , 2015 ). The longest filament that we could find microscopically was ~5 mm. However, natural filaments and cable networks that are not disrupted by sampling could be much longer. For marine sediments, fragment lengths of filaments up to 1.5 cm have been reported ( Pfeffer et al. , 2012 ). The detection of LDET in situ remains a difficult task. Our monitoring well provided water samples at 3 cm vertical resolution. Although this is probably the highest resolution for water sampling in aquifers of that depths reported to date, it is obviously not sufficient to record pH profiles at the mm range, which might be necessary to map LDET in situ . Moreover, pH profiles could also not be determined from fresh sediment cores, as cores commonly de-water upon retrieval during drilling, prohibiting the reconstruction of pore water geochemistry. Thus, analyzing sediment cores with molecular and microscopic tools is the only reliable way for detecting cable bacteria in aquifers so far. However, LDET might potentially be traced in situ by remotely assessing associated electric fields ( Revil et al. , 2010 ; Risgaard-Petersen et al. , 2014 ; Revil et al. , 2015 ). In fact, our results provide the first field evidence for biogeobatteries in aquifers comprising cable bacteria as electron conductors ( Revil et al. , 2010 ). By oxidizing sulfide, groundwater cable bacteria resemble the anode of a microbial fuel cell. Revil et al. (2015) showed direct oxidation of propylene glycol by electric currents through a conductive iron body in laboratory experiments ( Revil et al. , 2015 ). Such electric currents create electric potential anomalies, which might be a good monitoring tool for localizing hotspots of LDET in situ ( Naudet et al. , 2004 ; Atekwana and Slater, 2009 ; Revil et al. , 2010 ; Revil et al. , 2015 ). However, the direct link between LDET and the observation of electric potentials at contaminated sites still remains to be proven. Our field geochemical data indicated that oxygen and nitrate were at least 6 cm apart from detectable dissolved sulfide. As only distances below 3 cm for LDET have been reported so far, LDET might be fueled by other reduced sulfur compounds as electron donor such as precipitated FeS. This would be supported by our laboratory incubations where FeS turned out to be an excellent electron donor for LDET. It is also likely that sulfide produced by sulfate-reducing toluene degradation is immediately re-oxidized by LDET ( Figure 4b ) or precipitated as FeS in the field. In fact, a previous study conducted at the same site demonstrated strong sulfur cycling at the upper plume fringe, the place of LDET reported here ( Einsiedl et al. , 2015 ). The discovery of LDET catalyzed by cable bacteria in laboratory incubations with sediments from hydrocarbon-contaminated aquifers might provide a new perspective of microbial activities at the capillary or plume fringes. Even though the presence of cable bacteria at the plume fringes provides a first evidence for LDET in situ, the quantitative impact on the biogeochemistry and contaminant degradation remains to be investigated. The electric shortcut by the filaments could strongly increase electron fluxes ( Risgaard-Petersen et al. , 2014 ) across redox interphases ( Figure 4 ). Thus, it could extend the recently established plume fringe concept ( Cirpka et al. , 1999 ; Mayer et al. , 2001 ; Thornton et al. , 2001 ; Maier and Grathwohl, 2006 ; Anneser et al. , 2008 ; Bauer et al. , 2008 ; Winderl et al. , 2008 ; Meckenstock et al. , 2015 ), which states that electron acceptors are depleted in the core of contaminant plumes. Biodegradation is accordingly restricted to the plume fringes where electron acceptors are supplied from the outside by dispersion or diffusion ( Figure 4a ). Recycling of sulfate by LDET at the plume fringes might overcome the spatial separation of electron donors and acceptors to a certain extent and consequently lead to an enhancement of biodegradation as compared with a system otherwise fully controlled by dispersion ( Figure 4b ; Anneser et al. , 2008 ; Bauer et al. , 2008 ; Anneser et al. , 2010 ; Pilloni et al. , 2011 )."
} | 3,460 |
30519407 | PMC6262911 | pmc | 574 | {
"abstract": "Abstract Analysis of ecological networks is a valuable approach to understanding the vulnerability of systems to disturbance. The tolerance of ecological networks to coextinctions, resulting from sequences of primary extinctions (here termed “knockout extinction models”, in contrast with other dynamic approaches), is a widely used tool for modeling network “robustness”. Currently, there is an emphasis to increase biological realism in these models, but less attention has been given to the effect of model choices and network structure on robustness measures. Here, we present a suite of knockout extinction models for bipartite ecological networks (specifically plant–pollinator networks) that can all be analyzed on the same terms, enabling us to test the effects of extinction rules, interaction weights, and network structure on robustness. We include two simple ecologically plausible models of propagating extinctions, one new and one adapted from existing models. All models can be used with weighted or binary interaction data. We found that the choice of extinction rules impacts robustness; our two propagating models produce opposing effects in all tests on observed plant–pollinator networks. Adding weights to the interactions tends to amplify the opposing effects and increase the variation in robustness. Variation in robustness is a key feature of these extinction models and is driven by the structural heterogeneity of nodes (specifically, the skewness of the plant degree distribution) in the network. Our analysis therefore reveals the mechanisms and fundamental network properties that drive observed trends in robustness.",
"introduction": "1 INTRODUCTION Network analysis has become an important tool for ecologists seeking to understand the vulnerability of ecosystems to natural and anthropogenic disturbance. Recent research has centered on network approaches for improving our understanding of plant–pollinator communities and extinctions, especially in the light of the widely documented declines in key insect pollinators such as honeybees, bumblebees, and butterflies (Benton, 2006 ; Biesmeijer et al., 2006 ; Goulson, Lye, & Darvill, 2008 ; Senapathi et al., 2015 ). These trends are concerning for biodiversity, ecosystem function, and food security (Potts et al., 2010 ) as insect pollinators play a vital role in providing ecosystem services (Bailes, Ollerton, Pattrick, & Glover, 2015 ). They feed on nectar and pollen provided by plant species, and whilst doing this, facilitate the fertilization of plants via cross‐pollination (Free, 1993 ; Lubbock, 1897 ). In plant–pollinator systems, the community can be regarded as a bipartite network comprising two distinct guilds of organisms in which each node represents a species, and species are connected by edges indicating interactions, which may be directly observed, indirectly observed (e.g., pollen analysis), or inferred (Morales‐Castilla, Matias, Gravel, & Araújo, 2015 ). Models of community robustness based on observed plant–pollinator networks (available, e.g., from http://www.web-of-life.es and https://www.nceas.ucsb.edu/interactionweb/resources.html ) usually fall into one of two types. In the first (see for example Bastolla et al., 2009 ; James, Pitchford, & Plank, 2012 ), the community is modeled as a dynamical system, in which the population of each species is affected by the interactions that species has with others. The dynamics are typically run to fixation, and the populations at fixation used to determine community robustness. The second approach, adopted here, is to model the tolerance of the network to simulated extinctions (henceforth “knockout extinction models”). In ecology, this approach was applied first to multitrophic food webs (Dunne, Williams, & Martinez, 2002 ) and then mutualistic bipartite networks, especially plant–pollinator networks (Kaiser‐Bunbury, Muff, Memmott, Müller, & Caflisch, 2010 ; Memmott, Waser, & Price, 2004 ). Campbell, Yang, Shea, and Albert ( 2012 ) use a very similar approach to study the effects of forced species extinctions. The networks they analyze differ from those considered here, in that they are all generated by a (dynamic Boolean) model of plant–pollinator community formation (Campbell, Yang, Albert, & Shea, 2011 ). Knockout extinction models estimate the robustness of a plant–pollinator network by sequentially removing species of the primary type (e.g., plants) and recording the number of surviving species of the secondary type (e.g., pollinators), by applying some predetermined rule for species survival. Network robustness can then be determined from the area under the curve of the proportion of the secondary type that survive against the proportion of the primary type removed (Burgos et al., 2007 ; see Figure 1 a). Figure 1 The output of a knockout extinction model. (a) For a single extinction sequence, the number of surviving pollinator nodes a reduces as the number of plant nodes made extinct, p , increases until a = 0. Robustness ( R ) = 0.550 is the area under a ( p ), divided by the area of the rectangle, AP . (b) In all our extinction models, the value of R depends on the order in which plants are made extinct, so many simulations of random sequences of primary extinctions are used to produce a distribution of robustness values f ( R ) In the simplest, “Secondary Only” (SO) knockout models, primary extinctions from one guild lead only to secondary extinction of species in the other guild. Primary extinctions are chosen in a specific order—determined by the number of interactions a species has, for example—or in a random order (Dunne et al., 2002 ; Memmott et al., 2004 and Pocock, Evans, & Memmott, 2012 ). A key development by Vieira and Almeida‐Neto ( 2015 ) was to allow coextinction due to feedback between guilds, so permitting cascades of extinctions. The propagating extinction model of Traveset, Tur, and Eguíluz ( 2017 ) incorporates empirically estimated dependencies of plants on pollinators. In a different development, Kaiser‐Bunbury et al. ( 2010 ) allowed edge rewiring (pollinators switching from one plant to another) based on empirical evidence; others have explored robustness to edge, not node, knockouts (Santamaría, Galeano, Pastor, & Méndez, 2016 ; Valiente‐Banuet et al., 2015 ). SO models were used to show that the robustness of communities to random primary extinctions increases with network connectance, that is the fraction of the possible interactions that were actually observed (Dunne et al., 2002 ) and the resulting robustness was often interpreted in terms of network nestedness (Memmott et al., 2004 ). Vieira and Almeida‐Neto ( 2015 ) found that cascades were more likely in highly connected networks. However, more detailed investigation of the impact of network structure on robustness has been lacking. Most early empirical plant–pollinator networks were binary; interactions between pairs of species were either observed or not. However, researchers are increasingly measuring the frequency or importance of interactions to create weighted networks, yielding a better description of the interactions observed (Ings et al., 2009 and Memmott, 1999 ), and accounting better for undersampling biases (Bersier, Banašek‐Richter, & Cattin, 2002 ). More recent models have used weighted data in different ways: using node abundance to weight the binary outcomes (Kaiser‐Bunbury et al., 2010 ) or using empirically determined, weighted dependences of plant species on pollinators (Traveset et al., 2017 ). One of the features of knockout extinction models is that, when using random sequences of primary extinctions on a single empirical network, there is a broad distribution in the resulting robustness values (Figure 1 b). Robustness must therefore be a product both of structural heterogeneity of the network (e.g., Pastor, Santamaria, Mendez, & Galeano, 2012 ) and of the method of producing extinction sequences. The aim of this paper was to understand in detail which features of knockout models, and which properties of empirical ecological networks, are responsible for the central value and range of computed robustness distributions. To this end, we bring together a suite of models—a simple SO model and two simple propagating extinction models—and use them to compute the robustness of a number of empirical plant–pollinator networks in both binary and weighted form. The models were chosen for their simplicity and direct comparability, not to achieve ecological realism.",
"discussion": "4 DISCUSSION Robustness R is a valuable quantitative metric for describing and comparing the vulnerability of ecological networks to simulated extinctions. We confirm, through our framework of extinction models, that R is a consequence of both the model itself and the network structure. Our analysis reveals the mechanisms and fundamental network properties that drive observed trends in robustness. Knockout extinction models that calculate robustness have been around for over a decade and the list of ecological rules they employ is growing. Building on the models of Memmott et al. ( 2004 ), Kaiser‐Bunbury et al. ( 2010 ) and Vieira and Almeida‐Neto ( 2015 ), we have brought together a suite of directly comparable knockout extinction models and applied them here to plant–pollinator networks. We have used an extinction threshold (pollinators can go extinct before all their plants go extinct and vice versa) that can be applied to all nodes. This addition has an ecological motivation—plants may decline to extinction due to reducing pollination (as modeled by Traveset et al., 2017 ), and adds greatly to the flexibility of the model. Having T < 1 allows us to create weighted versions of our models and provides the potential for feedback between the trophic levels and, hence, avalanches of extinctions cascading across the network (e.g., as shown by Campbell et al., 2012 and Vieira & Almeida‐Neto, 2015 ). Cascades are more likely as T is decreased. We chose a middle value of T (0.5). The exact value chosen is not a vital ingredient of this work, but can make a big difference to mean robustness (Figure 3 , Supporting information Figure S1 ). We therefore recommend that researchers test at least the qualitative robustness of their conclusions to varying values of threshold. All our extinction models, in binary and weighted form, produce a broad distribution of robustness values f ( R ) for each network that we analyzed, indicating that there are aspects of the structure of the network that cause this variation. We found the degree distribution of the plants, in particular, to be an important driver of robustness variation. Plant–pollinator networks tend to have fewer plant species than pollinator species ( P < A ), so the potential for a skewed plant degree distribution is greater, thus making it more influential on robustness in our test network (Memmott, 1999 ). Of the six networks we analyzed, those that have one particularly highly connected plant (Ashton Court—Figure 4 , and Hickling—Supporting information Figure S5 ) have the broadest f ( R ); those with a more homogenous plant degree distribution are narrower. We note in passing that the largest plant degree is strongly correlated with nestedness in these networks (Supporting information Figure S7 ). Though “robustness” has in the past been used to suggest priorities for conservation or management (Devoto, Bailey, Craze, & Memmott, 2012 ; Pocock et al., 2012 ), extinction models are not an attempt to predict precisely how an ecosystem would collapse. They do, nonetheless, offer a means to quantify and compare the structure of ecological networks, but to do this we need to ensure we are comparing like‐for‐like. Plant–pollinator communities are increasingly described with weighted interactions. We found (Figure 4 , Supporting information Table S1 ) that introducing weighted interactions has the effect of amplifying the outcomes observed for binary data: the inter‐quartile range of the robustness distribution f ( R ) increases in all models for weighted networks, and the shifts in median robustness for DA and RW compared to SO are larger. Weights tend to increase the skew of the plant degree distribution because high‐degree species accumulate high edge weights and low degree species only gain a small fraction of the overall weight in the network. This exaggerates effects in f ( R ) and highlights the importance of including interaction weights in robustness analysis, and in exploring all of the distribution f ( R ), not just its central tendency. Future work should continue to explore the full effects of weighted data. There are different ways in which extinction models can use feedback between trophic levels and we developed two illustrative models: the Deterministic Avalanche (DA) and the Random Walk (RW) models. These models (and others like the cascade model developed by Vieira & Almeida‐Neto, 2015 ) may appear to be generating new outcomes, but in reality, they simply produce a nonrandom sample of robustness values from those generated by a simple SO model. The AC dataset generated a very wide range of R values, all of which can be realized in the SO models. The DA and RW models preferentially sample extinction sequences to produce skewed subsets of the SO outcomes (the P ! extinction sequences are not all equally likely, and some will be impossible). The DA Model preferentially samples nodes that are 1 step away from each other in the network and extinctions can “ripple out” from each trigger. In some cases, the DA model produces a double‐peaked f ( R ) distribution. This corresponds to networks where the highest plant degree, as a fraction of the number of pollinators, is large—the Ashton Court and Hickling networks for example. In contrast to DA, in the RW model plant extinctions tend to jump from plant to plant away from a trigger. Although both the DA and RW models are ecologically credible, they produce opposing results, demonstrating the influence of the model on the assessment of robustness. It is important for researchers using robustness models to have a clear justification for the model they use, and a clear understanding of how much their results are influenced by the model as well as the network data. All of these extinction models are designed to be applied to real ecological network data. Therefore, it is vital to consider the quality and reliability of the data being used. Empirical pollination networks vary hugely in sampling method, period of collection and taxonomic resolution, all of which can affect metrics of network structure. Factors such as relative species abundance and time of sampling can lead to over‐ or underestimating the degree of a plant species in a network (e.g., Blüthgen et al., 2006 ).This will affect the outcomes of knock‐on extinction models and could easily over‐ or underestimate the robustness and the importance of particular plant species. We caution against comparing the outcomes of extinction models across multiple networks, for example, in meta‐analyses or comparative analyses, without consideration of the data and the methods used to collect them. CaraDonna et al. ( 2017 ) highlight the potential pitfalls of assuming that a network constructed by aggregating samples over time is an appropriate representation of a community. Further work in understanding temporal variation and the description of fully resolved plant–pollinator networks is key to improving the utility of extinction models. Current robustness models still lack the biological realism needed to make reliable ecological predictions. They are, however, useful for understanding and separating the effects of mechanism and network structure. We recommend therefore that researchers seeking greater ecological realism in models pay due attention to the details of the models themselves. Ecological conclusions drawn from robustness models may become less surprising when model developments are taken into account. We hope that by improving our understanding of extinction models at a mechanistic level and by setting out different areas of model extension, our work will guide future developments in the analysis of the vulnerability of ecosystems to environmental change."
} | 4,087 |
18721812 | null | s2 | 576 | {
"abstract": "Quorum sensing (QS) is a communication mechanism exploited by a large variety of bacteria to coordinate gene expression at the population level. In Gram-negative bacteria, QS occurs via synthesis and detection of small chemical signals, most of which belong to the acyl-homoserine lactone class. In such a system, binding of an acyl-homoserine lactone signal to its cognate transcriptional regulator (R-protein) often induces stabilization and subsequent dimerization of the R-protein, which results in the regulation of downstream gene expression. Existence of diverse QS systems within and among species of bacteria indicates that each bacterium needs to distinguish among a myriad of structurally similar chemical signals. We show, using a mathematical model, that fast degradation of an R-protein monomer can facilitate discrimination of signals that differentially stabilize it. Furthermore, our results suggest an inverse correlation between the stability of an R-protein and the achievable limits of fidelity in signal discrimination. In particular, an unstable R-protein tends to be more specific to its cognate signal, whereas a stable R-protein tends to be more promiscuous. These predictions are consistent with experimental data on well-studied natural and engineered R-proteins and thus have implications for understanding the functional design of QS systems."
} | 343 |
26490133 | PMC4651109 | pmc | 578 | {
"abstract": "A simple, scalable, non-lithographic, technique for fabricating durable\nsuperhydrophobic (SH) surfaces, based on the fingering instabilities associated with\nnon-Newtonian flow and shear tearing, has been developed. The high viscosity of the\nnanotube/elastomer paste has been exploited for the fabrication. The fabricated SH\nsurfaces had the appearance of bristled shark skin and were robust with respect to\nmechanical forces. While flow instability is regarded as adverse to roll-coating\nprocesses for fabricating uniform films, we especially use the effect to create the\nSH surface. Along with their durability and self-cleaning capabilities, we have\ndemonstrated drag reduction effects of the fabricated films through dynamic flow\nmeasurements.",
"discussion": "Discussion In summary, the synthesis of a mechanically robust rough surface, which seems akin to\nthat of a bristled shark skin, exhibiting SH characteristics, through a relatively\ninexpensive novel roll-to-roll process has been demonstrated. The synthesis\ntechnique is simple to set up, reproducible, amenable to industrial scale production\nas long as we use larger size rolls, and can be adapted to widespread usage. The\nunderlying mechanism for the formation of the rough surface has been indicated to be\nfingering instabilities associated with high viscosity liquids subject to\nroll-to-roll processes. The characterization of SH surfaces was done through static\nmeans ( e.g., through contact angle measurements), as well as under\ndynamic/liquid flow conditions, where significant drag reduction through a pressure\ndrop reduction was observed. The significant merit of our approach for large scale\nfabrication of SH surfaces is that nano-/micro-scopic patterning or chemical\ntreatment is unnecessary for exhibiting superior SH characteristics. In addition,\nelectrical and thermal properties may be tuned through desired electrically\nconductive fillers."
} | 474 |
40321946 | PMC12047930 | pmc | 580 | {
"abstract": "Reservoir computers (RCs) provide a computationally efficient alternative to deep learning while also offering a framework for incorporating brain-inspired computational principles. By using an internal neural network with random, fixed connections—the ‘reservoir’—and training only the output weights, RCs simplify the training process but remain sensitive to the choice of hyperparameters that govern activation functions and network architecture. Moreover, typical RC implementations overlook a critical aspect of neuronal dynamics: the balance between excitatory and inhibitory (E-I) signals, which is essential for robust brain function. We show that RCs characteristically perform best in balanced or slightly over-inhibited regimes, outperforming excitation-dominated ones. To reduce the need for precise hyperparameter tuning, we introduce a self-adapting mechanism that locally adjusts E/I balance to achieve target neuronal firing rates, improving performance by up to 130% in tasks like memory capacity and time series prediction compared with globally tuned RCs. Incorporating brain-inspired heterogeneity in target neuronal firing rates further reduces the need for fine-tuning hyperparameters and enables RCs to excel across linear and non-linear tasks. These results support a shift from static optimization to dynamic adaptation in reservoir design, demonstrating how brain-inspired mechanisms can improve RC performance and robustness while deepening our understanding of neural computation.",
"introduction": "I. INTRODUCTION Reservoir computers [ 1 , 2 ] (RCs) offer a computationally efficient framework for implementing recurrent neural networks (RNNs), broadly reflecting key principles of the brain’s neocortex. RNNs process data through recurrent connections that generate complex activity patterns, making them well-suited for dynamic computations. RCs harness this feature using a three-layer architecture, where a fixed-weight, randomly connected RNN, the reservoir, transforms low dimensional input into high-dimensional internal representations. A trainable output layer then maps these representations to task-specific outputs. A key strength of RCs lie in their training efficiency; by keeping the reservoir fixed and training only the output layer, RCs achieves faster training and lower computational demand [ 3 – 5 ] compared with other machine learning approaches, such as convolutional neural networks (CNNs), long-short term memory networks (LSTMs), and transformers [ 6 ]. These alternatives typically rely on feed-forward architectures requiring extensive multiple-layer training via back-propagation [ 7 ]and substantial data and computational resources. The random yet fixed initialization of the reservoir poses a challenge for optimizing RC performance. This initialization determines the internal dynamics of the reservoir, which directly influence performance, but there is little guidance on how to systematically construct effective reservoirs. Theoretical studies [ 8 , 9 ] suggest that optimal performance may occur near the edge of chaos, indicating that maintaining a delicate dynamical balance within the reservoir is beneficial for effective design. This need for a delicate dynamical balance in RCs parallel the role of excitatory (E) and inhibitory (I) balance in RNNs of the brain’s neocortex, which is crucial for optimal brain function [ 10 ]. Disruptions in this balance are linked to altered states of consciousness, such as anesthesia [ 11 ], coma, and depression [ 12 ], which correspond to over-inhibited states, while conditions like epilepsy arise from excessive excitation [ 13 ].In neuronal network models, deviations from optimal E-I balance impair key aspects of information processing, including dynamic range and information capacity [ 14 ]. Traditional RCs capture some of this dynamical balance by including random positive as well as negative connections between neurons in the reservoir. However, the effects of changing the relative balance of excitation and inhibition remains underexplored. Further, in the neocortex, neurons and their connections have distinct excitatory and inhibitory roles, following Dale’s Law [ 15 , 16 ], where each neuron maintains only excitatory or inhibitory outgoing links. To bridge this gap, we introduce a brain-inspired RC architecture with distinct excitatory and inhibitory roles for neurons and their connections. Our design maintains a 4:1 ratio in the number of excitatory to inhibitory neurons and begins with a base structure that achieves an overall global E/I balance through a 1:4 ratio in the strengths of excitatory and inhibitory links, consistent with their approximate relative proportions in the brain [ 17 ]. By explicitly incorporating tunable E/I balance through adjustments to inhibitory link strengths, we enable RCs to achieve better performance across tasks while adhering to fundamental principles of neural organization. Specifically, we explore E-I balance by incorporating a local plasticity rule that adapts inhibitory weights to achieve target firing rates, inspired by activity homeostasis in neurobiology [ 18 – 22 ]. Unlike traditional RCs, which have fixed global balance from random network construction, this autonomous adaptation allows adjustable local and global E-I balance, improving performance across diverse tasks. Typical RC hyperparameter tuning is computationally expensive [ 23 ], whereas our biologically inspired approach enables autonomous self-adaptations that efficiently tune internal dynamics for broad applicability. While previous unsupervised learning approaches have investigated plasticity mechanisms in recurrent networks [ 24 – 27 ], and RCs [ 26 , 28 – 30 ] specifically, none have directly focused on tuning E-I balance. By introducing heterogeneous target rates, our model reduces the need for fine-tuning RC performance across linear tasks, such as memory recall, and nonlinear time-series prediction tasks. Additionally, we demonstrate that, relying on iterative tuning, networks can be designed to achieve desired levels of local balance through a one-step adjustment of randomly initialized inhibitory links, further improving efficiency and scalability crucial for large RCs. Our work builds on prior research[ 3 , 31 – 34 ]exploring the shared properties between biological neural networks and reservoir computers, particularly regarding the role of inhibition in these networks [ 35 , 36 ]. By demonstrating how local tuning of E-I balance enhances RC performance, our results bridge neuroscience and artificial intelligence, leveraging biological insights to improve machine learning.",
"discussion": "IV. DISCUSSION In this study, we investigate how tuning the excitatory-inhibitory balance in neural reservoirs can lead to significant performance improvements. We go beyond the generic global balance seen in typical RC implementations by probing the role of local balance in a brain-inspired excitatory inhibitory (E/I) reservoir. Specifically, we introduce a novel inhibitory adaptation mechanism that rapidly steers the network to appropriate levels of both local and global E/I balance to reach a high performing dynamical regime. This self-tuning, coupled with variability in target firing rates observed in biological neural networks, eliminates the need for extensive external fine-tuning and boosts performance across diverse tasks such as memory capacity and nonlinear time series prediction. Furthermore, we demonstrate that networks can be explicitly designed to reach desired levels of local balance through a single-step adjustment of randomly initialized inhibitory links, providing a more efficient and scalable alternative to iterative tuning. The role of excitation-inhibition balance in reservoir computing Excitatory-inhibitory (E-I) balance is a fundamental principle in neural systems, yet its role in reservoir computing has received relatively little attention [ 51 , 52 ]. Traditional reservoir computing approaches often use a hyperbolic tangent (tanh) activation function, which obscures the effects of E-I dynamics. Because neurons can output both positive and negative values, and synaptic weights can also be either sign, the distinction between excitation and inhibition becomes ambiguous. As a result, a form of global balance typically emerges simply from the random initialization of synaptic weights, rather than through any structural feature of the reservoir. In our study, we explicitly separated excitatory and inhibitory dynamics by employing a sigmoid neuron-like activation function and defining distinct populations of excitatory and inhibitory neurons. This separation allowed us to systematically explore how different E-I regimes shape reservoir dynamics and computational performance. We found that reservoirs operating in balanced to slightly inhibition-dominated regimes exhibit enhanced performance across tasks. Inhibitory neurons play a crucial role in preventing runaway excitation, ensuring that the network remains within a dynamical regime that supports efficient information processing. This asymmetrical preference toward inhibition is consistent with prior findings by deGraaf et al. [ 52 ], who showed that inhibition-dominated networks maintain balanced neuronal input and exhibit diverse firing rate variability, whereas excitation-dominated networks suffer from saturated firing rates. The latter reduces the effective dimensionality of network activity, leading to overly simplified output patterns. In line with these findings, our results suggest that while reservoirs can tolerate excess inhibition, increased excitation degrades performance—highlighting a fundamental asymmetry in how neural networks process information. The mechanisms governing E-I balance in biological brains are complex, ranging from anatomical constraints—such as fixed excitatory-to-inhibitory ratios—to dynamic synaptic adjustments that actively maintain equilibrium. One such anatomical constraint is Dale’s Law, which states that a given neuron releases either excitatory or inhibitory neurotransmitters, but not both [ 15 , 16 ]. While this principle is crucial in biological systems, our results suggest it is not essential for effective reservoir computation. Even when we shuffled synaptic weights, breaking the strict segregation of E and I neurons, we observed similar computational performance (see Supp. Fig. 3 ). This principle likely evolved as a consequence of biological constraints during neural development, where excitatory and inhibitory neurons develop distinct morphologies and neurotransmitter systems. However, further study is warranted to better understand the role of distinct excitatory and inhibitory populations in shaping network dynamics and computational properties. Notably, we find that strong RC performance in balanced and slightly over-inhibited regimes remains consistent whether E–I balance is tuned by adjusting the strength of inhibitory connections or by varying the proportion of inhibitory neurons (see Supp. Fig. 1 ), suggesting flexibility in how balance can be implemented in artificial systems. To reach an appropriate level of local balance in our reservoir computing models, we introduced an inhibitory adaptation rule that dynamically tunes inhibitory synapses at the neuronal level. While this rule draws inspiration from biological principles of firing rate homeostasis, it is not intended as a direct model of neural E-I regulation. Instead, it serves as a computationally efficient mechanism for improving reservoir function by leveraging biologically motivated constraints. Adaptive mechanisms make reservoirs more scalable by alleviating hyperparameter optimization Because typical RCs rely on generic randomly constructed neural reservoirs, incorporating bio-inspired principles offers a promising path for improving both performance and robustness. Prior research has explored various modifications to network topology of the reservoir, such as small-world connectivity and structured recurrent architectures, to enhance computational capacity [ 45 , 53 – 55 ]. However, most RC optimization strategies remain focused on task-specific performance and achieve it through extensive hyperparameter tuning. This process, often the most computationally demanding step in RC implementations, requires multiple training cycles to evaluate different configurations. The computational cost of each training step scales cubically with network size — O ( N 3 ) in typical implementations, where N is the number of neurons, making exhaustive optimization infeasible for large-scale systems. Even simple grid searches become prohibitively expensive when optimizing just a few hyperparameters, and while more sophisticated techniques like Bayesian optimization [ 56 , 57 ] or genetic algorithms [ 58 ] can improve efficiency, they still require numerous costly training iterations. To overcome this limitation in RCs, we propose shifting from performance-based optimization to dynamics-based adaptation . Rather than searching the high-dimensional parameter space for optimal configurations, our local adaptation rule enables reservoirs to self-organize into dynamical regimes that naturally support high performance. This approach significantly reduces the need for global hyperparameter tuning, making larger reservoir implementations computationally feasible. Additionally, we introduce a complementary non-local design rule that provides an efficient, one-step alternative, particularly useful for large-scale implementations where local adaptations, though far less costly than typical hyperparameter optimization, may still introduce undesired computational overhead. Many prior adaptation mechanisms [ 28 – 30 , 59 ] have demonstrated performance improvements in RCs, and our local E-I balance-based rule is one such approach. However, the important takeaway is not the specific implementation of this rule but the broader paradigm shift—from statically optimized reservoirs to dynamically adaptive systems. This perspective highlights a promising direction for addressing scalability challenges and improving the efficiency and robustness of RC implementations. Firing rate heterogeneity allows reservoirs to adapt to task-specific demands While adaptation reduces the need for manual tuning of multiple reservoir parameters, it introduces a new consideration: determining the optimal target firing rate. This target is inherently task-dependent due to a fundamental trade-off between linear and nonlinear processing needs. Neurons operating largely linear, i.e., in the middle, of their activation range (∼0.5 firing rate), maintain maximal sensitivity to input history, enhancing memory. Conversely, neurons operating inclusively towards non-linear aspects of the input-output function, exhibit more nonlinear responses, improving the network’s ability to perform complex transformations. Our results confirm this trade-off: memory-intensive tasks, such as memory capacity, perform best when neurons maintain intermediate firing rates and corresponding local balance between excitation and inhibition. In contrast, highly nonlinear tasks, like chaotic time-series prediction, benefit from deviations that enhance nonlinearity. This task dependence creates a challenge when designing adaptable RCs without reintroducing task-specific tuning of reservoir weights. To address this, we incorporate another biologically inspired principle: neuronal firing rate heterogeneity. In biological systems, distinct populations of neurons maintain different baseline firing rates, allowing them to fulfill diverse computational roles. Prior research has shown that combining linear and nonlinear nodes enhances multitask performance [ 48 ]. Importantly, we find that balanced reservoirs with heterogeneous firing rate targets also naturally balance memory and nonlinearity, yielding broad performance gains while eliminating task-specific tuning of firing rates. In these reservoirs, some neurons optimize memory retention, while others enhance nonlinear processing, reflecting the division of labor observed in biological neural circuits. This heterogeneity provides a flexible and robust computational architecture, enabling reservoirs to generalize more effectively across diverse tasks. By combining E/I balance with firing rate heterogeneity, our study highlights how biologically inspired mechanisms can enhance artificial neural computation. Our results demonstrate that appropriately balancing excitation and inhibition at the local level improves computational efficiency, while incorporating heterogeneity makes reservoirs more flexible across tasks. Moreover, by reducing reliance on hyperparameter optimization, our approach significantly enhances scalability, making it well-suited for larger and more complex learning systems. As AI models continue to grow in size and computational demand, biologically inspired adaptation mechanisms may offer a crucial step toward more efficient, self-organizing, and scalable neural architectures."
} | 4,274 |
38813885 | PMC11154151 | pmc | 581 | {
"abstract": "Abstract Life on Earth comprises prokaryotes and a broad assemblage of endosymbioses. The pages of Molecular Biology and Evolution and Genome Biology and Evolution have provided an essential window into how these endosymbiotic interactions have evolved and shaped biological diversity. Here, we provide a current perspective on this knowledge by drawing on decades of revelatory research published in Molecular Biology and Evolution and Genome Biology and Evolution , and insights from the field at large. The accumulated work illustrates how endosymbioses provide hosts with novel phenotypes that allow them to transition between adaptive landscapes to access environmental resources. Such endosymbiotic relationships have shaped and reshaped life on Earth. The early serial establishment of mitochondria and chloroplasts through endosymbioses permitted massive upscaling of cellular energetics, multicellularity, and terrestrial planetary greening. These endosymbioses are also the foundation upon which all later ones are built, including everything from land–plant endosymbioses with fungi and bacteria to nutritional endosymbioses found in invertebrate animals. Common evolutionary mechanisms have shaped this broad range of interactions. Endosymbionts generally experience adaptive and stochastic genome streamlining, the extent of which depends on several key factors (e.g. mode of transmission). Hosts, in contrast, adapt complex mechanisms of resource exchange, cellular integration and regulation, and genetic support mechanisms to prop up degraded symbionts. However, there are significant differences between endosymbiotic interactions not only in how partners have evolved with each other but also in the scope of their influence on biological diversity. These differences are important considerations for predicting how endosymbioses will persist and adapt to a changing planet.",
"conclusion": "Conclusion The fields of molecular evolution—and SMBE journals—have tracked decades of scientific discoveries that have revealed the origins, evolution, and global impacts of major endosymbiotic events. This knowledge has invited the reevaluation of long-held theories, including even the fundamental definitions of endosymbioses (i.e. organelles derived from endosymbioses vs. all other kinds of endosymbiotic interactions; see Husnik and Keeling 2019 ). While the classification of endosymbioses is partly a matter of semantics and theory, the evolutionary implications are important for understanding how endosymbiotic events have influenced biological diversity and how their interactions will persist and adapt to a changing planet. Our accumulated knowledge has revealed that there are indeed important categorical distinctions to make between endosymbiotic systems. We conclude by reviewing two of the more significant ones. The Important Distinctions between Endosymbioses The first major distinction between endosymbiotic events is their sharply contrasting scales of influence over the evolution of global biodiversity. At the broadest level, the endosymbiotic steps in eukaryogenesis permitted the adaptive radiation of single-celled eukaryotes, multicellularity, and the eventual evolution of plants and animals ( Lane and Martin 2010 ). The early establishment of mitochondria and chloroplasts is also the essential foundation upon which all other endosymbioses are built. While the later endosymbioses that followed are certainly responsible for the global-scale diversification of many organismal groups, they are comparatively narrow in their host associations and are relatively plastic ( Chomicki et al. 2019 ; Cornwallis et al. 2023 ). Nutritional endosymbioses in invertebrate animals, for example, have evolved repeatedly between a wide range of hosts, microbial partners, and environments ( Sudakaran et al. 2017 ; Sogin et al. 2021 ). Many independent events have given rise to a wide diversity of host lineages over space and time. In contrast, mitochondria and chloroplasts are each derived from singular events that enabled the evolution and diversification of everything that is not a prokaryote. The second important distinction between endosymbioses is that the physical and cellular relationships between partners differentially influence their long-term evolution. Mitochondria, which evolved in single-celled hosts, have proliferated along with nearly every eukaryotic cell, including those comprising multicellular organisms. This is not the case for most later nutritional endosymbionts in multicellular hosts. These endosymbionts generally exist only in highly specialized organs and cells restricted from most others including the germline ( Fronk and Sachs 2022 ). In vertically transmitted endosymbioses (e.g. those found in many insects), tissue and cellular restrictions put their endosymbionts in the perilous situation of having little to no control over their reproductive or evolutionary fates. Consequently, the cellular structures and genomes of vertically transmitted endosymbionts are whittled away to eventual extinction or replacement ( McCutcheon et al. 2019 ). For partnerships where endosymbionts are acquired from the environment (e.g. plant–rhizobial and deep-sea animal–chemosynthetic bacterial endosymbioses), the evolutionary consequences of these associations may be comparatively less severe on endosymbiont genomes and their independent cellular capabilities ( Young et al. 2006 ; Sogin et al. 2021 ). Taken together, multicellular hosts that established additional endosymbioses long after eukaryogenesis may have the latitude to evolve away from endosymbioses acquire more partners or swap endosymbionts with better ones ( Bennett and Moran 2015 ). They cannot as easily drop their dependence on mitochondria and, to a more limited extent, chloroplasts. These first endosymbionts have ensured their essentiality and near immortality among eukaryotic cells. Thus, it may be predicted that for as long as eukaryotes and photosynthesis persist on Earth, so too will mitochondria and chloroplasts. The same cannot be said for other kinds of endosymbioses.",
"introduction": "Introduction The evolution of all life—from prokaryotes to complex multicellular eukaryotes—has been shaped by symbiotic interactions with the immense microbial diversity that exists on the Earth ( McFall-Ngai et al. 2013 ). Such interactions generally range from antagonistic to beneficial, placing distinctive evolutionary pressures on the interacting partners ( Lynch and Hsiao 2019 ; Drew et al. 2021 ). In recent years, beneficial symbioses—including facultative and obligate interactions—have become much better understood as important drivers of biological complexity and diversity ( Archibald 2014 ; Douglas 2014 ; McFall-Ngai 2015 ; Chomicki et al. 2019 ; Perreau and Moran 2022 ). These interactions are diverse in terms of the phylogenetic array of hosts and microbes involved, the specific services that each provides, and the evolutionary mechanisms employed to sustain them. While such beneficial symbioses are diffuse among biological life, endosymbiotic interactions—microbes living inside the cells of a host—are among some of the most ancient and complex biological interactions known ( Archibald 2015a ). They generally arise when unexploited resources are available but out of reach for potential hosts ( Moran 2007 ). By bridging distant peaks between adaptive landscapes, endosymbionts provide novel phenotypes to their hosts that unlock environmental resources ( Fig. 1 ; Lynch and Hsiao 2019 ). As a result, endosymbionts are often removed from the open environment and become wholly dependent on their hosts ( Bennett and Moran 2015 ; Drew et al. 2021 ). Thus, endosymbioses are paragons of coevolution. They entail the complex evolution of integrated genomes, host tissues and organs, novel host cells and cell structures, and mechanisms of resource exchange and communication between the domains of life ( Keeling 2013 ; Martin et al. 2015 ; Wilson and Duncan 2015 ). Fig. 1. Simplified summary of the major endosymbiotic events that have led to significant leaps in the biological diversity and complexity of life. Endosymbionts provide novel phenotypes to their hosts permitting them to leap between adaptive landscapes with new trait axes and peaks. Except for the establishment of mitochondria during eukaryogenesis, all other endosymbioses are built on the more ancient ones that preceded them. Arrows track the evolutionary progression of these interactions. Peaks and labels illustrate some of the major endosymbiotic events. Over evolutionary time, endosymbioses have become ecologically pervasive, playing integral roles in shaping—and reshaping again and again—Earth's biological diversity. One could summarize life on our planet as comprising just the prokaryotes and a broad union of organisms derived from endosymbiotic interactions (i.e. anything with mitochondria, plastids, and beyond; Yutin et al. 2008 ; Martin et al. 2015 ; Archibald 2015a ; McCutcheon et al. 2019 ). As such, there is an intrinsic and even urgent need to understand the biology and ecology of these interactions. This knowledge is key to discerning the main origins and drivers of biological diversity, as well as to clarifying even our most basic biological and evolutionary theories. Pursuing such grand research goals requires the application of evolutionary principles. This framework elevates questions of “ how endosymbioses function” to “ why they function,” “ where they came from,” and “ why they even exist and persist in nature.” It further provides a predictive framework to project our understanding well past our contemporary moment in the evolution of life on Earth. Building an evolutionary framework into endosymbiosis research necessitates developing a baseline understanding of the diversity, origins, and evolutionary processes underlying these interactions. Over the past 40 years, researchers publishing in the journals, Molecular Biology and Evolution ( MBE ), and later Genome Biology and Evolution ( GBE ), have tackled these questions and greatly expanded our knowledge of endosymbioses. What we have learned is profound and voluminous. We take readers through this literature, tackling two basic questions: How have endosymbioses shaped life ? And how do endosymbioses evolve? A Primer on How Endosymbioses Shaped Biological Diversity With ever more sophisticated molecular tools and technologies, researchers publishing in MBE and GBE have traced the evolution of endosymbioses up and down the tree of life. Early phylogenetic approaches permitted the identification of endosymbiotic partners for many systems and the development of hypotheses about the origin and evolutionary processes shaping these interactions ( Moran 1996 ; Peek et al. 1998 ; Spaulding and von Dohlen 1998 ; Pisani et al. 2007 ). But the greatest accelerant of our understanding of endosymbioses—particularly since few endosymbiotic microbes can be cultured—is the advent of next-generation molecular sequencing ( McFall-Ngai 2015 ). These technologies cheaply expanded the ability to collect complete molecular information for all symbiotic partners (genomes, transcriptomes, proteomes, epigenomes, etc.) across populations, species, and groups ( Brown et al. 2015 ; Chong et al. 2019 ; Shinzato et al. 2021 ; Sun et al. 2021 ; Gould et al. 2022 ). Research employing these approaches has yielded novel and refined theories of how endosymbioses function and evolve (e.g. McCutcheon and Moran 2010 ; Sloan et al. 2014 ; Shapiro et al. 2016 ; Yang et al. 2020 ; Ip et al. 2021 ). Along the way, we have learned that endosymbionts appear to come from almost everywhere and do almost everything. They provide an array of metabolic and physiological services to their hosts, including the exponential upscaling of cellular energy and the bridging of nutritional deficits on land and in the sea ( Lane and Martin 2010 ; Hansen and Moran 2014 ; Sogin et al. 2021 ). From eukaryogenesis to the ability of insects to feed on plants, endosymbiosis has been a perpetual driver of biological complexity and diversity ( Fig. 1 ; Archibald 2014 ; Mills et al. 2022 ). The Original Endosymbioses The first known endosymbiosis of major biological significance occurred ~1.8 billion years ago when Asgard archaean formed an obligate endosymbiotic relationship with an alphaproteobacterium ( Sagan 1967 ; Fitzpatrick et al. 2006 ; Pisani et al. 2007 ; Yutin et al. 2008 ; Williams et al. 2013 ; Raval et al. 2023 ). This relationship gave rise to the mitochondria (also mitosomes and hydrogenosomes) and eukaryotes writ large. The precise origins and steps in the coevolutionary integration of the endosymbiotic interaction have been long debated ( Thiergart et al. 2012 ; Williams and Embley 2014 ; Geiger et al. 2023 ). (Note: 1.8 billion years of Earth's history is an immense amount of time and space for evolution to scramble its tracks—a common theme in endosymbiosis research.) Nevertheless, recent theory suggests that mitochondria arose in anaerobic conditions through the dependence of methanogenic archaea on H 2 provided by an alphaproteobacterial ancestor ( Martin and Müller 1998 ; Mills et al. 2022 ). The permanent establishment of this alphaproteobacterium into the mitochondria greatly expanded the cellular energy budgets of single-celled and multicellular eukaryotes, further assuming roles in cell cycle regulation, signaling, apoptosis, etc. ( Gray et al. 1999 ; McBride et al. 2006 ; Roger et al. 2017 ). The benefit of abundant and localized energy vis-à-vis the mitochondria was a necessary preadaptation for establishing all other endosymbioses that have followed. The mitochondria, by transferring genes to the nuclear genome, also provided genetic toolkits for integrating and sustaining later endosymbioses in more complex hosts (e.g. nutritional endosymbioses in some insects; reviewed by Mao et al. 2018 ). Relatively soon after eukaryogenesis (∼1.5 billion years ago), an ancestor to the Archaeplastida (algae and plants) established another significant endosymbiosis with a cyanobacterium ( Yoon et al. 2004 ; Rogozin et al. 2009 ; Keeling 2013 ). This relationship led to the primary establishment and evolution of chloroplasts, eukaryotic photosynthesis, massive increases in global primary productivity, and the literal greening of Earth ( Moreira et al. 2000 ; McFadden 2001 ). Mitochondria were a necessary partner for this event. It provided the fundamental bioenergetic framework to leverage solar energy and the protection of chloroplasts during cellular stress conditions and in the darkness of night ( Hoefnagel et al. 1998 ; Lane and Martin 2010 ; Mills et al. 2022 ). In return, the chloroplast endosymbiont provided a ready food source and metabolic support to its mitochondrial partner (e.g. sugars and oxygen; Raghavendra and Padmasree 2003 ; Oikawa et al. 2021 ). As a result, some mitochondrial and chloroplast metabolic activities are linked and coregulated by their hosts ( Zhang and Glaser 2002 ; Zhao et al. 2020 ; He et al. 2023 ). Some of their essential functions are even supported by the same dual-targeted genes (e.g. essential t-RNA synthesis; Peeters and Small 2001 ; Carrie et al. 2009 ; Yogev and Pines 2011 ). Thus, mitochondria and chloroplasts are, perhaps, the first dual endosymbiosis—a relatively common feature of later endosymbioses of plants and invertebrate animals. The ability to photosynthesize is such an important adaptive eukaryotic trait that ancestrally nonphototrophic eukaryotes (ancestors to euglenids, apicomplexans, cryptomonads, etc.) have repeatedly stolen the ability ( Yoon et al. 2005 ; Baurain et al. 2010 ; Dagan et al. 2013 ; Keeling 2013 ). Over evolutionary time, and through the process of secondary endosymbiosis, nonphotosynthetic eukaryotic hosts have intracellularly acquired archaeplastid endosymbionts many times and sometimes repeatedly (e.g. tertiary endosymbiosis; Douglas 1998 ; Keeling 2013 ). However, the arrangement for the interned photosynthetic eukaryote is less than ideal. The secondary host essentially dissolves the archaeplastid and absorbs necessary genes into its genome to sustain their stolen plastid ( Archibald 2015b ; Ponce-Toledo et al. 2019 ). Endosymbioses derived from secondary, tertiary, etc. origins have led to key biological diversity with Earth-changing outcomes ( Dorrell and Howe 2015 ). For example, Symbiodinium dinoflagellates, the product of a red algal secondary endosymbiosis, are themselves endosymbionts of a wide range of marine invertebrates (jellyfish, anemones, nudibranchs, etc.; LaJeunesse et al. 2018 ; Liu et al. 2018 ). Notably, Symbiodinium in corals enabled the massive diversification of marine reef systems, which are among the most diverse, important, and threatened ecosystems on the planet ( Plaisance et al. 2011 ; Levin et al. 2016 ). The Later Endosymbioses That Shaped Plant and Animal Diversity Beyond the serial establishment of mitochondria powerhouses and chloroplast primary productivity, disparate eukaryotic lineages have continued acquiring additional endosymbionts. These “later” endosymbioses provided key adaptive advantages to their hosts that generally include novel metabolisms and enhanced access to environmental resources ( Moran 2007 ; Archibald 2014 ). They have occurred in everything from single-celled eukaryotes (e.g. Paulinella ; Marin et al. 2005 ; Nowack et al. 2008 , 2011 ) to more biologically complex plants and invertebrate animals (e.g. Fabaceae leguminous plants, some marine deep-sea vent invertebrate animals, and hemipteran plant-feeding insects; Sloan et al. 2014 ; Manzano-Marín et al. 2015 ; Warshan et al. 2018 ; Ip et al. 2021 ). The importance of these endosymbioses in shaping biodiversity is now well in view. The diversity and success of land plants are attributable, in part, to endosymbiotic interactions with arbuscular mycorrhizae (AM; Parniske 2008 ; Bonfante and Genre 2010 ) and nitrogen-fixing cyanobacteria and rhizobia bacteria ( Kiers et al. 2003 ; Warshan et al. 2018 ). Up to 80% of land plant species engage in endosymbiosis with the ancient Glomeromycota AM ( Bennett and Groten 2022 ). By extending plants’ abilities to scavenge essential nutrients from soils (e.g. nitrogen, phosphorous, and minerals; Parniske 2008 ), endosymbiosis with AM provided a key adaptive advantage for plants in the terrestrial environment. This relationship was also likely an important factor in the early success of land plants as they established on Earth's barren landscapes >450 million years ago ( Delaux and Schornack 2021 ). In contrast, endosymbioses between land plants and bacteria are comparatively restricted, possibly because plant cell structures and physiology limit intracellular invasion by microbes ( Geurts et al. 2016 ; Delaux and Schornack 2021 ). Nevertheless, endosymbiotic bacteria in plants have contributed to the ecological success and diversification of several important plant groups. Nitrogen-fixing cyanobacterial endosymbioses have independently evolved in a range of hosts that include Gunnera , some liverworts, cycads, and ferns ( Rikkinen 2017 ; Warshan et al. 2018 ; Delaux and Schornack 2021 ). Similarly, root-nodulating rhizobial endosymbioses have led to the diversification of the ecologically and agriculturally important group of plant orders that includes Fabales, Cucurbitales, and Rosales ( Markmann and Parniske 2009 ). Invertebrate animals have been particularly successful at establishing additional endosymbioses. In an evolutionary framework, these exceedingly diverse interactions permitted hosts to thrive in totally unsuitable environments, often leading to global-scale adaptive species radiations ( Moran 2007 ). For example, a wide diversity of marine invertebrates (e.g. some clam, mussel, and snail species) ally with chemosynthetic bacterial endosymbionts for CO 2 fixation into consumable biomass and sugars ( Ozawa et al. 2017 ; Sogin et al. 2020 ; de Oliveira et al. 2022 ). These endosymbioses permit their hosts to dominate some of the most extreme and energy-limited environments on Earth, including oceanic sediments and deep-sea thermal vents ( Sogin et al. 2021 ). But this diversity of endosymbioses in invertebrate animals is just the tip of the iceberg. Far more diverse groups of invertebrate animals, including some nematodes and insects, owe their origins to endosymbioses ( Jiggins et al. 2002 ; Brown et al. 2015 ; Chong et al. 2019 ). In particular, insect endosymbioses have received intense attention in recent years. Insects have been dubbed a “fairly land” of endosymbiosis and for good reason ( Buchner 1965 ). These endosymbiotic interactions are responsible for at least 20% of insect species diversity (>1 million species) and underlie their terrestrial dominance ( Douglas 2011 ). Origination events are generally ancient (e.g. tens to hundreds of millions of years old; Bennett and Moran 2013 ; Patiño-Navarrete et al. 2013 ) and are too numerous to summarize (e.g. at least ∼50 independent origins in the order Hemiptera, alone; Bennett and Moran 2015 ; Sudakaran et al. 2017 ). The principal role of insect endosymbionts is to provide nutrition lacking in host diets ( Hansen and Moran 2014 ). For example, plant sap-feeding insects in the hemipteran order (cicadas, leafhoppers, aphids, whiteflies, etc.) have acquired a diverse array of bacteria to provide essential amino acids lacking in their plant phloem and xylem diets ( Sloan and Moran 2013 ; Santos-Garcia et al. 2014 ; Mao et al. 2017 ; Garber et al. 2021 ). Similar nutritional interactions are known from a broad diversity of insect groups, including ants, cockroaches, and tsetse flies to name just a few ( Williams and Wernegreen 2012 ; Medina Munoz et al. 2017 ; Kinjo et al. 2018 ). Insects often take these interactions much further by acquiring multiple endosymbiotic bacteria and fungi that have completely different origins and that make distinct contributions to their endosymbioses ( McCutcheon and Moran 2010 ; Weglarz et al. 2018 ). They are also actively evolving ever-novel endosymbioses with environmental bacteria ( Oakeson et al. 2014 ), leading to the routine replacement of ancient endosymbionts with younger ones ( Sudakaran et al. 2017 ). Evolving Biologically Complex Endosymbioses A major area of interest in evolutionary biology—and Society for Molecular Biology and Evolution (SMBE) journals—is the evolutionary processes that shape the establishment, integration, and long-term persistence of endosymbioses. We have previously described this process as an ever-deepening and spiraling “rabbit hole” ( Bennett and Moran 2015 ). In this framework, endosymbioses are shaped by at least four key factors: (i) the metabolic purpose of the endosymbiosis, (ii) the number of partner endosymbionts involved, (iii) the host’s abilities to transmit and support their endosymbionts across generations, and (iv) the age of the endosymbiosis. The first two factors dictate the minimum genetic repertoire endosymbionts must retain to fulfill their host-dependent functions (e.g. oxidative phosphorylation pathways in mitochondria and nutritional pathways in invertebrate animal endosymbionts; Gray et al. 1999 ; McCutcheon and Moran 2010 ). Many symbiotic systems further depend on multiple collaborative endosymbionts to provide single metabolisms (e.g. chloroplast–mitochondrial interactions and dual nutritional endosymbioses in insects; Douglas 2016 ; Gossett et al. 2023 ; He et al. 2023 ). The third factor dictates how strongly host selection and drift are in shaping endosymbiont evolution and the extent to which hosts can support their endosymbionts across generations ( Jiggins et al. 2002 ; Salem et al. 2015 ; Leftwich et al. 2020 ; Perreau and Moran 2022 ; Romero Picazo et al. 2022 ). The modality in which endosymbionts are acquired (i.e. environmental vs. vertical transmission) generally plays a large role in how these processes unfold over evolutionary time. Finally, as endosymbiotic relationships age, the effects of living in an endosymbiosis become more pronounced on the decreasing cellular and metabolic integrity of endosymbionts ( McCutcheon et al. 2019 ). Hosts, in turn, must continually adapt to sustain their endosymbionts ( Mao et al. 2018 ; Perreau and Moran 2022 ). Endosymbiont Evolution: Streamlined for Dysfunction Perhaps one of the most generalizable outcomes of endosymbiosis is that the genomes and related cellular functionality of endosymbionts are streamlined to fit their ecological and symbiotic requirements. In many cases, this process leads to the extreme reduction of microbial genomes to a mere fraction of those found in their free-living relatives ( Khachane et al. 2007 ; McCutcheon and Moran 2012 ). However, the obvious effects of genome streamlining are less pronounced in endosymbionts that are horizontally acquired, particularly those with life phases in the open environment ( Bright and Bulgheresi 2010 ; Fisher et al. 2017 ). For example, while rhizobia bacteria and AM contain genes necessary for their endosymbiotic interactions, they also tend to have large versatile genomes required to cope with complex soil environments (>7 and >150 Mb, respectively; Young et al. 2006 ; Tisserant et al. 2013 ). For vertically transmitted endosymbionts, the evolutionary procession shaping their reduced genomes has been relatively well characterized ( Khachane et al. 2007 ; Wernegreen 2015 ). Early in the establishment of an endosymbiotic interaction, endosymbiont genomes become adaptively streamlined to lose redundancy with those of their hosts and other endosymbiont partners ( Gray et al. 2001 ; Dale et al. 2003 ). Genes encoding redundant biosynthetic activities are purged via relaxed selection and eventual excision ( Bennett and Moran 2015 ; Wertheim et al. 2015 ). This process occurs even in systems with multiple obligate endosymbionts that evolve to perfectly complement each other to meet the needs of their hosts ( McCutcheon and Moran 2010 ; Monnin et al. 2020 ). However, early on in their establishment when endosymbiont population size and selection are reduced, their genomes can expand with noncoding content that rapidly obliterates redundant and nonessential genes ( Koga and Moran 2014 ; Oakeson et al. 2014 ). As this early upheaval settles, endosymbiont genomes shrink toward essential integrated metabolisms and functions. A distinct mode of adaptive genome streamlining occurred in mitochondria, plastids, and the chromatophore of Paulinella . The ancestors of these endosymbionts translocated sizable portions of their genomes to their hosts––a process known as endosymbiotic gene transfer (EGT; Dagan et al. 2013 ; Ku et al. 2015 ). The advantages of EGT are thought to lie in host control of gene expression, aiding endosymbiont escape from genetic drift, and economized energetics of protein production and transport ( Kelly 2021 ). The EGT evolutionary process, as well as extreme genome reduction, also occurs in the genomes of primary archaeplastid algal hosts unfortunate enough to become enveloped in secondary endosymbioses with other single-celled eukaryotes ( Keeling 2010 ; Uthanumallian et al. 2022 ). A significant evolutionary contrast can be made with endosymbionts that are established later in multicellular plant and animal hosts. They generally exhibit little evolutionarily significant EGT. These endosymbionts are rarely in direct contact with germline nuclei and have limited opportunity for heritable EGT to occur ( Nikoh et al. 2010 ). Finally, as vertically transmitted endosymbioses mature—particularly those found in invertebrate animals—endosymbionts become locked into the endosymbiotic relationship. They experience drastically reduced effective population sizes and strong intergenerational genetic bottlenecks ( Moran 1996 ; Woolfit and Bromham 2003 ; Vogel and Moran 2013 ; Hendry et al. 2016 ). Along with the reduction in their DNA repair mechanisms, drift and the inability to fix errors exaggerate rates of molecular evolution, accumulation of deleterious mutations, and extreme base pair compositional biases (e.g. Douglas et al. 2001 ; Schelkunov et al. 2015 ; Waneka et al. 2021 ). Over time, accumulated mutations cause genes that underlie critical cellular metabolisms and functions to be lost, including the independent ability to regulate genome expression, synthesize membranes and transport metabolites and resources, and even synthesize essential resources required by the host and partner endosymbionts ( Kuo et al. 2009 ; Bennett et al. 2016 ). These evolutionary processes take their toll on genome size and function, leading to the very smallest known genomes carved out of free-living ancestors (i.e. often just tens to hundreds of kilobases; Gray et al. 2001 ; Bennett and Moran 2013 ; Moran and Bennett 2014 ; Sibbald and Archibald 2020 ). They also create extreme situations where host lineages and species must adapt to the distinct molecular identities and needs of their endosymbionts through genomic compensation or the acquisition of novel partners ( Bennett et al. 2016 ; Chong et al. 2019 ; Forsythe et al. 2021 ; Biot-Pelletier et al. 2023 ). Host Evolution: Evolutionary Problem Solvers Hosts need to overcome a few basic evolutionary challenges in order to sustain and integrate successful endosymbioses with ever-changing partners ( Bennett and Moran 2015 ). These challenges generally include a way to stably exchange essential metabolic and cellular resources, communicate and regulate shared activities, and support ongoing endosymbiont genome degradation. The diverse ways in which hosts meet these challenges depend on the identity of the interacting partners, host anatomy and physiology (e.g. plants vs. animals), and the genetic and genomic constraints of each partner symbiont. All endosymbioses depend on evolving a means of resource sharing. The sharing of essential metabolites and nutrition is critical to the function and maintenance of endosymbioses (e.g. insect dependence on essential amino acids; Russell et al. 2014 ; Spinelli and Haigis 2018 ). The evolution of exchange mechanisms has been accomplished in many ways. The solution depends on the nature of the endosymbiosis and whether endosymbionts retain membrane transport systems and other capabilities in their genomes. Endosymbionts with larger genomes, for example, retain transporters that can handle metabolite exchange ( Toft and Fares 2008 ; Hehenberger et al. 2016 ). For ancient endosymbionts that no longer encode some or all transporters, hosts contribute them to their membranes and often the entire membrane as well ( Price et al. 2011 ; Duncan et al. 2016 ; Cunningham and Rutter 2020 ). But, for unyielding endosymbionts with large independent genomes, such as those acquired from the environment, hosts can either sequester them to elicit and absorb excreted metabolites (e.g. root-nodulating rhizobia; Markmann and Parniske 2009 ) or simply consume them (e.g. chemosynthetic systems; Sogin et al. 2021 ). Due to their tiny genomes, organelles and many endosymbionts also depend on the import of large host nuclear-encoded proteins to perform even their most basic cellular functions. For example, to share protein resources with mitochondria and plastids, eukaryotic hosts have evolved—independently—complex import systems (Tim–Tom and Tic–Toc, respectively; Soll and Schleiff 2004 ; Wiedemann and Pfanner 2017 ). In Paulinella —which has in many ways independently replayed chloroplast evolution—protein import re-evolved but the mechanisms appear to have distinct origins, possibly involving the Golgi apparatus ( Singer et al. 2017 ). Similarly, the endosymbionts of invertebrate animals with highly degraded genomes also depend on protein imports, but the mechanisms are currently unclear ( Nakabachi et al. 2014 ; Mao et al. 2018 ). Due to their many independent origins of endosymbioses in invertebrate animals, the mechanisms responsible for protein import across lineages may very well be cobbled together from distinct genes and cellular machineries. Host endosymbiont cell–cell regulation and communication are essential for the stable long-term integration of successful endosymbioses. How this has been evolutionarily accomplished across the range of endosymbioses is diverse and complex, and much remains to be understood for many systems. Nevertheless, some mechanisms have been identified in key endosymbioses. In chloroplasts and mitochondria, for example, a process of retrograde signaling modulates host genome activities in response to a range of organelle functions, metabolite presence and abundances, and accumulation of reactive oxygen species ( Wang et al. 2020 ). In plants, both mycorrhizae and rhizobia can use glycan signals to target root cells to establish endosymbiotic association and root nodule formation ( Gough and Cullimore 2011 ). Plant hosts also excrete flavonoid signals that toggle bacterial expression of specific genes important in nodule formation and maintenance ( Spaink 2000 ). After establishment, RNA appears to also play some regulatory roles in nutrient exchange ( Xu et al. 2018 ). In marine endosymbioses between corals and algal dinoflagellates, a complex array of glycans, reactive oxygen species, RNA, and lipids are similarly involved in cell–cell communication and symbiotic regulation ( Rosset et al. 2021 ). For insects, less is currently known about how they regulate and communicate with their diverse nutritional endosymbionts. Some bacterial symbionts with larger genomes maintain complex abilities to monitor and respond to their environments in ways that influence their hosts’ function. These bacterial capabilities include eukaryote targeting effectors and quorum sensing, which are mechanisms for attenuating the host-level fitness costs of harboring endosymbionts ( Sanchez-Contreras et al. 2007 ; Enomoto et al. 2017 ; Hinzke et al. 2019 ). However, in the vast majority of ancient insect endosymbioses, these systems have long been lost. An emergent property of some systems is that host-level regulation of endosymbionts may be accomplished through nutrition and metabolite monitoring and exchange. In pea aphids, for example, metabolite transporters sense and regulate the flow of metabolites in response to essential amino acid concentrations ( Wilkinson et al. 2007 ; Duncan et al. 2023 ). Finally, maintaining endosymbionts, particularly as they age and degrade into complete dependence, requires the evolution of a multitude of support mechanisms. This process often leads to complex reconfiguration of host genomes. The most extreme is perhaps that of the eukaryotic nuclear genome, which is a core patchwork of archaeal, mitochondrial endosymbiont, and other prokaryotic genes ( Ribeiro and Golding 1998 ; Pisani et al. 2007 ; Ku et al. 2015 ; Brueckner and Martin 2020 ). Accommodating and economizing the early endosymbioses with the mitochondria’s ancestor was accomplished, in part, by absorbing and repurposing parts of its genome through EGT ( Martin et al. 1998 ). A similar process permitted the capture and establishment of chloroplasts ( Sibbald and Archibald 2020 ), as well as the continued acquisition of plastids through secondary endosymbioses ( Ponce-Toledo et al. 2018 ). For the establishment of later endosymbioses, such as those in insects, the core toolkit is the eukaryotic genome. However, endosymbionts are generally restricted to distinct cells and tissues (e.g. bacteriocytes) that undergo evolutionary modification into tailored support apparatuses ( Buchner 1965 ). The gene expression of these cells is reprogrammed to differentially express thousands of genes responsible for the maintenance and regulation of endosymbionts (e.g. Sloan et al. 2014 ; Luan et al. 2015 ). These symbiont-support genes are derived from a range of origins to meet the specific needs of particular endosymbionts. For example, membrane transporters are typically lost from tiny endosymbiont genomes in insects. They often undergo extensive duplication in the host genome and reassignment to the host-endosymbiont interface ( Price et al. 2011 ; Duncan et al. 2016 ). Another source of aid for later endosymbioses is the preexisting mitochondrial support genes the hosts acquired through ancient EGT. Up to hundreds of these genes are either dual-targeted to mitochondria and nutritional endosymbionts, or they have been duplicated and completely reassigned to support only the latter ( Mao et al. 2018 ). Occasionally, however, the host genome alone cannot close gaps in the metabolic and cellular functions of their endosymbionts. When no other mechanisms are available, hosts resort to acquiring genes through horizontal gene transfer from other infecting bacteria ( Sloan et al. 2014 ; Bublitz et al. 2019 ). They may also acquire additional symbionts along with their entire genomes ( Deng et al. 2023 ). In essence, hosts pull every evolutionary trick they can to meet the needs of their ever-degrading endosymbionts."
