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35310624 | PMC8928408 | pmc | 1,548 | {
"abstract": "Synthetic chemical fertilizers are a fundamental source of nutrition for agricultural crops; however, their limited availability, low plant uptake, and excessive application have caused severe ecological imbalances. In addition, the gravity of environmental stresses, such as salinity and water stress, has already exceeded the threshold limit. Therefore, the optimization of nutrient efficiency in terms of plant uptake is crucial for sustainable agricultural production. To address these challenges, we isolated the rhizospheric fungus Curvularia lunata ARJ2020 (AR11) and screened the optimum doses of biochar, silicon, and potassium phosphate (K 2 HPO 4 ), and used them—individually or jointly—to treat rice plants subjected to salt (150 mM) and drought stress (20–40% soil moisture). Bioassay analysis revealed that AR11 is a highly halotolerant and drought-resistant strain with an innate ability to produce gibberellin (GA 1 , GA 3 , GA 4 , and GA 7 ) and organic acids (i.e., acetic, succinic, tartaric, and malic acids). In the plant experiment, the co-application of AR11 + Biochar + Si + K 2 HPO 4 significantly improved rice growth under both salt and drought stresses. The plant growth regulator known as abscisic acid, was significantly reduced in co-application-treated rice plants exposed to both drought and salt stress conditions. These plants showed higher Si (80%), P (69%), and K (85%) contents and a markedly low Na + ion (208%) concentration. The results were further validated by the higher expression of the Si-carrying gene OsLSi1 , the salt-tolerant gene OsHKT2 , and the OsGRAS23 ’s drought-tolerant transcriptome. Interestingly, the beneficial effect of AR11 was significantly higher than that of the co-application of Biochar + Si + K 2 HPO 4 under drought. Moreover, the proline content of AR11-treated plants decreased significantly, and an enhancement of plant growth-promoting characteristics was observed. These results suggest that the integrated co-application of biochar, chemical fertilizers, and microbiome could mitigate abiotic stresses, stimulate the bioavailability of essential nutrients, relieve phytotoxicity, and ultimately enhance plant growth.",
"conclusion": "Conclusion To sum up, our study demonstrated that the co-application of Si (2 mM), K 2 HPO 4 \n ( 3 mM) and biochar (3%) with isolate AR11 could significantly optimize the nutrient uptake efficiency of plants, modulate plant growth regulators and stress-responsive genes to confer salt (150 mM) and drought (20–40%) of soil moisture level] stress tolerance in rice. The strategies defined in this study could contribute to reducing the use of fertilizers, minimizing production costs and environmental pollution, decreasing food toxicity, and promoting sustainable agriculture.",
"introduction": "Introduction Sustainable agricultural practices have been recognized as a pragmatic economic approach to attain the goal of food security ( Francis and Porter, 2011 ). However, one of the biggest challenges for the sustainability transitions is the management of chemical fertilizers and environmental stresses, such as drought and salinity ( Butcher et al., 2016 ; Mark Ibekwe et al., 2017 ). Salinization-related reduction in crop yield in semiarid and arid region of world accounts to 18–43% and more than three percent of soil resources are affected globally ( Singh, 2021 ). Similarly, the percentage of lands affected by drought has doubled in the last 40 years, affecting farmers more than any other natural hazards ( FAO, 2021 ). It is predicted that, by the first half of the twenty first century, water drought events will dramatically worsen, reaching record levels never seen before in the history of mankind ( Premanandh, 2011 ). Forecasts predict a 1.3-fold increase in water requirements in the agricultural sector by 2025 ( Premanandh, 2011 ). In addition, the world’s population is rapidly increasing and is expected to reach nine billion by 2050 ( Fariduddin et al., 2019 ). In these scenarios, the mitigation of salt stress and droughts is crucial to overcome the probable risk of food insecurity ( Zhai et al., 2019 ). Understanding the physiological context of salt and water deficiency stresses is essential for their alleviation and to develop long-term management strategies ( Dawood et al., 2021 ). In general, salt and drought stresses harm plant metabolism, causing ionic imbalance, osmotic stress, oxidative damage, damage of cellular structures, and a decrease in gas exchange rates, which ultimately lead to plant death ( Bhusal et al., 2021 ; Khan et al., 2021 ; Nabi et al., 2021 ). Plants acquire several strategies to cope with abiotic stresses and, among these, ionic adjustment such as Na + /K + ratio, and endogenous phytohormones such as ABA regulation play a key role in modulating the overall physiological system ( Garg and Bhandari, 2016 ; Zaid et al., 2021 ). Under salinity stress, plants accumulate toxic ions which causes ionic toxicity; in response, amassing of soluble osmolytes and synthesis of reducing agents occur which aids to osmotic adjustment and nutrients balance ( Golldack et al., 2014 ; Dawood et al., 2021 ). Under drought stress, plants mobilize the ABA channel that activates guard cells to regulate stomatal conductance and sustain high moisture level, by influencing the leaf and soil water potential ( Harb et al., 2010 ). It has been reported that the plants’ ionic system is extensively regulated by the balanced presence of sodium, potassium, phosphorus, and silicon ( Etesami, 2018 ). Bargaz et al. (2016) showed that the phosphorus (P) supply induced salt tolerance in Phaseolus vulgaris through the acquisition of K + and Ca ++ . Similarly, the role of silicon (Si) in improving plant physiology, growth, and yield has been widely reported in numerous studies ( Kim et al., 2014 ; Zhu and Gong, 2014 ). The enhancement of P, K, and Si uptake in plants was shown to mitigate several stresses in plants, including both biotic (e.g., pathogens) ( Bakhat et al., 2018 ), and abiotic ones, such as heavy metal stress, water stress, thermal stress, and salt stress ( Zaid et al., 2018 ). Therefore, it can be concluded that the proper utilization of Si, P, and K could improve plant metabolism and growth under diverse environmental conditions. The nutritional sources of plants largely depend on synthetic fertilizers. According to the Food and Agriculture Organization (FAO) ( Food and Agriculture Organization of the United Nations, 2017 ), the world consumption of nitrogen, phosphorus, and potassium (NPK) was estimated at about 186.67 million tonnes in 2016, and P demand specifically increased by 2.2% in 2020, but the utilization efficiency by plants is limited to 10–15% ( Simpson et al., 2011 ). Soil nutrient depletion has been adversely affecting poverty-stricken farmers worldwide ( Yang et al., 2009 ). The net accumulation of toxic elements in agriculture is increasing due to the overuse of synthetic chemicals and to industrialization, which result in water eutrophication and environmental hazards ( Simpson et al., 2011 ; Bouraoui and Grizzetti, 2014 ; Ali et al., 2019 ). The scientific community has repeatedly warned that the long-term, continuous application of chemical fertilizers without adequate organic amendments may degrade soils to such an extent that they may become extremely low or non-responsive to inorganic fertilizers ( Fofana et al., 2008 ; Tittonell and Giller, 2013 ; Tounkara et al., 2020 ). Plant growth-promoting microorganisms (PGPMs) and biochar are the emerging alternatives to agrochemicals for the production sustainable food ( Yang et al., 2009 ; Abhilash et al., 2016 ; Ali et al., 2017 ). PGPMs have been widely reported to enhance plant growth-promoting traits such as through abscisic acid (ABA) regulation ( Cohen et al., 2009 ), gibberellin (GA) production ( Bottini et al., 2004 ), and phosphate, potassium, and silicate solubilization ( Adhikari et al., 2020a ). Similarly, biochar has been widely reported to facilitate PGPM growth, prevent disease incidence, stabilize heavy metal concentrations, increase soil fertility, and mitigate severe biotic and abiotic stresses ( Farhangi-Abriz and Torabian, 2017 ; Gavili et al., 2019 ). Under stress conditions, these biostimulants enhance plant physiology through the modulation of phytohormones, including ABA, jasmonic acid (JA), gibberellic acid (GA), and salicylic acid (SA) ( Waqas et al., 2012 ). In addition, they were reported to strengthen the antioxidant system through the modulation of amino acids under stress ( Qadir et al., 2020 ). Moreover, biochar and PGPMs were shown to have a functional role in lowering Na + uptake and enhancing K + ion uptake in plants exposed to salt stress, while also enhancing the water holding capacity of soils and plants under drought stress ( Ali et al., 2017 ; Khan et al., 2019a ). Salinity and drought were shown to be crucial limiting factors for the growth of major cereal crops such as rice ( Kumar et al., 2013 ; Tang et al., 2019 ). Rice is the most important staple crop, consumed by billions of people globally, representing 75% of the total calorie intake in Asian countries ( Xia et al., 2019 ). As mineral elements—such as Si, P, and K—play a key role in the increase of several biotic and abiotic stresses, an efficient strategy to boost their uptake efficiency in plants is still lacking. Hence, there is an urgent call for developing a sustainable agriculture contrivance in perspective of climate change, abiotic stresses, excess fertilizer demand, environmental pollution and high-production cost. To address these challenges, in the present study, we identified a highly drought- and salt-tolerant fungus and assessed its plant growth-promoting traits. The effects of this fungal species on the modulation of rice physiology under salt and drought stresses were elucidated along with those of biochar, inorganic silicon, phosphorous, and potassium.",
"discussion": "Discussion There is an urgent need to counteract the effects of environmental stresses, chemical pollution, climate change, and economic inflation on agriculture ( Arif et al., 2019 ; Gupta et al., 2020 ). As biostimulants (biochar and PGPMs) are widely reported as ecologically safe agents for enhancing the efficiency of inorganic fertilizers and mitigating environmental stresses ( Grobelak et al., 2015 ; Hossain et al., 2017 ; Zou et al., 2021 ), in the present study we elucidated how these agents mobilize synthetic Si, P, and K contents and modulate the internal physiology of rice plants exposed to drought and salt stress conditions. The use of PGPMs and biochar is a promising tool for crop improvement; biochar creates favorable condition for sustaining microbiomes which facilitates symbiotic association of PGPMs in plant rhizosphere ( Zhang et al., 2018 ; Gorovtsov et al., 2020 ). As PGPMs are widely reported for producing secondary metabolites and mediating nutrient recycling; they would further intensify the nutrient assimilation that maintains various metabolic processes and confer stress tolerance in crops ( Egamberdieva et al., 2018 ; Khan et al., 2019b ; Kazerooni et al., 2021 ). In our experimental process, we observed combined application of Si, P, K, AR11, and biochar produced a symbiotic effect that downregulated the Na influx and enhanced the uptake of K, Si, and P contents by plants, therefore maintaining the ionic balance in their metabolic system. Excessive salt concentrations cause ionic disturbances in plants, and drought further exacerbates the effect of salinity on crops causing osmotic stress, which may ultimately lead to cell death ( Sahin et al., 2018 ). The results of the current study showed that the plant growth was tremendously improved with the combined application of Si, PK, AR11, and biochar. Previous study has reported that GA producing microbes could play a key role in regulating endogenous phytohormones under salt and drought stress ( Khan et al., 2015 ). Additionally, organic acid chelates ions, dissolutes the insoluble mineral ions and enhance bioavailability to plant uptake ( Ullah et al., 2015 ). Since the C. lunata AR11 strain has the potential to spontaneously produce GA (GA 1 , GA 3 , GA 4 , and GA 7 ), and organic acids that can possibly boost the uptake of essential elements under salt and drought stress conditions. The researchers are now desperately conscientious in developing the approach of optimizing fertilizer use efficiency and mitigating stress for sustainable agriculture. Despite the efforts, limited success has been achieved in terms of improving the utilization efficiency of synthetic fertilizers ( Tounkara et al., 2020 ). Thus, the main goal of the present study was based on enhancement of nutrient use efficiency. In our study, the application of AR11 and biochar significantly increased the uptake of Si, P, and K in rice plants. Ma et al. (2006) first succeeded in revealing a suppression of OsLsi1 results in reduced silicon uptake in rice. Moreover, Ma et al. (2007) further discovered efflux transporter of silicon OsLsi2 which revealed a unique phenomenon in nutrient transport in rice. Thus, a higher uptake values of Si in our results were further correlated with the higher expression of the Si transporter gene OsLsi1 . Moreover, the ABA stress hormone was observed to be low in the plants treated with co-application, under both stress conditions. As ABA regulates stomatal conductance, which in turn regulates the transpiration rate in plants, a higher ABA level or depletion represents a higher stress level ( Chen et al., 2020 ; González-Guzmán et al., 2021 ). The results obtained in this study indicate the low-stress level present in plants, which is further validated by the higher expression of the stress-responsive genes OsGRAS23 and OSHKT2 . Our results are supported by various previous studies which have shown that the application of exogenous Si, K 2 HPO 4 , biochar, and plant growth-promoting microorganisms improved Si, K, and P contents and OsLsi1 expression and decreased the ABA level in plants under abiotic stress ( Kim et al., 2014 ; Adhikari et al., 2020b ; Khan et al., 2020 ). These results are further supported by findings reported in various previous studies, for example, Gong et al. (2006) and Gengmao et al. (2015) showed that Si reduced Na + uptake under salt stress. The combined effect of biochar and silicon was shown to improve several physiological traits, like seed yield and oil quality, in Helianthus annuus under water deficit stress ( Seleiman et al., 2019 ). Reactive oxygen species (ROS), a by-product of aerobic metabolism are recognized as oxygen radicals which are highly vulnerable for normal plant metabolic processes ( Zaid and Wani, 2019 ). To neutralize these negative effects, plants activate antioxidant system to produce phenolic content ( Bilal et al., 2018 ; Decros et al., 2019 ). Our results showed that AR11 application combined with biochar, silicon, and potassium phosphate significantly increased polyphenol contents. These results agree with Li et al. (2012) , Hashem et al. (2015) , and Ikram et al. (2018) who reported the role of biostimulants in strengthening the antioxidant system of plants under abiotic stresses. Altogether, our results coincide with the findings of several authors: Danish and Zafar-ul-Hye (2019) showed biochar and PGPR co-application improved yield of wheat under drought stress, Asaf et al. (2018) reported GA producing fungus Aspergillus flavus conferred salt tolerance in Glycine max . Similarly, Biju et al. (2017) and Cui et al. (2021) reported Si application alleviated drought stress in crops by regulating enzymes, osmolytes, oxidative metabolism, and nutrient assimilation. Adnan et al. (2020) and Kaya et al. (2003) showed the supplementation of potassium and phosphorus coupled with phosphate solubilizing bacteria ameliorated salt stress in maize, pepper and cucumber plants. These evidence suggest that our methodology covers the magnitude of all of these stress mitigation tools with a single comprehensive approach."
} | 4,044 |
37170163 | PMC10173534 | pmc | 1,549 | {
"abstract": "Lignocellulosic biomass is an attractive non-food feedstock for lactic acid production via microbial conversion due to its abundance and low-price, which can alleviate the conflict with food supplies. However, a variety of inhibitors derived from the biomass pretreatment processes repress microbial growth, decrease feedstock conversion efficiency and increase lactic acid production costs. Microbial tolerance engineering strategies accelerate the conversion of carbohydrates by improving microbial tolerance to toxic inhibitors using pretreated lignocellulose hydrolysate as a feedstock. This review presents the recent significant progress in microbial tolerance engineering to develop robust microbial cell factories with inhibitor tolerance and their application for cellulosic lactic acid production. Moreover, microbial tolerance engineering crosslinking other efficient breeding tools and novel approaches are also deeply discussed, aiming to providing a practical guide for economically viable production of cellulosic lactic acid.",
"conclusion": "Conclusions In this review, we summarize the inhibitors derived from lignocellulosic biomass pretreatment and their molecular toxic mechanisms, and construction of tolerant LA strains based on microbial tolerance engineering. However, economic competitiveness challenges still exist. Fortunately, with the development of efficient technologies (such as high-throughput screening, multiplexed automated ALE systems, and CRISPR/Cas9 gene editing tool), construction efficiency of strain tolerance modification can be accelerated. In addition, microbial tolerance engineering crosslinking other novel approaches including designing biomass and synergistic microbial consortia can also further improve economic competitiveness for cellulosic LA production."