} | 9,328 |
32070345 | PMC7029506 | pmc | 582 | {
"abstract": "The efficiency of industrial fermentation process mainly depends on carbon yield, final titer and productivity. To improve the efficiency of l -lysine production from mixed sugar, we engineered carbohydrate metabolism systems to enhance the effective use of sugar in this study. A functional metabolic pathway of sucrose and fructose was engineered through introduction of fructokinase from Clostridium acetobutylicum . l -lysine production was further increased through replacement of phosphoenolpyruvate-dependent glucose and fructose uptake system (PTS Glc and PTS Fru ) by inositol permeases (IolT1 and IolT2) and ATP-dependent glucokinase (ATP-GlK). However, the shortage of intracellular ATP has a significantly negative impact on sugar consumption rate, cell growth and l -lysine production. To overcome this defect, the recombinant strain was modified to co-express bifunctional ADP-dependent glucokinase (ADP-GlK/PFK) and NADH dehydrogenase (NDH-2) as well as to inactivate SigmaH factor (SigH), thus reducing the consumption of ATP and increasing ATP regeneration. Combination of these genetic modifications resulted in an engineered C. glutamicum strain K-8 capable of producing 221.3 ± 17.6 g/L l -lysine with productivity of 5.53 g/L/h and carbon yield of 0.71 g/g glucose in fed-batch fermentation. As far as we know, this is the best efficiency of l -lysine production from mixed sugar. This is also the first report for improving the efficiency of l -lysine production by systematic modification of carbohydrate metabolism systems.",
"conclusion": "Conclusions For the first time, metabolic engineering of carbohydrate metabolism systems was identified as a critical factor for efficiently producing l -lysine from mixed sugar in C. glutamicum . The carbohydrate uptake system of strain was reconstructed and the intracellular ATP was complemented by enhancing ETP. We showed that hetero-expression of ScrK and introduction of optimized non-PTS were effective for increasing substrate consumption rate and l -lysine production from mixed sugar. Furthermore, substitution of the promoter of ndh by strong tuf promoter and deletion of the transcriptional regulator SigH further increased the l -lysine production and the highest substrate consumption rate, and these provided an efficient strategy for improving the efficiency of l -lysine production. The target strain K-8 produced 221.3 ± 17.6 g/L at a productivity of 5.53 g/L/h and a carbon yield of 0.71 g/g glucose in fed-batch fermentation. To the best of our knowledge, those are the highest value for l -lysine production by fed-batch fermentation in the references. However, this yield of l -lysine in strain K-8 is still lower than the theoretical level (i.e., 0.81 g/g glucose). Thus, there is still plenty of room to enhance the yield of l -lysine. The most important by-product of strain K8 was pyruvate-family amino acids. Although adequate ATP could be used for pyruvic carboxylase (PYC) as cofactor, the activity of PYC was inhibited by l -aspartate [ 56 ]. Further improvement may be achieved by increasing the activity of PYC, for example, by overexpression of PYC-coding gene pyc or by site-specific mutagenesis of pyc to relieve feedback inhibition. Another potential strategy is to reduce the flux into biosynthetic pathway of pyruvate-family amino acids. In addition, intracellular NADPH plays an important role in l -lysine production [ 1 ]. Therefore, how to effectively improve availability of intracellular NADPH is an important problem to be solved in further improving l -lysine production strains.",
"discussion": "Results and discussion Hetero-expression of ScrK increases l -lysine production in C. glutamicum K-1 from mixed sugar According to our previous results [ 7 ], total sugar concentration in beet molasses, supplied by COFCO Biochemical (Anhui) Co., Ltd., (Anhui, China), was about 50%, and especially sucrose was the most important components (more than 47%). Sucrose was assimilated and phosphorylated by sucrose-PTS (PTS Suc ) in C. glutamicum to yield sucrose-6-phosphate, and then sucrose-6-phosphate was hydrolyzed to glucose-6-phosphate and fructose by sucrose-6-phosphate hydrolase [ 35 ]. However, there is no fructokinase (ScrK) in C. glutamicum [ 36 ], resulting in that the intracellular fructose should be firstly excreted into extracellular and then re-assimilated via fructose-PTS (PTS Fru ). Previous results indicated that hetero-expression of ScrK is beneficial to increase the production of NADPH-dependent products with sucrose or mixed sugar as carbon source [ 7 , 37 – 39 ]. Therefore, hetero-expression of ScrK from C. acetobutylicum at pfkB gene loci was set as a potential strategy for enhancing l -lysine production on mixed sugar in this study. As a result, the resulted strain C. glutsmicum K-2 showed no fructose efflux, whereas the extracellular fructose in original strain C. glutamicum K-1 was increased at the initial stage of fermentation on CgXII IP M-medium (Fig. 1 a). There was no obvious difference in DCW between C. glutamicum K-1 and C. glutamicum K-2, but the maximal specific growth rate (μ max. ) of C. glutamicum K-2 (μ max. = 0.23/h) was higher than that of C. glutamicum K-1 (μ max. = 0.20/h) (Fig. 1 b). The similar results were also obtained in the previous reports [ 7 , 39 ]. However, these results are contrast to the results reported by Zhang et al. [ 38 ], in which DCW of recombinant strain with hetero-expression of ScrK was 15.5% lower than that of control strain. A possible reason is that more carbon source was used to synthesize target product. Consistent with the effect on cell growth, hetero-expression of ScrK obviously increased the l -lysine production on CgXII IP M-medium (Fig. 1 c). In addition, the sugar consumption rate of strain K-2 increased to 7.74 ± 0.45 (mmol C)/(g DCW)/h, which was 76.7% higher than that of strain K-1 (4.38 ± 0.47 (mmol C)/(g DCW)/h) (Table 1 ). The point is that hetero-expression of ScrK is ineffective in cell growth and l -lysine production with glucose as sole carbon source (Table 1 ), possibly because of the absence of fructose in culture [ 7 ]. Fig. 1 Effect of hetero-expression of fructokinase-coding gene in C. glutamicum on glucose (filled diamond, green lines), fructose (filled triangle, red lines) and sucrose (filled square, blue lines) consumption in CgXII IP -medium ( a ), and on cell growth ( b ) and l -lysine production ( c ). a -1 Indicates strain K-1 and a -2 indicates strain K-2. b, c Filled square and blue lines indicate strain K-1, whereas filled diamond and red lines indicate strain K-2. The data represent mean values and standard deviations obtained from two independent cultivations Table 1 DCW, Cell growth rate, l -lysine production, and substrate consumption rate of original strain C. glutamicum K-1 and its derivatives in shake flask experiments Strains Sugar Glucose Fructose Sucrose Molasses DCW μ max. P Lys q s DCW μ max. P Lys q s DCW μ max. P Lys q s DCW μ max. P Lys q s a K-1 10.9 ± 1.2 0.25 24.1 ± 1.6 6.26 ± 0.57 8.6 ± 0.5 0.2 17.2 ± 1.8 5.13 ± 0.40 9.9 ± 0.8 0.24 18.9 ± 1.2 5.18 ± 0.32 10.3 ± 1.2 0.2 15.8 ± 2.1 4.38 ± 0.47 K-2 10.6 ± 0.5 0.25 24.3 ± 2.9 6.25 ± 0.66 8.9 ± 0.5 0.21 18.5 ± 1.7 6.06 ± 0.37 10.1 ± 0.6 0.24 23.9 ± 1.6 8.07 ± 0.51 9.8 ± 0.7 0.23 21.3 ± 2.1 7.74 ± 0.45 K-3 1.8 ± 0.2 0.06 0.5 ± 0.06 1.02 ± 0.13 2.1 ± 0.2 0.07 1.3 ± 0.3 1.21 ± 0.05 9.9 ± 1.0 0.23 24.2 ± 1.3 8.06 ± 0.54 9.8 ± 0.3 0.23 22.0 ± 1.8 7.80 ± 0.56 K-4 5.3 ± 0.7 0.12 10.4 ± 1.5 3.38 ± 0.54 9.7 ± 0.6 0.22 23.5 ± 2.2 8.05 ± 0.31 9.8 ± 1.3 0.23 24.1 ± 2.2 8.10 ± 0.72 10.0 ± 1.5 0.24 22.5 ± 1.9 7.95 ± 0.52 K-5 8.9 ± 1.0 0.18 25.3 ± 2.2 7.89 ± 0.71 10.0 ± 1.3 0.22 23.8 ± 3.0 8.06 ± 0.74 9.7 ± 0.9 0.22 24.7 ± 2.7 8.12 ± 0.53 9.8 ± 1.0 0.22 22.8 ± 2.5 7.99 ± 0.63 K-6 10.1 ± 1.4 0.23 26.0 ± 3.0 8.73 ± 0.48 10.2 ± 0.8 0.23 24.9 ± 2.5 8.21 ± 0.57 10.0 ± 1.0 0.23 25.4 ± 1.8 8.33 ± 0.75 9.8 ± 0.6 0.22 23.5 ± 1.5 8.16 ± 0.48 K-7 11.4 ± 1.2 0.26 26.4 ± 1.8 10.62 ± 0.55 10.8 ± 1.3 0.24 25.4 ± 2.1 9.75 ± 0.65 11.1 ± 1.6 0.25 26.0 ± 2.5 9.92 ± 0.49 10.7 ± 0.9 0.24 24.1 ± 2.5 9.72 ± 0.98 K-8 10.8 ± 0.7 0.24 27.6 ± 2.5 11.87 ± 0.34 10.5 ± 0.5 0.24 26.4 ± 2.8 10.92 ± 0.64 10.4 ± 0.9 0.24 27.0 ± 1.9 11.11 ± 0.62 10.5 ± 1.2 0.24 25.2 ± 1.8 10.87 ± 0.74 The culture media was CgXII IP -medium with 40 g/L of glucose, fructose, beet molasses or 20 g/L of sucrose as sole carbon source DCW: Dry cell weight (g/L); μ max.: The maximal specific growth rate (/h); P Lys : The production of l -lysine (g/L); q s : The substrate consumption rate ( q s ; mmol C/(g DCW)/h) a The data was based on the sucrose consumption rate In addition, fed-batch fermentation was carried out in a 1-L jar fermenter containing 0.25 L fermentation media to test the production performance of strain K-2. Compared with strain K-1, hetero-expression of ScrK had a trend to increase l -lysine production (Fig. 2 a). The l -lysine production of strain K-2 reached to 187.3 ± 11.7 g/L, which was 9.0% higher than that of strain K-1 (171.8 ± 5.6 g/L). However, it cannot be ignored that strain K-2 accumulated large amount of by-products, especially lactate, l -alanine and l -valine (Fig. 2 b). The main reason for this is that carbohydrate uptake by PTS will produce pyruvate (Fig. 3 ), thus leading to overflow metabolism [ 13 ]. Therefore, the next plan is to modify the carbohydrate uptake system in strain K-2 to reduce the amount of by-products. Fig. 2 Comparison of cell growth (filled circle, blue lines), substrate consumption (filled square, green lines) and l -lysine production (filled diamond, red lines) ( a ), and by-products accumulation ( b ) of different C. glutamicum strains in fed-batch fermentation. Signals denote: Strain K-1 (dotted lines or blue bars) and strain K-2 (full lines or red bars). The data represent mean values and standard deviations obtained from three independent cultivations Fig. 3 Schematic representation of l -lysine biosynthesis pathway and variant pathways for uptake of glucose, fructose and sucrose in C. glutamicum . Red lines indicate the introduced exogenous pathway. Pink lines indicate the strengthened endogenous pathway. “×” indicates gene deletion. Scr B: Sucrose-6-phosphate hydrolase, ScrK: Fructokinase, GlK: Glucokinase, PFK: Phosphofructokinase, GAPDH: Glyceraldehyde-3-phosphate dehydrogenase, PGK: Phosphoglycerate kinase, PYK: Pyruvate kinase, PYC: Pyruvate carboxylase, PPC: Phosphoenolpyruvate carboxylase, NDH-2: NADH dehydrogenase, Op. Optimized Deletion of ptsG and ptsF genes cause a decrease in carbohydrates consumption in C. glutamicum The ptsG and ptsF genes encode membrane-bound glucose-specific EIIABC (EII Glc ) and fructose-specific EIIABC (EII Fru ), respectively [ 39 ]. EII Glc and EII Fru facilitate glucose and fructose movement across membrane, respectively (Fig. 3 ). Theoretically, deletion of ptsG and ptsF genes reduces the intake of sugar via PTS and thus compels strain to assimilate sugar via non-PTS. In order to verify this inference, ptsG and ptsF genes were deleted in strain K-2. However, the target strain C. glutamicum K-2 Δ ptsG Δ ptsF ( i.e. , C. glutamicum K-3) showed bad cell growth and l -lysine production as compared with strain K-2 on CgXII IP -medium with glucose or fructose as sole carbon source (Table 1 ). This is because PTS is the major carbohydrates uptake system in C. glutamicum [ 23 ]. In addition, although C. glutamicum possess IGS for assimilating glucose and fructose, the key components (i.e., myo -inositol permease and glucokinases) show the low expression level and the low affinity for glucose and fructose [ 18 , 19 , 21 ]. Interestingly, deletion of ptsG and ptsF genes is ineffective in cell growth and l -lysine production with sucrose or molasses as carbon source (Table 1 ), because glucose and fructose in culture is negligible. However, it should be noted that the production performance of strain K-3 (including DCW, l -lysine production and sugar consumption rate) was dramatically disturbed by deletion of ptsG and ptsF genes in fed-batch fermentation (Additional file 1 : Fig. S1). C. glutamicum exhibits a strong preference for glucose as carbon source [ 8 ]. And fermentation medium and feed solution mainly contain glucose (see “ Methods ” section) [ 10 ]. These may be the reasons for the bad production performance of strain K-3 in fed-batch fermentation. In consideration of the physiology of strain K-3 and the functional role of IGS, the expression levels of myo -inositol permease and glucokinases in strain K-3 should be increased to enhance the participation of IGS in carbohydrates uptake in l -lysine producer. Over-/hetero-expression of IolT1, IolT2, ATP-GlK and ADP-GlK accelerates carbohydrates consumption in PTS Glc - and PTS Fru -deficient strains As mentioned above, C. glutamicum possess IGS for carbohydrate uptake, but myo -inositol permease and glucokinases show the low expression level and the low affinity for glucose and fructose [ 18 , 19 , 21 ]. To overcome these defects and to push carbohydrate into cell via IGS, the dominant strategy is to increase the expression level of myo -inositol permease and glucokinases. myo -inositol permease (e.g., IolT1 or IolT2) and glucokinases (e.g., PpgK or ATP-GlK) have been demonstrated to redirect carbohydrate uptake by IGS in C. glutamicum [ 19 – 22 ]. However, the expression of IolT-coding gene iolT1 is repressed by IolR [ 18 ], and deletion of IolR-coding gene iolR causes certain negative effects on strain [ 17 ]. Therefore, the special promoter of iolT1 ( i.e. , P o6 ) with two point mutations at position -113 (A → G) and -112 (C → G) replaced the nature promoter of iolT1 according to the reports by Brusseler et al. [ 17 ], and the nature promoter of IolT2-coding gene iolT2 was replaced by P tuf promoter, and then the second copy of iolT2 gene with P tuf promoter was introduced in strain K-3 genome, resulting in the final engineered strain C. glutamicum K-4. In response to these modifications, the cell growth, l -lysine production and sugar consumption rate of strain K-4 were increased by ~ 1.9 times, 19.8 times and ~ 2.3 times as compared with strain K-3 on CgXII IP -medium with glucose as sole carbon source, respectively (Table 1 ). The similar positive results were also found in fructose (increased by ~ 3.6 times, 17.1 times and ~ 5.7 times, respectively), whereas the increase of cell growth and sugar consumption rate was not obvious on sucrose and molasses (Table 1 ). These results indicated that IolT1 and IolT2 participate in the uptake of glucose and fructose, which were consistent with previous reports [ 21 , 22 ]. Interestingly, this was also linked to an increase in the activity of PEP carboxylase (PPC) and pyruvate kinase (PYK) (Table 2 ). PPC catalyzes the biosynthesis of oxaloacetate from PEP, and PYK catalyzes the biosynthesis of pyruvate from PEP (Fig. 3 ) [ 1 ]. IGS turned away from PEP to phosphorylate carbohydrate, resulting in that a large amount of intracellular PEP can be used as substrate for PPC and PYK [ 14 ]. However, although the productivity of strain K-4 was improved as compared with strain K-3, it was lower than that of strain K-2 with glucose as sole carbon source (Table 1 ). Lindner et al. indicated that glucokinase must be required in PTS-independent glucose uptake system to phosphorylate glucose [ 24 ]. However, glucokinase from C. glutamicum shows the low affinity for glucose with K m values of 1.0 mmol/L [ 40 ]. In addition, many studies have demonstrated that it is necessary to co-overexpression of glucokinase to get the most out of IGS [ 20 , 22 ], indicating that the expression level of glucokinase-coding gene was low in C. glutamicum . Table 2 In vitro activities of some key enzymes in genetically modified C. glutamicum strains and original strain C. glutamicum K-1 on CgXII IP -medium with glucose as carbon source Strains PTS Glc GlK PFK ScrK PPC PYK NDH-2 F o F 1 -ATPase ATP ADP ATP ADP K-1 1.73 ± 0.27 0.046 ± 0.004 ND 0.13 ± 0.01 ND ND 0.50 ± 0.09 1.47 ± 0.10 7.31 ± 1.52 0.39 ± 0.04 K-2 1.71 ± 0.12 0.045 ± 0.001 – 0.15 ± 0.02 – 0.11 ± 0.02 0.51 ± 0.12 1.47 ± 0.18 8.16 ± 2.03 0.44 ± 0.02 K-3 ≤ 0.1 0.057 ± 0.003 – 0.02 ± 0.00 – 0.03 ± 0.02 0.17 ± 0.05 0.56 ± 0.09 2.35 ± 0.55 0.13 ± 0.02 K-4 – 0.073 ± 0.007 – 0.07 ± 0.02 – 0.07 ± 0.05 0.42 ± 0.05 1.14 ± 0.15 7.88 ± 1.64 0.41 ± 0.03 K-5 – 0.312 ± 0.018 – 0.21 ± 0.04 – 0.09 ± 0.02 0.95 ± 0.14 2.23 ± 0.21 8.43 ± 1.78 0.52 ± 0.07 K-6 – 0.325 ± 0.021 0.21 ± 0.01 0.26 ± 0.03 0.15 ± 0.03 0.11 ± 0.06 0.79 ± 0.07 2.78 ± 0.18 8.45 ± 2.25 0.53 ± 0.01 K-7 – 0.331 ± 0.019 0.21 ± 0.03 0.27 ± 0.05 0.14 ± 0.03 0.11 ± 0.03 0.80 ± 0.13 2.74 ± 0.25 26.27 ± 8.76 0.60 ± 0.08 K-8 – 0.354 ± 0.043 0.18 ± 0.04 0.32 ± 0.04 0.10 ± 0.02 0.13 ± 0.05 0.87 ± 0.09 2.59 ± 0.16 29.73 ± 5.84 0.91 ± 0.05 The unit of specific enzyme activity is U/(mg protein) All data are meaning values of three determinations of three independent experiments with ± SD “–” represents no test, and ND represents no detection Glucokinase catalyzes the phosphorylation of glucose to glucose-6-phosphate using ATP, ADP or inorganic polyphosphates (PolyP) as phosphoryl donor [ 19 , 41 ]. C. glutamicum has two types of glucokinases, i.e., GlK Cg (ATP-dependent enzyme; ATP-GlK, encoded by glk Cg ) and polyphosphate-glucose phosphotransferases (PolyP/ATP-dependent enzyme; PpgK, encoded by ppgK ) [ 19 ], but PpgK plays a chief part in phosphorylation of glucose [ 40 ]. However, PpgK shows a low affinity for glucose with K m values of 1.0 mmol/L [ 40 ]. In order to increase the glucose consumption of strain K-4, the native GlK Cg was replaced by GlK from Bacillus subtilis 168 ( i.e. , GlK Bs ) with a high affinity for glucose ( K m = 0.24 mmol/L) [ 42 ]. In addition, the strong tuf promoter was located at the front of GlK Bs -coding gene glk Bs . The resulting strain K-5 exhibited significantly increased glucose consumption rate, cell growth and l -lysine production as compared with strain K-4 (Table 1 ). The final production of l -lysine by strain K-5 was 25.3 ± 2.2 g/L (0.63 g/g glucose), which is 143.3% and 4.1% higher than that of strain K-4 and K-2, respectively. This advantage was also found in the fed-batch fermentation (Fig. 4 ). In the fed-batch fermentation, the l -lysine production of strain K-5 reached to 209.0 ± 21.6 g/L, which was 11.6% higher than that of strain K-2 (Fig. 4 c). Although most of the test by-products in strain K-5 were reduced, the accumulation of acetate and ethanol was significantly increased as compared with strain K-2 (Fig. 4 d). In addition, the intracellular NADH and ATP levels were decreased in strain K-5 (Table 3 ). ScrK and GlK Bs catalyze the phosphorylation of fructose and glucose to fructose-6-phosphate and glucose-6-phosphate using ATP as phosphoryl donor, respectively [ 39 , 42 ]. Introduction of ScrK and GlK Bs in strain K-5 could increase ATP consumption rate, thus perturbing the intracellular ATP balance. NADH can be oxidized to generate ATP [ 43 ], thereby meeting the demand of cell for ATP. It should be noted that the biosynthesis of acetate and ethanol involved in ATP and NADH regeneration (Fig. 3 ). This might be the reason why acetate and ethanol were significantly increased in strain K-5. Consistent with the previous results [ 44 ], the shortage of ATP has significantly impact on cell growth (Table 1 and Fig. 4 a). To overcome this defect, the availability of ATP should be increased by reducing the consumption of ATP or/and by increasing ATP regeneration. Fig. 4 Comparison of cell growth ( a ), substrate consumption ( b ) and l -lysine production ( c ), and by-products accumulations ( d ) of strains K-2, K-4, K-5 and K-6 in fed-batch fermentation. Signals denote: Strain K-2 (filled triangle, green lines or bars), strain K-4 (filled square, black lines or bars), strain K-5 (filled circle, blue lines or bars) and strain K-6 (filled diamond, red lines or bars). The data represent mean values and standard deviations obtained from three independent cultivations Table 3 Comparison of intracellular nucleotides concentrations in original strain and the genetically defined C. glutamicum strains (μmol/(g DCW)) Strains NADH NAD + NADH/NAD + NADPH NADP + NADPH/NADP + ATP K-1 1.69 ± 0.22 7.68 ± 0.83 0.22 ± 0.03 1.71 ± 0.12 1.47 ± 0.15 1.16 ± 0.10 4.98 ± 0.42 K-2 1.67 ± 0.23 7.59 ± 0.66 0.22 ± 0.01 1.74 ± 0.21 1.42 ± 0.19 1.23 ± 0.13 4.87 ± 0.53 K-3 0.28 ± 0.02 2.02 ± 0.24 0.14 ± 0.02 0.25 ± 0.04 0.30 ± 007 0.82 ± 0.09 0.98 ± 0.13 K-4 1.03 ± 0.15 5.42 ± 0.78 0.19 ± 0.03 0.99 ± 0.17 0.94 ± 0.12 1.05 ± 0.14 2.68 ± 0.19 K-5 1.51 ± 0.18 7.57 ± 0.81 0.20 ± 0.01 1.57 ± 0.11 1.38 ± 0.15 1.14 ± 0.13 2.92 ± 0.35 K-6 1.54 ± 0.22 7.69 ± 0.83 0.20 ± 0.02 1.48 ± 0.18 1.31 ± 0.16 1.13 ± 0.10 3.08 ± 0.32 K-7 0.93 ± 0.23 8.58 ± 0.67 0.11 ± 0.03 1.41 ± 0.12 1.25 ± 0.13 1.13 ± 0.21 3.43 ± 0.45 K-8 0.98 ± 0.22 8.91 ± 0.79 0.11 ± 0.01 1.16 ± 0.14 1.05 ± 0.10 1.10 ± 0.08 4.69 ± 0.47 Exponentially growing cells cultured in CgXII IP with 40 g/L glucose as sole carbon source in shake flasks were used for analysis All data are meaning values of three determinations of three independent experiments with ± SD Lately, ADP-dependent glucokinase ( i.e. , ADP-GlK) was discovered in Archaea and Mammalian, which used ADP as phosphoryl donor for the phosphorylation of glucose [ 45 , 46 ]. In Methanococcus maripaludis , a bifunctional enzyme ( i.e. , ADP-GlK/PFK) with ability to phosphorylate glucose and fructose-6-phosphate has been reported [ 47 ]. In order to reducing the consumption of ATP, we introduced ADP-GlK/PFK from M. maripaludis into strain K-5. The resulting strain K-6 showed good properties in glucose consumption rate, cell growth, l -lysine production and by-products accumulation either in shake-flasks or in fed-batch fermentation as compared with strain K-5 (Table 1 and Fig. 4 ). Moreover, the intracellular NADH and ATP levels in strain K-6 were slightly higher than that of strain K-5 (Table 3 ), and this may be why strain K-6 showed the better cell growth and the lower accumulation of by-products than that of strain K-5 (Fig. 4 a, d). ADP-GlK/PFK catalyzed the phosphorylation of glucose with ADP as phosphoryl donor rather than ATP [ 47 ], thus avoiding over-utilizing ATP in carbohydrate uptake. Interestingly, the activity of PYK was increased by 25% as compared with strain K-5 (Table 2 ). This may be because PYK is activated by AMP [ 48 ], while AMP will be synthesized from ADP in the ADP-GlK/PFK-catalyzed reactions (Fig. 3 ). Compared with strain K-2, however, strain K-6 exhibited the decreased intracellular NADH and ATP levels (Table 3 ), thereby hampering the cell growth and increasing the accumulation of acetate and ethanol (Fig. 4 ). The formation of acetate and ethanol involved in NADH and ATP regeneration (Fig. 3 ), thus making as much ATP for cell as possible. Overexpression of the NDH-2 benefits the further increase in carbohydrates consumption in PTS Glc - and PTS Fru -deficient strains Compared with strain K-1, the l -lysine production was drastically increased in fed-batch fermentation (from 171.8 ± 5.6 g/L to 215.2 ± 10.3 g/L), whereas the cell growth was markedly disturbed in strain K-6 because of the insufficient of ATP (Figs. 2 a, 4 a, c and Table 3 ). These results indicated that ATP may be a limiting factor for further increasing the production efficiency of l -lysine in strain K-6. ATP can be synthesized either by substrate level phosphorylation (SLP) or by electron transport phosphorylation (ETP) [ 44 ]. ETP, also known as oxidative phosphorylation, involved in the transfer of electrons from NADH to oxygen and the phosphorylation of AMP/ADP to synthesize ATP (Fig. 3 ) [ 49 ]. NADH dehydrogenase from C. glutamicum (i.e., NDH-2, encoded by ndh gene) is a quinone-dependent dehydrogenase, which links with the inner layer of the cytoplasmic membrane [ 49 ]. To enhance ATP synthesis by ETP, the promoter of ndh was substitute by the strong tuf promoter in this study. Consistent with the previous results [ 50 ], NADH/NAD + ratio in the resulting strain C. glutamicum K-7 was significantly decreased as compared with strain K-6 (from 0.20 ± 0.02 to 0.11 ± 0.03; Table 3 ). As expected, the glucose consumption rate in shake-flasks increased from 8.73 ± 0.48 (mmol C)/(g DCW)/h in strain K-6 to 10.62 ± 0.55 (mmol C)/(g DCW)/h in strain K-7 (Table 1 ). In addition, the other substrates (i.e., fructose, sucrose and molasses) consumption rate was also increased during overexpression of NDH-2 in strain K-6 (Table 1 ). The research has shown clearly that high level of NADH/NAD + ratio inhibits the activity of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and pyruvate dehydrogenase (PDHC), which are the rate-limiting enzymes in glycolysis [ 51 ]. Therefore, the effect of overexpression of NDH-2 on increasing substrate consumption rate was likely due to the increased activity of GAPDH and PDHC in C. glutamicum . Consistent with the previous results [ 52 ], however, the intracellular ATP levels in strain K-7 were slightly higher than that of strain K-6 (Table 3 ), and the l -lysine production was not obviously increased in strain K-7 (Table 1 and Fig. 5 c). In contrast, strain K-7 accumulated a large amount of by-products, especially pyruvate, acetate, l -alanine and l -valine (Fig. 5 d). This is presumably due to impairment of the intracellular NADH/NAD + balance for l -lysine production and hence resulting in increasing the by-products biosynthetic pathway to regenerate NADH and ATP. In addition, overflow metabolism could be another reason for by-products accumulation [ 13 ]. As can be seen from Table 3 , a large amount of pyruvate accumulated in the cell instead of entering into l -lysine biosynthetic pathway. Fig. 5 Comparison of cell growth ( a ), substrate consumption ( b ) and l -lysine production ( c ), and by-products accumulations ( d ) of strains K-6, K-7 and K-8 in fed-batch fermentation. Signals denote: Strain K-6 (filled square, blue lines or bars), strain K-7 (filled triangle, green lines or bars) and strain K-8 (filled diamond, red lines or bars). The data represent mean values and standard deviations obtained from three independent cultivations Deletion of the sigH gene decreases the accumulation of by-products in C. glutamicum strain Base on the chemiosmotic theory of energy coupling, the synthesis of ATP from ETP involved in energy release by transfer of electrons from NADH to oxygen and energy transfer by phosphorylation of AMP/ADP [ 53 ]. F O F 1 -ATPase catalyzed the formation of ATP (i.e., the phosphorylation of AMP/ADP) by proton motive force (pmf) [ 44 ]. Although F O F 1 -ATPase was not essential for growth of C. glutamicum on glucose, inactivation of F O F 1 -ATPase decreased the specific glucose uptake rate and mRNA levels of genes involved in amino acid biosynthesis [ 44 ]. Therefore, it is a useful strategy for increasing l -lysine production by genetically modifying F O F 1 -ATPase. We tried to increase the expression level of F O F 1 -ATPase by overexpression of F O F 1 -ATPase-coding gene, but it was not successful as there were eight subunits of F O F 1 -ATPase (encoded by atpBEFHAGDC ) [ 44 ] (data not shown). As described by Barriuso-Iglesias et al. [ 54 ], F O F 1 -ATPase-coding gene was expressed at pH 9.0 because it is regulated by SigmaH factor (i.e., SigH). However, the optical pH of C. glutamicum in l -lysine fermentation process is neutral pH (i.e., pH ≈ 7.0). In order to increase the expression level of F O F 1 -ATPase-coding gene at neutral pH, we deleted SigH-coding gene sigH to construct a SigH-deficient strain C. glutamicum K-8. Consistent with previous results [ 54 ], the expression level of atpB was increased (increased by about 4.3 times), whereas the expression level of sigH was disappeared in strain K-8 as compared with strain K-7 at pH = 7 (Additional file 1 : Fig. S2). Conversely, the expression level of atpB in strain K-8 was lower than that of strain K-7 at pH = 9 (Additional file 1 : Fig. S2). These results showed that deletion of SigH increased the activity of F O F 1 -ATPase at neutral pH (Table 2 ). We also observed that the increased l -lysine production rate was induced by elevating ATP supply from ETP (Table 1 and Table 3 ). In other words, the more ATP is available in the cytosol, the more carbon flux will be entered into l -lysine biosynthetic pathway (Fig. 5 ). Previous researches have pointed out that interdiction of ATP regeneration from ETP decreased the specific glucose uptake rate in C. glutamicum [ 44 , 55 ]. Our study again confirmed these viewpoints that strain K-8 showed the best substrate consumption rate among the test strains (Table 1 ). The titer of l -lysine reached 221.3 ± 17.6 g/L at a productivity of 5.53 g/L/h and a carbon yield of 0.71 g/g glucose at 40 h (Fig. 5 c). Those are the highest value for l -lysine production in fed-batch fermentation (Table 4 ), demonstrating that this engineered C. glutamicum strain is a competitive platform strain for l -lysine production. Table 4 Overview on the production of l -lysine by metabolic engineered C. glutamicum Strains Culturing methods Final titers (g/L) Productivity (g/L/h) Yield (g/g glucose) References K-8 Batch 27.6 0.58 0.69 This work Fed-batch 221.3 5.53 0.71 This work AM03 Batch 27.7 0.46 0.35 [ 60 ] ZL-92 Fed-batch 201.6 5.04 0.65 [ 61 ] JL‑69P tac‑M gdh Fed-batch 181.5 3.78 0.65 [ 3 ] JOV2-C7 a Batch 13.2 0.33 0.31 [ 62 ] LYS-12 Fed-batch 120 4.0 0.55 [ 10 ] MH20-22B∆leuA a,b Batch 21.6 0.3 0.22 [ 63 ] a Achieved in shake‑flask fermentation b Estimated from reference"
} | 7,491 |
35515611 | PMC9053625 | pmc | 583 | {
"abstract": "Triboelectric nanogenerators (TENGs) with excellent flexibility and high outputs are promising for powering wearable/wireless electronics with electricity converted from ubiquitous mechanical energies in the working environment. In this work, the effects of the dielectric properties and thickness of the electrification film on the performance of the TENG are discussed. BaTiO 3 nanoparticles are added into poly(vinylidene fluoride) (PVDF) to improve the dielectric constant of the composite film. The TENG using a BaTiO 3 /PVDF nanocomposite film with 11.25 vol% BaTiO 3 as the tribo-negative electrification layer is demonstrated to be the optimized one, and generates an open-circuit voltage of 131 V and transferred short-circuit charge density of 89 μC m −2 , 6.5 fold higher than those of a TENG using bare a PVDF layer. Furthermore, by reducing the thickness of the BaTiO 3 /PVDF film to 5 μm, the voltage and charge density increase to 161 V and 112 μC m −2 , respectively, and an instantaneous peak power density of 225.6 mW m −2 is obtained.",
"conclusion": "4. Conclusion In summary, we report an effective approach to improve the performances of a TENG through increasing the dielectric constant and decreasing the thickness of the electrification layer. BaTiO 3 nanoparticles were added into the PVDF matrix. The dielectric constant of the BaTiO 3 /PVDF composite increases with the BaTiO 3 content. As the BaTiO 3 concentration increased to 11.25 vol%, the dielectric constant values raised from 7.96 to 25 at 1000 Hz. Though the output performances of the TENG are positively proportional to the dielectric constant of the composite, the generated static charges could decrease at higher filler content. Therefore, the optimized BaTiO 3 content is determined to be 11.25 vol%, which shows a five-fold enhancement in transferred charge density compared to bare PVDF-based TENG. By further decreasing the thickness of composite films to 5 μm, the Q sc increases to 114 μC m −2 , corresponding to 200% improvement compared to the TENG with 45 μm thick composite electrification layer. The enhanced output performance of the 5 μm composite film-based TENG shows good stability and durability.",
"introduction": "1. Introduction The rapid advancement of miniaturized electronic devices has led to their extensive applications in wireless, portable, wearable and distributed smart devices. Nevertheless, the green energy resource to provide sustainable power for these devices has always been a major concern. Harvesting ubiquitous mechanical energies from the working environments of electronic devices has been proven to be one of the most promising strategies. Among various energy harvesting technologies, triboelectric nanogenerators (TENGs), 1 based on the coupling of triboelectrification and electrostatic induction, 2 have emerged as a promising technique to convert mechanical energy into electricity due to their high output voltage, simple fabrication, 3 flexibility and low cost. 4–7 As an energy harvester, further enhancing the output performance is critical to speed up their practical applications. There have been many reports on improving the output power of TENGs. Several strategies have been developed, such as the device structure optimization, 8–10 excitation circuit, 11,12 vacuum environment, 13 and corona charge bombardment. 14–16 From a materials perspective, optimizing the tribo-electrification film is essential. By increasing the effective contact area, the tribo-electrified static surface charge density can be improved. Many methods have been utilized to enlarge the effective contact area, such as plasma etching, 17 electrospinning technique, 18–20 as well as the use of silicon mold for pattern transfer. 21,22 Alternatively, surface modification is adopted to tune the tribo-negativity of a dielectric film, so as to improve the generated static charge density. Improving the dielectric properties of triboelectric materials is also an effective method. Zhai et al. 23 have improved the performances of TENG by adding titania monolayer to poly(vinylidene fluoride) (PVDF) to improve dielectric constant. Lee et al. 24 synthesized PtBA-grafted PVDF copolymers with high dielectric constant, which improved TENG's performance several times. Inorganic fillers with high dielectric constant or conductive fillers were also reported to be embedded in the polymer matrix, such as carbon nanotubes, 25 graphene, 19 ceramic materials, 26–29 and metal–organic framework materials. 30 Other than these properties, the thickness of the electrification layer can also affect the output performances, but its effect is not well studied experimentally. In this work, dielectric properties and thickness of the tribo-electrification film were optimized to improve the output performances of TENG. BaTiO 3 nanoparticles were added into a PVDF polymer matrix. BaTiO 3 /PVDF nanocomposite film showed enhanced output performances when it was employed as a negative triboelectric layer in a two-electrode TENG. The volume fraction of the BaTiO 3 was optimized, and the BaTiO 3 /PVDF composite film with 11.25 vol% BaTiO 3 filler (40 μm thick) generated open-circuit voltage of 131 V and transferred short-circuit charge density of 89 μC m −2 , 6.5 folds higher than TENG using bare PVDF. Furthermore, by reducing the thickness of BaTiO 3 /PVDF film to 5 μm, the voltage and charge density increased further to 161 V and 112 μC m −2 , respectively. The electrification layer of 5 μm BaTiO 3 /PVDF composite film generated a peak power density of 225.6 mW m −2 at a load resistance of 100 MΩ. The device was used for charging commercial capacitors and glowing LEDs. Furthermore, the enhanced output performance of the 5 μm composite film-based TENG shows good stability and durability under continuous operation of 4000 cycles.",
"discussion": "3. Results and discussion The schematic diagram of the fabrication process for the BaTiO 3 /PVDF nanocomposite films is shown in Fig. 1a . BaTiO 3 nanoparticles were mixed with PVDF in the DMF solvent, followed by preparing the composite film with a doctor-blade casting method. The XRD patterns of BaTiO 3 nanoparticles were shown in Fig. 1b . The XRD peaks suggested that BaTiO 3 has a tetragonal phase, 31 indicating appreciable ferroelectricity. 32 It can be seen from Fig. 1c that the diameter of BaTiO 3 nanoparticles is uniformly about 100 nanometers. After removing the nanocomposite films from the substrate, it can be seen that it is white and quite flexibility ( Fig. 1d ). The thickness of BaTiO 3 /PVDF nanocomposite films was controlled to be about 40 μm, as seen from Fig. 1e . The volume fraction of the BaTiO 3 was varied (3.18–16.47 vol%) and a bare PVDF film was also prepared for comparison. The XRD patterns of BaTiO 3 /PVDF nanocomposite films with BaTiO 3 content of 0 vol%–16.47 vol% are shown in Fig. S1a. † With the increase of BaTiO 3 fillers, the peaks of PVDF become weaker, but the intensity of BaTiO 3 peaks increases. The FTIR spectra of bare PVDF and BaTiO 3 /PVDF nanocomposite with different concentrations of BaTiO 3 have been carried out (Fig. S1b † ). The data implied that there is no significant phase change in crystalline phases when changing the BaTiO 3 volume fraction. The results suggest that the adding of BaTiO 3 nanoparticles had negligible effect on the crystalline structure of polymer matrix. The thickness of PVDF and all BaTiO 3 /PVDF films were measured by the cross-sectional scanning electron microscopy (SEM) images, as shown in Fig. 2a and S2. † It is clear that the thickness of all films was approximately about 40 μm. From the surface morphologies of the bare PVDF and nanocomposite films in Fig. 2b and S3, † it can be seen that BaTiO 3 nanoparticles were dispersed uniformly in the matrix, indicating good compatibility between BaTiO 3 nanoparticles and the PVDF. The energy dispersive spectroscopy (EDS) mapping (Fig. S4 † ) also showed that all the elements are evenly distributed throughout the nanocomposite films. We used the atomic force microscope (AFM) to evaluate the surface topography of the BaTiO 3 /PVDF films, as shown in Fig. 2c . It can be observed that the surface roughness increases with increasing in BaTiO 3 content. Fig. 2 SEM and AFM images of the BaTiO 3 /PVDF films. (a) Cross-sectional and (b) surface morphology SEM images for composite films with different BaTiO 3 volume fractions. (c) 3-dimensional AFM images of the composite films. The dielectric properties for the PVDF and BaTiO 3 /PVDF nanocomposite films were further measured at room temperature, as shown in Fig. 3a . The PVDF displayed a dielectric constant of 7.96 at 1000 Hz, which is basically consistent with previous reports. 33,34 It is clear that the dielectric constant increased with the increase of the BaTiO 3 volume fraction, owing to the higher dielectric constant of BaTiO 3 compared to bare PVDF. The dielectric constant of the 11.25 vol% and 16.47 vol% BaTiO 3 /PVDF films is as high as 25 and 29, respectively. Fig. 3 (a) Frequency dependence of dielectric constant values for composite films with different BaTiO 3 volume fractions from 0 to 16.47 vol%. (b) Comparison of experimental and theoretical dielectric constant at 1000 Hz of BaTiO 3 /PVDF films. To better understand the dielectric behavior of the BaTiO 3 /PVDF composite films, several theoretical models have been employed and comparisons have been done between the experimental and theoretical values ( Fig. 3b ). The following equations are used of calculate the values of dielectric constant of the nanocomposites: (i) Series mixing equation: 35 1 (ii) Maxwell–Wagner equation: 36 2 (iii) Bruggeman equation: 37 3 (iv) Lichtnecker logarithmic equation: 35,38 4 ln ε = v B ln ε B + v p ln ε p where ε , ε B and ε p are the dielectric constant of the BaTiO 3 /PVDF composite films, BaTiO 3 and PVDF, respectively; v B , v p are the volume fraction of BaTiO 3 and PVDF, respectively. It can be observed that Lichtnecker logarithmic equation has smallest discrepancy with the experimental among these models. With the increase of the amount of BaTiO 3 , the filling phase is impossible to achieve an ideally uniform dispersion and a certain degree of aggregate is inevitable, leading to deviation of the experimental values from the theoretical values. Aggregate white dots of BaTiO 3 can be seen from Fig. 2b and S3 † at high volume fraction. From Fig. S5(a) and (b), † it is noted that the dielectric loss of all samples was less than 0.5, which means that the dielectric constant values are quite reliable. A two-electrode TENG was fabricated for evaluating the electrification properties of the composite films, as shown in Fig. 4a . The composite film was employed as a triboelectric negative layer and a nylon film was used as the tribo-positive layer. The contact effective area is fixed to be 2 × 2 cm 2 . An as-fabricated TENG device is shown in Fig. S6. † The working mechanism of the contact-separation mode TENG is schematically illustrated in Fig. 4b . 39–41 When the nylon film contacts with the BaTiO 3 /PVDF film ( Fig. 4b-i ), tribo-electrification occurs with static charges generated at two tribo-materials surfaces with opposite signs because of their differences in work function. According to the triboelectric series table, 42 PVDF will be negatively charged and the nylon will be positively charged. When the two layers begin to separate from each other ( Fig. 4b-ii ), the electron will flow from the top Cu electrode to the bottom electrode through the external circuit, so as to achieve local charge equilibrium, i.e. the process of static induction. If at open-circuit state, the unscreened charges will induce a potential difference between the two Cu electrodes. Once the two triboelectric layers are far away enough (the maximum transferred charges can be achieved at a separation distance about ∼ 1 mm 43,44 ), charge equilibrium will be achieved and the charge transfer through the external circuit will be finished ( Fig. 4b-iii ). As the top layer begins to be compressed again ( Fig. 4b-iv ), electrons are driven to flow from the bottom electrode to the top electrode, yielding an electrical output signal with an opposite sign. Finally, when the device is fully compressed ( Fig. 4b-i ), the balance in electric charge is retained, completing one cycle of electricity generation. Repeating the compress-release motions, mechanical motions are converted into pulsed AC currents. The built-up potential difference between the two electrodes at open-circuit condition can be confirmed by the finite-element simulation using the COMSOL Multiphysics software, as shown in Fig. S7. † We performed the surface potential distribution at the contact state to the maximum release state. At contact state (5 μm gap distance between the Nylon and PVDF film), the top and bottom surface has no potential difference. When the distance is 1 mm, a large potential difference between the two surfaces can be observed and a surface charge density of 15 μC m −2 is obtained. Fig. 4 Triboelectric performances of the TENG. (a) Schematic structural components of the TENG device. (b) Working mechanism of the contact-separation mode TENG. The harvested (c) output voltage, (d) current and (e) transferred charge density signals of TENG based the BaTiO 3 /PVDF films. As shown in Fig. 4b-iii , ε 1 and ε 2 are the dielectric constant of the BaTiO 3 /PVDF film and the nylon, respectively; d 1 and d 2 are the thicknesses of the BaTiO 3 /PVDF film and the nylon, respectively. The gap z can be varied under mechanical motions. After several cycles of contacting, the surface static charge areal density σ c reaches saturation. When the z is increased, a potential difference ( V ) induced by the triboelectric charges drives electrons to flow through the external load, leading to the accumulation of free electrons in the electrode 45 ( i.e. transferred charge density, σ ( z )). The electric fields in the two tribo-material layers and in the gap are given by: 46 5 6 7 where ε 0 is vacuum permittivity (8.854 × 10 −12 F m −1 ). The potential drop between the two electrodes is then described as 8 V = σ ( z )[ d 1 / ε 1 + d 2 / ε 2 ] + z [ σ ( z ) − σ c ]/ ε 0 Under open-circuit condition, the transferred charge density σ ( z ) = 0, the open-circuit voltage can be described as: 9 V oc = − zσ c / ε 0 Under short-circuit condition, the voltage between the two electrodes is zero. Then, the σ ( z ) can be derived as 10 The σ ( z ) saturates at a maximum separation distance of about 1 mm. 43 According to eqn (10) , σ ( z ) is proportional to the dielectric constant of the tribo-material layers and inversely proportional to the thickness of tribo-material layers. So, to get larger σ ( z ), we should enlarge dielectric constant but decrease the thickness of the electrification layer. The open-circuit voltage ( V oc ), short-circuit current ( I sc ) and transferred charge density ( σ ( z )) of the TENGs were recorded. To evaluate the effects of BaTiO 3 fillers, the thickness of BaTiO 3 /PVDF film and nylon film, contact force (12 N), and speed (1 m s −1 ) are all fixed to be the same. The electrical output V oc , I sc and σ ( z ) of TENGs based on the bare PVDF film and BaTiO 3 /PVDF nanocomposites films were shown in Fig. 4c–e , respectively. The V oc of TENG was increased from 20 V to nearly 132 V as the filler concentration of BaTiO 3 was varied from 0 to 11.25 vol%. At the same time, the I sc and σ ( z ) of the TENG were enhanced from ≈0.9 to 4.1 μA and ≈15 to 88 μC m −2 , respectively. When the filler concentration of BaTiO 3 was further increased, the V oc , I sc and σ ( z ) values of TENG showed an obvious decrease. The V oc values of TENG dropped to nearly 76 V, by increasing the filler concentration of BaTiO 3 to 16.47 vol%. The I sc and σ ( z ) values were decreased to ≈2.3 μA and ≈55.2 μC m −2 , respectively. Results reveal that output performance tends to increase with the BaTiO 3 volume fraction below 11.25 vol%; further increasing the BaTiO 3 concentration, the output performances began to decline. As shown in Fig. 2a and S2, † the thickness of the PVDF and all BaTiO 3 /PVDF nanocomposite films are about the same, which implies that the effect of the thickness on the output performances is negligible. The improvement of the output performances can be largely attributed to the increase in the dielectric constant of the composite films, in accordance with eqn (10) . However, it is still needed to explain the decrease of the output performances when the BaTiO 3 volume fraction is larger than 11.25 vol%. According to the previous works, 47,48 increasing the film's roughness is an important way to enhance the output signals of TENG, due to the more generated static charge densities ( σ c ) by increasing the effective contact areas for electrification. However, there are trade-off effects of the BaTiO 3 fillers on the static charge densities. As shown by the surface SEM images in Fig. 2b and S3, † the areal fraction of exposed BaTiO 3 nanoparticles on the surface will also increase with their volume fraction. As PVDF (high tribo-negativity in tribo-series table) is a much better tribo-electrification material than inorganic BaTiO 3 , the decrease of the PVDF matrix surface area can also lead to the decrease in generated static charge σ c . Therefore, there is an optimum volume fraction of BaTiO 3 , above which the static charge σ c and the output performances of the TENG will decrease. This is accordant with the variation trend of the measured performances, and the optimized BaTiO 3 volume fraction is 11.25 vol%. Since the BaTiO 3 volume fraction is optimized to be 11.25 vol%, the output performances can be further enhanced by decreasing the thickness of the composite film. Therefore, we fixed the concentration of BaTiO 3 to be 11.25 vol%, and prepared composite films of different thicknesses. Four films of different thickness were prepared (5 μm, 26 μm, 31 μm, 45 μm), as shown in Fig. 5a , and the thickness of Nylon is fixed at 10 μm (Fig. S8 † ). It was clearly observed that the output transferred charge densities dramatically increase with the decrease of the thickness ( Fig. 5d ). When a 5 μm BaTiO 3 /PVDF nanocomposite film was used as the negative electrification layer, the output performance reaches the maximum. The V oc , I sc and σ (z) are 160 V, 6.2 μA and 114 μC m −2 ( Fig. 5b–d ), respectively, correspondent to 180%, 270%, and 200% improvement comparing with that using 45 μm thick electrification layer. Besides, the instantaneous power density and current density of 5 μm thick-based TENG was obtained ( Fig. 5e ). As the resistance increases, the current density decreases and the output power density increases first and then decreases. The maximum power density reaches 225.6 mW m −2 at a load resistance of 100 MΩ. Fig. 5 (a) Cross-sectional SEM images of different thicknesses with 11.25 vol% BaTiO 3 /PVDF composite films. (b) The open-circuit voltage, (c) short-circuit current and (d) transferred charge density of different thickness 11.25 vol% BaTiO 3 /PVDF-based TENGs. (e) The output current density and power density of TENG based on the 5 μm BaTiO 3 /PVDF nanocomposite films with 11.25 vol% of BaTiO 3 . (f) Lightening LEDs and charging commercial capacitors by the TENG. (g) The stability test of the TENG. To show that capability of TENG as a power source, commercial capacitors have been charged by the TENG. Rectifying circuit is used to convert the generated AC current into DC current to charge commercial capacitors. The 2 μF and 10 μF capacitors can be charged to about 4 V and 2 V in 6 min, respectively. 16 LEDs connected in series can also be lightened by the TENG ( Fig. 5f ). Lastly, the 5 μm composite film-based TENG shows stable output performances for over 4000 cycles of repeated testes, suggesting the good stability and durability ( Fig. 5g ). It is noted that rigid the acrylic is used as the substrate for the convenience of the measurement. However, the device can certainly be more flexible if flexible substrate is used for substrate."