} | 448 |
37813937 | PMC10562416 | pmc | 1,550 | {
"abstract": "Memristive devices that rely on redox-based resistive switching mechanism have attracted great attention for the development of next-generation memory and computing architectures. However, a detailed understanding of the relationship between involved materials, interfaces, and device functionalities still represents a challenge. In this work, we analyse the effect of electrode metals on resistive switching functionalities of NbO x -based memristive cells. For this purpose, the effect of Au, Pt, Ir, TiN, and Nb top electrodes was investigated in devices based on amorphous NbO x grown by anodic oxidation on a Nb substrate exploited also as counter electrode. It is shown that the choice of the metal electrode regulates electronic transport properties of metal–insulator interfaces, strongly influences the electroforming process, and the following resistive switching characteristics. Results show that the electronic blocking character of Schottky interfaces provided by Au and Pt metal electrodes results in better resistive switching performances. It is shown that Pt represents the best choice for the realization of memristive cells when the NbO x thickness is reduced, making possible the realization of memristive cells characterised by low variability in operating voltages, resistance states and with low device-to-device variability. These results can provide new insights towards a rational design of redox-based memristive cells.",
"conclusion": "Conclusions In this work, we have systematically analysed the effect of electrode metals on the switching properties of NbO x -based memristive cells. For this purpose, Au, Pt, Ir, TiN, and Nb electrodes have been analysed in memristive cells based on NbO x grown by anodic oxidation on a Nb substrate exploited as a counter electrode. Results show that the choice of the TE is crucial for regulating the electronic transport mechanism in the pristine state as well as in regulating device electroforming and resistive switching performances. In this context, it is shown that Au and Pt represent the best choice for realising memristive devices based on NbO x, . It is worth noticing that further investigation, such as with temperature-dependent characterization, is required to better understand the mechanism of electronic transport regulated by interfaces. In addition, further investigation is required to understand the correlation between the thickness-dependent resistive switching characteristics and metal electrode properties.",
"introduction": "Introduction Memristive devices whose functionalities rely on resistive switching (RS) phenomena represent promising candidates for next generation memories as well as for the development of neuromorphic computing architectures 1 – 6 .The simplest way to realise a memristive device is by sandwiching an insulator material between two metal electrodes, to achieve a so-called metal–insulator-metal (MIM) structure. In these devices, the switching mechanism relies not only on the insulator material but also on the choice of metal electrodes and the metal–insulator interface properties 4 , 7 – 10 . In this context, an mportant class of RS devices is represented by the Valence Change Memory (VCM) cells where the insulator material is sandwiched in between an electrochemically inert material where the switching process takes place (i.e., active interface) and a counter material that usually forms an ohmic contact 10 . As insulator layers, a wide range of transition metal oxides have been considered 9 – 12 . Among metal oxides, NbO x thin films have recently attracted great attention as insulator layers for the realization of memristive devices 13 – 33 . Besides resistive switching devices have been realized by depositing NbO x thin films by means of radio frequency (RF) sputtering, Atomic Layer Deposition (ALD), Physical Layer Deposition (PLD), and other Complementary Metal Oxide Semiconductor (CMOS) compatible techniques 16 , 18 , 19 , 26 , anodic oxidation has been recently proposed as an alternative grown technique for the realization of NbO x thin films with resistive switching capabilities 21 , 22 , 25 , 34 . While in these works the resistive switching capabilities have been analysed by considering peculiar metal electrode configurations, a relationship in-between the choice of metal electrodes and resistive switching functionalities in NbO x -based devices still have to be established. In the present work, we systematically investigated the effect of different top electrode material on resistive switching properties of NbO x -based memristive devices. For this purpose, the effect of several top electrodes (TEs) materials on NbO x grown by anonic oxidation on a Nb substrate exploited also as counter electrode has been analysed by keeping fixed the Nb counter electrode. Analysed TE materials include high work function metals such as Au, Pt and Ir and a low oxygen affinity material compound such as TiN. As a reference, a symmetrically contacted NbO x with Nb electrodes have been also considered. A detailed analysis of the pristine states of the fabricated cells is reported, analysing how the choice of the TE material influences the electronic transport properties. By analysing the electroforming process and resistive switching functionalities, selection criteria for the choice of the TE materials in NbO x memristive cells are discussed.",
"discussion": "Results and discussion Electrode-dependent electronic transport properties in the pristine state Resistive switching devices were fabricated with the typical MIM structure in which a thin layer of anodic NbO x is sandwiched between a common bottom electrode (BE) of Nb and a TE metal chosen among Au, Pt, Ir, TiN, and Nb. An example of the cross-section of the device structure can be found in Fig. 1 a in which it can be appreciated the compactness of the NbO x layer and the smoothness of the BE interface due to the anodic oxidation process, as confirmed also by Transmission Electron Microscopy (TEM) analysis reported in our previous work 25 . Note that a detailed chemical and structural analysis revealed that the NbO x is amorphous and is characterised by the presence of Nb(+ 5) oxidation state on the top of the anodized film and Nb(+ 2) oxidation state at the interface with the common Nb BE, as investigated in our previous work 25 . Note that while the same oxidation states are then found after the deposition with the Au TE 25 , chemical properties at the metal/NbO x interface with the TE may locally slightly vary the oxide stoichiometry due to the interaction with the TE metal. To have a comparison between the effect of different TE metals, each type of cell was studied with the same electrical scheme, with the BE grounded and the bias voltage applied to the TE. Figure 1 ( a ) Cross-section of a Au/NbO x /Nb cell showing the compactness of the anodic grown oxide layer (scale bar is 100 nm, image is in false colours). Typical pristine state curves for the structure NbO x /Nb terminated by ( b ) Au, ( c ) Pt, ( d ) Ir, ( e ) TiN, and ( f ) Nb. Measurements are referred to 60 nm NbO x devices contacted by 50 × 50 µm 2 top electrodes. The pristine states (i.e., the resistance state before switching events) of memristive cells with different electrodes have been investigated through I-V characteristics reported in Fig. 1 b–f (log scale plots can be found in Supplementary Figure S1 ). In this case, the electronic transport mechanism is regulated by metal–insulator interfaces where Schottky barriers are expected. In this context, the MIM structure can be represented as back-to-back Schottky diodes with the NbO x series resistance 35 – 37 . As expected, results show that the choice of the TE electrode metal strongly influences the Schottky barrier at the TE-NbO x interface, influencing the electronic transport mechanism and the resulting I-V characteristics of memristive cells in the pristine state. Asymmetric diode-like behaviours were observed in the case of Pt and Au top TE arise from the high blocking character of these metal electrodes when reversely biased. Instead, the lower pristine state resistance of devices contacted by Ir, TiN, and Nb electrodes can be ascribed to the lower blocking character of metal-oxide interface when these metals are exploited as TE. As can be observed, an almost symmetric characteristics in the case of TiN and Nb electrodes can be observed. In this context, it is worth noticing that an almost symmetric characteristic was observed in symmetrically contacted devices with Nb electrode, even if interfacial properties of the Nb BE exploited also as NbO x grown substrate during anodic oxidation are in principle expected to be different from Nb TE deposited by sputtering on the previously grown NbO x . Based on the previous discussion, the I-V characteristics in the pristine state can be explained on the basis of the physical properties of the hetero-junction Nb/NbO x /TE, in which the entity of the barriers at interfaces should reflect differences between the metal work function of the electrode and the electron affinity of the NbO x . In principle, the higher the barrier difference the higher the blocking character of the metal–insulator interface should result, thus limiting the electronic current in the pristine state. However, no clear trends between the pristine state resistance and the work function of the top electrode can be observed, as shown in Fig. 2 (details on the work functions and theoretical Schottky barrier height at the TE/NbO x interface can be found in Supplementary Information Tab. ST1 ). Here, it is possible to observe that, even if Pt shows the highest theoretical barrier difference, Au-terminated devices are the ones characterised by the most insulating pristine state. Note that no clear trends in between the pristine resistance and the metal work function of electrodes were previously reported also in case of TaO x switching layers 38 . A similar behaviour was also previously observed in case of Cu/CuO/TE devices where the exploitation of Ag as TE material gave rise to a more insulating pristine state with respect to Au, which on the contrary exhibits a higher work function 39 . This is because, besides the Schottky barrier height, the blocking character of the metal–insulator interface is regulated also by the interface chemistry 40 . It is worth mentioning that the interface resistance can be influenced by the presence of interfacial oxide(s) 41 . The probability for formation of an oxide can be estimated by the electronegativity (or alternatively one can referrer to the standard electrode potential) of the electrode metal, as discussed in previous works 42 , 43 . The resistance of the interfacial oxide is primarily determined by the point-defect structure and band structure, that determine the ionic and electronic conductivities. Figure 2 Work function Φ M for the investigated TE metals (black dots), cell resistance for the different TE metal extracted at V = 1 V (blue dots) and at V = -1 V (red dots). Details on work functions can be found in Supplementary Tab. ST1 . The effect of metal electrodes on the electroforming process As a first programming step, the electroforming process is necessary to initialise the NbO x -based memristive cells. A schematization of the electroforming process can be found in Fig. 3 a. The TE is negatively biased and subjected to a voltage sweep, in the meanwhile the Nb BE is grounded. In this case, the electroforming process can be attributed to the ionic conduction of the mobile chemical species which in this case includes both niobium and oxygen ions (or, alternatively speaking, by the oxygen vacancies) driven under the action of the electric field. Since the transport number of niobium ions is lower than the one of the oxygen ions, we can assume in principle that the process could be attributed mainly to the migration of oxygen ions 20 . In the meanwhile, the Nb in the oxide layer, reduces to its low oxidation state and a channel composed of a sub-stoichiometric NbO x is expected to grow from the BE toward the TE. At the end of the process, the channel bridges the two electrodes, and the device reaches what is called the low resistance state (LRS). During this process, the oxide layer experiences a soft breakdown which permanently alters its structure. To prevent a possible hard breakdown of the device related to Joule overheating, a compliance current (CC) is externally applied to limit the maximum current allowed during the electroforming process. It is worth noticing that this mechanism can be influenced by reactions at the metal/NbO x interface, as expected in case of TiN and Ir electrodes. In this context, it has been previously shown that TiN can react with transition metal oxides such as HfO to form TiOx and TiON, and similar processes may happen with NbO x 44 . Similarly it has been shown that Nb and Ir can interact forming Ir3Nb 45 , so the formation of compounds at the NbO x /Ir interface cannot be excluded. Figure 3 ( a ) Schematization of the forming process in a typical TE/NbO x /Nb VCM cell, where the process is activated by applying a negative voltage sweep on the active TE while the BE is grounded. The electroforming process rely on the formation of a sub-stoichiometric conductive channel related to the migration of ionic species inside the NbO x active material (blue spheres represent the oxygen vacancies whilst the green ones indicate Nb ions in a lower valence state). Once the forming voltage is reached, the current in the I-V plots abruptly reaches the value of the set current compliance. Forming voltage characteristics of NbO x -based devices contacted by ( b ) Au, ( c ) Pt, ( d ) Ir, ( e ) TiN, and ( f ) Nb TEs. Arrows and numbers in the I-V curves specify the temporal evolution of the I-V hysteretic loop. Typical electroforming characteristics of NbO x memristive cells with different TEs are reported in Fig. 3 b–f. As it can be observed, for each TE metal it was possible to find proper stimulation conditions to form the cell under test. Indeed, for each TE the electroforming was characterised by an initial step in which the current follows the typical Schottky behaviour due to the barrier at the TE contact, a subsequent current jump in correspondence of the electroforming voltage where the current saturates reaching the compliance value, and a following low resistance state I–V characteristic going back to the origin due to the formation of the conductive channel. While a nearly linear I–V characteristic was observed after electroforming in Au, Pt and Ir contacted devices, the LRS state characteristics are affected by non-linearity effects in TiN and Nb contacted devices. Based on the previous results, electroforming curves of different devices have been acquired to statistically evaluate how the different TE metal affects the electroforming process. For this purpose, the forming voltage was identified as the first voltage value at which the current equals the CC value. Results of the statistical analysis are reported in Fig. 4 . What is evident in this case is that TEs responsible for higher pristine state resistance result in higher forming voltages. In addition, in the case of Au, Pt, and Ir (electrodes with higher pristine state resistance), statistical analysis was performed also by considering NbO x layers with a reduced 30 nm thickness, showing that a reduction of the oxide thickness results in a reduction of the electroforming voltage (details of forming curves of devices with reduced thickness in Supplementary Fig. S2 ). These observations are in accordance with previous works 46 , where fixed the device area, the electroforming voltage reduces by reducing oxide layer thickness, independently on the method chosen to grow the oxide layer. Figure 4 Statistical analysis of electroforming voltages of NbO x -based cells contacted by different TE metals. Measurements have been performed by considering the 60 nm NbO x devices, with TE size of 50 × 50 µm, while dashed data refer to electroforming voltages for the 30 nm NbO x devices. In boxplots, midlines represent median values, squares represent the mean values, boxes the 25th and 75th percentiles, and whiskers the minimum and maximum values. The effect of metal electrodes on resistive switching Differently from the electroforming process, which is characterised by localised redox reactions that progressively lead to the formation of the conductive channel, the switching mechanism is a process that involves the localised formation/rupture of the filament previously formed, making it possible to program the devices to switch between a LRS and a high resistance state (HRS). A schematization of the switching mechanism can be found in Fig. 5 a, in which a pre-electroformed device experiences the rupture of the channel near the TE electrode when a positive voltage sweep is applied, making the device switching from the LRS to the HRS (RESET process) and conversely, its restoration, with the consequent passage from HRS to LRS (SET process) when the opposite polarity voltage sweep is applied. The switching mechanism can be triggered multiple times allowing the device to experience the SET and the RESET phases in a cyclic way, resulting in the typical hysteretic characteristic of memristive devices. Figure 5 b–f show typical I–V curves of the NbO x cells contacted with different TE metals. Au and Pt terminated cells show the typical hysterical I–V curves which are representative of bipolar RS devices. While Ir also exhibited the capability to switching in bipolar way, these cells exhibited high instability. Instead, TiN and Nb devices, although showing some changes in the internal resistance state of the device when stimulated, did not exhibit reproducible switching characteristics after electroforming due to the permanence of the device in the ON state without the possibility of recovering the HRS. Additional data on resistive switching exemplary characteristics of unstable Ir, TiN and Nb-terminated devices can be found in Supplementary Figure S3 . In this context, results suggest that for obtaining good switching capability it is necessary to have asymmetric MIM structures characterised by an ohmic contact and a low oxygen affinity metal at the counter electrode, which is in principle true for TiN, being a low oxygen affinity metal, but in the end, this one and Nb act like ohmic contact at the TE 33 , 47 . Figure 5 ( a ) Schematics of the switching mechanism of a VCM device by looking at the formation and rupture of the conductive channel under the application of an external bias on the top electrode (the bottom is grounded during this operation). Typical shapes of I-V curves for different top electrodes: ( b ) Au, ( c ) Pt, ( d ) Ir, ( e ) TiN and ( f ) Nb. The arrows and the numbers indicate the sweep direction. Switching behaviour of Pt and Au terminated cells Even if the switching capability was observed in each NbO x cell, only the Au and the Pt terminated devices show stable switching behaviour, while the RS characteristic was observed to be unstable and characterised by low reproducibility in the case of Ir. For the Au and the Pt terminated devices we performed an endurance test, which represents one of the common figures of merit when a RS device is studied 48 . In this case, we compare the switching behaviour of the two types of metal contact tested for a 200 full-sweep cycles (measurements stopped even if the devices were able to switch in a stable way). In Fig. 6 the results of the endurance tests for devices with Au and Pt TEs. RS characteristics, endurance, SET/RESET voltages over cycling, and SET/RESET distributions in case of Nb/NbO x (60 nm)/Au, Nb/NbO x (60 nm)/Pt, and Nb/NbO x (30 nm)/Pt are reported in Fig. 6 a–l, respectively. For the sake of completeness, results for Nb/NbO x (30 nm)/Au devices are reported in Supplementary Fig. S4 , For all devices, I–V curves acquired during the test at the 1st, 50th, 100th, 150th, and at last cycle superimposed to the median on the whole test are reported. Figure 6 Endurance test of the good switching TE cells. First row displays results for Au TE and 60 nm NbO x : ( a ) some I-V curves of 200 cycles endurance test (cyan curves) with superimposed median (yellow curve), ( b )HRS and LRS extracted by reading the resistance a Vread = -0.2 V, ( c )VSET and VRESET for each cycle and ( d ) grouped in histograms. Same results reported for Pt TE and 60 nm oxide ( e ), ( f ), ( g ) and ( h ) and for Pt TE and 30 nm oxide ( i ), ( j ), ( k ) and ( l ). In all cases, the RS behaviour was characterised by low cycle-to-cylce variability in terms of device LRS/HRS resistance states and SET/RESET voltages, except for Nb/NbO x (30 nm)/Au devices. Concerning operating voltages, Au-terminated devices are characterized by lowest and more stable SET and RESET voltages. Furthermore, a slightly higher ON/OFF ratio can be observed in case of Pt-terminated devices. While device-to-device variability of Au-terminated devices was already analysed in our previous work 25 , we have here investigated the device-to-device variability of Pt terminated devices focusing on cells with 30 nm NbO x . Figure 7 a shows a collection of I-V curves representing the medians of the endurance tests for each device under test. For each device the statistical distribution of the SET and RESET voltages have been collected in Fig. 7 b, showing that the RESET process endows a lower inter device and device-to-device variability compared to the SET process. Figure 7 c shows the box plot distributions for the HRS and LRS acquired during each endurance test, showing that the LRS is characterised by a lower device-to-device variability compared to the HRS. For completeness, a retention test on both LRS and HRS of a 30 nm thick Pt-terminated cell is reported in Supplementary Fig. S5 , showing the capability of Pt terminated devices to retain HRS and LRS for 10 3 s. Figure 7 Device-to-device variability. ( a ) Representative resistive switching characteristics of different cells based on 30 nm oxide thickness devices and terminated with Pt. Curves represent the median I-V curve obtained over 50 cycles. Arrows and number indicate the sweep direction. ( b ) box plot collecting the SET and the RESET voltages for each endurance test. ( c ) statistical distribution of HRS and LRS collected during the endurance test for each device. Data acquired on 13 devices. Box plots were obtained from 50 consecutive cycles on the same device. Midlines represent median values, squares represent mean values, boxes the 25th and 75th percentiles, and whiskers the 5th and 95th percentiles."
} | 5,700 |
20593757 | null | s2 | 1,553 | {
"abstract": "Major ampullate (dragline) spider silk is a coveted biopolymer due to its combination of strength and extensibility. The dragline silk of different spiders have distinct mechanical properties that can be qualitatively correlated to the protein sequence. This study uses amino acid analysis and carbon-13 solid-state NMR to compare the molecular composition, structure, and dynamics of major ampullate dragline silk of four orb-web spider species ( Nephila clavipes , Araneus gemmoides , Argiope aurantia , and Argiope argentata ) and one cobweb species ( Latrodectus hesperus ). The mobility of the protein backbone and amino acid side chains in water exposed silk fibers is shown to correlate to the proline content. This implies that regions of major ampullate spidroin 2 protein, which is the only dragline silk protein with any significant proline content, become significantly hydrated in dragline spider silk."
} | 228 |
40196698 | PMC11974898 | pmc | 1,554 | {
"abstract": "The metabolic activity of microbial communities is essential for host and environmental health, influencing processes from immune regulation to bioremediation. Given this importance, the rational design of microbiomes with targeted functional properties is an important objective. Designing microbial consortia with targeted functions is challenging due to complex community interactions and environmental heterogeneity. Community-function landscapes address this challenge by statistically inferring impacts of species presence or absence on function. Similar to fitness landscapes, community-function landscapes are shaped by both additive effects and interactions (epistasis) among species that influence function. Here, we apply the community-function landscape approach to design synthetic microbial consortia to degrade the toxic environmental contaminant bisphenol-A (BPA). Using synthetic communities of BPA-degrading isolates, we map community-function landscapes across increasing BPA concentrations, where higher BPA means greater toxicity. As toxicity increases, so does epistasis, indicating that collective effects become more important in degradation. Further, we leverage landscapes to rationally design communities with predictable BPA degradation dynamics in vitro . Remarkably, designed synthetic communities are able to remediate BPA in contaminated soils. Our results demonstrate that toxicity can drive epistatic interactions in community-function landscapes and that these landscapes can guide microbial consortia design for bioremediation.",
"introduction": "Introduction Microbial communities drive processes essential for human and environmental health. Humans have manipulated environmental conditions for millennia to recruit microbial communities with desirable properties, from food fermentation [ 1 ] to wastewater treatment [ 2 ]. However, using wild or enriched communities to perform a functional role of interest faces challenges. For example, fecal microbial transplants can effectively treat deadly infections [ 3 , 4 ], but patient outcomes can be variable and sometimes detrimental [ 5 ]. Similarly, in a remediation context, where enriched communities can decontaminate pollutants in soils or water, the process is often slow | 6 , 7 ] and challenging for decontamination of complex or toxic compounds such as polycyclic aromatic hydrocarbons [ 8 , 9 ]. These challenges have driven recent interest in synthetic ecology: engineering communities with desirable functional properties. However, rationally designing such consortia is difficult because community function is affected by a myriad of factors, including gene expression [ 10 ], ecological interactions [ 11 – 13 ], and environmental variables [ 14 ]. As a result, dissecting the relationship between the species present, their abundances, and higher-level community community functions such as metabolism can be a challenge. One route to overcoming this challenge is to exploit a conceptual analogy with fitness landscapes. Community-function landscapes, which map community composition to function [ 13 , 15 , 16 ], borrow directly from the notion of fitness landscapes in genetics which map a genotype to an organism’s fitness [ 17 ]. Remarkably, this simple approach can accurately predict how community function depends on composition [ 15 ] through elucidating additive effects (average effect of a strain on a community’s function) and epistatic interactions (average effect of a pair or group of strains on a community’s function beyond their individual additive effects) [ 18 ]. However, with few exceptions [ 19 ], most work inferring community-function landscapes targets simple synthetic communities [ 20 ] and does not solve critical applied problems in biotechnology. Furthermore, community-function landscapes are typically inferred in a single environmental condition [ 15 , 19 ]. It is unclear how the landscape structure depends on fluctuating environmental conditions such as nutrient availability. The dependence of the landscape on the environment is especially critical for the deployment of engineered communities in natural environments where resident consortia and variable environmental conditions might render any designed consortia ineffective [ 21 ]. Thus, there remains a pressing need to test the approach on a challenging applied community design problem where environmental variation might play a role. One biotechnological process that could benefit from environment-influenced rational community design is bioremediation. In this context, dangerous chemicals that contaminate water and soil environments can be remediated by the metabolic activity of microbes [ 22 ]. Contaminant removal is commonly approached through both single-strain and community-level bioremediation [ 6 , 23 – 25 ]. However, communal bioremediation attempts are limited due to a lack of rational design principles to engineer communities [ 14 , 26 , 27 ]. One major target of bioremediation efforts is bisphenol-A (BPA), a widely used industrial chemical and pollutant [ 28 , 29 ] whose removal offers significant health benefits [ 30 ]. Bacteria can utilize BPA as a carbon source during aerobic respiration [ 31 – 33 ]. Although BPA degradation by individual bacterial strains has been extensively studied [ 34 , 35 ], many fail to achieve complete degradation [ 34 ] due to the toxicity of BPA and the byproducts of BPA degradation [ 36 – 40 ]. Some studies suggest that communities can overcome the toxicity of BPA and its intermediates [ 41 ], however, there is no rational route to selecting strains to include in BPA degrading consortia. Part of the challenge of selecting strains is that we lack definitive knowledge of the genes responsible for every step of a BPA degradation pathway, as only a handful of steps have been characterized [ 31 – 33 , 38 , 42 , 43 ]. Furthermore, there has been little study of the role of environmental factors in BPA bioremediation [ 34 ]. Here we address these challenges by exploiting a community-function landscape approach to design BPA-degrading communities across a range of increasing BPA concentrations. Inferred landscapes show that collective effects, which arise in landscapes through epistatic statistical interactions between taxa, increase with higher levels of the contaminant. In addition, we find that the structure of the landscape changes smoothly as the BPA concentration rises. We then use this landscape to design communities with desired rates of BPA degradation in vitro . Finally, we show that our designed communities remediate BPA in contaminated soils. Together, our results highlight the power of community function landscapes in understanding community function under varying environmental conditions and as a tool for the statistical engineering of communities with real-world applications.",
"discussion": "Discussion Using a library of BPA-degrading isolates, we showed that the community-function landscape of BPA degradation can be learned statistically from ensembles of communities where BPA degradation dynamics are quantified. Learning the landscape statistically required only knowledge of the strains present in the community, and we inferred these landscapes across a range of initial BPA concentrations. Inspection of these landscapes showed that as BPA concentration increased (and with it, toxicity), so did epistasis. We concluded that collective effects become more important as toxicity rises, and indeed monocultures cannot degrade BPA at the highest concentration. We then showed that what we learned from this landscape translated into the remediation of BPA in soil samples. Functional landscapes are most easily inferred when they are relatively smooth with limited epistasis [ 15 ] or when global non-linear transformations render the landscape approximately additive [ 17 ]. Indeed, most of the previous work on landscapes falls into one of these two categories. The BPA degradation landscape presented here offers a slightly different picture: the landscape shows smooth, low-ruggedness at low BPA concentrations and higher ruggedness at high BPA levels ( Fig. 4 ). However, we do observe a difference in model fits between the low and high concentrations, where higher concentrations were more difficult to fit ( Fig. 3B ). We attribute this difficulty to the sparse sampling of 70 communities (out of a possible ~ 65, 000) in a regime where epistatic terms dominate the landscape ( Fig. 5 ). Despite this difficulty, our success highlights an example of characterizing a rugged community function landscape. Previous explorations of community-function landscapes are mostly limited to a stable environment and exploring changes in community composition [ 15 , 19 , 51 ]. Our results demonstrate the power of functional landscapes as a tool for understanding environmental changes. In particular, we showed that the functional landscape changes with initial BPA concentration smoothly, to the extent that SoftImpute accurately predicts BPA degradation of communities ( Fig. 2D ) and that regression coefficients vary in a low-dimensional manner ( Fig. S7 ). Our work serves as a proof of principle for analyzing community function landscapes across other environmental gradients or even multiple environmental gradients simultaneously (such as the effect of pH on BPA degradation landscape). The low-rank regressor formalism developed here ( Fig. S12 ) serves as a generic method for inferring landscapes across environmental gradients. We showed that the smooth variation in the landscape is accompanied by epistasis that increases with BPA toxicity. At low BPA concentrations, the presence of degraders is the primary driver of degradation, with statistical interactions between them generally competitive ( Fig. 5 ). The importance of epistatic interactions for BPA degradation rises with toxicity, where at the highest BPA concentration measured, no individual strain can degrade BPA ( Fig. 4D ). The importance of epistatic interactions in our landscapes is especially evident in the community of all strains - Mix20 - not being the fastest to degrade BPA in all five concentrations measured ( Fig. 1C ). This result shows that composition matters in a non-trivial manner. The observation is consistent with the presence of many instances of both statistical cooperation (negative γ coefficients, speeding up degradation) and competitive (positive γ coefficients, slowing down degradation) effective interactions between strains. Our work provides unique insight into the power of a functional landscape in determining epistatic interactions that are biologically relevant. Previous efforts in bioremediation have encountered difficulties in engrafting bacteria into soils [ 52 , 53 ], resulting in low bioremediation efficiency [ 24 ]. Some studies have attempted to model complex interactions between invaders and resident consortia to design communities for remediation [ 7 ]. Our work highlights a practical application of the theory posited by Silverstein et al. [ 23 ] in combination with community function landscapes to address this problem. This success paves the way for simplifying similar endeavors for other bioremediation targets."
} | 2,810 |
35451841 | PMC9974066 | pmc | 1,556 | {
"abstract": "Memristors\nare candidate devices for constructing artificial neurons,\nsynapses, and computational networks for brainlike information processing\nand sensory-motor autonomous systems. However, the dynamics of natural\nneurons and synapses are challenging and cannot be well reproduced\nwith standard electronic components. Halide perovskite memristors\noperate by mixed ionic–electronic properties that may lead\nto replicate the live computation elements. Here we explore the dynamical\nbehavior of a halide perovskite memristor model to evaluate the response\nto a step perturbation and the self-sustained oscillations that produce\nanalog neuron spiking. As the system contains a capacitor and a voltage-dependent\nchemical inductor, it can mimic an action potential in response to\na square current pulse. Furthermore, we discover a property that cannot\noccur in the standard two-dimensional model systems: a three-dimensional\nmodel shows a dynamical instability that produces a spiking regime\nwithout the need for an intrinsic negative resistance. These results\nopen a new pathway to create spiking neurons without the support of\nelectronic circuits."