} | 5,080 |
26555246 | PMC5029208 | pmc | 584 | {
"abstract": "Coral and algal holobionts are assemblages of macroorganisms and microorganisms, including viruses, Bacteria, Archaea, protists and fungi. Despite a decade of research, it remains unclear whether these associations are spatial–temporally stable or species-specific. We hypothesized that conflicting interpretations of the data arise from high noise associated with sporadic microbial symbionts overwhelming signatures of stable holobiont members. To test this hypothesis, the bacterial communities associated with three coral species ( Acropora rosaria , Acropora hyacinthus and Porites lutea ) and two algal guilds (crustose coralline algae and turf algae) from 131 samples were analyzed using a novel statistical approach termed the Abundance-Ubiquity (AU) test. The AU test determines whether a given bacterial species would be present given additional sampling effort (that is, stable) versus those species that are sporadically associated with a sample. Using the AU test, we show that coral and algal holobionts have a high-diversity group of stable symbionts. Stable symbionts are not exclusive to one species of coral or algae. No single bacterial species was ubiquitously associated with one host, showing that there is not strict heredity of the microbiome. In addition to the stable symbionts, there was a low-diversity community of sporadic symbionts whose abundance varied widely across individual holobionts of the same species. Identification of these two symbiont communities supports the holobiont model and calls into question the hologenome theory of evolution.",
"conclusion": "Conclusions The results here support the holobiont model where the microbial and macrobial members have individual evolutionary trajectories. Although there are stable associations between the bacteria and macrobes, these associations are not exclusive and therefore not heritable as a unit. Our emerging view is that coral and algal holobionts assemble and then the environment selects for membership. As the environment changes, there is reassembling and the acquisition of new members. This is similar to the adaptive bleaching model proposed by Buddemeier and Fautin (1993 ). As with the Symbiodinium , our analysis suggests that the stable bacterial symbionts are found on multiple hosts. The fact that the individual elements of the holobiont have remained separate for several hundreds of millions of years also suggests that the unit of selection is at the individual biont level. Until compelling contradictory evidence is presented, the gene-centric proposal of the hologenome is subject to all of the criticisms of the selfish gene hypothesis, as well as the challenges presented by this study (that is, heritability). At this stage, the evidence points toward holobionts. However, to really understand the complex dynamics in these systems, there is a need for strong mathematical models, extensive sampling of naturally occurring holobionts and statistics, such as the AU test.",
"introduction": "Introduction The coral holobiont is the assemblage of the coral animal and its symbionts (viruses, Symbiodinium , protozoa, endolithic algae, fungi, Archaea and Bacteria, microfauna) and functions as an ecological unit ( Rohwer et al. , 2002 ; Knowlton and Rohwer, 2003 ). Viruses, mostly phage, are the most abundant and diverse members of the holobiont, followed by Bacteria and Archaea ( Kellogg, 2004 ; Wegley et al. , 2007 ; Marhaver et al. , 2008 ; Vega Thurber et al. , 2009 ). Phage are hypothesized to provide the holobiont with a specific immune system ( Barr et al. , 2013a , b ), in addition to a large reservoir of functional genes ( Marhaver et al. , 2008 ; Dinsdale et al. , 2008; Vega Thurber et al. , 2009 ; Kelly et al. , 2014 ). The Bacteria and Archaea perform a wide array of metabolic functions that influence the physiology of the holobiont, including cycling of carbon, nitrogen, iron and sulfur ( Lesser et al. , 2004 ; Wegley et al. , 2007 ; Beman, 2007 ; Siboni et al. , 2008 ; Raina et al. , 2009 ; Fiore et al. , 2010 ; Kimes et al. , 2010 ). The microbial symbionts may ward off potentially pathogenic bacteria through niche exclusion and production of antibiotics ( Ritchie, 2006 ; Nissimov et al. , 2009 ; Rypien et al. , 2010 ). Similar to corals, other reef organisms such as sponges ( Taylor et al. , 2004 ; Schmitt et al. , 2012 ) and various algal guilds ( Lachnit et al. , 2009 ; Barott et al. , 2011 ; Lachnit et al. , 2011 ; Nelson et al. , 2013 ) harbor diverse microbial communities that have been shown or hypothesized to provide ecological services to the holobiont ( Lachnit et al. , 2009 ; see review of algal–bacterial associations in Egan et al. , 2013 ). The health of the holobiont is linked to the composition of its viral and microbial constituents, which can become disrupted by natural and anthropogenic stressors, usually leading to blooms of pathogen-related viruses and bacteria ( Vega Thurber et al. , 2008 ; Vega Thurber et al. , 2009 ), as well as an increased incidence of diseases ( Bourne et al. , 2008 ). However, there are documented cases where stressors such as nitrogen ( Siboni et al. , 2012 ) and temperature ( Berkelmans and Van Oppen, 2006 ) may increase microbial symbionts that confer environmentally important physiologies, as well as cases where the bacterial communities return to their original composition after a stressor is removed ( Bourne et al. , 2008 ; Sweet et al. , 2011 ). Even though coral reef holobiont research has been very active for over a decade, the stability of these symbioses over time and space remains contentious. Specifically, do viral and microbial symbionts stably associate with their coral or algal hosts? A survey of the literature is summarized in Table 1 and shows that almost half of the reports find species-specific associations, while the remainder demonstrate environment-driven associations between bacteria and corals. This same question arises for algae, as algal-associated bacterial communities show both species specificity ( Lachnit et al. , 2009 , 2011 ) and environmentally driven dynamics ( Lachnit et al. , 2011 ; Case et al. , 2011 ). To parameterize dynamical models of the holobiont, the field needs robust statistical tests of membership. Here we characterized >350 000 16S rDNA reads from 131 bacterial communities associated with five hosts: three coral species ( Acropora hyacinthus , Acropora rosaria and Porites lutea ) and two functional groups of benthic algae (Turf and crustose coralline algae (CCA)). A new statistical approach, the Abundance-Ubiquity (AU) test, was developed to objectively identify members of the bacterial community that were stable or sporadic members of these benthic holobionts. Our results support the hypothesis that coral and algal holobionts harbor both a stable microbiome and a locally variable suite of Bacteria.",
"discussion": "Discussion All macroorganisms associate with microbes, forming ecological assemblages called holobionts ( Gordon et al. , 2013 ). Reef corals and algae are some of the best-studied holobionts, but it remains unclear how stable these associations are over time and space ( Table 1 ). Here we developed a statistical method called the AU test to identify different associative patterns of bacterial members of the holobiont. The AU test showed that coral and algal holobionts harbor two different groups of bacterial symbionts: (i) sporadic symbionts, whose abundance varies significantly between individual holobionts of the same species or guild, and (ii) stable symbionts, which are usually found associated with the holobionts of a particular grouping. There were no obligate, exclusive symbionts (that is, a single bacteria species always associated with a single host and not found on other hosts). Sporadic symbionts Sporadic symbionts were variably associated with an individual host, but as a group sporadic symbionts were abundant within the holobiont community. The sporadic symbionts are likely the product of either stochastic events or in responses to environmental pressures. In support of the latter hypothesis, other studies have shown that biogeochemical regimes ( Siboni et al. , 2012 ; Kelly et al. , 2014 ) or position on the reef (for example, internal reef zones versus reef periphery; Schöttner et al. , 2012 ) influence reef- and organism-associated microbes. Kushmaro and colleagues ( Siboni et al. , 2012 ) showed that excess nitrogen led to an enrichment of denitrifying Bacteria and Archaea. Changes in the symbiont members can lead to holobionts better adapted for particular conditions, such as the switching of Symbiodinium clades with depth ( Rowan and Knowlton 1995 ; Toller et al. , 2001 ). Local adaption of the whole reef microbome to local conditions (for example, nutrient regimes) has also been observed in the Line Islands ( Kelly et al. , 2014 ). If horizontal acquisition of microbes is the main mechanism for holobiont responses to changing environmental conditions, then adaption is best described as an ecological dynamic. This means that long-term, evolution trajectories are most probably not selected at the level of the holobiont but rather at the species level (that is, swapping of individual members in and out of the holobiont). Reassessing the ubiquity of a coral symbiont The Porites astreoides symbiont PA1 ( Endozoicomonas sp.) is ubiquitously associated with P. astreoides from Panama to Bermuda ( Rohwer et al. , 2002 ). Here we found a Gammaproteobacteria with 99% sequence similarity to PA1, which was found only on P. lutea samples collected from Kingman (10% of all P. lutea samples). Kvennefors et al. (2010) also reported that bacteria similar to PA1 were common on A. hyacinthus and Stylophora pistillata from the Great Barrier Reef, and we observed several additional OTUs that were closely related to PA1 on all the five hosts studied here. Additionally, Bayer et al. (2013) demonstrated that species from the Endozoicomonas genera closely associated with tissue of the coral S. pistillata . Therefore, PA1 and its close relatives appear to associate generally with coral reef macrobes and the abundance of this group is subject to local unknown conditions. Stable symbionts Stable bacterial symbionts were relatively rare and highly diverse. These stable symbionts are likely associated with host-constructed niches that are less sensitive to the surrounding environment. For example, Actinobacter and Ralstonia spp. are closely associated with coral gastrodermal cells and endosymbiotic dinoflagellates, suggesting these bacteria occupy specific niches within the macrobial host ( Ainsworth et al. , 2015 ). Despite this niche specificity, stable symbionts were not holobiont specific. That is, any holobiont species has a group of stable symbionts; however, these symbionts may also be found on other hosts. This is similar to what has been found for the human and other mammalian microbiomes ( Muegge et al. , 2011 ; Human Microbiome Project Consortium, 2012 ). Additionally, the diversity of these stable symbionts might have a role in the overall community stability ( Yachi and Loreau 1999 ). Holobionts or hologenomes? The Holobiont model posits ecological assemblages of symbionts that may change over the lifetime of the macroorganism. The term holobiont was originally coined by Margulis and Fester (1991) and later refined by Rohwer et al. (2002) and Knowlton and Rohwer (2003) to explain the emerging observations about ubiquitous, dynamical symbioses between macrobes and microbes (for example, Symbiodinium and bacteria in corals). The main premises of the holobiont model are: (1) all macroorgansisms form symbioses with microbes, (2) the bionts (viruses, microbes, protists, macrobe and so on) assemble, disassemble and reassemble in different combinations based on ecological dynamics (for example, environmental conditions, migration, predation and so on), and (3) each biont member of the holobiont belongs to a metapopulation with their own evolutionary trajectory. The bionts may be evolutionarily selected based on interactions with each other (that is, coevolution), but they are not obligate symbionts (this work) and they migrate between holobionts and/or other reservoirs (for example, seawater, sediment and so on). The Hologenome Theory of Evolution was based on the holobiont model and incorporates the exact same premises with one important exception: heredity. To quote from Zilber-Rosenberg and Rosenberg (2008 ), ‘(1) All animals and plants establish symbiotic relationships with microorganisms. (2) Symbiotic microorganisms are transmitted between generations. (3) The association between host and symbionts affects the fitness of the holobiont within its environment. (4) Variation in the hologenome can be brought about by changes in either the host or the microbiota genomes; under environmental stress, the symbiotic microbial community can change rapidly.' Therefore, the only strong difference between these two models is the transmission between generations (that is, heredity; Figure 3 compares and contrasts these two models). Here we test the hypothesis that symbionts are transmitted by looking across populations of coral and algal holobionts. If bacteria are transmitted between generations, then these bacteria should be ubiquitous and have strong phylosymbiotic signals ( Brucker and Bordenstein, 2013 ). The analysis here shows that this is not true; the stable bacterial symbionts are neither ubiquitous nor specific to one host. Ainsworth et al. (2015) came to similar conclusions sans an AU-like test. Similar to Symbiodinium , the vast majority of bacterial symbionts are moving in and out of the holobiont. This is also similar to what has been observed in the human microbiome ( Human Microbiome Project Consortium, 2012 ), and the AU test described here can be used to assess the generalization of this phenomenon across all holobionts. Based on the observations presented here and in Table 1 , we propose that the holobiont model best describes what has been observed for macrobe–microbe associations. One place where additional clarity is needed is when two bionts become linked in an obligate manner (for example, Ochman and Moran 2001 ; Moran et al. , 2008 ; and Brucker and Bordenstein 2013 ; Bordeinstein and Theis, 2015) . Endosymbiotic theory and the subsequent models for understanding this type of symbiosis are established ( Margulis and Fester, 1991 ). To differentiate between obligate symbioses and the more flexible, holobiont-type found in corals, algae and human, we propose that there is a bifurcation called aboluta iunctio for ‘absolute linkage' where two symbionts become one, indivisible biont (based on some to be determined by probability). The attraction of incorporating aboluta iunctio into models of symbioses is that it would be both mathematically tractable and translatable into laboratory and field studies. What to do with the hologenome? Despite the eloquent defense of the term by Rosenberg et al. , 2007 , the term hologenome has become divisive and the older term metagenome should be used to describe the collective genetic material of a particular sample (holobiont or otherwise). As for the Hologenome Theory of Evolution, without clear evolutionary mechanisms, including heredity, this is a misnomer. It might be useful to develop the theory as a null model for multilevel selection, but this will require more quantitative precision than has been forthcoming. Statements that hologenomes are integrated such that they are free from conflict ( Rosenberg et al. , 2007 ; Rosenberg and Zilber-Rosenberg, 2013 ) has eroded any serious consideration by evolutionary theorists. This assertion is clearly not true; symbiont members of the assemblages frequently kill or weaken the holobiont in corals ( Kline et al. , 2006 ; Vega Thurber et al. , 2008 ; Vega Thurber et al. , 2009 ). Further, the very name hologenome implies a gene-centric approach reminiscent of the selfish gene. And while it is possible to measure the outcomes of evolutionary processes at the genetic level, this information is only relevant within the context of its environment (for example, in a cell). Bacteria, Symbiodinium , corals and the other bionts of the holobiont have remained separate entities despite close association for >200 million years ( Timmis et al. , 2004 ; Life and Death of Corals Reefs, chapter 5) and it is the bionts, not just their genetic material, that determines selectable phenotypes ( Grafen and Ridley 2007 )."
} | 4,169 |
28367229 | PMC5372337 | pmc | 586 | {
"abstract": "Background Bioenergy with carbon capture and storage (BECCS) has come to be seen as one of the most viable technologies to provide the negative carbon dioxide emissions needed to constrain global temperatures. In practice, algal biotechnology is the only form of BECCS that could be realized at scale without compromising food production. Current axenic algae cultivation systems lack robustness, are expensive and generally have marginal energy returns. Results Here it is shown that microbial communities sampled from alkaline soda lakes, grown as biofilms at high pH (up to 10) and high alkalinity (up to 0.5 kmol m −3 NaHCO 3 and NaCO 3 ) display excellent (>1.0 kg m −3 day −1 ) and robust (>80 days) biomass productivity, at low projected overall costs. The most productive biofilms contained >100 different species and were dominated by a cyanobacterium closely related to Phormidium kuetzingianum (>60%). Conclusion Frequent harvesting and red light were the key factors that governed the assembly of a stable and productive microbial community. Electronic supplementary material The online version of this article (doi:10.1186/s13068-017-0769-1) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusion In this study, the cultivation of a mixed-community microbial biofilm at high pH and alkalinity is shown to comprise a productive and stable phototrophic carbon capture system. High volumetric productivities were observed for a mixed microbial community dominated by a cyanobacterium closely related to P. kuetzingianum (maximum 1022 g m −3 day −1 , average 862.91 g m −3 day −1 ) grown at pH 9, 0.5 mol L −1 total carbonates and 80 µmol photon m −2 day −1 . This high productivity combined with the predicted fourfold lower cost of a high pH, high alkalinity system [ 9 ] has the potential to reduce the overall cost of phototrophic carbon capture systems. Fatty acid content and profiles were consistent with previously reported axenic cultures of cyanobacteria. It should be noted that further cost reductions will still be needed to make production of biofuels from algae economically feasible, and there is no consensus on the best method of growing and harvesting algal biomass. Furthermore, production of higher added-value products such as pharmaceuticals or animal feedstock might not be possible with mixed microbial communities. The present study is an excellent starting point to investigate additional improvements to further reduce the costs of CO 2 conversion with algae.",
"discussion": "Discussion The sampled alkaline soda lakes each harboured a completely different microbial community, as shown by nonmetric multidimensional scaling analysis. The same analysis showed that during photobioreactor cultivation, these communities became very similar to each other. This suggests that photobioreactor cultivation induced strong and reproducible ecological selection. In all photobioreactors from all four soda lakes, an increase in illumination intensity led to a dramatic shift in microbial community structure between days 85 and 98. This shift might have been caused by more prolific biofilm growth, leading to anoxic conditions, especially during dark periods when no oxygen was produced. These conditions selected for anoxygenic phototrophs at the expense of oxygenic phototrophs. For this reason, a second set of experiments were performed, where the biofilms were regularly harvested whenever a decrease in oxygen production was observed. To prevent the emergence of anoxic pockets during growth, the geometry of the flat panel photobioreactors was changed to create a more even distribution of the fresh medium, which might have prevented the occurrence of anoxia during dark periods. All the bioreactors in this second experiment showed long-term, robust productivity over the 82-day period. In this regard, a microbial community might outperform an axenically cultivated single strain of algae, which can show a loss of productivity caused by contamination [ 27 , 28 ]. The use of high alkalinity and high pH likely also limited the contamination. By providing only red or blue light, it was attempted to selectively grow either cyanobacteria or diatoms. Indeed, the biofilms grown under red light were dominated (>60%) by a population related to the cyanobacterium P. kuetzingianum , while the blue biofilms were dominated by a population related to the diatom N. thermalis. White-light biofilms contained a mixture of these two populations. The phototrophs serve as the sole or primary autotroph, suppling oxygen and photosynthetically derived carbon to the heterotrophic community, while the heterotrophs might promote the growth of phototrophs by proving key metabolites and scavenging wastes [ 29 , 30 ]. The microbial communities in all three bioreactors were stable over time with one minor community disturbance at day 25 (red) and day 26 (white) observed. The red-light biomass was superior to both the blue-light and white-light photobioreactor biomass both in terms of productivity and ease of harvesting. Volumetric productivities of the red-light photobioreactor were a maximum of 429 g m −3 day −1 , and were stable over the course of the 82 day experiment. The red-light biomass formed a thin biofilm on the walls of the photobioreactor. This biofilm was easily removed from the walls into the liquid medium via minor agitation or a low-pressure air flow. When the liquid medium was removed from the photobioreactor, the biomass easily flowed with it. The harvested biomass rapidly coagulated in a flask forming a single globule of biomass. This biomass could easily be separated from the bulk suspension by physical removal with a pipette tip. To what extent these harvesting approaches might work at large scale is a topic for further research. Despite the large body of work showing a diversity–productivity relationship, the practical implication of using algal species diversity for enhancing productivity or stability of biotechnological systems remains largely untested. It has only been explored on a small scale in relatively few studies, using either constructed algal assemblages with ten or fewer species [ 31 , 32 ] or natural assemblages with a species richness of at most 19 [ 33 ]. The evidence of a polyculture approach is promising so far. For example, Corcoran and Boeing [ 32 ] showed that the most species-rich polycultures ( n = 6) were stable and recovered from short-term negative grazing effects by rotifers, whereas the monocultures experienced persistent negative effects. In the present study, the community disturbance at days 25–26 did not cause a corresponding decrease in oxygen production and the dominant phototrophic microbial community had re-established itself by day 39 without any human intervention (other than the usual harvesting cycling). The microbial community from the HP bioreactor was similar to the previous red-light bioreactor, except for the presence of a small amount of a diatom related to N. thermalis. The HP bioreactor also displayed the highest volumetric productivities of the photobioreactors tested, up to 1022 g m −3 day −1 , average 862.91 g m −3 day −1 at 80 µmol photon m −2 day −1 of red light in a 16:8 light and dark cycle. These productivities are on par with most reported previous findings for axenic cultures. Nascimento et al. [ 34 ] showed a volumetric productivity of 730 g m −3 day −1 for an axenic culture of Chlorella vulgaris grown in a 12:12 light and dark cycle under 140 µmol photons m −2 day −1 . All other strains tested in that survey showed biomass productivities between 200 and 340 g m −3 day −1 . Volumetric productivities of up to 1900 g m −3 day −1 have been observed for suspended cultures of Phaeodactylum tricornutum in an airlift tubular bioreactor [ 35 ]. 1470 g m −3 day −1 was measured for Chlorella sorokiniana cultivated as a suspended culture in an inclined tubular bioreactor [ 36 ] and 420 m −3 day −1 was measured for Spirulina platensis in an airlift tubular system [ 37 ]. The surface areal productivity of the HP bioreactor was also typical of a biofilm photobioreactor (maximum 3.6 g m −2 day −1 ). Various types of algal biofilm systems have been developed. For example, growth of P. tricornutum on the twin-layer biofilm photobioreactor achieved a surface biomass productivity of 1.8 g m −2 day −1 [ 38 ]. Boelee et al. [ 39 ] designed a flow-lane biofilm bioreactor and achieved areal production rates between 2.7 and 4.5 g m −2 day −1 . Interestingly, their system was inoculated with municipal wastewater and the subsequent biofilm was dominated by the cyanobacterium Phormidium autumnale . The rotating algal biofilm cultivation system (RAB) inoculated with C. vulgaris has demonstrated productivities of 3.51 g m −2 day −1 (laboratory scale) [ 18 ] and 5.80 g m −2 day −1 (pilot scale) [ 40 , 41 ]. Schnurr et al. [ 42 ] cultivated Nitzschia palea and Scenedesmus obliquus biofilms on a flat plate parallel horizontal photobioreactor with areal productivities of 2.8 and 2.1 g m −2 day −1 . These results suggest that the cultivation of a mixed-community microbial biofilm at high pH and alkalinity results in a system that is both stable and highly productive compared to common axenic culture systems. The total lipid content of the HP bioreactor biomass on day 20 was 13.4% dwt. This value is consistent with reported literature values for other Cyanobacteria such as Spirulina (7–13% dwt) and Oscillatoria (7% dwt) grown under nutrient-replete conditions [ 43 ]. Interestingly, the total lipid content of some freshwater Phormidium and Oscillatoria sp. is substantially higher when directly sampled from the environment, 26.7 and 28.1%, respectively [ 44 ]. The highest lipid yields under nutrient-replete cultivation conditions are typically seen for the Chlorophyta (green algae), 13–31% dwt, and the Bacillariophyceae (diatoms), 21–51% dwt [ 34 , 43 ]. The lipid productivity of the biomass on day 20 was calculated to be 115.4 mg L −1 day −1 . This value is remarkably high compared to other axenic culture systems and is a product of the very high volumetric productivity and the average cyanobacterial lipid content of this microbial biomass. Few species of microalgae have been shown to have lipid productivities above 115 mg L −1 day −1 : C. vulgaris (204.91 mg L −1 day −1 ) [ 34 ], Ettlia oleoabundans (164 mg L −1 day −1 ) [ 43 ] and Amphora (160 mg L −1 day −1 ) [ 43 ]. The dominance of the C 16 fatty acid is common amongst cultured Phormidium species [ 45 ]. Overall, the biomass lipids were 34.4% saturated, 42.9% monounsaturated (MUFA) and 22.8% polyunsaturated (PUFA). As opposed to fuels composed of PUFAs, the high proportion of saturated and MUFAs (combined 77.2%) may incur fewer problems with fuel polymerization during combustion [ 46 ]."
} | 2,733 |
36002385 | PMC10092099 | pmc | 587 | {
"abstract": "Abstract Advances in flexible electronic devices and robotic software require that sensors and controllers be virtually devoid of traditional electronic components, be deformable and stretch‐resistant. Liquid electronic devices that mimic biological synapses would make an ideal core component for flexible liquid circuits. This is due to their unbeatable features such as flexibility, reconfiguration, fault tolerance. To mimic synaptic functions in fluids we need to imitate dynamics and complexity similar to those that occurring in living systems. Mimicking ionic movements are considered as the simplest platform for implementation of neuromorphic in material computing systems. We overview a series of experimental laboratory prototypes where neuromorphic systems are implemented in liquids, colloids and gels.",
"conclusion": "5 Conclusion The goal of brain‐inspired neuromorphic computing is to offer an effective replica of the human brain's functionality through the use of electrical components. We overviewed the properties and materials of liquid, colloidal and gel neuromorphic systems, compared them and discussed various liquid based synaptic devices as well as their neuromorphic applications. To simulate synaptic functions, these gadgets use an aqueous solution. These liquid‐based artificial synapses have potential applications in biocompatible devices and constitute a new paradigm to explore innovative computational protocols at the liquid state. Comparative characteristics of the devices reviewed are summarised in Table 3 . We find that neuromorphic device \n [47] \n and Analog : 2D‐SnO 2 memtransistor \n [71] \n are devices with shortest cycles. Memory: 2D‐SnO 2 memtransistor and Analog : 2D‐SnO 2 memtransistor \n [71] \n are devices whose pre‐processing time is comparable with their cycle lengths. Actuator, \n [85] \n 2D‐SnO 2 memtransistor and Analog:2D‐SnO 2 memtransistor \n [71] \n have longest life time. Transistor, \n [87] \n 2D‐SnO 2 memtransistor and Analog:2D‐SnO 2 memtransistor \n [71] \n can survive largest number of cycles.\n Table 4 Comparative analysis of neuromorphic devices. \n Device \n \n Cycle, ms \n \n Pre‐processing \n \n Density, device /mm 2 \n \n \n Voltage range, V \n \n Current range \n \n Power consumption \n \n Life time \n \n Re‐usability, cycles \n \n Self‐Healing \n \n Refs. \n \n MoO 3 device \n \n N/A \n \n N/A \n \n N/A \n \n −60 to 60 \n \n 0.1 nA to 10 μA \n \n N/A \n \n N/A \n \n N/A \n \n No \n \n [70] \n \n Memory: 2D‐SnO 2 memtransistor \n \n 1000 (DC sweep) \n \n 1 min \n \n N/A \n \n 15 V to −15 V \n \n 0.1 μA \n \n 1.5–1.8 μW \n \n 3 months \n \n 1000 (DC sweep) \n \n N/A \n \n [71] \n \n Analog : 2D‐SnO 2 memtransistor \n \n \n \n 1 min \n \n N/A \n \n 15 V to −15 V \n \n 60–85 nA \n \n 1.9 pJ \n \n 3 months \n \n \n \n N/A \n \n [71] \n \n Transistor \n \n N/A \n \n N/A \n \n N/A \n \n −4 to 4 \n \n 0.4 to 53 μA \n \n 0 \n \n N/A \n \n N/A \n \n No \n \n [72] \n \n Memristor \n \n N/A \n \n N/A \n \n N/A \n \n −1.5 to 1.5 \n \n −100 to 140 \n \n 150 μW \n \n 20000 s \n \n 100 \n \n No \n \n [75] \n \n neuromorphic device \n \n 5 \n \n N/A \n \n 0.44 cm −2 \n \n \n −8 to 5 \n \n 0 to 0.1 mA \n \n 69.6 nJ/event \n \n 600 s \n \n 200 \n \n No \n \n [47] \n \n Memristor \n \n N/A \n \n N/A \n \n N/A \n \n −3 to 3 \n \n −0.1 to 0.1 A \n \n N/A \n \n N/A \n \n N/A \n \n No \n \n [13] \n \n Optoelectronic device \n \n 200 \n \n N/A \n \n N/A \n \n 0 to 0.8 \n \n 11 nA \n \n 18 \n \n N/A \n \n N/A \n \n No \n \n [78] \n \n Li‐ion transistor \n \n N/A \n \n N/A \n \n N/A \n \n 1.8 to 3.5 \n \n \n \n N/A \n \n N/A \n \n \n \n No \n \n [81] \n \n Actuators \n \n 2000 \n \n N/A \n \n 1.1 g cm −3 \n \n \n N/A \n \n N/A \n \n 100 mW \n \n \n \n N/A \n \n No \n \n [85] \n \n Transistor \n \n 200 \n \n N/A \n \n N/A \n \n −2 to 2 \n \n 1.1 to 13.5 μA \n \n 0.24 μW \n \n N/A \n \n N/A \n \n No \n \n [86] \n \n Transistor \n \n 2000 \n \n N/A \n \n 150 cm −2 \n \n \n −1.5 to 1.8 \n \n 80 to 600 nA \n \n 0.00018 \n \n N/A \n \n 500 \n \n No \n \n [87] \n \n Transistor \n \n 557 \n \n N/A \n \n N/A \n \n −1 to 2 \n \n 0 to 6.44 mA \n \n 0.32 μW \n \n N/A \n \n N/A \n \n NO \n \n [89] \n \n Memristor AgNO3: PVDF‐HFP : IL \n \n 100 \n \n NO \n \n N/A \n \n −10 to 10 \n \n 1 μA to 1 mA \n \n N/A \n \n \n \n 100 \n \n NO \n \n [48] \n \n Memristor AgNO3 : PEO : IL \n \n 100 \n \n NO \n \n N/A \n \n −5 to 5 \n \n 1 μA to 1 mA \n \n N/A \n \n \n \n 500 \n \n NO \n \n [48] \n Wiley‐VCH GmbH",
"introduction": "1 Introduction Complex systems can be correspondingly abstracted in algorithmic formats to describe phenomena that have traditionally been cognition avoided. Such as the complexities of biological sensorial‐actuation networks, through which phenomena such as “intelligence” are hypothesized even in organisms without a nervous system. The sensor‐actuator collections represent the first order of cybernetic systems, which have been extensively studied and replicated. \n [1] \n Such applications of computational concepts and the development of experimental devices in that field enclasp “unconventional computing”.[ \n 2 \n , \n 3 \n ] The term “neuromorphic” was invented by Carver Mead in the 1990s to refer to very large‐scale of integration computing systems (VLSI) with mixed analog/digital signals, inspired by the neuro‐biological architecture of the brain. \n [4] \n A neuromorphic feature of an engineered system mimics the structure or function of a single or multiple components of the Metazoan nervous system. Typically, this involves attempts to replicate the phenomenon of synaptic plasticity: self modulation of the excitability of neuron‐neuron junctions (synapses), towards replicating state retention (‘learning’) via a process of entrainment with graduated input (‘neuromodulation’). Neuromorphic devices, as an unconventional computational model, are worth researching owing to certain features of their biological counterparts, such as massive parallelism, emergence, and low power consumption, which are highly desirable for imitation.[ \n 5 \n , \n 6 \n , \n 7 \n , \n 8 \n , \n 9 \n , \n 10 \n , \n 11 \n , \n 12 \n , \n 13 \n ] ‘Neuromorphic engineering’ emerged as an interdisciplinary field of research that focusing on building electronic neural processing systems that directly imitate the biophysics of real neurons and synapses,[ \n 14 \n , \n 15 \n , \n 16 \n ] or ultimately allow direct communication with neurons. \n [17] \n Recently, the definition of the term neuromorphic has expanded in two additional directions. \n [18] \n Initially, the term neuromorphic was used to describe spike‐based processing systems that were engineered to discover large‐scale computational neuroscience models. Second, neuromorphic computations involve specific electronic neural architectures that implement neural and synaptic circuits. \n [19] \n \n Neuromorphic computing hardware requires physical models at three different levels: (1) individual components such as artificial synapses and neurons, (2) Networks of these neurons and synapses, and (3) Learning rules and training methods. \n [20] \n Historically, early attempts at understanding the mammalian brain focused on the physical aspects of neurons including the McCulloch–Pitts neuron \n [21] \n and Rosenblatt perceptron, \n [22] \n which formed the basis for further development. Briefly, the cell body of a neuron collects and sums the charges generated by synaptic connections in the dendrites until the total charge reaches a threshold after which the neuron fires a spike along the axon. \n [23] \n The resulting spike is transmitted to other neurons connected to that synapse, which depending on the synaptic weight can augment or inhibit the signal. A more accurate Hodgkin‐Huxley physiological model \n [24] \n includes differential equations with more than 20 different parameters such as the concentrations of K + and Na + ions, which became the basis for subsequent approximations.[ \n 25 \n , \n 26 \n ] Subsequent research in neuroscience shifted the focus to the conceptual basis of higher levels of learning, cognition, and behaviour of neuronal populations, which the resulting models became the basis for Neural network architecture (ANN) (for example, Hopfield networks) and learning rules (for example, Hebbian learning). \n [27] \n \n In this regard, systems such as Spiking Neural Networks (SNN), the third generation of neural networks, \n [28] \n are extremely representative. However, there is important cross‐fertilisation between the technologies needed to develop efficient SNNs and the more traditional non‐spiking neural network technologies, known as artificial neural networks (ANNs), which are usually time‐based. \n [29] \n \n Early successes in neuromorphic computing have relied heavily on conventional electronic materials. In particular, spiking neural networks composed of silicon‐based Complementary Metal Oxide Semiconductor (CMOS).[ \n 30 \n , \n 31 \n , \n 32 \n ] Since CMOS chips have disadvantages such as inefficient and high energy consumption synaptic operations based on volatile random access memory (RAM), considerable effort has been focused on non‐volatile memory (NVM) as a basis for neuromorphic computing. \n [33] \n Among the empirical understanding of NVMs we should mention the Resistive Switching Devices (RSDs), to whom the memristors \n [34] \n belong, which has a transition between different impedance modes that can be related, for example, to binary information. Such a voltage‐controlled, reversible, stable transmission depends on several nano scale phenomena. \n [35] \n The simultaneous presence of NVM and multi‐mode switching in memristors[ \n 36 \n , \n 37 \n ] gives CMOS‐memristive hybrid circuits promising for edge computing and the Internet of Things, so that local processing of analog and digital data on mobile devices reduces the need for cloud access. \n [38] \n \n Conventional Von‐Neumann computers based on CMOS technology do not have the inherent capabilities to learn or deal with complex data such as the human brain. \n [29] \n To overcome the limitations of digital computers, considerable research efforts have been made around the world to develop profoundly different approaches, inspired by biological principles. One such approach is the development of neuromorphic systems, namely computer systems that mimic the type of information processing in the human brain. \n [39] \n \n To mimic the synaptic functions of the brain, nonlinearity, memory features and rich systems dynamics are needed.[ \n 41 \n , \n 42 \n ] Dynamics, understood as evolution of systems in time (or time‐ordering of evolution steps) is the turning point of all neuromorphic computing systems. Exotic concepts, like time crystals (time crystal is a quantum system of particles whose lowest‐energy state is one in which the particles are in repetitive motion), \n [43] \n are considered as universal models for neuromorphic information processing. \n [44] \n This concept is fully in line with idea of polychronization: computation with spiking neurons operating in desynchronized fashion, thus forming a complex spatiotemporal fabric of oscillations \n [45] \n so important for cognitive processes. \n [46] \n One of the ways to achieve perfect mimicking of neuronal dynamics and information processing is replication of ionic movements in the nervous system (Figure 1 ). Therefore, it is important to note that ions move easily in liquids \n [47] \n and in soft matter in general. \n [48] \n Gels, viscous and non‐homogeneous media are especially interesting in this context ‐ complex diffusion and other transport phenomena, described in terms of fractional calculus, are ideal for mimicking complex dynamics of neural systems.[ \n 49 \n , \n 50 \n ] The term iontronics has been coined to describe electronic‐like devices and systems based on ion as information carriers. \n [51] \n In numerous cases iontronic devices are based on membranes with pores of controllable dimensions, which leads to anomalous transport phenomena.[ \n 52 \n , \n 53 \n ] These phenomena, in turn, embodied in devices called nanofluidic memristors, are proposed as a bio‐inspired information processing platform.[ \n 54 \n , \n 55 \n ]\n Figure 1 Schematic of (a) biological synapse, compared to organic artificial synapses with working mechanisms of (b) charge trapping, (c) conductive filament, (d) ion migration, (e) floating gate, and (f) dipole alignment. Reproduced with permission. \n [40] \n Copyright 2019, American Chemical Society (ACS) Publications. Electrochemical process devices have shown promising synaptic properties that are useful in artificial synaptic devices because the electrochemical reactions of ions can mimic the movement of ions in the nervous system.[ \n 56 \n , \n 57 \n ] Along with synaptic functionalities, interaction of metal ion with neural extracellular matrix in the brain was postulated to be responsible for metal, most probably via modulation of synaptic plasticity. \n [58] \n Resistive switching and extremely fast (at least as compared with living neurons) responses up to the range of hundreds of kHz have been observed in electrochemical devices, which allow the expansion of biological functions. \n [59] \n \n The Liquid State Machine (LSM) \n [60] \n is a nervous system‐inspired algorithm that mimics the brain's ability to process spatio‐temporal data. Of course, this particular term ‘liquid’ does not mean that the physical system is in a liquid state, but rather refers to the surface of a liquid that is affected by input forces and creates a pattern of reciprocal waves. A single LSM network can be used as a general intelligent processor that processes different data streams on a single stream to extract different features. \n [61] \n The flow of the LSM model training process is as follows: \n Initialization. Each neuron in the fluid is randomly selected as an inhibitor or excitator, depending on the ratio of inhibitory or excitatory neurons. The entire set of connections and their corresponding synaptic strengths are initialized. A set of inputs u ( t ) are fed into the input layer. The liquid response is calculated based on step (1). The responses in the previous time step are fed into the output layer and are also stored for the next time step (to calculate liquid response). The Liquid response is used to train the next category, using a specific training algorithm and update rule. Repeat steps 2–5 on all of the input training sets. \n [61] \n \n \n The liquid state machine (LSM) \n [60] \n mimics the cortical columns in the brain. Cortical microstructures are thought to represent non‐linearly input stream into a high‐dimensional state space. This high‐dimension representation is then used as input to other areas in the brain where learning is possible. The cortical microcircuits have a sparse representation and (slowly) fading memory. The microprocessor state is in the ‘forgets’ state for a certain period of time. While LSMs may be able to mimic certain functions in the brain, it should be noted that LSMs cannot be used to explain how and why the brain functions. \n [62] \n \n Liquid marbles (LMs) are spherical microlitre quantities of fluid with a coating of superhydrophobic particles that can be tens to thousands of micrometers in diameter.[ \n 63 \n , \n 64 \n ] LM devices are able to perform computation through a variety of non‐standard logics, where the LMs are considered as data or otherwise, to contain data (i. e. chemical reactants), which may interact with other LMs via collisions that will result in data translation or transfer via ricochets or coalescence.[ \n 65 \n , \n 66 \n ] By exploiting the principles of collision‐based computing, \n [67] \n LM computing devices may be used to implement non‐standard, collision‐based conservative logic. \n [68] \n The integration of LM properties, such as collisions that their results may have been engineered (reflection or integration), and the potential for chemical reactions between two heterogeneous fluid cores after collisions, further reinforces the traditional conservative logic toolkit. \n [13] \n \n In the present review, we first briefly introduce the liquid, colloidal, and gel neuromorphic systems, followed by the review of various liquids, colloids and gels synaptic devices and their achieved results in neuromorphic computing."
} | 3,993 |
38971854 | PMC11227571 | pmc | 588 | {
"abstract": "Accumulating evidences are challenging the paradigm that methane in surface water primarily stems from the anaerobic transformation of organic matters. Yet, the contribution of oxygenic photosynthetic bacteria, a dominant species in surface water, to methane production remains unclear. Here we show methanogenesis triggered by the interaction between oxygenic photosynthetic bacteria and anaerobic methanogenic archaea. By introducing cyanobacterium Synechocystis PCC6803 and methanogenic archaea Methanosarcina barkeri with the redox cycling of iron, CH 4 production was induced in coculture biofilms through both syntrophic methanogenesis (under anoxic conditions in darkness) and abiotic methanogenesis (under oxic conditions in illumination) during the periodic dark-light cycles. We have further demonstrated CH 4 production by other model oxygenic photosynthetic bacteria from various phyla, in conjunction with different anaerobic methanogenic archaea exhibiting diverse energy conservation modes, as well as various common Fe-species. These findings have revealed an unexpected link between oxygenic photosynthesis and methanogenesis and would advance our understanding of photosynthetic bacteria’s ecological role in the global CH 4 cycle. Such light-driven methanogenesis may be widely present in nature.",
"introduction": "Introduction Atmospheric methane (CH 4 ), one of the most important greenhouse gases, reached an exceptionally high concentration of 1912 part per billion in 2022 1 , 2 . This calls for an immediate action to understand and address CH 4 emission problems. Freshwater ecosystems such as rivers, streams, lakes, oceans, and wetlands, play a vital role in contributing to the global atmospheric CH 4 budget 3 . It is widely recognized that CH 4 in freshwater ecosystems is primarily produced via the transformation of organic matters in anoxic profundal and littoral sediments 4 , 5 . Nevertheless, despite the limited exchange between the oxic surface layers of freshwater ecosystems and sediments due to the deep water columns, a prevalent CH 4 supersaturation was observed 6 . This unexpected phenomenon, also known as the methane paradox wherein methane concentrations exceed atmospheric equilibrium values, suggests the existence of a significant CH 4 production process that has yet to be defined. Photosynthetic bacteria hold a dominant presence in the surface layers of freshwater ecosystems and exhibit excellent phototactic motility and versatile metabolic patterns 7 . Their interaction with other coexisting microorganisms significantly influences the biogeochemical cycle of elements via harnessing solar light as an energy source. The correlation between photosynthetic bacteria and CH 4 production under illumination has been reported previously 8 , 9 , but the underlying mechanisms are yet to be elucidated. It is likely that anoxygenic photosynthetic bacteria act as photosensitizers, driving the CO 2 -to-CH 4 conversion with anaerobic methanogenic archaea when being cocultured in an anoxic layer 10 . The role of oxygenic photosynthetic bacteria in the context of CH 4 supersaturation is largely unknown. This oversight arises from the traditional belief that methanogenic archaea are highly sensitive to oxygen exposure and can only thrive in highly reduced, anoxic environments 11 . However, the coexistence of oxygenic photosynthetic bacteria and anaerobic methanogenic archaea occurs in various natural habitats, such as microbial mats, soil crusts, and aerobic epilimnion of an oligotrophic lake 12 – 14 . The in situ detection of the close attachment between methanogenic archaea and photosynthetic bacteria in these oxygenated and methane-rich environment, along with the finding that methanogens can survive oxygen exposure 15 , suggested their potential interactions through direct nutrient exchange or signal transduction 16 , 17 . Thus, a comprehensive understanding of photosynthetically regulated CH 4 production is of ecological and biogeochemical importance, and will offer valuable insights into global CH 4 cycle with implications for climate change. Here, we demonstrated the methanogenesis involved in the coculture of Cyanobacterium Synechocystis sp. strain PCC6803 (hereafter PCC6803) and Methanosarcina barkeri (hereafter M. b ). PCC6803 is a model oxygenic photosynthetic bacterium that can perform solar energy conversion of water and CO 2 to carbohydrates and oxygen. In the absence of light, the produced carbohydrates are metabolized to generate CO 2 and ATP through a respiratory system, creating an anoxic microenvironment suitable for microbial methanogenesis 18 . Meanwhile, M. b as a model methanogen was chosen owing to its widespread environmental presence with physiological and metabolic diversity 19 . It is reported that iron exists in many open water systems and is quantitatively the most important trace metal in photosynthetic bacteria 20 . Over 99% of the dissolved Fe pool is complexed by organic ligands 21 . Therefore, Fe-ethylenediaminetetraacetic acid (Fe-EDTA) was selected as a typical iron species in this study due to its stability and solubility in aqueous solutions. The results showed that CH 4 production by the interaction of oxygenic photosynthetic bacteria and anaerobic methanogenic archaea was significantly enhanced through the redox cycling of Fe-ethylenediaminetetraacetic acid (Fe-EDTA), involving both syntrophic methanogenesis and abiotic methanogenesis during the periodic dark-light cycles (Fig. 1 ). Specifically, in darkness, the organics and H 2 produced by PCC6803 during dark fermentation were utilized as carbon sources and reducing equivalents by M. b for syntrophic methanogenesis under anoxic conditions. The significantly lowered hydrogen pressure by M. b , in turn, created more thermodynamically favorable conditions for PCC6803. In contrast, in illumination, the photosynthesized organic compounds and intermediate products by PCC6803 served as potential methyl donors (-CH 3 ). Meanwhile, the simultaneously produced O 2 stimulated reactive oxygen species (ROS) production by M. b . Along with the Fenton reaction with Fe-EDTA, various methyl donors were oxidized by ROS to form methyl radicals (•CH 3 ) as intermediates that eventually resulted in abiotic methanogenesis under oxic conditions. Further studies indicated that other model oxygenic photosynthetic bacteria and anaerobic methanogenic archaea were also able to conduct this light-driven methanogenesis process. These findings not only unveil an unexpected link between oxygenic photosynthesis and methanogenesis, but also advance our understanding of the ecological role of photosynthetic bacteria in the global CH 4 cycle. Fig. 1 Schematic illustration of CH 4 production in the presence of oxygenic photosynthetic bacteria. a Syntrophic methanogenesis under anoxic conditions in darkness. b Abiotic methanogenesis under oxic conditions in illumination.",
"discussion": "Discussion Unlike the previous studies that CH 4 production by oxygenic photosynthetic bacteria could be progressed by the demethylation of methylphosphonates or the conversion of fixed inorganic carbon into CH 4 52 , 53 , this work has elucidated an unappreciated but potentially widespread pathway for CH 4 production. The alternating phases of photosynthetic oxygen evolution (oxic) and respiratory oxygen consumption (anoxic) are essential for methanogenesis, which could be achieved under varying light times, even a light-dark cycle of 12 h-12 h that simulates a full day (Supplementary Fig. 18 ). The light-driven methanogenesis experiments were also conducted on the roof of the Research Center for Water Resources and Security Building at Fujian Agriculture and Forestry University in Fuzhou, China (latitude: 26.05 o N, longitude: 119.14 o E) under natural sunlight (from 08:00 to 20:00 with an average solar heat flux of ~0.5 kW m −2 ), with ambient temperatures ranging between 25 °C and 37 °C. A similar methanogenesis process was also observed (Supplementary Fig. 19 ). In conclusion, besides the abiotic methanogenesis under illumination, there exists a co-evolved, specific interaction during syntrophic methanogenesis by oxygenic photosynthetic bacteria and anaerobic methanogenic archaea in darkness. Specifically, M. b , in the absence of other cell types except PCC6803, were benefiting from photosynthetic organic matter production. It was estimated that 5.9% of gross primary production was diverted to CH 4 formation. Meanwhile, due to the CO 2 /H 2 methanogenesis of M. b , PCC6803 were benefiting from the lowered hydrogen pressure, creating more thermodynamically favorable conditions for the dark fermentation of PCC6803. Oxygenic photosynthesis has been recognized as the most important metabolic innovation on Earth, enabling life to harness energy and reducing power directly from sunlight and water, thus liberating it from the constraints of geochemically derived reductants 54 . Consequently, these diverse and intriguing oxygenic photosynthetic bacteria contain considerable metabolic flexibility, utilizing numerous unconventional central carbon metabolic pathways and novel enzymes for autotrophic, mixotrophic, and heterotrophic growth, tailored to their specific ecological niches 22 , 55 . Considering the extensive coexistence and interaction of diverse microbial species in natural and engineered ecosystems 56 , along with ferruginous environment on Earth (e.g., oceans with abundant Fe(II) and Fe(III)-carboxylate complexes), syntrophic methanogenesis by oxygenic photosynthetic bacteria and anaerobic methanogenic archaea creates more thermodynamically favorable conditions for both microorganisms. Thus, this light-driven methanogenesis process, involved both syntrophic methanogenesis (under anoxic conditions in darkness) and abiotic methanogenesis (under oxic conditions in illumination) during the periodic dark-light cycles, surpasses the conventional methane production pathways (i.e., acetoclastic methanogenesis and hydrogenotrophic methanogenesis), and potentially making a more significant contribution to the global CH 4 cycle. The inference was supported by the correlation between CH 4 supersaturation and photosynthesis 8 , 9 , 57 . Various potential mechanisms for CH 4 production by phototrophic microorganisms having been extensively investigated, including the photosynthesis-driven metabolism 9 , 53 and ROS-driven demethylation of methyl donors 46 – 49 . Our study innovatively demonstrated the synergistic interaction between these two mechanisms, along with the Fe redox cycles. However, the existence of such methanogenesis by oxygenic photosynthetic bacteria and anaerobic methanogenic archaea in the natural environments requires further validation with multiple complementary approaches, including the evaluation of in situ CH 4 profiles and microbial composition, incubation experiments with freshwater microbial cultures using NaH 13 CO 3 as a supplementation carbon source, and the assessment of the exact contribution of both abiotic and biotic pathways. Meanwhile, recent studies have indicated the potential importance of various metal elements in the evolution of oxygenic photosynthesis, such as manganese 58 . Therefore, the potential involvement of other metal elements in such light-driven methanogenesis warrants further evaluation."
} | 2,849 |
27814362 | PMC5096674 | pmc | 590 | {
"abstract": "Bacteria frequently lose biosynthetic genes, thus making them dependent on an environmental uptake of the corresponding metabolite. Despite the ubiquity of this ‘ genome streamlining ’, it is generally unclear whether the concomitant loss of biosynthetic functions is favored by natural selection or rather caused by random genetic drift. Here we demonstrate experimentally that a loss of metabolic functions is strongly selected for when the corresponding metabolites can be derived from the environment. Serially propagating replicate populations of the bacterium Escherichia coli in amino acid-containing environments revealed that auxotrophic genotypes rapidly evolved in less than 2,000 generations in almost all replicate populations. Moreover, auxotrophs also evolved in environments lacking amino acids–yet to a much lesser extent. Loss of these biosynthetic functions was due to mutations in both structural and regulatory genes. In competition experiments performed in the presence of amino acids, auxotrophic mutants gained a significant fitness advantage over the evolutionary ancestor, suggesting their emergence was selectively favored. Interestingly, auxotrophic mutants derived amino acids not only via an environmental uptake, but also by cross-feeding from coexisting strains. Our results show that adaptive fitness benefits can favor biosynthetic loss-of-function mutants and drive the establishment of intricate metabolic interactions within microbial communities.",
"introduction": "Introduction Bacterial genomes are highly dynamic in terms of both size and composition [ 1 ]. The extensive variation in gene repertoires that characterizes prokaryotic genomes can be caused by genome expansion via horizontal gene transfer and gene duplication or, alternatively, contraction due to gene loss. Interestingly, comparative analyses have provided evidence that gene loss may in fact be quantitatively more important for determining the size of prokaryotic genomes than the gain of new genetic information [ 1 – 3 ]. Indeed, as sequencing technologies improve, more and more microorganisms are being discovered that feature tremendously small genomes [ 4 ]; some of which are even smaller than the suggested minimal genome size for cellular life of ~300 kb [ 5 ]. Analyzing the genetic content of these reduced genomes revealed—besides a lack of dispensable elements [ 6 ]—also the elimination of seemingly essential biosynthetic functions. For example, reconstructing metabolic networks from sequence data to predict the phenotype of the focal organism unraveled that the majority of bacterial genomes analyzed lacked the biosynthetic capability to produce several essential building block metabolites such as amino acids, vitamins, or even nucleobases [ 7 – 10 ]. Surprisingly, the list of genotypes that cannot produce certain metabolites autonomously (hereafter: auxotrophic genotypes ) does not only include host-associated bacteria such as pathogens [ 11 ] or endosymbionts [ 12 – 14 ], which potentially obtain the required metabolites from their host’s cytoplasm, but also free-living bacteria such as Prochlorococcus and Pelagibacter [ 15 , 16 ] that are known to mainly inhabit nutrient-poor environments. The ubiquity of biosynthetic loss-of-function mutations in bacteria that inhabit ecologically disparate environments begs an explanation: Which evolutionary mechanisms have favored a loss of biosynthetic genes over metabolic autonomy in these bacteria? Two main hypotheses have been put forward to explain these striking observations. First, genetic drift may drive gene loss in bacteria that are obligately associated with eukaryotic hosts. These bacteria experience nutrient-rich and rather constant environmental conditions [ 17 ] and frequently undergo reductions in population sizes during host-to-host transmission. As a consequence of these periodic population bottlenecks, the effects of drift may override those of selection [ 2 ]. A lack or a drastically reduced frequency of recombination may further accelerate the fixation of non-beneficial or deleterious mutations [ 12 ]. This hypothesis is mainly supported by evidence stemming from comparative genomic analyses [ 18 – 20 ]. In addition, selection experiments, in which bacterial populations were repeatedly subjected to single-cell bottlenecks, resulted in bacteria with strikingly reduced genomes [ 21 ]. The second main hypothesis that has been proposed to account for the ‘ streamlining ’ of bacterial genomes is that natural selection favors loss-of-function mutants in environments, in which the gene is no longer required [ 2 , 22 – 24 ]. This line of reasoning has been mainly applied to bacteria with free-living lifestyles or those that face nutrient-poor conditions. The large effective population size bacteria experience in these environments likely increases the efficacy of selection. For instance, it has been previously shown that advantageous mutations occur frequently in experimentally evolved bacterial populations of large effective population sizes [ 25 ]. As a consequence, loss-of-function mutations or gene deletions that increase a cell’s fitness are more likely to fix in the population [ 2 ]. Adaptive benefits of losing genes may stem, for example, from an increased cellular economization [ 26 ] or a saving of production costs, when certain metabolites can be derived from the environment [ 8 , 23 ]. Indeed, previous studies that compared the Darwinian fitness of engineered mutants lacking the ability to biosynthesize certain metabolites to non-mutated wild type cells revealed that metabolic auxotrophies can be beneficial, when the required biosynthetic product is sufficiently available in the environment [ 7 , 8 , 22 , 23 ]. Furthermore, fitness-increasing deletion mutations have also been observed in bacterial populations adapting to environments, in which the lost functions were not required for survival [ 27 , 28 ]. Even though these studies suggest that selection can possibly account for the commonly observed loss of biosynthetic genes from bacterial genomes, the frequencies with which these mutations arise in nutrient-containing environments as well as the fitness effects they exert on the corresponding mutants remain generally unclear. Therefore, direct experimental evidence for natural selection driving the loss of biosynthetic functions, and thus facilitating a metabolic adaptation to the current nutrient environment, is lacking. To unravel whether fitness advantages can indeed drive the loss of biosynthetic functions from bacterial genomes in nutrient-containing environments, we serially propagated eight replicate populations of the initially metabolically autonomous (hereafter: prototrophic ) bacterium Escherichia coli in amino acid-replete (hereafter: AA regime ) or -deficient environments (hereafter: non-AA regime ). After 2,000 generations of evolution in the presence of 20 amino acids, 75% of the experimental populations evolved amino acid auxotrophies–on average for 10 amino acids per strain. Surprisingly, auxotrophic mutants also evolved in the non-amino acid environment, albeit at lower frequencies than in the amino acid-containing environment. The evolution of metabolic auxotrophies was adaptive when amino acids were present in the environment and–surprisingly–also when other co-occurring genotypes could provide the required amino acids. A genomic analysis of derived auxotrophic genotypes revealed that distinct genetic changes in both structural and regulatory genes caused the adaptive loss or deactivation of biosynthetic functions. Our analysis indicates that adaptive advantages can drive the evolution of metabolic auxotrophies in bacteria and thus foster their obligate dependency on the biotic and abiotic environment.",
"discussion": "Discussion Why are metabolic auxotrophies so common in natural microbial communities? Hypothesizing that adaptive benefits may account for the frequently observed loss of metabolic functions, our evolution experiment revealed that prototrophic Escherichia coli cells rapidly evolved metabolic auxotrophies when adapting to environments that contained all of 20 different AAs. Interestingly, also serial propagation in AA-free environments resulted in the emergence of genotypes that had a lost the ability to autonomously produce some amino acids, yet the number of auxotrophies per strain, the number of auxotrophic strains per population, and the number of populations containing auxotrophs was significantly lower relative to populations that evolved under AA-replete conditions. In line with prior expectations, auxotrophic genotypes that evolved in AA-containing environments gained an adaptive advantage over their evolutionary ancestor, yet the observed fitness benefit was contingent on the presence of AAs in the environment. Surprisingly, evolved auxotrophs also derived amino acids from coexisting prototrophic cells and this interaction was stabilized by negative frequency-dependent selection. Multiple genetic routes lead to the inactivation of AA biosynthetic abilities, including mutations in both regulatory and structural genes. Moreover, reconstructing all mutations identified in auxotrophic genotypes in the ancestral WT background revealed that most auxotrophy-causing mutations that were considered, resulted in an increased fitness of the corresponding mutant in AA-containing environments, thus strongly suggesting that they were selectively favored under the experimental conditions. A main outcome of the evolution experiment was that adaptive benefits drove the rapid loss of biosynthetic functions when the focal metabolites were sufficiently present in the cell-external environment. These findings are in line with previous analyses, which revealed a significant fitness advantage synthetically engineered, auxotrophic mutants gained over competing prototrophic types when AAs were sufficiently present in the environment [ 7 , 8 , 23 , 30 ]. What could explain these adaptive benefits? One explanation could be that the loss of biosynthetic functions in the presence of metabolites in the environment results in energetic savings for a cell that might be due to the saving of protein production costs [ 7 , 8 , 23 , 36 ]. Alternatively, a regulatory or metabolic rewiring of cells could provide auxotrophs with a growth advantage in AA-deficient conditions—for example by changing fluxes through the cells’ metabolic networks [ 37 ]. Another explanation could be that auxotrophic cells do not only use amino acids as building block metabolites, but also catabolize AAs. This could explain the advantage auxotrophic genotypes gain relative to prototrophic cells, which autonomously produce the AAs they require for growth. Which of these mechanisms causes the fitness increase of auxotrophic genotypes in AA-containing environments, however, remains unknown and should be subject to further investigation. A prediction that follows from our observations is that metabolic auxotrophies should rapidly evolve whenever bacteria are cultivated in AA-rich media or inhabit environments with increased AA-availabilities [ 8 , 38 , 39 ]. Indeed, metabolic auxotrophies have been repeatedly reported to arise in laboratory-based evolution experiments [ 11 , 40 , 41 ] or have been detected in natural microbial communities [ 7 – 10 , 26 , 30 , 42 – 44 ]. In our experiment, derived auxotrophs always coexisted together with metabolically autonomous prototrophs. A strikingly similar pattern has been previously observed in populations of Pseudomonas aeruginosa that adapted to the lungs of cystic fibrosis (CF) patients: both prototrophic and auxotrophic strains have been isolated from the AA-rich mucus that fills the lungs of these CF patients [ 44 , 45 ]. Independent of whether or not AAs were present in the selective environment, auxotrophs that evolved in our evolution experiment always obtained AAs also from other community members such as the coexisting prototrophs. Two mechanisms are conceivable how auxotrophs obtained the AAs they required for growth: metabolites might be exchanged among genotypes via diffusion through the cell-external environment [ 46 – 48 ] or, alternatively, in a contact-dependent manner [ 49 , 50 ]. Recently it has been described that auxotrophic cells of E . coli can produce so-called ‘nanotubes’ to directly obtain cytoplasmic AAs from other bacterial cells [ 50 ]. These structures likely minimize the costs to the AA-producing cell by reducing the loss of AAs to the cell-external environment. Thus, the formation of nanotubes of AA-starved bacteria might be interpreted as a strategy to survive under AA-limiting conditions. Such a scenario could explain the evolution of auxotrophic genotypes in the non-AA regime. A contact-dependent exchange mechanism might also have allowed growth of auxotrophic genotypes after depletion of amino acids in the cell-external environment. Analyzing the genomes of derived mutants unveiled a diverse spectrum of mutations that caused the observed phenotypes. The finding that auxotrophic genotypes bore on average significantly more loss-of-function mutations than the cognate prototrophs strongly suggests the adaptive loss of functions resulted in the observed auxotrophies ( Fig 5D ). In contrast to expectations, deactivation of amino acid biosynthetic pathways via a deletion of the corresponding structural genes was much less common than the loss of regulatory elements with putative roles in AA metabolism ( Fig 5 , S2 Table ). Interestingly, auxotrophies that evolved in the non-AA regime were most likely due to mutations that down-regulated the expression levels of AA biosynthetic genes, while most auxotrophies that evolved in the AA-containing environment were caused by a complete loss of enzyme-coding regions or an inactivation of the corresponding regulatory elements. This pattern likely mirrors differences in the two selective regimes. While the environment that did not contain AAs penalized any newly evolved auxotroph, whose metabolic deficiency could not be compensated by any of the prototrophic types present, the AA-replete condition likely permitted many more different auxotrophs to increase in frequency. Indeed, the only auxotrophs that could be detected in the lines that evolved under AA-free conditions had lost the ability to produce leucine, lysine, and tryptophan, which incur relatively low metabolic costs [ 51 ] and are thus cheaper for the corresponding prototrophs to produce. In contrast, in the AA-replete environment, many more auxotrophic mutants evolved, with all replicate populations displaying a core set of common auxotrophies ( S1 Fig ). Since both epistatic interactions among mutations [ 7 ] and metabolic costs to produce the corresponding amino acids [ 8 ] determine the fitness consequences of a biosynthetic loss-of-function mutation in E . coli , the observation of such strikingly parallel changes likely reflects selective constraints acting on the evolved populations. Fitness consequences resulting from the individually reconstructed, auxotrophy-causing mutations were in a majority of cases different from the fitness levels the derived auxotrophic strain achieved ( Fig 6 ). Given that all evolved genomes analyzed contained multiple mutations, the fact that one individual mutation could not in all cases explain the fitness of the whole organism likely resulted from epistatic interactions among mutations that have been previously shown to strongly affect the fitness of auxotrophic genotypes [ 7 ]. This means that those auxotrophy-causing mutations that did not cause an increased fitness in the reconstructed mutant may have been adaptive in the genetic background in which the mutation arose. In addition, also an accumulation of multiple beneficial mutations in the same genetic background could explain why reconstructing the auxotrophy-causing mutation in the ancestral background was not in all cases sufficient to reconstitute the fitness level of the derived genotype, from which this mutation originated. Unfortunately, due to a lack of clear evolutionary lineages, these hypotheses escape an experimental validation. Interestingly, some of the mutations that did not cause an AA auxotrophy were found to be neutral (1 case) or even deleterious (2 cases) ( Fig 6 ). These mutations likely hitchhiked on another adaptive mutation that arose in the focal genotype–a phenomenon that has been observed previously in other selection experiments [ 25 , 52 ]. Theoretical predictions of metabolic auxotrophies in otherwise uncharacterized bacterial genomes have been largely based on whether or not a given biosynthetic pathway exists in the focal organism [ 7 – 9 ]. Due to a lack of understanding of the underlying regulatory networks, these approaches usually neglect the multifarious genetic routes that can possibly cause metabolic auxotrophies. Consequently, previously published estimates that only consider the absence or presence of biosynthetic genes [ 7 – 9 ] likely underestimate the true number of auxotrophic prokaryotes in nature dramatically. Given that the fitness advantage multiply auxotrophic bacteria gain are strongly affected by epistatic interactions among the auxotrophy-causing mutations [ 7 ], mutationally-induced regulatory changes could represent an effective bypass of this evolutionary constraint. The adaptive loss of metabolic capabilities and the emergent dependence on other co-occurring strains as observed in this study have significant ramifications for the evolution of bacterial genomes. A striking pattern that emerges when genomes of multiple different bacterial clones are sequenced that have coexisted together for extended time periods, is not only the frequent loss of many biosynthetic functions from their genomes, but often also a high degree of metabolic complementarity on the genomic level. Examples involve both free-living bacterial communities [ 53 ] and consortia of endosymbiotic bacteria, whose metabolite production is intricately interwoven between their eukaryotic host [ 12 , 54 , 55 ] and other coevolving bacteria [ 14 , 56 , 57 ]. In the latter case, loss of biosynthetic functions has been suggested to arise as a consequence of drift resulting from periodic bottlenecks leading to low population sizes. Empirical evidence, however, suggests that the bottleneck size experienced by bacterial symbionts during transfer between insect hosts is usually around 10 3 CFUs [ 58 , 59 ]. This population size is strikingly similar to the number of bacterial cells that were serially passaged in our evolution experiment. Assuming the cytoplasm of host cells is as nutrient-rich as the medium used in this study, the evolution of metabolic complementarities in insect endosymbionts could be selectively favored as well. Given the ease, with which metabolic auxotrophies evolve, thereby rendering the resulting mutants dependent on other coexisting organisms, it is conceivable how this event can set the stage for a coevolutionary race, in which the interacting partners may further benefit from losing additional metabolic functions. This race will most likely favor those loss-of-function mutants, which are fitter than other competing genotypes given the presence of a donor that can sufficiently compensate for their deficiencies. This ‘black-queen’-like process [ 46 , 48 ] can then lead to coadaptations on both sides. Indeed, our observation that the AA-evolved auxotrophs grew significantly better when cocultured with the derived prototrophs than their evolutionary ancestor supports this interpretation ( Fig 4A ). In the long-run, this process should lead to metabolic networks that are intricately interconnected between multiple different bacterial genotypes. Ultimately, the findings of our study may provide a plausible explanation of why most bacterial species known are difficult to cultivate under laboratory conditions [ 60 , 61 ]: they have most likely adapted to the nutritional biotic and abiotic environment they encountered in nature, which complicates a reproduction of these conditions in the laboratory."
} | 5,054 |
26892169 | PMC4759530 | pmc | 592 | {
"abstract": "We discuss the influence of surface structure, namely the height and opening angles of nano- and microcones on the surface wettability. We show experimental evidence that the opening angle of the cones is the critical parameter on sample superhydrophobicity, namely static contact angles and roll-off angles. The textured surfaces are fabricated on silicon wafers by using a simple one-step method of reactive ion etching at different processing time and gas flow rates. By using hydrophobic coating or hydrophilic surface treatment, we are able to switch the surface wettability from superhydrophilic to superhydrophobic without altering surface structures. In addition, we show examples of polymer replicas (polypropylene and poly(methyl methacrylate) with different wettability, fabricated by injection moulding using templates of the silicon cone-structures.",
"discussion": "Results and Discussion In this study, we altered the flow rate of SF 6 and O 2 and the etching time , and kept the rest of the parameters unchanged, as the gas flow rate is the governing parameter for tuning the opening angles of the resulting cones 26 31 32 . As the absorbance of light varies with the geometric features of the structures, such as, height, aspect ratio, and period of the structures 26 31 32 33 34 , our samples do not necessarily appear black; some are black and some appear brownish. Superhydrophobic silicon samples Figure 1a shows scanning electron microscopy (SEM) images of Si surfaces prepared by RIE for 8 min, using SF 6 of 70 sccm and O 2 of various flow rates: 50, 70, 90, and 130 sccm respectively. For simplicity, we denote samples of different SF 6 and O 2 flow rates and etching time as . Therefore 70–50–8 means a sample processed with = 70 sccm, and = 50 sccm for 8 min. Morphologies of samples vary from sharp hierarchical needles (70–50–8), tapered cones (70–70–8 and 70–90–8), to rounded domains (70–130–8), which are macroscopically uniform while microscopically stochastic. According to the cross-sectional SEM images, structures prepared with different O 2 flow rates are of different heights: with increasing O 2 rate, the structure height reduces from 1.540 ± 0.544, 0.733 ± 0.079, 0.557 ± 0.084, to 0.264 ± 0.026 μm. This trend is consistent with results obtained by other groups 26 31 32 . As the height of the fabricated structures ranges from a few hundred nanometers to a few micrometres, we refer to our structures as nano- and microcones. Results of static water contact angle measurements of these surfaces in comparison to the flat Si surface are shown in Fig. 1b . For samples with the hydrophobic 1 H, 1 H, 2 H, 2 H-perflourodecyltrichlorosilane (FDTS) coating, with increasing O 2 flow rate, we measured decreasing static water contact angle. By assuming that most samples exhibit a (tapered) cone shape, the geometry of these samples is determined by only two parameters, the height and the opening angle of the cones ( Fig. 2a ). It is thus necessary to discuss which geometric parameters dominate the wetting properties of a chemically fixed surface. As depicted in Fig. 2a , the water-air interface is almost flat on the microscopic scale at a quasi-equilibrium 9 28 . The micro-contact angle at the water-solid sidewall ( Fig. 2a ) is thus determined by the advancing contact angle of the flat FDTS surface. This is to say, for the non-wetting condition, the Cassie-Baxter state 35 , or an intermediate Cassie-impregnating state 28 36 , ; while for the complete wetting situation, the Wenzel state 37 , . Hence for the non-wetting condition, the opening angle of an individual cone has to fulfil 2 α ≤ 51.0° ± 1.8°. Figure 2b summarizes the opening angles of 14 samples and their corresponding wetting states. Among all 14 measured samples, two are sticky, that is water droplets were pinned to the surface even when the surface was positioned upside down; and the rest are non-sticky, that is water droplets rolled/slid off from the surfaces at a certain surface inclination. Apparently, all the non-sticky samples have opening angles smaller than the threshold value 51.0° ± 1.8°. The two sticky samples have however opening angles of 49.5° ± 10.2° (70–130–8) and 2 α = 47.8° ± 7.4° (70–130–12), which are above or comparable to the expected threshold. Interestingly, for Si-70–130–8, all 5 tested drops were pinned; while for Si-70–130–12, only 3 out of 5 drops were pinned, which fits very well with this threshold hypothesis. It is worth mentioning that here we use the critical angle to draw a distinct line between the slippery and sticky surfaces. However, the degree of slipperiness and stickiness is always gradual. In fact, the wetting test on sample Si-70–130–12 shows that depending on structural imperfections on the microscale, the surface can be either “slippery” and “sticky” on macroscale. Though results from Fig. 2b fit the critical opening angle assumption without considering the influence of the structural height, the influence of structural height on the wetting properties of individual samples should not be excluded completely. To understand the influence of the opening angles and structure heights, we plot water contact angle and roll-off angle of different samples with respect to both and ( Fig. 3 ). Interestingly, the static and dynamic contact angles do not show obvious dependence on but exhibit an apparent linear dependence on 2 α for the cones. The apparent contact angle at equilibrium of a rough surface is usually described by the Cassie-Baxter equation for the non-wetting state 9 35 where , is the solid fraction. For a cone geometry depicted in Fig. 2a , , for hexagonal close-packed cones. Equation 1 can be rewritten as, According to the Cassie-Baxter equation (Eq. 1 ), the larger the solid fraction , the smaller the , as both and the Young contact angle are larger than 90°. The θ e ~ 2 α dependence from Fig. 3b can only be achieved if h / H increases with increasing 2 α , indicating that the penetration depth depends on 2 α for the given geometry. Such dependence is consistent with our data analysis, as shown in Fig. 3c , where the water penetration degree h / H is shown to depend on 2 α linearly. Here h / H is calculated by Equation 3 , using the experimentally measured angles 2 α and , and ranges from 5% up to 50%. The wetting situations described by the Cassie and Wenzel models are usually considered binary; there is no gradual transition from Cassie to Wenzel state (or vice versa); a surface should be either non-wetted or completely wetted. However, several studies have revealed that an intermediate wetting state exists, which can be reversed back to the Cassie state when certain conditions are met 16 28 36 38 . Although we here use the Cassie-Baxter model to explain the linear dependence of , it does not necessarily mean that the intermediate state does not exist for some of the geometries presented. We now turn to describe the dynamic properties of the surfaces. The linear increase of roll-off angle with increasing opening angle shown in Fig. 3b also indicates that the structures are partially wetted with various water penetration degrees. Such a wetting situation can be understood in two ways: 1. The structures are not perfect – some are sharper and others are blunter. Therefore, there is a stochastic mixture of totally non-wetted and wetted cones on the microscale, which results in the partial wetting situation on macroscale. 2. In an ideal situation, all cones are identical and arranged in hexagonal close packed order. The cones are wetted exactly the same way on the microscale with certain degree of water penetration. The wetting situation of structures fabricated by RIE mostly falls into the first category due to the nature of the RIE technique 29 . The macroscopic wetting behaviour is none-the-less the same for both cases. The roll-off angle is determined by the balance of the driving force along the rolling plane and the friction during the movement, which is also called the pinning force, and is proportional to the contact angle hysteresis and the surface tension of the liquid. For a flat surface, the relation between a roll-off angle and the contact angle hysteresis can be explained by the Furmidge equation 39 . where is the gravity force acting on the droplet, which is constant for all measured droplets, d is the width of the drop base viewed along the rolling/sliding direction, and and are the receding and advancing contact angles respectively. For rough surfaces, instead of using the width d of the drop, the effective three-phase contact length along the rolling direction should be used 18 21 . For the hexagonal closed packed cone model geometry, this length is given by . The Furmidge equation can thus be rewritten as: When we plot the roll-off angle versus contact angle hysteresis ( Fig. 3d ), we are able to fit the plot with a second order polynomial fit. The second order dependence implies, for our structures, that has a linear dependence on the contact angle hysteresis. Such dependence is supported by the plot of versus contact angle hysteresis ( Figure S3 ). It is worth mentioning that Equations 3 and 5 are derived based on the hexagonal closed packed cone geometry, which is the most representative geometry of the fabricated nano and microcones. However, not all samples exhibit the closed packed geometry, as can be seen from both Fig. 1 and Fig. S1 . In the supporting information (SI) , we discuss several other packing geometries, which nonetheless only change some prefactors in Equations 3 and 5 . Superhydrophobic polymers Though much effort has been invested to study the wetting properties of silicon samples, the final goal is to develop a technique that can be integrated into existing fabrication techniques for solid materials, such as injection moulding of polymers. As a starting point and a simple demonstration of our technique, we chose structures 70–70–8 and 70–90–8 for master origination and a low surface energy material polypropylene (PP, γ PP ≈ 29 mN/m 40 ) for injection moulding. Though Si-70–50–5 exhibits the strongest superhydrophobicity, replication fidelity of this structure could be low due to its hierarchical nature; while 70–70–8 and 70–90–8 with FDTS coating still have high superhydrophobicity and are easy to replicate. The main steps for replicating polymer surfaces through Si masters are shown in Fig. 4a . The prepared Si master serves as a template for forming textured Ni shim, and is removed by warm KOH bath after the Ni formation ( supporting information ). The Ni shim is then used as a mould for injection-moulding textured PP samples. A more detailed fabrication procedure can be found in the supporting information (SI) and our previous publications 28 41 . Both replicated PP samples exhibit cone morphology after injection moulding ( Fig. 4b ), indicating that the structures have been transferred from their Si masters successfully. However, the cones of PP-70–70–8 are much shorter than those of PP-70–90–8, though their masters are quite the opposite: the cones of Si-70–70–8 are 170 nm higher than those of Si-70–90–8. The replication quality of injection moulded polymer samples depends mainly on the filling quality of the polymer melt and the demoulding of the structures from the mould. The filling of micro-cavities in Ni-70–70–8 is more difficult than in Ni-70–90–8, as the former has a higher aspect ratio (Si-70–70–8 around 3.6 and Si-70–90–8 around 2.1) 42 43 . On the other hand, the higher surface area of structure 70–70–8 results in a higher friction during the demoulding, which might cause deformation or even fracture of the moulded polymer cones 44 . Interestingly, the water wettability of the two samples however follows the trend of their silicon masters: PP-70–70–8 is more superhydrophobic than PP-70–90–8, as summarized in Table 1 . As discussed previously, the structure height contribute less to the superhydrophobicity compared to the structure opening angle or the structure aspect ratio, which explains why samples with shorter cones exhibit higher superhydrophobicity. For samples of the same surface morphology, their superhydrophobicity is mainly dominated by the surface chemistry. As FDTS has a lower surface energy than PP, the resulting Si surface coated with FDTS should be more superhydrophobic than the PP surface, if we assume both have the same surface structure. The measured water contact angles of Si-70–70–8 and PP-70–70–8 follow such a rule, i.e. Si-70–70–8 is slightly more superhydrophobic than PP-70–70–8. However, PP-70–90–8 is more superhydrophobic than its silicon master Si-70–90–8. Such a deviation from the theory could possibly be due to the fact that the structure of PP-70–90–8 is different than its Si master Si-70–90–8. During demoulding, the PP cones might be stretched, which might form sharper cones than their Si masters. Superhydrophilic silicon samples The complete wetting can be described by the Wenzel equation 37 with being the surface roughness factor for the cone shape model, which equals the surface area divided by the apparent area of the rough surface. For θ Y < 90°, creating surface roughness would lead to more hydrophilic surfaces, and even superhydrophilic surfaces, according to the Wenzel model. The water contact angle of unstructured Si surface after plasma cleaning is 35.5 ± 4.1°, and for all samples, as calculated from the opening angle 2α measured on the SEM images. The resulting should thus be much smaller than 10° for the structured surfaces. We therefore tested the wetting properties of samples processed by the same SF 6 and O 2 flow rates as the ones shown in Fig. 1a , but without FDTS coating ( Fig. 5 ). All surfaces exhibited strong superhydrophilicity during the measurements: the water droplet spread on the surface immediately after impacting the surface. The static contact angles of all samples are smaller than 5 degree. Interestingly, according to the Wenzel equation, for complete wetting, the higher the surface roughness, the smaller is the contact angle if θ Y < 90°. However, here we find the highest contact angle ∼10° for the surface with the highest . The high water contact angle of 70–50–8 is perhaps the result of the hierarchical nature of this sample surface ( Fig. 1a ). Hejazi et al. suggested that for hierarchical surfaces there could be two wetting states at different levels of roughness; structures that contribute to the first order roughness, the small spikes on top of the cones are completely wetted, while structures that contribute to the second order roughness, the cones might be partially wetted (air trapped) 45 . Such a multi-degree wetting situation can result in a higher apparent contact angle , as demonstrated by Hejazi et al. , which is consistent with our experimental observation on superhydrophilic Si-70–50–8. Hydrophilic polymer replicas For a simple demonstration of our method, we textured PMMA to generate the hydrophilic function, as the surface energy of PMMA ( γ PMMA ≈ 39 mN/m 40 ) is relatively high among polymers and can be injection-moulded. To further improve the wettability of the textured surface, we treated the textured surface with O 2 plasma for 25 s. As proved by AFM results ( Figure S6 ), there is no obvious surface structure modification within such a short treatment. Figure 6 represents PMMA samples fabricated through the silicon master 70–70–8, which is the same one used for fabricating PP samples. Here the height of the cones is only around 120 nm, which is similar to that of PP–70–70–8, but also much smaller than that of the silicon master Si–70-70–8. Such a small height is probably due to the same reason: the polymer melt did not fully fill the Ni cavities, and/or the cones broke during the demolding. The Ni insert used for injection moulding was examined by SEM afterwards, however no obvious residual polymer was found in the holes ( Figure S7 ). We therefore attribute the low structure height to the incomplete filling of the submicron cavities. Though the structures are a bit lower than expected, the surface wettability has increased after texturing: The water contact angle is for flat untreated PMMA, for treated PMMA and for treated and textured PMMA. We can thus conclude that by texturing and surface treating PMMA, we can mass–produce surfaces with high water wettability. In summary, we have discussed the influencing geometric parameters on surface wettability at a given surface chemistry. From our experimental results, the opening angles of the nano- and microcones play a dominant role on both the static contact angle and the roll-off angle of a surface: the static and dynamic contact angles of superhydrophobic silicon samples exhibit linear dependence on the opening angles of the cones, whereas there is no obvious dependence of them on the height of the cones. Such a rule also applies to polymer samples, injection moulded using the corresponding silicon masters. In addition, we have demonstrated that the same structures used for superhydrophobic applications also can be used to fabricate superhydrophilic surfaces, without any further surface modifications required. The multi-order roughness of the hierarchical surface, however, reduces the superhydrophilicity slightly, due to the different wetting states of the various levels of roughness."
} | 4,380 |
26998520 | PMC4795665 | pmc | 595 | {
"abstract": "Researchers successfully design materials with extremely low ice adhesion.",
"introduction": "INTRODUCTION Ice accretion and its subsequent removal is a safety hazard for aircrafts, power lines, motor vehicles, marine structures, communication towers, and wind turbines ( 1 ). The most common methods for ice removal are extremely energy-intensive ( 2 ), and there exists a strong need to develop methods where ice is passively removed from a surface (that is, no external energy input) ( 3 ). Previously, there have been numerous publications related to developing “icephobic” surfaces ( 2 – 13 ). Such surfaces use different approaches including delaying droplet freezing time ( 5 , 13 – 15 ), preventing frost formation ( 6 , 8 , 12 ), and lowering τ ice ( 2 – 4 , 6 – 9 , 11 , 13 , 16 ). Icephobic surfaces can be defined by an ice adhesion strength τ ice < 100 kPa ( 13 ). In comparison, structural materials like aluminum or steel have extremely high τ ice , around 1600 and 1400 kPa, respectively ( 3 ). However, to passively remove ice with no external energy input, such as on airplane wings, power lines, or boat hulls, extremely low values of τ ice are required. For example, Dou et al . ( 16 ) found that a strong breeze detached ice when τ ice ≤ 27 ± 6 kPa. Previous work has shown that, on different, high modulus solids, τ ice = B γ(1 + cosθ rec ), where B is an experimental constant, γ is the surface tension of water, and θ rec is the receding water contact angle ( 2 ). For nontextured surfaces, this provides a theoretical lower limit for τ ice of ~150 kPa (as the maximum θ rec water ≈ 120°). Superhydrophobic surfaces display an ultrahigh θ rec water through the incorporation of texture and have been shown to have τ ice as low as 50 kPa. However, an increasing body of work suggests that even these moderately low ice adhesion values cannot be maintained due to condensation and frost formation ( 5 , 6 , 13 – 15 , 17 ). To date, the lowest ice adhesion values have only been reported using lubricants (τ ice = 16 kPa) or gels (τ ice = 0.4 kPa) ( 8 , 18 – 20 ). Lubricated surfaces purportedly achieve low ice adhesion by minimizing the contact angle hysteresis on the surface through the formation of a low surface energy (typically highly fluorinated) lubricating free-oil layer. But again, the icephobicity for such surfaces can be short-lived, as the oil may be displaced and removed by water droplets ( 7 ) or frost ( 12 ), or during accreted ice removal (fig. S1A). Overall, there are no reports of durable icephobic surfaces that maintain or even exhibit τ ice < 15 kPa. Here, we study the ice adhesion of elastomers. Elastomers are viscoelastic, that is, they can demonstrate both solid- and liquid-like properties. We control the viscoelastic nature of our elastomers in two ways. First, we modify the cross-link density ρ CL of our elastomers to alter their physical stiffness ( G = RT ρ CL , assuming isotropy, where G is the shear modulus and R is the universal gas constant). The stress required to shear a hard block (such as ice) from a soft film (such as an elastomeric coating) is given by τ = A ( W a G / t ) 1/2 , where A is an experimental constant, W a is the work of adhesion, and t is the thickness of the soft film ( 21 , 22 ). This is a macroscopic relationship that predicts the shear stress required to cleave two surfaces apart, a process that occurs through interfacial cavitation ( 21 , 23 ). Second, we alter the no-slip boundary condition ( 24 ) at the ice-elastomer interface through the addition of uncross-linked, polymeric chains. In solid-solid contact, conservation of momentum usually dictates that the velocity at the interface is zero or that there is no slip. However, if the polymeric chains within the elastomer are sufficiently mobile, slippage (that is, a nonzero slip velocity) can occur at the solid-solid interface, as has been observed previously for polymer melts ( 25 , 26 ), adhesives ( 24 ), and rubbers ( 23 ). When a hard surface slides over a soft elastomer, such as during interfacial slippage, the shear stress to slip at the interface is given by τ = Gfa / kT or τ ∝ G 1 . Here, f is the force needed to detach a single chain of segmental length a , k is the Boltzmann’s constant, and T is the temperature ( 27 , 28 ). By tailoring ρ CL for different elastomeric coatings, and by additionally embedding miscible, polymeric chains to enable interfacial slippage, we show that it is possible to systematically design icephobic coatings with extremely low ice adhesion (τ ice < 0.2 kPa). Overall, we have designed a comprehensive library of more than 100 icephobic surfaces that can be rough, smooth, hydrophobic, or hydrophilic, as shown in fig. S2A (also see Table 1 ). It is clear from fig. S2 that the variations in τ ice for the different icephobic coatings developed in this work cannot be explained by variations in the parameter 1 + cosθ rec . For soft surfaces, this is because the interface either cavitates or slips before the work of adhesion is reached ( 21 ). Table 1 A library of icephobic surfaces. The coating fabrication methodology and resulting ice adhesion strengths, cross-link densities, and water contact angles for all the samples fabricated in this work. SG, Sylgard; SO, silicone oil; PS, polystyrene; PIB, polyisobutylene; PFPE, perfluoropolyethers; FPU, fluorinated polyurethane polyols; PMPS, polymethylphenyl siloxane; UVA, ultraviolet A; RT, room temperature; NS, no slippage (no oil is added to the coating); IS, interfacial slippage (miscible oil has been added but no lubricating liquid layer forms) [confirmed by atomic force microscopy (AFM), optical microscopy, and the shape of the force versus time curves]; L, lubricated [excess oil (either intentionally or otherwise) is added to the coating, forming a thick lubricating layer] (confirmed using the same methods as for interfacial slippage). Polymer base Nonreactive oil wt % Reactive oil wt % Cure (°C/hour) ρ CL (mol/m 3 ) τ ice average (kPa) τ ice min. (kPa) τ ice max. (kPa) Type θ adv /θ rec (°) A SG 184 10:1 — — — — 150/24 307±8 264 245 340 NS 120/94 B SG 184 10:1 — — — — 80/2 333±45 47 36 57 IS 131/26 C SG 184 20:1 — — — — 80/2 112±1 178 147 251 NS 129/45 D SG 184 4:1 — — — — 80/2 33±45 89 42 165 IS 127/36 E SG 184 3:1 — — — — 80/2 268±2 15 6 29 L 122/76 F SG 184 2:1 — — — — 80/2 222±9 14 6 23 L 118/77 G SG 184 5:2 — — — — 80/2 267±21 16 8 26 L 112/100 H SG 184 1:1 — — — — 80/2 162±5 14 6 29 L 112/89 I SG 184 10:1 100-cP SO 25 — — 80/2 219±13 35 26 56 IS 123/89 J SG 184 10:1 100-cP SO 50 — — 80/2 72±11 87 40 120 IS 114/94 K SG 184 10:1 100-cP SO 75 — — 80/2 — 55 30 71 IS 114/94 L SG 184 10:1 — — PMHS 25 80/2 215±10 10 1.0 31 L 105/103 M SG 184 10:1 — — PMHS 50 80/2 75±13 67 31 121 IS 118/101 N SG 184 10:1 — — PMHS 75 80/2 — 17 4.9 39 L 121/102 O SG 184 1:1 100-cP SO 25 — — 80/2 32±2 173 58 237 IS 124/86 P SG 184 1:1 100-cP SO 50 — — 80/2 13±2 46 17 74 IS 124/82 Q SG 184 1:1 100-cP SO 75 — — 80/2 — 18 0.15 47 IS 104/103 R SG 184 1:1 — — PMHS 25 80/2 102±5 17 1.0 40 L 125/104 S SG 184 1:1 — — PMHS 50 80/2 14±4 6 0.7 30 L 106/105 T SG 184 1:1 — — PMHS 75 80/2 — 9 0.35 31 L 105/103 U SG 184 10:1 100-cP SO 25 PMHS 25 150/24 536±97 64 50 78 IS 119/95 V SG 184 10:1 100-cP SO 15 PMHS 15 80/2 — 31 1.0 137 L 108/104 W SG 184 10:1 100-cP SO 10 PMHS 10 150/24 459±9 74 40 116 IS 123/90 X SG 184 10:1 — — PMHS 10 80/2 283±9 37 4.0 71 IS 114/100 Y SG 184 10:1 — — PMHS 10 150/24 284±41 173 122 234 NS 121/78 Z SG 184 10:1 — — PMHS 20 80/2 197±4 45 19 82 IS 109/105 AA SG 184 10:1 — — PMHS 20 150/24 348±28 64 34 92 IS 118/93 BB SG 184 10:1 — — PMHS 25 150/24 452±9 302 275 346 NS 103/84 CC SG 184 10:1 100-cP SO 25 PMHS 15 150/24 405±27 58 41 73 IS 112/104 DD SG 184 10:1 100-cP SO 20 PMHS 20 80/2 107±2 37 9.1 67 IS 109/100 EE SG 184 10:1 100-cP SO 25 PMHS 25 80/2 150±8 35 5.1 77 IS 116/99 FF SG 184 10:1 100-cP SO 25 PMHS 10 150/24 290±25 41 24 55 IS 112/108 GG SG 184 10:1 5-cP SO 25 — — 80/2 181±5 145 109 178 IS 121/90 HH SG 184 10:1 1000-cP SO 25 — — 80/2 153±7 45 33 53 IS 100/85 II SG 184 10:1 10000-cP SO 25 — — 80/2 67±2 81 13 226 L 120/104 JJ SG 184 10:1 SO AP 1000 25 — — 80/2 216±3 66 12 171 L 113/78 KK SG 527 1:1 — — — — 150/24 0.68 ‡ 14 7.6 25 NS 130/89 LL 1:9 SG 527:184 100-cP SO 25 — — 150/24 182±11 14 7.3 18 IS 112/103 MM 1:3 SG 527:184 100-cP SO 25 — — 150/24 123±2 10 5.5 17 IS 111/104 NN 1:1 SG 527:184 100-cP SO 25 — — 150/24 76±1 9 5.5 12 IS 112/102 OO 3:1 SG 527:184 100-cP SO 25 — — 150/24 46±2 6 3.7 8 IS 114/101 PP 3:1 SG 527:184 — — — — 150/24 50±2 10 4 49 IS 123/100 QQ 1:3 SG 527:184 — — — — 150/24 104±5 141 130 154 NS 122/95 RR 1:1 SG 527:184 — — — — 150/24 110±5 19 6.7 37 IS 117/88 SS 9:1 SG 527:184 100-cP SO 25 — — 150/24 8.0±0.8 6 4.1 7 IS 121/98 TT 9:1 SG 527:184 — — — — 150/24 9.1±0.9 134 132 139 NS 121/96 UU PFPE — — — — UVA 5 min 160±35 238 200 281 NS 115/93 VV PFPE Krytox 100 25 — — UVA 5 min 96±24 31 17 53 IS 115/95 WW PFPE Krytox 105 25 — — UVA 5 min 124±33 31 16 55 IS 104/98 XX PFPE Krytox 103 25 — — UVA 5 min — 12 10 13 IS 114/91 YY PFPE — — CN4002 10 UVA 5 min — 45 33 51 L 117/91 ZZ FPU — — — — 80/72 1098±98 538 257 627 NS 103/72 AB * FPU — — — — 80/72 475±14 394 334 479 NS 105/73 AC * FPU — — — — 80/72 316±17 284 204 399 NS 101/73 AD * FPU Krytox 100 25 — — 80/72 1142±158 595 538 713 IS 101/72 AE * FPU Krytox 105 25 — — 80/72 1112±77 392 283 520 IS 105/72 AF * FPU — — NCO C50 75 150/24 1332±48 246 194 320 IS 108/84 AG * FPU 100-cP SO 5 NCO C50 75 80/72 82 61 100 IS 109/82 AH * FPU 100-cP SO 10 NCO C50 75 80/72 49 22 66 IS 106/96 AI PS — — — — RT/24 447,000 ‡ 336 189 370 NS 97/86 AJ PS 200 M w PS 25 — — RT/24 — 424 271 569 IS 103/74 AK PS 200 M w PS 50 — — RT/24 — 570 378 642 IS 109/58 AL PS 540 M w PS 25 — — RT/24 — 477 454 510 IS 100/79 AM PS SO AP 1000 25 — — RT/24 — 92 59 112 L 103/97 AN PS PMPS 10 — — RT/24 — 354 218 491 IS 98/84 AO PS PMPS 5 — — RT/24 — 333 217 498 IS 99/84 AP PIB — — — — RT/24 8,000 ‡ 395 335 453 NS 125/56 AQ PIB Polybutene 25 — — RT/24 — 288 220 419 IS 128/56 AR PIB Polybutene 50 — — RT/24 — 459 341 620 IS 130/17 AT PIB Polybutene 75 — — RT/24 — 268 176 442 IS 128/72 AU VytaFlex10 — — — — RT/24 26±7 144 84 254 NS 52/12 AV VytaFlex40 — — — — RT/24 95±14 151 118 192 NS 80/26 AW VytaFlex60 — — — — RT/24 290±17 261 157 360 NS 82/23 AX † VytaFlex40 Vegetable 20 — — RT/24 53±4 10.5 4.6 22 L 68/21 AY * VytaFlex40 Cod liver 15 — — RT/24 29±2 27 9 51 IS 75/12 AZ * VytaFlex40 100-cP SO 10 RT/24 — 41 18 83 L 82/45 BA VytaFlex40 — — NCO Di-50 1 RT/24 47±3 109 51 179 IS 96/49 BB VytaFlex40 — — NCO Di-50 5 RT/24 52±2 101 42 232 IS 110/56 BC VytaFlex40 — — NCO Di-50 10 RT/24 34±7 139 49 243 IS 113/60 BD * VytaFlex40 100-cP SO 10 NCO Di-100 50 RT/24 21±1 11 6 15 IS 97/89 BE * VytaFlex40 — — NCO C50 50 RT/24 42±0.4 44 25 55 IS 106/81 BE † VytaFlex40 100-cP SO 5 NCO C50 50 RT/24 36 18 57 IS 100/85 BF * VytaFlex40 100-cP SO 10 NCO C50 50 80/72 11 6 17 IS 95/86 BG * VytaFlex40 — — NCO C50 75 RT/24 171±4 49 38 65 IS 102/85 BH * VytaFlex40 100-cP SO 10 NCO C50 75 RT/24 9 3 12 IS 91/82 BI * VytaFlex40 1000-cP SO 10 NCO C50 75 RT/24 10 5 14 IS 99/90 BJ * VytaFlex40 5-cP SO 10 NCO C50 75 RT/24 18 12 24 IS 102/83 BK VytaFlex40 10,000-cP SO 10 NCO C50 75 RT/24 19 14 31 IS 102/92 BL VytaFlex40 100-cP SO 5 — — RT/24 — 77 70 90 L 70/42 BM VytaFlex40 100-cP SO 10 — — RT/24 — 80 58 91 L 68/42 BN VytaFlex40 100-cP SO 15 — — RT/24 — 98 68 128 L 65/41 BO VytaFlex40 100-cP SO 20 — — RT/24 — 93 76 107 L 67/42 BO VytaFlex40 Vegetable 5 — — RT/24 62±2 128 77 200 IS 79/23 BQ VytaFlex40 Vegetable 10 — — RT/24 62±4 238 233 247 IS 89/48 BR VytaFlex40 Vegetable 15 — — RT/24 49±2 121 91 151 IS 32/20 BS VytaFlex40 Vegetable 20 — — RT/24 53±4 173 141 227 IS 43/34 BT VytaFlex40 Cod liver 5 — — RT/24 — 129 107 166 IS 67/29 BU VytaFlex40 Cod liver 10 — — RT/24 — 70 56 85 IS 59/34 BV VytaFlex40 Cod liver 15 — — RT/24 — 110 100 120 IS 46/34 BW † VytaFlex40 Cod liver 15 — — RT/24 29±2 4 2 9 IS 43/25 BX † VytaFlex40 Vegetable 15 — — RT/24 52±1 11 3 15 IS 88/44 BY † VytaFlex40 Safflower 2.5 — — RT/24 63±0.5 30 20 43 IS 100/32 BZ * VytaFlex40 Safflower 5 — — RT/24 50±0.5 11 9 16 IS 82/28 BA * VytaFlex40 Safflower 10 — — RT/24 45±5 6 4 12 IS 72/24 CB † VytaFlex40 Safflower 15 — — RT/24 33±1 4 1 7 IS 67/29 CC † VytaFlex40 Safflower 20 — — RT/24 32±0.4 6 3 11 L 56/44 CD † VytaFlex40 Safflower 25 — — RT/24 45±2 4 2 6 L 52/43 CE VytaFlex40 Cod liver 20 — — RT/24 — 97 76 114 L 34/21 *Films that were spray-coated (500 mg/ml). All others are spin-cast at 1500 rpm for 60 s (200 mg/ml). †Films that were dip-coated (500 mg/ml). ‡Approximated from the elastic modulus of the polymer.",
"discussion": "RESULTS AND DISCUSSION Mechanisms for low ice adhesion We first attempted to understand the effects of interfacial slippage and ρ CL on τ ice , using a shear-based (Mode-II) ice adhesion test, conducted at −10°C (see Materials and Methods) ( 2 ). To do so, we tested four representative polydimethylsiloxane (PDMS) samples: high ρ CL PDMS (ρ CL = 307 ± 8 mol/m 3 ), low ρ CL PDMS (ρ CL = 50 ± 2 mol/m 3 ), high ρ CL PDMS with oil (ρ CL = 290 ± 25 mol/m 3 , 25 wt % silicone oil), and low ρ CL PDMS with oil (ρ CL = 46 ± 2 mol/m 3 , 25 wt % silicone oil). For high ρ CL PDMS (unaltered Sylgard 184), τ ice = 264 ± 19 kPa ( Fig. 1A ), which matches reported literature values of 200 to 300 kPa ( 2 , 10 ). To achieve a surface with interfacial slippage and the same ρ CL as Sylgard 184, we added both silicone oil (which lowers ρ CL ) and polymethylhydrosiloxane (PMHS; which raises ρ CL ) until the equivalent ρ CL was achieved. Such a surface has τ ice = 58 ± 5 kPa, a fivefold reduction over unaltered Sylgard 184, highlighting the effect of interfacial slippage provided by the miscible chains. Note that by maximizing the miscibility between the elastomeric network and the chains causing interfacial slippage, we avoid the formation of a liquid layer on top of the substrate that can be easily abraded (discussed later) ( 6 , 7 , 12 ). For PDMS with a lower ρ CL and devoid of any uncross-linked chains (see Materials and Methods), we found τ ice = 33 ± 2 kPa. This is five times lower than the theoretical minimum of τ ice = 150 kPa, without the use of lubricating layers, fluorination, or texture. Indeed, coatings with values of τ ice < 10 kPa can be fabricated without oil, solely by lowering ρ CL significantly (see coatings KK, PP, and RR in Table 1 ). Similarly, chemically grafted chains that can induce interfacial slippage can lower the ice adhesion to values as low as τ ice = 11 ± 4 kPa (fig. S1D). Both mechanisms can be used independently to fabricate surfaces with lower ice adhesion than anything previously reported. Indeed, when both mechanisms are used in concert, these effects are amplified. Accordingly, for low ρ CL PDMS with interfacial slippage, we measured τ ice = 6 ± 1 kPa. Fig. 1 Mechanisms responsible for low ice adhesion. ( A ) PDMS-based coatings having low or high ρ CL , with or without interfacial slippage. ( B ) Relationship between ρ CL and τ ice for coatings without interfacial slippage. Error bars are 1 SD, and the best fit is found using the method proposed by York et al. ( 44 ). The slope is 0.51 ± 0.04. ( C ) Variation of τ ice with ρ CL for coatings with interfacial slippage. The best-fit slope is 1.01 ± 0.03. ( D ) Ice-reducing potential I * as a function of ρ CL . Error bars are 1 SD, and for the best-fit curve shown, R 2 = 0.89. We fabricated a series of different icephobic coatings (see Materials and Methods) from PDMS, polyurethane rubbers (PU), fluorinated polyurethane polyols (FPU), and perfluoropolyethers (PFPE), with ρ CL varying from 0.68 to 1203 mol/m 3 , as measured by solvent swelling using Flory-Huggins theory ( 29 ) and confirmed by Mooney-Rivlin analysis (fig. S3) ( 30 ). To enable interfacial slippage, we embedded the elastomers with either silicone, Krytox, vegetable oil, cod liver oil, or safflower oil (see Materials and Methods). Earlier, we stated that τ ice ∝ G 1/2 for elastomeric surfaces in the absence of interfacial slippage. When we measured τ ice for surfaces devoid of any uncross-linked chains (that is, no added oil), we observe this dependence precisely ( Fig. 1B ). Because of interfacial cavitation, the ice abruptly detached from these coatings ( Fig. 2B ). For the different elastomers tested here, we found no significant impact of elastomer chemistry/surface energy on τ ice . The variation in ice adhesion strength was dominated by the changes in ρ CL . Fig. 2 Force versus time curve analysis. ( A and B ) Force versus time curves for a lubricant (PMHS oil) and lubricated (coating R) surfaces. The number next to each curve is the order in which the testing was performed. ( C ) Representative surfaces from fig. S1B, where the ice unadheres by interfacial cavitation. Note the abrupt drop in force once the ice has detached. Depending on the cross-link density, the ice adhesion can be low or high, but the mechanism for detachment remains the same. ( D ) The FPU (coating ZZ), which has no uncross-linked chains, causes ice to detach by interfacial cavitation, which results in high but consistent ice adhesion values. ( E ) In contrast, the PU coating (ρ CL = 33 ± 1 mol/m 3 , 15 wt % safflower oil) shows interfacial slippage. Note the persistence of a nonzero sliding force long after the ice has moved from its original location. Comparing (A) to (E), it is apparent that lubricated surfaces lose their oily layer quite rapidly, transitioning to high ice adhesion surfaces. ( F ) In contrast, varying the cross-link density on surfaces exhibiting interfacial slippage, the τ ice values can also be low or high, but the mechanism for detachment remains the same. When interfacial slippage is enabled, τ ice ∝ G 1 , assuming perfect molecular contact between the ice and the coated substrate ( 27 ). As we started with liquid water that was subsequently frozen, this assumption should hold. We confirmed this linear relationship for a number of different icephobic systems, as shown in Fig. 1C . Because of the interfacial slippage, the frictional force persisted long after the ice had unadhered from its original location (see Fig. 2 , E and F). Thus, we can differentiate elastomers with and without interfacial slippage either by the dependence of τ ice on ρ CL or by comparing the shape of their force versus time curves over multiple icing/deicing cycles. To predict the ice adhesion strength–reducing potential of interfacial slippage for different elastomers, we developed the dimensionless parameter I * (see the Supplementary Materials). I * is the ratio between τ ice for an elastomer without ( τ ice no-slip ) and with interfacial slippage ( τ ice slip ), and is given as I * = τ ice no-slip τ ice slip = C ρ CL (1) where C is a constant. For 14 different elastomeric surfaces (see Materials and Methods), we precisely made samples with equivalent ρ CL , both with and without interfacial slippage. Our measured I * values match the trend predicted by ( Eq. 1 ) quite well ( Fig. 1D ). The two important points to note here are that (i) a low ρ CL can help achieve extremely low values of τ ice and (ii) interfacial slippage is most effective in lowering τ ice for surfaces having a low ρ CL . For example, enabling interfacial slippage for the FPU (ρ CL = 1098 mol/m 3 ) only gives I * = 1.6, whereas for soft PDMS (ρ CL = 8.5 mol/m 3 ), I * = 24. Moreover, by fitting the data shown in Fig. 1D , we find that C ≈ 83 mol 1/2 m −3/2 . This has the physical interpretation that, for ρ CL > 7000 mol/m 3 , the addition of oil (or enabling interfacial slippage) will have no effect on τ ice (see fig. S2B). The cross-linked network with such a high ρ CL is too stiff to allow for significant chain mobility. I * predictions from ( Eq. 1 ) work well even for systems that only have physical entanglements. For example, adding 25, 50, or 75 wt % liquid polybutene to polybutadiene (ρ CL , ~8000 mol/m 3 ) ( 31 ) resulted in statistically equivalent τ ice values as compared to polybutadiene with no embedded polybutene, that is, I * = 1.0. The same was found for PS (ρ CL , ~450,000 mol/m 3 ) ( 31 ) embedded with liquid, low–molecular weight PS (see Materials and Methods; Table 1 ). When designing surfaces with interfacial slippage, a thick, lubricating layer can form if the added oil/polymeric chains start to phase-separate from the elastomer. We performed a number of experiments to differentiate lubricated surfaces from surfaces with interfacial slippage. The easiest way to check for a lubricating layer is touching the surface by hand. The layer can also be detected through controlled abrasion or by repeatedly measuring τ ice over multiple icing/deicing cycles ( Fig. 3A ). This free liquid layer is also readily viewable in optical micrographs or AFM phase images ( Fig. 3 , C and D). Lubricated systems are also mechanistically different from surfaces with interfacial slippage because they rely on extremely low contact angle hysteresis (CAH) to achieve their properties ( 8 ). Further, the friction on lubricated surfaces is independent of ρ CL but heavily reliant on the oil viscosity ( 32 ). In contrast, our icephobic surfaces using interfacial slippage can have high CAH ( Table 1 ), survive harsh mechanical abrasion that should remove any lubricating surface layer (discussed below), display τ ice values that depend strongly on ρ CL ( Fig. 1 , B to D), and are independent of oil viscosity ( Fig. 3B ). Fig. 3 Comparison between interfacial slippage and lubrication. ( A ) Variation of τ ice with the number of icing/deicing cycles. See Materials and Methods for a description of each coating’s fabrication. The values of τ ice for both the lubricant and the lubricated systems increase with an increasing number of icing/deicing cycles (see Fig. 2 , A and B, for force versus time curves). In comparison, there is no change in τ ice values for the surfaces with interfacial slippage over multiple icing/deicing cycles. ( B ) Variation in τ ice with oil viscosity. Values of τ ice for lubricated surfaces strongly depend on the oil viscosity and follow a typical Stribeck relationship ( 32 ). In comparison, the values of τ ice for surfaces with interfacial slippage are markedly independent of viscosity (coatings BH, BI, BJ, and BK in Table 1 ). ( C ) AFM phase images and optical micrographs of the PU coating with 15 wt % safflower oil. The surface does not have a lubricating oil layer. Note that the AFM phase image looks equivalent to the PU coating without oil (fig. S4C). ( D ) AFM phase images and optical micrographs of the PU coating with 10% silicone oil. The lubricating oil layer is clearly visible on the surface. Initially, we stated that superhydrophobic surfaces may not be icephobic due to wetting of their porous texture by condensing water droplets or frost. However, if the icephobicity arises from low ρ CL and interfacial slippage, superhydrophobic surfaces can be icephobic, even when fully wetted. Using a silicon mold with a square array of holes, we fabricated icephobic (τ ice = 26 ± 3 kPa), PDMS-based micropillars (see Materials and Methods). Droplets of water placed on such a surface display superhydrophobicity, with θ water adv / θ water rec = 165°/161° and a low roll-off angle of 3° ( Fig. 4 ). Such surfaces effectively repel water (above 0°C) through minimizing the solid-liquid contact area and solid ice (below 0°C) through low ρ CL and interfacial slippage. The differing mechanisms allow for a superhydrophobic surface to remain icephobic even when the surface is fully frosted. The PDMS-based coatings can also be used to imbue icephobicity to other textured surfaces, such as different wire meshes, yielding values as low as τ ice mesh = 2.4 ± 0.5 kPa (fig. S5). Fig. 4 Superhydrophobic and icephobic surfaces. ( A ) Droplets of water placed on icephobic PDMS pillars (coating I in Table 1 ) display superhydrophobicity, with θ water adv / θ water rec = 165°/161° and a low roll-off angle of 3° (inset). For 20 successive icing/deicing cycles on such surfaces, we measured τ ice = 26 ± 3 kPa. Such surfaces effectively repel liquid water through minimizing the solid-liquid contact area and solid ice through low ρ CL and interfacial slippage. The differing mechanisms allow the surface to remain icephobic even after the surface is fully frosted. ( B and C ) SEM micrograph of the icephobic pillars before and after ice adhesion testing. The pillars are not removed during ice adhesion testing. Durability of icephobic coatings To initially characterize the durability of our icephobic coatings, we evaluated force versus time curves, and thereby τ ice , for our surfaces over repeated icing/deicing cycles (see Materials and Methods). For surfaces damaged during the icing/deicing process, the shape of the force versus time curves changes, and τ ice increases, with increasing icing/deicing cycles. Both lubricated surfaces, as well as surfaces too soft to prevent physical damage, display such behavior within 10 icing/deicing cycles ( Figs. 2 , A and B, and 3A). However, these soft surfaces often offer almost immeasurably low τ ice . We measured τ ice = 0.15 ± 0.05 kPa for our most icephobic surface (fig. S1B). This is one of the lowest τ ice reported thus far and over five orders of magnitude below τ ice for aluminum. Ice slides off such surfaces solely under its own weight (movie S1). However, additional icing/deicing cycles begin to degrade the surface, raising τ ice (fig. S1B). Durable surfaces with interfacial slippage, typically having higher ρ CL , maintain their low ice adhesion values (τ ice = 3.6 ± 1.0 kPa) over repeated icing/deicing cycles ( Fig. 3A ) and show self-similar force versus time curves ( Fig. 2E ). To illustrate the significant advantage of coatings that repel ice through low ρ CL in conjunction with interfacial slippage, we conducted two simple tests for durability: repeated icing/deicing and relatively mild abrasion (see Materials and Methods). We compare our coatings’ performance to other state-of-the-art icephobic coatings, such as commercial superhydrophobic surfaces (NeverWet), lubricant-infused surfaces ( 8 ), extremely low–surface-energy fluorodecyl polyhedral oligomeric silsesquioxane (POSS) coatings ( 2 ), and commercially available icephobic coatings (NuSil R-2180). As fabricated, our PU coating (coating CB; ρ CL = 33 mol/m 3 , 15 wt % safflower oil, θ adv /θ rec = 67°/29°, CAH = 38°) shows an order of magnitude reduction in τ ice over the other state-of-the-art coatings considered here. Further, after just 10 icing/deicing cycles, all other coatings, except those fabricated here, exhibit ice adhesion strengths >200 kPa (with the exception of the commercial coating NuSil R-2180, which is a low ρ CL PDMS). Additionally, after mild abrasion, only our PU coating remains icephobic, with an ice adhesion strength 2500% lower than any other coating relying on lubrication or low surface energy. We additionally tested our PDMS-based coating (coating OO), which can be repeatedly iced but is mechanically very poor, and a PU-based coating, where we intentionally added excess safflower oil (20 wt %) to form a lubricating, free-oil layer (coating CC; see Materials and Methods). There is statistically no difference in τ ice values between the lubricated and interfacial slippage PU-based coatings initially or after 10 icing/deicing cycles (see Table 1 , fig. S1C, and Fig. 5D ). However, the lubricated PU coating easily delaminates from essentially all coated substrates ( Fig. 5C , left inset) due to the presence of the free-oil layer. Similarly, slippery liquid-infused porous (SLIPS)–based surfaces using costly, fluorinated lubricants suffer a 10-fold increase in ice adhesion after just a few icing/deicing cycles (fig. S1A). Thus, there is a marked advantage to producing interfacial slippage–based icephobic coatings. Finally, note that a Si wafer treated with a PDMS-silane, a surface exhibiting interfacial slippage ( 24 ) due to pendent chains ( 33 ), also exhibits very low ice adhesion (τ ice = 11 ± 4 kPa; see fig. S1D and Fig. 5D ). In comparison, a Si wafer coated with a low surface energy fluorinated silane exhibits relatively high ice adhesion (τ ice = 248 ± 57 kPa; fig. S1D). However, these thin silane coatings can be abraded away relatively easily ( Fig. 5D ). Fig. 5 Durability of the different icephobic coatings developed in this work. ( A ) Outdoor testing of a PDMS-based coating (coating NN; see Table 1 ) for 4 months during winter 2014. On 12 Febuary, the uncoated panel was covered with a ~7-mm layer of glaze, the type of ice with the strongest adhesion ( 1 ). No ice had accreted on the coated panel. On 4 March, snow followed a night of freezing rain, which completely covered the uncoated panel. The coated panel only had a small amount of accreted ice remaining. ( B ) Half-coated license plate during outdoor winter 2013 testing, with ice only accreted on the uncoated side. ( C ) Mechanical abrasion of three different icephobic coatings. The PDMS (coating NN) and lubricated PU (coating CC) were easily damaged and delaminated within 20 abrasion cycles, whereas the PU with interfacial slippage (coating CB) survives over 5000 cycles while maintaining low ice adhesion. ( D ) Comparison of coatings in this work with other state-of-the-art icephobic surfaces. Also, additional durability characterizations are presented for the PU coating with interfacial slippage. For details on each coating and test configuration, see Materials and Methods. To demonstrate the real-world potential of our durable icephobic surfaces, we conducted outdoor testing during the winter months of 2013 and 2014 in Ann Arbor, MI (see Materials and Methods). Over the 4 months of exposure, both snow and ice accreted severely on an uncoated glass panel. The coated panel often had snow settle on it, but all ice that formed was quickly sheared off even from mild winds ( Fig. 5A ) ( 34 ). After 4 months of exposure, the contact angles and τ ice for the coated surface were the same as before testing, highlighting the coating’s durability. Finally, we conducted extensive durability testing ( Fig. 5 , C and D) on our icephobic polyurethane (coating CB) including Taber abrasion (ASTM D4060), acid/base exposure, accelerated corrosion (ASTM B117), thermal cycling, and peel testing (ASTM D3359) (see Materials and Methods). We also measured τ ice over 100 icing/deicing cycles and evaluated the coating in a temperature range from −5° to −35°C (fig. S5B). After 5000 abrasion cycles, causing more than 600 μm of thickness loss, the coating remains icephobic because icephobicity is an inherent property of the coating. PDMS-based coatings (coating NN) or lubricated PU–based coatings (coating CC), though equally icephobic initially, are completely abraded away (and/or delaminated) after <20 cycles ( Fig. 5C ). The use of high surface energy elastomers, and the lack of a free-oil layer, allows us to create coatings that adhere very well to any underlying substrate. We observed no increase in τ ice even after 10 successive peel tests on steel, copper, aluminum, and glass, or after thermal cycling between −10° and 70°C. The average ice adhesion strength for this coating after all durability testing is τ ice = 9 ± 2 kPa. We additionally subjected our icephobic polyurethane to a tensile stress of 2.5 MPa, causing the elastomer to elongate by 350% without breaking or losing its icephobic properties ( Fig. 5C , right inset, and movie S2). Additional tensile testing showed strains in excess of 1000% (fig. S3). The developed, extremely durable coatings can be spun, dipped, sprayed, or painted onto essentially any underlying substrate of any size. Finally, we had the extremely low ice adhesion strengths for multiple surfaces independently verified by Mode-I type (peel test) and Mode-II (zero-degree cone) adhesion testing at the U.S. Army’s Cold Regions Research and Engineering Laboratory (CRREL) (see fig. S5A) ( 35 ). Overall, in this work, we discuss two universal attributes, cross-link density and interfacial slippage, which can be used to systematically tailor ice adhesion for elastomeric surfaces, irrespective of material chemistry. It was found that interfacial slippage makes the biggest impact on the ice adhesion strength of low cross-link density elastomers. Using this understanding, we fabricate a range of different, mechanically durable, long-lasting icephobic surfaces from a wide range of material systems. We foresee such extremely durable, icephobic coatings having immediate, worldwide applications across various industrial sectors, academic disciplines, and engineering endeavors."
} | 8,232 |
39809795 | PMC11732973 | pmc | 596 | {
"abstract": "Genome-scale metabolic models (GSMM) are commonly used to identify gene deletion sets that result in growth coupling and pairing product formation with substrate utilization and can improve strain performance beyond levels typically accessible using traditional strain engineering approaches. However, sustainable feedstocks pose a challenge due to incomplete high-resolution metabolic data for non-canonical carbon sources required to curate GSMM and identify implementable designs. Here we address a four-gene deletion design in the Pseudomonas putida KT2440 strain for the lignin-derived non-sugar carbon source, p -coumarate ( p -CA), that proved challenging to implement. We examine the performance of the fully implemented design for p- coumarate to glutamine, a useful biomanufacturing intermediate. In this study glutamine is then converted to indigoidine, an alternative sustainable pigment and a model heterologous product that is commonly used to colorimetrically quantify glutamine concentration. Through proteomics, promoter-variation, and growth characterization of a fully implemented gene deletion design, we provide evidence that aromatic catabolism in the completed design is rate-limited by fumarase hydratase (FUM) enzyme activity in the citrate cycle and requires careful optimization of another fumarate hydratase protein (PP_0897) expression to achieve growth and production. A double sensitivity analysis also confirmed a strict requirement for fumarate hydratase activity in the strain where all genes in the growth coupling design have been implemented. Metabolic cross-feeding experiments were used to examine the impact of complete removal of the fumarase hydratase reaction and revealed an unanticipated nutrient requirement, suggesting additional functions for this enzyme. While a complete implementation of the design was achieved, this study highlights the challenge of completely inactivating metabolic reactions encoded by under-characterized proteins, especially in the context of multi-gene edits.",
"introduction": "Introduction Computational and data-driven approaches in synthetic biology enable strain performance improvements typically inaccessible using traditional strain engineering approaches. One successful paradigm uses growth coupling design algorithms to query genome-scale metabolic models (GSMM) to reroute the metabolic flux towards a desired outcome, inactivating competing reactions that divert the substrate away from product synthesis. By implementing the required gene deletions predicted by the algorithms, metabolism is rewired so that the production of a target metabolite is concomitant with microbe growth. Thus growth is paired to the synthesis of the chosen product and can substantially improve the productivity of the targeted metabolite. This approach can also be used in tandem with other tools like serial passaging for laboratory evolution and with rational strain engineering 1 – 3 . Growth coupled production has been demonstrated using relaxed thresholds for product yield 4 – 7 and with model carbon substrates (i.e., glucose) 8 , 9 , but there are fewer examples of such approaches for non-sugar carbon streams 10 – 12 from renewable bioenergy streams like depolymerized plastics or aromatics from plant-derived lignocellulosic biomass. The challenge of using growth coupling algorithms for alternative carbon streams is that while co-utilization of several non-sugar carbon molecules has been described in many reports 13 – 17 , the existing metabolic data has yet to capture how carbon flows through various metabolic routes or account for enzyme constraints in specific multi-deletion mutant strains expressing heterologous gene pathways. This is especially true outside of glucose (or sugar) catabolism, where the understanding of carbon flux is less robust. When modeling does not reflect true cellular metabolism, these approaches are more likely to generate gene deletion targets that are ineffective. Growth coupling algorithms that promise substantial improvements to yields, making production essential for growth are termed “strong” growth coupling 4 , 8 . These algorithms require numerous gene modifications which are challenging to achieve in practice. Both partial and complete implementation of such designs, again oftentimes in conjunction with other approaches, have shown useful applications for bioproduction 8 , 18 – 20 and reveal key insights into the host strain physiology. We use a previously developed strong growth coupling design in Pseudomonas putida KT2440 that pairs a lignin-derived monomer, p- coumarate ( p -CA) to glutamine 21 (Fig. 1 ), a valuable platform chemical 22 – 24 . We further use the heterologous expression of a non-ribosomal peptide, indigoidine, an alternative for industrial indigo 25 – 27 . Indigoidine is generated by condensation of two glutamine molecules using two molecules of ATP, one Mg2+ and two flavin mononucleotides (FMN) as cofactors (Fig. 1E ), and it is often used as a proxy for glutamine since it can be measured using quantitative colorimetric assays for efficient strain prototyping 26 , 28 , 29 . Non-model carbon sources pose a challenge for growth-coupled strategies, as substrate toxicity (pertinent for p- CA) can be a challenging baseline for strain engineering 30 . We used c onstrained m inimal c ut s ets (cMCS), a computational approach 4 , 8 that provides strong coupling solution-sets or cutsets, where each cutset consists of reactions (and the corresponding genes) that need to be deleted for the growth coupled production phenotype. For bioconversion to glutamine/indigoidine, we showed that a partially implemented cutset (three out of the four demanded genes were deleted) enabled phenotypic growth coupling but as expected, generated the product at lower levels than the predicted yield 21 . As we encountered unexpected strain behavior after three of the four deletions were introduced, we used an ensemble of methods to characterize the triple deletion strain. However, since implementation of the complete cutset was not accomplished, a hypothesis that emerged is that a full cutset may allow higher productivity as predicted for the strong growth coupling design without additional refinement, or provide key insights into the role of the reactions involved in this design. Fig. 1 Growth coupled cutset for the bioconversion of p -coumarate to glutamine and Indigoidine. A Yield envelope for this study. The complete growth coupling solution described with the orange fill area (Design 1b) has a potential for 76% of the predicted maximum theoretical yield (MTY) of indigoidine from p- CA. The yield is shown for a range of substrate uptake rates from 0 to 10 mmol/gDCW/h. For comparison, the predicted biomass formation rate and yields for a partial cutset 21 are included for comparison as the gray area fills in the yield envelope. B Simplified metabolic map showing the four required gene interventions necessary to implement the Design 1b cutset in P. putida . Three genes are involved in fumarase hydratase activity, and one permease must be deleted. C The encoded reactions and genomic locations of the three genes involved in fumarase hydratase activity. D The different approaches taken to modulate the expression of PP_0897, another fumarate hydratase gene, need to be deleted for a complete cutset implementation. E Schematic representation of the indigoidine production module using two molecules of l -glutamine converted to the blue pigment, a non-ribosomal peptide. Here, we characterize an engineered P. putida strain containing the complete cutset and corresponding heterologous final product formation. We demonstrate that the fourth gene in this cutset, PP_0897, is involved in cellular activity beyond its annotation as a fumarate hydratase. The approaches involved promoter titration analysis, gene and metabolite complementation, DNA damage sensitivity analysis, and comparison of strain growth rates. Concomitantly, constraint-based analyses using proteomics data also showed limitations of growth and production on p- CA medium and were in good agreement with the experimental results.",
"discussion": "Discussion Fumarate and fumarate hydratase play a crucial role in connecting central metabolism to many cellular functions that are not captured by the genome-scale metabolic model. Deletion analyses of homologous fumC fumarate hydratases have been used to probe its function in many processes; a P. aeruginosa PAO1 homolog of PP_0897 was shown to have promiscuous activity on converting mesaconate to (S)-citramalate 46 . PP_0897 homologs in other species, including E. coli and yeast have been reported to indirectly enhance the DNA damage response to double-strand breaks; the metabolite fumarate modulates the bacterial chemotaxis response 37 – 39 . Deletion strains of the eukaryotic yeast homolog of PP_0897, FUM1 , are viable but growth on non-fermentative carbon sources requires medium supplementation with either aspartic acid, asparagine, or serine 47 , parallel with our observations on nutrient auxotrophy in the D1b_gf ∆PP_0897 strain. However, in this study, implementation of this final deletion resulted in a strain that did not grow nor produced per the predicted design. While modulation of the PP_0897 gene enabled systems that recovered growth and production phenotypes, some aspects of the results require additional studies for a better mechanistic understanding. Measurement of glutamine and glutamate levels revealed that glutamate cellular pools could be modulated by changing PP_0897 levels in the D1b_gf strain. However, we did not observe a stoichiometric conversion of these intermediates to the final product indigodine, suggesting cofactor imbalances or other growth and pathway protein expression impacts that remain obscure. In this regard, proteomics data proved instrumental in understanding the strain behavior when PP_0897 activity was altered and could be used to develop context-specific models that confirmed the narrow flux required for the FUM reaction. Ultimately, the best experimental solution obtained from the Design 1 strains is those that permitted some activity through this reaction, outperforming strains where the node was fully inactive. Flux through this node is required for biomass formation as well as specific final product yields demanded by the growth coupling design. The key insight of this study comes from the double sensitivity analysis that enabled the evaluation of a single gene intervention representing a limiting enzymatic reaction on both growth and production. Specifically, double sensitivity analysis maps the production of the target metabolite and implementation of a gene deletion while optimizing for growth and fitness. In the ideal situation, the ratio of biomass to final product remains consistent irrespective of specific growth rates. In contrast, we find an inconsistent growth rate to product formation rate ratio. An intervention of up to three steps yields a configuration with a broad permissible growth and acceptable indigoidine production space, whereas titrating down the expression of the last gene, PP_0897 changes the ratio of growth rate to indigoidine yield. These results imply that the PP_0897 deletion in the cutset is inviable because the permissible space for growth and production is constrained below a threshold acceptable for biological function. As a result, neither production nor growth can occur. While such a double sensitivity analysis is not a conventional approach used to analyze growth-coupled designs, it accurately recapitulates post hoc the experimental evidence in this study. The data-curated model from the promoter titration strain exhibits the overconstrained permissible design space for the growth-coupled phenotype in these scenarios. Advancement in bioproduction and biomanufacturing necessitates the implementation of large sets of gene interventions for strain design and the solutions encountered here may be generally applicable for other final products and hosts. Non-model hosts (e.g., P. putida 48 , P. taiwanensis 49 , Vibrio natriegens 50 ), non-canonical carbon sources (e.g., lignin-derived aromatics 17 , the C 1 carbon, formate 51 ) or poorly studied growth formats (e.g., membrane or other alternative bioreactors formats 52 , 53 ) are increasingly found advantageous in these biomanufacturing scenarios. Our methods characterizing p- CA as a carbon source highlighted a limitation in applying predictions from GSMMs. Extending these methods to other non-model hosts, non-canonical carbon sources, and growth formats mentioned may also require a similar understanding of the designs from computational predictions to enable experimental implementation. As a learning from this strain development cycle, we propose that a stringent threshold for evaluating cutsets may reconcile imperfections in models with their implementation. Enzymatic reactions selected for removal are mapped back to their corresponding coding sequences. Previously, we assumed reactions in the metabolic model mapped to genes encoding only one biological activity and excluded only those that were potentially essential. While we now know that fumarate hydratase PP_0897 is not an essential gene in P. putida , its activity (or its isozymes) was still required for growth when using nonconventional carbon streams like p- CA in the D1b_gf strain background. As such cutsets should be filtered to remove essential genes, known multi-functional enzymes, and sequences where multiple proteins are encoded in the same DNA sequence in a sense-antisense configuration 54 . An analysis of available RB-TnSeq 55 datasets indicates several other TCA cycle enzymes may result in a dependency similar to that observed for PP_0897. Specifically, when a gene locus is absent from the P. putida RB-TnSeq library, it may suggest that a transposon in that locus is lethal and represents an essential gene. However, targeted genetics and corresponding mutants in homologs have been reported in other Pseudomonads and suggest that a mutant in that gene could be recoverable under the appropriate conditions. For example, it is likely that attempts to fully inactivate the citrate synthase or succinate dehydrogenase, for example, would also be difficult. These additional precautions will preserve cutset fidelity as enzymatic reactions are translated to encoded genes selected for strain engineering. Algorithms that output potentially over-constrained solutions via strong growth coupling are a powerful approach to obtaining strong production phenotypes that are able to meet the stringent titer, rate, and yield demands necessary for an economically viable bioconversion process using renewable carbon streams 56 . These approaches will become more valuable as additional catabolic profiles are discovered and introduced into host platforms 49 , 57 , 58 . Modifying the current growth coupling workflow that accounts for these additional findings in conjunction with functional genomics and ALE could enable streamlined strains for biomanufacturing."