} | 286 |
38545459 | PMC10965485 | pmc | 1,557 | {
"abstract": "Increasing atmospheric carbon dioxide levels, a reduction of arable land area and the dependence of first and second generation biotechnology feedstocks on agricultural products, call for alternative, sustainable feedstock sources for industrial applications. The direct use of CO 2 or conversion of CO 2 into other single carbon (C1) sources have great potential as they might help to reduce carbon emissions and do not compete with agricultural land use. Here we discuss the microbial use of C1 carbon sources, their potential applications in biotechnology, and challenges towards sustainable C1-based industrial biotechnology processes. We focus on methanol, formic acid, methane, syngas, and CO 2 as feedstocks for bioprocesses, their assimilation pathways, current and emerging applications, and limitations of their application. This mini-review is intended as a first introduction for researchers who are new to the field of C1 biotechnology.",
"conclusion": "8 Conclusion and outlook Use of C1 carbon sources in industrial applications bears a lot of potential towards sustainable and carbon neutral or negative bioprocesses. However, each process should be designed considering its own requirements and specifications. Gaseous substrates such as CO 2 , methane or syngas can be used for the production of renewable chemicals, as well as in more futuristic applications like CO 2 fixation in a Martian atmosphere [ 83 ]. However, mass transfer limitations in gas fermentations remain as an obstacle to solve, although several strategies are proposed [ 84 ]. Liquid C1 sources like methanol or formate are easier to use in large scale bioprocesses either with native or synthetic microorganisms. Methane and syngas are way more abundantly available, however to a large extent from fossil resources. The renewable production of methanol and formate [ [85] , [86] , [87] ], as well as syngas [ 88 ] is on the rise, but still need to be massively upscaled to serve as feedstocks for a C1 biotechnology that has a relevant impact on the global carbon balance [ 89 ] ( Fig. 2 ). Fig. 2 Single carbon substrates for biotechnology. The main gaseous and liquid single carbon substrates and their advantages and disadvantages are highlighted. Tank sizes illustrate the respective relative annual production to date. Fig. 2 So far, electricity demand for the reduction of CO 2 for future applications and scaling-up of bioprocesses with high productivities are the main challenges of sustainable C1-based productions to compete with conventional production cycles. Current synthetic strains are not yet efficient enough for large-scale industrial use and require further metabolic engineering, although some promising initial results have been published [ 48 , 77 ]. Expanding the substrate utilization range and integrating new synthetic pathways including their thermodynamic and kinetic models may help to facilitate the engineering of more efficient pathways with high MDF and low energy requirements for less thermodynamically favorable reactions. Electrochemical reduction of CO 2 into methanol or formate as an electron or carbon source is a promising approach, even though these processes are not yet competitive with fossil resources and more study is required especially focusing on the scale-up. However, as faradaic efficiencies for the conversion of CO 2 into formate is higher than methanol, formate use and formatotrophy might gain more attention in the future [ 90 ]. Gaseous C1 substrates (CO 2 and CO) are abundant and their bio-conversion into durable products would be among potential solutions to reduce greenhouse gas concentrations in the atmosphere. CO 2 and CO are main fractions of synthesis gas (together with H 2 ), and their fermentation bears great potential for environmentally beneficial production. Fermentation of gaseous substrates has some critical limitations however, first of all low mass transfer rates from the gas phase to the liquid fermentation broth, limiting volumetric productivities [ 84 ]. Liquid C1 substrates (methanol, formate) are favored by high water miscibility, which enables fast transfer to the fermentation broth, so that productivities are not limited by substrate supply. Beyond that, the ease of storage and transport of liquid substrates are major advantages over gaseous feedstocks. While the production of chemicals from C1 substrates is widely researched ( Table 1 ), only a limited number has already reached pilot or production stage. We conclude from these cases that processes using the WL pathway (which leads to acetyl-CoA) are mainly established for production of C2 molecules like acetate and ethanol, or to other derivatives of acetyl-CoA like acetone or butyrate. Among other C1 pathways no clear product pattern can be identified which may be a sign for higher versatility of processes using these pathways. The feasibility of an individual process is determined by several parameters, productivity being an important one. Others, such as achievable products and aeration demand need to be considered as well, making the best substrate and process for a given product a multi-parameter decision.",
"introduction": "1 Introduction Rising atmospheric carbon dioxide (CO 2 ) levels are the main reason for the ongoing global climate crisis. In the year 2023 the record levels of more than 420 ppm of CO 2 were reached and the trend is still increasing [ 1 ] and global temperatures already increased by 1.1 °C (Fanning and Hickel, 2023; Morice et al., 2021). There is an urgent need for a reduction of greenhouse gas emissions and there are multiple agreements, most prominently the Paris Agreement, aiming to limit global warming to 2 °C compared to pre industrial levels [ 2 ]. A key point to fulfill these goals is to reduce or even stop the use of fossil fuels. Biotechnology could serve as a technology to help reduce the carbon footprint [ 3 ]. Amino acids, citric acid, and bio-ethanol production are examples where biotechnological processes are performed in large scales. Often these processes are based on substrates like sugars which are in direct competition with human consumption [ 4 , 5 ]. By using lignocellulosic biomass this competition can be reduced but complex pre-treatment steps of the biomass are necessary [ 6 ]. A possible future lies in single carbon (C1) feedstocks, which are either abundant in the air as CO 2 or can be produced from it, and so helping to reduce carbon emissions. This review gives an overview of main sources, microbial assimilation pathways and biotechnological use of selected C1 carbon sources, namely methanol, formic acid, CO 2 , methane, and syngas. Single carbon molecules are interconvertible by oxidation and reduction, respectively. Microbes make use of this to convert C1 substrates to two main precursors, formaldehyde and formate, which are the entry points into assimilation for most of the natural assimilation pathways ( Fig. 1 ). Fig. 1 Overview of the major native single carbon assimilation pathways. Methanol assimilation via formaldehyde is achieved by the RuMP cycle (orange) or the XuMP cycle (blue). These cycles bear high similarity to the CBB cycle for CO 2 assimilation (green). Formaldehyde can be further oxidized to formate and assimilated via the serine cycle (yellow) or the reductive glycine pathway (purple). The latter is not cyclic but a linear pathway, same as the Wood Ljungdahl pathway (brown). Abbreviations: AcCoA – acetyl coenzyme A, CH 2 -THF – methylene tetrahydrofolate, DHA – dihydroxyacetone, DHAP – dihydroxyacetone phosphate, E4P – erythrose 4-phosphate, Fald – formaldehyde, FBP – fructose bisphosphate, For – formate, G3P – glyceraldehyde 3-phosphate, Gly – glycine, Glyc – glycerate, Glx – glyoxylate, H6P – hexulose 6-phosphate, HPyr – hydroxypyruvate, Mal – malate, MeOH – methanol, OAA – oxaloacetate, PEP – phosphoenolpyruvate, Pyr – pyruvate, Ru5P – ribulose 5-phosphate, RuBP – ribulose bisphosphate, S7P – sedoheptulose 7-phosphate, SBP – sedoheptulose bisphosphate, Ser – serine, Xu5P – xylulose 5-phosphate. Fig. 1"
} | 2,017 |
33922571 | PMC8122867 | pmc | 1,559 | {
"abstract": "Reservoir computing (RC) is an attractive paradigm of a recurrent neural network (RNN) architecture, owning to the ease of training and existing neuromorphic implementation. Its simulated performance matches other digital algorithms on a series of benchmarking tasks, such as prediction tasks and classification tasks. In this article, we propose a novel RC structure based on the coupled MEMS resonators with the enhanced dynamic richness to optimize the performance of the RC system both on the system level and data set level. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to set forth this point, we compare three typical RC structures, a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks, and the couple-nonlinearity RC structure with double-feedbacks. Specifically, four different tasks are enumerated to verify the performance of the three RC structures, and the results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. These results prove that coupled MEMS resonators offer an interesting platform to implement a complex computing paradigm leveraging their rich dynamical features.",
"conclusion": "4. Conclusions We propose the coupled MEMS resonators can optimize the performance of the RC system based on the enhanced dynamic richness that is composed of linear richness and nonlinear richness. In order to set forth this point, we compare three typical RC structures with a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks that is treated as a transition structure, and the couple-nonlinearity RC structure with double-feedbacks. We also describe the evolution for the promotion of the performance of the three structures. At the system level, we use the delay characteristic diagram and memory capacity to illustrate that the delayed feedback loop increases the linear richness, and use the output signal timing diagram and the histogram of the virtual nodes to illustrate that the coupled resonator increases the nonlinear richness of the system. At the data set level, four different tasks, the parity benchmark task, nonlinear autoregressive moving average (NARMA) task, isolated spoken digits recognition, and human action recognition, are investigated to prove that CRSD can improve the accuracy of the system to a certain extent. These results have significant implications with respect to hardware implementation of the reservoir and open new future possibilities for exploring real-world applications of coupling resonators in the RC system.",
"introduction": "1. Introduction Artificial neural networks (ANN) have played an important role in the current boom in artificial intelligence (AI), especially with the invention of the Internet of Things (IoT) and ubiquitous sensing [ 1 ]. ANN has strong self-learning adaptability, parallel information processing capabilities, and nonlinear mapping capabilities, but they require complex and time-consuming algorithms to train the connection weights in the large-scale network. When interested in processing time-dependent tasks, standard feed-forward ANNs can no longer meet the requirements, so the recurrent neural network (RNN) has gradually become the mainstream [ 2 , 3 , 4 ]. RNN requires complex algorithms to train the connection weights, however, which causes slow convergence and consumes lots of calculations [ 5 , 6 ]. Therefore, reservoir computing (RC) gradually attracted attention because the weights of its recursive network are initialized randomly and untrained. Reservoir computing (RC) is a brain-inspired computational framework suited for temporal data processing, owing to its derivation from the recurrent neural network (RNN) [ 5 , 7 ]. The main difference between RC and conventional RNNs is that the weights on the recurrent connections in the reservoir are not trained, but only the weights in the readout are trained, which avoids the well-known limitations of RNN, such as iterative parameter optimization and the condition of convergence [ 8 ]. More importantly, the fixed reservoir without updating is suitable for hardware implementation using various nonlinear dynamical systems. The hardware reservoir needs two crucial properties, nonlinearity and fading memory, which can be realized by using numerous randomly interacting nonlinear nodes or using a time-delayed nonlinear system. Actually, RC based on the time-delayed nonlinear system greatly reduces the implementation difficulty, which has been successfully demonstrated in a wide variety of systems, such as memristor [ 9 , 10 ], electronic [ 8 , 11 , 12 ], spintronic [ 13 , 14 , 15 ], optoelectronic [ 16 , 17 , 18 ], all-optical [ 19 , 20 , 21 , 22 ], quantum [ 23 ], and mechanical resonator [ 24 , 25 , 26 , 27 ]. In spite of these encouraging results, the RC implementation based on the single nonlinear node with single time-delay feedback adopted in these previous works has a limited memory capacity. The memory capacity is a key property of the RC that allows the processing of dynamical signals. The RC structures with the multiple delayed feedback or bidirectionally coupled nodes was proven to increase the processing speed of the system by boosting the memory length of the RC system, which are discussed numerically in detail in Appeltant’s doctoral dissertation [ 28 ]. Furthermore, this pioneering work inspires many studies on different configurations for the time-delayed RC. Particularly, double optical feedbacks RC structures are proposed to increase prediction performance under the condition that the long delay time and short delay time meet the specific relationship [ 29 ]. In addition, the mutually delay-coupled semiconductor lasers structure was studied for enhancing calculation speed and accuracy of the RC systems [ 30 , 31 , 32 ]. Despite these instructive works based on the optoelectronic devices promoting research on RC with the multiple delayed feedbacks and coupled nonlinear nodes, the potential of RC with other nonlinear devices and the reason why the coupled RC structure improves the accuracy has not yet been fully investigated. In this work, we present a couple-nonlinearity RC structure with double-feedbacks (CRSD) based on the coupled MEMS resonators with the enhanced dynamic richness to promote the performance of the RC. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to elaborate this point in detail, we compare three typical RC structures, a single-nonlinearity RC structure with a single-feedback (SRSS), a single-nonlinearity RC structure with double-feedbacks (SRSD), and the couple-nonlinearity RC structure with double-feedbacks (CRSD). In addition, we describe the evolution for the promotion of the performance of the three structures. Furthermore, four different tasks—the parity benchmark task, nonlinear autoregressive moving average (NARMA) task, isolated spoken digits recognition, and human action recognition—are investigated to prove the feasibility of these RC structures. The results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. The rest of this paper is organized as follows. In Section 2 , we describe the methods we used in this paper, including the three typical structures of the RC system, the nonlinear mapping node of the RC structure, and the dynamic richness analysis of three RC structures. In Section 3 , we give a detailed discussion about the numerical results of our system on four typical tasks. In Section 4 , the paper ends with a brief conclusion and an outlook for possible future work.",
"discussion": "3. Results and Discussion The performance of the system lies in the following two aspects, prediction ability and classification ability, which are assessed by four typical data sets to prove that the coupled nonlinear resonators improve the dynamic richness of the RC system. The prediction ability is numerically investigated via the parity benchmark prediction task and NARMA task, while the classification ability is discussed via the TI-46 isolated word task and human action recognition task. 3.1. Parity Benchmark Prediction Task The prediction ability of the system is first assessed by means of the parity benchmark task. The parity function is considered as a benchmark, as it requires both memory ability and nonlinear computational capability [ 40 ]. For this task, u t is a binary time-dependent signal that randomly switches between two states −1 and +1, where τ is its switching period. The n t h -order parity function can be defined as: (7) P n = ∏ i = 1 n u t − i − 1 τ , \nwhere n is the order of benchmark, τ is the period, and P n is the training target of this n t h -order parity function. As n increases, the difficulty of this prediction task will gradually increase. When n = 1 , the benchmark performs a linearly separable data set and easy to predict [ 41 ]. As n > 1 , the data of P n at this time are related to the previous n data. Namely, the system needs to have at least a n τ memory length to process this data set. One hundred fifty training data and 50 testing data are used for our test and we choose the parameters, θ = 0.1 ms , N = 400 , f = 352 , 500 H z, β = 1 , and V d c = 20 V , V a c = 2 V for the better success rate. The success rate of P 2 ~ 4 = 100 % , P 5 = 95 % , and P 6 = 88 % is reported in [ 24 ]. The method of parameter selection has been explained in our previously published articles [ 42 ] and will not be explained here. Figure 7 shows the performance of SRSS and SRSD when the order n equals 7. Because the success rate of P 1 ~ P 6 has already achieved 100% for two RC systems, we do not show the result of P 1 ~ P 6 here. The parameters of these two RC systems are the same except for the delay feedback gain. The delay feedback gain α = 5 in SRSS while α 1 = 2.5 and α 2 = 2.5 in SRSD. The black dotted line represents the 50 target value of P 7 . Compared with the result of SRSS (red line), the result of SRSD (blue line) is closer to the target value. At the same time, the success rate is 78% for SRSS and 100% for SRSD. Since the success rate has already up to 100% for n = 1 , 2 , 3 , 4 , 5 , 6 , 7 with SRSD, there is no room for improvement, so we did not use CRSD to further optimize this data set. The result of this prediction task shows SRSD with another delay feedback loop can improve the success rate of the RC system by increasing its linear richness. 3.2. NARMA Prediction Task The nonlinear autoregressive moving average (NARMA) prediction task is one of the most popular tasks in the RC community and it is also a more difficult task than the parity benchmark. In this prediction task, the RC system is trained to predict the behaviors of the system, for instance, a nonlinear autoregressive moving average (NARMA) of order m driven by white noise. The m th order NARMA task is given by the following recursive formula: (8) y n + 1 = 0.3 y n + 0.05 y n ∑ i = 0 m − 1 y n − 1 + 1.5 u n − m + 1 u n + 0.1 , \nwhere y n is the output target of the system, m is the order of this benchmark, and u t stands for the white noise, which is the random input derived from a uniform distribution over the interval (0, 0.5). In this task, the RC system is trained in a sequence of 1000 data length and tested in the following sequence of 1000 data length. The performance metric used to evaluate NARMA is normalized mean square error (NMSE), NMSE = 1 L ∑ n = 1 L y n − y ^ n 2 / v a r y , where y n is the target and y ^ n is the prediction result of the system. We use three RC systems (SRSS, SRSD, and CRSD) to test the performance of this data set, and the same parameters were set as N = 50 , β = 3.6 , and V d c = 80 V , and V a c = 2 V . In particular, θ exhibits a crucial role in this task and we set θ = 0.01 ms to enhance the connection of current input to the previous virtual node state. The prediction difficulty of the system increases exponentially with the increase of order m . We test the prediction accuracy of the three RC systems under the conditions of m = 1 , 2 , 5 , and 10. Figure 8 shows the performance of three RC systems for the NARMA prediction test. The lines with different colors represent the different orders of prediction. The driving frequency f = 352 , 000 H z in SRSS and SRSD, while f = 264 , 300 H z in CRSD. The delay feedback loop α = 0.6 in SRSS, while α 1 = 0.4 , α 2 = 0.4 in SRSD and CRSD with two delay feedback loops. In the same structure, the NMSE increases gradually with the increase of order because of increasing difficulty. Under the condition of the same order m , the NMSE decreases as the structure is optimized from SRSS to SRSD to CRSD at the same time. When m = 1 , CRSD reduces the NMSE of the system by around 70% from SRSS, and when m = 2 , 5 , CRSD reduces the NMSE of the system by around 50%. Concurrently, CRSD reduces the NMSE of the system by around 35%. The results of this complicated prediction task show CRSD with two delay feedback loops and the coupled resonators can decrease NMSE by increasing its linear richness and nonlinear richness. 3.3. TI-46 Isolated Word Classification Task Through the isolated spoken word task, the classification performance of reservoir computing when inputting complex data is evaluated. This task is the classification of isolated audio sequences from a subset of the National Institute of Standards and Technology Texas Instrument-46 Corpus (NIST TI-46 Corpus) [ 43 ]. The data set has 500 audio sequences, each audio sequence represents a number (0–9), recorded ten times by five different female speakers. Five hundred audio waveforms with different time lengths are sampled at a rate of 12.5 kHz. Every input representing a spoken digit is preprocessed using the Lyon cochlear ear model [ 44 ] before injecting into the RC system. In the training phase, ten weight vectors are calculated for the ten numbers in the vocabulary, and then the winner-takes-all method is applied to select the actual numbers. Each classifier is trained to output a value of 1 when presented with an utterance of the digit corresponding to its category, and 0 otherwise. The highest averaged classifier corresponds to the correct digit. The performance indicator used to evaluate the isolated word classification is the word error rate (WER), namely, the fraction of digits incorrectly classified. There are only 500 sequences in the subset corpus, so we divide them into 10 parts and use the tenfold cross-validation method to avoid the influence of the specific division of available data in some processes (such as training and testing), so as to make the results more convincing. Ten sections were randomly selected, including nine for training and one for testing. In this task, we use three RC systems (SRSS, SRSD, and CRSD) to test the performance, and the same parameters were set as N = 100 , θ = 0.1 ms , β = 200 , and V d c = 20 V , V a c = 2 V . The driving frequency f = 349 , 200 H z is in SRSS and SRSD, while f = 244 , 300 H z in CRSD; the delay feedback loop is α = 0.4 in SRSS, while α 1 = 0.2 , α 2 = 0.2 in SRSD and CRSD with two delay feedback loops. The original result is WER = 0.2% in SRSS. After increasing the linear richness of the system, the result is improved to WER = 0.12% in SRSD. CRSD improves the dynamic richness of the system and the result is optimized to WER = 0.04%. In order to visualize how the time-dependent data separation occurs and understand the recognition capabilities of the different RC structures, the t-distributed stochastic neighbor embedding (t-SNE) technique [ 45 ] is used to represent input data of the TI-46 data set in a 2D figure. The t-SNE technique is a nonlinear dimensionality reduction technique used to map high-dimensional data to a low-dimensional space with two dimensions or three dimensions. During data reduction, the probability of two vectors becoming neighbors is preserved, so that the structure in the data can be visualized. Figure 9 shows the t-SNE result of two structures. For all of the data points of the digit isolated word, each number is represented by a colored dot. Figure 9 a shows the result of the TI-46 raw data only after processing by the Lyon cochlear ear model. Since all the color points seem to be randomly distributed, there is no data separation. In particular, numbers in the same category will not form separate clusters. For the data set after preprocessing by SRSS, data separation can clearly be seen in Figure 9 b, correlating with the word error rate of 0.2%. A few clusters such as Digit 8 and Digit 9, however, are very close to some clusters, and some even overlap with others. After optimized by the CRSD, the t-SNE result is shown in Figure 9 c with WER = 0.04%. The clusters of Digit 8 and Digit 9 can be relatively well separated from the clustering of other numbers and corroborate the high recognition rates exhibited by CRSD. The results of this isolated word classification task show that CRSD can decrease the WER by increasing its dynamic richness. 3.4. Human Action Recognition Task Recognizing human actions in video streams is a difficult task in computer vision. This task was first applied to RC systems in 2019 [ 46 ] as a standard data set for testing RC systems. This recognition task is a subset of the KTH video database [ 47 ], which contains four different scenarios. For brevity, we limited the data set in the first scenario, called “S1,” which included an outdoor video of six different motions (walking, jogging, running, boxing, hand waving, and hand clapping) performed by 25 subjects. All videos were recorded with a static camera and 25 fps against a uniform background and then sampled down to 160 × 120 pixels of spatial resolution. The length of each motion is different, the average is 4 s. Our dataset consists of 25 × 6 × 4 = 600 sequences, each combination consisting of 25 subjects, 6 actions, and 4 replicates. These 600 motion sequences are preprocessed by the histograms of oriented gradients (HOG) algorithm [ 47 ], which is used to extract spatial and shape information from a single video frame. HOG features are performed by the computer, with a cell size of 8 × 8 and a block size of 2 × 2, individually for each frame of every sequence. The size of each frame is 160 × 120 pixels, and the HOG algorithm returns 19 × 14 × 4 × 9 = 9576 features per frame. These features are fed into the RC system, which outputs the values of the virtual nodes, and then trains it to classify each frame. The classifiers are implemented by defining six binary output nodes. Training them to output 1 for a frame of the corresponding class and 0 for the other classes, which is the same as training method for TI-46 isolated word classification task. Classification by frame is obtained by selecting the node with the largest output, namely the winner-take-all method. The final determination of a video sequence is given by the classes that belong to the majority of the frames in the sequence. In this recognition task, we also use three RC systems (SRSS, SRSD, and CRSD) to test the performance, and the same parameters were set as N = 3000 , θ = 0.1 ms , β = 0.01 , and V d c = 20 V , V a c = 2 V . The driving frequency was f = 350 , 000 H z in SRSS and SRSD, while f = 244 , 000 H z in CRSD. The delay feedback loop was α = 0.2 in SRSS, while α 1 = 0.1 , α 2 = 0.1 in SRSD and CRSD with two delay feedback loops. Figure 10 displays the confusion matrices [ 48 ] for three different RC systems. After processing by SRSS, the success rate is only 73.3%. Relatively good recognition results were obtained for the hand gestures (walking, jogging, and running), but for fast spatial movements (jogging and running) that are difficult to distinguish, the recognition results are very unsatisfactory. After optimizing the linear richness, the success rate rises to 89.33% in SRSD. The hand gestures can get perfect accuracy and the accuracy of fast spatial movements is greatly improved. The CRSD optimizes the nonlinear richness of the system, so the success rate up to 92%. Compared with SRSD, the accuracy of jogging and walking are relatively improved. In addition, we test the effect of the number of virtual nodes N on the accuracy rate of the CRSD system. The number of virtual nodes N is a crucial parameter for RC because it determines the speed as well as the performance of the system. If N is relatively small, the performance will decrease, but the system operation speed will increase on the contrary. A higher N usually means better state diversity, which improves performance at the expense of lower computing speed. Because this recognition video data set has a much higher data complexity than the other three above, more virtual nodes are needed to process the data to increase the accuracy rate. The photonic computer gets the accuracy rate = 91.3% at the condition of N = 16 , 384 [ 46 ]. In our test, the accuracy rate as a function of the number of virtual nodes is shown in Figure 11 . In the range of N = 400 to N = 3000 , as the number of virtual nodes increases, the accuracy rate increases significantly. When N > 3000 , the accuracy rate fluctuates in a small range and gradually stabilizes. The simulation time increases rapidly as the number of virtual nodes increases. If increasing the number of virtual nodes does not increase the accuracy rate, then too large a number of virtual nodes will seriously affect the calculation efficiency. As a compromise between the performance and the efficiency, we choose N = 3000 in this task."
} | 5,521 |
23346384 | PMC3548177 | pmc | 1,561 | {
"abstract": "This work is devoted to the investigation of the methanogenic archaea involved in\nanaerobic digestion of cattle manure and maize straw on the basis of terminal\nrestriction fragment length polymorphism (T-RFLP) analysis of archaeal 16S rRNA\ngenes. The biological diversity and dynamics of methanogenic communities leading\nto anaerobic degradation of agricultural organic wastes with biogas production\nwere evaluated in laboratory-scale digesters. T-RFLP analysis, along with the\nestablishment of archaeal 16S rRNA gene clone libraries, showed that the\nmethanogenic consortium consisted mainly of members of the\ngenera Methanosarcina and Methanoculleus, with\na predominance of Methanosarcina spp. throughout the\nexperiment.",
"introduction": "INTRODUCTION One of the most effective methods for reducing the negative effects of the waste from\nthe agricultural and processing industries on the environment is their anaerobic\ndigestion. Anaerobic digestion of wastes is accompanied by the destruction of most\norganic components and production of biogas consisting of methane (50–75%) and\ncarbon dioxide (25–50%), with trace amounts of other components. In contrast\nto bioethanol and biodiesel mostly produced from energy crops, biogas is obtained\nduring utilization of residual biomass and various organic wastes [1– 7 ], such as cattle manure. However, due to the\nlow biodegradability of manure, its utilization in anaerobic reactors is\ncharacterized by an insignificant biogas yield. Anaerobic co-digestion of manure and\nplant biomass promotes substrate hydrolysis, optimizes the distribution of nutrients\nin the bioreactor, thus activating microbial growth and the biomethane yield\n[8, 9 ]. The co-digestion of several\ndifferent substrates has been actively investigated over the past years\n[9– 13 ]. The first three stages of anaerobic co-digestion (hydrolysis, acidogenesis, and\nacetogenesis) are performed by bacterial communities; the fourth stage is performed\nby aceticlastic and hydrogenotrophic methanogens, which consume acetate, molecular\nhydrogen, and carbon dioxide to produce methane [1, 6, 14 ]. Independently of the mode of digestion (psychro-, meso-, or thermophilic) and\nfeedstock composition, the major participants in methanogenesis are the members of\nthe orders of Methanomicrobiales and/or\n Methanosarcinales [2, 5, 7, 15– 18 ]. However, there is a lack of information about the changes\nin microbial association during methanogenic fermentation. The present study was devoted to the investigation of pathways for utilization of\nagricultural wastes (manure and maize straw) with biogas production in\nlaboratory-scale biogas reactors and to studying the diversity, structure, and\ndynamics of the methanogenic communities involved in this process using modern\nmethods of molecular biology. The determination of the composition and dynamics of\nthe microbial communities in biogas reactors, jointly with the analysis of substrate\ndestruction, is aimed at revealing the potential for intensification of the\nanaerobic process. The use of the universal phylogenic marker 16S rRNA and T-RFLP\n(terminal restriction fragment length polymorphism) will contribute to the study of\nthe composition and temporal changes to the microbial consortium.",
"discussion": "RESULTS AND DISCUSSION Table 1 Main configurations of anaerobic digestion of cattle manure and maize\nstraw Digester* Organic loading rate**,g oTS \nday –1 Substrate composition, g day –1 Biogas yield under standard conditions, L\ng –1 oTS Biogas composition pH Acid capacity, g L –1 NH 4 + -N, g\nL –1 cattle manure straw total*** CH 4 , % CO 2, % H 2 S, ppm. R 4.13 74.1 723.6 28.2 857 0.40 58.7 40.2 3450 7.63 1.49 1.20 71.2 518.7 26.3 857 0.36 59.8 38.7 2216 7.50 1.90 1.24 71.7 694.6 26.3 857 0.33 55.6 42.9 2145 7.61 1.80 1.16 R 4.14 74.1 723.6 28.2 857 0.40 59.3 39.8 4183 7.66 1.42 1.22 71.2 518.7 26.3 857 0.38 58.4 40.2 1928 7.53 1.66 1.28 71.7 694.6 26.3 857 0.37 56.7 42.1 2092 7.58 1.43 1.31 R 4.15 72.1 723.6 83.7 857 0.39 58.1 41.1 ~5000 7.75 1.54 1.47 68.6 518.7 78.1 857 0.39 59.3 39.2 2234 7.56 1.28 1.39 69.1 694.6 78.1 857 0.39 56.8 42.6 2373 7.74 1.37 1.26 R 4.16 72.1 723.6 83.7 857 0.41 58.6 40.6 4558 7.76 1.51 1.54 68.6 518.7 78.1 857 0.38 59.0 40.1 2056 7.54 1.53 1.36 69.1 694.6 78.1 857 0.39 57.2 41.5 3155 7.61 1.37 1.27 * Digester parameters are presented at three sampling times, when\nmethanogenic communities were analyzed (except for biogas yield, biogas\ncomposition, and pH, with values averaged over 1 week). ** Organic total solids. *** Water was added to final concentration of 857 ml day\n –1 . The use of renewable energy sources, in particular various types of organic waste, is\nan essential aspect of “green technologies” for biofuel production\n[ 1 ]. The aim of this work was to\ninvestigate the dynamics of methanogenic associations during the conversion of\ncattle manure and maize straw in model mesophilic digesters. Table 1 lists the major operational parameters\nof the anaerobic digestion of agricultural waste as substrates. Anaerobic biomass\ndestruction was carried out in four laboratory-scale digesters with an operating\nvolume of 30 L at 38 о C. In the reactors R 4.13 and R 4.14, cattle\nmanure and maize straw were co-digested; the reactors R 4.15 and R 4.16 were loaded\nwith manure and extruded maize straw. The organic loading rate (OLR) was varied from\n71.2 to 74.1 g oTS day -1 (organic total solids) in the\nreactors R 4.13 and R 4.14. In the reactors R 4.15 and R 4.16, the OLR was lower and\nvaried from 68.6 to 72.1 g oTS day -1 . Throughout the\nexperiment the HRT was kept constant (35 days). Depending on the particular\nfeedstock, the biogas yield varied from 0.33 to 0.41 L g -1 \n oTS with a methane content of 56–60%. As can be seen in\n Table 1 , pH in all\nbioreactors was maintained at approximately 7.5–7.8; acid capacity ranged\nbetween 1.3 and 1.9 g L -1 , and ammonium concentration varied from 1.2\nto 1.5 g L -1 . These parameters are favorable for methanogenesis [ 23 ]. Table 2 Results of sequencing of archaeal 16S rRNA gene clones and experimentally\ndetermined terminal restriction fragments (T-RF) Clone, bp Nearest relative (GenBank accession No) / coincidence % Taxonomic status in accordance with RDP 10 MseI-T-RF, bp HaeIII-T-RF, bp ar_B9 (863) Uncultured archaeon clone: FA69 (AB494258) / 99% Methanoculleus sp. 37 67 ar_A1 (864) Uncultured Methanoculleus sp. Clone: DMMR219\n(HM218939) / 99% Methanoculleus sp. 36 67 OTU 1 Methanoculleus sp. I 36/37 67 ar_A2 (863) Uncultured archaeon clone: MTSArc_G8 (EU591664) / 99% Methanoculleus sp. 499 67 OTU 2 Methanoculleus sp. II 499 67 ar_E12 (864) Uncultured archaeon clone: WA50 (AB494245) / 100% Methanocorpusculum sp. 97 241 OTU 3 Methanocorpusculum sp. 97 241 ar_E10 (567) Uncultured euryarchaeote clone: B35_F_A_A05 (EF552199) / 99% Methanosarcina sp. 557 220 ar_H2 (873) Uncultured euryarchaeote clone: B35_F_A_A05 (EF552199) / 99% Methanosarcina sp. 557 220 OTU 4 Methanosarcina sp. I 557 220 ar_E6 (873) Uncultured archaeon clone: SA42 (AB494252) / 99% Methanosarcina sp. 859 220 ar_F10 (873) Uncultured archaeon clone: SA42 (AB494252) / 99% Methanosarcina sp. 858 220 OTU 5 Methanosarcina sp. II 858/859 220 ar_G8 (874) Uncultured archaeon clone: SA42 (AB494252) / 99% Methanosarcina sp. 877 220 OTU 6 Methanosarcina sp.III 877 220 The biological diversity and dynamics of methanogenic communities digesting cattle\nmanure and maize straw were investigated by amplification, cloning, restriction\nanalysis, and sequencing of the archaeal 16S rRNA genes. The methanogenic\nassociation structure was determined at three sampling points with a 1-month\ninterval. Amplification, cloning, sequencing of archaeal 16S rRNA, and T-RLFP analysis revealed\na relatively large diversity of archaeal species in the reactors. During the T-RLFP\nanalysis of archaeal 16S rRNA gene amplicons containing FAM flurophor were treated\nwith endonucleases MseI and HaeIII. Belonging of the peaks in T-RLFP patterns to\ncertain phylogenic groups was determined by the length of the terminal restriction\nfragments (T-RF) of 16S rRNA gene clones. In total, 9 clones were selected from the\nclone library for sequencing. The clones were classified into 6 operational\ntaxonomic units (OTUs) on the basis of their T-RF lengths ( Table \n2). Three phylotypes were attributed to the order\n Methanomicrobiales (OTU 1, OTU 2, OTU 3), and three were\nattributed to the order Methanosarcinales (OTU 4, OTU 5, OTU 6). Up\nto 22 different T-RFLP profiles (with abundance of more than 1%) were detected by\nT-RLFP analysis of 16S rRNA genes using MseI restrictase. Since the main T-RFs in\nthe reactors were identified, we identified the methanogens playing the key role in\nbiogas production. Fig. 1 Dynamics of methanogenic communities in the digesters R 4.13 and R 4.14\nbased on T-RFLP analysis (determined with the restriction enzyme\nMseI) Fig. 1 shows the distribution\nof groups of methanogens (community dynamics) during anaerobic digestion of manure\nand straw (R 4.13 and R 4.14). This distribution was obtained based on MseI\nrestriction profiles (results of HaeIII restriction are not shown). In the first\nsample, when the organic loading rate was 74.1 g oTS day -1 ,\nthe T-RFLP analysis revealed the predominance of methanogens of the genus\n Methanosarcina and hydrogenotrophic methanogens of the genus\n Methanoculleus in the archaeal community of the bioreactors R\n4.13 and R 4.14. Thus, the total ratio of representatives of\n Methanosarcina sp. (OTU 4, OTU 5, and OTU 6) and\n Methanoculleus sp. (ОТU 1, ОТU 2) was\n65 and 15%, respectively, of the total T-RF peak areas in the reactor R 4.13. In the\nreactor R 4.14, methanogens of the genera Methanosarcina (75%) and\n Methanoculleus (9%)were detected. Other archaeal members with\nlow abundance (1–3%) were classified into the minor groups. A decrease in OLR\nto 71.2 g oTS day –1 with a subsequent increase to 71.7\ng oTS day –1 resulted in a change in the composition of\nthe microbial community. Thus, the relative abundance of members of the genus\n Methanosarcina (OTU 4, OTU 5, OTU 6) in the two next sampling\npoints reached 70/47% and 35/49% values for the reactors R 4.13 and R 4.14,\nrespectively. The relative abundance of the species of the genus\n Methanoculleus (ОТU 1, ОТU 2) in the\nreactors R 4.13 and R 4.14 was 8/31 and 9/32%, respectively (two next sampling\npoints). Hydrogentrophic methanogens from the genus Methanocorpusculum were\nfound among the minor associations and they comprised less than 2% of the total T-RF\narea. Furthermore, the major peak corresponding to 336 bp was detected in T-RLFP\npatterns; however, this phylotype was not present among the cloned archaeal 16S rRNA\ngenes, and, hence, it was assigned to the unidentified group of the methanogenic\ncommunity. It is clear from Fig. 2 that\nthe composition of the methanogenic communities from the bioreactors R 4.15 and R\n4.16 with manure and extruded straw as the used substrates was represented by\nsimilar groups as those detected in the reactors R 4.13 and R 4.14. The OLR at three\nsampling points for the R 4.15 and R 4.16 reactors were 72.1, 68.6, and 69.1 g\n oTS day –1 , respectively. The members of the genera\n Methanosarcina (70, 54, and 63% of the total abundance in three\nsampling points, respectively) and Methanoculleus (15, 25, and 25%\nof the total abundance in three sampling points, respectively) were the predominant\ntaxons in the digester R 4.15. Reactor R 4.16, as well as R4.15, was dominated\nbymembers of thegenera Methanosarcina (81, 69, and 51%) and\n Methanoculleus (6, 17, and 28%). Similar to that in the\nreactors R 4.13 and R 4.14, high abundance of the T-RF peak corresponding to 336 bp\nwas detected; however, the taxonomic group of archaea corresponding to this\nrestriction length profile was not determined. Fig. 2 Dynamics of methanogenic communities in the digesters R 4.15 and R 4.16\nbased on T-RFLP analysis (determined with the restriction enzyme MseI) These findings substantiate the possibility of effective co-digestion of manure and\nmaize straw, yielding biogas. It has been demonstrated that members of the genera\n Methanosarcina and Methanoculleus prevail\nthroughout the fermentation process. In addition, the methanogenic community\ndynamics during utilization of organic waste has been investigated for the first\ntime. Methanoculleus species utilize hydrogen and carbon dioxide\nfor methanogenesis [ 2 ], whereas the members of\nthe genus Methanosarcina are likely to decompose acetate yielding\nmethane and carbon dioxide or to utilize hydrogen, carbon dioxide, and methylated\ncompounds yielding methane [ 24 ]. In all\nlikelihood, the increased concentration of organic acids in the reactors inhibits\nrepresentatives of the strictly aceticlastic genus Methanosaeta and\nstimulates thedevelopment of Methanosarcina spp. [ 14 , 25 ]."
} | 3,233 |
30271948 | PMC6123781 | pmc | 1,562 | {
"abstract": "The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2 S )-pinocembrin in Escherichia coli , to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L −1 . The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.",
"introduction": "Introduction Recent technical advances in synthetic biology, including rapid DNA assembly 1 , genome editing 2 , comprehensive pathway refactoring 3 , high-throughput screening 4 , and powerful pathway design tools 5 , are enabling the increased automation of microbial chemical production processes 6 , 7 . Academic and industrial biofoundries are increasingly adopting an engineering approach based on the iterative application of the DBTL cycle that has long been a central element of product development in traditional engineering disciplines 8 . Here we present an automated DBTL pipeline for the rapid prototyping and optimization of biochemical pathways in microbial chassis, which integrates a unique combination of these new technologies. The pipeline is designed to be agnostic regarding the target compound and runs from the in silico selection of candidate enzymes, through automated parts design, statistically guided and robot-assisted pathway assembly, rapid testing and rationalized redesign, providing an iterative DBTL cycle underpinned by computational and laboratory automation. This is a major step forward toward automating the DBTL cycle to develop biomanufacturing solutions for industrial chemical production.",
"discussion": "Discussion We have successfully implemented an automated DBTL pipeline to streamline the process of microbial engineering for chemical production. The pipeline integrates and combines tools of the Design–Build–Test–Learn cycle of metabolic engineering, providing relatively simple robust protocols. Crucially the pipeline is designed with the potential of operating in a target agnostic manner, applicable to any feasible chemical target. To develop and demonstrate its capabilities, the pipeline was applied to optimize the production of the flavonoid (2 S )-pinocembrin and the alkaloid ( S )-reticuline in E. coli . By considering relatively few design parameters, large numbers of pathway variants were designed and we demonstrated how using statistical sampling of the initial design space, coupled with automated laboratory protocols for pathway assembly and testing, allowed rapid prototyping in vivo. Both pathways were optimized to produce >50 mg L −1 of target, making them competitive with current state-of-the-art titers. For pinocembrin, identification of the key design factors influencing final production titers contributed to a second DBTL round using standardized statistical protocols, which resulted in a further 40-fold improvement in pinocembrin production. The titers observed from the two libraries ranged over three orders of magnitude (Fig. 2 ), clearly demonstrating the importance of pathway design and optimization. Testing such a large design space is only feasible using this kind of approach, combining intelligent sampling and automated workflows. The efficiency of the automated pipeline is clearly demonstrated, as it led to a 500-fold increase in titers for the flavonoids pathway with competitive titers up to 88 mg L −1 , from the screening of just 65 (16 + 25 + 24) variants out of 23,328 (2592 × 9) possible designs. Furthermore, for the alkaloids pathway screening of just 14 variants out of 2592 possible designs identified pathways with reticuline titers equivalent to the current state of the art. Our present pipeline can achieve a full iteration in 2 months, including gene synthesis (typically 1 month) and sequencing (3 days). A second iteration involving parts that have been already ordered could be then accomplished in 3 weeks. These results outperform previously reported DBTL approaches 12 , 13 in terms of fold increase in titers compared with number of screened variants. As the application of these strategies becomes more widespread, it is anticipated that DBTL pipeline methodologies for engineering biology will provide new faster, highly predictable and sustainable routes to valuable chemical diversity. We envision deriving common design rules and applying state-of-the-art machine-learning techniques in future cases involving larger data sets and design spaces. Similarly, propagation of design rules will be implemented as part of the pipeline through a workflow that will translate inferred design rules between factors into design constraints for the redesign of the next iteration. The workflow could also be extended beyond part selection to other factors like enzymes, alternative pathways, process conditions, etc.–e.g., finding alternatives to overcome identified bottlenecks. Designed to be applicable to any target compound, the pipeline is intended to be compatible for automation and therefore able to work without any prior knowledge of successful strategies. Our application of an automated DBTL pipeline demonstrates how these strategies can efficiently lead to the discovery and rapid optimization of high-performance pathways, providing the tools to enable a new era in automated agile biomanufacturing."