} | 3,790 |
26057581 | PMC4650609 | pmc | 597 | {
"abstract": "Direct interspecies electron transfer (DIET) between Geobacter species and Methanosaeta species is an alternative to interspecies hydrogen transfer (IHT) in anaerobic digester, which however has not been established in anaerobic sludge digestion as well as in bioelectrochemical systems yet. In this study, it was found that over 50% of methane production of an electric-anaerobic sludge digester was resulted from unknown pathway. Pyrosequencing analysis revealed that Geobacter species were significantly enriched with electrodes. Fluorescence in situ hybridization (FISH) further confirmed that the dominant Geobacter species enriched belonged to Geobacter metallireducens . Together with Methanosaeta species prevailing in the microbial communities, the direct electron exchange between Geobacter species and Methanosaeta species might be an important reason for the “unknown” increase of methane production. Conductivity of the sludge in this electric-anaerobic digester was about 30% higher than that of the sludge in a control digester without electrodes. This study not only revealed for the first time that DIET might be the important mechanism on the methanogenesis of bioelectrochemical system, but also provided a new method to enhance DIET by means of bioelectric enrichment of Geobacter species.",
"discussion": "Discussion Recently, DIET from Geobacter species to Methanosaeta species has been confirmed in defined co-culture of G. metallireducens and M. harundinacea 7 as well as in brewery wastewater digesters 13 . Metatranscriptomic analysis revealed that the genes for CO 2 reduction pathway in M. harundinacea were highly expressed, which caused that Methanosaeta species had the capacity to directly accept the electrons from Geobacter species for reduction of CO 2 to CH 4 35 . With the co-existence of Geobacter species and Methanosaeta species in an anaerobic digester, DIET is expected to be another important way to produce methane. Geobacter species is one of the most metabolically active microorganisms in the anaerobic environments, such as soils and sediments 17 , making DIET potential to contribute a considerable part of methane production in the world. However, the population of Geobacter species is pretty scare in waste activated sludge ( Fig. 3B and Fig. 4 ), which makes DIET difficult to take place. Recently, some reported that the conductive carbon material, such as granular activated carbon (GAC) 36 , biochar 37 , carbon cloth 38 and carbon nanotube 39 , were added into the methanogenic digesters to enhance conversion of wastes to methane via DIET. Differently, although a pair of graphite electrodes installed into the reactor (R3) also possibly serving as a similar conductive material to enhance the electron exchange in DIET, the increased methane production was insignificant (P > 0.05) as compared with the reactor with no electrodes (R2) ( Fig. 1A ). The lack of Geobacter species was the major reason limiting DIET for methane production during anaerobic sludge digestion. With an electric supply imposed on the electrodes, although the changes of relative abundance of Methanosaeta species was not apparent, the enrichment of Geobacter species was obviously observed in the suspended sludge and especially in the anodic biofilm ( Fig. 3 ). Bond and Lovley 24 first revealed that electrode reduction could support the growth of Geobacter species. Further studies reported that Geobacter species usually adapt to grow with electrodes or Fe (III) oxides as electron acceptors 21 . In agreement, the electrodes with the power supply installed into the anaerobic digester created a favorable condition to enrich Geobacter species. Further FISH analysis showed that the dominant Geobacter species enriched in the anodic biofilm as well as in the suspended sludge of R1 was Geobacter metallireducens , which were well-known as the microorganism capable of DIET for methane production in defined co-cultures 7 10 as well as in anaerobic methanogenic digesters 13 40 . Unlike other Geobacter species, Geobacter metallireducens not only utilize acetate as substrate for extracellular electron transfer but also utilize other SCFAs and alcohols 17 . It made Geobacter metallireducens more likely to grow in the anode of bioelectrochemical system fed with complex substrates as compared with other Geobacter species 41 42 43 44 . Another potential evidence to support this was that the current density of R1 dropped from day 18 to 30 ( Fig. 1C ). This was because acetate as the most favorite substrate for Geobacter species to produce electricity was almost depleted at day 15 ( Fig. S2 ) . Afterwards, with enriching Geobacter metallireducens , it began to again utilize propionate or other SCFAs which allowed to recover the electricity production. The electrically conductive pili produced by Geobacter species for long-range electron exchange is the important mechanism for DIET 45 46 . If Geobacter species could exchange electron with methanogens through its conductive pili, the conductivity of sludge likely increased due to the participation of conductive pili 13 47 . The conductivity (μS/cm) in the suspended sludge before and after digestion presented a highly linear growth with the increase of VSS (mg/L) ( Fig. 5 ). The average conductivity (slope of the curve, μS/cm/VSS) in the initial sludge (0.7121 ± 0.0025 μS/cm/VSS) and in the digested sludge of R2 (0.7550 ± 0.0045 μS/cm/VSS) were similar, both about thirty percentage points lower than that in the digested sludge of R1 (0.9614 ± 0.0079 μS/cm/VSS). The higher conductivity of the digested suspended sludge of R1 might be resulted from the direct interspecies electron exchange between the two species. It is worth mentioning that bioelectrochemical methanogenesis in most of recent literatures was ascribed to the anodic oxidation of organics coupled with the cathodic reduction of CO 2 into CH 4 31 32 48 . Some considered that the more diverse communities formed on electrodes was a result for the increase of producing methane 49 . All of the present reports on bioelectrochemical methanogenesis have ignored the potential of DIET from Geobacter species to Methanosaeta species for methane production. Actually, the mechanism of anodic oxidation in the bioelectrochemical system was just that exoelectrogenic bacteria like Geobacter species transfer electrons from the oxidation of organic matters to electrodes. This study highly suggested that Methanosaeta species might be another sink to accept electron from Geobacter species in bioelectrochemical system. Also, the electric energy supply for the electrodes inserted into is quite lower compared with the increased energy from methane production ( Fig. 6 ). Energy income from the increased methane production (W CH4 ) of R1 (as compared with R2) reached 7.8 × 10 4 J during the initial 33 days calculated by the formula (3), while the sum of electricity energy supply (W E ) for the electrodes of R1 was only 7487.2 J calculated by the formula (2). It meant that the extra energy income was 10.5 folds (10.5 = 78757.4 J [W CH4 ] / 7487.2 J [W E ]) of the electric energy supply during 33 days. Normally, the disconnection of the voltage supply in the bioelectrochemical system (opened R1) should be operated to further clarify the effects of DIET on methanogenesis. However, with Geobacter species gradually enriched in R1, the available substrates were progressively exhausted in this batch experiment. Assuming in the continuous feed mode, after the Geobacter species was enriched DIET was likely to continuously occur even shifting to the voltage-off state. It might obtain higher energy efficiency. After 51 days experiments, the organic matter removal and sludge reduction are illustrated in Table S1 (see supplementary material). From this table, the effluent TSS, VSS and TCOD in R2 was 10320 ± 960 mg/L (mean ± standard deviation), 33500 ± 400 mg/L and 19007.2 ± 165.3 mg /L respectively, which was still similar to that in R3 (103150 ± 850 mg/L, 33600 ± 950 mg/L and 18940.5 ± 428.3 mg/L respectively). While, the effluent TSS, VSS and TCOD in R1 (96100 ± 700 mg/L, 31310 ± 700 mg/L and 16219.7 ± 256.0 mg/L) was lower than that in the other two reactors. It indicated organic matter removal and sludge reduction was enhanced with addition of a pair of electrodes in this study."
} | 2,111 |
34875063 | PMC8892533 | pmc | 598 | {
"abstract": "ABSTRACT The mutual nutritional cooperation underpinning syntrophic propionate degradation provides a scant amount of energy for the microorganisms involved, so propionate degradation often acts as a bottleneck in methanogenic systems. Understanding the ecology, physiology and metabolic capacities of syntrophic propionate-oxidizing bacteria (SPOB) is of interest in both engineered and natural ecosystems, as it offers prospects to guide further development of technologies for biogas production and biomass-derived chemicals, and is important in forecasting contributions by biogenic methane emissions to climate change. SPOB are distributed across different phyla. They can exhibit broad metabolic capabilities in addition to syntrophy (e.g. fermentative, sulfidogenic and acetogenic metabolism) and demonstrate variations in interplay with cooperating partners, indicating nuances in their syntrophic lifestyle. In this review, we discuss distinctions in gene repertoire and organization for the methylmalonyl-CoA pathway, hydrogenases and formate dehydrogenases, and emerging facets of (formate/hydrogen/direct) electron transfer mechanisms. We also use information from cultivations, thermodynamic calculations and omic analyses as the basis for identifying environmental conditions governing propionate oxidation in various ecosystems. Overall, this review improves basic and applied understanding of SPOB and highlights knowledge gaps, hopefully encouraging future research and engineering on propionate metabolism in biotechnological processes.",
"conclusion": "Concluding remarks on genomic organization, hydrogenases and formate dehydrogenases While there are some common patterns in the genomic organization of the mmc pathway and the content of hydrogenases and formate dehydrogenases, the number of observed differences suggests the existence of diverse strategies involved in the oxidative metabolism of known SPOB. Clearly, Ca . Cloacimonetes diverges from other SPOB, but the lack of cultivable representatives impedes further characterization of their involvement in the syntrophic propionate oxidation process. Moreover, to the best of our knowledge, only [NiFe] hydrogenase from S. fumaroxidans has yet been biochemically characterized to function in terminal reduction of protons (de Bok et al . 2002 ). The functions of hydrogenases are currently inferred through sequence homology to other characterized enzymes, although sometimes even structurally very similar enzymes can show distinct activities, as pointed out above for a presumably non-bifurcating [FeFe] A3 hydrogenase from Ca . Cloacimonetes (Losey et al . 2020 ). Consequently, it is likely that new strategies for propionate oxidation and energy conservation by SPOB will be uncovered in the future. Expanding the current understanding of how these steps are managed by the microbial community can help in formulating new strategies to overcome the problem of propionate accumulation in methanogenic reactors.\n\nCONCLUSIONS AND PERSPECTIVES Over recent decades, research efforts within iterative cultivation experiments with pure and mixed cultures, thermodynamic calculations and omics approaches have increased understanding of syntrophic propionate oxidization. The results of these efforts have demonstrated dispersed taxonomic placement of SPOB and key SPOB traits, including ability to use methylmalonyl-CoA or the dismutating pathway for propionate degradation and capability to circumvent thermodynamic constraints by transferring reduced compounds (H 2 , formate) or directly relocating electrons to a hydrogenotrophic methanogen. The broad taxonomic heterogeneity of known SPOB, belonging to the two phyla Firmicutes and Deltaproteobacteria and indicatively even a third phylum, Ca . Cloacimonetes, brings many challenges in the research field, as it makes generalization of SPOB difficult. Mounting evidence obtained using combinations of enrichment and omic analyses indicates even wider taxonomic and metabolic versatility of SPOB. Identification of key functional gene(s) for syntrophic propionate degraders or gene expression related to specific SPOB activities (e.g. involved in their interspecies communication or activities carried out to come into close physical proximity with cooperating partners) would help overcome some of these limitations. The current progress within the field of SAOB and identification of key genes in the Wood–Ljungdahl pathway suitable as marker genes (Singh 2021 ) could be a source of inspiration. There is also a need for experimental analyses that span a larger range of anaerobic digestion systems and environments than hitherto studied, using a combination of cultivation and omic analyses to classify SPOB based on activity within a community, rather than based on their genotypes. It is known that just a few of the characterized SPOB specialize solely in syntrophic cooperation and that most have at least one alternative mode of electron disposal (e.g. sulfate reduction) or mode of growth (e.g. fermentative or autotrophic). Hence, syntrophic cooperation might only be a stopgap for many representatives of the SPOB. Still, identification of a functional gene encoded by all SPOB would facilitate identification of key players in more complex settings. This would allow information to be gathered on how the propionate-degrading capability of different SPOB relates to environmental conditions and would enable identification of biotic and abiotic drivers controlling their activity, especially with respect to the bottlenecks associated with propionate degradation in anaerobic digesters. Critical SPOB traits have yet to be identified, although capability to operate one of the biochemical pathways (the methylmalonyl-CoA or the dismutating pathway) for propionate oxidation and acetate formation can be argued to be a unique SPOB feature. However, there are considerable variations in gene repertoire, gene organization and enzymatic activities between the species. It can be hypothesized that indirect and/or direct electron transfer is well-organized and system-integrated, but it might in fact be unpredictable and difficult to analyse and control, especially in engineered digester systems. Unique features for adaptation to a syntrophic lifestyle, such as synchronized amino acid biosynthesis and transport with a cooperating methanogen, or altered expression of chemotaxis genes in response to a methanogenic partner, have been found in SPOB. However, whether these are a characteristic shared by all SPOB and unique for SPOB (hence not found in other syntrophs) remains to be determined, as little has been explored within the syntrophic world (Sieber, McInerney and Gunsalus 2012 ). Important discoveries within recent research have revealed some intriguing metabolic capabilities and enzymatic activities of SPOB. We hope that this review will inspire research on key unknowns warranting further investigation, including enzymatic activities for translocation of propionate across the cell membrane, the connection between the first and the last step (i.e. propionate activation and acetate generation; steps 1 and 11 in Fig. 2 ) in the intracellular propionate degradation pathways of SPOB, and how Candidatus SPOB (such as Cloacimonetes) conduct the energetically unfavorable oxidation of succinate to fumarate. Continuing research relating to the possible division of labor to amino acid biosynthesis and amino acid or fructose exchange between syntrophic interacting strains (Kato, Kosaka and Watanabe 2009 ; Hidalgo-Ahumada et al . 2018 ), promotion of syntrophic propionate oxidation by intermediates such as succinate (Pan et al . 2021 ) and potential flagellum-mediated syntrophic interaction (Hidalgo-Ahumada et al . 2018 ) is highly important in this regard. Communications by exchanging quorum-sensing molecules and connections via membrane-derived nanotubes have been demonstrated in anaerobic cultures that interact nutritionally in tight cell-cell interactions (Pande et al . 2015 ; Ranava et al . 2021 ). This raises questions as to whether syntrophic bacteria, which rely on finding a suitable partner microorganism in order to conduct that metabolic activity, exhibit similar behavior. A scouting study on this topic revealed a positive correlation between enhanced abundance of species involved in acetate, propionate and ethanol degradation and presence of genes for quorum sensing (Yin et al . 2020 ). Another open question regarding the metabolic capabilities of SPOB is possible capability for bidirectional use of the methylmalonyl-CoA pathway. Propionate production via the methylmalonyl-CoA pathway is feasible from an enzymatic biochemical point of view. From an ecological perspective, bidirectional use of the pathway could benefit SPOB, especially in ecosystems with fluctuating hydrogen levels. Hypothetically, if the hydrogen concentrations become too high to sustain propionate oxidation via the methylmalonyl-CoA pathway, operating the pathway in a propionate-producing direction would help to counteract excessive hydrogen accumulation and enable SPOB to survive under adverse conditions. However, bacteria operating the methylmalonyl-CoA pathway in a propionate-producing direction (ΔG°´ = −74 kJ/reaction) would not be able to compete for H 2 with hydrogenotrophic methanogens (ΔG°´ = −130.8 kJ/reaction; Table S9 , Supporting Information ). Thus it can be argued that, in environments with high H 2 levels and no activity of hydrogenotrophic methanogens (e.g. due to low pH), bacteria could thrive on operating the methylmalonyl-CoA pathway in the ‘reverse’ propionate-producing direction. It can even be speculated that bacteria with bidirectional use of the methylmalonyl-CoA pathway could act as hydrogen scavengers for SPOB operating the dismutating pathway ( Figures S1 , S2 and Table S9 , Supporting Information ). In such a system, the SPOB would benefit from the hydrogen scavenging in two ways, through sustainably low hydrogen concentration and recycling of additional propionate. For this system to work for prolonged periods in practice, hydrogen would need to be produced continuously by other organisms, hydrogenotrophic methanogens would need to be inactive, and the acetate level would need to be lower than the propionate level. Hydrogen dark fermentation could be such a system but is currently merely a theory, so cultivation experiments including variations of SPOB are needed. Advanced knowledge in this area, coupled with insights on bidirectional use of the Wood-Ljungdahl pathway by SAOB when growing in syntrophy with methanogens (Müller, Sun and Schnürer 2012 ), could help to discern specific syntrophic attributes such as that hypothesized above. With multiple process studies reporting lower propionate levels in bioreactors upon addition of selected supportive and conductive materials, biotechnological research is currently making strides towards the development of real, applicable and reactor management approaches. However, the myriad of inter-species interactions sustaining anaerobic degradation processes complicates identification of the actual mechanisms involved in microbe–material interactions in more complex settings. This calls for research focusing on the biochemistry, underpinning the observed effects on process function achieved by the supportive material, including synergistic effects of the ambient environment. Identification of potential drivers for establishment of direct electrical communication, i.e. DIET between SPOB and cooperating partners, is an area that raises interesting questions, such as how factors that at first glimpse do not seem to benefit SPOB activity (e.g. ethanol addition) can increase the rate of propionate degradation. The answer may lie in the suggestion that DIET-capable cooperating partners for SPOB are enriched by such addition, or in the highly speculative suggestion that an existing electron transfer network can function as a ‘high voltage line’ to which other species can connect their electron transport wire. Combined cultivation trials, conductive measurements, electronic microscopy analyses and omic approaches to evaluate the mechanisms of electron transfer, and the impact on the interspecies connection from addition of supportive and conductive materials to diverse SPOB communities, could help to answer these questions. To help predict the outcome of adding widely diverse materials to support syntrophic propionate-oxidizing communities, links between different environmental conditions (e.g. temperature and pH) and the effect on the specific material on microbial activity need to be established. Subsequent research should then examine whether the positive effects of the supportive material vary depending on the microbial species and its cooperating partner, or on its competitive advantage, and whether the material also provides the microbial community with higher resistance to fluctuations in environmental conditions such as ammonia, H 2 , formate or acetate levels. Within all the above areas, but in particularly regarding novel enzymatic activities, division of labor for biosynthesis, promotion of propionate degradation by intermediates and mediation of cooperating interactions between SPOB and methanogens, we strongly encourage further research to obtain fundamental knowledge on syntrophic traits. Given the current substantial interest in syntrophic microorganisms in anaerobic habitats, we are optimistic about future advances in answering unresolved fundamental questions about SPOB metabolism and the strategies and mechanisms these organisms use for interspecies cooperation. A more holistic understanding of syntrophic interactions would open up new avenues of innovation for future biotechnologies and approaches that can be implemented in engineered systems for more robust process control. Prediction of methane emissions from anaerobic soils/sediments and adaptation of syntrophic propionate oxidation communities to the reality of global changes in temperature is another research area of biogeochemical and practical significance.",
"introduction": "INTRODUCTION: PROPIONATE—A KEY INTERMEDIATE IN ANAEROBIC DEGRADATION Propionate is an important intermediate in anaerobic degradation and a significant precursor for biomethane production in engineered production systems (Mah et al . 1990 ; Ahring, Sandberg and Angelidaki 1995 ). Research on anaerobic zones of ecosystems has also revealed potential importance of propionate conversion to methane, resulting in emissions of methane as a potent greenhouse gas (Glissmann et al . 2004 ; Lueders, Pommerenke and Friedrich 2004 ; Schmidt et al . 2015 ). In biogas-producing anaerobic degradation systems and anaerobic environments such as rice fields, sediments and oil reservoirs, propionate arises as a product of fermentation and acidogenesis (Fig. 1 ). The dominant propionate formation routes vary in different habitats. The main sources in anaerobic digesters, dark fermentation processes and sediments are degradation of odd-numbered fatty acids, carbohydrates, amino acids, aromatic compounds or lactate (Laanbroek et al . 1983 ; Gallert and Winter 2005 ; Sanchez et al . 2021 ). In oil reservoirs, propionate is formed in metabolism of oil hydrocarbons and carbohydrates (Yang et al . 2017 ), whereas in rice fields propionate is produced by bacteria in the rhizosphere that ferment saccharides and lactate excreted from plant roots, but interestingly also from carbon dioxide (CO 2 ) and acetate (Conrad and Klose 1999 , 2000 ). In the digestive system of animals, propionate is produced by break-down of dietary fiber and further fermentation of sugars, amino acids (derived from proteins) and lactate (Koh et al . 2016 ; Louis and Flint 2017 ). Figure 1. Overview of the main anaerobic process of organic biomass degradation (natural and engineered) with the focus on syntrophic propionate oxidation. Equations 1–6 correspond to the equations listed in the main text. SPOB—syntrophic propionate-oxidizing bacterium, SAOB—syntrophic acetate-oxidizing bacterium, MA—methanogenic archaeon and DIET—direct interspecies electron transfer. The fate of propionate, thereafter is governed by the availability of electron acceptors in the anaerobic ecosystem. For example, in environments containing sulfur compounds, the availability of an electron acceptor (e.g. sulfate) will benefit sulfate-reducing bacteria using propionate and acetate as a carbon and energy source. As these oxidized sulfur species are energetically more favorable electron acceptors than CO 2 , methanogens will be outcompeted. In habitats with restricted availability of electron acceptors other than CO 2 , such as biogas reactors, rice fields, peatlands and oil reservoirs, propionate will instead be converted to methane (Kaspar and Wuhrmann 1978 ; Schmidt et al . 2016 ; Chen et al . 2020 ; Jin et al . 2021 ). In these methanogenic ecosystems, propionate degradation proceeds through a closely interlinked multispecies cooperation between syntrophic propionate-oxidizing bacteria (SPOB) and hydrogen (H 2 )/formate- and acetate-utilizing methanogens (Stams 1994 ; Fig. 1 ). In bioreactors treating protein-rich waste, the ammonia (NH 3 ) concentration readily reaches levels that inhibit aceticlastic (acetate-utilizing) methanogens and, under these conditions, propionate is converted by ammonia-tolerant SPOB and acetate removal has instead been shown to be accomplished by syntrophic acetate-oxidizing bacteria (SAOB) in cooperation with hydrogenotrophic (H 2 -utilizing) methanogens (Singh, Schnürer and Westerholm 2021 ; Fig. 1 ). In both high and low ammonia conditions, the interplay with cooperating H 2 - and acetate/formate-utilizing partners is an important factor, as propionate degradation is energetically unfavorable in the presence of reaction products, mainly H 2 , formate and acetate (Müller et al . 2010 ). In engineered biogas systems, propionate degradation is of particular importance since propionate build-up, often in combination with high acetate levels, is a common consequence of disturbance in the process and can cause a severe decrease in productivity (Hill, Cobb and Bolte 1987 ; Ma et al . 2009 ; Westerholm et al . 2015 ). This calls for strategies to overcome restraints involved in propionate degradation in such systems. Syntrophic bacteria have fascinated microbiologists for decades, as these organisms habitually thrive at the limits of what is considered energetically possible. Their adaptation to inter-species cooperation and diverse capability to switch between syntrophic and non-syntrophic lifestyles are other intriguing aspects highlighted in relevant research (Morris et al . 2013 ). Concerted efforts over the years have advanced understanding of SPOB and their taxonomic distribution and metabolic characteristics. With regard to metabolic functioning, four routes for syntrophic propionate oxidation have been proposed, viz . the methylmalonyl-CoA (mmc), lactate, hydroxypropionyl and dismutating pathways (Patón, Hernández and Rodríguez 2020 ). At present, the mmc pathway is the most investigated (Kato, Kosaka and Watanabe 2009 ; Sedano-Núñez et al . 2018 ) and, even in that case, many aspects remain to be elucidated with regard to gene identity and organization and enzymatic activities. Recently, iterative community-level functional investigations and reclassifications have been undertaken to capture the range of microbial taxa involved in conversion of propionate to methane, and many hypotheses on their functionality have been generated. However, further research is needed into both basic and applied questions, in order to underpin the use of anaerobic degradation systems that represent biotechnological solutions to generate renewable energy (biogas, H 2 ), manage waste and recover nutrients by using the anaerobic digestion residue as biofertilizer. Further progress in syntrophic propionate oxidation research would also benefit modeling work on anaerobic microbiomes in anoxic environments that cause greenhouse gas emissions, contributing to global warming. In this review, we aim to provide a comprehensive and structured description of current knowledge on syntrophic bacteria involved in propionate oxidation in methanogenic environments. We compile and discuss recent and earlier advances that laid the foundation for understanding SPOB taxonomy, habitats, metabolism, kinetics and interspecies networking, and highlight current knowledge gaps. Our objective is to inspire future research and stimulate cross-disciplinary discussion that can further define the intriguing syntrophic associations involved in propionate degradation."
} | 5,204 |
34164390 | null | s2 | 600 | {
"abstract": "Current sources of fermentation feedstocks, i.e. corn, sugar cane, or plant biomass, fall short of demand for liquid transportation fuels and commodity chemicals in the United States. Aquatic phototrophs including cyanobacteria have the potential to supplement the supply of current fermentable feedstocks. In this strategy, cells are engineered to accumulate storage molecules including glycogen, cellulose, and/or lipid oils that can be extracted from harvested biomass and fed to heterotrophic organisms engineered to produce desired chemical products. In this manuscript, we examine the production of glycogen in the model cyanobacteria, "
} | 160 |
26870011 | PMC4736303 | pmc | 601 | {
"abstract": "In marine sediments the anaerobic oxidation of methane with sulfate as electron acceptor (AOM) is responsible for the removal of a major part of the greenhouse gas methane. AOM is performed by consortia of anaerobic methane-oxidizing archaea (ANME) and their specific partner bacteria. The physiology of these organisms is poorly understood, which is due to their slow growth with doubling times in the order of months and the phylogenetic diversity in natural and in vitro AOM enrichments. Here we study sediment-free long-term AOM enrichments that were cultivated from seep sediments sampled off the Italian Island Elba (20°C; hereon called E20) and from hot vents of the Guaymas Basin, Gulf of California, cultivated at 37°C (G37) or at 50°C (G50). These enrichments were dominated by consortia of ANME-2 archaea and Seep-SRB2 partner bacteria (E20) or by ANME-1, forming consortia with Seep-SRB2 bacteria (G37) or with bacteria of the HotSeep-1 cluster (G50). We investigate lipid membrane compositions as possible factors for the different temperature affinities of the different ANME clades and show autotrophy as characteristic feature for both ANME clades and their partner bacteria. Although in the absence of additional substrates methane formation was not observed, methanogenesis from methylated substrates (methanol and methylamine) could be quickly stimulated in the E20 and the G37 enrichment. Responsible for methanogenesis are archaea from the genus Methanohalophilus and Methanococcoides , which are minor community members during AOM (1–7‰ of archaeal 16S rRNA gene amplicons). In the same two cultures also sulfur disproportionation could be quickly stimulated by addition of zero-valent colloidal sulfur. The isolated partner bacteria are likewise minor community members (1–9‰ of bacterial 16S rRNA gene amplicons), whereas the dominant partner bacteria (Seep-SRB1a, Seep-SRB2, or HotSeep-1) did not grow on elemental sulfur. Our results support a functioning of AOM as syntrophic interaction of obligate methanotrophic archaea that transfer non-molecular reducing equivalents (i.e., via direct interspecies electron transfer) to obligate sulfate-reducing partner bacteria. Additional katabolic processes in these enrichments but also in sulfate methane interfaces are likely performed by minor community members.",
"conclusion": "Conclusions Here we described physiological characteristics of AOM communities at different temperatures from the Elba cold seeps and the Guaymas Basin hydrothermal vent area. We identified inorganic carbon as the dominant carbon source of AOM communities in all three tested AOM cultures, and hence provide additional evidence that all studied ANME and their partner bacteria are autotrophs. Further stable isotope probing experiments should consider this finding with respect to the selection of labeled carbon sources. We found no indications for a capability of ANME to reverse their metabolism towards net methanogenesis. In contrast, we showed the presence of specific known methanogens ( Methanococcoides spp., Methanohalophilus spp.) in all studied low and medium temperature AOM enrichments. Those methanogens can be enriched and isolated using methylated compounds. In the enrichments but also in methane-rich sediments their substrate might be formed as byproduct of AOM or as decay product of AOM biomass. Furthermore, we were able to enrich sulfur-disproportionating bacteria from different non-thermophilic AOM enrichments, which however, are not identical with the known and abundant AOM partner bacteria (Seep-SRB1, Seep-SRB2, or HotSeep-1), but represent known or novel disproportionating bacteria. In the thermophilic (G50) enrichment, neither sulfur disproportionation nor disproportionating taxa were observed. Also the prominent AOM partner bacteria in the studied low and intermediate temperature enrichment did not respond to elemental sulfur addition, which makes transfer of zero-valent sulfur in these enrichments highly unlikely. In summary, we narrowed down metabolic capabilities of the AOM core community, the ANME and their syntrophic partner bacteria. ANME thrive as obligate methane-oxidizing, but autotrophic organisms, which, however, depend on specific partner bacteria that are obligate autotrophic sulfate-reducers. Other metabolic processes observed in AOM cultures and natural enrichments, such as methanogenesis and sulfur disproportionation, are meanwhile likely performed by specialized minor community members.",
"introduction": "Introduction In the anoxic marine subsurface large amounts of the potential greenhouse gas methane are formed by microbial and thermal degradation of organic matter. Hence methane is highly abundant in the marine subsurface (Reeburgh, 2007 ). The efflux of methane from sediments into the water column is however limited, which is mostly due to the effective barrier of methanotrophic microorganisms. The quantitatively most important sink is the coupling of methane oxidation to the reduction of sulfate (AOM) according to the net reaction:\n (1) CH 4 + SO 4 2 − → HCO 3 − + HS − + H 2 O \nwith an energy yield of only −34 kJ per mol substrate turnover at standard conditions (Knittel and Boetius, 2009 ). AOM is performed in dual species microbial consortia of anaerobic methane-oxidizing archaea (ANME), which are closely related to known methanogens, and partner bacteria affiliated to canonical sulfate reducers of the Desulfosarcina/Desulfococcus clade (Hinrichs et al., 1999 ; Boetius et al., 2000 ; Orphan et al., 2001 ; Knittel et al., 2005 ) or of the HotSeep-1 group (Krukenberg et al., under review). Currently three major clades of ANME archaea are known. ANME-2 is the most prominent methanotrophic clade at marine cold gas seeps (Orphan et al., 2002 ; Mills et al., 2003 ; Wegener et al., 2008b ; Knittel and Boetius, 2009 ). The temperature at those sites is usually between 4 and 14°C (Knittel and Boetius, 2009 ). ANME-3 often occurs at mud volcanoes (i.e., Håkon Mosby Mud Volcano; Niemann et al., 2006 \n in situ temperature −1.5°C) and the Eastern Mediterranean seepages (14°C; Omoregie et al., 2008 ). To our knowledge so far ANME-3 does not proliferate in vitro . The third phylogenetic group ANME-1 has been originally described at cold seeps (Hinrichs et al., 1999 ), but it is particular abundant in diffusive sulfate methane interfaces (Thomsen et al., 2001 ; Lanoil et al., 2005 ; Harrison et al., 2009 ; Aquilina et al., 2010 ) and in microbial mats and chimney structures at methane seeps in the Black Sea (Michaelis et al., 2002 ), in situ temperature of 10°C. In hydrothermally heated sediments such as in the Guaymas Basin (AOM activity up to 70°C) ANME-1 perform thermophilic methane oxidation (Teske et al., 2002 ; Holler et al., 2011b ; Dowell et al., 2016 ). All ANME clades form dense consortia with deltaproteobacterial partners, which belong either to Seep-SRB1a from the Desulfosarcinales/Desulfococcus subcluster; (Schreiber et al., 2010 ), Seep-SRB2 from the Desulfbacterales subcluster (Kleindienst et al., 2012 ) or Desulfobulbus (mostly ANME-3; Niemann et al., 2006 ). The partner of thermophilic ANME-1 is HotSeep-1 (Holler et al., 2011b ; Wegener et al., 2015 ). Different naturally enriched AOM communities proliferated in vitro (Nauhaus et al., 2002 ; Krüger et al., 2005 ; Holler et al., 2009 ), however cultivation at low temperatures (≤20°C) repeatedly selected for ANME-2, although several source sediments were dominated by other clades (ANME-1 from the Black Sea or ANME-3 at Håkon Mosby Mud Volcano; Holler et al., 2009 ; own data). The principles underlying this selective growth of ANME-2 in vitro have so far not been resolved. Only cultivation at elevated temperatures sustained ANME-1 (Holler et al., 2011b ). The potential of ANME to perform methanogenesis has been repeatedly suggested. This hypothesis based on experiments with natural enrichments (Bertram et al., 2013 ), on thermodynamic constrains (Alperin and Hoehler, 2009 ) and on the phylogenetic proximities and genomic similarities of ANME and known methanogens (Lloyd et al., 2011 ). Furthermore, using radiotracer co-occurrence of AOM and methane formation have been repeatedly measured (Treude et al., 2007 ; Orcutt et al., 2008 ) and ANME-1 archaea have been found to be abundant in potentially methanogenic sedimentary horizons (Lloyd et al., 2011 ). However, tracer transfer from product (DIC) into the reactant pool (methane) might also be explained as inherent process of AOM as suggested by Holler et al. ( 2011a ). The certainly least understood feature of AOM is how archaea and bacteria interact in the characteristic dual-species consortia. The activation and complete oxidation of methane via a reversal of the well-described methanogenesis pathway can be confidently assigned to the ANME archaea (Hallam et al., 2004 ; Meyerdierks et al., 2010 ; Thauer, 2011 ; Stokke et al., 2012 ; Wang et al., 2014 ). The fate of the released electrons including the localization of sulfate reduction is instead so far controversial. Based on their phylogenetic classification as Deltaproteobacteria (Knittel et al., 2003 ; Schreiber et al., 2010 ; Kleindienst et al., 2012 ) and the presence of genes and enzymes of sulfate reduction (Milucka et al., 2013 ; Wegener et al., 2015 ), all different partner bacteria are likely involved in the sulfur cycle. Different mechanisms for the interaction of ANME and partner bacteria have been suggested. For the sediment-free Isis Mud Volcano AOM enrichment (Mediterranean Sea), incomplete sulfate reduction in ANME-2 and zero-valent sulfur transfer to disproportionating partner bacteria was proposed (Milucka et al., 2012 ). Instead, for AOM communities in Hydrate Ridge sediments (Coast off Oregon, USA) cytochrome-mediated direct interspecies electron transfer between ANME-2 and their sulfate-reducing partner was proposed (McGlynn et al., 2015 ). For the interaction of thermophilic ANME-1 and their sulfate-reducing HotSeep-1 partner direct interspecies electron transfer via nanowires and cytochromes was proposed (Wegener et al., 2015 ). Here we retrieved three sediment-free AOM enrichments derived from methane-percolated coastal sands off the Mediterranean Island Elba (Italy; enriched at 20°C; E20) as well as a mesophilic enrichment (37°C; G37) and a thermophilic enrichment culture (G50) from the Guaymas Basin. We described community compositions and membrane lipid patterns of these enrichments and performed physiological experiments to test metabolic capabilities attributed to AOM community members including chemoautotrophy, methanogenesis and sulfur disproportionation. Findings were evaluated in further AOM enrichments obtained from different seep sites.",
"discussion": "Results and discussion Cultivation, microbial diversity and archaeal intact polar lipids in the studied enrichment cultures The original sediment samples from Guaymas Basin and the Elba seeps showed already high methane-dependent sulfide production when incubated at AOM conditions (about 0.15 μmol g dw - 1 ; gram dry weight; E20; Ruff et al., this issue to 0.5 and 1.25 μmol g dw - 1 in Guaymas Basin; G37 and G50; Holler et al., 2011b ). In E20, cells were separated from the sandy matrix (see Materials and Methods). All samples were further enriched for AOM by cultivation in anoxic marine sulfate-reducer medium equilibrated with 0.225 MPa methane and 0.025 MPa carbon dioxide headspace. Cultivation was performed at the respective temperature optima of 20°C (E20) and 37°C and 50°C (G37/G50). From the development of sulfide production rates and dilution frequencies we estimated doubling times of 69 days (G37) and 55 days (G50; Supplementary Figures 1A,B ). Due to repeated subsampling for experiments similar required long-term incubations are yet not available for E20, but we expect doubling times in the range of other cold-adapted enrichments (2–7 month; Girguis et al., 2005 ; Nauhaus et al., 2005 ). The studied meso- and thermophilic cultures from Guaymas Basin grew faster than the before studied cold-adapted deep sea AOM enrichments (i.e., 7 month in Hydrate Ridge enrichments; Nauhaus et al., 2007 ). Hence, after repeated dilution and cultivation a sediment-free state (<100 mg background sediment per liter culture) was reached after 1.5–2 years in the Guaymas Basin cultures. Cultures were maintained at sulfide production rates of 100–250 μmol l inoculum - 1 d −1 . The microbial composition of the three enrichments were analyzed by sequencing archaeal and bacterial 16S rRNA genes (Figures 1A,B , Table 1 , Supplementary Figure 2 ). In E20 the most sequence-abundant archaeal group was ANME-2 (subgroups ANME-2a, ANME-2b, ANME-2c). Using catalyzed reporter deposition-fluorescence in situ hybridization (CARD-FISH) we showed that ANME-2 archaea formed tightly packed consortia with Seep-SRB2 partner bacteria (Figure 1C ). G37 consisted of likewise densely packed dual-species consortia of ANME-1 and Seep-SRB2 partner bacteria. (Figure 1D ) The dominance of ANME-1 and Seep-SRB2 is typical for moderately heated surface sediments of the Guaymas Basin seeps (Dowell et al., 2016 ). As shown for 60°C thermophilic AOM enrichments before also the G50 enrichment was dominated by ANME-1 and their partner bacteria HotSeep-1 (Wegener et al., 2015 ; Figures 1A,B ). Compared to the low and medium temperature enrichment cell types were strongly mixed but less densely packed (Figure 1E ), which may indicate a less established partnership than in the low-temperature enrichments. Figure 1 Comparison of community composition, typical microbial aggregates and archaeal lipids of the three AOM enrichment cultures. (A,B) Comparison of normalized archaeal and bacterial clone numbers retrieved from the enrichment (for clone number see Table 1 ; short, badly aligning sequences were not considered here). (C–E) Fluorescence in situ hybridization of dual-species aggregates in the enrichment (E20: red = ANME-2-538, Treude et al., 2005 ; green = DSS658, Manz et al., 1998 ; G37: red = ANME-1-350, Boetius et al., 2000 , green = DSS658; G50: red = ANME-1-350, green = HotSeep-1-590, Holler et al., 2011b ; bars scale 10 μm). (F) Major archaeal membrane intact polar lipid types defined by hydrophobic core groups OH-AR, hydroxyarchaeol; AR, archaeol; MAR, macrocyclic archaeol; GDGT, glyceroldibiphytanylglyceroltetraether. At higher temperatures ANME-1 archaea tend to produce predominantly GDGTs, likely a temperature adaption (for details and 60°C example see Table 2 ). Table 1 Analyzed clones from 16S rRNA gene libraries established from sediment-free methane-oxidizing anaerobic enrichment cultures from Elba and the Guaymas Basin . Phylogenetic group Enrichment E20 G37 G50 G60 G50 & 60 ARCHAEA Euryarchaeota Methanomicrobia ANME-1 ANME-1a 71 (83%) 85 (99%) 70 (88%) 155 (93%) ANME-1b 1 (1%) Methanosarcinales ANME-2 ANME-2a-2b 9 (11%) ANME-2b 23 (30%) ANME-2c 46 (58%) 6 (7%) Others 3 1 1 Thermoplasmata 6 1 6 7 Thermococci 1 1 Thaumarchaeota 1 1 Crenarchaeota 1 1 Total sequences analyzed 79 86 86 80 166 BACTERIA Proteobacteria Deltaproteobacteria HotSeep-1 41 (48%) 48 (74%) 89 (59%) Seep-SRB1 7 (9%) 1 (1%) 1 (1%) Seep-SRB2 35 (46%) 60 (88%) Others 2 5 4 4 Betaproteobacteria 1 1 Bacteroidetes 6 Spirochaetes 3 Chloroflexi 1 Planctomycetes 5 Firmicutes 3 Candidate division OP-3 37 3 40 Candidate division OP-8 1 6 6 Candidate division JS1 2 Others 13 1 3 7 10 Total sequences analyzed 76 68 86 65 151 Bold numbers represent sequences targeted by probes used for CARD-FISH (Figures 1C–E ) . Our results of the E20 enrichment and also prior in vitro cultivation at low-temperatures (≤20°C; i.e., Hydrate Ridge, Mediterranean seeps such as Amon Mud Volcano, Black Sea; Holler et al., 2009 ) showed that low-temperature enrichments of mixed communities always led to ANME-2-dominated enrichments (Supplementary Figure 2 ), whereas ANME-1 is usually not sustained in vitro . In contrast, cultivation at elevated temperatures (≥37°C) led to ANME-1-dominated enrichments, even from sites that harbored mixed communities (Table 1 , c.f.; Holler et al., 2011b ; Kellermann et al., 2012 ). The different temperature optima and growth ranges of ANME-1 and ANME-2 might be due to their cell membrane structure. The ANME-2 in the E20 enrichments assemble their membranes from double layers of diether lipids (intact archaeols) such as hydroxylated (PG)phosphatidylglycerol archaeol (Figure 1F , Table 2 ). ANME-1 are instead able to condense diethers to tetraether lipids (Kellermann et al., under review). Hence in the G37 enrichment culture an about 1:1 mixture of diether and tetraether lipids (i.e., glyceroldialkylglyceroltetraether GDGTs) was detected, whereas the high-temperature enrichments (G50 and also G60; the latter only shown in Table 2 ) contained between 80 and 94% tetraether lipids. The formation of GDGT might allow higher temperature optima (Kellermann et al., 2012 ) or better resistance in starvation periods (Schouten et al., 2003 ; Rossel et al., 2008 ). This observation might also explain the predominance of ANME-1 in most deep sulfate-methane interfaces or in inner parts of microbial chimneys where they have to survive under often minimal substrate concentration. The adaption to harsh conditions or limited substrate availability may, on the other hand, also explain their inability to compete with ANME-2 during cultivation at low temperatures and high substrate availability. Table 2 Relative composition of archaeal lipids in the three studied enrichments, and for comparison, composition of lipids in G60 . ANME-2 ANME-1 20°C 37°C 50°C 60°C DIETHER LIPIDS Archaeol-based lipids 1Gly-AR 9 2Gly-AR 18 7 9 3 GN-1G-AR 3 PG-AR 12 25 6 2 Pent-PG-AR 2 PE-AR 2 Macrocyclic archaeol based lipids PE-MAR 3 4 2 Hydroxy-archaeol based lipids 1Gly-OH-AR 31 PG-OH-AR 19 1 PE-OH-AR 1 44 PI-OH-AR 1 98 80 19 7 TETRAETHER LIPIDS PG-GDGT-PG PG-GDGT 5 1Gly-GDGT 1 1 5 2Gly-GDGT 1 14 79 88 SUMMARY AR 46 32 15 5 MAR 3 4 2 OH-AR 52 45 Tetraether 2 20 80 94 Values given in percent (%). Headgroups: Gly, glycosyl; GN-1G, (N-acetyl)-glucosamine-monoglycosyl; PG, phosphatidylglycerol; PE, phosphatidylethanolamine; PI, phosphatidylinositol; PE, phosphatidylethanolamine. Core lipid: AR, archaeol; MAR, macrocyclic archaeol; OH-AR, hydroxyarchaeol; GDGT, glyceroldibiphytanylglyceroltetraether . Origin of biomass carbon in AOM-performing microbial enrichments To interpret natural biomass stable isotope signals and to perform stable isotope studies the dominant biomass carbon sources of the active organisms need to be identified. For AOM methane and inorganic carbon have been suggested as carbon sources (Hinrichs et al., 1999 ; Blumenberg et al., 2005 ; Wegener et al., 2008a ; Kellermann et al., 2012 ). Here we studied inorganic carbon and methane assimilation into AOM communities using a radiotracer assay with respective labeled carbon sources and tracked the assimilation into the bulk sample. In all three cultures mainly inorganic carbon was assimilated, whereas only 3–15% of the biomass carbon derived from methane (Figure 2 ). In the absence of methane as energy source, assimilation of inorganic carbon dropped to about 1/10 of the values measured under AOM conditions. This shows that the microbial activity and carbon fixation in the studied cultures strongly depended on the presence of methane and the process of AOM, respectively. During the oxidation of 1 mol methane only 10–40 mM of carbon (mostly of inorganic origin) were incorporated. The rates of inorganic carbon assimilation measured here are in the upper range of growth/ carbon fixation reported in earlier studies (Nauhaus et al., 2007 ; Wegener et al., 2008a ). However, in those studies extremely slow-growing AOM enrichments were investigated with doubling times of approximately 7 months, e.g., for enrichments from Hydrate Ridge. Figure 2 Assimilation of carbon sources in relation to reducing equivalent transfer assuming an average oxidation state of organic carbon of 0 . Red = methane carbon assimilation; light gray = DIC assimilation in the absence of methane; dark gray = DIC assimilation in the presence of methane; error bars = standard deviation, n = 3 per treatment; blue bars = methane-dependent DIC assimilation as difference between incubations with and without methane, therefore no error bars. In all cultures assimilation of inorganic carbon strongly exceeds methane carbon assimilation, suggesting that the latter is likely methane-derived DIC assimilation. The predominant use of inorganic carbon as carbon source for assimilation is in line with earlier observations stating “chemoorganoautotrophy” for mesophilic ANME-1 (Kellermann et al., 2012 ). This growth mode seems to be consistent in cold-adapted and thermophilic methane-oxidizing enrichments. The minor amounts of methane carbon incorporation observed here and in earlier studies (Wegener et al., 2008a ) should also be interpreted as assimilation of methane-derived inorganic carbon. The assimilation of methane-derived CO 2 and further isotope fractionation might also explain the extremely low carbon isotope values. Carbon fixation in ANME proceeds most likely via the acetyl-CoA pathway (Koga and Morii, 2005 ; Meyerdierks et al., 2010 ), which causes the highest 13 C-discrimination (Preuß et al., 1989 ). It is furthermore consistent with the observation of lowest 13 C-lipid values in highly active AOM sites, where pore water inorganic carbon derives mostly from methane, thus is also strongly depleted in 13 C. In less active AOM sites rather moderate 13 C-signatures of archaeal lipids are observed (Elvert et al., 2005 ). Methanogenesis in the AOM cultures Using radiotracer isotope assays (i.e., 14 CO 2 ) transfer of inorganic carbon into the methane pool has been shown for many different AOM systems. This phenomenon has been repeatedly interpreted as capacity of ANME to thrive as methanogens (Orcutt et al., 2008 ; Lloyd et al., 2011 ). However, alternatively this tracer transfer was related to enzymatic back reactions. All three studied cultures showed substantial tracer transfer from DIC into the methane pool amounting to 2–5% of the methane-dependent sulfate reduction rate (Figure 3 ). This tracer transfer is independent of a net formation of methane, as in none of the three cultures methane formation was observed without addition of further methanogenic substrates. Hence, in agreement with earlier hypothesis (Holler et al., 2011a ) the observed tracer flux should be seen as intrinsic back reaction during the oxidation of methane in ANME, which proceeds on the same pathways as methanogenesis (Hallam et al., 2004 ), but is not an energy conserving net reaction. Figure 3 Production of 14 C-methane from 14 C-bicarbonate relative to AOM rates (here determined by production of 35 S-sulfide from 35 S-sulfate) in the three studied AOM enrichments incubated under AOM conditions at their respective temperature optima (error bars = standard deviation n = 5 per treatment) . We furthermore aimed to induce methanogenesis in the AOM enrichments with typical substrates for methanogens. Therefore, we incubated 1:10 diluted AOM enrichments in sulfate-free medium with different methanogenic substrates and screened those enrichments for methane formation. Hydrogen, acetate and carbon monoxide addition did not cause methane formation in any of the 3 studied enrichments (Table 3 ), also after extended incubation times of several months (data not shown). However, in the E20 and the G37 cultures methylated substrates (methanol, methylamine) were largely converted to methane within 18 days of incubation (Figures 4A–D ). In contrast, the G50 AOM enrichment culture did not show methanogenic activity even after prolonged incubation of 60 days with these two substrates. Table 3 Stimulation of methanogenesis and sulfate reduction in enrichments from Elba (E20) and Guaymas Basin (G37 and G50) using different substrates (methanogenesis w/o sulfate) . Substrate E20 G37 G50 Methanogenesis AOM control + + + No-substrate control 0 0 0 Hydrogen 0 0 0 Formate 0 0 0 Acetate 0 0 0 Methanol +++ +++ 0 Methylamine +++ +++ 0 Sulfate reduction AOM control + + + No-substrate control 0 0 0 Hydrogen 0 ++ +++++ Carbon monoxide 0 0 0 Methyl sulfide 0 0 0 Methanol 0 0 0 Acetate 0 0 0 Formate 0 0 0 Propionate 0 0 0 “ +”, expected rate measured; “++”, instant rates low, but rates exceed AOM after longer time; “+++”, instant rate low, but rapidly higher than AOM; “+++++”, rate instantly 3 times higher than during AOM; “0”, no rate detectable . Figure 4 Methanogenesis and methanogenic archaea in AOM cultures. (A–D) Methane production in 1:10 dilutions of the E20 and G37 AOM enrichments after addition of methanol or methylamine (10 mM) to the enrichments; open and filled circles, two replicate incubations. (E) Phylogenetic affiliation of methanogens (blue) isolated in dilution-to-extinction approaches with methanol and methylamine. Using the dilution-to-extinction approach with methylamine or methanol we yielded cultures of methanogenic archaea from the E20 and G37 enrichments. Sequencing of the 16S rRNA gene amplified from the enrichments identified all methylamine cultures as relatives of Methanococcoides spp., whereas organisms in methanol cultures were identified as relatives of Methanohalophilus spp. (Figure 4E ). As methylotrophs, both methanogenic cultures grow on methanol and methylamine. Generally, methylotrophic methanogens grow rapidly, and are hence relatively easy to cultivate (Sowers and Ferry, 1983 ; Kendall and Boone, 2006 ). We also retrieved those groups in archaeal 16S rRNA gene tag datasets of the enrichments (Table 4 ). Both groups contributed between 1 and 3‰ of all archaeal sequences retrieved from the E20 and G37 enrichments. Furthermore, we screened the additional low temperature (4–20°C) methanotrophic enrichment cultures (Supplementary Table 2 ) for methanogens. All those enrichments contain few but also up to 10‰ sequences that align with Methanococcoides or Methanohalophilus . In contrast, in the G50 only a single read aligned to Methanococcoides . ANME archaea, however, were not enriched in any of the methanogenic enrichments which clearly indicates that ANME cannot thrive as methanogens. Table 4 Methanogenic and sulfur-disproportionating minor community members . OTU0.02 * E20 GB 37 GB 50 GF Organism ** Sequence accession number A-Otu00017 2.8 1.3 0.1 4.3 Methanococcoides KT899737 A-Otu00024 2.9 1.8 − 7.0 Methanohalophilus KT899738 B-Otu00016 − − − 1.0 Desulfocapsa KT899741 B-Otu00114 8.6 − − − Elba-DISP1 KT899742 B-Otu00373 − 2.2 − − GB-DISP1 KT899739 ; KT899740 * Based on 454 pyrosequencing of the 16S rRNA V3-V5 region; ** presented organisms had a taxonomy quality score of 100; numbers report detected sequences as parts of 1000 (‰) . Minor populations of methanogens also regularly appear in sulfate methane interfaces (Wegener et al., 2008b ; Ruff et al., 2015 ), where they likely also thrive on methylated substrates. These substrates (i.e., methanol, methylamines, and methyl sulfides) are not competitively used by other anaerobic microorganisms with potential higher energy yields including sulfate reducers (King, 1984 ; Kiene et al., 1986 ; Lovley and Klug, 1986 ). Yet, the source of methylated substrates in those environments and in the studied laboratory enrichments is unclear. Three possible sources are outlined here: (I) Active ANME may leak methylated compounds, as was shown for aerobic methanotrophs (Xin et al., 2004 ). This might be in particular true for ANME-1 which lack the methylenetetrahydromethanopterin reductase (Mer) enzyme from a strict reversal of methanogenesis. Meyerdierks and coworkers proposed a bypass via the formation of methanol or methylamine as intermediates which would be oxidized via alcohol dehydrogenases (Meyerdierks et al., 2010 ). Leakage of these intermediates would be a source of methylated compounds. (II) The strong reversibility of the enzymes involved in AOM, particular of the methyl-CoM reductase (Thauer and Shima, 2008 ; Holler et al., 2011a ) may lead to the formation of trace amounts of methylated substrates. These trace production of methylated substrates should not be confused with the proposal of methyl sulfide as intermediates between ANME and partner bacteria (Moran et al., 2008 ). Trace production of methylated compounds during AOM or (III) alternatively during decay of microbial biomass might be sufficient to sustain the low numbers of methanogens observed in our enrichments and in sulfate methane interfaces. An experimental detection of these compounds is however challenging as they are efficiently consumed by the methanogenic side communities. In G50 methanogenesis could not be stimulated. The considerably higher maintenance energy at elevated temperatures (Tijhuis et al., 1993 ) might be the reason for the lack of methanogens and stimulation of methanogenesis here. Our results allow an alternative explanation for the observed stimulation of methane and lipid production in Black Sea mats by methylated compounds as demonstrated by Bertram et al. ( 2013 ). This production is unlikely caused by ANME archaea, but should be rather interpreted as growth of specific methanogenic side communities. Hydrogenotrophic sulfate reduction and sulfur disproportionation in the AOM enrichments We tested the capabilities of the three enrichments to metabolize sulfate with alternative energy sources. As shown before HotSeep-1, the sulfate-reducing bacterium in thermophilic AOM, instantly reacts on hydrogen with elevated sulfide production and growth uncoupled from ANME-1 (Wegener et al., 2015 ). However, besides G50, also the G37 culture showed sulfide production on hydrogen as substrate. Rates quickly exceeded those of parallel incubations on methane. Following this observation we cultivated the sulfate reducers from the sediment-free AOM enrichment using the dilution-to-extinction approach. The retrieved cultures were characterized by direct 16S rRNA gene sequencing. The 16S rRNA gene sequence obtained from one of the cultures affiliated to the larger cluster of Seep-SRB2 bacteria, but was clearly not identical (only 93% sequence similarity which is below the proposed threshold of 94.5% for a genus; Yarza et al., 2014 ) with the Seep-SRB2 partner bacterium found in this mesophilic and in the cold-adapted AOM culture (Figure 5F ). In another hydrogenotrophic culture a bacterium related to Desulfatitalea tepidiphila was obtained. The mesophilic D. tepidiphila was described to grow as autotroph by hydrogen-dependent sulfate reduction or alternatively by using thiosulfate as electron acceptor and various organic carbon sources as electron donor (Higashioka et al., 2013 ). Hydrogenotrophic sulfate reduction could not be stimulated in the E20 culture, which likewise is dominated by Seep-SRB2 partner bacteria. Hence it is unlikely that the meso- or psychrophilic AOM partner bacteria can thrive on hydrogen, therewith confirming earlier results which excluded hydrogen as intermediate in low-temperature AOM or as (alternative) substrate of their partner bacteria (Nauhaus et al., 2002 ). Figure 5 Sulfur disproportionation and sulfate reduction in AOM enrichment cultures . (A–C) Comparison of developments of sulfide concentrations in the three AOM enrichments under AOM conditions [methane (0.2 MPa) plus sulfate (20 mM; open circles)] and during addition of colloidal sulfur (20 mM; filled circles; two replicates) within 18 days. (D,E) Comparison of sulfide and sulfate production in zero-valent sulfur amendments of E20 and G37; disproportionation has not been observed in G50. The observed approximate 3:1 stoichiometry between sulfide and sulfate production is characteristic for disproportionation of elemental (zero-valent) sulfur. (F) Phylogenetic affiliation of sulfur-disproportionating (red) and sulfate-reducing (green) bacteria within the Deltaproteobacteria based on nearly full-length 16S rRNA sequences retrieved from high dilutions of AOM-active cultures supplied with elemental sulfur. To investigate the response of the AOM cultures to additions of zero-valent sulfur and therewith to test the observation made by Milucka et al. ( 2012 ) in an ANME-2 dominated AOM enrichment derived from Mediterranean mud volcano, we supplied aliquots of the three cultures with freshly prepared colloidal sulfur solution and tracked the development of the chemical endmembers of disproportionation, sulfide and sulfate. As described before (Wegener et al., 2015 ) sulfur disproportionation was absent in the thermophilic AOM culture (Figure 5C ). In contrast, E20 and G37 responded to elemental sulfur addition with rapid sulfide and sulfate production tightly following the 3:1 stoichiometry characteristic for the disproportionation of elemental sulfur (Figures 5D,E ). Disproportionation stopped when sulfide concentrations reached approximately 3 mM (E20) or 7 mM (G37). A 7:1 stoichiometry between sulfide and sulfate production, as described for another Mediterranean enrichment (Isis Mud Volcano; cultivated at 20°C and dominated by ANME-2; Milucka et al., 2012 ) has not been observed in any of our enrichments. Using a dilution-to-extinction approach with colloidal sulfur as only available electron donor we repeatedly isolated specific strains of sulfur-disproportionating bacteria from the two natural AOM enrichments. Interestingly in the dilution series from G37 we repeatedly isolated a single bacterium (hereon called GB-DISP1) that is basically identical to the one isolated on hydrogen (Figure 5F ). GB-DISP1 is a rare member in the G37 AOM enrichment, accounting for about 9‰ of the bacterial 16S rRNA gene sequences. The under AOM conditions dominant bacterium Seep-SRB2 however, did not respond to additions of elemental sulfur, hence pointing toward a neutral role of elemental (zero-valent) sulfur in mesophilic AOM proceeding in the G37 enrichment. Growth experiments with the enriched GB-DISP1 showed that it can grow as sulfate-reducing hydrogenotroph (with activity doubling time of 3 days), it can couple sulfur reduction to hydrogen oxidation (activity doubling time 1 day) or it grows as sulfur-disproportionating bacterium (with activity doubling times of about 1 day). Using hydrogen as electron donor GB-DISP1 thrives at sulfide concentrations of up to 20 mM. Instead via sulfur disproportionation GB-DISP1 grows well to sulfide concentrations of up to 5 mM. Above this value sulfide production slows down and sulfide production levels off at around 7 mM. At these sulfide concentrations the energy yield of sulfur disproportionation at 37°C is reduced to approximately −10 kJ mol −1 elemental sulfur turnover (Finster, 2008 ), which is about the minimum free energy yield (ΔG min ) to sustain microbial metabolism (Hoehler, 2004 ). The E20 dilution-to-extinction series with elemental sulfur yielded several replicates of a single bacterium, hereon called Elba-DISP1, with high identity to the uncultivated deltaproteobacterial bacterium MSBL7 (Pachiadaki et al., 2014 ) and the isolated disproportionating species Desulfurivibrio alkaliphilus (Sorokin et al., 2008 ). D. alkaliphilus was described as halophilic chemoautotrophic sulfate reducer, capable to thrive on sulfur disproportionation even without supplying a sulfide sink (Poser et al., 2013 ). Unlike described for D. alkaliphilus , we did not succeed to grow Elba-DISP1 as hydrogenotrophic sulfate or elemental sulfur reducer, thus Elba-DISP1 may exclusively thrive as sulfur-disproportionating bacterium. Furthermore, we searched for sulfur disproportionation in the cold seep AOM enrichment culture “GF” retrieved from the Gullfaks oil field (Norwegian North Sea). Indeed also this culture responded to elemental sulfur addition with its disproportionation. The microorganism enriched from the GF culture was Desulfocapsa sulfoexigens (>99% 16S rRNA gene identity), one of the first described sulfur-disproportionating microorganism (Finster et al., 1998 ). In contrast to many other sulfur-disproportionating enrichments we were able to proliferate the enriched sulfur-disproportionating cultures without the addition of iron as sulfide sink as so far only shown for halophiles by Poser et al. ( 2013 ). However, due to the limited sulfide tolerance and expected low growth yields of all studied cultures their cell densities remained rather low. We searched for sequences related to disproportionating bacteria in the 16S rRNA gene tag libraries of the other AOM enrichments that proliferated for up to 15 years in the laboratory. The genus Desulfocapsa was found in 5 of 10 enrichments, whereas Elba-DISP1 appeared only in the Black Sea enrichment with more than 0.5‰ of the sequences (Supplementary Table 2 ). Bacteria related to GB-DISP1 were not found in other enrichments than in the GB37 enrichment. Hence we conclude that disproportionating bacteria are a general impurity of AOM enrichment cultures. The abundant partner bacteria, namely Seep-SRB1a, Seep-SRB2 and HotSeep-1, neither respond to elemental sulfur additions nor to any other potential added molecular intermediate (except hydrogen in G50 as discussed above, see Table 3 ). The absence of stimulation by potential intermediates supports an obligate syntrophic role of Seep-SRB1a, and Seep-SRB2, and an electron transfer in AOM by direct interspecies electron transfer as suggested for thermophilic and psychrophilic AOM (McGlynn et al., 2015 ; Wegener et al., 2015 ). Due to the lacking cultivability of these dominant partner bacteria, zero-valent sulfur as primary intermediate exchanged in AOM is less likely. Sulfur-disproportionating bacteria such as Desulfocapsa sp. have also been repeatedly identified in reduced ecosystems and in particular at cold seeps (Lloyd et al., 2006 ; Sylvan et al., 2012 ; Ruff et al., 2015 ). A direct connection of these groups to AOM is meanwhile unlikely in those environments, as they appear in rather low numbers compared to the known partner bacteria (Table 4 ; Supplementary Table 2 ). The sulfur source for the disproportionating bacteria in the strictly anaerobic laboratory AOM enrichments is so far unknown. It might be elemental sulfur produced by ANME archaea, which show characteristic sulfur inclusions (Milucka et al., 2012 ). Furthermore, also the cultivation medium will provide at least trace amounts of elemental sulfur that is produced when sodium sulfide is used as reducing agent. Furthermore, also any leak of oxygen during cultivation will lead to formation of zero-valent sulfur. Activity directly after medium exchange (medium is prepared with about 0.5 mM sulfide) is likely sufficient for the responsible sulfur-disproportionating bacteria to survive later inactivity at increased sulfide concentrations (regular medium change at approximately 12–15 mM sulfide). These short periods of activity are likely sufficient to thrive in the infrequently diluted AOM enrichments. At higher temperatures increased demands of maintenance energy may not have allowed survival of disproportionating bacteria in the G50 culture. In the environment they might thrive on elemental sulfur produced by sulfide-oxidizing bacteria or chemical oxidation of sulfide or being involved in the cryptic sulfur cycle rather below the sulfate methane interfaces (Holmkvist et al., 2011 )."