} | 1,496 |
36260135 | PMC9581835 | pmc | 1,563 | {
"abstract": "Poly(3-hydroxybutyrate) (PHB) is a microbially produced biopolymer that is emerging as a propitious alternative to petroleum-based plastics owing to its biodegradable and biocompatible properties. However, to date, the relatively high costs related to the PHB production process are hampering its widespread commercialization. Since feedstock costs add up to half of the total production costs, ample research has been focusing on the use of inexpensive industrial side streams as carbon sources. While various industrial side streams such as second-generation carbohydrates, lignocellulose, lipids, and glycerol have been extensively investigated in liquid fermentation processes, also gaseous sources, including carbon dioxide, carbon monoxide, and methane, are gaining attention as substrates for gas fermentation. In addition, recent studies have investigated two-stage processes to convert waste gases into PHB via organic acids or alcohols. In this review, a variety of different industrial side streams are discussed as more sustainable and economical carbon sources for microbial PHB production. In particular, a comprehensive overview of recent developments and remaining challenges in fermentation strategies using these feedstocks is provided, considering technical, environmental, and economic aspects to shed light on their industrial feasibility. As such, this review aims to contribute to the global shift towards a zero-waste bio-economy and more sustainable materials.",
"conclusion": "Conclusion and outlook PHB is a promising bioplastic with a broad range of applications, however, to date, its production cost is limiting widespread utilization. As the carbon source contributes to approximately 50% of the total cost and the sustainability of currently used carbon sources is questionable, attention is drawn to low-cost and more environmentally friendly substrates, such as industrial side streams. Therefore, this mini-review presents a critical evaluation of the use of these side streams as substrates for sustainable PHB production and compares this to state-of-the-art processes using first-generation substrates, taking into account technical, economic, and environmental considerations. As concerns the carbohydrate and lignocellulosic side streams, strategies that eliminate upstream hydrolysis steps, such as the use of recombinant strains, SSF, or CBP, appear beneficial to reduce the overall costs, yet further advances will be required to increase their PHB production performance. Especially lignocellulosic side streams have been presented as attractive feedstocks because of their abundance and low cost, though the extensive pre-treatment required to break their recalcitrant structure cannot be overlooked. In contrast, waste cooking oils, as well as crude glycerol, can be used with no or limited pre-treatments and have shown to be competitive substrates for PHB production, particularly the former owing to its high conversion yield. Looking at future prospects, however, it should be noted that the supply of crude glycerol is vulnerable as this heavily depends on the demand for biodiesel. Besides industrial biomass side streams, also industrial C1 gas emissions have been considered as alternative substrates for PHB production. They benefit from the attractive environmental advantage of being a CCU technology, which also entails an economically interesting perspective given the increasing emission taxes. Concerning CO 2 fixation, additional H 2 needs to be provided, thereby substantially increasing the feedstock cost. Nonetheless, considerable progress is being made to lower the cost of renewable electricity and establish large-scale green H 2 production. Although the utilization of real industrial gas emissions has not yet been investigated, promising results have been obtained using synthetic gas streams. Here, the safe and sufficient supply of O 2 was identified as the main bottleneck. This could be circumvented by an innovative best-of-both-worlds strategy involving the indirect utilization of C1 gases through liquid intermediates, allowing for efficient C1 gas fixation as well as PHB production. Future research will be required to conclude if this challenging two-stage process could indeed be technically as well as economically feasible. In conclusion, several highly-promising side streams were presented with the potential to establish a more sustainable PHB production process by combining: (1) optimized and minimal pre-treatment, (2) a suitable (engineered) PHB-producing strain, and (3) implementation of an advanced fermentation strategy. Nevertheless, further advances in these three aspects, as well as others, such as PHB purification and application, could still improve the overall process performance. Furthermore, whereas all cited results, including some competitive with the current state-of-the-art processes, have been obtained on a lab-scale, scale-up studies will be crucial to demonstrate the industrial performance of the complete process.",
"introduction": "Introduction While plastics have become almost indispensable with widespread applications in various sectors, their ever-increasing usage has severe environmental impacts. The carbon footprint of traditional fossil-based plastics amounts to 1.7 Gton CO 2 eq. per year, or 4% of the global greenhouse gas emissions (Zheng and Suh 2019 ). In addition, after a service life of often less than one year, also their disposal has a disastrous impact on the environment as a staggering amount of 20 Mton of plastic ends up in the oceans every year (Borrelle et al. 2020 ). Although regulations are being put in place to reduce plastic usage, such as bans on single-use plastics, various industries are in need of alternative, more sustainable materials (Nielsen et al. 2020 ). In this context, polyhydroxyalkanoates (PHAs), which are polyesters that can be produced by prokaryotes from renewable resources, are witnessing a major surge of interest (Li et al. 2016 ). These biopolymers are known for their biodegradation under various conditions, including in marine environments (Kabir et al. 2020 ). Poly(3-hydroxybutyrate) (PHB) is by far the most occurring and extensively studied representative of the PHA family with material properties similar to conventional polypropylene (PP), hence, suitable for a wide range of applications (Kumar et al. 2020 ). In particular, its barrier properties result in a great potential for food packaging, while its biocompatibility makes PHB suitable for medical and pharmaceutical products (Koller 2014 ; Israni and Shivakumar 2019 ). Nevertheless, despite its bio-based nature, interesting material properties, and excellent biodegradability, today’s annual PHB production is estimated at only 4 kton, whereas the global bioplastics market amounts to 2.4 Mton (Koller and Mukherjee 2022 ). Due to its relatively high production cost of approximately 3 €/kg, PHB currently struggles to be cost-competitive with established fossil counterparts such as PP with a market price of about 1 €/kg. In this respect, the choice of feedstock is crucial as it typically constitutes up to 50% of the total PHB production cost (Koller 2019 ). Although current commercial processes achieve high PHB production performances, they use high-purity first-generation substrates which are not only economically unfavorable but raise ethical concerns as well. Consequently, the use of industrial side streams as inexpensive and more sustainable feedstocks could offer a solution. This, however, entails new challenges related to the accessibility of the carbon source, the presence of impurities, and the restricted assimilation by PHB-producing microorganisms. Therefore, this mini-review provides a comprehensive and critical overview of the use of industrial side streams for fermentative PHB production. First, the state-of-the-art PHB production processes using conventional, pure carbon sources are described as a benchmark. Afterward, a concise overview is provided of the industrial side steams, where their feasibility as an alternative feedstock is discussed based on the attained PHB production performance and required pre-treatments, taking into account technical, environmental, and economic considerations. Notably, this mini-review does not aim to cover the large number of carbon sources described in literature. In contrast, it rather puts focus on the most promising ones for PHB production, which were selected based on state-of-the-art research results considering the full spectrum of biomass side streams as well as C1 gas emissions. As such, it presents the most important inexpensive heterotrophic and autotrophic feedstocks to make PHB biosynthesis on a larger scale economically efficient, and, at the same time, upgrade waste streams and save resources of food and fodder relevance typically used in PHB production to date."
} | 2,222 |
39876645 | PMC7617567 | pmc | 1,565 | {
"abstract": "Abstract To better understand how the biocatalyzed depolymerization of polyesters works, model molecules are needed to develop activity assays and determine enzymatic kinetic parameters. In this communication the chemical synthesis and characterization of 2‐hydroxyethyl furan‐5‐carboxylic acid and bis(2‐hydroxyethyl) furan‐2,5‐dicarboxylates as potential model molecules to further study the enzymatic depolymerization of poly(ethylene furanoate) was investigated.",
"conclusion": "Conclusions In this work a facile and environmentally friendly procedure to obtain the glycol‐containing diesters BHET and BHEF, with high yield and purity above 95 % was reported. Furthermore, a novel mild strategy to obtain monoesters in good yield from symmetric aromatic diesters employing a hydrolysis‐followed transesterification reaction. In this second case, the successful purification process has also proven to be particularly complicated because of the poor solubility of the products and the high boiling point of the EG.",
"discussion": "Results and Discussion Aiming to synthesize mono and diesters of FDCA and ethylene glycol (EG) (Figure 1 ), the constituent monomers of PEF, the information regarding the synthesis of these molecules available in the literature is sparse, especially regarding the preparation of mono esters.\n Figure 1 Structures of the synthesized compounds bis(2‐hydroxyethyl) furan‐2,5‐dicarboxylate (BHEF) (top, light green) and 2‐hydroxyethyl furan‐5‐carboxylic acid (MHEF) (bottom, dark green). Therefore, the preparation of bis(2‐hydroxyethyl) terephthalate (BHET) was selected as the starting point. BHET can be synthesized with several protocols however, most reactions dealing with BHET do not report yields, are under patent and were found to be far from the ideal of eco‐sustainability since they either use harmful reagents, harsh conditions, expansive metal catalysts or using various derivatives and protective groups. Moreover, some of these methodologies are directed towards a large‐scale industrial synthesis and use harmful reagents like ethylene oxide, \n [10] \n or obtain this monomer by chemically depolymerizing PET in different conditions[ \n 11 \n , \n 12 \n , \n 13 \n ] (high pressure and temperature, and using metallic catalyst). Others reported procedures include transesterification of dimethyl terephthalate either using metal catalysis \n [14] \n or imidazolium salts, \n [15] \n and the alkoxycarbonylation of aryl iodides with Mo(CO) 6 . \n [16] \n The direct rection between TPA and EG was only performed in a flow reactor with a yield of 97.5 % at 180 °C in 9 h. \n [17] \n The furane counterpart was again obtained either from the metal‐catalyzed transesterification of the corresponding dimethyl ester \n [18] \n or from 5‐hydroxymethyl‐2‐furfuraldehyde, using a multi‐step approach. \n [19] \n \n The MHET synthesis was less reported than the diester, ethylene oxide \n [20] \n was again used but only alkoxycarbonylation \n [16] \n and protective groups \n [21] \n were found to be an effective strategy to selectively obtain the desired product; no procedure for the obtainment of MHEF was instead found. This led to the search for simpler and straightforward strategies to obtain these molecules. For this reason, following the green chemistry principles, a simple acid‐catalyzed Fischer esterification (Scheme 1 ) was carried out. Scheme 1 Synthesis of bis hydroxyethyl diesters through Fischer esterification. Once the solution cools down, NaHCO 3 is added to neutralize and deprotonate any residual diacid with the diester that is then extracted with AcOEt. This reaction in literature was carried out only on FDCA using benzene as the reaction solvent, with a total yield of 54 %. \n [22] \n When carrying out the reaction on TPA using toluene as a safer alternative to benzene (Table 1 , entry 1) no conversion was observed as the TPA was unable to dissolve in the solvent and the small amount of EG. Seeing that the reaction in solvent was not working, toluene was removed from the reaction system and a 10‐fold excess of EG was used (Table 1 , entry 2). Using this protocol after ~30 min the reaction mixture became homogeneous and, after 24 h the product was recovered with 80 % yield. The limitation of this methodology lies in the fact that EG partially dissolves in ethyl acetate, carrying on with the organic phases, so to completely remove it from the product a lyophilization step is required (white solid, purity>95 %). The reaction was then further optimized (Table 1 , entry 3) by reducing the reaction time to 6 h. After the successful optimization of the BHET synthesis, the process was scaled‐up by performing the reaction on 1 gram of TPA (Table 1 , entries 4 and 5) that led to a yield of 98 % and a 95 % purity of the recovered compound. The procedure was then applied for the synthesis of BHEF. The first synthesis (Table 1 , entry 6) led to a yellow solid (BHET was instead white), although the amount of coloured impurity is negligible and almost not detectable at 1 H‐NMR, the reaction was repeated (Table 1 , entry 7) using a 2‐necked flask equipped with a nitrogen inlet. The reaction proceeded in the same fashion, providing a crystalline white solid; most likely the atmospheric oxygen is capable of oxidizing and therefore partially degrading the furan ring. Lastly, in Table 1 , entry 8 the scale‐up of the reaction on 1 gram is reported obtaining analogues results. Using FDCA as the aromatic diacid the extra 8 equivalents of glycol reported on entry 4 were not needed since FDCA is more polar and has a higher solubility than TPA.\n Table 1 Fischer esterification of terephthalic acid or furan dicarboxylic acid with ethylene glycol. \n Entry \n \n Monomer \n \n Diacid/EG ratio \n \n Time \n [h] \n \n Yield \n % \n \n Scale \n [mg] \n \n Literature a \n \n \n FDCA \n \n 1 : 3 \n \n 24 \n \n 54 \n \n 500 \n \n 1 b \n \n \n TPA \n \n 1 : 3 \n \n 24 \n \n Traces \n \n 500 \n \n 2 \n \n TPA \n \n 1 : 30 \n \n 24 \n \n 90 \n \n 500 \n \n 3 \n \n TPA \n \n 1 : 30 \n \n 6 \n \n 90 \n \n 500 \n \n 4 \n \n TPA \n \n 1 : 30 \n \n 6 \n \n 65 \n \n 1000 \n \n 5 \n \n TPA \n \n 1 : 38 \n \n 6 \n \n 98 \n \n 1000 \n \n 6 \n \n FDCA \n \n 1 : 30 \n \n 6 \n \n 72 \n \n 500 \n \n 7 c \n \n \n FDCA \n \n 1 : 30 \n \n 6 \n \n 75 \n \n 500 \n \n 8 c \n \n \n FDCA \n \n 1 : 30 \n \n 6 \n \n 72 \n \n 1000 \n H 2 SO 4 (95 %) was used as catalyst, 1–2 drops on less than 1 g scale, 3–5 drops on 1 g scale. Temperature is 110 °C. [a] Solvent used: benzene. [b] Solvent used: toluene. [c] Under nitrogen atmosphere Wiley‐VCH GmbH After the synthesis of BHEF, the synthesis of the monoesters was carried out. This proved to be particularly challenging because, while the two reagents in stoichiometric amounts should generate only the desired product, there is always a certain percentage of diester and residual diacid. The latter is impossible to extract separately and extremely difficult to remove using column chromatography because of its similarity and interactions with the product. While for 2‐hydroxyethyl furan‐5‐carboxylic acid (MHEF) there were no reported synthesis, for 2‐hydroxyethyl terephthalic acid (MHET) there are few synthetic routes available. Again, most of these strategies were under patent, did not report any yield and used ethylene oxide or metal catalysts. Several synthetic strategies reported in Table 2 were attempted. Fischer esterification was not possible this time due to the need to keep the molar ratio close to 1 and lack of a suitable solvent. Therefore, we tried the opposite route, a basic hydrolysis of the previously synthesized diester. In entries 1–5 different hydrolytic conditions, changing solvent, base, time, and temperature were tested, but sadly none of these were able to provide an acceptable yield with high purity and low diacid content. Only entry 3 produced a good yield with excellent purity however, the recovered product was not the hydroxyethyl monoester, but the ethyl monoester derived from the side reaction with ethanol (Scheme 2 ) which was used as solvent. Thus, the base‐catalyzed transesterification appears to be much faster than the subsequent hydrolysis reaction.\n Table 2 Strategies to obtain monoesters of terephthalic acid or furan dicarboxylic acid. \n Entry \n \n Reagent \n A \n \n Reagent \n B \n \n Ratio \n \n Reagent \n C \n \n Solvent \n \n T \n [C°] \n \n Time \n [h] \n \n % Yield of monoester \n \n 1 \n \n BHET \n \n NaOH \n \n 1 : 1 \n \n – \n \n Water \n \n R.T. \n \n 18 \n \n Traces \n \n 2 \n \n BHET \n \n NaOH \n \n 1 : 1 \n \n – \n \n Glycol \n \n R.T. \n \n 18 \n \n Traces \n \n 3 \n \n BHET \n \n NaOH \n \n 1 : 1 \n \n – \n \n Ethanol \n \n R.T. \n \n 18 \n \n 50 a \n \n \n 4 \n \n BHET \n \n LiOH \n \n 1 : 1 \n \n – \n \n Water/acetone \n \n 80 \n \n 2.5 \n \n 10 \n \n 5 \n \n BHET \n \n LiOH \n \n 1 : 1 \n \n – \n \n Water/THF \n \n 80 \n \n 2.5 \n \n 22 \n \n 6 \n \n TPA \n \n EG \n \n 1 : 1.3 b \n \n \n DCC \n \n DMF \n \n 0 – R.T. \n \n 6 \n \n Traces \n \n 7 \n \n TPA \n \n EG \n \n 1 : 1.3 b \n \n \n SOCl 2 \n \n \n EG \n \n R.T. \n \n 26 \n \n 0 \n \n 8 \n \n TPA \n \n Iodoethanol \n \n 1 : 1.3 \n \n NaHCO 3 \n \n \n DMF \n \n 85 \n \n 25 \n \n Traces \n \n 9 \n \n DMT \n \n NaOH \n \n 1.15 : 1 \n \n – \n \n EG \n \n R.T. \n \n 14 \n \n 50 \n \n 10 \n \n DMFu \n \n NaOH \n \n 1.15 : 1 \n \n – \n \n EG \n \n R.T. \n \n 14 \n \n 69 \n \n a Not the desired product \n b Ratio is Reagent A to Reagent C, EG is in large excess Wiley‐VCH GmbH Scheme 2 Formation of the hydrolyzed transesterification byproduct. Due to the lack of success with these strategies, three different coupling methods to obtain the desired product were carried out. A sub‐stoichiometric amount of TPA to preferably obtain the (easier to remove) diester instead of the diacid was used. In Table 2 , entry 6 the results of a Steglich esterification \n [23] \n are reported which led to the formation of short oligomers (see ESI, Figure S1) and only traces of the desired product could be recovered. Table 2 , entry 7 relates to the activation of the reagents with thionyl chloride however, we recovered only a small fraction from aqueous phases containing mostly TPA since most of the substrate was again converted to a polyester. To avoid this polycondensation, we decided to use 2‐iodoethanol in basic conditions to selectively achieve a mono substitution; \n [24] \n the reaction should have lasted 4 h but even after 24 h only traces of product were found on TLC. Then, building on the previous observations, the hydrolysis of dimethyl terephthalate (DMT) using EG as solvent (Table 2 , entry 9) was performed. Despite the high viscosity and the reaction proceeding mostly in heterogeneous condition, due to the low solubility of DMT, a large amount of monoester (50 % yield), contaminated with some TPA, a small amount of residual DMT and mostly BHET was obtained, proving that transesterification proceeds faster than the hydrolysis (Scheme 3 ). Scheme 3 Hydrolysis‐followed transesterification to obtain monoesters. After the initial extraction with AcOEt to remove the diesters it is possible to eliminate the glycol either by lyophilization or vacuum distillation. Once the glycol is removed MHET is dissolved in basic water, precipitated adding HCl and filtered. Since this molecule is intended to be used in enzymatic assays, purity must be exceptionally high, with inverse phase chromatography and even solid phase extraction (SPE) cartridges can be of great use to remove any remaining TPA, allowing the obtainment of pure MHET (>99 %) with a 50 % yield. Moreover, the cartridge can be run multiple times and uses water as solvent. Lastly in Table 2 , entry 10, the same methodology to the synthesis of MHEF by starting from dimethyl furan dicarboxylate (DMFu) was applied. In this case the reaction was again completely homogeneous thanks to the higher polarity of the furan ring, sadly this also caused the MHEF to not precipitate in acidic water as opposed to MHET, moreover the small loading SPE were not enough to separate it from residual FDCA. We then resorted to use flash chromatography to separate and purify the product, obtaining an off‐white solid with up to 69 % yield on a 3‐gram scale. To assess the purity, the obtained compounds were characterized by mono‐ and bidimensional NMR, HPLC‐MS, IR and GC‐MS when possible. Melting point determination was also performed."
} | 3,025 |
38593383 | PMC11044582 | pmc | 1,566 | {
"abstract": "A three-terminal\nmemristor with an ultrasmall footprint of only\n0.07 μm 2 and critical dimensions of 70 nm ×\n10 nm × 6 nm is introduced. The device’s feature is the\npresence of a gate contact, which enables two operation modes: either\ntuning the set voltage or directly inducing a resistance change. In I – V mode, we demonstrate that by\nchanging the gate voltages between ±1 V one can shift the set\nvoltage by 69%. In pulsing mode, we show that resistance change can\nbe triggered by a gate pulse. Furthermore, we tested the device endurance\nunder a 1 kHz operation. In an experiment with 2.6 million voltage\npulses, we found two distinct resistance states. The device response\nto a pseudorandom bit sequence displays an open eye diagram and a\nsuccess ratio of 97%. Our results suggest that this device concept\nis a promising candidate for a variety of applications ranging from\nInternet-of-Things to neuromorphic computing.",
"conclusion": "Conclusion Our\nwork successfully demonstrates the development and characterization\nof a three-terminal memristor with an ultrasmall footprint of 0.07\nμm 2 and critical dimensions of 70 nm × 10 nm\n× 6 nm. We show how a gate voltage applied to the device can\nbe used to manipulate the operation point, as evidenced by DC characterization,\nor initiating the resistance switching, as demonstrated by 1 kHz pulsed\nmeasurements. DC characterization shed light on the underlying memristive\noperation of the proposed device concept with a median set voltage\nof 0.4 V (ungated case). When a gate voltage is applied, we observe\na set voltage shift from 0.325 V (−1 V gated) to 0.55 V (+1\nV gated), corresponding to a shift of 69.2%. Pulse measurements of\n1 kHz are carried out with the gate as the driving force to change\nthe device resistance. We reported an endurance of over 2.6 M pulses\nwith two distinct resistance distributions. Using the same type of\nmeasurement, we compare four devices for 500k pulses and find only\nminor device-to-device variations. To demonstrate the device’s\npotential in practical applications, under random inputs, we test\nits response to PRBSs of varying orders from 7 to 13. Our measurements\nindicate reliable operation with an open eye diagram and success rates\nof approximately 97%. These promising results enable a range\nof potential applications.\nIn the realm of neuromorphic computing, our device should facilitate\nspike probability tuning in artificial neurons and the adjustment\nof synaptic weights. Furthermore, in addition to 2T-memristive devices, 11 3T-memristive devices are attracting attention\nfor applications to reservoir computing. 46 Here, the nonlinear electrical response of 3T-ion-gating transistors\nplays a crucial role in mapping inputs to a high-dimensional feature\nspace. The time scales and voltages utilized in this work also suggest\npotential for Internet-of-Things applications. Finally, our findings\nare promising candidates for the development of memristive logic systems.",
"discussion": "Results/Discussion Device\nConcept and Two-Terminal Characterization Our\ntunable 3T memristor with nanoscale dimensions (0.07 μm 2 footprint) is depicted in Figure 1 . We first show the device in a 2T operation\n(IV cycles, ungated) as proof of the memristive origin of the switching\nmechanism. This plot will further serve as a reference for the gated\nexperiments below. Figure 1 Three-terminal (3T) memristor overview and 2T operation.\n(a) False-color\ntop view scanning electron microscope image of the device. The “gate”\nelectrode (Pt) is on the left, in green; the active “source”\nelectrode (Ag) comes from the top, in red; and the “drain”\nelectrode (Pt) is on the right, in blue. (b) Schematic device cross\nsection. Between the Ag source and Pt drain, a bilayer nitride layer\n(2 nm TiN on 4 nm SiN) is used to confine the ion migration in the\n2 nm SiO 2 layer toward the Pt gate. A 4 nm TiN, 4 nm HfO 2 bilayer is used to prevent ion migration toward the gate.\nThe bottom Pt drain has a width of 70 nm confining the device-under-testing\n(DUT). (c) Schematic of the measurement setup and input signal. A\ntriangular voltage sweep is applied to the source with a source-measure\nunit (SMU) that limits the current to a compliance current ( I cc ) and measures the current through the memristor.\n(d) DC IV characteristics of 50 overlaid cycles without the gate (floating),\nfocused on the region of interest. In blue, the median value of all\nmeasurements is shown. (e) Histogram of extracted V set at 90% I cc . The presented device is based on a 2T memristor with Ag as\nthe\nactive top electrode, also called the source in this work, and Pt\nas the inert bottom electrode, the drain, with SiO 2 as\nthe switching medium. The 3T functionality is enabled by introducing\nanother Pt electrode as a side gate. This additional electrode will\nallow us to influence the memristor operation by either shifting the\nset voltage or triggering the resistance change itself between source\nand drain. A false-colored top view scanning electron microscope\n(SEM) image,\nalong with a zoomed-in 3D sketch, is provided in Figure 1 (a) and (b) to illustrate the\nstructure of the device-under-testing (DUT). From the cross section,\nwe see that the source and drain are separated by 2 nm TiN and 4 nm\nSiN. These layers confine the ion migration to the SiO 2 active region. As can also be seen in Figure 1 (b), the device has a width of only 70 nm.\nThus, our active region is confined to the nanoscale with a height\nof 6 nm, width of 10 nm toward the gate, and width of 70 nm from the\nbottom Pt drain electrode, leading to critical dimensions of 70 nm\n× 10 nm × 6 nm = 4200 nm 3 . Additional SEM images\ncan be found in the Supporting Information, Figures S1 and S2 . The in-house fabrication starts with a standard\nSi wafer with 200\nnm of thermally grown SiO 2 on top. The drain is patterned\nby electron beam lithography (EBL). Subsequently, we etch 50 nm into\nthe SiO 2 with reactive ion etching (RIE) and deposit 3\nnm Ti and 47 nm Pt using electron-beam evaporation (EBE). Afterward,\na nitride bilayer (4 nm SiN, 2 nm TiN) is grown with atomic-layer\ndeposition (ALD) to avoid filament formation directly between the\ndrain and source. Next, the source is patterned by EBL and deposited\nusing EBE (1 nm Cr, 24 nm Ag, 17 nm Pt, 3 nm Cr). To allow for filament\nformation at an interface, the gate region is patterned with the EBL\nand subsequently physically etched by ion bombardment. Next, the switching\nmedium (2 nm SiO 2 ) as well as a blocking layer of 4 nm\ninsulating TiN and 4 nm HfO 2 are deposited via ALD. The\ngate is patterned with EBL using an MMA/PMMA double-layer resist and\ndeposited with EBE at an angle. Finally, after a last photolithography\nstep, all buried electrodes are opened by removing the covering dielectrics\nusing RIE. To prove the memristive behavior of the fabricated\ndevices, we\nfirst characterize their 2T (source–drain) part. DC measurements\nare carried out by grounding the drain, applying a voltage V SD on the source, and letting the gate float.\nIn Figure 1 (b), the\ncontacted electrodes and in Figure 1 (c) the setup are schematically shown. After initial\nformation with a floating gate and a compliance current I cc = 10 nA, we sweep V SD from\n0 V to 4 V to −2 V and back to 0 V with a sweep rate of 0.05\nV/step and repeated it in 50 cycles. We used this extended voltage\nrange to capture all of the switching events. In Figure 1 (d), the regions of interest\ncontaining the set voltage of all 50 measurements are overlaid on\ntop of each other with the median value shown in blue. The full sweep\ndata can be found in Supporting Information Figure S5(a) . The set voltage V set , at\nwhich the devices turn on, is defined as the point where the current\nreaches 90% of the compliance current I cc = 1 μA. We plot the distribution of V set as a histogram in Figure 1 (e). A narrow distribution can be observed with an\naverage V set well below 1 V (mean of 0.39\nV, median of 0.40 V, and standard deviation of 0.05 V). We find the\nON-resistance to be 1.65 MΩ, which is in line with the literature. 13 , 42 , 43 Additional pulsed set time and\nretention measurements show a fast 2T set of around 1 μs and\na volatile operation. These measurements can be found in the Supporting\nInformation, Figures S3 and S4 , respectively. Gate-Induced Operation Point Tuning To explore the\npotential of the 3T memristor, we begin by applying a constant gate\nvoltage offset while still performing DC IV measurements between the\nsource and drain electrodes. This allows us to gain insight into the\npossibility of adapting the operation point with the gate contact\nand thereby increasing the range of applications of the device. To demonstrate the gate-induced operation point tuning, we apply\nthe voltage signals, as shown in Figure 2 (a). The essential difference from the 2T\noperation is that the gate electrode is now alternated between two\nconstant offsets. On the Ag top electrode (source), we apply a triangular-shaped\nvoltage sweep from 0 V to 1.1 V and back, with a sweep rate of 0.025\nV/step using a source-measure unit (SMU, Keysight B2912a) and a current\ncompliance of I cc = 200 nA. The Pt bottom\nelectrode (drain) is grounded. On the Pt gate electrode, two different\nvoltages, V GD,1 = −1 V and V GD,2 = +1 V, are applied, represented by the\nblue and orange curves in the V GD plot,\nrespectively. To account for potential shifts of the memristor over\nsuccessive cycles, we alternated V GD between\nconsecutive cycles. In total, we carried out 50 I – V measurements, which corresponds to 25\nfull cycles per gate offset. Two representative measurement cycles\nare listed in Figure 2 (b). The full measurement data can be found in Supporting Information Figure S5(b) . By applying a gate voltage V GD,1 = −1 V (shown in blue), the set\nvoltage is lower than in the ungated experiments previously discussed\nand has a value of V set,1 = 0.325 V. Conversely,\nwhen we apply a gate voltage V GD,2 = +1\nV (shown in orange), the set voltage shifts to a value of V set,2 = 0.55 V. This corresponds to a shift\nin the operation point of 69.2% due to the gating of the device. The\nmeasurements shown in Figure 2 (b) are characterized by V set that corresponds to the median of the 25 full cycles per gate voltage.\nTo complete the analysis, we extracted the set voltages of all cycles\nat 90% I cc = 200 nA and plotted this\nquantity in a histogram in Figure 2 (c). The gate voltage creates two well-distinguishable\nstates without any overlap between their distributions. The cumulative\nprobabilities of the distributions are plotted in the same figure.\nWe evaluate the ON-resistance in both cases as well: For V GD,1 = −1 V, we find 640 kΩ, and for V GD,2 = +1 V, we find 1.28 MΩ. Additionally,\nthe reset voltage analysis as well as continuous gate sweep measurements\ncan be found in the Supporting Information, Figures S6 and S7 , respectively. Figure 2 “Set-voltage-tuning” mode.\n(a) Measurement schematic.\nA triangular voltage signal is applied to the source ( V SD in dark gray), while the drain is grounded. During\nconsecutive cycles, the gate voltage is alternated between two offsets, V GD,1 = −1 V (blue) and V GD,2 = +1 V (orange). (b) DC IV characteristics showing\na representative cycle of each V GD offset,\ni.e., one corresponding to the median value of V set from the 50 measurements shown in (c), focused on the region\nof interest. The difference between V set,1 = 0.325 V (blue) and V set,2 = 0.55 V\n(orange) corresponds to a 69.2% shift in V set . (c) Histogram of 50 alternating measurements for V GD,1 = −1 V (blue) and V GD,2 = +1 V (orange). V set is evaluated at\nthe point where the current reached 90% of the compliance current I cc = 200 nA. Adding the gate voltage enables us to shift the set voltage up\nand down compared to the ungated case. This hints at the gate voltage\nplaying a role in facilitating or hindering filament formation in\nthe memristor depending on the polarity of the applied voltage. A\ngate voltage influences the electric field within the SiO 2 and at the interfaces of the remaining two electrodes. We assume\nthat this changes several reaction steps necessary for filament formation:\noxidation at the Ag electrode, distribution of the silver ions inside\nSiO 2 , and reduction of silver ions. This in turn will\ninfluence the filament formation dynamics. This interplay between\ngate voltage and filament reactions allows us to tune the set voltage\nor initiate the resistance change, as discussed below. We assume that\nthe negative gate voltage increases the rate of Ag + ion\nformation, thereby increasing the silver reduction rate at the filament,\nwhich corresponds to a lower V set . In\ncontrast, the positive gate voltage hinders the ion formation, thereby\nreducing the reduction rate at the filament and causing an increase.\nThese effects are visualized in Figure 3 . Figure 3 Visual illustration of the operation mechanism. Inside\nthe outlined\nred box, representing the SiO 2 layer, Ag atoms and ions\nare depicted in two shades of gray. (a) Applying a negative gate voltage\nis assumed to increase the silver ion concentration and facilitate\nfilament formation. (b) Applying a positive gate voltage is assumed\nto decrease silver ion concentration and hinder filament formation. Gate-Triggered Resistance Change In the following,\nwe show that the resistive state of our 3T memristor can be changed\nby modulating the gate bias. For this purpose, the input voltage on\nthe source and a series resistor is fixed, and the gate voltage is\nvaried. Furthermore, the measurement method is changed from DC IV\ncycles to 1 kHz pulsed operation to show the dynamical response of\nthe device and allow for comprehensive endurance measurements. The experimental setup is schematically shown in Figure 4 (a). Two input signals are\nsupplied by an arbitrary waveform generator (AWG, Agilent 33500B)\nand monitored with an oscilloscope (Keysight MSO9104A), which is also\nused to evaluate the state of the device-under-testing (DUT). Figure 4 Representative\n1 kHz memristor state modulation by applying gate\nvoltage pulses. (a) Measurement schematic showing the bias path in\nblue, the device-under-testing (DUT) in green, and the gating path\nin purple. An arbitrary waveform generator (AWG) supplies the constant\nbias voltage V B and the pulsed gate voltage V GD . The DUT consists of three contacts: source\n(S), gate (G), and the drain (D, ground). The bias path consists of\nAWG channel 1 and a current-limiting resistor ( R Icc ) that connects to the source of the DUT. The gate path\nconsists of AWG channel 2, which is connected to the gate of the DUT.\n(b) Input voltages from the AWG measured at V B (blue) and V GD (purple). The\ninput bias voltage V B is held constant\nat 1.5 V, while the gate voltage is modulated between V GD = ±V with a 1 kHz frequency. (c) Measured source–drain\nvoltage V SD . We observe a typical response\nfor capacitive charging overlaid onto the resistance change of our\nmemristor. (d) Device resistance ( R DUT ) converted from V SD . R DUT follows the shape of V GD ; the higher the V GD , the higher the\ndevice resistance, and vice versa. The AWG channel 1 supplies a constant bias voltage V B , which is monitored with an oscilloscope. Next, a resistor R Icc = 1 MΩ is inserted between AWG channel\n1 and the DUT to limit the current and protect the DUT. Between R Icc and the source contact, we read out the\nsource–drain voltage V SD across\nthe DUT. The drain contact is grounded. The bias path is shown in\nblue in Figure 4 (a),\nand the DUT in green. The resistance between source and drain together\nwith R Icc acts as a voltage divider, enabling\nus to calculate the resistance R DUT of\nthe DUT. Channel 2 of the AWG supplies the square-shaped gate voltage\npulses with 1 kHz frequency, displayed in purple in Figure 4 (a). It is read out with the\noscilloscope as V GD . A representative\nmeasurement is shown in Figure 4 (b)–(d). The input bias voltage is\nheld constant at V B = 1.5 V, while the\ngate voltage is modulated between ±2 V, as depicted in Figure 4 (b) in blue and purple,\nrespectively. The gate voltage is increased compared to that before\nto account for the faster measurement speed. The voltage between source\nand drain, V SD ( Figure 4 (c)), exhibits a capacitive charging response,\nwhich results from the high resistances present in the circuit, thus\nbringing the setup close to its RC limit. The increased voltage V SD compared to the set voltages of the I – V measurements stems from the\npulsed operation happening on a faster time scale and is commonly\nknown as the voltage–time dilemma. 44 The device resistance, R DUT , is extracted\nas discussed before from V SD and is reported\nin Figure 4 (d) in green.\nThe resistance of the device follows the shape of the gate voltage:\na higher gate voltage increases the DUT resistance, whereas a lower\ngate voltage decreases it. This indicates that the gate voltage may\ninfluence the filament growth between source and drain, a negative\ngate voltage favoring this process, while a positive one hindering\nit. By evaluating the data at the last point prior to the gate voltage\ntransition, we extract an average high-to-low resistance ratio of\n18.9. The retention of the gated resistance states is shown in Figure S8 . Leakage current measurements\nto the gate with the same voltages\nas used in this work (±1 and ±2 V) were carried out. Leakage\ncurrents were found to be ≤1.6 nA, hence significantly smaller\nthan the source–drain current in compliance ( I SG , I DG ≪ 100 nA ≤ I SD,cc ). The full analysis can be found in Table S2 . We prioritized reliability and\nendurance during the aforementioned\nmeasurements and therefore chose high resistance values in this experiment,\nwhich leads to a high RC time constant and, accordingly, to a slow\nresponse (see Methods section “ Pulsed Measurements ”). To ensure a precise\ndata evaluation, we determine the resistance state at the end of each\npulse. Further investigations using lower resistances and therefore\nallowing for higher speeds are of high interest, too. They would require\nthe DUT to withstand higher currents without degradation, which has\nyet to be explored. Endurance and Reproducibility Bringing\nthe 3T device\ninto applications requires long device endurance, reproducible operation,\nand low device-to-device variability. We first conducted endurance\nmeasurements by collecting a total of 2.6 million cycles in packs\nof 100 000 cycles, using the same device as in all previously shown\nmeasurements and labeled “Device 1”. In between data\nrecordings, the device is continuously cycled. As a result, the actual\nnumber of cycles is even larger than that shown here. We read out\nthe device state immediately before the gate voltage V GD transition from low to high or high to low. Hence,\neach cycle contains a data point for the positive and negative pulse.\nThe same setup as that in Figure 3 (a) is used. Figure 5 displays the resistance of the device under test ( R DUT ) over the 2.6 million cycles in subplot\n(a). To further analyze the data, the corresponding histogram is shown\nin Figure 5 (b). This\nrepresentation clearly indicates that there is no overlap between\nthe two gated states. We find that for a gate voltage of V GD = −2 V, the device resistance has a mean of\n1.5 MΩ (median of 1.5 MΩ) with a standard deviation of\n0.12 MΩ. In the case of V GD = +2\nV, we extract a mean of 12.8 MΩ (median of 12.0 MΩ) and\na standard deviation of 3.2 MΩ. Using the mean, we found a ratio\nof 8.5. The results show that the device reliably switches over the\nentire 2.6 million cycles. We did not encounter a single failure,\ndemonstrating its excellent endurance capabilities. Figure 5 Endurance and device-to-device\nvariability. (a) R DUT plotted over 2.6M\ncycles. Each cycle consists of a\npositive and a negative 500 μs voltage pulse applied to the\ngate with V GD = ±2 V. R DUT is evaluated at the last point of the pulse. The experiment\nis performed, and the data are collected in packs of 100k pulses.\n(b) Associated histogram shows a mean of 1.5 MΩ (median of 1.5\nMΩ) for the negative gate pulse (blue) and a mean of 12.7 MΩ\n(median of 12.0 MΩ) for the positive gate pulse (orange), respectively.\n(c) Comparison of R DUT measured over 500k\ncycles for four devices displaying minor device-to-device variation.\nResults from device 1 were used in all previous figures. Second, we evaluated the performance of multiple devices\nto investigate\nthe reproducibility of the 3T memristor. For this purpose, we again\nuse Device 1 and compare it with three other devices. We gather 500 000\ndata points per device to ensure large enough statistics for a fair\ncomparison of all devices. Different voltage settings are used for\neach device so that the low resistance states are well aligned (see Table 2 in the Methods section “ Pulsed Measurements ”). The data collection is done in the same way as before,\nand the results are plotted in Figure 5 (c). Each device exhibits two distinct states with\na narrow distribution around their mean value. Additionally, the variability\nbetween devices is low, which allows us to define a common threshold\nto distinguish the two states for all devices. This demonstrates the\nrepeatability and consistency of the device’s gated operation\nconcept. Application: Pseudorandom Bit Sequences To evaluate\nfurther the device’s performance under practical application\nscenarios, we conducted tests using pseudorandom bit sequence (PRBS)\ndata. A PRBS of order k is a sequence of pseudorandom\nbits of length of 2 k – 1. It contains\nshorter sequences of equal bits of various lengths. This mimics the\npossible bit combinations that might occur in a practical scenario,\nsimilar to the MNIST data set. In our experiments, the bits were translated\nto V GD = ±2 V. The PRBS test serves\nas a benchmark to measure the device’s ability to handle and\nwithstand dynamic inputs as used in high-speed data transmission or\ncomputation applications. Furthermore, it allows investigating the\ndevice’s resilience to random data and potentially long sequences\nof the same input without deteriorating in quality. The gate\nvoltage ( V GD ) is coded as +1 for positive\nvalues and −1 for negative values. An exemplary input signal\nis illustrated in Figure 6 (a). Each single data point is encoded by a 1 ms pulse. Similarly,\nthe DUT state is coded depending on the measured V SD compared with a threshold voltage. We code the output\nas +1 for V SD > V th and −1 for V SD < V th . From the measurement in Figure 6 (b), it can be deduced that V th = 0.145 is a suitable choice. By comparing\nthe two data strings (input encoded in V GD and output read out from V SD ), we calculated\nthe success rate. For the example shown in Figure 6 , it is 100%. The resistance of the DUT in\nthis example is plotted in Figure 6 (c) for completeness and comparison. Figure 6 Pseudorandom bit sequence\nmeasurements: Representative data and\neye diagram of a full data set. (a) Using the same setup as in Figure 3 (a), we apply V B = 0.3 V (blue) and V GD = ±2 V (purple). (b) Measured source–drain voltage, V SD . The threshold voltage V th = 0.145 V is marked with a gray dashed line. (c) Corresponding\ndevice resistance R DUT . (d) Eye diagram\nbased on 202 000 cycles using V SD to show the switching dynamics over two periods (T) of measurement\ndata. For a complete picture with longer\ndata sets, we repeat these measurements\nin packs of 50 000 bits for a total of 250 000 bits\nand evaluate the DUT state again at the last point before the V GD flank. We repeated this measurement for four\nPRBSs of different order (PRBS-7, PRBS-9, PRBS-11, and PRBS-13). By\ncomparing the PRBS input data with the extracted device state, we\ncan calculate the success rate. It varies between 96.6% and 97.4%\ndepending on the PRBS used, as can be seen in Table 1 . We\nobserve no degradation in performance for higher PRBS orders and a\nstable threshold voltage ( V th ). Table 1 Pseudorandom Bit Sequence Measurements a PRBS 7 9 11 13 Success\nrate 96.6% 97.3% 97.4% 97.1% V th 0.145 0.145 0.145 0.145 a Success rate and threshold voltage\nfor PRBS of orders 7, 9, 11, and 13. Table 2 Bias Voltage V B and Gate Voltage V GD Applied to\nthe Four Devices Shown in Figure 4 (c) Device 1 2 3 4 V B (V) 1.8 1.5 2 2 V GD (V) ±2 ±2 ±3 ±1.5 The eye diagram ( Figure 6 (d)) is a visual representation of the signal\nquality and\nshows the transitions between and fluctuations within the two states.\nTo obtain such an eye diagram, all periods of the measurement data\nare overlaid. In our case, the period is T = 1 ms.\nThe eye is found to be wide open for a PRBS-13 input of 202k cycles.\nWe found an eye height 45 of 6 mV and an\neye opening of 6.3 σ. This implies a high level of noise immunity.\nThe switching times are found to be well within the cycle period,\nindicating that the device has the potential to operate at higher\nspeeds than the current 1 kbit/s."