} | 9,940 |
39179548 | PMC11344147 | pmc | 602 | {
"abstract": "Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO 2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO 2 memristor including endurance (>10 12 ), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces.",
"introduction": "Introduction To interact with the real world effectively, advanced robotics should be equipped with a crossmodal sensing-computing system that can perceive multimodal surroundings, recognize objects, and provide real-time feedback 1 – 3 . For example, when working in extreme and hazardous environments (e.g., surgery, ruins rescue, and submarine survey), single-modal sensing systems of robotics have difficulty in executing complex tasks, and is even prone to harm to devices and users 4 , 5 . With crossmodal sensing and processing capabilities, intelligent robots could access comprehensive object features (e.g., color, shape, texture, softness, temperature, odor, and motion) and provide real-time information feedback 6 – 8 , which is of value in autonomous driving, environment exploration, and human-machine interactions. State-of-the-art complementary metal-oxide-semiconductor (CMOS) sensing-computing systems have been used to realize complicated and dexterous multisensory processing and motion control functions 3 , 9 , 10 . However, these systems are in massive system designs with separated sensing-computing architecture and sophisticated analog-digital conversion, which causes serious latency in processing and inevitably increases the power consumption. In contrast, the biological multisensory system provides a crossmodal in-sensor computing paradigm, with multimodal fusion and parallel processing capabilities, facilitating robust and energy-efficient object recognition 11 , 12 . As shown in Fig. 1a , when a person touches an object, analog perception signals (e.g., pressure, temperature, etc.) can be pre-processed and synchronously encoded into neuronal trains through sensory neurons 13 . The spike-encode information is then conveyed to the cerebral cortex, where the information transmitted is post-processed to accurately recognize object properties and avoid damage caused by dangerous signals. Fig. 1 Bio-inspired crossmodal intelligent in-sensor computing system. a Schematic diagram of the multisensory processing in biological proprioceptive system. b Schematic illustration of the spiking crossmodal in-sensor computing system. Integrated flexible crossmodal spiking sensory neurons (CSSNs) consisting of high-performance flexible VO 2 memristors and sensors. The CSSNs can crossmodally encode signals (temperature and pressure) into spikes, working like the sensory neuron does in the biological system. Then, these spikes are input into a spiking reservoir network for crossmodal recognition and feedback. This spiking reservoir network behaves like the cerebral cortex in a biological system. Bioinspired in-sensor computing systems have emerged as a promising candidate for the multimodal perception and processing of analog signals from the physical world, alleviating data transmission bottlenecks across sensor-processor interfaces 14 . One of the emerging devices in this field is memristor 15 , 16 , which works more efficiently than CMOS devices such as the synapses 17 – 19 and neurons 20 – 22 , thanks to its simple two-terminal structures and abundant ion dynamics, allowing for in-sensor computing. Remarkably, the neuromorphic system based on memristors has been successfully demonstrated to sense and process visual 23 – 25 , tactile 26 – 29 , and auditory information 30 , 31 . However, realizing a flexible in-sensor computing system with crossmodal spike encoding and real-time haptic feedback capabilities remains challenging. To implement such a flexible system, several fundamental challenges need to be overcome. First, flexible memristor non-idealities, such as low durability, noticeable cycle-to-cycle (C2C) and device-to-device (D2D) variability, make them challenging to build high-performance neuromorphic systems on large-area flexible substrates. Second, it is difficult to achieve crossmodal in-sensor spike encoding by simply combining sensors with conventional memristors, due to the need of additional conversion circuits for signal acquisition at the multiple sensor nodes, resulting in more time- and energy-consuming. Third, it is required that the flexible in-sensor computing system accurately identifies multimodal objects and provides real-time haptic feedback for real applications in human-machine interaction. In this work, a flexible memristor-based crossmodal spiking sensory neuron (CSSN) is developed to process multimodal signals and provide haptic-feedback for in-sensor computing system (Fig. 1b ). The CSSNs perform in-sensor spike encoding to capture and extract critical features from the crossmodal signals, like the sensory neuron does in the biological system. Then, the encoded information is delivered and classified in the spiking reservoir network which behaves like cerebral cortex in biological system, as shown in Fig. 1 . The CSSN unit consists of two key components: a flexible temperature-sensitive memristor and a flexible pressure sensor. A forming-free flexible VO 2 memristor is fabricated at low temperature (280 °C) by introducing a Cr 2 O 3 buffer layer, exhibiting excellent yield (~97.3 %), ultrahigh endurance (> 10 12 cycles), low C2C and D2D variation (0.72% and 3.73%, respectively), fast respond speed (< 30 ns), and high flexibility (bendable to a radius of 1 mm). This device can not only achieve neuron firing by threshold switching (TS) behavior but also sense the temperature by the intrinsic temperature-sensing capability of the VO 2 material. By coupling with a flexible pressure sensor, the CSSN can synchronously encode bimodal signals (e.g., pressure and temperature) as spikes with different frequencies. Furthermore, the CSSN can perform real-time haptic feedback to external signals by utilizing integrated, flexible hardware. By leveraging CSSNs as the feature extraction and fusion layer of multimodal signals, a crossmodal in-sensor spiking reservoir computing system is demonstrated for dynamic object identification and real-time signal feedback. Compared to single-modal sensory recognition (83.6% and 79.1% for sole pressure and temperature, respectively), the multimodal system shows higher recognition accuracy (98.1%) and exhibits more realistic sensing feedback under bimodal sensing fusion.",
"discussion": "Discussion Here, a flexible CSSN is realized based on a high-performance flexible VO 2 memristor for crossmodal in-sensor encoding. The memristor possesses electroforming-free TS behavior with high yield (97.8%), ultra-high endurance (> 10 12 ), high C2C and D2D uniformity (0.72% and 3.73%, respectively), fast respond speed (< 30 ns), and excellent flexibility (bendable to a radius of 1 mm). The CSSN can synchronously encode temperature and pressure stimulus to spikes based on the coupled piezoresistive sensor and inherent temperature sensitivity of the VO 2 , achieving crossmodal in-sensor encoding capability. A flexible hardware system based on CSSN is then designed, which can provide real-time haptic-feedback to external signals by encoding different spike events. A crossmodal in-sensor spiking reservoir computing system is demonstrated based on this CSSN, achieving recognition accuracy of 98.1% for dynamic objects and real-time sensing-feedback. The crossmodal in-sensor computing concept introduced here is a novel approach for sensing and processing multimodal signals directly within artificial neurons without complex data transmission. This simplifies the complexity of the hardware implementation and amplifies the functionalities for processing information at sensing terminals. We have provided a comprehensive overview of recent advancements in artificial in-sensor spike encoding neurons 25 , 29 , 57 – 62 , summarized in Table 1 . Our CSSNs offer several advantages, including flexibility that support multimodal sensory inputs like tactile and temperature stimuli, emulating the human somatosensory system. Moreover, CSSNs demonstrate excellent endurance exceeding 10 12 cycles and operate within an energy range of 3.9-50 nJ per spike. This positions them favorably in terms of energy efficiency and durability compared to other state-of-the-art in-sensor spike encoders. CSSNs support a wide range of applications, including dynamic object recognition and human-machine interactions. They are ideally suited for creating compact, versatile wearable sensory computing systems, potentially revolutionizing the landscape of portable and wearable technology. However, some challenges remain. The growth temperature of our VO 2 device is significantly lower than that of previously reported devices 23 , 29 , 32 , 33 , further reduction while maintaining high performance is challenging. Despite the promising flexibility and stability of our flexible memristor, scaling CSSN array for mass integration without compromising performance is challenging. Additionally, managing the energy consumption of sensory neurons as the number of sensory nodes increases remains a challenge. There is substantial scope for enhancements through the co-optimization of materials, device architecture, and circuit design to overcome these limitations and fully realize the potential of the advanced sensory neurons. Table 1 Comparison with reported in-sensor encoding neurons Sensory signals Crossmodal sensory Sensory components Flexibility Endurance Energy/spike Coding-related applications Ref. Optical No 1 M No >100 2.1–20.3 nJ Machine vision 25 Pressure &Temperature Yes 1 M + 1PS No >10 6 / Object recognition 29 Optical No 1 M No >10 4 ~190 nJ a Image segmentation 57 Temperature No 1 M No >10 3 ~0.15 nJ a Edge detection 58 Physiological signals No 2 T No / ~0.5 μJ a Neuromorphic bio-interface 59 Optical No 1 M No >500 ~32 pJ a Pattern recognition 60 Pressure & optical Yes 1 T + 1PS No / ~8 nJ a / 61 Optical No 1 T No >10 8 ~0.1 nJ Motion detection 62 Pressure &Temperature Yes 1 M + 1PS Yes >10 12 3.9-50 nJ Human-machine interaction & dynamic object recognition This work a The energy consumption per spike is calculated approximately from the P–t, V–t and I–t curves in these reference papers, respectively. To unify the benchmark, all the sensory components in these reference papers are equivalent to three categories: memristor (M), transistor (T), and pressure sensor (PS)."
} | 2,934 |
31338603 | PMC6650513 | pmc | 603 | {
"abstract": "The development of stretchable smart electronics has attracted great attentions due to their potential applications in human motions energy collection systems and self-powered biomechanical tracking technologies. Here, we present a newly stretchable all-rubber-based thread-shaped triboelectric nanogenerator (TENG) composed of the silver-coated glass microspheres/silicone rubber as the stretchable conductive thread (SCT) and the silicone rubber-coated SCT (SSCT) as the other triboelectric thread. The stretchable all-rubber-based thread-shaped TENG (SATT) generates an open-circuit voltage of 3.82 V and short-circuit current of 65.8 nA under the 100% strain and can respond to different finger motion states. Furthermore, the self-powered smart textile (SPST) woven by the SCT and SSCT units has two kinds of working mechanisms about stretch-release and contact-separation modes. The stretching-releasing interaction between knitting units can generate an open-circuit voltage of 8.1 V and short-circuit current of 0.42 μA, and the contacting-separating mode occurs between cotton and two types material outside the SPST producing peak voltage of 150 V and peak current of 2.45 μA. To prove the promising applications, the SPST device is capable to provide electrical energy to commercial electronics and effectively scavenge full-range biomechanical energy from human joint motions. Therefore, this work provides a new approach in the applications of stretchable wearable electronics for power generation and self-powered tracking. Electronic supplementary material The online version of this article (10.1186/s11671-019-3085-9) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusion In summary, this paper demonstrates a newly designed stretchable all-rubber-based thread-shaped wearable electronics by using silver-coated glass microspheres and silicone rubber as source materials. The SATT with 100% strain can convert tensile mechanical energy into electric energy via electrostatic effect and is demonstrated as a self-powered sensor to quantitatively track the finger joint motions. Moreover, the SCT and SSCT triboelectric threads are woven into SPST with traditional plain weave, which generates the open-circuit voltage of 8.1 V and short-circuit current of 0.42 μA through the stretching-releasing interaction between knitting units and the maximum output power of 163.3 μW at external load resistance of 120 MΩ in the SPST-cotton tapping way. With the stable and large output voltage performance, the SPST had been identified as an effective power source to supply the electrical energy for commercial electronics. Being stretchable and wearable, the SPST provides an effective solution for harvesting biomechanical energy from human joint motions and will be expected to develop great potential in the applications of in medical systems and self-powered smart tracking technologies.",
"introduction": "Introduction Wearable electronics with comfort, soft, and breathability integrated on textiles or clothes have been widely used in many fields, such as biomedical monitors [ 1 – 3 ], bionic-robots [ 4 – 6 ], human-interactive interfaces [ 7 , 8 ], military, and consumer electronics [ 9 – 11 ], which is the perfect embodiment of the boom advancement in technology and brings a lot of convenience and advantages to our life. However, for powering these wearable electronics, traditional batteries and supercapacitors are difficult to meet their energy requirements due to the technical bottlenecks of structural rigidity, limited lifetime, extra device weight, and environmental pollution. Consequently, it is an urgent problem to explore a newly sustainable power supply for wearable electronics. For wearable applications, human motion mechanical energy is ubiquitous and relatively stable that is expected to be converted into electricity by the wearable electronics in operation, developing into a sustainable self-powered multi-functional electronic device [ 12 , 13 ]. Therefore, it is a promising method for using human motion mechanical energy harvesting technology to achieve a self-powered wearable device, which could convert the measured signals into power supply signals. Among various approaches, the triboelectric nanogenerators (TENGs) [ 14 – 17 ] base on triboelectric electrification and electrostatic induction can efficiently scavenge human motions mechanical energy, which is regarded as a sustainable power or a self-powered sensor due to lightweight, cost-effectiveness, high efficiency, robustness, and wide selection of materials. Recently, developing thread-shaped TENGs acted as self-powered wearable electronics have been demonstrated their merits in monitoring human physiological signals including body motions detecting, skin tactile sensing, pulse frequency testing, etc. Hongzhi Wang has delivered a thread-like sensor with built-in wavy structure design to detect and discriminate the joint movements of human bodies [ 18 ]; however, the stretchability of sensor is a critical hurdle in complex limb motions with large strain. Moreover, the smart textile electronics composed of the thread-shaped TENGs have shown their advantages in human motions energy collection systems owing to easily integrated with clothes. Wang and co-workers have sewn the wearable smart textile into a garment to become a power cloth [ 19 ] or realized TENG textiles based on well-designed weaving yarns method [ 20 ]; however, the stable high-output performance is still a challenging problem for practical applications. Besides, most stretchable electrodes in previous wearable electronics are achieved by serpentine metal foils [ 21 , 22 ], deposition on pre-strained soft substrate [ 23 , 24 ], and metal nanowires [ 25 ], obstructing the smart textile electronics to tolerate wearing use and large-scale fabrication. Here, in order to address the above issues, we present a new type of SATT with double-helix structure, consisting of “silver-coated glass microspheres/silicone rubber” as the SCT thread and “silicone rubber-coated SCT” as the SSCT thread. Due to the good compatibility of the ultra-stretchable elastomer matrix material, the SATT can easily obtain a high stretchability of 100% to realize conformal assembly in stretchable electronic systems. The SATT with a length of 5 cm generates an output voltage of 3.82 V and output current of 65.8 nA, which could be acted as an active wearable sensor for tracking the finger motion states. Moreover, the SPST woven by the SCT and SSCT units generates the output voltage of 8.1 V and current of 0.42μA in the stretch-release mode and the maximum power can reach up to 163.3 μW in the contact-separation mode. Thus, the SPST is capable to supply the electrical energy for commercial electronics to maintain normal operating state, meanwhile can effectively harvest full-range biomechanical energy from human joint motions, providing a great significance for promoting the development of practical stretchable and wearable energy harvesters.",
"discussion": "Results and Discussion The SATT consists of two double-helix all-rubber-based threads: one is the SCT using silver-coated glass microspheres uniformly dispersed into silicone rubber matrix, and the other is the SSCT using the silicone rubber-coated SCT. The detailed fabrication process of the SATT is illustrated in Fig. 1 a. The silver-coated glass microspheres (75 wt%) were blended into the ultra-elasticity silicone rubber by mixing process, which was subsequently extruded and vulcanized through the screw extrusion machine to achieve the conductive composite thread (Fig. 1 a I). Then the five stretchable conductive threads were selected to be coiled together used as SCT electrode, and the ends of the threads were tied to prevent untwisting during subsequent manufacturing (Fig. 1 a (II)). Considering the strong ability to gain electrons, the silicone rubber with superior mechanical property was carefully chosen as a wrapping material to encapsulate the electrode. Namely, the SSCT was prepared and considered as the other triboelectric thread (Fig. 1 a (III)). Finally, the SCT and SSCT were intertwined with each other to form a stretchable, wear-resistant, and low-cost all-rubber-based thread-shaped TENG with double-helix structure (Fig. 1 a (IV)). The cross-sectional scanning electron microscopy (SEM) image of SSCT is shown in Fig. 1 b. It is obvious that the five conductive threads are coated tightly by silicone rubber to achieve an all-in-one structure aiming at more induced charges occurring on the internal conductive threads. As shown in Fig. 1 c, d, the silver-coated glass microspheres with different diameters are closely embedded in silicone rubber, which could appear three-dimensional conductive network structure in the rubbery matrix. Consequently, the SCT has an outstandingly conductive property and remarkably stretchable ability. To further demonstrate the good compatibility of homogeneous organic matrix, the SEM images of the enlarged in the connection position between SCT and coated silicone rubber are shown in Fig. 1 e, f. Apparently, there is no gap between conductive threads and coated silicone rubber so that they are implemented a well-designed integrated structure. Figure 1 g displays the resulted SATT with double-helix energy-scavenging threads, and the lower image of Fig. 1 g shows the stretchability of the SATT. The result presents that the thread-shaped TENG can be elongated up to ≈ 100%, which is overwhelmingly superior to the previous reports about thread-based TENG [ 26 – 28 ]. Fig. 1 a Schematic diagram for fabricating process of the SATT device. b – d The SEM image of the SSCT cross-section view at different magnifications. e , f The SEM image of the connection position between SCT and coated silicone rubber at different magnifications. g Photographs of the prepared SATT with demonstrations of being stretched at ≈ 100% strain. Despite fabricated by complex double-helix structure, the SATT can be approximated as a large number of capacitors connected in parallel without considering the edge effect. Thus, the working mechanism of SATT could be simplified into the typical contact-separation process between the SCT and SSCT in the stretching-releasing cycles. The electricity-generating mechanism of the SATT based on the coupling effects of contact electrification and electrostatic induction is depicted in Fig. 2 a. In the original state, the surface of the silicone rubber takes the negative charges, while an equivalent positive charge is generated on the electrode, respectively, due to the contact electrification. When a tensile stress is applied to the SATT, the distance between the silicone surface and electrode increases and causes an electric potential difference. The electrons flow between two electrodes through the external circuits, resulting in the formation of an electrical current. Until the distance is quite far away, there is an equilibrium state of electrons stopping the transfer. When the tensile stress is released, the electrons flow inversely between the electrodes to realize a charge balance. After the SATT is fully restored to the original state, the charges are completely neutralized again. Thus, the SATT could generate output electrical energy in the continuous stretching-releasing periodic motions. Fig. 2 a Power generation mechanism of SATT at stretching-releasing process. b The simulation results of the potential distributions using COMSOL software. c The resistances of conductive threads with the length of 5 cm at different strain mounts. d The tensile force experienced by different numbers of conductive threads as functions of strain amount. e The tensile durability test of the SCT within 100% strain. f The output voltages and currents of conductive threads at different strain mounts. g The open-circuit voltage of SATT with a length of 5 cm at 100% strain. h Enlarged view of the area indicated by the dashed black box in panel g Furthermore, we establish a finite element method (FEM) simulation based on the COMSOL software to quantitatively analyze the working mechanism of the SATT. In this model, the two tribo-charge densities of ± 1μC/m 2 are assigned on the thread surfaces. It is worth to note that the amount of initial charges on the thread surfaces only affects the calculated electrical potential; however, the relative changing trend of the electrical potential will be invariant. Figure 2 b shows the electrical potential distributions of the SATT at different tensile forces. When the external stretched force does not exist, the potential difference of the whole device is almost zero. As the SATT is stretched outward, the positive and negative tribo-charges are separated, and the potential difference will be increased. Consequently, it is evident that the simulation results by COMSOL software are consistent with the theoretical analysis process of above work mechanism. For comfortably stretchable electrode, electrical conductivity is an adequately important factor. The proposed stretchable thread-shaped electrode with silver-coated glass microspheres dispersed in silicone rubber elastomer is stretched at different strains to cause varied electric conductivity. It is necessary to systematically study the relationship among the number of conductive threads, the length of the stretch, and the resistance of the electrode. Figure 2 c shows the resistances of one to five conductive threads with a length of 5 cm at different strain mounts. Within the range of 50% strain, the resistances of electrodes with different numbers of conductive threads are almost unchanged under the stretching and releasing process. However, with the strain amount increasing, the more numbers of conductive threads, the lower resistance value of the electrode. Figure 2 d shows the tensile force experienced by different numbers of conductive threads as functions of strain amount. Obviously, the tensile force will enlarge as the numbers of conductive threads increase. Considering the easier to be stimulated by tensile force, the five intertwined conductive threads are selected as the SCT electrode in this work. The tensile durability of the SCT within 100% strain was performed, as shown in Fig. 2 e. The results indicate that the SCT is an excellent conductive elastomer especially exhibiting highly stable reversibility. Additionally, the electrical output performances of the double-helix energy-scavenging threads were carried out, as shown in Fig. 2 f. As the increasing number of conductive threads, the contact areas between electrode and silicone rubber are enlarged, resulting in more transferred charges between triboelectric threads under the stretching-releasing motions. Accordingly, both the output open-circuit voltage and short-circuit current increase. Figure 2 g presents that the SATT with the length of 5 cm can generate the open-circuit voltage of 3.82 V and the short-circuit current of 65.8 nA at 100% strain. The enlarged view of one voltage cycle is shown in Fig. 2 h. It is worthy of note that the response and recovery times of the SATT composed of SCT and SSCT are 48 ms and 220 ms at 1 Hz, respectively. Consequently, the SATT is expected to be used as self-powered tensile sensing electronic to monitor human physiological signals. The mechanical energy from human motions has been the frequently used energy resources because of its various advantages such as universality, renewability, and stability. Smart textiles and intelligent clothes collecting the mechanical energy from human motions have been widely researched [ 29 – 31 ]. However, due to the lack of excellent stretchability, the comfort of smart textiles based on the flexible strips is an extremely important factor hindering the development of intelligent fabrics. In view of the excellent stretchable characteristic of the SATT device, a lightweight, comfortable, and wearable self-powered textile is put forward here. The SCT and SSCT units were woven into SPST with traditional plain weave. The schematic illustration and photograph of the SPST device (5 × 7 cm 2 ) are demonstrated in Fig. 3 a, b. It is worth noting that biological movements are normally considered as elongated in 5–30% strain, which requires a much higher strain compatibility of the wearable electronics to ensure long-term stable operation under mechanical tension [ 32 – 34 ]. Figure 3 c presents the stretching schematic graph of the intentionally stretched 100% strain of the SPST device using a linear motor. The stretching-releasing working mechanism of SPST is the same as that of SATT that the focus is to connect all STC terminals as the testing port and the electrodes in SSTC together as the other testing port. The open-circuit voltage and short-circuit current of the SPST device are about 8.1 V and 0.42 μA in the process of stretching-releasing excitation, respectively (Fig. 3 d, e). Owing to the high stretchability and stable output performances, the SPST could be acted as a self-powered monitoring device to scavenge the stretching kinetic energy for human joints. Fig. 3 a The schematic illustration of the SPST. b The photo image of the SPST. c The stretching schematic graph of the SPST at the 100% strain. d The output voltage and e output current of the SPST at the periodic stretching-releasing cycles Furthermore, considering that the SPST device appears contact-separation process with other clothing fabrics during the actual human movements, the output performances with SPST-cotton tapping were achieved in the periodical tapping process of the linear motor (Fig. 4 a). The electricity generation mechanism with SPST-cotton tapping is depicted in Fig. 4 b. In the periodical tapping cycles, the contact-separation mode occurs between cotton and the two types of material outside the SPST. Thus, the electrostatic induction charges flow between the electrodes of the SPST. Figure 4 c, d displays the open-circuit voltages and short-circuit currents under the force of 100 N. Remarkably, the open-circuit voltage of the SPST is about 150 V at different tapping frequencies, which is independent of the operation frequency. However, the short-circuit currents of the SPST are about 0.96, 1.31, 1.55, 1.77, and 2.45 μA with frequencies of 0.5, 1, 1.5, 2, and 3 Hz, respectively. This is because the time for contact-separation becomes shorter as higher frequencies so that the equal numbers of charges causes a larger current (Isc = dQsc/dt). Furthermore, the SPST acted as an energy supply device usually connects with the external load in practical application. Additional file 1 : Figure S1 presents the output voltages as a function of external load resistances from 1 MΩ to 1 GΩ. The output powers of the SPST connected to external loads with various levels can be obtained, since the output power is defined by U 2 /R. Clearly, the output power increases at first and then decreases, reaching a maximum value of 163.3 μW when the external load resistance is about 120 MΩ. In addition, the stability testing of the SPST was conducted for 10,000 cycles, as shown in Additional file 1 : Figure S2. Obviously, the SPST’s output voltage did not decline in periodic testing cycles, thus the SPST has the remarkable long lifetime. The electricity generated from the SPST-cotton tapping can be stored into the capacitors to supply power for wearable electronics. Figure 4 e shows that the charging curves of various capacities at frequency of 3 Hz and force of 100 N. The voltage of a 0.47 μF capacitor can be charge to 14 V for 150 s. With the capacity of the capacitor increasing, it takes longer to reach the same high voltage. Owing to the outstanding output performances, the SPST-cotton device could directly turn on LEDs and power up a commercial electric watch by the electrical energy stored in the capacitor (Fig. 4 f and Additional file 2 : Video S1, S2). These results present that the SPST device can provide electrical energy for commercial electronics to maintain normal operation. Fig.4 a The schematic illustration of the SPST-cotton tapping. b The electricity generation mechanism with SPST-cotton tapping. c The open-circuit voltages and d short-circuit currents with SPST-cotton tapping at different tapping frequencies. e Measured voltage curves of various capacitors at frequency of 3 Hz and force of 100 N. f The LEDs and electric watch were driven by the SPST-cotton device Being stretchable and easy to be assembled in most parts of the body, the thread-shaped TENG can be acted as an active wearable electronic device for detecting the body motions. As shown in Fig. 5 a and Additional file 2 : Video S3, the SATT device was fixed on a subject’s index figure to respond five bending-releasing motion states. Clearly, the output voltage peaks increase with the enlarging of motion amplitude, namely, the output monitoring signals are determined by the magnitudes of the stretching motions. The behaviors confirm that the SATT can be used as a self-powered active sensor without an external power to quantitatively characterize the finger motion states. Furthermore, the open-circuit voltages of the SPST woven by SCT and SSCT units are stable and independent of the operation frequency, which could be used as the output signals of motion monitoring. As shown in Fig. 5 b, c, the SPST was fixed on the joints of human body to perform energy harvesting and condition monitoring. When the flexion and extension behaviors from elbow and knee appear, the stretch-release mode from SPST and the contact-separation mode from SPST-cotton produce, resulting in the alternating electric signals generated. Obviously, the SPST device richly meets the requirement about the elastic property for smart textile, and the output voltages could reach about 105 V and 116.9 V at the maximum bending angles of elbow and knee joints, respectively. The response output currents are about 0.73 μA and 0.89 μA, respectively. Consequently, the carefully designed SPST provides a promising power supply method for wearable electronics by scavenging body joints motion energy and will play an extremely important role in the applications of patients’ rehabilitation training and track activity. Fig. 5 a The SATT as a self-powered active sensor for detecting finger motion states. b The SPST is fixed on the elbow c the knee to perform energy harvesting and condition monitoring"
} | 5,636 |
32587586 | PMC7298970 | pmc | 606 | {
"abstract": "Cell-density dependent quorum sensing (QS) is fundamental for many coordinated behaviors among bacteria. Most recently several studies have revealed a role for bacterial QS communication in bacteriophage (phage) reproductive decisions. However, QS based phage-host interactions remain largely unknown, with the mechanistic details revealed for only a few phage-host pairs and a dearth of information available at the microbial community level. Here we report on the specific action of eight different individual QS signals (acyl-homoserine lactones; AHLs varying in acyl-chain length from four to 14 carbon atoms) on prophage induction in soil microbial communities. We show QS autoinducers, triggered prophage induction in soil bacteria and the response was significant enough to alter bacterial community composition in vitro . AHL treatment significantly decreased the bacterial diversity (Shannon Index) but did not significantly impact species richness. Exposure to short chain-length AHLs resulted in a decrease in the abundance of different taxa than exposure to higher molecular weight AHLs. Each AHL targeted a different subset of bacterial taxa. Our observations indicate that individual AHLs may trigger prophage induction in different bacterial taxa leading to changes in microbial community structure. The findings also have implications for the role of phage-host interactions in ecologically significant processes such as biogeochemical cycles, and phage mediated transfer of host genes, e.g., photosynthesis and heavy metal/antibiotic resistance.",
"introduction": "Introduction Bacteriophages may infect bacterial host cells via lytic and lysogenic cycles, both of which have shown ecological significance. For instance, lytic cycles of reproduction can impact population and community dynamics through lysis of host cells effectively re-routing dissolved organic carbon and other nutrients back to the dissolved pool, a process referred to as the “viral shunt” ( Danovaro et al., 2008 ; Sullivan et al., 2017 ). Lysogenic cycles in which the phage genome is inserted into the host cell genome without killing the host, may promote host fitness and regulate metabolic functions through selective expression of certain phage encoded genes and transcriptional regulators without production of progeny phage particles ( Hurwitz and U’Ren, 2016 ; Howard-Varona et al., 2017 ; Williamson et al., 2017 ). Among the temperate phage, the mechanisms that control lysis-lysogeny decisions in natural environments remain unknown. The “piggyback-the-winner” (PtW) theory of phage-host population dynamics predicts that high microbial cell densities promote lytic to temperate (lysogenic) switching, highlighting the importance of lysogenic reproductive cycles at high host cell abundances ( Knowles et al., 2016 ). Some microscopic counting-based examinations and viral metagenomic analyses provide evidence for PtW theory ( Reyes et al., 2010 ; Knowles et al., 2016 ). In contrast, the “kill-the-winner” (KtW) theory predicts that lytic infections are more prevalent and suppress the fastest growing hosts during times of high host cell densities, while lysogenic conversions are stimulated at low host cell abundances ( Thingstad and Lignell, 1997 ; Weitz and Dushoff, 2008 ; Maslov and Sneppen, 2017 ). The long-standing KtW paradigm has also gained empirical support ( Payet and Suttle, 2013 ; Brum et al., 2016 ; Liang et al., 2019c ). Both PtW and KtW suggest host-cell density may guide the viral reproductive strategies, although the paradigms propose contrasting fashions of host-cell density influences. Thus, cell density-dependent quorum sensing (QS) might have an important role in the lysogeny-lysis switch of temperate phages. Most recently, the molecular communication between viruses and between viruses and bacteria has shed light on the mechanism underpinning the phage lysogeny-lysis decisions in a few phage-host model systems ( Erez et al., 2017 ; Dou et al., 2018 ; Liang and Radosevich, 2019 ; Silpe and Bassler, 2019a ). Quorum sensing functions as cell-density dependent communication among bacteria and enables coordinated gene expression following fluctuations in population density ( Miller and Bassler, 2001 ; McCready et al., 2019 ). QS bacteria produce signaling molecules, such as different types of N-Acyl homoserine lactones (AHLs), with the concentration of released signaling molecules dependent upon bacterial population density ( Fuqua and Greenberg, 2002 ; Jemielita et al., 2018 ; Wellington and Greenberg, 2019 ). Thus, QS plays a major role in adaptive survival and collective activity of bacterial communities. In an initial investigation evaluating the potential impact of QS on lysogeny-lysis switching, Ghosh et al. (2009) assessed the prophage induction response to exogenously added AHL mixtures of N- (butyl, heptanoyl, hexanoyl, ß-ketocaproyl, octanoyl, and tetradecanoyl) homoserine lactones and demonstrated that AHLs triggered viral production (i.e., switching from lysogenic to lytic viral reproduction) in soil and groundwater bacteria. The AHL-mediated prophage induction mechanism was demonstrated to be an SOS-independent process by using the single-gene knock-out mutation in the model system of Escherichia coli with λ-prophage ( Ghosh et al., 2009 ). Similar studies by Silpe and Bassler (2019a ; b ) revealed that the lysogeny-lysis switch of a Vibrio phage can be induced by the host-produced QS autoinducers, in which the phage lysogeny-lysis decisions directly respond to host QS molecular signals and cell density. It is important to note that microbially produced QS molecules in Silpe and Bassler (2019a ; b ) reports as well as Ghosh et al. (2009) have the same prophage induction response as exogenously added autoinducers suggesting that the QS-mediated prophage-induction mechanisms are likely operative in natural systems. However, the potential significance of this phenomena at the microbial community level has only been demonstrated by Ghosh et al. (2009) . Communication among phages through phage-encoded arbitrium peptides was first described by Erez et al. (2017) , and the following studies ( Dou et al., 2018 ; Wang et al., 2018 ; Gallego del Sol et al., 2019 ; Stokar-Avihail et al., 2019 ) revealed the molecular basis for the production, detection, and consequences of the short signaling peptides on phages lysogeny-lysis decisions. Notably, these reports also showed that phages communicate only with their close relatives using a very specific arbitrium peptide, which suggests that phage communication peptides act in a taxon-specific manner just as bacterial QS signals. Inspired by the above studies, especially the phage-bacterium QS connections, we hypothesized that any single QS signal should only induce prophages within a small subset of closely related host bacteria. Toward that end, we tried to determine the impacts of the addition of individual AHL signaling molecules on prophage-induction and further assess the resulting impact of phage-mediated host cell lysis on bacterial community composition in vitro using microbial communities extracted directly from soil.",
"discussion": "Discussion This study revealed the lysogeny-lysis switch of some temperate phages is responsive to QS autoinducers and the phenomenon may be more widespread than the handful of well-characterized phage-host systems reported. The molecular basis of a host QS autoinducer controlling a phage lysogeny-lysis decision has been recently characterized ( Silpe and Bassler, 2019a ). Subsequent studies reported phage responses to other types of host autoinducers ( Laganenka et al., 2019 ; Silpe and Bassler, 2019b ). Bacteria communicate with their close relatives using specific QS signals, thus phage-bacterium QS connections may also be molecular structure dependent ( Liang and Radosevich, 2019 ). Building on this idea, we hypothesized that any single QS signal should only induce prophages within a small subset of closely related host bacteria. In this study, eight AHLs of varying molecular weight and structure were selected for evaluation of phage responses of lysogeny-lysis switching and its significance in structuring the exposed well mixed bacterial communities extracted and purified from a single soil source in vitro . Significant differences in viral abundance were observed in the treated microbial cell suspensions exposed to AHL1, 2, 3, 4, 5, and 7 compared with the control suspensions. The increase of viral abundance in AHL-treated suspensions was consistent with a burst of viral production from prophage induction, especially in a relatively few host taxa, and was in agreement with our hypothesis. Other recent studies also reported prophage induction by QS molecules in bacterial hosts, such as Enterococcus faecalis ( Rossmann et al., 2015 ) and E. coli ( Laganenka et al., 2019 ), leading to viral production and bacterial lysis. Interestingly, Oh et al. (2019) discovered that though different prophages were induced by environmental cues they achieved differential induction responses indicating a unique governing system for each prophage. A significant decrease in total cell abundance was typically not observed upon exposure to AHLs. This too is consistent with the hypothesis if each AHL triggered prophage induction in a relatively narrow range of perhaps less numerically abundant taxa. In order to test this hypothesis in future studies, investigating bacterial community responses under addition of cocktails of multiple AHLs to the same bacterial suspensions should be considered. Additionally, meta-genomic and meta-transcriptomic analyses during the induction assay could reveal specific prophage-host responses to AHL-exposure. Two other possibilities may also be consistent with the observed results of the treatment-driven changes in viral and bacterial abundances. One possibility is that some of the increased abundance of fluorescence particles from AHL-treated cell suspensions may have been DNA-carrying membrane vesicles secreted/released by AHL-treatment ( Toyofuku et al., 2017 ). Previous reports have indicated that membrane vesicles play important roles in QS and can increase epifluorescence-based viral counts ( Li et al., 2016 ; Biller et al., 2017 ). The other possibility may be that the produced phages during treatment were released without host lysis ( Rakonjac et al., 1999 ; Xue et al., 2012 ; Loh et al., 2019 ). Therefore, future work is needed to elucidate the effects of QS on viral and bacterial populations and advance our understanding of this important phenomenon which may have important ecological consequences in nature. Other experimental approaches that will be useful in confirming the findings reported here might include qPCR quantification of bacterial 16S rRNA gene abundance and phage marker genes, transmission electron microscopy enumeration and morphological description of viral particles. Even a small collection of lysogenic bacterial taxa triggered to enter the lytic cycle could result in changes in taxonomic composition. We inspected the bacterial community diversity based on species richness and Shannon index. Significantly lower Shannon indices but no significant changes in species richness were observed after AHL treatment. These results suggest that AHL treatment decreased species evenness potentially by suppressing a subset of bacterial species and resulting in increased relative abundance of some other species. Further analysis of Pielou’s evenness across the samples showed that the species evenness was significantly lowered in AHL1 ( P < 0.05), AHL3 ( P < 0.05), AHL5 ( P < 0.01), and AHL7 ( P < 0.01) compared to the control suspensions which further supported the proposed mechanism of AHL treatment influencing the bacterial community structure. In contrast, mitomycin C treatment, as a broad-spectrum inducing agent, also possessing broad toxicity, commonly used for prophage induction ( Williamson et al., 2007 ; Knowles et al., 2017 ; Liang et al., 2020 ), prompted an increase in both species richness and diversity relative to untreated controls. Mitomycin C treatment brought about a significant increase in viral abundance ( P < 0.001) and notable decrease in bacterial abundance ( P < 0.01) through a wide range of viral lysis, thus contributing to higher richness and evenness in bacterial taxonomic profiles. The measurement of Pielou’s evenness in these suspensions demonstrated that mitomycin C treatment significantly increased the species evenness compared to the control group ( P < 0.01). Since viral production has been shown to be correlated to nutrient cycling and bacterial metabolism, the prophage induction can also contribute to resource redistribution and bacterial community composition ( Liang et al., 2019c , b ; Oh et al., 2019 ). To further examine the putative effects of AHL-dependent prophage induction on bacterial community structure, we determined changes in relative abundance (expressed as log2fold changes relative to control cell suspensions) of the affected taxa. Up to 10 bacterial genera decreased in relative abundance after any single AHL treatment, and a total of 23 different genera for all AHL treatments combined. Decreased relative abundance of Lysobacter , Novosphingobium , Sphingomonas , Bosea , and Nocardioides was observed in at least two AHL treatments. The significant decrease in density of the AHL-targeted bacterial genera, e.g., Lysobacter , Novosphingobium , and Sphingomonas , suggests AHL-directed transition of prophages from lysogeny to lysis in these bacteria. While AHLs caused reduced relative abundances of some bacterial taxonomic groups, AHL treatment also resulted in an increased relative abundance of some bacterial taxa, e.g., classes of Gamma-Proteobacteria, Bacilli and Flavobacteria although the positive affect on some taxa may be indirectly attributed to prophage induction and lysis of other susceptible taxa and/or some growth of the non-susceptible groups. However, if the observed increases in relative abundance were strictly due to growth of certain taxa on the extractable soil dissolved organic carbon (DOC) that was in the induction assays then the patterns would have been similar in all the treatments instead of the differing patterns that were observed. Interestingly, the relative abundance of Nocardioides and Bacilli , gram-positive genera, varied significantly in the presence of AHL, a QS autoinducer in gram-negative bacteria which may be hard to explain. However, previous studies have shown that many of Nocardioides and Bacilli members can degrade AHLs leading to quorum quenching ( Dong et al., 2002 ; Yoon et al., 2006 ; Hong et al., 2012 ; Vinoj et al., 2014 ; Kusada et al., 2019 ) thus likely having undetermined important associations with AHL-producing bacterial groups. Microbial interactions within the community are also an important factor to be considered for variations in community composition and diversity ( Tian et al., 2018 ). However, the explanations of abundance variations of Nocardioides and Bacilli cannot be demonstrated by the current results in this research. Quorum sensing has been demonstrated to be widespread among bacteria ( Polkade et al., 2016 ; Liao et al., 2018 ; Ling et al., 2019 ), however, AHLs have not been shown as QS signals for many of the AHL-impacted bacterial groups in the present study, such as Nocardioides , Streptomyces , Pedobacter , and Verrucomicrobium . While we consider the AHL-mediated prophage induction as the main driving force of differences in the bacterial community composition we observed, other factors should also be considered. For example, QS autoinducers can regulate bacterial collective behaviors such as virulence and biofilm formation and influence inter- and intra-population interactions within the community which thus may influence bacterial community structure ( Kim et al., 2016 ). There are also reports showing that AHLs can be utilized by some bacteria for growth ( Huang et al., 2003 ). So, the selective impact of AHLs themselves needs to be further considered. Mitomycin C treatment resulted in decreased abundance of 53 bacterial genera. Though the observed decreases may have resulted from virus-mediated host cell lysis upon prophage induction or simply from direct toxicity of the mitomycin C, these broad-spectrum changes clearly contributed to the observed increases in evenness of bacterial species profiles and thus increases in the community diversity. Increased proportion of specific bacterial groups observed in AHL or mitomycin C treatment might be derived from competitive release ( Loudon et al., 2014 ). Growth of some rare bacterial species might also be promoted which contributed to the increased species richness in mitomycin C treated suspensions. Susceptible bacterial species were lysed by chemical induction of prophages allowing the remaining competitors to utilize the resources more fully, and the remaining members of the bacterial community may also have access to the nutrients released through the viral shunt ( Danovaro et al., 2008 ; Kuzyakov and Mason-Jones, 2018 ). Lysogeny has been demonstrated to be widespread and a common viral life strategy in nature and shown to have links with the dynamics of the nutrient regime and host density ( Howard-Varona et al., 2017 ). Chemical induction assays, like mitomycin C, have been adopted to assess lysogeny among viral and bacterial communities ( Ghosh et al., 2009 ; Knowles et al., 2017 ; Kronheim et al., 2018 ). Though the phage-bacterium connections through QS was recently discovered, the QS based prophage-induction are largely unknown except for a few phage-host pairs none of which were derived from soil, with little known about the influence of prophage induction on microbial community dynamics. In this study, we focused on the inducing effects of eight AHLs among different well-known bacterial autoinducers on microbial community structure. Our findings revealed that a broad range of bacterial taxonomic groups were putatively susceptible to prophage induction by these host autoinducers (i.e., AHLs), and we also demonstrated how transitions from lysogeny to lysis of temperate phages responsive to different host autoinducers can have pivotal roles in influencing bacterial community structure. This research provides theoretical and methodological foundation for future study of phage-bacterium communication and the lysogeny-lysis switch of soil viral communities. For future study, the results reported here should be further investigated by including analysis of lysed bacteria as template for 16S rRNA sequence analyses. In addition, metagenomic and metatranscriptomic analyses before during and after induction assays may reveal more specific and direct evidence supporting QS-controlled lysogeny-lysis switching and the hypothesis addressed in this study may resolve more statistically robust relationships and provide unique high-resolution views of virosphere responses to host autoinducers."
} | 4,785 |
25038317 | null | s2 | 607 | {
"abstract": "Division of labor is commonly observed in nature. There are several theories that suggest diversification in a microbial community may enhance stability and robustness, decrease concentration of inhibitory intermediates, and increase efficiency. Theoretical studies to date have focused on proving when the stable co-existence of multiple strains occurs, but have not investigated the productivity or biomass production of these systems when compared to a single 'super microbe' which has the same metabolic capacity. In this work we prove that if there is no change in the growth kinetics or yield of the metabolic pathways when the metabolism is specialized into two separate microbes, the biomass (and productivity) of a binary consortia system is always less than that of the equivalent monoculture. Using a specific example of Escherichia coli growing on a glucose substrate, we find that increasing the growth rates or substrate affinities of the pathways is not sufficient to explain the experimentally observed productivity increase in a community. An increase in pathway efficiency (yield) in specialized organisms provides the best explanation of the observed increase in productivity."
} | 298 |
28696067 | PMC5609283 | pmc | 608 | {
"abstract": "The decline of coral reefs due to anthropogenic disturbances is having devastating impacts on biodiversity and ecosystem services. Here we highlight the potential and challenges of microbial manipulation strategies to enhance coral tolerance to stress and contribute to coral reef restoration and protection."
} | 77 |
38271267 | PMC10901607 | pmc | 610 | {
"abstract": "Abstract Reef-building corals (Scleractinia, Anthozoa, Cnidaria) are the keystone organisms of coral reefs, which constitute the most diverse marine ecosystems. Since the first decoded coral genome reported in 2011, about 40 reference genomes are registered as of 2023. Comparative genomic analyses of coral genomes have revealed genomic characters that may underlie unique biological characteristics and coral diversification. These include existence of genes for biosynthesis of mycosporine-like amino acids, loss of an enzyme necessary for cysteine biosynthesis in family Acroporidae, and lineage-specific gene expansions of DMSP lyase-like genes in the genus Acropora . While symbiosis with endosymbiotic photosynthetic dinoflagellates is a common biological feature among reef-building corals, genes associated with the intricate symbiotic relationship encompass not only those shared by many coral species, but also genes that were uniquely duplicated in each coral lineage, suggesting diversified molecular mechanisms of coral-algal symbiosis. Coral genomic data have also enabled detection of hidden, complex population structures of corals, indicating the need for species-specific, local-scale, carefully considered conservation policies for effective maintenance of corals. Consequently, accumulating coral genomic data from a wide range of taxa and from individuals of a species not only promotes deeper understanding of coral reef biodiversity, but also promotes appropriate and effective coral reef conservation. Considering the diverse biological traits of different coral species and accurately understanding population structure and genetic diversity revealed by coral genomic analyses during coral reef restoration planning could enable us to “archive” coral reef environments that are nearly identical to natural coral reefs.",
"conclusion": "Concluding Remarks Coral genomic data have highlighted diverse biological characters among stony corals, e.g. loss of CBS genes in the family Acroporidae, suggesting different degrees of dependence on symbiotic algae, depending on the coral lineage, MAA gene clusters, specific gene expansions of DL-L genes in Acropora ( Fig. 1 ), and lineage-specific symbiosis-related genes, suggesting diversified molecular mechanisms of coral–algal symbiosis ( Fig. 2 ). Various reef-building corals with different biological characteristics interact to support the rich biodiversity of coral reefs, and it is important to comprehend these diverse characteristics for studying coral biology and coral conservation. High-resolution population genomic studies using genome-wide SNP analyses have also revealed hidden, complex population structures of corals, indicating that when establishing effective coral conservation plans, careful, local-scale, and species-specific conservation policies will be needed. Moreover, similar, unexpected population complexities may be expected for marine species other than scleractinian corals with similar reproductive modes, especially broadcast spawners. Coral genome information, boosted by NGS advances, is accumulating steadily, and 12 reference genomes of the family Symbiodiniaceae have been reported in NCBI as of September 2023. Accumulating coral genomic data from a wide range of taxa and from individuals of a species not only promotes deeper understanding of coral reef biodiversity but also promotes appropriate and effective coral reef conservation. Considering the diverse biological traits of different coral species and accurately understanding population structure and genetic diversity revealed by coral genomic analyses during coral reef restoration planning could enable us to “archive” coral reef environments that are nearly identical to natural coral reefs. Hopefully, coral genome analysis will contribute to the preservation of the richness of coral reefs for future generations."
} | 970 |
27877107 | PMC5099523 | pmc | 611 | {
"abstract": "Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.",
"introduction": "1. Introduction Deep learning is achieving outstanding results in various machine learning tasks (He et al., 2015a ; LeCun et al., 2015 ), but for applications that require real-time interaction with the real environment, the repeated and often redundant update of large numbers of units becomes a bottleneck for efficiency. An alternative has been proposed in the form of spiking neural networks (SNNs), a major research topic in theoretical neuroscience and neuromorphic engineering. SNNs exploit event-based, data-driven updates to gain efficiency, especially if they are combined with inputs from event-based sensors, which reduce redundant information based on asynchronous event processing (Camunas-Mesa et al., 2012 ; O'Connor et al., 2013 ; Merolla et al., 2014 ; Neil and Liu, 2016 ). This feature makes spiking systems attractive for real-time applications where speed and power consumption are important factors, especially once adequate neuromorphic hardware platforms become more widely available. Even though in theory (Maass and Markram, 2004 ) SNNs have been shown to be as computationally powerful as conventional artificial neural networks (ANNs; this term will be used to describe conventional deep neural networks in contrast with SNNs), practically SNNs have not quite reached the same accuracy levels of ANNs in traditional machine learning tasks. A major reason for this is the lack of adequate training algorithms for deep SNNs, since spike signals (i.e., discrete events produced by a spiking neuron whenever its internal state crosses a threshold condition) are not differentiable, but differentiable activation functions are fundamental for using error backpropagation, which is still by far the most widely used algorithm for training deep neural networks. A recently proposed solution is to use different data representations between training and processing, i.e., training a conventional ANN and developing conversion algorithms that transfer the weights into equivalent deep SNNs (O'Connor et al., 2013 ; Diehl et al., 2015 ; Esser et al., 2015 ; Hunsberger and Eliasmith, 2015 ). However, in these methods, details of statistics in spike trains that go beyond ideal mean rate modeling, such as required for processing practical event-based sensor data cannot be precisely represented by the signals used for training. It is therefore desirable to devise learning rules operating directly on spike trains, but so far it has only been possible to train single layers, and use unsupervised learning rules, which leads to a deterioration of accuracy (Masquelier and Thorpe, 2007 ; Neftci et al., 2014 ; Diehl and Cook, 2015 ). An alternative approach has recently been introduced by O'Connor and Welling ( 2016 ), in which a SNN learns from spikes, but requires keeping statistics for computing stochastic gradient descent (SGD) updates in order to approximate a conventional ANN. In this paper we introduce a novel supervised learning method for SNNs, which closely follows the successful backpropagation algorithm for deep ANNs, but here is used to train general forms of deep SNNs directly from spike signals. This framework includes both fully connected and convolutional SNNs, SNNs with leaky membrane potential, and layers implementing spiking winner-takes-all (WTA) circuits. The key idea of our approach is to generate a continuous and differentiable signal on which SGD can work, using low-pass filtered spiking signals added onto the membrane potential and treating abrupt changes of the membrane potential as noise during error backpropagation. Additional techniques are presented that address particular challenges of SNN training: Spiking neurons typically require large thresholds to achieve stability and reasonable firing rates, but large thresholds may result in many “dead” neurons, which do not participate in the optimization during training. Novel regularization and normalization techniques are proposed that contribute to stable and balanced learning. Our techniques lay the foundations for closing the performance gap between SNNs and ANNs, and promote their use for practical applications. 1.1. Related work Gradient descent methods for SNNs have not been deeply investigated because both spike trains and the underlying membrane potentials are not differentiable at the time of spikes. The most successful approaches to date have used indirect methods, such as training a network in the continuous rate domain and converting it into a spiking version. O'Connor et al. ( 2013 ) pioneered this area by training a spiking deep belief network based on the Siegert event-rate approximation model. However, on the MNIST hand written digit classification task (LeCun et al., 1998 ), which is nowadays almost perfectly solved by ANNs (0.21% error rate in Wan et al., 2013 ), their approach only reached an accuracy around 94.09%. Hunsberger and Eliasmith ( 2015 ) used the softened rate model, in which a hard threshold in the response function of leaky integrate and fire (LIF) neuron is replaced with a continuous differentiable function to make it amenable to use in backpropagation. After training an ANN with the rate model they converted it into a SNN consisting of LIF neurons. With the help of pre-training based on denoising autoencoders they achieved 98.6% in the permutation-invariant (PI) MNIST task (see Section 3.1). Diehl et al. ( 2015 ) trained deep neural networks with conventional deep learning techniques and additional constraints necessary for conversion to SNNs. After training, the ANN units were converted into non-leaky spiking neurons and the performance was optimized by normalizing weight parameters. This approach resulted in the current state-of-the-art accuracy for SNNs of 98.64% in the PI MNIST task. Esser et al. ( 2015 ) used a differentiable probabilistic spiking neuron model for training and statistically sampled the trained network for deployment. In all of these methods, training was performed indirectly using continuous signals, which may not capture important statistics of spikes generated by real sensors used during processing. Even though SNNs are well-suited for processing signals from event-based sensors such as the Dynamic Vision Sensor (DVS) (Lichtsteiner et al., 2008 ), the previous SNN training models require removing time information and generating image frames from the event streams. Instead, in this article we use the same signal format for training and processing deep SNNs, and can thus train SNNs directly on spatio-temporal event streams considering non-ideal factors such as pixel variation in sensors. This is demonstrated on the neuromorphic N-MNIST benchmark dataset (Orchard et al., 2015 ), achieving higher accuracy with a smaller number of neurons than all previous attempts that ignored spike timing by using event-rate approximation models for training.",
"discussion": "4. Discussion We proposed a variant of the classic backpropagation algorithm, known as the most widely used supervised learning algorithm for deep neural networks, which can be applied to train deep SNNs. Unlike previously proposed techniques based on ANN-to-SNN conversion methods (Diehl et al., 2015 ; Esser et al., 2015 ; Hunsberger and Eliasmith, 2015 ), our method can optimize networks by using real spike events from neuromorphic vision sensors during training. We found that regularization of weight and threshold parameters are critical to stabilize the training process and achieve good accuracy. We also proposed a novel normalization technique for backpropagating error gradients to train deep SNNs. We have shown that our novel spike-based backpropagation technique for multi-layer fully-connected and convolutional SNNs works on the standard benchmarks MNIST and PI MNIST, and also on N-MNIST Orchard et al. ( 2015 ), which contains spatio-temporal structure in the events generated by a neuromorphic vision sensor. We improve the previous state-of-the-art accuracy of SNNs on both tasks and achieve accuracy levels that match those of conventional deep networks. Closing this gap makes deep SNNs attractive for tasks with highly redundant information or energy constrained applications, due to the benefits of event-based computation, and advantages of efficient neuromorphic processors (Merolla et al., 2014 ). We expect that the proposed technique can better capture the timing statistics of spike signals generated from event-based sensors, which is an important advantage over previous SNN training methods. Recent advances in deep learning have demonstrated the importance of working with large datasets and extensive computational resources. The MNIST benchmark, under these considerations needs to be considered too small for evaluating the scaling of architectures and learning methods to larger applications. Furthermore, the dataset is not meant as a benchmark for SNNs, because it does not provide spike events generated from real sensors. Nevertheless, it remains important since new methods and architectures are still frequently evaluated on MNIST. In particular, almost all recently published SNN papers are tested on MNIST, where it remains the only dataset allowing comparisons. The N-MNIST benchmark (Orchard et al., 2015 ), which was recorded directly with neuromorphic vision sensors, is a more meaningful testbed for SNNs, even though it is still small in size, similar to the original MNIST. The fact that all events were generated following the same saccade patterns for all samples was a choice made by the creators of the dataset, and might lead to networks learning the particular spatial patterns of the saccades. It is thus unknown how classifiers trained on N-MNIST would generalize to different movement patterns, and possibly the accuracy for arbitrary saccade patterns would degrade. Just as hardware acceleration through GPUs has been critical to advance the state of the art in conventional deep learning, there is also an increasing need for powerful hardware platforms supporting SNN training and inference. Parallelizing event-based updates of SNNs on current GPU architectures remains challenging (Nageswaran et al., 2009 ), although the option of simply time-stepping the simulated SNNs on GPUs has not been carefully evaluated yet. Neuromorphic processors (Camunas-Mesa et al., 2012 ; Merolla et al., 2014 ; Indiveri et al., 2015 ) are improving to make inference in deep networks more efficient and faster (Esser et al., 2016 ), but applying the training methods introduced in this paper additionally at least requires the measurement of spike statistics during runtime. The limited numerical precision of neuromorphic hardware platforms may require further adaptations of the training method, hence, at this point a hardware speed-up of onchip SNN training is not yet feasible, but remains an important topic for further research. It may be that a platform such as SpiNNaker (Furber et al., 2013 ), which consists of a mesh of specialized ARM processors, could be used to simulate the forward propagation through the SNN while simultaneously collecting the necessary statistics for backprop training. Here we have presented only examples where spiking backpropagation was applied to feed-forward networks, but an attractive next goal would be to extend the described methods to recurrent neural networks (RNNs) (Schmidhuber, 2015 ), driven by event-based vision and audio sensors (Neil and Liu, 2016 ). Here the advantages of event-based sensors for sparsely representing precise timing could be combined with the computational power of RNNs for inference on dynamical signals."