} | 6,288 |
36857605 | null | s2 | 1,567 | {
"abstract": "Characterizing the diverse, root-associated fungi in mine wastes can accelerate the development of bioremediation strategies to stabilize heavy metals. Ascomycota fungi are well known for their mutualistic associations with plant roots and, separately, for roles in the accumulation of toxic compounds from the environment, such as heavy metals. We sampled soils and cultured root-associated fungi from blue grama grass ("
} | 105 |
36857605 | null | s2 | 1,568 | {
"abstract": "Characterizing the diverse, root-associated fungi in mine wastes can accelerate the development of bioremediation strategies to stabilize heavy metals. Ascomycota fungi are well known for their mutualistic associations with plant roots and, separately, for roles in the accumulation of toxic compounds from the environment, such as heavy metals. We sampled soils and cultured root-associated fungi from blue grama grass ("
} | 105 |
36857605 | null | s2 | 1,569 | {
"abstract": "Characterizing the diverse, root-associated fungi in mine wastes can accelerate the development of bioremediation strategies to stabilize heavy metals. Ascomycota fungi are well known for their mutualistic associations with plant roots and, separately, for roles in the accumulation of toxic compounds from the environment, such as heavy metals. We sampled soils and cultured root-associated fungi from blue grama grass ("
} | 105 |
34351429 | PMC8378938 | pmc | 1,570 | {
"abstract": "ABSTRACT Marine sponges play a major ecological role in recycling resources on coral reef ecosystems. The cycling of resources may largely depend on the stability of the host–microbiome interactions and their susceptibility to altered environmental conditions. Given the current coral to algal phase shift on coral reefs, we investigated whether the sponge-associated bacterial communities of four sponge species, with either high or low microbial abundances (HMA and LMA), remain stable at two reefs sites with different coral to algae cover ratios. Additionally, we assessed the bacterial community composition of two of these sponge species before and after a reciprocal transplantation experiment between the sites. An overall stable bacterial community composition was maintained across the two sites in all sponge species, with a high degree of host-specificity. Furthermore, the core bacterial communities of the sponges remained stable also after a 21-day transplantation period, although a minor shift was observed in less abundant taxa (< 1%). Our findings support the conclusion that host identity and HMA–LMA status are stronger traits in shaping bacterial community composition than habitat. Nevertheless, long-term microbial monitoring of sponges along with benthic biomass and water quality assessments are needed for identifying ecosystem tolerance ranges and tipping points in ongoing coral reef phase shifts.",
"introduction": "INTRODUCTION Coral reefs are among the most productive ecosystems on Earth while residing under oligotrophic conditions owing to the efficient cycling of nutrients (Done et al . 1996 ). On coral reefs, benthic communities are fueled by primary producers, such as macroalgae and dinoflagellates in corals, which in turn release a significant part (up to 50%) of their photosynthetic “over” production as dissolved organic matter (DOM; Wild et al . 2004 ; Naumann et al . 2010 ; Tanaka et al . 2011 ; Hansell and Carlson 2015 ). Sponges are important nutrient cyclers within coral reef benthic communities (Diaz and Rützler 2001 ; de Goeij et al . 2013 ; Pawlik and McMurray 2020 ). Through their high filtering capacity and their close association with diverse symbiotic microorganisms, they play a key role in the uptake, processing and release of (in)organic nutrients within their ecosystems (Maldonado et al . 2012 ; de Goeij et al . 2017 ; Pita et al . 2018 ; Zhang et al . 2019 ). Specifically, the processing of DOM by sponges puts them into a unique position with regard to the cycling of resources within oligotrophic ecosystems. Sponges can turnover DOM—by far the largest potential organic resource on reefs, but unavailable as food source to most other reef heterotrophs—at rates close to the daily gross primary production rates of an entire reef (de Goeij et al . 2013 ). Over the past decades, climatic events in combination with direct anthropogenic disturbances have resulted in considerable changes in benthic community structures worldwide, including a shift from coral to algal dominance on many coral reefs (Hughes 1994 ; McManus and Polsenberg 2004 ; Hoegh-Guldberg et al . 2007 ; Mumby and Steneck 2008 ; Conversi et al . 2015 ). Filamentous turf- and macroalgae are found to release higher quantities of bioavailable DOM than corals, which causes higher ambient bacterioplankton production, including the growth of potential pathogens, described as the microbialization of coral reefs (Haas et al . 2011, 2016 ; ; Morrow et al . 2013 ; Nelson et al . 2013 ; Cárdenas et al . 2018 ). Increased microbial respiration and pathogenic mechanisms at the coral–algae interface can weaken or even kill corals, which frees space for further algal growth, especially under warmer conditions (Kline et al . 2006 ; Smith et al . 2006 ; Rohwer et al . 2010 ; Silva et al . 2021 ). This mechanism, referred to as the DDAM (dissolved organic carbon, disease, algae and microorganisms) negative feedback loop, can catalyze reef destruction, such as dominance shifts from reef-building coral to non-reef-building fleshy algae (Dinsdale and Rohwer 2011 ; Barott and Rohwer 2012 ). An increase of sponge biomass due to changing nutrients conditions and the subsequent increased release of inorganic nutrients have both been hypothesized to further exacerbate algal growth at the expenses of corals (Bell et al . 2013 ; Pawlik et al . 2016 ; Lesser and Slattery 2020 ). However, experimental and field data to corroborate these hypotheses are very limited. Ex-situ experiments suggest that sponges may cope better than other reef organisms with the effects of future higher temperatures and lower seawater pH (Fang et al . 2013 ; Bell et al . 2018 ), but expected changes in biomass and composition have, to our knowledge, not been reported for sponges at the ecosystem level. A total of two ex-situ studies have observed differential processing of naturally sourced coral and macroalgal-DOM by sponges, showing that macroalgal-DOM is more rapidly assimilated and recycled by the sponge holobiont, which suggests that macroalgae release a more labile food source for sponges than corals (Rix et al . 2017 ; Campana et al . 2021a ). However, little is known about the role of the sponge microbial symbionts in the differential recycling of coral- and macroalgal-DOM by sponges. Recent nano-scale secondary ion mass spectrometry (NanoSIMS) studies have tremendously boosted our understanding of host versus symbiont processing of DOM within sponge holobionts. Both sponge cells and symbionts are found to rapidly take up carbon and nitrogen derived from DOM (Achlatis et al . 2019 ; Rix et al . 2020 ; Hudspith et al . 2021a ) and were found to translocate these nutrients from sponge host to the microbial symbionts (Hudspith et al . 2021a ). Moreover, sponges that host high microbial abundances (HMA) within their tissue were shown to rely much more on symbiont-processing of DOM (∼ 60% of total) than low microbial abundance (LMA) species (< 0.7% of total; Rix et al . 2020 ; Hudspith et al . 2021b ). In HMA sponges, the associated bacteria can constitute up to 40% of their biomass and have a strong sponge-specific community composition, while LMA sponges generally possess a bacterial community more similar to that of the ambient seawater in abundance and composition (Taylor et al . 2007 ; Easson and Thacker 2014 ; Gantt et al . 2019 ). Seawater bacterioplankton adopts different metabolic strategies when exposed to coral- versus algal-DOM and its community composition becomes dominated by Alphaproteobacteria with coral-DOM, while Bacteroidetes and Gammaproteobacteria become more abundant with algal-DOM (Nelson et al . 2013 ; Haas et al . 2016 ). These bacterial lineages are also found in association with sponges, but it is unknown whether similar, or any, shifts in sponges-associated bacterial communities are found when exposed to different relative contributions of ambient coral and algae communities. We compared if the sponge-associated bacterial communities change when exposed to different relative contributions of corals and macroalgae. Therefore, we sampled four different sponge species (representing different morphology, phylogeny and abundances of associated microbes) in situ at two reef sites that were characterized by different benthic communities (i.e., a site with high-coral/low-macroalgal relative cover and vice versa ) on the Caribbean reefs of Curaçao. Additionally, we performed a reciprocal transplantation experiment of two sponge species between the two reef sites to assess sponge-associated bacterial community composition changes after three weeks of reallocation.",
"discussion": "DISCUSSION This study compared the in-situ bacterial community composition of four sponge species sampled at two reef sites with different relative coral to algae benthic cover composition and assessed the stability of two of these host-symbiont relationships through a reciprocal transplantation experiment between the two reef sites. We found that the sponge-associated bacterial communities were generally host-specific and no major bacterial community shifts were observed in the tested sponges between sites, nor after reciprocal transplantation. Site comparison of benthic community composition and water quality The two reef sites showed significant differences in the relative benthic cover composition, with higher coral and lower algae cover at Site 1 compared to Site 2, as predicted during initial site selection (based on visual observation; Fig. 1 ). The water quality assessment at the two sites shows bacterioplankton abundances and (in)organic nutrient concentrations that are well in the range of previously measured concentrations using similar methods in Curaçao reef waters (e.g., van Duyl and Gast 2001 ; Mueller et al . 2014 ; Campana et al . 2021a ). It may seem contradictory that the high-coral-to-low-algae Site 1 was characterized by significantly higher dissolved organic carbon (DOC) concentrations, as macroalgae are known to release higher quantities of DOC than corals (Haas et al . 2011 ; Mueller et al . 2014 ). However, DOC released by macroalgae can stimulate bacterioplankton production (Nelson et al . 2013 ), causing a decrease in DOC standing stock and increase in ambient bacterioplankton abundance as found for reefs worldwide (Haas et al . 2016 ). Strangely, bacterioplankton abundances were not found to be significantly higher in Site 2 and that could be due to the higher abundance of sponges at that site, which efficiently feed on bacterioplankton, but this needs to be tested in future studies. Also, the bioavailability of DOM does not necessarily depends on its concentration, but on its composition. The interaction between bacterioplankton and sponge holobionts in the cycling of DOM (components), referred to as “the battle for sugar” on reefs (de Goeij et al . 2017 ) is still largely unknown. Stable sponge bacterial communities between sites and after transplantation Overall, sponge-associated and seawater bacterial communities were conserved between the two sampling sites, with the exception of the phyla Nitrospirae in A. conifera and Cyanobacteria in the seawater bacterioplankton. These differences could be driven by the differences in organic and inorganic nutrients availability at the two sites; indeed, Nitrospirae are well known nitrogen-oxidizing bacteria in sponges (Feng and Li 2019 ) and possibly also involved in organic carbon uptake (Campana et al . 2021b ), while free-living Cyanobacteria are capable of both inorganic carbon and nitrogen fixation (Foster et al . 2013 ). Nonetheless, an opposite (but non-significant) change in the relative abundance of Nitrospira between the two sites was noticeable in the other HMA species P. angulospiculatus . Therefore, other environmental parameters, such as light conditions, turbidity, water flow or even sponge functional traits, such as morphology (massive versus encrusting growth forms) could be involved in these differences. Some differences were also found in the bacterial community composition of N. erecta after the transplantation experiment. However, it should be noted that the bacterial groups which abundance significantly changed after the transplantation accounted for less than 1% of the overall bacterial community of N. erecta . Furthermore, the change in relative abundances was similar at both sites (i.e., a decrease in Dadabacteria with an increase in Planctomycetes ), therefore the shift seems to be an effect of the transplantation to a new site rather than the difference in environmental conditions present at the respective sites after transplantation. The greatest separation was found between the bacterial communities of the seawater bacterioplankton and that of the sponges and between high versus low microbial abundance (HMA vs LMA) sponges. Based on community composition and diversity indices, the two HMA sponges, P. angulospiculatus and A. conifera , were clearly differentiated from the two LMA sponges, S. ruetzleri and N. erecta . While the HMA sponges hosted more diverse but homogeneous symbiont communities, LMA sponges were characterized by few dominant groups and a community composition more similar to that of the seawater bacterioplankton, as reported in numerous LMA and HMA host species collected from diverse geographic regions (Schmitt et al . 2011 ; Bayer et al . 2014 ; Erwin et al . 2015 ; Moitinho-Silva et al . 2017 ; Gantt et al . 2019 ). Furthermore, the bacterial community composition of the species described in our study fits well within the groups of LMA and HMA indicator taxa identified by Moitinho-Silva et al . ( 2017 ). The LMA species N. erecta was dominated by the LMA indicator taxa Proteobacteria (mostly Alphaproteobacteria ) with a lower contribution of Cyanobacteria , as already reported in Panama (Easson and Thacker 2014 ). Consistently with previous work, the LMA species S. ruetzleri was dominated by Cyanobacteria , Proteobacteria and Actinobacteria , but it was also the species with the most variable community composition among replicates (Rua et al . 2015 ). The HMA species P. angulospiculatus and A. conifera , were characterized by the HMA key indicator taxa Chloroflexi , Acidobacteria and Actinobacteria (Olson and Gao 2013 ), and by less abundant, but characteristic taxa such as Gemmatimonadetes , Dadabacteria , PAUC34f and Poribacteria (Erwin et al . 2015 ). In conclusion, our results support the role of the host identity and of the HMA-LMA status in shaping symbiont community composition, an emerging paradigm in sponge microbiology (Moitinho-Silva et al . 2017 ). Although the relative abundance of some bacterial phyla varied between the two sites, sponge host identity remained the major factor shaping the associated bacterial community composition before and after reciprocal transplantation. A stable host-specific partnership between the sponge host and its microbial symbionts has been observed in several studies along different environmental gradients (Lee et al . 2010 ; Erwin et al . 2012 ; Reveillaud et al . 2014 ; Erwin et al . 2015 ; Souza et al . 2017 ). For example, the sponge Aplysina cavernicola , found in low-light habitats (deeper waters or in submarine caves), did not show a change in its microbial community composition following a three-month long transplantation from their original location to shallower and light-exposed sites (Thoms et al . 2003 ). The sponges Cymbastela stipitata and Aplysina cauliformis maintained conserved microbial communities despite exposure (5 days to 4 weeks, respectively) to high levels of nutrients (Gochfeld et al . 2012 ; Luter et al . 2014 ). The same was true for the symbiotic bactrial, archaeal and eukaryotic communities of the Great Barrier Reef sponge species Rhopaloeides odorabile under elevated inorganic nutrients levels and temperatures for a week (Simister et al . 2012 ). Across longer time spans, sponge-associated bacterial communities exhibited a high degree of seasonal stability especially in HMA sponge hosts, despite large fluctuations in temperature and irradiance (Erwin et al . 2012 ; Erwin et al . 2015 ). Nonetheless, some shifts in sponge microbial community composition were found under seasonal and temporal scales and after transplantation between intertidal and subtidal environments (Cao et al . 2012 ; White et al . 2012 ; Weigel and Erwin 2017 ). The lack of a major shift in the sponge-associated bacterial communities between sites implies that the tolerance limits to changes in environmental conditions have not yet been met for these sponges. It does not imply that both the microbiome or the biomass of sponges are resilient to future reef regime shifts, but that the difference in benthic community composition and related environmental conditions observed were not yet strong enough to cause a significant change in the sponge-associate bacterial communities. Phase shifts or alternative states within ecosystems are usually triggered by a critical threshold or tipping point, which causes the ecosystem to change from one state to a new state. Reef environments can be seen as nested ecosystems because they are characterized by complex networks, which interact at different scales, from microbes to ecosystem level (Pita et al . 2018 ). In sponges, key functions carried out by the sponge microbiome, influence the whole sponge holobiont, which in turn interact with the surrounding holobionts, influencing community structure and ecosystem functioning (Pita et al . 2018 ). Vice versa environmental and biological stressor can act at multiple scales altering the various components of these nested ecosystems. Each ecosystem level can have a certain buffering capacity against perturbations. Therefore, the strength of these perturbations can be a major factor in determining the ability of the holobiont to maintain a stable state. Additionally, a possible response to change could be the acquisition of novel functions without shifts in taxonomic composition, therefore we would need to improve our understanding not only of the composition but also of the functionality of the sponge-associated microbial communities under coral reefs phase shifts. To understand the role and the resilience of sponge holobionts under reef phase shifts, not only the microbial composition of sponges need to be assessed, but also their biomass and functional changes therein. A major part of sponge diversity (Vicente et al . 2021 ) and biomass (Kornder et al . 2021 ) on reefs can be “hidden” in so-called cryptic habitats (e.g., coral overhangs, crevices and cavities) and are usually missed in traditional surveys. Extensive baseline data on sponge biomass and microbiome composition accompanied by benthic biomass and water quality assessments are therefore needed to identify the ecosystem buffering capacity and tipping points in ongoing coral reef phase shifts. A more comprehensive approach can enable intervention before the key ecosystem functions provided by sponges are lost."
} | 4,569 |
27242397 | PMC4865656 | pmc | 1,572 | {
"abstract": "Trade-off between reproducibility of neuronal activities and computational efficiency is one of crucial subjects in computational neuroscience and neuromorphic engineering. A wide variety of neuronal models have been studied from different viewpoints. The digital spiking silicon neuron (DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmetic circuits. We expanded the DSSN model and found appropriate parameter sets with which it reproduces the dynamical behaviors of the ionic-conductance models of four classes of cortical and thalamic neurons. We first developed a four-variable model by reducing the number of variables in the ionic-conductance models and elucidated its mathematical structures using bifurcation analysis. Then, expanded DSSN models were constructed that reproduce these mathematical structures and capture the characteristic behavior of each neuron class. We confirmed that statistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductance models. Computational cost of the DSSN model is larger than that of the recent sophisticated Integrate-and-Fire-based models, but smaller than the ionic-conductance models. This model is intended to provide another meeting point for above trade-off that satisfies the demand for large-scale neuronal network simulation with closer-to-biology models.",
"conclusion": "4. Conclusion In this paper, we expanded the DSSN model so that it can support the RS, FS, LTS, and IB classes, according to the following steps. Firstly, we reduced the number of variables of the Pospischils' ionic-conductance model utilizing the Keplers' method. The reduced models contained three variables for the RS, FS, and LTS classes and four variables for the IB class. We then elucidated their mathematical structures, including their structures on the phase portraits. The FS class is generically considered to exhibit a Hopf bifurcation; however, it yielded a saddle-node bifurcation in the Pospischils' model. Secondly, we determined the appropriate parameter sets that produce the same mathematical structures in the DSSN model as those in the reduced models. To support the IB class, the four-variable DSSN was developed by adding a slowest variable. It dynamically tunes the structure of the fast system. If ϕ 0 , ϕ 1 , and ϕ 2 are equal to ϕ, the four-variable DSSN model is compatible with the three-variable one. Finally, it was confirmed that the DSSN models behave very similarly to the Pospischils' models in response to several magnitudes of step input. The similarity was quantitatively confirmed by measuring the C V and L V statistics of the spike sequences, and confirming that both neuron models have the same statistical properties in each neuron class. The model is solved by Euler's method (Δ t = 0.0001[s]), and is realized by only one multiplier that consumes significant digital circuit resources. The DSSN model will consume more circuit resources than the IZH model because it has more variables, while the number of multiplication per a numerical integration step is the same. But it captures more aspects of the neuronal activity because it is not an I& F-based model. As is apparent from the equations, the implementation cost of the DSSN model is far lower than the ionic-conductance models. Circuit implementation of this model will be reported in our future publication. In software simulation, the computational cost of the DSSN mode is much lower than that of the ionic-conductance models, and comparable with that of the IZH per a numerical integration step. We conducted simulation of two million spikes, the DSSN and IZH models in the RS mode consumed 18.51 and 2.76 s, respectively. The DSSN model required over five times longer calculation, because the step of the DSSN model (0.0001[s]) is five times smaller than that of the IZH model (0.0005[s]). If the fixed point operation is used, it will be simulated faster. The DSSN model may be also useful for computer simulation in which more realistic model than I&F-based models are required. By the expansion in this work, the DSSN model supports the RS, FS, LTS, and IB classes as well as the Class I and II in the Hodgkin's classification, Class I * , square-wave bursting, elliptic bursting. We expect it can be a basis for silicon- and software- based spiking neuronal networks that capture the dynamics in the nervous system approximately, which can contribute to the neuroscience from the viewpoint of analysis by construction and the neuromorphic engineering. In this work, we tuned the parameters manually. We will work on an auto-fitting method for the DSSN model with an error function based on the mathematical structure.",
"introduction": "1. Introduction Silicon neuronal networks have gained remarkable attention in recent years. The silicon neuronal network is composed of dedicated circuit that solves the differential equations of a neuron and synapse model. On account of their parallel and distributed structures, silicon neuronal networks can simulate neuronal activities with low power consumption and in high speed, potentially realizing an extremely large-scale network comparable to that of the human brain in future. Their analog circuit implementation consumes ultra-low power down to several nano watts per silicon neuron (Brink et al., 2013 ; Kohno and Aihara, 2014 ; Mandloi et al., 2014 ), however, it includes technical hurdles of fabrication mismatch and temperature dependence to construct a large-scale network. On the other hand, digital circuit implementation solves this limitation because it is far less sensitive to these factors, though power consumption tends to be higher than the analog circuit implementations. A digital silicon neuronal network comprising 1 million spiking neurons and 256 million synapses has been implemented on a 5.4-billion transistor chip (Merolla et al., 2014 ). This Application Specific Integrated Circuit (ASIC) chip calculates asynchronously in real-time and consumes just 63 mW. Merolla et al. adopted the leaky integrate-and-fire (LIF) model, one of the most simple neuronal models. The LIF model describes the dynamics of the neuronal membrane potential, which is perturbed by a stimulus inputs over time and converges slowly to the resting potential. It is computationally efficient and suitable for the large-scale implementation. After refining the synaptic efficacy by a learning process, the ASIC was applied to a multi-object detection and classification. In silicon neuronal networks, diversified neuronal models are used due to a trade-off between the reproducibility of neuronal activity and computational efficiency. For instance, ionic-conductance models can reproduce a neuronal activity accurately but demands excessive computational resources in large-scale implementations. In contrast, neuronal models that approximate a spiking process by resetting the state variable [integrate-and-fire (I&F) -based models], such as the LIF, exponential I&F (Fourcaud-Trocmé et al., 2003 ), adaptive exponential I&F (Brette and Gerstner, 2005 ), and Izhikevich (IZH) models, can be implemented at low computational cost. However, it has reduced reproducibility of complex neuronal activities. For example, these models assume fixed maximum membrane potentials during the spike process, whereas these potentials are nonuniform in the nervous system (Alle and Geiger, 2006 ). Reduction of computational cost benefits the power consumption, system size, and response speed of silicon neuronal networks regardless of implementation types (ASIC, Field Programmable Gate Array, and massively-parallel CPUs). These factors are particularly important for their application to neuromorphic systems (e.g., for robot control) that are required to occupy a small space and operate with restricted power supply. They are also important to implement with realistic power consumption and system size a large-scale network comparable to the human brain that is composed of about a hundred billion neurons. The DSSN model (Kohno and Aihara, 2007 ) is a qualitative neuronal model designed for efficient implementation in a digital arithmetic circuit. It is a non-I&F-based model that can realize several neuronal activities including the Class I and II in the Hodgkin's classification (Hodgkin, 1948 ). Because this model does not abbreviate the calculation of the spiking process, it can reproduce the gradient response in Class II neurons (Wang and Rinzel, 2003 ). Li et al. ( 2012 ) constructed an auto-associative memory with 256 fully connected digital spiking silicon neuron (DSSN) models on an FPGA. They reported that a network of Class II neurons yields higher retrieval performance than that of Class I neurons in the associative memory task. Recently, a Hebbian learning rule was applied to this network (Li et al., 2013 ). In Osawa and Kohno ( 2015 ), it was reported that the Class II mode of the IZH model with a standard parameter setting has discontinuous phase resetting curve (PRC) and the auto-associative memory constructed in the same way does not deliver higher performance than the Class I-mode IZH model. Increasing the value of a parameter can solve this problem, however, it considerably distorts the spiking waveform. Pospischil et al. ( 2008 ) have found appropriate parameter sets for an ionic-conductance model that replicate experimental data in the well-known four classes of cortical and thalamic neurons; regular spiking (RS), fast spiking (FS), intrinsically bursting (IB), and low-threshold spike (LTS). A most typical cortical neuron class, RS is characterized by spike-frequency adaptation; that is, the spike frequency decreases over time in response to a constant stimulus input. Conversely, FS neurons maintain firing at a constant frequency. IB and LTS are the neuron classes with bursting ability. Neurons in the IB class generate a burst firing immediately at the onset of a stimulus, then continue spiking until its termination. The LTS class also exhibits the bursting. Neurons in the LTS class generate a burst firing just after the termination of a sufficient hyperpolarizing stimulus. Their model equations were constructed by integrating several ionic-conductance models reported in the previous researches and contains up to seven variables. For the RS and FS classes, they applied an auto fitting procedure that minimizes the error function of spike intervals by the simulated annealing method. The IZH model supports these four classes. The Pospischils' ionic-conductance model for the IB class generates a long silent phase following the burst firing ( Figure 11 -left), but the IZH model produced uniform silent phases (Figure 1 ). Moreover, the response to larger inputs evidently differed from that of Pospischils' model. Because it is not elucidated completely what properties of the neuronal activities are playing the key roles in the information processing in the brain, developing a simple non-I&F model that supports a wide variety of neuronal activities can be significant for both scientific and engineering purposes, even if it consumes computational resources than I&F-based models. Figure 1 Behaviors of the Izhikevich model in the IB class . Stimulus step input I stim rise at t = 0.02, where (A) \n I stim = 10, (B) 15, and (C) 30, respectively. A numerical integration step dt is 0.05 ms. In Nanami and Kohno ( 2015 ), we reduced the dimensions of Pospischils models by Keplers method and analyzed their mathematical structures by bifurcation analysis. We also reported parameter sets where the three-variable DSSN model (Kobayashi et al., 2011 ) produces similar activities to our target classes. While we did not evaluate their similarity, it was clear that the parameter set for the IB class could not reproduce the transient firing patterns from the first bursting phase to the later spiking phase. In this work, we cleared this limitation as follows. Firstly, we repeated the parameter search and found three-variable DSSN model parameter sets for RS, FS, and LTS modes that reproduce corresponding mathematical structures of the reduced Pospischils models and statistical characteristics of spiking patterns. Secondly, for the IB class, we incorporated an additional slow variable and found a parameter set that reproduces corresponding mathematical structures and the statistical characteristics of spiking patterns. The statistical evaluation was performed using Cv and Lv (Shinomoto et al., 2003 ) that are utilized to characterize the spiking activities of neuronal cells. The remainder of this paper is organized as follows. Section 2 introduces our neuron model, its basic concepts, and details of its construction. The model is tested and evaluated by simulation in Section 3. Section 4 summarizes the work and suggests ideas for future."