} | 3,310 |
25873997 | PMC4395902 | pmc | 613 | {
"abstract": "Background The growing concern regarding the use of agricultural land for the production of biomass for food/feed or energy is dictating the search for alternative biomass sources. Photosynthetic microorganisms grown on marginal or deserted land present a promising alternative to the cultivation of energy plants and thereby may dampen the ‘food or fuel’ dispute. Microalgae offer diverse utilization routes. Results A two-stage energetic utilization, using a natural mixed population of algae ( Chlamydomonas sp. and Scenedesmus sp.) and mutualistic bacteria (primarily Rhizobium sp.), was tested for coupled biohydrogen and biogas production. The microalgal-bacterial biomass generated hydrogen without sulfur deprivation. Algal hydrogen production in the mixed population started earlier but lasted for a shorter period relative to the benchmark approach. The residual biomass after hydrogen production was used for biogas generation and was compared with the biogas production from maize silage. The gas evolved from the microbial biomass was enriched in methane, but the specific gas production was lower than that of maize silage. Sustainable biogas production from the microbial biomass proceeded without noticeable difficulties in continuously stirred fed-batch laboratory-size reactors for an extended period of time. Co-fermentation of the microbial biomass and maize silage improved the biogas production: The metagenomic results indicated that pronounced changes took place in the domain Bacteria, primarily due to the introduction of a considerable bacterial biomass into the system with the substrate; this effect was partially compensated in the case of co-fermentation. The bacteria living in syntrophy with the algae apparently persisted in the anaerobic reactor and predominated in the bacterial population. The Archaea community remained virtually unaffected by the changes in the substrate biomass composition. Conclusion Through elimination of cost- and labor-demanding sulfur deprivation, sustainable biohydrogen production can be carried out by using microalgae and their mutualistic bacterial partners. The beneficial effect of the mutualistic mixed bacteria in O 2 quenching is that the spent algal-bacterial biomass can be further exploited for biogas production. Anaerobic fermentation of the microbial biomass depends on the composition of the biogas-producing microbial community. Co-fermentation of the mixed microbial biomass with maize silage improved the biogas productivity.",
"conclusion": "Conclusions A combination of bioH 2 and biogas production by a mixture of nonsterile microalgae and natural bacterial flora was demonstrated. In a closed system, the mutualistic bacteria consumed the O 2 evolved by the algae and created a sufficiently anaerobic environment for algal H 2 evolution without damaging the photosynthetic apparatus of the algae. With the help of the bacterial partners, the algae succeeded in capturing light energy by photosynthetic water splitting and evolved H 2 at the same time without the need for further manipulation of the system, such as sulfur deprivation. H 2 production through the use of a mixture of microalgae and syntrophic bacteria started earlier than the H 2 evolution following sulfur deprivation, although sulfur-deprived C. reinhardtii produced bioH 2 for a longer period of time. AD and biogas evolution from the nonsterile microalgal-bacterial biomass yielded a gas enriched in CH 4 relative to the commonly used maize silage. The specific biogas production estimated on the basis of the organic material input, however, was smaller than that from maize silage. The addition of maize silage to the algal-bacterial mixed biomass increased the C/N ratio considerably and improved the balanced digestibility of the microbial biomass. The metagenome analysis of the microbial communities present in the AD reactors revealed the persisting impact of the microalgae and their bacterial companions on the composition of the AD microbial community within a few days. The large amount of bacteria belonging in the genera Rhizobium and Burkholderia , dosed together with the microalgal biomass, significantly changed the bacterial community composition. Co-fermentation of the algal-bacterial biomass with maize silage compensated somewhat for the Rhizobium and Burkholderia predominance due to the 50% lower loading of the microbial biomass on an organic dry matter basis. In the control reactors fed with maize silage, the microbial taxa belonging in the phyla Firmicutes and Bacteroidetes persisted. Interestingly, the pronounced alterations observed in the domain Bacteria did not affect the composition of the domain Archaea . The order Methanosarcinales predominated in the Archaeal community regardless of the substrate composition.",
"introduction": "Introduction Biomass utilization for energy generation is commonly regarded as a major contributor to the achievement of renewable energy production targets [ 1 - 4 ]. Energy carriers from biomass are currently predominantly produced through the use of terrestrial plants [ 5 ]. The intensive exploitation of land for the cultivation of crops destined for biofuel production, however, may exert a negative impact on the global supply and the price of food and feed [ 6 ]. The search for alternative biomass sources still continues. Economically and environmentally friendly solutions should be found. Huge energetic and biorefinery opportunities are offered by the conversion of solar energy via the use of photosynthetic microorganisms. Hence, the interest in photosynthetic microorganisms (and especially microalgae) is growing worldwide. The microalgae are a large and diverse group of microscopic, photoautotrophic, or photoheterotrophic organisms, which grow profusely in both salt and fresh natural waters [ 7 ]. Microalgae are able to double their biomass much faster than terrestrial plants, and they therefore produce more biomass per hectare than higher plants do [ 8 ]. The relatively small land area needed to cultivate microalgae may be arable or marginal land, which further decreases the competition for agricultural land and smothers the ‘food or fuel’ dispute [ 7 ]. Microalgae can be harvested practically all year round, hence improving the biomass production efficacy and eliminating numerous storage problems. Cultivation is possible in closed photobioreactors or in open ponds. Open systems are usually considered to be economical, while closed systems are more efficient from the aspect of biomass production and control of the cultivation parameters [ 9 , 10 ]; either concept may therefore be competitive in diverse applications [ 11 ]. Additional beneficial features of a microalgal biomass include versatility and the variety of utilization for energetic purposes such as biohydrogen (bioH 2 ), bioethanol, biodiesel, and biogas production [ 12 - 14 ], besides biorefinery applications [ 14 - 16 ]. The important properties of a microalgal biomass to be used in anaerobic digestion (AD) include high contents of lipids and/or carbohydrates and a lack of recalcitrant lignin [ 12 ]. The lipid and carbohydrate content amounts up to 50% of the biomass dry weight in some strains [ 10 , 17 ]. Research on the AD of algal biomass started more than 50 years ago [ 18 ]. Until recently, only a few studies followed up this line of research [ 19 - 24 ]. Levels of biogas productivity from various fresh and salt water algal strains have been compared under mesophilic conditions [ 25 ]. The biogas potential was found to depend strongly on the species and on the cell disruption method applied. The CH 4 content of the gas evolved from the microalgae was 7% to 13% higher than that from maize silage [ 25 ]. A closed-loop system to convert the algal biomass to biogas and electricity has been tested [ 26 ]. The microbial communities thriving in anaerobic digesters fed with algal biomass have not been investigated extensively. The archaeal community formed during microalgal fermentation was recently analyzed by next-generation sequencing [ 27 ]. Some microalgae, such as the most extensively studied green microalga Chlamydomonas reinhardtii , have the noteworthy ability to produce H 2 via a photosynthetic water-splitting reaction coupled with the dark hydrolysis of storage materials [ 28 - 30 ]. Sulfur deprivation becomes a standard method through which to switch the algal metabolism from photoautotrophy to dark heterotrophic H 2 generation. The two-step process during which the cells undergo major metabolic and biochemical changes demands considerable energy input both by the process operators and by the algae. Naturally formed, mixed algal-bacterial microbial communities have been observed to have beneficial effects on algal growth [ 31 - 34 ]. The mutualistic relationship involves supplying the algae with important growth factors, notably vitamin B12, by the bacterial partner in exchange for organic nutrients [ 35 - 39 ]. Little is known about H 2 production by algal-bacterial systems [ 40 ]. A recent study proposed that by consuming the O 2 generated photosynthetically by the algae, the bacteria maintain an anaerobic environment suitable for algal bioH 2 production [ 41 ]. This may eliminate the need for the sulfur-deprivation step [ 28 - 30 ]. In this study, we modeled a two-stage biorefinery process, that is, H 2 production in the first stage by an algal-bacterial mixed biomass grown under nonsterile photoheterotrophic conditions, with biogas generation from the residual biomass in the second stage. The composition of the microalgal-bacterial mixture was monitored during the process by using next-generation DNA sequencing technology.",
"discussion": "Results and discussion H 2 production by the mixed algal-bacterial system H 2 accumulated in the reactor headspace and concomitantly O 2 disappeared in time when a mixture of Scenedesmus sp. and Chlamydomonas sp. was cultivated under nonsterile conditions together with their natural mutualistic bacterial partners (AB + S culture), which consumed the O 2 produced by the algae. The results were compared with the H 2 evolution by a mixture of the pure cultures of the two microalgae supplemented with hydrogenase-deficient Escherichia coli cells (AE + S culture) and by sulfur-deprived, bacterium-free algal cultures (A-S culture) (Figure 1 ). Striking differences were observed in terms of accumulated H 2 yields and the commencement and duration of H 2 evolution. Figure 1 \n H \n 2 \n accumulation (A) and O \n 2 \n content (B) in the headspaces of the various cultures in time. Orange circles: mixed algal-bacterial co-culture (AB + S); green squares: algal-bacterial mixture with added E. coli ΔhypF (AE + S); blue triangles: sulfur-deprived bacterium-free co-culture of Chlamydomonas sp. and Scenedesmus sp. (A-S); red diamonds: bacterium-free co-culture of Chlamydomonas sp. and Scenedesmus sp. without sulfur deprivation (A + S). In the headspace of the growing algal-bacterial culture, the O 2 level decreased from 21% to 4.5% in 12 h (Figure 1 B). The low O 2 level allowed H 2 evolution by the algal biomass after 8 h and 1.15 ± 0.09 mL H 2 L −1 was produced during the next 16 h, confirming earlier observations in similar systems (Figure 1 A) [ 41 ]. The mutualistic bacteria were eliminated from the algal culture by photoautotrophic cultivation on minimal medium supplemented with rifampicin. H 2 production was not observed of the bacterium-free algal culture (A + S), because O 2 was not consumed by the mutualistic bacteria and the biosynthesis of the O 2 sensitive hydrogenases was repressed (Figure 1 A,B). The facultative anaerobic wild-type E. coli tends to consume O 2 when it is available. Under anaerobic conditions, E. coli evolves H 2 by using its own hydrogenases [ 42 ]. In order to eliminate the contribution of H 2 production by E. coli , a pleiotropic hydrogenase mutant (Δ hypF ) strain was used in these experiments so that only the facultative anaerobic property of this bacterium is functioning. Addition of E. coli ΔhypF cells and acetate to the pure algal culture (AE + S) efficiently reduced the level of O 2 from 21% to 4% in 2 h. Pronounced H 2 production accompanied this condition (1.52 ± 0.04 mL H 2 L −1 ) (Figure 1 A). The bacterial cell number in the spontaneously formed algal-bacterial culture (AB + S) was markedly lower than in the algal- E. coli ΔhypF co-culture (AE + S), which may explain why H 2 generation by the AE + S started earlier than without the O 2 scavenger E. coli strain (Figure 1 ). These data were compared with the H 2 production by the mixture of the pure algal strains using the photoheterotrophic TRIS-acetate-phosphate medium (TAP) and employing the sulfur-deprivation method [ 43 , 44 ]. The sulfur-deprived pure Scenedesmus sp. and Chlamydomonas sp. mixture (A-S culture) became anaerobic after 20 h as opposed to the 2 to 8 h in the case of AB + S and AE + S. H 2 evolution starts when anaerobic conditions are established; therefore, the difference in time required to reach anaerobicity is critical for the efficacy of the process. Additional benefits from practical aspect are the lower cost of alga production under nonsterile conditions and the elimination of labor- and cost-intensive transfer of algae into the sulfur-deficient medium. The highest level of H 2 generation by the A-S (1.91 ± 0.12 mL H 2 L −1 ) was reached after 4 days (Figure 1 A), which exceeded the H 2 production of the AE + S culture only by about 20%. In view of the exceptionally thick cell walls of the Scenedesmus strains, the H 2 productivity may have been partly diffusion-limited in the mixed algal culture, which may explain the lower H 2 yield of A-S relative to the pure culture of sulfur-deprived Chlamydomona s sp. 549 strain (2.63 ± 0.04 mL H 2 L −1 ) reported earlier [ 41 ]. Taken together, these experiments demonstrated that algal-bacterial natural mutualistic consortia are superior to the bacterium-free sulfur-deprived algal cultures from the aspect of H 2 evolution. There are two possible reasons why the H 2 production ceased after about 24 h in the algal-bacterial co-cultures cultivated in TAP medium (see the ‘Materials and methods’ section). First, the H 2 yield depends on the H 2 partial pressure in a closed system [ 45 ]. Removal of the product H 2 from the headspace allows the extension of the production time, leading to sustainable H 2 evolution (data not shown). Secondly, in separate experiments, we have demonstrated that the depletion of acetate also results in a rapid loss of the mutualistic bacteria [ 41 ]. This can be remedied by the systematic addition of acetic acid to the system. Acetate is a low-value commodity produced in a number of anaerobic fermentative processes. The limiting factors of this bioH 2 production methodology appear to be relatively easy to overcome. H 2 production by algae under nonsterile conditions may make this approach economically viable on a large scale. Biogas production from algal-bacterial mixed biomass The levels of biogas production from the various biomass substrates were determined after a 1-month of start-up and stabilization phase, that is, in weeks 1 to 4 of the experiment. During this time, the reactors were fed with the AB + S substrate to ensure that all the remaining and digestible biomass from the inoculum (containing pig slurry and maize silage) had been degraded and did not contribute to the biogas formation. Gas production data were collected during weeks 5 to 9, when the evolved gas was produced from the AB + S biomass. Biogas generation from the algal-bacterial mixture was compared with co-fermentaion of the alga-rich biomass and maize silage, and reactors fed with maize silage were used as controls. The CH 4 concentration in the gas made from the AB + S biomass substrate was 58% to 61%, which is comparable to previous findings [ 25 , 26 , 46 ]. The biogas CH 4 content from maize silage alone was 50% to 52%, as found previously [ 47 ]. The co-fermentation of algal-bacterial biomass with maize silage, in a ratio of 1:1, on the basis of organic dry matter (oDM), yielded a CH 4 content of 54% to 57%, an intermediate value between those for maize silage and the algal-bacterial biomass. The daily average generated biogas volumes were as follows: from maize silage 3.20 L day −1 , from co-fermentation 3.15 L day −1 , and from algal-bacterial mixture 2.20 L day −1 . Figure 2 shows the specific average CH 4 production values (mL) calculated for g oDM −1 . Figure 2 \n Specific CH \n 4 \n production from the various biomasses. \n For the appreciation of the potential value of the AB + S biomass as biogas substrate, its advantages relative to the widely used maize silage have to be taken into account. Most importantly, the AB + S biomass can be cultivated under nonsterile conditions on lands not useful for agricultural production and can be continuously harvested during extended period of the year. Although several technical issues related to the large-scale production of AB + S biomass for energetic purpose remain to be elaborated, this material may effectively replace a large portion of maize silage in the biogas reactors. VOAs/TAC ratio indicated stable operation The ratio of the volatile organic acids (VOAs) and the total alkaline capacity (TAC) is an appropriate measure of the functional stability of the anaerobic digestion process [ 48 , 49 ]. A VOAs/TAC ratio below 0.1 means that the reactor needs feeding, whereas at a ratio ≥0.5 the biomass input is excessive and the process is out of balance. During the experiments, the average content of VOAs was 1.5 g L −1 and the average TAC was between 9 and 10 g CaCO 3 L −1 in all cases. Figure 3 shows the weekly measured VOAs/TAC ratios. Figure 3 \n Weekly measured VOAs/TAC ratios. The area between the dashed red lines indicates the optimum range. A constant value of VOAs/TAC is a reliable indicator of a stable fermentation process. The organic loading rate was on the low side and allowed stable and balanced operation. NH 4 + accumulation From the decomposition of nitrogen-containing compounds, ammonia (NH 3 ) is formed, which is present in the aqueous medium in the form of ammonium ion (NH 4 + ) [ 50 ]. Values above 3,000 mg NH 4 + L −1 may have a negative effect on the methanogenic community [ 51 , 52 ]. During the anaerobic fermentation, slight fluctuations in the weekly NH 4 + concentrations were observed. In the case of using the algal-bacterial mixture, the NH 4 + content tended to increase but remained under the critical 3,000 mg NH 4 + L −1 level (Figure 4 ). Co-fermentation efficiently balanced this elevated NH 4 + level. Figure 4 \n Weekly measured NH \n 4 \n + \n concentrations. The dashed red line indicates the highest value recommended by the various studies. The effect of the C/N ratio The ideal C/N ratio for AD is 20 to 30 [ 53 , 54 ], because the microbes in the anaerobic reactor can utilize carbon (C) 20 to 30 times faster than nitrogen (N) [ 54 ]. The risk of C starvation increases if the C/N ratio is lower than 20; the methanogens are inhibited by the high NH 3 accumulation, making the AD process vulnerable. At the other end of the spectrum, if the C/N ratio exceeds 30, the concentration of volatile fatty acids escalates, leading to process inhibition. The C/N ratios of the substrates used in this work are presented in Table 1 . During the fed-batch continuous AD of microalgae and their mutualistic bacterial flora (AB + S), the nitrogen content increased. The initial C/N ratio of the AB + S biomass was low, 5.3. The nitrogen content increased as the fermentation progressed (Figure 5 ), accompanied by a slight but persistent free N concentration increase. Co-fermentation of the algal-bacterial biomass with maize silage, which had a C/N ratio of 45.3, led to a less pronounced N accumulation, indicating a buffering effect of the maize silage. In the reactors fed with maize silage alone, the N level remained nearly constant (Figure 5 ). Table 1 \n The initial substrate compositions \n \n Substrate \n \n Wet mass N (mg g \n −1 \n ) \n \n Wet mass C (mg g \n −1 \n ) \n \n C/N ratio \n \n TS (%) \n \n oDM (%) \n Maize silage 4.35 196.86 45.3:1 41.19 94.59 Algal-bacterial mix 18.65 98.33 5.3:1 30.30 97.71 TS = total solids, oDM = organic dry material. Figure 5 \n Changes in N content during the AD of various substrates. Green: AB + S, orange: co-fermentation, blue: maize silage. Olsson et al . reported that feeding AD reactors with a high proportion of microalgal biomass in co-fermentation with waste water sludge had a negative effect under both thermophilic (55°C) and mesophilic (37°C) conditions, possibly because of the high N content of the microalgal biomass [ 55 ]. Co-fermentation of a microalgal biomass with waste paper improved the AD performance [ 56 ], presumably in consequence of the higher C/N ratio of the mixed substrate and the induction of cellulase biosynthesis by the paper sludge. In our case, co-fermentation of the algal-bacterial biomass with the cellulose-rich maize silage likewise enhanced the biogas productivity. Microbial community The composition of the microbial community was established at four time points: at the start of feeding with the selected substrate (start), 1 week later (week 1), when the system was working at full capacity (week 5), and at the end of the process (week 9). The microbial community compositions of the substrates were determined separately. Microbiological compositions of the substrates The microbial flora of the maize silage included representatives of the genera Lactobacillus and Acetobacter , as expected (Figure 6 A). Lactobacilli produce lactate from mono- and disaccharides [ 57 ]. Upon ensilaging, the accumulating acid decreases the pH and preserves the green plant material. Members of the genus Acetobacter primarily contribute to acetate production [ 58 ]. Figure 6 \n Microbial compositions of the substrates: (A) Maize silage, (B) AB + S. The communities at domain, phylum, class, and genus levels are indicated. The mixture of Chlamydomonas sp . and Scenedesmus sp . microalgae was cultivated under nonsterile conditions and contained copious amounts of the mutualistic bacteria (Figure 6 B). Rhizobium species predominated in the bacterial population. Rhizobium is well known for its syntrophic interaction with plants and mutualism has also been observed in the cases of several microalgal species [ 36 , 39 ]. The major probable driving force behind this association is vitamin B 12 , which the algae needs for growth but cannot synthesize. Rhizobium is there to supply the algae with vitamin B 12 in exchange for fixed carbon. The growth rate and the resistance to environmental stresses improve as a result of the algal-bacterial interactions [ 36 , 39 ]. Other forms of mutualism between microalgae and bacteria have also been recognized [ 31 - 34 ]. The biogas-producing microbial community The distribution of the microbial taxa in the biogas-producing microbial community at the beginning of the experiments was very similar to that found in earlier studies on reactors fed with pig manure and maize silage [ 59 ], in good agreement with starting the reactors with inocula from a mesophilic industrial biogas facility digesting such substrates. These results may therefore be regarded as an internal control validating the metagenome sequencing method. In the following detailed analysis of the metagenomic results, the unidentified sequences are disregarded. Microbial community of maize silage AD (domain Bacteria) Only relatively minor and trivial rearrangements occurred in the relative distribution of the bacterial taxa during the experimental period (Figure 7 ). This is not surprising in view of the fact that the reactors were sustained on pig manure and maize silage prior to the start of the experiment. In the domain Bacteria , the most abundant strains belong in the phylum Firmicutes . Pronounced changes were seen in the phylum Proteobacteria . Some of the Proteobacteria were apparently displaced by Firmicutes and Bacteroidetes . In the phylum Firmicutes , the orders Clostridiales and Bacteroidales predominated (Figure 8 ). Among the Clostridiales , the genus Clostridium increased in abundance, followed by the genus Bacillus . In the order Bacteroidales , the genus Bacteroides predominated (data not shown). Figure 7 \n Changes in the domain Bacteria of the microbial community at phylum level. (A) Maize silage, (B) co-fermentation, and (C) algal-bacterial biomass. Figure 8 \n Changes in the domain Bacteria of the microbial community at the order level. (A) Maize silage, (B) co-fermentation, and (C) algal-bacterial biomass. Microbial community of co-fermentation (domain Bacteria) Co-fermentation of the algal-bacterial mixture with maize silage provoked major changes in the composition of the bacterial community within a week as compared with the AD of maize silage (Figures 7 and 8 ). At the starting time point, there was no difference between the reactors fed with the various substrate compositions, indicating that the same microbial community was established during the start-up phase and the initial conditions were therefore identical. Supplying the reactors with a 1:1 mixture of microbial biomass and maize silage instigated a rearrangement within the biogas-producing microbial community. Representatives of the phylum Proteobacteria gradually predominated in the community, and within the taxon, the orders Rhizobiales and Burkholderiales prevailed (Figure 8 ). At higher resolution, a marked accumulation of the genera Rhizobium and Burkholderia was evident as the experiment progressed, although the phylum Proteobacteria displayed a diverse representation at the start. At the same time, members of the phylum Firmicutes and to a lesser degree those belonging to the phylum Bacteroidetes lost their significance within the AD community. The majority of bacteria belonging in these taxa have gained a reputation as outstanding cellulose degraders and H 2 producers, both of these metabolic activities being crucial for efficient biogas production from plant biomass. Microbial community of microalgal-bacterial fermentation (domain Bacteria) A noteworthy fast response by the biogas-producing microbial community was observed when the substrate added to the reactors was changed from the mixture of pig slurry and maize silage to the algal-bacterial biomass. The reaction was less pronounced, but similar when the reactors were fed with a 1:1 mix of plant and microbial biomasses, as discussed above. The main outcome of this reorganization was the predominance of the phylum Proteobacteria , which surpassed the phyla Firmicutes and Bacteroidetes . The genera Rhizobium and Burkholderia were introduced into the reactor with the substrate (Figure 6 ) and accumulated in time (Figure 8 ), in spite of the relatively low daily organic loading rate. Either the decomposition was too slow to convert the total administered bacterial biomass to biogas, or the Rhizobia multiplied faster than their anaerobic degradation. Rhizobium species survive in free living form under anaerobic conditions, taking advantage of their nitrate respiration capability [ 60 , 61 ], but it is unlikely that their growth rate exceeds that of the anaerobic degradation by the biogas microbial community. The substrate was stored at −20°C for about 3 months before being fed into the reactors. It seems likely that the build-up of Proteobacteria in time is due to their relatively slow decomposition under the AD conditions. In this respect, it is noteworthy that the relative abundance of eukaryotic sequences in the reactors also increased in time (Figure 9 ). The eukaryotic DNA accumulation from the algal biomass was twice that from the maize, suggesting that the algal cell wall may be more resistant than that of the maize silage to microbial degradation. Figure 9 \n Eukaryotic sequences in the reactors. Green: AB + S, orange: co-fermentation, blue: maize silage. This implies that the biogas potential of the algal biomass is higher than that of the bacterial biomass, although a correct mass balance is difficult to achieve because of the complexity of the organic materials in the reactor. The domain Archaea In the domain Archaea , a microbial composition was found that was distinct from those observed in previous studies in reactors fed with ‘conventional’ substrates [ 59 , 62 - 66 ]. The class Methanomicrobia represented the domain Archaea in great abundance. The Methanomicrobia are able to operate all three routes of methanogenesis [ 67 ]. The order Methanomicrobiales was the most prevalent from the start, and at higher resolution, the members of the genus Methanosarcina predominated. Seasonal changes or other uncontrolled factors may also be responsible for these alterations in the AD communities [ 68 - 70 ]. At any rate, the genus Methanosarcina remained predominant in all fermentations tested in this study (Figure 10 ). Interestingly, in a previous study, involving the use of next-generation sequencing mcrA genes, the order Methanosarcinales was also found to be predominant in the AD of a mixture of waste water sludge and a nonsterile, unidentified algal biomass [ 27 ]. In the Archaeal community converting that substrate to biogas, the acetotrophic genus Methanosaeta (order Methanosarcinales ) was identified as the prevailing taxonomic unit. Members of the genus Methanosaeta were present in our study too, although in less abundance. Figure 10 \n Distribution of the domain Archaea in the microbial community at the order level. (A) Maize silage, (B) co-fermentation, and (C) algal-bacterial biomass."
} | 7,492 |
38622279 | PMC11018857 | pmc | 615 | {
"abstract": "Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a “next-generation” reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore the inherent limitations of finite-past memory traces in this intriguing proposal. A lower bound from Fano’s inequality shows that, on highly non-Markovian processes generated by large probabilistic state machines, next-generation reservoir computers with reasonably long memory traces have an error probability that is at least \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sim 60\\%$$\\end{document} ∼ 60 % higher than the minimal attainable error probability in predicting the next observation. More generally, it appears that popular recurrent neural networks fall far short of optimally predicting such complex processes. These results highlight the need for a new generation of optimized recurrent neural network architectures. Alongside this finding, we present concentration-of-measure results for randomly-generated but complex processes. One conclusion is that large probabilistic state machines—specifically, large \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines—are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.",
"conclusion": "Conclusion The striking advances made by RNNs in predicting a very wide range of systems—from language to climate—have not been accompanied by markedly improved explorations of how much structure they fail to predict. Here, we introduced and illustrated such a calibration. We addressed the task of leveraging past inputs to forecast future inputs, for any stochastic process. We showed that \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$P_\\text {e}^\\text {min}$$\\end{document} P e min —the minimal time-averaged probability of incorrectly guessing the next input, minimized over all possible strategies that can operate on historical input—can be directly calculated from a data source’s generating \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machine. This provides a benchmark for all possible prediction algorithms. We compared this optimal predictive performance with a lower bound on various RNNs’ \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$P_\\text {e}$$\\end{document} P e —the actual time-averaged probability of incorrectly guessing the next input, given the state of the model. We found that so-called next-generation RCs are fundamentally limited in their performance. And we showed that this cannot be improved on via clever readout nonlinearities. In our comparison of various prediction models, we tested next-generation RCs with highly-correlated inputs that are challenging to predict. This input data was generated from large \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines. The \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines are the optimal prediction algorithm, and the minimal probability of error for these data are known in closed-form. Our extensive surveys showed, surprisingly, that models from RCs with linear readout to next-generation RCs of reasonable size to LSTMs all have a probability of prediction error that is \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sim 50\\%$$\\end{document} ∼ 50 % greater than the theoretical minimal probability of error. The fact that simple large random \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines generate such challenging stimuli might be a surprise. Recently, though, it was reported that tractable \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines can lead to “interesting” processes 11 , 12 . We showed that these processes provide even more of a challenge for next-generation RCs. At first, it may seem that this new calibration is somewhat useless, both theoretically and from a practical point of view. For instance, it is perhaps not surprising that RCs, NGRCs, and maybe even LSTMs perform poorly on highly non-Markovian processes such as the ones used here. However, with N nodes, one can find N predictive features that potentially reach far back into the past even though one might naively think that the N features correspond to the last N time points. Secondly, the processes used here are not of general interest, as large random \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines do not correspond to real-world signals in structure. However, one can manufacture \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines that do have the structure of real-world signals, as any real-world signal can be represented by an \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machine. Then, the calibration here can improve the RC or RNN’s ability to predict real-world signals. This potential research program extends even to nonstationary real-world data. Both natural language and natural data from the physical world can be understood as stochastic processes which, in principle, have some \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machine representation. While we focused on stationary processes in this manuscript, nonstationary processes can be accommodated via simple adaptations of our methods, where the unifilar HMM of the process would have a unique start state and possible absorbing states. Finally, next-generation RCs—that do indeed outperform typical RCs with the same number of readout nodes—are fundamentally limited in prediction performance by the nature of their limited memory traces. We suggest that effort should be expended to optimize standard RCs that do not suffer from the same fundamental limitations—so that memory becomes properly incorporated and typical performance improves.",
"introduction": "Introduction Success in many scientific fields centers on prediction. From the early history of celestial mechanics we know that predicting how planetary objects move stimulated the birth of physics. Today, predicting neuronal spiking drives advances in theoretical neuroscience. Outside the sciences, prediction is quite useful as well—predicting stock prices fuels the finance industry and predicting English text fuels social media companies. Recent advances in prediction and generation are so impressive (e.g., GPT-4) that one is left with the impression that time series prediction is a nearly solved problem. As we will show using randomness- and correlation-calibrated data sources, this hopeful state of affairs could not be further from the truth. Recurrent neural networks 1 , of which reservoir computers are a prominent and somewhat recent example 2 , have risen to become one of the major tools for prediction. From mathematics’ rather prosaic perspective, recurrent neural networks are simply input-dependent dynamical systems. Since input signals to a learning system affect its behavior, over time it can build up a “memory trace” of the input history. This memory trace can then be used to predict future inputs. There are broad guidelines for how to build recurrent neural networks 1 and reservoir computers that are good predictors 2 . For instance, a linearized analysis shows that one wants to be at the edge of instability 3 . However, a theory of how these recurrent neural networks work optimally is lacking; though see Ref. 4 . Recently, a new architecture was introduced for prediction called a “next-generation reservoir computer”, whose memory trace intriguingly only included the last few timesteps of the input, while demonstrating low prediction error with simultaneously small compute power 5 . The general impression from these and many additional reports is that these recurrent neural networks have conquered natural stimuli, including language 6 , video 7 , and even climate data 8 . They have certainly maximized performance on toy tasks 9 , 10 that test long memory. This noted, it is unknown how far they are from optimal performance on the tasks of most importance, such as prediction of language, video, and climate. We need a calibration for how far away they are from nearly-perfect prediction. And this suggests developing a suite of complex processes for which we know the minimal achievable probability of error in prediction. In the service of this goal, the following adopts the perspective that calibration is needed to understand the limitations inherent in the architecture of the next-generation reservoir computers and to understand how well state-of-the-art recurrent neural networks (including next-generation reservoir computers) perform on tasks for which optimal prediction strategies are known. This calibration is provided by time series data generated by a special type of hidden Markov model specialized for prediction called \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines. We find, surprisingly perhaps, that large random multi-state \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines are an excellent source of complex prediction tasks with which to probe the performance limits of recurrent neural networks. More to the point, benchmarking on these data demonstrates that reasonably-sized next-generation reservoir computers are inherently performance limited: they achieve no better than a \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sim 60\\%$$\\end{document} ∼ 60 % increase in error probability above and beyond optimal for “typical” \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machine tasks even with a reasonable amount of memory. A key aspect of the calibration is that the optimalities are derived analytically from the \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machine data generators, providing an objective ground truth. This increase in error probability above and beyond the optimal increases to \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$10^5\\%$$\\end{document} 10 5 % if interesting 11 , 12 stimuli are used. Altogether, we find that state-of-the-art recurrent neural networks fail to perform well predicting the high-complexity time series generated by large \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines. In this way, next-generation reservoir computers are fundamentally limited. Perhaps more surprisingly, a more powerful recurrent neural network 9 also has an increase in error probability above and beyond the minimum of roughly \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$50\\%$$\\end{document} 50 % for these new prediction benchmarks. Section “ Background ” reviews reservoir computers, recurrent neural networks, and \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon$$\\end{document} ϵ -machines. Section “ Prediction error bounds ” derives a lower bound on the average rate of prediction errors. Section “ Results ” describes a new set of complex prediction tasks and surveys the performance of a variety of recurrent neural networks on these tasks. Section “ Conclusion ” draws out the key lessons and proposes new calibration strategies for neural network architectures. Such objective diagnostics should enable significant improvements in recurrent neural networks."
} | 4,168 |
37577626 | PMC10418233 | pmc | 616 | {
"abstract": "Microbial communities are shaped by the metabolites available in their environment, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. To this end, we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the diverse assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively smaller ones, is necessary and sufficient to recapitulate all of our experimental observations. In addition to pointing to a fundamental principle of community assembly, the divergence-complexity effect has important implications for microbiome engineering applications, as it can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions.",
"introduction": "Introduction Understanding how complex microbial communities assemble is important for addressing open challenges in microbial ecology with applications that range from medicine 1 , 2 to climate change mitigation 3 – 5 . Studies in natural 6 , 7 and laboratory 8 – 11 settings have investigated the reproducibility of assembly dynamics across a range of environmental conditions leading to seemingly contradictory results. Under certain conditions, microbial community assembly appears to be highly deterministic, as different communities are driven by strong environmental selection towards a specific steady state independent of their initial composition 8 . Under other conditions, however, environmental selection is weaker, resulting in highly variable assembly of communities with more dependence on their initial composition 9 . Uncovering what properties govern this variability in microbial community assembly constitutes one of the fundamental questions of microbial ecology 12 and is crucial for successful microbiome engineering, which aims to steer communities towards a desired structure in a given environment 13 . Here, we combined experimental measurements and computational modeling to investigate the interplay of initial composition and environmental selection in determining community assembly and its variability. We followed the dynamic assembly of diverse microbial communities inoculated from different soil samples grown on carbon sources of increasing metabolic complexity. By tracking how closely these communities resembled each other over time, we found that the effect of environmental selection on communities depended on the metabolic complexity of the environment itself. Specifically, different microbial communities diverged in their taxonomic composition across a gradient of increasingly complex metabolic conditions, suggesting that the forces dominating microbial community assembly shift from strong to weak environmental selection in increasingly complex conditions. By constructing a consumer resource model that recapitulates this effect, we additionally learned that this divergence-complexity relationship depends on a hierarchical structure of metabolite transformations (e.g. polysaccharides to oligosaccharides to monosaccharides), but does not depend on the distribution of these metabolic functions across taxa. Our results point to an ecosystem organization principle that can help reconcile seemingly incompatible observations of divergence in different conditions and provide guidelines for which environments may be more susceptible to microbiome engineering projects. The divergence-complexity effect hypothesis To assess the strength of environmental selection on community assembly, one would ideally compare how the trajectories of multiple distinct microbial communities diverge in taxonomic composition across a set of conditions. A key question we ask is whether distinct communities assembled in the same condition tend to become taxonomically similar and how the degree of similarity depends on the metabolic complexity of the environment. For example, we can imagine how different microbial communities that initially vary in taxonomic composition ( Fig. 1a ) may converge in composition over time when grown in one environment (strong environmental selection, Fig. 1b ) while those same communities may diverge in another environment, arriving at alternative stable states (weak environmental selection, Fig. 1c ). To quantify the degree to which different communities diverge taxonomically from each other when grown in a given condition, we calculate the difference (beta diversity) in their compositions as they develop towards a steady state ( Methods ; Fig. 1d ). We initially identified existing data that could indicate whether and how community divergence would indeed depend on environmental conditions. We re-analyzed two independent studies that both explored how a collection of diverse microbial communities assembled over time, but did so under very different conditions. When one study, Goldford et al. 8 , cultured communities in (simple) glucose media, communities converged ( Fig. 1e ). By contrast, when Bittleston et al. 9 cultured communities in (complex) acidified cricket media, they diverged ( Fig. 1f ). In both cases, the initial communities differed substantially from each other and then immediately became more similar; however, communities enriched on glucose ultimately converged significantly more, despite starting with greater variation in initial community composition ( Fig. 1g ). Based on the striking discrepancy in the degree of divergence across these two studies we formulated the hypothesis that divergence increases with the metabolic complexity of the provided resources ( Fig. 1d ), a relationship that we will refer to as the divergence-complexity effect. Community divergence increases with metabolic complexity In order to directly test the divergence-complexity effect, we designed an experiment to quantify the divergence of microbial communities grown in conditions of increasing metabolic complexity ( Methods ; Fig. 2a ). To assess divergence, we sourced six microbial communities from forest soils that are generally diverse and distinct from each other 14 , even over small (centimeter) spatial scales 7 . Each microbial community was grown in nine different minimal media, each supplemented with equimolar concentrations of at least one carbon source commonly found in soils 15 : (1) citrate, (2) glucose, (3) cellobiose, (4) cellulose, (5) lignin, (6) citrate + glucose, (7) citrate + glucose + cellobiose, (8) citrate + glucose + cellobiose + cellulose, or (9) citrate + glucose + cellobiose + cellulose + lignin. In testing the divergence-complexity effect, we consider metabolic complexity to increase from citrate to lignin (in line with the number of metabolic byproducts expected from each metabolite 16 ). We included single- and mixed-metabolite conditions in order to test the divergence-complexity effect with increasing complexity of each metabolite (single), as well as increasing resource diversity (mixed). Each microcosm, containing one source community growing in one condition, was serially passaged ten times, in intervals of three days. 16S rRNA sequencing was performed and amplicon sequence variant (ASV) counts were generated for the initial soil inocula and microcosm communities at days 3, 6, 9, 12, and 33. Supporting our hypothesis of the divergence-complexity effect, we observed that divergence increased with metabolic complexity ( Fig. 2b – f ). In accordance with previous studies ( Fig. 1e – f ), our source communities initially differed from each other and then immediately converged and stabilized once introduced to laboratory conditions ( Supp. Fig. 1 , Fig. 2d ). Once stabilized, we observed the divergence-complexity effect on single- and mixed-metabolite conditions, separately ( Fig. 2e ). Within single-metabolite conditions, the communities converged strongly on simple metabolites, while they diverged to increasingly distinct states on the more complex metabolites ( Fig. 2b , 2d – f ). Similarly, community divergence increased from the least (citrate + glucose) to the most diverse (all metabolites) mixed-metabolite conditions ( Fig. 2c – f ). Interestingly, the effect is stronger in single-metabolite conditions than in mixed-metabolite conditions ( Fig. 2e ), suggesting that assembly dynamics are sensitive to the order in which different metabolites become available through trophic interactions 17 . These trends are detectable at each sampled time point ( Fig. 2f ) and when we re-computed divergence at the Family taxonomic level ( Supp. Fig. 3 – 4 ). Because bacteria often differ in metabolic function at the Family level 18 , this latter result suggests that our communities, which assemble to distinct taxonomic compositions, may also be engaging in distinct metabolic activities. The degree of divergence in complex conditions appears to be particularly sensitive to differences in initial community composition. Communities sourced from different locations, but that were initially similar to each other, did not necessarily converge to similar final states ( Fig. 2b – c ), suggesting that they may be traversing a rugged structure-function landscape in complex conditions, where slight differences in initial composition can lead to distinct final compositions 19 . Conversely, replicate microcosms assembled from the same source did cluster together ( Supp. Fig. 2 ), suggesting that while assembly dynamics are indeed complex (initially similar communities can diverge), they are reproducible and not diverging merely due to stochasticity. These trajectories show that complex environments can support a greater number of discrete alternative stable states than simple environments. Community diversity dynamically correlates with divergence and implicates the role of specialists In order to gain a deeper understanding of the divergence-complexity effect, we investigated how alpha diversity within each individual community correlates with divergence across communities. In particular, two separate principles could jointly give rise to the divergence-complexity effect. The first principle, “metabolic complexity begets diversity”, where community diversity increases with increasing metabolic complexity, has been experimentally documented in both natural and synthetic communities 11 , 16 . A proposed second principle, “diversity begets divergence”, could result from the expectation that more diverse communities have more variation in the abundance of each microbe, leading to higher divergence across communities. If metabolic complexity yields diversity and diversity yields divergence, we would expect higher divergence in increasingly complex conditions, leading to the divergence-complexity effect. Consistent with these expectations, we observed a strong linear relationship between diversity and divergence, which strengthened over time, indicating that specific changes in community assembly drive the rise of divergence. The slope of the diversity-divergence relationship increased over time ( Fig. 3a – b ), despite the fact that diversity itself, on average, decreased ( Fig. 3a ). In other words, over time, the same degree of divergence is maintained by communities with reduced diversity. For divergence to remain relatively stable while diversity decreases ( Fig. 2d ), taxa endemic (i.e. specific) to each community must persist while a set of species shared across communities universally go extinct within each condition. One possible explanation is that these persistent taxa are metabolic specialists, which produce enzymes that target specific biochemical bonds 20 . We hypothesize that functionally redundant 21 specialists that differ between communities and target complex metabolites are less evenly distributed across communities than taxa that specialize on simpler metabolites, driving the divergence-complexity effect. Specialists are more endemic in complex conditions To investigate the role of specialists in the divergence-complexity effect, we explored the distribution of taxa across experimental conditions and source communities. If specialists drive the diversity-complexity effect, we would expect to see that specialists are increasingly endemic, or unevenly distributed across source communities, in more complex conditions. To quantify the degree of specialization, we computed a condition-specificity metric for each taxon (ASV) in each condition, and then assessed whether specialization and endemism depended on metabolic complexity. We defined condition-specificity for each taxon and condition as the fraction of source communities in which that taxon was found at the final sampling time point on that given condition. In particular, if a taxon only occurs in one condition, its condition-specificity is 1 and will be referred to as a “specialist”. In accordance with our expectations, we observed that more complex conditions (particularly single-metabolite ones) had greater condition-specificity ( Fig. 4a ) and more specialists ( Fig. 4b ). These results alone are encouraging, but are not sufficient for linking specialists to the divergence-complexity effect, which would additionally require specialists to differ between communities in the same condition. While we observed an enrichment of condition-specific taxa in complex conditions, it hypothetically could be the case that these same taxa were found across all source communities, in which case communities in complex conditions would not diverge more than those in simple conditions (H1 Fig. 4d ). However, when we count the occurrence of each taxon in each condition and source community, we find that condition-specific taxa are also source community-specific (endemic; Fig. 4c ). As a result, taxa that specialize on complex metabolites are less evenly distributed across communities than taxa that specialize on simpler metabolites, and are therefore heavily implicated in mediating the divergence-complexity effect (H2 Fig. 4d ). Trophic resource transformations reproduce divergence with consumer resource models To better understand what aspects of the organisms and their environment are necessary for the diversity-complexity effect, we performed a series of simulations with microbial consumer-resource models (CRMs; Methods ) 22 . In particular, we wanted to corroborate our hypothesized mechanism of the diversity-complexity effect, that divergence correlates with metabolic complexity and emerges from endemism of specialist taxa. CRMs are dynamical ecological models where consumers are defined by the set of resources they prefer (consumer preferences) and resources are able to be transformed by consumers into other resources following consumption (resource transformations). Overlapping consumer preferences give rise to competition, while the exchange of secreted transformed products can generate cross-feeding interactions 22 . Taxonomic structure in CRMs can be represented by specifying “families” of consumers that have similar resource preferences (specialization; Supp. Fig. 6c – d ). Metabolic structure can be represented by assuming that upon metabolization, resources of a given type transform into resources of another specific type in a hierarchical fashion 23 ( Supp. Fig. 6a – b ). In natural ecosystems, consumer preferences and resource transformations are typically arranged in a trophic structure, where taxa specialize in the hierarchical consumption of environmentally available metabolites and cross-feed the resulting (simpler) byproducts to taxa at subsequently lower trophic levels 17 . In order to understand whether the divergence-complexity effect could emerge solely from these ecological forces (consumer preferences and resource transformations), we assumed physiological parameters such as rates of consumer growth, consumer maintenance, resource utilization, resource energy density, and leakage (the fraction of transformed resource that is secreted) to be uniform across all consumers and resources 22 , 23 . To investigate the role of taxonomic and metabolic structure in community divergence, we closely mimicked our experimental design and measured divergence of simulated communities using four different CRM configurations that captured combinations of trophic structures of consumer preferences and resource transformations, as well as corresponding random controls, similar to those shown to be sufficient to reproduce a number of ecological properties 23 ( Fig. 5 , Supp. Fig 6 , Methods ). Trophic consumer preferences were defined with “families” of specialists and generalists ( Supp. Fig 6c ). In line with our experimental observations ( Fig. 4 ), we set the diversity of specialists to be proportional to the complexity of the resource type they prefer. Trophic resource transformations were defined such that complex resources successively transformed into simpler ones in a hierarchical fashion ( Supp. Fig. 6a ). Random controls of consumer preferences ( Supp. Fig 6d ) and resource transformations ( Supp. Fig 6b ) were also generated, where consumers preferred resources of any type and resources transformed into others of any type, respectively. Combinations of these four parameterizations led to the following four model configurations: trophic consumer preferences and resource transformations (fully structured; Fig. 5a ), random preferences and trophic transformations (resource structured; Fig. 5b ), trophic preferences and random transformations (consumer structured; Fig. 5c ), and random preferences and transformations (fully random; Fig. 5d ). All four model configurations were initialized with six source communities and seven conditions (four single and three mixed resource conditions) and growth dynamics were simulated until reaching a steady state. Surprisingly, our simulations showed that trophic structure of the resource transformations alone was necessary and sufficient to reproduce the divergence-complexity effect in both single- and mixed-resource conditions ( Fig. 5a – f ). Notably, even when consumer preferences were random, the divergence-complexity effect was still observed as long as resource transformations were structured ( Fig. 5a , b , e ). However whenever resource transformations were random, all communities diverged equally, irrespective of metabolic complexity, and thus there was no divergence-complexity effect ( Fig. 5c , d , f ). In addition to qualitatively reproducing the divergence-complexity effect, our model recovered, as emergent properties, further non-trivial trends detected in our experiment. For example, in model configurations with trophic resource transformations, the divergence-complexity effect is greater for single resource conditions than mixed ones ( Fig. 5e ), as observed experimentally ( Fig. 2f ). Additionally, the maximum divergence in single resource conditions exceeds that of mixed conditions ( Fig. 5a – b ; Fig. 2e ). These model configurations also reproduced our downstream analyses, such as the correlation between divergence and diversity ( Fig. 5g ; Fig. 3b ) and the tendency for specialists to be endemic ( Fig. 5h ; Fig. 4d ). The reproduction of these patterns with physiologically neutral consumers (uniform physiological parameters) and resources implicates the trophic metabolic structure in resource transformations as the driving mechanism of the divergence-complexity effect.",
"discussion": "Discussion Compelled by recent experiments which found that microbial community diversity increases with metabolic complexity 11 , 16 , we sought to reconcile contradictory interpretations of whether microbial communities tend to converge 8 or diverge 9 in the same conditions. By jointly revisiting these two propositions, we uncovered a new, reproducible, and quantitative ecological principle, the divergence-complexity effect, which has important consequences for ecological theory and microbiome engineering. While previous work explored community assembly by modulating the complexity of metabolic conditions 11 , 16 , 24 or the variability of source communities 8 – 10 , the divergence-complexity effect could be observed only by systematically varying both, i.e. analyzing multiple source communities under increasingly complex conditions. We found that divergence correlates strongly with diversity, which is driven by an enrichment of specialists in complex conditions. We concluded our analysis by reproducing these results using consumer resource model simulations, which provide insights into the potential ecological mechanisms of the divergence-complexity effect. While our experimental results are robust and reproducible, they necessarily rely on specific design constraints. Experimental choices that could be revisited in future studies include the passaging time, chosen here to be three days, as used in other microbial community assembly studies with complex metabolites 9 ; the selection of metabolites, which constitute a representative, but oversimplified version of the metabolic complexity of soil environments; and the focus on taxonomic divergence (through 16S amplicon sequencing) rather than functional divergence, which would require a comprehensive profiling of microbial functions with metagenomic or metatranscriptomic sequencing. We designed our simulations to represent the ecological structure of microbial communities and organization of metabolites as accurately as possible; however, our consumer resource models lacked the encoding of certain granular processes such as diffusion, transcriptional regulation, and antimicrobial defense. Inclusion of these processes with other ecological models 25 could help to reveal further mechanistic insights into the diversity-complexity effect. The most surprising result from our simulations was how structured resource transformations (where complex metabolites are progressively degraded into simpler ones), but not consumer preferences, were required for reproducing the divergence-complexity effect ( Fig. 5 ). This result disappears completely when the resource transformations are uniformly random. A possible interpretation of this result is that microbial community assembly and dynamics are strongly dependent on the actual structured architecture of metabolism, which differs substantially from a network of random transformations. We cannot rule out the possibility that adding more parameters, and increasing the realism of simulations may affect our results. For example, we could parameterize our models to incorporate the trade-offs that are known to exist between enzyme production and growth rate in nutrient limited conditions 26 . However, since our current model captures so many of our observations, including the similarities and subtle differences between the single- and mixed-metabolite conditions, it lends confidence to the dominant role that the architecture of metabolism plays in community structure, corroborating previous reports 16 . Importantly, the divergence-complexity effect has direct implications for the engineering of microbial communities towards any target, suggesting that metabolically complex environments may be more susceptible to microbiome engineering than simple ones. Potential targets for microbiome engineering include correcting the dysbiosis in the human gut 2 and increasing the carbon stabilization capacity of soils 5 , among many other microbially-regulated traits. The consequences of the diversity-complexity effect are encouraging for efforts along these lines, since complex environments may be more likely to support an alternative community that is equally stable as the original one, but with potentially increased expression of a trait of interest. Culturing techniques such as directed evolution, where a set of microbial communities undergoes iterative rounds of perturbation and artificial selection in order to assemble high-performing communities 27 , offer an ideal strategy for exploring the different alternative states that a complex environment can support. Future research is required in order to understand how, in light of functional redundancy 28 , the divergence in taxonomic composition that we observe relates to divergence in functional composition, since modifying functional activity is commonly the goal of microbiome engineering efforts. Ultimately, we envisage that the awareness of the divergence-complexity effect may help microbial ecologists reframe the role of environmental selection in microbial community assembly and enable further research into the engineering of complex microbially-regulated environments"
} | 6,324 |
37771337 | PMC10525310 | pmc | 617 | {
"abstract": "Spiking neural network (SNN) is a brain-inspired model with more spatio-temporal information processing capacity and computational energy efficiency. However, with the increasing depth of SNNs, the memory problem caused by the weights of SNNs has gradually attracted attention. In this study, we propose an ultra-low latency adaptive local binary spiking neural network (ALBSNN) with accuracy loss estimators, which dynamically selects the network layers to be binarized to ensure a balance between quantization degree and classification accuracy by evaluating the error caused by the binarized weights during the network learning process. At the same time, to accelerate the training speed of the network, the global average pooling (GAP) layer is introduced to replace the fully connected layers by combining convolution and pooling. Finally, to further reduce the error caused by the binary weight, we propose binary weight optimization (BWO), which updates the overall weight by directly adjusting the binary weight. This method further reduces the loss of the network that reaches the training bottleneck. The combination of the above methods balances the network's quantization and recognition ability, enabling the network to maintain the recognition capability equivalent to the full precision network and reduce the storage space by more than 20%. So, SNNs can use a small number of time steps to obtain better recognition accuracy. In the extreme case of using only a one-time step, we still can achieve 93.39, 92.12, and 69.55% testing accuracy on three traditional static datasets, Fashion- MNIST, CIFAR-10, and CIFAR-100, respectively. At the same time, we evaluate our method on neuromorphic N-MNIST, CIFAR10-DVS, and IBM DVS128 Gesture datasets and achieve advanced accuracy in SNN with binary weights. Our network has greater advantages in terms of storage resources and training time.",
"conclusion": "5. Conclusion This study proposes a construction method of ultra-low latency adaptive local binary spiking neural network with an accuracy loss estimator, which balances the pros and cons between full precision weights and binarized weights by choosing binarized or non-binarized weights adaptively. Our network satisfies the requirement of network quantization while keeping high recognition accuracy. At the same time, we find the problem of long training time for BSNNs. Therefore, we propose the GAP Layer, in which a convolution layer is used to replace the fully connected layer, and a global average pooling layer is used to solve the binary output problem of SNN. Because of the binary output, SNN usually needs to run multiple time steps to get reasonable results. Finally, we find that when the BSNN is stable, the binary weight processed by the sign function is difficult to change, which leads to the bottleneck of network performance. Therefore, we propose binary weight optimization to reduce the loss by directly adjusting the binary weight, which makes the network performance close to the full-precision network. Experiments on traditional static and neuromorphic datasets show that our method saves more storage resources and training time and achieves competitive classification accuracy compared with existing state-of-the-art BSNNs.",
"introduction": "1. Introduction Courbariaux et al. ( 2015 ) proposed Binary Connect, which pioneered the study of binary neural networks. Binarization can not only minimize the model's storage usage and computational complexity but also reduce the storage resource consumption of model deployment and greatly accelerate the inference process of the neural network. In the field of convolution neural networks (CNNs), many algorithms have been proposed and satisfactory progress has been made. However, conventional quantization techniques end up in either lower speedup or lower accuracy because these works fail to dynamically capture the sensitivity variability in the input feature map values. Therefore, we are motivated to apply different levels of quantization for different feature map values. Some researchers have embarked on the study of mixed-precision algorithms, which has led to many hardware accelerator designs. Chang et al. ( 2021 ) designed a reconfigurable CNN processor, which can reconstruct the computing unit and the on-chip buffer according to the computing characteristics of the model with mixed-precision quantization. Jiang et al. ( 2020 ) designed the PRArch accelerator architecture which support both conventional dense convolution and aggregated sparse convolution and implement mixed-precision convolution on fix-precision systolic arrays. Song et al. ( 2020 ) proposed an architecture that utilizes a variablespeed mixed-precision convolution array. It can achieve a significant improvement in performance with a small loss of accuracy. Spiking neural networks, as the third generation of neural networks, is a computational paradigm that simulates the biological brain based on the dynamic activation of binary neurons and event-driven (Illing et al., 2019 ; Tavanaei et al., 2019 ). Using the time sparsity of binary time series signals can improve the computational energy efficiency on special hardware (Mead, 1990 ; Xu et al., 2020 ). The combination of SNNs and binary networks has gradually attracted more and more attention (Srinivasan and Roy, 2019 ; Lu and Sengupta, 2020 ; Kheradpisheh et al., 2022 ). However, it is still a great challenge to train SNNs due to their non-differentiable activation function. In order to maintain good accuracy, some researchers choose to use pre-training to obtain parameters from artificial neural networks (ANNs) (Cao et al., 2015 ; Lu and Sengupta, 2020 ; Wang et al., 2020 ; Xu et al., 2022b ). The pre-training of ANN gives up the advantage of SNNs in temporal and spatial information processing. In recent years, some studies have successfully trained binarized SNNs (BSNNs) directly. For example, Jang et al. ( 2021 ) used the Bayesian rule to train BSNNs directly, and Kheradpisheh et al. ( 2022 ) used time-to-first-spike coding in the direct training of the network. To maintain the energy efficiency and reasonable recognition accuracy of BSNNs, we propose accuracy loss estimators (ALE) and binary weight optimization (BWO). We use them to construct an ultra-low latency adaptive local binary spiking neural network. In addition, we apply global average pooling (GAP) structures to improve the speed of the networks further. To illustrate the superiority of our model, we conduct experiments on several datasets, our model dramatically improves the performance of BSNNs, and our contributions can be summarized as follows: Inspired by the mixed weight training, we design the ALE. When the network is trained, ALE will automatically select binary weight or full precision weight for training to solve the problem of large precision loss in the full binary weight training. We use the GAP layer instead of the fully connected layer to reduce the amount of calculation and change the output layer of SNNs to alleviate the phenomenon that it takes a long time to train BSNNs directly. To reduce the error caused by the binary weight in the backpropagation, we propose the BWO, which can directly adjust the binary weight based on the error. This method further reduces the error of networks and improves their performance."
} | 1,839 |
27112950 | PMC4845381 | pmc | 618 | {
"abstract": "Background Anaerobic digesters become unstable when operated at a high organi c loading rate (OLR). Investigating the microbial community response to OLR disturbance is helpful for achieving efficient and stable process operation. However, previous studies have only focused on community succession during different process stages. How does community succession influence process stability? Is this kind of succession resilient? Are any key microbial indicator closely related to process stability? Such relationships between microbial communities and process stability are poorly understood. Results In this study, a mesophilic anaerobic digester for treating food waste (FW) was operated to study the microbial diversity and dynamicity due to OLR disturbance. Overloading resulted in proliferation of acidogenic bacteria, and the resulting high volatile fatty acid (VFA) yield triggered an abundance of acetogenic bacteria. However, the abundance and metabolic efficiency of hydrogenotrophic methanogens decreased after disturbance, and as a consequence, methanogens and acetogenic bacteria could not efficiently complete the syntrophy. This stress induced the proliferation of homoacetogens as alternative hydrogenotrophs for converting excessive H 2 to acetate. However, the susceptible Methanothrix species also failed to degrade the excessive acetate. This metabolic imbalance finally led to process deterioration. After process recovery, the digester gradually returned to its original operational conditions, reached close to its original performance, and the microbial community profile achieved a new steady-state. Interestingly, the abundance of Syntrophomonas and Treponema increased during the deteriorative stage and rebounded after disturbance, suggesting they were resilient groups. Conclusions Acidogenic bacteria showed high functional redundancy, rapidly adapted to the increased OLR, and shaped new microbial community profiles. The genera Syntrophomonas and Treponema were resilient groups. This observation provides insight into the key microbial indicator that are closely related to process stability. Moreover, the succession of methanogens during the disturbance phase was unsuitable for the metabolic function needed at high OLR. This contradiction resulted in process deterioration. Thus, methanogenesis is the main step that interferes with the stable operation of digesters at high OLR. Further studies on identifying and breeding high-efficiency methanogens may be helpful for breaking the technical bottleneck of process instability and achieving stable operation under high OLR.",
"conclusion": "Conclusions This study investigated the microbial diversity and dynamicity during four consecutive phases (stable, deterioration, recovery, and new stable) induced by OLR disturbance in an anaerobic digester used for treating FW. The results show that there was no clear correlation between ecological parameters and process stability. Most of the bacteria showed redundant functional adaptation to increased OLR. Therefore, new steady-state microbial community profiles were observed after disturbance. However, the genera Syntrophomonas and Treponema appeared to be resilient groups; their abundances were closely related to process deterioration. This observation provides insight into the key microbial groups that control the operation of anaerobic digesters. Moreover, the succession of methanogens during the disturbance phase was unsuited for the metabolic function needed at high OLR. This contradiction was the fundamental reason for the process deterioration. Thus, methanogenesis is the restricting step that impedes stable and efficient operation of digesters. Identifying and breeding high-efficiency methanogens will be helpful for breaking the technical bottleneck of process instability and achieving stable operation during high OLR. These findings improve the understanding of the correlation between microbial communities and process stability, and provide a theoretical basis for the efficient and stable operation of anaerobic digesters for treating FW.",
"discussion": "Results and discussion Reactor performance Time series of OLR, methane yield, volatile solid removal rate (VS r ), pH, total volatile fatty acids (VFA) and alkalinity (TA), VFA/TA, CH 4 , CO 2 , acetate, propionate, total ammonia–nitrogen (TAN), and Free ammonia (FAN) are shown in Fig. 1 . TAN and FAN increased continuously during Phase I; at Day 45, their concentrations were 1767 and 83 mg L −1 , respectively. It has been reported that a FAN level of about 100 mg L −1 and TAN level of 3000 mg L −1 caused inhibition in an anaerobic digester [ 13 ]. Therefore, the effect of TAN and FAN on the methane yield was negligible during this stage. A stable methane yield and VS r were obtained, and the other state parameters were all relatively constant, assuring a steady-state process during Phase I. Fig. 1 Process performance of anaerobic digester. Evolution of OLR, VS r , methane yield ( a ), pH, total VFA, TA ( b ), VFA/TA, CH 4 , CO 2 ( c ) and acetate, propionate, FAN, TAN ( d ) in digester during the experiment To induce the process of deterioration, a stepwise OLR disturbance was introduced during Phase II. As shown in Fig. 1 , an increase in OLR from 3 to 4 g VS L −1 d −1 had no observable effect on the process efficiency or reactor stability. When the OLR was increased to 5 g VS L −1 d −1 , a slight increase in VFA was observed, which was accompanied by a slight decrease in TA. This anomaly may have been caused by FAN inhibition, as the concentration of FAN exceeded 100 mg L −1 at Day 67. However, these parameters did not continue to deviate from their original levels, but achieved a new steady-state, and the process efficiency was not affected. When the OLR was further increased to 6 g VS L −1 d −1 , FAN continuously increased to 114 mg L −1 at Day 82, then the VFA concentration rapidly increased from 3100 (Day 82) to 9443 mg L −1 (Day 90). Although acetate was still the dominant component of VFAs, propionate increased by 20-fold. In addition, the concentrations of butyrate and valerate also increased (data not shown). Fermentative microbial communities have much faster growth kinetics than methanogens. Under high OLR, the rapidly proliferating bacteria hydrolyzed organics to VFAs, but the slow-growing and even inhibited methanogens could not directly or indirectly degrade the generated VFAs in time, which resulted in VFA accumulation [ 14 , 15 ]. The accumulated VFAs lowered the TA in the digester, and reduced the pH to suboptimal values, which further exacerbated the toxic effect on the methanogens. Eventually, all these factors resulted in reduced AD efficiency. After OLR stress, process recovery is essential, and a drastic decrease in OLR is the most common way to achieve this [ 16 ]. Considering the severe acidification, the loading of the digester was halted during Phase III to accelerate process recovery. As shown in Fig. 1 , VFAs gradually restored to their normal ranges as the recovery time increased. In contrast, the methane content not only recovered, but also reached a higher level. This may be because as the VFAs were consumed, HCO 3 − that was previously combined with VFAs was released, causing the TA in the digester to increase. The increased TA increased the pH in the digester, resulting in less CO 2 spilled from the liquid phase, and the relative content of gaseous methane increased. The higher TA concentration and pH at this stage confirmed this inference. The increase in pH shifted the balance between FAN and ammonium ions and caused a sharp increase in FAN, resulting in FAN >200 mg L −1 at Day 115. However, the high FAN level did not inhibit the process performance, possibly because the microbial communities were acclimated by the step-wise increased ammonia concentration. As reported by Yenigun and Demirel, ammonia inhibition to mesophilic AD with acclimated inoculum is triggered mostly at levels of 2800–6000 mg L −1 TAN and 337–800 mg L −1 FAN [ 17 ]. After 1 month of process recovery, the digester was re-fed. A transition period with low OLR was first introduced to reduce the loading impact; then the operational conditions of Phase IV were set as the same as those for Phase I. As shown in Fig. 1 , the process performance of these two stages was comparable; high TAN (2810 ± 53 mg L −1 ) and FAN (134 ± 18 mg L −1 ) did not have a toxic effect on the AD process. The methane yield, VS r , and VFA/TA during these two stages were similar. However, the VFA and TA concentrations during Phase IV were slightly higher, possibly because of the microbial community shift. Pyrosequencing analysis Pyrosequencing was performed to monitor the microbial community during each phase in the digester. The qualified nucleotide sequence reads were grouped into operational taxonomic units (OTUs) at a distance level of 3 % to estimate the phylogenetic diversities of microbial communities. Table 1 summarizes the sample information and statistical results used for each sample. As shown in Table 1 , no significant changes were observed in the richness of archaea during the experiment; in contrast, the richness of bacteria slightly fluctuated, but the fluctuations appeared to be random. There were significant differences between community evenness among samples derived from different operational phases for both bacteria and archaea. The estimated Jaccard indices showed that the archaeal community was highly stable during the whole operational process. In contrast, the bacterial community was more dynamic, but their dynamics appeared to be correlated with time. Therefore, there was no clear correlation between these ecological parameters and process stability. Previous studies have tried to link these ecological parameters with process stability. For example, Carballa et al. and Werner et al. found a positive correlation between community evenness and performance of anaerobic reactors [ 18 , 19 ]. Ziganshin et al. and Regueiro et al. concluded that bacterial diversity and richness are not associated with process stability, but archaeal populations are correlated with reactor performance [ 20 , 21 ]. In contrast, Dearman et al. suggested that global microbial diversity is not important for developing a functionally successful anaerobic microbial community [ 22 ]. Thus, it is still controversial what level of community complexity a healthy, well-balanced, efficient microbial consortium should have for the production of biogas. Moreover, judging the process stability of a digester according to general ecological parameters is not a sophisticated method. Therefore, to clarify the relationship between process stability and microbial community, further investigations on specific community succession under different process stages are necessary. Table 1 Sample information and statistical results Samples Reads OTU Richness Gini coefficient Jaccard similarity index (%) Day 45 Day 90 Day 120 Day 150 Archaea Day 45 6221 35 36 (5)a 0.786 (6.5E−02)b – 99.49 97.23 97.27 Day 90 6552 32 36 (11)a 0.767 (8.9E−02)a – 99.60 99.47 Day 120 6879 31 32 (5)a 0.775 (8.3E−02)ab – 99.78 Day 150 5892 33 40 (15)a 0.850 (5.2 E−02)c – Bacteria Day 45 6625 173 192 (15)a 0.901 (4.0E−02)a – 74.14 73.40 73.41 Day 90 6614 189 212 (16)ab 0.949 (2.0 E−02)c – 65.38 58.00 Day 120 7256 215 239 (17)b 0.956 (1.7E−02)d – 96.03 Day 150 7789 196 238 (25)b 0.931 (2.5 E−02)b – Numbers in brackets stand for standard errors, and the different letters show a significantly different among samples at different operational phase (P < 0.05) Bacterial communities in response to OLR disturbance The bacterial sequence distributions at the phylum level are shown in Fig. 2 , and Table 2 further deconstructs the bacterial sequence at the class and genus levels. The majority of sequences from Phase I were assigned to the phyla Bacteroidetes, Firmicutes, Chloroflexi, Spirochaetae and Synergistete. After the process deterioration caused by overloading, the relative abundance of the above phyla all decreased. In contrast, the abundance of Actinobacteria increased from 0.03 to 1.41 %, and the amount of Tenericutes sharply increased from 0.08 to 13.30 %. Tenericutes -affiliated bacteria are facultative anaerobes. Under anaerobic conditions, they produce organic acids, which can be used by acidoclastic methanogens [ 7 ]. Phylum Actinobacteria may also be responsible for hydrolyzing and degrading FW into VFAs, and some bacteria in Actinobacteria produce propionate [ 23 , 24 ]. Thus, the proliferation of Tenericutes and Actinobacteria may be related to the high VFA yield during Phase II. The abundance of class Clostridia (phylum Firmicutes ) also sharply increased during Phase II. Members of Clostridia are capable of performing diverse fermentation pathways. Apart from their role in hydrolysis and acidogenesis, they are also involved in acetogenesis and syntrophic acetate oxidation (SAO) [ 7 , 25 ]. They are also efficient hydrogen producers; their proliferation suggests that excessive H 2 was generated in the digester [ 26 ]. Once the hydrogenotrophs failed to degrade the produced H 2 in time, the degradation of VFAs is disturbed. This may be the cause of the acid accumulation during Phase II. Syntrophomonas was a representative genus in class Clostridia . Syntrophomonas -related bacteria are syntrophic fatty-acid-oxidizing bacteria, which can convert various organic acids to H 2 and acetate for subsequent hydrogenotrophic methanogenesis (HM) [ 27 , 28 ]. Their proliferation during Phase II was consistent with the sharp increase in propionate and the accumulation of butyrate and valerate. The relative abundance of genus Treponema within phylum Spirochaetes also increased from 0.5 to 3.28 % during Phase II. Members of Treponema are likely homoacetogens, which consume H 2 and CO 2 to produce acetate [ 29 ]. Homoacetogenesis is typically observed under psychrophilic conditions, as homoacetogens have a better ability to adapt to low temperatures compared with hydrogenotrophic methanogens [ 30 ]. It has been reported that homoacetogenesis cannot compete with HM under mesophilic or thermophilic conditions because of its lower energy yield. However, Wang et al. observed the coexistence of Treponema and hydrogenotrophic methanogens in a mesophilic digester used for treating sewage sludge with H 2 influent [ 29 ]. Siriwongrungson et al. found that homoacetogenesis can act as an alternative pathway for H 2 consumption during thermophilic AD of butyrate under suppressed methanogenesis [ 30 ]. Thus, adverse circumstances may induce the proliferation of homoacetogens in suboptimal conditions, which may play a key role in optimizing the performance of the system. In this study, the amount of Treponema dramatically increased during Phase II, which may have been induced by the high H 2 stress in the digester. Moreover, the excessive H 2 may have been converted to methane by both direct (HM) and indirect (homoacetogenesis and acetoclastic methanogenesis (AM)) pathways. Fig. 2 Taxonomic classification of the bacterial communities at the phylum level. Phyla making up less than 1 % of total composition in all the samples were classified as others Table 2 Taxonomic compositions of bacterial communities at the class and genus level Taxonomic compositions Relative abundance (%) Class/genus Day 45 Day 90 Day 120 Day 150 \n Clostridia \n \n 8.18 \n \n 26.82 \n \n 45.19 \n \n 45.53 \n Coprothermobacter \n 0.05 0.12 1.54 1.62 Fastidiosipila \n 1.69 3.69 2.83 0.62 Gelria \n 0.53 0.30 2.60 3.33 Sedimentibacter \n 0.24 1.22 0.47 0.08 Syntrophaceticus \n 0.00 0.00 2.43 1.26 Syntrophomonas \n 0.50 1.78 1.68 0.89 \n Bacteroidia \n \n 27.58 \n \n 23.16 \n \n 27.43 \n \n 31.71 \n Alkaliflexus \n 0.14 1.24 4.26 0.60 Bacteroides \n 2.84 1.30 7.57 0.77 Petrimonas \n 18.85 17.54 6.92 3.50 Proteiniphilum \n 0.78 2.59 8.57 26.37 VadinBC27_wastewater - sludge_group \n 4.33 0.17 0.10 0.37 \n Synergistia \n \n 6.48 \n \n 3.98 \n \n 7.65 \n \n 4.57 \n Aminobacterium \n 0.08 0.21 1.78 2.03 Thermovirga \n 4.82 2.24 3.25 1.13 \n Spirochaetes \n \n 8.27 \n \n 6.95 \n \n 2.29 \n \n 1.40 \n Candidatus_cloacamonas \n 0.00 0.00 0.00 0.03 Spirochaeta \n 2.87 3.07 0.41 0.40 Treponema \n 0.50 3.28 1.47 0.86 \n Thermotogae \n \n 0.11 \n \n 0.00 \n \n 0.30 \n \n 1.54 \n 060F05 - B - SD - P93 \n 0.00 0.00 0.30 1.51 \n Mollicutes \n \n 0.08 \n \n 13.31 \n \n 0.80 \n \n 1.66 \n Acholeplasma \n 0.08 13.27 0.80 1.66 \n Actinobacteria \n \n 0.33 \n \n 1.41 \n \n 0.37 \n \n 0.05 \n Actinomyces \n 0.29 1.36 0.33 0.03 Only identified genera with relative abundances higher than 1.0 % in at least one sample are listed The relative abundances of bacterial classes are in italics During Phases III and IV, the relative abundance of class Clostridia continuously increased, but no VFA accumulation was observed, possibly because an efficient pathway for H 2 consumption occurred. In contrast, the abundance of genus Syntrophomonas decreased during Phase III and was then restored to the same level as Phase I during Phase IV. Meanwhile, the amounts of another representative genus Syntrophaceticus suddenly increased during Phase III. Genus Syntrophaceticus has the opposite metabolic function as Treponema . They are syntrophic acetate oxidizing bacteria (SAOB), oxidizing acetate to CO 2 and H 2 , which in turn can be converted into methane by hydrogenotrophic methanogens [ 25 ]. Compared to AM, the concurrent reactions of SAO and HM are less efficient for acetate degradation. However, the tolerant acetate oxidizers and hydrogenotrophic methanogens are expected to continue to function in more hostile environments [ 31 ]. As shown in Table 2 , this genus was not found during Phases I or II, but a high abundance was detected during Phases III and IV, indicating the key role of SAO in acetate degradation during recovery and new stable stages. This observation reveals the inefficiency of acetoclastic methanogens, and emphasizes the importance of HM during the last two stages. Correspondingly, the abundance of genus Treponema decreased during Phase III and reached its original level during Phase IV. This observation indicates that the proliferation of Treponema is consistent with process deterioration, and it may be used as a potential warning indicator of process instability. Moreover, the relative abundance of other syntrophic bacteria also changed considerably. For example, the amounts of class Synergistia (phylum Synergistetes ) increased during Phase III. This class includes numerous bacteria that can efficiently degrade complex organic materials and ferment lactic or acetic acid to H 2 and CO 2 [ 23 ]. Their predominance indicates that a syntrophic relationship with hydrogenotrophic methanogens occurred in the digester. The succession of class Thermotogae (phylum Thermotogae ) also supported the inference. Thermotogae was only detected during Phase III and IV, its representative genus 060F05 - B - SD - P93 can produce exopolysaccharides (EPS), which are used in the formation of stable cellular aggregates and facilitate interspecies H 2 transfer [ 32 ]. These successions show the irreversible bacterial communities before and after disturbance and may also imply a shift in the methanogenesis pathway at Phase III and IV. Methanogen communities in response to OLR disturbance A shift in the metabolic pathways and metabolites of bacteria directly affects the composition and behavior of methanogens. Figure 3 shows the succession of methanogens in response to OLR disturbance at the genus level. The acetoclastic methanogen Methanothrix and hydrogenotrophic methanogens Methanospirillum and Methanoculleus were the dominant genera during the overall experimental period. The genus Methanosarcina , whose metabolic features are diverse and include both acetotrophic and hydrogenotrophic pathways, was also detected, but abundances were always low (1.24–4.90 %). Specifically, during Phase I, Methanothrix was the most dominant genus with an abundance of 46.97 %, followed by Methanospirillum (35.35 %) and Methanoculleus (9.89 %). The VFA accumulation and ammonia inhibition led to process deterioration during Phase II, however the relative abundance of susceptible Methanothrix increased to 58.47 %. This abnormal phenomenon has been discussed in our previous study [ 4 ]. Moreover, the predominant hydrogenotrophic methanogens shifted from Methanospirillum to Methanoculleus . As we know, the H 2 affinity of Methanoculleus was higher than Methanospirillum ; thus, this succession reduced the H 2 consumption efficiency, which was not consistent with the succession of bacteria under high OLR. Generally speaking, a shift in community structure is always in the direction of the species dealing with the stress conditions and adaptations to the new environment [ 14 ]. Thus, methanogens should shift towards a genus with a higher H 2 consumption rate such as Methanobacterium . However, the present study, as well as many others, identified the predominance of Methanoculleus under stress conditions [ 1 , 2 , 33 ]. Its dominance over other hydrogenotrophic methanogens may be related to its tolerance of high ammonia concentrations [ 1 ]. In addition, Methanoculleus species have a higher gene content compared to other genera that are involved in specific pathways and some that are directly involved in methanogenesis. More specifically, they can use different secondary alcohols as electron donors for methanogenesis [ 33 , 34 ]. These features may be advantageous for the survival of Methanoculleus sp. in different environments and their dominant presence in digesters. Fig. 3 Taxonomic compositions of methanogens at the genus level. Genera making up less than 1 % of total composition in all the samples were classified as others During the recovery phase (Phase III), Methanothrix was still the most dominant methanogen with an abundance of 60.60 %, and hydrogenotrophic methanogens were relatively low in abundance. However, it is likely that methane was mainly produced via the HM pathway, as mentioned above, the increased abundance of Syntrophaceticus indicates the low efficiency of acetoclastic methanogens. Moreover, with the further proliferation of syntrophic bacteria (e.g., classes Clostridia, Synergistia and Thermotogae ) and the decrease in alternative hydrogenotrophs (genus Treponema ), the process performance was restored to a normal level. The contradiction between abundance and function may be explained by microbial activity. Schauer-Gimenez et al. observed that the relative abundance of Methanospirillum and Methanoculleus was less than a quarter of that of Methanothrix , but the specific methanogenic activity (SMA) for H 2 uptake was 188-fold higher than that of acetate [ 35 ]. Shigematsu et al. also found that although the proportion of Methanothrix in their system was as high as to 88 %, SAOB and hydrogenotrophic methanogens converted the acetate in the digester [ 36 ]. In addition, because the feed during the recovery stage was ceased, the changes in archaeal communities during Phase III might be due to the different decay rates of the archaea rather than their different growth rates. Methanoculleus became the most dominant methanogen during Phase IV, which may be related to the high ammonia concentration. Although the high ammonia concentration did not cause process inhibition at this stage, it changed the microbial community composition, and promoted proliferation of the tolerant Methanoculleus. Relationship between process stability and microbial community There are three basic mechanisms for maintaining microbial community function independent of process disturbance: resistance (populations are able to withstand changes without variations in composition), resilience (populations respond to disturbances and have the ability to rebound following disturbances), and redundancy (a disturbed population can be replaced by a new group with the same function, thus a change in community composition will not affect system performance) [ 37 ]. Applying these concepts to anaerobic microbiomes, some studies have suggested that hydrolytic and acidogenic bacteria rely on functional redundancy or resistance to maintain overall function, whereas syntrophic populations tend to be more resilient [ 38 , 39 ]. Some researchers have even extended these concepts to specific microorganisms. For example, Carballa et al. speculated Methanobacteriales as resistant, Methanothrix as redundant, and Methanosarcina and Methanomicrobiales as resilient and redundant [ 38 ]. Goux et al. concluded that Bacteroidales were resistant to high VFA and low pH [ 2 ]. The relationship between resistant and redundant populations and process stability is unintelligible, while the succession of resilient groups is closely related to the process performance. Thus resilient groups may play an important role in indicating the actual state of AD. All three mechanisms were observed in this study. The behavior of the microbial community during the entire disturbance event is inferred and summarized as follows. The increased OLR caused the proliferation of acidogenic bacteria (phyla Tenericutes and Actinobacteria ), and the resulting high VFA yield induced an increase in the abundance of syntrophic acetogenic bacteria (class Clostridia ). However, the abundance of total hydrogenotrophic methanogens decreased, and the ammonia accumulation shifted the dominant hydrogenotrophic methanogens from Methanospirillum to Methanoculleus , which further decreased the H 2 consumption rate. This opposite behavior resulted in the uncoupling of acetogenic bacteria and the methanogens, thus they could not effectively complete the syntrophy. This stress induced the propagation of Treponema as alternative hydrogenotrophs. However, Methanothrix species are well known as susceptible groups, and their metabolic activity may have also been affected by the high ammonia concentration. Thus, the excessive acetate was not degraded in time (Fig. 1 ). The mismatch between bacteria and methanogens caused the accumulation of VFAs, which lowered the pH and buffer capacity of the digester and resulted in a decrease in methane content and yield, finally resulting in process deterioration. During the recovery stage, increased metabolic activity allowed hydrogenotrophic methanogens to out-compete Treponema , leading to a decline in Treponema . Hydrogenotrophic methanogens efficiently degraded the accumulated VFAs accompanied by syntrophic partners ( Clostridia and Synergistia ); the depletion of VFAs resulted in a decrease in fatty-acid-oxidizing bacteria ( Syntrophomonas ). In addition, as the ammonia inhibition decreased the activity of Methanothrix species, the tolerant acetate oxidizers (genus Syntrophaceticus ) proliferated. Excessive acetate was converted to methane through concurrent reactions (SAO + HM). At this time, the dominant methanogeneic pathway likely shifted from acetoclastic to hydrogenotrophic. During Phase IV, the digester was re-fed and gradually operated under the same conditions as in Phase I. Though similar process performance was observed, the microbial composition changed significantly and new steady-state microbial community profiles were shaped after the disturbance (Table 2 ). The overall microbial community was functionally redundant, however classes Clostridia and Bacteroidia were always the dominant groups in the digester, indicating their resistance. The genera Treponema and Syntrophomonas were sensitive to the disturbance, but rebounded afterwards, suggesting that they are potentially resilient groups. As the relative abundances of the genera Treponema and Syntrophomonas were closely related to the process stability, we infer that they may be key microbial indicators closely related to the stability of anaerobic digesters."
} | 7,026 |
24613991 | null | s2 | 619 | {
"abstract": "Bioengineered spider silk block copolymers were studied to understand the effect of protein chain length and sequence chemistry on the formation of secondary structure and materials assembly. Using a combination of in vitro protein design and assembly studies, we demonstrate that silk block copolymers possessing multiple repetitive units self-assemble into lamellar microstructures. Additionally, the study provides insights into the assembly behavior of spider silk block copolymers in concentrated salt solutions."
} | 129 |
22052235 | null | s2 | 621 | {
"abstract": "Sediment microbial fuel cells (SMFCs) have been used as renewable power sources for sensors in fresh and ocean waters. Organic compounds at the anode drive anodic reactions, while oxygen drives cathodic reactions. An understanding of oxygen reduction kinetics and the factors that determine graphite cathode performance is needed to predict cathodic current and potential losses, and eventually to estimate the power production of SMFCs. Our goals were to (1) experimentally quantify the dependence of oxygen reduction kinetics on temperature, electrode potential, and dissolved oxygen concentration for the graphite cathodes of SMFCs and (2) develop a mechanistic model. To accomplish this, we monitored current on polarized cathodes in river and ocean SMFCs. We found that (1) after oxygen reduction is initiated, the current density is linearly dependent on polarization potential for both SMFC types; (2) current density magnitude increases linearly with temperature in river SMFCs but remains constant with temperature in ocean SMFCs; (3) the standard heterogeneous rate constant controls the current density temperature dependence; (4) river and ocean SMFC graphite cathodes have large potential losses, estimated by the model to be 470 mV and 614 mV, respectively; and (5) the electrochemical potential available at the cathode is the primary factor controlling reduction kinetic rates. The mechanistic model based on thermodynamic and electrochemical principles successfully fit and predicted the data. The data, experimental system, and model can be used in future studies to guide SMFC design and deployment, assess SMFC current production, test cathode material performance, and predict cathode contamination."
} | 430 |
34676921 | PMC11469148 | pmc | 622 | {
"abstract": "Abstract The underwater superoleophobicity of a coating is often caused by its preferential water affinity, which, however, normally weakens the substrate adhesion property. In this work, a new strategy is reported for achieving strong underwater adhesion between a well‐designed amphiphilic polyurethane coating and a diverse range of substrates while also rendering the coating surface's superoleophobicity. When the coating, which is a mixture of an amphiphilic polyurethane and a water miscible solvent, is immersed in water, the hydrophobic segments aggregate to orientate and pile along the surface of substrates via a segment orientation mechanism triggered by solvent exchange with water penetration to exert strong adhesion. At the same time, the hydrophilic segments will physically crosslink to form a hydrogel coating, endowing the substrate with underwater superoleophobicity. This work provides a facile, versatile, and scalable approach for the future design of superoleophobic coatings in a water environment.",
"conclusion": "3 Conclusion A well‐designed PCAPU coating successfully achieved both superoleophobic and strong substrate adhesion properties in water. The underlying mechanism of these achieved properties is based on the unique orientations of the PTMG and PEG segments. Particularly, the solvent involved in the APU solution is critical in forming the migration environment in which the desired orientation can occur. This work provides a facile and versatile strategy to achieve a coating with both superoleophobic and strong substrate bonding characteristics in water. This new approach can significantly contribute to the development of multifunctional coatings with a wide range of vital applications, such as antifouling surfaces, antibacteria materials, and antiblocking materials, etc.",
"introduction": "1 Introduction Nature has created various biological surfaces with anti‐oil‐fouling properties that can survive in aqueous environments, including organic and/or inorganic composites, such as shark skins and clamshells. [ \n \n 1 \n \n ] The anti‐oil‐fouling property is highly desirable for many artificial materials and devices used for underwater activities since these surfaces must remain clean to sustain their functionalities even when subjected to complex oil‐contaminated aqueous environments. [ \n \n 2 \n \n ] Underwater superoleophobic surfaces with contact angles > 150° toward various oily liquids, exhibit outstanding anti‐oil‐fouling performance in aqueous environments. [ \n \n 3 \n \n ] Typically, these surfaces are fabricated by introducing nano/microscale roughness and a high‐energy water‐favoring composition. [ \n \n 4 \n \n ] These methods, however, either require a sophisticated manufacturing process, limiting their practical applications, [ \n \n 5 \n \n ] or inherently weaken the underwater substrate adhesion property. Though covalent bonding can be introduced to promote the adhesion of hydrophilic materials to substrates (for example, by grafting reaction and interfacial polymerization, [ \n \n 6 \n \n ] etc.), it requires tailor designed substrate surfaces, a tedious and complex process that makes it unsuitable for large area use. [ \n \n 7 \n \n ] Therefore, developing a facile, versatile, and scalable approach to endow the coating with both superoleophobicity and strong substrate adhesion in a water environment is a critical necessity. [ \n \n 8 \n \n ] \n In this work, we provide a new noncovalent coating strategy, based on a physically crosslinked amphiphilic polyurethane (PCAPU) with the combined features of superoleophobicity and strong adhesion onto a variety of substrates in a water environment. On various substrates, a mixture of designed amphiphilic polyurethane and a water miscible solvent, e.g., dimethylacetamide (DMAc) was applied. After immersing these coated substrates in water, the orientation of hydrophobic segments toward the substrate can occur via a segment orientation mechanism, causing the coating to strongly adhere onto the substrate surface. Meanwhile, the formation of a physically crosslinked hydrogel from the hydrophilic segments can result in the generation of an underwater superoleophobic surface.",
"discussion": "2 Results and Discussion 2.1 Design of the PCAPU Hydrogel An amphiphilic polyurethane (APU) was designed and synthesized by employing poly(ethylene glycol) (PEG 2000) and poly(tetramethylene glycol) (PTMG 2000) as the hydrophilic and hydrophobic components, respectively. As shown in Figure \n 1 a , DMAc was selected as the reaction medium since it is water miscible and can dissolve both the PEG and PTMG. Then, via a reaction between PEG/PTMG and excess isophorone diisocyanate (IPDI), isocyanate groups were first introduced to both ends of the PEG and PTMG chains. Subsequently, by adding stoichiometric water (H 2 O) to the system, the end‐functionalized PEG and PTMG chains were randomly connected, bridging by the urea structure (Figure S1 , Supporting Information), and the APU–DMAc solution was obtained. To confirm the successful synthesis of APU, nuclear magnetic resonance ( 1 H NMR) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and gel‐permeation chromatography (GPC) were performed (Figure S2 , Supporting Information). Figure 1 Fabrication process and the surface morphologies of PCAPU. a) Schematic illustration of the synthesis of APU and the preparation of PCAPU. The hydrophilic PEG and hydrophobic PTMG chains (4:1) were first end‐functionalized with isocyanate groups by reacting with excess IPDI in DMAc. After adding H 2 O, the end‐functionalized PEG and PTMG chains were connected randomly, bridging by urea structure. The coated substrates were then immediately immersed in deionized water to form a PCAPU hydrogel coating. b–e) Wrinkle‐like morphologies of the PCAPU coated surface with obvious upheavals captured by digital camera (b), optical microscopy (c), and SEM at the microscale (d) and nanoscale (e). The wrinkle‐like structure is similar to the wrinkled skin of human fingers exposed to water over an extended period of time, where the epidermis swells but the dermis does not. Through detailed characterization and analysis in the following text, it is proved that the morphological upheavals indicate the swelling‐induced enlargement of the PEG dominated surface area, whereas the layer tightly bonded to the substrates represents the nonswelling PTMG dominated area. A PCAPU hydrogel coating with both strong substrate adhesion and superoleophobicity in water was obtained by painting the prepared APU–DMAc solution onto a substrate and immersing the coated substrate in water (Figure 1a ). Moreover, the immersion time should be long enough (>24 h) to allow sufficient exchange of DMAc and water and ensure a negligible amount of DMAc remains in the final formed hydrogel (Figure S3 , Supporting Information). Interestingly, after immersion, a wrinkled surface with obvious upheavals was observed (Figure 1b ). This wrinkle‐like structure was also captured using optical microscopy and scanning electron microscopy (SEM) (Figure 1c–e ). All of these images show that this wrinkle‐like structure is quite similar to the wrinkled skin of human fingers exposed to water for an extended period of time, where the epidermis swells but the dermis does not. [ \n \n 9 \n \n ] According to this point of view, it is reasonable to hypothesize that the formation of coating wrinkles is caused by the swelling‐induced enlargement of the PEG dominated surface area with a nonswelling PTMG dominated bottom layer that is tightly bonded to the substrates during the solvent exchange process. [ \n \n 10 \n \n ] \n 2.2 Underwater Superoleophobicity of the PCAPU In terms of the coating's underwater superoleophobic property, the water could be firmly trapped within the PCAPU network, due to the strong interactions between water molecules and the hydrophilic PEG chain segments, giving the PCAPU coating excellent repellency against oily liquids. Following this mechanism, as expected, though different underwater contact angles of oily liquids (OCAs) have been obtained for different types of oils (including n ‐hexadecane, n ‐hexane, toluene, diiodomethane, peanut oil, and crude oil), these OCA values are quite close to each other at the level of 150°. The OCAs for n ‐hexane, toluene, peanut oil, and crude oil, etc., on the coating surface all exceeded 150° demonstrating the coating surface's underwater superoleophobicity ( Figure \n 2 a ). Moreover, a diiodomethane droplet, with a density greater than water (unfavorable for the droplet to float in aqueous environments) was able to slide quickly on a PCAPU coated tin plate (Figure 2b ; and Video S1 , Supporting Information) with an underwater oil sliding angle (OSA) of ≈5° (Figure S4 , Supporting Information). The OSA was also measured for n ‐hexadecane droplet, which is around 7° (Figure S4 , Supporting Information). The small sliding angles less than 10° proved that these oil droplets can easily roll off the PCAPU coating reflecting the coating surface's underwater superoleophobicity. Additionally, underwater force measurements were conducted to quantitatively characterize the oil repellency of these coated surfaces. In detail, a droplet of n ‐hexadecane was inserted into a probe which was then gradually pressed onto the surface of a PCAPU coated tin plate. Following the appearance of significant deformation on the n ‐hexadecane droplet, the probe was subsequently pulled upward. The inset photographs in Figure 2c (Video S2 , Supporting Information) illustrate the shape changes of the oil droplet during the process of contacting, preloading, and detaching from the coated surface. During the detachment process, no shape distortion occurred, and no oil residue remained on the coated surface. The adhesion forces generated during this process are depicted in Figure 2c , where the adhesion forces are extremely low (≈0 µN), confirming the PCAPU's underwater superoleophobicity. Similar low adhesion forces (≈0 µN) were found with other oily liquids, including— n ‐hexane, toluene, and peanut oil (Figure S5 , Supporting Information). Figure 2 Characterization of the underwater superoleophobic performance. a) Underwater oil contact angle (OCA) analysis. On the PCAPU coating surface, the OCAs of various oily liquids ( n ‐hexane, toluene, peanut oil, and crude oil) all exceeded 150° (though slightly lower for n ‐hexadecane, diiodomethane and pump oil). For tests involving oil droplets with densities lower than that of water, the tin plate was turned upside down, with the coating (the blue layer) facing downward. b) Different moments when a diiodomethane droplet ( ρ > ρ H 2 O ) quickly slid off the PCAPU coating surface. These sliding moments indicate that there is no resistance or adhesion between the oil droplet and the coated substrate surface, indicating that the produced PCAPU coating is superoleophobic. c) The evolution of adhesion forces with the preloading of an n ‐hexadecane droplet on the coated surface. To quantitively characterize the oil repellency of the PCAPU coated surface, the advancing‐receding force measurements were performed in water. The adhesion force of an n ‐hexadecane droplet is extremely low during the detachment. The inset photographs depict the shape changes of the oil droplet during the process of contacting (left), preloading (lower right), and detaching (upper right) from the PCAPU coated surface, where no shape distortion or oil residue was found after detachment. Furthermore, to verify the versatility of the anti‐oil‐fouling ability of this PCAPU coating, its application on irregularly shaped substrates was also tested. A mixture of water and Oil Red O dyed n ‐hexane was used to mimic a polluted marine environment with an oil spill (the n ‐hexane layer was floating on the water surface), and then plastic dolphin models with and without PCAPU coating were immersed in this liquid mixture. As presented in Figure \n 3 a (Videos S3 and S4 , Supporting Information), the red n ‐hexane residue is clearly visible on the uncoated dolphin's skin, even after removing it from the liquid mixture and flushing it with a large amount of water. In contrast, the coated dolphin remained clean whether it was in or out of the “polluted water.” On the other hand, except for applications on substrates with smooth surface, the anti‐oil‐fouling performance of the PCAPU coating was also examined on rough and porous substrates, such as polypropylene (PP) nonwoven, poly(vinylidene fluoride) (PVDF) membranes, and poly(tetrafluoroethylene) (PTFE) microfiltration (MF) membranes. As expected, the crude oil dropped onto these coated surfaces was easily rinsed away with water demonstrating excellent anti‐oil fouling capability (Figure S6 and Videos S5 – S7 , Supporting Information). To summarize, all of the above results provide strong evidence for the PCAPU coating's superior and universal underwater superoleophobicity or anti‐oil‐fouling ability on various substrates. Figure 3 Underwater anti‐oil‐adhesion properties of the PCAPU coating. a) The appearance of uncoated and coated plastic dolphins following immersion in a liquid mixture of water and n ‐hexane (dyed red with Oil Red O to mimic a polluted marine environment). Under the “polluted” environment (middle section), there is obvious red n ‐hexane residue on the uncoated dolphin's skin, even after being taken out and flushed with a large amount of water (right section). Meanwhile, the coated dolphin remained clean both within and outside of the “polluted water,” demonstrating the PCAPU coating's superior underwater superoleophobicity. b) Application of the PCAPU coating to protect the inner surfaces of soft pipes. Crude oil (5 mL) was injected into the uncoated and coated silicon tubes (radius ≈8.0 mm). The appearance of the tubes after being rinsed with water shows a clear difference, with the coated silicon tube exhibiting an excellent anti‐oil‐adhesion property. In addition to the superoleophobicity, the PCAPU coating displayed remarkable flexibility due to the physically crosslinked network and the composition of soft PEG and PTMG segments, in contrast to the rigid nature of superoleophobic coatings generated from inorganic composites. [ \n \n 11 \n \n ] This characteristic was verified by gripping, bending, and twisting a PCAPU‐coated silicone rubber substrate (Figure S7a and Video S8 , Supporting Information). Meanwhile, the distortion of the substrate had no effect on the coating's anti‐oil‐adhesion performance. As demonstrated in Figure S7b and Video S9 (Supporting Information), the crude oil poured onto the bent area of a coated tin plate can be easily rinsed away with water. Furthermore, the PCAPU‐coated silicon pipe was utilized to explore the potential application of this PCAPU coating on protecting the inner surfaces of soft pipes, as shown in Figure 3b , by rinsing with water, the coated pipe exhibited an excellent ability of anti‐crude‐oil adhesion. To further prove this practical self‐cleaning ability, additional experiments were carried out by polluting the uncoated and dry PCAPU coated tin substrates in air using crude oil. As shown in Figure S8 (Supporting Information), the crude oil can be easily and completely cleaned off from the PCAPU coated surface by water, while under the same condition, the crude oil still stuck firmly to the untreated tin surface. 2.3 Strong Substrate Adhesion of the PCAPU Remarkably, in addition to superoleophobicity, a strong underwater adhesion to substrates such as organic and inorganic materials, as well as hard and soft surfaces, was achieved. The adhesion forces measured via lap shearing adhesion test [ \n \n 12 \n \n ] and the 90° peeling test [ \n \n 13 \n \n ] reflect this significant underwater substrate adhesion property. As shown in Figure \n 4 a , the shear strengths of the coating attached to a range of substrates were evaluated, including the hydrophilic tin plate (tin), aluminum (Al), hydrophobic polyester (PET), polypropylene (PP), silicone rubber (SR), and even PTFE. Based on the obtained results, we can conclude that regardless of the substrate, a minimum shear strength higher than 15 kPa was achieved. The different shear strength value measured for various substrates can be ascribed to the varying substrate composition which resulted in different synergetic interfacial adhesion interactions between the PCAPU materials and the substrates, e.g., hydrogen bonding, hydrophobic interactions, and Van der Waals forces, etc. Figure 4b shows the shear strengths between the tin plate and the coatings at various water immersion times. It can be found that, even after being stored in an aqueous environment for more than 60 days, the coated tin plate could support a weight of 200 g (Figure 4b ). This confirms the excellent underwater bonding ability of the coated substrates once more. Figure 4 Underwater substrate adhesion strength of the PCAPU coating. a) Shear strengths of coatings applied onto various hydrophilic and hydrophobic substrates. The shear strength (>15 kPa at minimum) measured on various substrates proved the PCAPU coating's strong adhesion property and versatility. b) Lap shearing adhesion test on coated tin plates with varying immersion times. The inset photograph shows that the PCAPU coated tin plates can withstand a 200 g weight even after 60 days in water, demonstrating the superior adhesion strength of the PCAPU coating. c,d) Control experiments for a coated tin (c) and PTFE (d) plates with and without DMAc treatment were designed to illustrate the significance of the solvent exchange process in attaining underwater substrate adhesion. After several seconds of immersion, the bottom section developed a wrinkle‐like structure, whereas the upper region showed no discernible alteration. Within 10 min, the upper coating had peeled off, and was hanging on the substrate supported by the bottom coating, which had a strong adhesion to the substrate. e) The coated tin plate's distinct bonding properties with another water miscible solvent utilized—NMP. In the middle area, the shed off coating with no wrinkle‐like structure was observed indicating the significance of the solvent exchange process (DMAc/NMP and water) in obtaining the underwater adhesion property. A 90° peeling test was also performed to ascertain the substrate adhesion performance of the PCAPU coating. As shown in Figure S9 (Supporting Information) the PCAPU hydrogel coating exhibited strong interfacial toughness with various hydrophilic and hydrophobic substrates (though it did not reach a super adhesion level when compared to previous work reported [ \n \n 14 \n \n ] that only targeted super adhesion performance (Table S1 , Supporting Information)). It is noteworthy that the challenging problem we aim to solve here is not the design of a super adhesion coating, but rather the development of a facile and versatile strategy for overcoming the incompatibility of underwater superoleophobicity and substrate adhesion. This is a significant bottleneck problem in the field of hydrogel adhesion underwater due to its hydrophilic feature. Based on an ingenious hydrophobic and hydrophilic segment orientation mechanism, without the requirement of tailor designed substrate surface, we have simultaneously realized underwater superoleophobicity (on various types of oil) and strong substrate adhesion on a diverse range of substrates (even PTFE). As a result, a facile and versatile solution to the bottleneck problem in the field of underwater hydrogel adhesion has been provided, while future work targeting MPa‐level substrate adhesion can be conducted to further improve this strategy. Additionally, the effect of different ratios of hydrophilic (PEG) and hydrophobic segments (PTMG) on underwater superoleophobicity and substrate adhesion was also studied. As depicted in Figure S10a,d (Supporting Information), the PCAPU hydrogel coating will entirely slide off the substrate when its composition lacks hydrophobic PTMG (PEG:PTMG = 5:0). The equilibrium water content (EWC) was adopted to calculate the swelling extent of the coating and reflect its superoleophobicity. As shown in Figure S10b (Supporting Information), the EWC diminished as the PEG:PTMG ratio decreased, and it is almost zero when the ratio of PEG:PTMG = 0:5, indicating poor anti‐oil property. Although a stronger adhesion can be achieved when the ratio of PEG:PTMG = 1:1 and 1:4 (Figure S10d , Supporting Information), the anti‐oil‐adhesion performance is not acceptable, where a certain amount of adhesive force (e.g., ≈8 µN for PEG:PTMG = 1:1) exists between the n ‐hexadecane droplet and the coating during detachment (Figure S10c and Video S10 , Supporting Information). The PCAPU hydrogel coating with the PEG:PTMG = 4:1 achieved excellent underwater superoleophobicity as well as strong substrate adhesion (as demonstrated in Figures 2 , 3 , 4 ). Hence, the PCAPU with a PEG and PTMG ratio of 4:1 was appropriately chosen to demonstrate the technology developed in this work. 2.4 Segment Orientation Mechanism Subsequently, to understand the mechanism behind the simultaneous realization of the underwater superoleophobicity and strong substrate adhesion of the PCAPU coating, the effect of the involvement of DMAc in APU on the property achievement was investigated. First, a control experiment was designed by fabricating a dried APU (without solvent) coated tin plate. In order to compare, solvent was added to the bottom coated area of the tin plate (Figure 4c ). In Figure 4c , the different adhesion behavior can be clearly seen after immersing the coated tin plate in water with and without DMAc treatment (Video S11 , Supporting Information). After several seconds of immersion, the wrinkle‐like structure was observed from the bottom area, while no obvious changes were observed from the upper region, which was not treated with DMAc. Consequently, the upper section of the coating shed off from the substrate after a short period of time (<10 min), and then hung on the substrate by the supporting force from the bottom part of the coating, which remained strongly adhered to the substrate. The different underwater adhesion performances were also observed when PCAPU coatings with and without DMAc were applied to PTFE surface (Figure 4d ; and Video S12 , Supporting Information) and other hydrophobic/hydrophilic surfaces (Figure S11 and Videos S13 – S16 , Supporting Information). Furthermore, besides DMAc, another water miscible solvent, N ‐methyl pyrrolidone (NMP), was employed to confirm the crucial role of the solvent (Figure 4e ), where distinct substrate bonding abilities were clearly observed from the coating with and without NMP/DMAc treatment. The abovementioned experimental and characterization results demonstrate how the PCAPU materials function to achieve both a superoleophobic surface and strong adhesion to various substrates, and why a wrinkle‐like structure can be formed. The functioning mechanism can be divided into three processes ( Figure \n 5 a )—1) Solvent exchange: After immersing the APU–DMAc coated substrate in water, a gradual permeation of water into the APU–DMAc mixture would begin, instead of instantly surrounding the APU chains with a water environment. Progressively, the DMAc phase would be substituted by water from the top surface of the coating to the bottom. Thereby, the temporary coexistence of the water phase and the DMAc phase would enable the subsequent unique orientation of the PTMG and PEG segments; 2) Orientation of hydrophobic segments: Given that the PTMG segments are hydrophobic and can be well dissolved in DMAc, with water permeation, these segments would migrate with the DMAc phase (move toward the substrate) and ultimately be squeezed onto the coating–substrate interface. This strong tendency would result in the PTMG layer being tightly attached to the substrate, which can protect the interfacial hydrogen bonds between the PCAPU segments (urethane or urea groups) and the substrates (e.g., hydroxyl groups from hydrophilic surfaces and fluorine groups of PTFE) by preventing their preferential interaction with water from forming a hydration layer, thus the final observed high adhesion strength to various substrates with a morphological sunken layer is achieved; 3) Orientation of hydrophilic segments: With the water permeation, the hydrophilic PEG segments would stretch and migrate outwardly toward the water phase (opposite to the substrate). [ \n \n 15 \n \n ] Afterward, the strong hydrogen bonds present at each joint point can stabilize this physical intermolecular crosslinking, producing a robust hydrogel network and appearing as morphological upheavals. This hydrogel surface endows the coating with superoleophobicity thereby allowing chain movements while maintaining the network topology giving the coating flexibility. Based on these three subtle processes, the underwater superoleophobicity and strong substrate adhesion can be achieved simultaneously. Figure 5 Segment orientation mechanism and the resulted PEG and PTMG dominated layers in PCAPU. a) Schematic illustration of the solvent exchange process and the orientation mechanism of the hydrophobic/hydrophilic segments. Different immersion behavior of the coated substrate with and without the DMAc treatment: the APU chains in the upper coated area will be surrounded by water immediately, while the lower coated section will undergo a water–solvent penetration process. Subsequent detachment and bonding mechanism after immersion: the different immersion processes will cause two different distributions of the hydrophobic segments—randomly distributed (without a solvent exchange process) or oriented (with a solvent exchange process), which will correspondingly result in the formation of hydrogen bonds between water (for the randomly distributed case) or the substrates (for the oriented case) with the PCAPU, and thus lead to two distinct performance—detachment or bonding of the coating. b) The 3D and cross‐section views of the obtained PCAPU coating captured by laser scanning confocal microscopy (LSCM). A hydrophobic aggregation‐induced emission (AIE) indicator was employed as a fluorescent molecular probe. The hydrogel coating exhibited a strong fluorescence at the bottom, indicating the squeezed hydrophobic PTMG segments on the coating–substrate interface. Weak fluorescence was observed from the top layer of the coating indicating the loosely distributed PEG segments. In contrast to the DMAc/NMP treated area, for the substrate area treated with only dried APU, the hydrophobic PTMG segments only undergo homogeneous aggregation when immersed in water, and are well‐distributed throughout the coating matrix, with no orientation toward the substrate (Figure 5a ). This resulted in the contact of the substrate with a considerable amount of PEG segments which tended to swell in water and form a hydration layer with no wrinkle‐like structure. This layer blocked the formation of hydrogen bonds between the coating and the substrates, which eventually caused coating's detachment from the substrate. It is worth noting that, as emphasized above, the aim of this work is not only to achieve substrate adhesion property, but there are also two prerequisites: 1) in a water environment and 2) simultaneous superoleophobicity. Therefore, unlike previous work that used solvent changes only to modulate adhesive properties, we focused on the simultaneous realization of underwater superoleophobicity and strong substrate adhesion based on the hydrophilic and hydrophobic segment orientation mechanism triggered by the solvent exchange. Therefore, it is not the superficial phenomenon—solvent exchange—but the fundamental segment orientation that allows the coating to simultaneously achieve both properties in water. The novel segment orientation mechanism will not be affected by the substrate nature, which guaranteed the versatility of this new coating strategy for various substrates, even the low energy substrates such as polypropylene and PTFE. To further verify this proposed segment orientation mechanism and also the two dominant layers (PEG dominated surface area and the PTMG dominated bottom area) in the wrinkle‐like structure, aggregation‐induced emission (AIE) indicator—8‐anilino‐1‐naphthalenesulfonic acid was dissolved into the APU–DMAc solution as a fluorescent molecular probe. The 3D and cross‐section views of the obtained PCAPU coating with AIE indicator were recorded using the laser scanning confocal microscope (LSCM). As illustrated in Figure 5b , the bottom area of the PCAPU coating, i.e., the coating–substrate interface, exhibited a strong fluorescence, indicating an intense aggregation of the AIE indicators caused by the migration of hydrophobic PTMG segments toward the substrate with the DMAc phase. While from the upper layer of the coating, only weak fluorescence can be observed, demonstrating a loosely distributed PEG area. The PEG dominated surface area can be further confirmed by the decisive role of PEG segments in the swelling behavior of PCAPU. Thickness study was conducted for the PCAPU coatings fabricated with different PEG:PTMG ratios (5:0, 4:1, 1:1, 1:4, 0:5). As shown in Figures S12b and S10a (Supporting Information), PCAPU coating with the ratio of PEG:PTMG = 4:1 displayed the maximum thickness and the most obvious wrinkle‐like structure. The thickness of the coating formed with pure PEG segments (PEG:PTMG = 5:0) exhibited a relative lower value although with a higher EWC (Figure S10b , Supporting Information), since the coating shed off from the substrate due to the lack of PTMG composition resulting in no wrinkle‐like structure. In the case of other ratios (PEG:PTMG = 1:1, 1:4, and 0:5), the thicknesses reduced with the decrease of PEG content, and no wrinkle‐like structure was generated from the pure PTMG coating. Overall, the above results intuitively proved again the segment orientation mechanism during the PCAPU coating formation and the PEG‐ and PTMG‐dominated bottom and surface layers in its resulted wrinkle‐like structure."
} | 7,565 |
26423494 | null | s2 | 623 | {
"abstract": "The fabrication and advanced function of large area biomimetic superhydrophobic surfaces (SHS) and slippery lubricant-infused porous surfaces (SLIPS) are reported. The use of roll-to-roll nanoimprinting techniques enabled the continuous fabrication of SHS and SLIPS based on hierarchically wrinkled surfaces. Perfluoropolyether hybrid molds were used as flexible molds for roll-to-roll imprinting into a newly designed thiol-ene based photopolymer resin coated on flexible polyethylene terephthalate films. The patterned surfaces exhibit feasible superhydrophobicity with a water contact angle around 160° without any further surface modification. The SHS can be easily converted into SLIPS by roll-to-roll coating of a fluorinated lubricant, and these surfaces have outstanding repellence to a variety of liquids. Furthermore, both SHS and SLIPS display antibiofouling properties when challenged with Escherichia coli K12 MG1655. The current article describes the transformation of artificial biomimetic structures from small, lab-scale coupons to low-cost, large area platforms."
} | 270 |
34370382 | PMC8351387 | pmc | 625 | {
"abstract": "Abstract Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness‐costly metabolites through natural selection. In this strategy, metabolic cross‐feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co‐evolved wild‐type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B‐group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange.",
"introduction": "Introduction The long‐term Escherichia coli evolution experiment (Lenski, 2017 ) has highlighted how evolution under well‐controlled conditions can be used to gain fundamental insights into adaptive processes. These experiments have helped, for example, in gauging the predictability of the evolutionary outcomes (McDonald, 2019 ) and uncovered the divergence between the fitness trajectories and mutation rates of clonal asexual populations (Maddamsetti et al , 2015 ). Adaptive laboratory evolution is by now also a well‐established tool for the development of microbial strains with improved biotechnological characteristics (Dragosits & Mattanovich, 2013 ). The applications include adaptation to harsh process conditions (Wallace‐Salinas & Gorwa‐Grauslund, 2013 ; Stella et al , 2019 ), improved substrate utilization (Zhou et al , 2012 ) and boosting the growth rates of metabolically engineered strains (Portnoy et al , 2011 ; Tenaillon, 2018 ). Further, adaptive laboratory evolution of co‐cultures has enabled studying the emergence and stability of interspecies interactions such as antagonism (Koskella & Brockhurst, 2014 ) and metabolite exchange (Mee et al , 2014 ; Harcombe et al , 2018 ). These studies primarily focus on establishing microbial models of cross‐feeding, with less emphasis on the molecular basis of adaptations. Further, while amino acid cross‐feeding is common in natural communities (Machado et al , 2021 ) and has also been the basis of synthetic communities (Wintermute & Silver, 2010 ), cross‐feeding involving other nutrients has been less well studied. Adaptive laboratory evolution approach does not require prior knowledge of the genetic elements underlying the trait that we wish to improve. Thus, this approach can be applied to arbitrarily complex traits and to organisms that are not amenable to genetic engineering. Adaptive laboratory evolution is particularly attractive when the use of engineered organisms is restricted due to legislative or consumer preference considerations, for example in fermented food products (Burgess et al , 2006 ). The fundamental requirement for applying adaptive laboratory evolution, whether for natural or engineered organisms, is that the trait of interest is correlated to the fitness under the selection conditions (Winkler et al , 2013 ). While this minimal requirement underlines the elegance and the success of adaptive laboratory evolution, it also underscores its limited applicability to the traits that impose a toll on the cell fitness, such as metabolite secretion. To enable improvement of fitness‐costly metabolite secretion while keeping the advantages offered by adaptive laboratory evolution, we here used mutualistic cross‐feeding to exert selection pressure for increased production of the target compound. This approach makes the target production amenable to natural selection despite its cost to the producer. Consider a mutualistic community with two members wherein each partner depends on the other for one or more essential metabolites. Secretion of these metabolites will be directly coupled to the growth of both community members. Any or all of these compounds, despite their fitness costs for the secretors, can then be subjected to improvement via adaptive laboratory evolution by selecting for the overall community growth. We tested this concept in microbial communities consisting of lactic acid bacteria (LAB) that can naturally produce B‐group vitamins (riboflavin or folate) and engineered Saccharomyces cerevisiae strains auxotrophic for one of the two vitamins. When grown under nitrogen excess, the yeast secretes amino acids for which the LAB strains are naturally auxotrophic (Ponomarova et al , 2017 ). The yeast‐lactic acid bacterial community thus satisfies the requirement for obligate mutualism. The lactic acid bacterial strains, which are not engineered, as well as the target products, riboflavin and folate, are relevant for food biotechnological applications. Beyond this direct industrial relevance, our study establishes a proof of concept for the feasibility of improving fitness‐costly traits using mutualistic communities.",
"discussion": "Discussion Our results demonstrate that natural selection can be used to improve production of a desired compound by coupling it with the community fitness through cross‐feeding. The link between the selection pressure exerted by the cross‐feeding and the improved secretion phenotype was evident in the lack of enhancement in the monoculture evolution experiments. At the same time, the absence of riboflavin from the environment or genetic predisposition of the selected parental strain does not lead to increased riboflavin secretion. The selection was also selective to the target vitamin, even when the two compounds used here, riboflavin and folate, originate from closely related pathways. Mechanistically, genomic, proteomic, flux balance and metabolomic analyses revealed concordant changes in the regulation of vitamin biosynthesis and precursor supply (Fig 4 ). The increased abundances of the riboflavin and the purine biosynthetic enzymes, as well as the accumulation of glutamine and glycine, are in accord with the previous genetic engineering studies in L. lactis and Bacillus subtilis (Burgess et al , 2004 ; Schwechheimer et al , 2016 ). The relatively small number of altered protein and metabolite abundances further shows the targeted nature of the selection pressure. The focussed nature of these changes is important also from an application perspective; the evolved isolates are therefore likely not to have alterations in other traits relevant for biotechnological processes. Figure 4 Key metabolic changes in L. plantarum following coevolution with yeast Bacterial pathways associated with the biosynthesis of riboflavin and its main precursors, and amino acids secreted by yeast are shown. Metabolites and proteins with at least a twofold change in abundance and FDR‐adjusted P < 0.05 are highlighted. In a mutualistic community, growth of all members is directly linked with that of the entire community. The phenotypes which co‐operate more also receive higher rewards from their partners, an observation which has also been made for coevolution of auxotrophic Escherichia coli strains (Zhang & Reed, 2014 ). Elsewhere, coevolution of mutualistic E. coli and Salmonella enterica resulted into the majority of S. enterica cells exhibiting a co‐operating phenotype (Harcombe, 2010 ), while the growth rate of the co‐cultures improved in expense of the individual growth rate of co‐operating E. coli (Harcombe et al , 2018 ). In our case, the evolved bacterial isolates with enhanced riboflavin production not only exhibited improved growth but also supported a larger community population. The increased cost of riboflavin production was thus more than compensated for, through increased supply and more efficient utilization of amino acids secreted by the yeast. Additionally, we show that the selection pressure for increased production is linked to the presence of the mutualistic partner and not with the absence of the compound from the environment. Our results, together with previous observations of amino acid exchange (Mee et al , 2014 ), demonstrate that increased secretion of costly metabolites is an exploitable strategy for adaptive laboratory evolution. Bacterial cells with decreased or limited secretion capabilities could constitute cheaters that can lead the whole population to collapse (Hardin, 1968 ; Rankin et al , 2007 ; Jones et al , 2015 ). Yet, the majority of the isolates from the evolved communities exhibited improved secretion, and only 1 of 48 communities collapsed. These observations indicate that poor secretors did not have a sufficient fitness advantage over high riboflavin secretors. As expected for an obligate mutualism, and illustrated by our simulations (Fig EV4 ), improved vitamin secretion by bacteria results in positive feedback through increased availability of amino acids from yeast. This explains the amplified advantage to the co‐operators, but not how “common goods”—riboflavin and amino acids—are not more frequently exploited by the cheaters. One likely explanation is the experimental conditions that we chose. All our evolution experiments were performed without shaking, and, as we show, the bacterial and yeast cells tend to form aggregates. Indeed, spatial segregation is one of the commonly suggested mechanisms of cheater suppression and mutualistic interaction stabilization (Momeni et al , 2013 ; Pande et al , 2016 ; Marchal et al , 2017 ). By affecting the distribution and availability of resources, spatial segregation allows preferential benefit to the co‐operating partners (Dal Mitri et al , 2011 ; Hillesland, 2018 ; Co et al , 2020 ). In agreement, our simulations showed that increased mixing strength negatively affects the percentage of stronger mutualists (Fig EV4 ). Together, our experimental and simulation results show that the coevolution is robust against the emergence of non‐co‐operating phenotypes, while the fitness burden of increased vitamin production is balanced by evolution of better nutrient utilization. The high secretors thus remained prevalent. Bacterial strains that produce either riboflavin or folate can be used to produce functional food with increased vitamin levels (LeBlanc et al , 2020 ). As our evolved bacterial isolates are not genetically engineered and retain their secreting phenotype in the absence of their yeast partner as well as when cultured in milk, they can be readily used in biotechnological applications to address nutritional deficiencies (Carrizo et al , 2020 ) or to treat patients with inflammatory gut diseases (von Martels et al , 2020 ). More broadly, our coevolution approach enables targeted improvement of fitness‐costly metabolites through the operational simplicity offered by natural selection in laboratory evolution. The method can be readily applied to organisms wherein genetic engineering is yet not possible or restricted, and also in cases where the genetic basis of the trait of interest is complex and/or unknown. Beyond its biotechnological potential, the coevolution approach will also be valuable in addressing fundamental ecological questions on the emergence of cross‐feeding and mutualism in microbial communities."
} | 2,940 |
30813538 | PMC6462982 | pmc | 627 | {
"abstract": "Microbial cooperation pervades ecological scales, from single-species populations to host-associated microbiomes. Understanding the mechanisms promoting the stability of cooperation against potential threats by cheaters is a major question that only recently has been approached experimentally. Synthetic biology has helped to uncover some of these basic mechanisms, which were to some extent anticipated by theoretical predictions. Moreover, synthetic cooperation is a promising lead towards the engineering of novel functions and enhanced productivity of microbial communities. Here, we review recent progress on engineered cooperation in microbial ecosystems. We focus on bottom-up approaches that help to better understand cooperation at the population level, progressively addressing the challenges of tackling higher degrees of complexity: spatial structure, multispecies communities, and host-associated microbiomes. We envisage cooperation as a key ingredient in engineering complex microbial ecosystems.",
"conclusion": "6. Concluding Remarks We reviewed recent work on microbial cooperation from a synthetic biology perspective, taking advantage of both simple ecological principles and a bottom-up approach to tackle the complexity of microbial communities. We described specific mechanisms favoring cooperation in microbial ecosystems, such as privatization of public goods and spatial structure. As we dealt with more complex community structures, we discussed emergent properties that result from cooperative interactions between two or more genotypes, e.g., the ability to thrive in environments where none of the species alone would survive. Finally, we highlighted that, by taking advantage of the cooperative interactions between microbes and simple model organisms, such as C. elegans , we can engineer the host microbiome to perform novel tasks. We envision that synthetic cooperation will have several applications in the near future. From a biomedical perspective, engineering native microbial symbionts in order to enhance their cooperative abilities [ 119 ] could help to develop better therapies for, e.g., obesity, diabetes, and inflammatory bowel disease. Other potential applications of synthetic cooperation lay in the field of conservation biology, if paired with careful assessment of risks and ethical evaluations. Anthropogenic climate change, globalization, and urbanization are altering environmental conditions, causing a widespread loss of biodiversity and ecosystem functions. Engineering higher collective tolerance to a changing environment, as well as recovering ecological functions that might have already been lost could provide valuable tools to cope with the environmental disturbances arising in our present time [ 29 , 120 ].",
"introduction": "1. Introduction Cooperation emerges at multiple scales of complexity in microbial ecosystems. Clonal populations are among the simplest microbial ecosystems that can be studied, and yet, they provide a convenient laboratory arena to analyze several cooperative behaviors in microbes [ 1 , 2 ]. These include extracellular digestion of resources [ 3 , 4 ], protection against antibiotics [ 5 ], and even the formation of fruiting bodies, a much rarer event that enhances the fitness of a small fraction of the population at the expense of the majority [ 6 ]. Inspection of natural communities reveals widespread cooperative interactions occurring not only within cells sharing a genotype [ 7 ], but also between different strains or species; see Table 1 . Such heterotypic interactions are commonly known as mutualisms, and they can give rise to a variety of behaviors in microbial consortia, e.g., cross-feeding [ 8 ], cross-protection [ 9 ], and division of labor [ 10 ]. These behaviors can be influenced by specific lifestyles that microbial communities adopt, such as the formation of spatially-structured biofilms [ 11 ]. In a way, microbial cooperative skills can even transcend the small size of unicellular organisms as, for example, in microbiomes, where microbes engage in symbiotic relationships with their hosts. Despite the progresses made in the past decades in disentangling microbe–microbe and host–microbe interactions, we still have limited understanding of microbial cooperation in natural communities [ 12 ]. What mechanisms promote cooperative interactions? How does cooperation shape the dynamics of complex microbial communities, or more generally, how can cooperators endure exploitation by cheaters? Since The Origin of Species was published, this question has puzzled evolutionary scientists [ 13 , 14 , 15 , 16 , 17 , 18 ], Charles Darwin included. Assuming a simple scenario in which cooperators pay a fitness cost in order to help their neighbors altruistically, cheaters could easily beat cooperators by exploiting any available public good while avoiding its costs. Hence, for cooperation to be an evolutionarily-stable strategy, additional mechanisms promoting cooperation have to be at play [ 18 , 19 ]. The recent blooming of synthetic biology [ 20 ] has provided a convenient platform to interrogate cooperation in microbial systems. Editing wild strain genomes has allowed manipulating microbial strategies within a population in order to understand how microbes face social dilemmas [ 1 , 21 , 22 , 23 , 24 , 25 ]. Moreover, engineering mutualisms between multiple genotypes recently provided insights into heterotypic partnerships such as cross-feeding interactions [ 8 ], collective resistance to antibiotics [ 9 ], and spatial self-organization [ 26 ]. Engineered symbiosis is progressively opening new avenues to explore interactions that benefit both microbial consortia and their associated hosts. Beyond improving our understanding of microbial interactions, a major goal in synthetic biology is to engineer complex microbial ecosystems for industrial [ 27 , 28 ], bioremediation [ 29 ], or therapeutic purposes [ 30 , 31 ]. To this aim, a better understanding of how cooperative feedbacks could be used to enhance the productivity and stability of different engineered consortia is needed. Here, we review recent advances on synthetic cooperation in microbial ecosystems. We focus on cooperative and parasitic interactions (see Table 1 ) in synthetic microbial ecosystems from an ecological perspective, rather than focusing on the specific genetic circuits to engineer these systems, which were reviewed, e.g., by McCarty et al. [ 32 ] and by Brophy et al. [ 33 ]. In the following, we start by discussing low complexity systems in simple laboratory environments, progressively moving on to more complex microbial ecosystems (see Figure 1 ). Each of the following sections covers a specific scale of complexity: well-mixed populations, spatially-structured environments, multispecies communities, and host-associated microbial communities. While reviewing several key drivers of microbial cooperation at these different scales, we highlight the potential impact of cheaters that exploit collective benefits. We discuss several mechanisms that promote cooperation against cheaters in microbial ecosystems, as well as how transitions between cooperators and cheaters could be used in microbial community engineering."
} | 1,805 |
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