} | 3,217 |
26558385 | PMC4641648 | pmc | 1,573 | {
"abstract": "Insect societies are complex systems, displaying emergent properties much greater than the sum of their individual parts. As such, the concept of these societies as single ‘superorganisms’ is widely applied to describe their organisation and biology. Here, we test the applicability of this concept to the response of social insect colonies to predation during a vulnerable period of their life history. We used the model system of house-hunting behaviour in the ant Temnothorax albipennis . We show that removing individuals from directly within the nest causes an evacuation response, while removing ants at the periphery of scouting activity causes the colony to withdraw back into the nest. This suggests that colonies react differentially, but in a coordinated fashion, to these differing types of predation. Our findings lend support to the superorganism concept, as the whole society reacts much like a single organism would in response to attacks on different parts of its body. The implication of this is that a collective reaction to the location of worker loss within insect colonies is key to avoiding further harm, much in the same way that the nervous systems of individuals facilitate the avoidance of localised damage.",
"introduction": "Introduction Colonies of social insects can achieve extraordinary levels of collective organisation, and as such, they are often referred to as single ‘superorganisms’ [ 1 ]. This may be manifest in evolutionary terms; certain insect societies can be single units of selection [ 2 ], or in physiological terms; with some social insect colonies displaying caste polymorphism which mirrors cell specialisation in individuals [ 3 – 5 ]. Furthermore, collective behaviour may confer spatially specific advantages in response to threats such as predation at the group level. In single organisms, antagonistic stimulation has markedly contrasting effects depending on its location. This is exemplified by the Xenopus tadpole, that, when touched on the tail, will flex it in the opposite direction to the stimulus, but when touched on the head, will swim away in a random direction [ 6 ]. Similarly, the scallop Euvola ziczac will ‘jump’ to avoid starfish that come into contact with its outer shell mantle, but perform longer escape swims when a starfish touches its dorsal ears [ 7 ]. Simple but specific behaviours such as these are effective in mitigating risks to the individual, and as such, it may be advantageous for colonies of social insects to react differentially to predation in an analogous fashion. The use of alarm pheromones by ants is well documented. Some species employ both ‘aggressive’ alarms, to draw nest mates towards potential intruders, and ‘panic’ alarms; to facilitate evasion or evacuation when faced with an insurmountable foe [ 8 ]. Another strategy, seen in the weaver ant Oecophylla longinoda , involves both chemical and physical cues; alarm pheromones will excite major workers to attack a predator, while causing minor workers to remain within the nest, and only if the nest itself is then disturbed will the minors respond [ 9 ]. While such examples demonstrate the complexity of colony-level reactions to predation, they make use of chemical rather than tactile signals, and often elicit responses dependent on the proximity to a pheromone source, rather than the location of the pheromone within the colony [ 1 ].Thus, to understand better how social insects react to differing types of predation, it may be advantageous to assess this in a species where predation can be simulated at discrete loci within the colony. The ant Temnothorax albipennis provides a suitable model with which to do this, as specific individuals can be removed from different parts of colonies, enabling the experimental assessment of varying forms of perturbation. Furthermore, in this genus, it has been well-established that the spatial organisation of workers within a colony is strongly linked to their roles, and thus it is possible to target and manipulate workers with known task propensities [ 10 , 11 ]. This species will also migrate to nests of better quality even if their current nest is intact, in so-called move-to-improve emigrations [ 12 ]. The emigration process itself provides a unique tool with which to assess colony-level responses to predation, as differing behaviours may be manifest in the rate of emigration to new nests that would otherwise be missed under normal circumstances. There is evidence to suggest that ants of this species are able to monitor mortality risk outside the nest. In the experiments of Richardson et al. [ 13 ], scouting workers leaving the nest were removed sequentially and the interval measured until the next scout appeared. In the control, individually marked scouts were allowed to return to the nest and the time taken for the next ‘new ant’ (i.e. one never seen outside of the nest before) to emerge was measured. In such controls, the time taken for each new ant to exit the nest increased over the observation period. This is to be expected given the well-known division of labour in such ants; some workers will mostly stay within the nest while others will habitually go outside, and given that the transition from working inside to working outside is a slow and regulated process. However, when scouts were removed upon emergence from the nest, the exit rate of new scouts was even slower, indicating that those within the nest were actively avoiding going outside due their nest mates’ failure to return. This process is likely made possible through the spatial structure of the workforce in these colonies, as workers that are prone to leave the nest have spatial fidelity zones near the nest entrance, and thus can monitor outgoing and incoming traffic [ 10 , 11 ]. In conjunction with this, a recent study using a closely related species; Temnothorax rugatulus , has found that during migrations, ants respond differently to the alarm pheromone 2, 5-dimethylpyrazine (DMP) dependent upon its perceived context [ 14 ]. Taking this into account, it seems likely that T . albipennis may also make use of context in shaping colony responses to threat. Previous experiments have also shown that when the nest is destroyed, causing forced emigration, colonies can still choose the better and more distant of two nest sites presented to them [ 15 ]. Here we employ the same design but do not destroy the current nest, instead we subject the ants to various predation scenarios. As such, we are able to assess the effects of predation both upon the rate of migration and upon final nest choice. In our experiments, we remove individuals from the periphery of colony scouting activity, and from within the nest itself, simulating predation targeted at very different parts of the colony. Additionally, we compare these scenarios to that of an emergency emigration, in which the original nest is destroyed, using data from [ 15 ] and a control treatment, in which no ants are removed. Using these techniques, we then assess the varying responses of the colony during migration to a new nest.",
"discussion": "Discussion Our results show a concerted anti-predation response consistent with the superorganism concept. Simulated predation of peripheral scouting workers markedly inhibited the progression of emigrations over time. This phenomenon was characterised by both later discovery of, and a slower build-up of scouting ants within the good nests in the peripheral predation group compared to the control, nest predation and nest destruction groups ( Fig 2 ). In contrast, simulated predation of individuals from within the nest increased emigration speed. There was a significantly faster build-up of workers over time in the good nests within the nest-predation group, compared to the control and peripheral predation groups ( Fig 2 ). This increased emigration rate was lower, but not statistically different to that triggered by an emergency emigration; however, the dynamics after worker removal from within the nest differed substantially from those when the nest was destroyed. Specifically, when the nest was destroyed, the number of workers built up at a similar rate in both new nests, suggesting that destruction of the nest led to a loss of colony cohesion ( Fig 2D ). By contrast, only two or three ants visited the excellent nest at any one time in the nest predation group, implying that the colony as a whole maintained its unity of choice ( Fig 2B ). Moreover, it seems unlikely that this difference in dynamics was due simply to scouting ants being ‘intercepted’ by the good quality nest in the nest predation treatment and then failing to upgrade to the excellent one. This is because the difference in quality and distance were the same for the control and peripheral predation groups, yet several colonies still migrated to the excellent nests. However, in the nest predation group, all ten experimental colonies moved to the good nest and were still there after 24 h ( Table 1 ). Taken together, our results suggest that colonies will retract into their current nest in response to scout loss at the periphery, but vacate rapidly in response to predation of workers from within the nest, while maintaining a level of cohesion absent from emergency emigrations [ 19 ]. Furthermore, although we found that the effect of scout removal on final emigration choice up to 24 hours after the start of the experiment was not statistically significant; our results suggest that such an effect cannot be ruled out. Previous studies have shown that colonies will migrate to farther-away excellent nests under forced emigration conditions [ 15 ], but here we show that this is not the case during migrations under the influence of worker loss. These findings are consistent with a robust form of predator avoidance behaviour, implemented at the colony level, and there are several potential mechanisms by which this may occur. The loss of specialist scouts may play a role in retarding colony exploration levels; however studies have shown that T . albipennis colonies have highly flexible task allocation structures, so would potentially be able to replace lost scouts [ 20 ]. Moreover, as has been shown earlier [ 13 ], that worker exit rate under such predation conditions is significantly slower than would be expected in response to depletion of scout specialists, or as a consequence of the spatial organisation of tasks [ 10 , 11 ]. In view of this, it seems more plausible that down-regulation is involved in reducing worker exits when nest mates do not return from scouting [ 13 ]. This process acts much in the same way as the withdrawal reflex in response to harmful stimuli in single organisms [ 21 ], and effectively avoids unnecessary mortality when predators are present outside the nest. In concert with this, the ‘evacuation’ response of colonies to predation targeted within the nest ensures that the vulnerable brood and queen are removed from harm. However, during the process, the colony retains its cohesion [ 19 ] and migrates directly into a single nest. In contrast, when the original nest is destroyed; colonies begin to migrate into both available new nests. As such it appears that two different threats, destruction of the nest, and loss of workers from within the nest, elicit comparable rates of evacuation but that the dynamics of worker accumulation in the new nests are different as the colony searches for a new home. The underlying mechanism behind such nest-evacuation behaviour is likely multi-faceted. There is a possibility that the queen plays a role in regulating colony behaviour, as seen in some primitively eusocial insects [ 22 ]. However, in Temnothorax albipennis, the queen is always passively transported during migrations, and though this event usually takes place half way through any given emigration, the presence of a queen, in itself, is not essential in order for migrations to occur [ 23 ]. Pheromones may well be involved, as seen in the closely-related species T . rugatulus . In this species, when workers release the alarm pheromone 2, 5-dimethylpyrazine (DMP) in a dangerous environment distant from the colony, nest mates will avoid the area, while when the same pheromone is released within their home nest, workers are instead attracted in order to mount a defence [ 14 ]. In this scenario, context plays a central role in colony responses to threat signals. However, our results suggest that if similar signals are used in T . albipennis , they elicit a very different response when released within the home nest, leading to aversion rather than attraction. Although it might be argued that removal of ants from within the nest is a highly artificial scenario, the perceived threat it poses is likely tantamount to attacks by slave-raiding ants or invertebrate predators, and thus it is not unreasonable that ants in this genus may have evolved specific solutions [ 24 ]. Furthermore, such a response is consistent with other species that have small colony sizes, for which escape is often the preferable option [ 25 ], especially in cases where many potential new nest sites are present [ 26 ]. Additionally, as scout mortality may occur over a large area, the use of alarm pheromones to stop workers leaving the nest could prove unfeasible. In view of this, it is tempting to speculate that T . albipennis makes use of both the down-regulation of record dynamics to ameliorate scout losses [ 13 ], and alarm pheromones in order to facilitate rapid and cohesive nest evacuation when faced with a threat in their current home. Jointly, these two behaviours would make for a fluid and reactive solution to the issue of predation, whereby feedback throughout the colony elicits action by all of its members, regardless of their individual exposure to the threat. Analogous systems that make use of modularised threat responses can be observed in numerous other unrelated taxa [ 6 , 7 ]. In some species, responses may be modularised in relation to threat identity, rather than location, such as in the differing responses of the Cicada killer wasp Sphecius Speciosus to territorial invaders, and the reactions of Polistine wasps to predators [ 27 , 28 ]. Additionally, parallels may be seen between the functioning of Temnothorax albipennis colonies, and that of individual vertebrates. One such example is the organisation of neurones responsible for the c-start escape reflex in teleost fish [ 29 ]. This process is thought to be controlled by two neuronal groups, sometimes referred to as the A1 and A2 groups. When a predator approaches from the posterior, the A1 neurones are maximally activated and the fish performs a turn away from the predator, while when a predator approaches from the anterior, both sets of neurones are activated, causing the fish to escape in the opposite direction [ 29 ]. The reactions of two different groups of workers in our experiments; those scouting and those within the nest, act in a similar way to these two groups of neurons; they illicit a response specifically appropriate to harm directed at their part of the colony. Our findings further elucidate the mechanisms that facilitate anti-predator behaviour within social insects, and concur with previous studies that suggest T . albipennis colonies are robust to worker loss [ 30 ]. Moreover, we have found that as in many other aspects, superorganisms may benefit from reacting as a single entity to the threat of predation. This highlights the propensity for ant colonies to employ a multi-organismal ‘nervous system’ to deal with challenges, and provides rich potential for future studies."
} | 3,918 |
33367204 | null | s2 | 1,575 | {
"abstract": "Neuromorphic computing uses basic principles inspired by the brain to design circuits that perform artificial intelligence tasks with superior energy efficiency. Traditional approaches have been limited by the energy area of artificial neurons and synapses realized with conventional electronic devices. In recent years, multiple groups have demonstrated that spintronic nanodevices, which exploit the magnetic as well as electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits. Among the variety of spintronic devices that have been used, magnetic tunnel junctions play a prominent role because of their established compatibility with standard integrated circuits and their multifunctionality. Magnetic tunnel junctions can serve as synapses, storing connection weights, functioning as local, nonvolatile digital memory or as continuously varying resistances. As nano-oscillators, they can serve as neurons, emulating the oscillatory behavior of sets of biological neurons. As superparamagnets, they can do so by emulating the random spiking of biological neurons. Magnetic textures like domain walls or skyrmions can be configured to function as neurons through their non-linear dynamics. Several implementations of neuromorphic computing with spintronic devices demonstrate their promise in this context. Used as variable resistance synapses, magnetic tunnel junctions perform pattern recognition in an associative memory. As oscillators, they perform spoken digit recognition in reservoir computing and when coupled together, classification of signals. As superparamagnets, they perform population coding and probabilistic computing. Simulations demonstrate that arrays of nanomagnets and films of skyrmions can operate as components of neuromorphic computers. While these examples show the unique promise of spintronics in this field, there are several challenges to scaling up, including the efficiency of coupling between devices and the relatively low ratio of maximum to minimum resistances in the individual devices."
} | 520 |
27051861 | PMC4820372 | pmc | 1,577 | {
"abstract": "During their early life stage as planktonic larvae, reef-building corals do not rely on their photosynthesizing symbionts for nutrition.",
"introduction": "INTRODUCTION In (sub)tropical shallow-water reefs, most scleractinian corals live in mutualistic endosymbiosis with dinoflagellates of the genus Symbiodinium (“zooxanthellae”) located within their gastrodermal cells. The dinoflagellates photosynthetically fix carbon (C), assimilate nitrogen (N), and translocate essential compounds (for example, lipids, glucose, and amino acids) to their animal host, thereby supporting its metabolic requirements for growth, skeletal formation, and reproduction [reviewed in ( 1 )]. A large fraction (20%) of symbiotic corals reproduce by brooding, and emitting planktonic larvae (planulae), which disperse and settle to form new colonies in a process fundamental to the expansion of reefs and the maintenance of their diversity. The energy budget of planulae is critical to their longevity and dispersal potential ( 2 , 3 ). It is well established that planulae, upon emission, are very rich in maternally derived endogenous lipids (up to 70% by weight) and protein reserves. Planulae from many corals also host autotrophic dinoflagellate symbionts, transferred either vertically from the mother colony or horizontally from the ambient seawater. The amount of energy stores largely determines the larval survivorship and competency period ( 2 , 4 – 7 ). However, in constrast to adult corals, the autotrophic contribution of dinoflagellates to the metabolism of the planula and the replenishment of its energy stores is poorly constrained, with constrasting evidence arguing both for and against the significant translocation of photosynthates ( 4 , 8 – 12 ). We performed a stable isotope pulse-chase experiment on newly released (less than 12 hours since emission) symbiotic planulae of the reef-building coral Pocillopora damicornis (Linnaeus, 1758). These planulae contain endosymbiotic dinoflagellates, in principle, permitting an immediate autotrophic nutrient contribution to the metabolism of their host.",
"discussion": "RESULTS AND DISCUSSION The genotype composition of the coral-dinoflagellate symbiotic association is known to influence its biological functions ( 13 ). Therefore, we first determined the taxonomy of each symbiotic partner (that is, the “ Symbiodinium ” photosymbionts and the “ Pocillopora damicornis ” host) at the clade and type levels with multiple independent markers. Classification of the coral host was assessed using three molecular markers, in addition to skeletal micromorphology and lunar timing of the release of planula larvae. Genetically, planula larvae and adult colony nubbins are classified as clade 1 type β lineage of P. damicornis , according to the sequence of their entire internal transcribed spacer (ITS), specifically their ITS2 region in nuclear DNA, and according to their open reading frame region in mitochondrial DNA (mtORF) ( 14 , 15 ). Morphologically, the adult skeletal corallites corresponded to the P. damicornis clade 1 type β morphotype ( 16 ). Colonies released their planula larvae a few days before full moon, corresponding to reproductive type B in Hawaii ( 17 ). Together, these results show that our biological material belongs to the clade 1 type β lineage of the P. damicornis species complex. This lineage was recently redescribed as Pocillopora acuta (Lamarck, 1816), a species previously synonymized with P. damicornis (Linnaeus, 1758) ( 16 ). Taxonomical classification of the Symbiodinium endosymbionts was assessed at the clade and type levels on the basis of the marker sequences in the 18 S and internal transcribed region, and specifically in the ITS2 region in ribosomal DNA ( 18 , 19 ). These nuclear markers indicated that, in both planulae and adult colonies, the dinoflagellate symbionts belonged to clade C, ITS2 type C1, without diversity detected via restriction enzyme profiling of cloned sequences. Planulae were labeled for 6 hours in light with [ 13 C]bicarbonate and [ 15 N]nitrate (which cannot be assimilated by coral host cells), followed by a 66-hour chase period in natural seawater with normal C and N isotopic compositions under a 12-hour light/12-hour dark cycling. Photosynthetic incorporation of 13 C and assimilation of 15 N by the dinoflagellate endosymbionts and the subsequent translocation of labeled compounds toward planula host tissue were quantified with subcellular resolution by combining transmission electron microscopy (TEM) and nanoscale secondary ion mass spectrometry (NanoSIMS) imaging ( Figs. 1 and 2 ) ( 20 , 21 ). Here, we describe the dynamic inorganic C and N assimilation and translocation process in the planulae throughout the pulse-chase experiment, and discuss this autotrophic contribution in comparison with that in adult colonies of the same lineage of P. damicornis , pulse-chase-labeled independently under similar experimental conditions ( 22 ). Fig. 1 Photosynthetic C fixation, nitrate assimilation, and translocation in newly released symbiotic P. damicornis planulae. ( A and B ) Histological sections of a planula, collected less than 12 hours after emission. coel, coelenteron; di, endosymbiotic dinoflagellate; ep, epiderm; ga, gastroderm; ld, lipid droplet; mes, mesentery; yp, yolk platelet. ( C to I ) Average 13 C and 15 N enrichments measured in the dinoflagellate cells (C to E) and in the planula host tissue during the pulse-chase experiment (F to I). Data are shown as box-whisker plots, with black horizontal bars indicating average values. Significant differences (Wilcoxon rank-sum test) are indicated between labeled and unlabeled control corals (*) and between samples from two consecutive time points ( + ). Fig. 2 Visualization of C and N assimilation into dinoflagellates and translocation into host tissue in adult P. damicornis corals and their newly emitted planulae. Each row includes a representative TEM micrograph of the dinoflagellate-containing host tissues and its corresponding quantitative NanoSIMS 13 C/ 12 C and 15 N/ 14 N isotopic maps during the pulse (6 hours) and chase (until 48 hours) under light/dark cycling. Data for adult corals are derived from a recent study ( 22 ). Scale bars, 5 μm. di, dinoflagellate; ep, epiderm; ga, gastroderm. Within the planula dinoflagellate endosymbionts ( Fig. 1 , A and B), both 13 C and 15 N were rapidly incorporated into different cell compartments and were already detectable after 30 min into the pulse period, during which the isotopic enrichments increased quasi-linearly ( Fig. 1 , C to E, insets). Over the subsequent 66-hour chase period, the 13 C enrichment in starch granules and lipid droplets experienced an ~80% drop ( Fig. 1D ). This rate of turnover in dinoflagellate C reserves (mainly starch granules and lipid droplets) was much lower than that observed in adult P. damicornis colonies where, under similar experimental conditions, a comparable (that is, ~80%) 13 C depletion was seen within just 18 hours into the chase ( 23 ). This difference might arise from (i) the lower translocation rate of photosynthates toward the host tissue in coral planulae compared to adults, (ii) the lower basal metabolism (respiration rate) of dinoflagellates in coral larvae, and/or (iii) the ability of gastrodermal cells in planulae to supply their endosymbionts with a substrate for respiration (for example, neutral lipids), rendering them less dependent on their own C reserves. In contrast, the 15 N enrichment of dinoflagellates (and their subcellular compartments) decreased by ~70% within the first 18 hours into the chase, then remained essentially stable until the end of the experiment ( Fig. 1 , C to E). Such fast 15 N depletion was not previously observed in adult corals ( 22 – 24 ), and it was not accompanied by a corresponding increase in the planula host tissue, which would indicate translocation. Within the planula host tissue, the gastroderm benefited more than the epiderm from the translocation of metabolic compounds from the dinoflagellates ( Fig. 1 , F and G): both 13 C and 15 N enrichments slowly increased during the chase, reaching average values of ~170 and ~100‰, respectively ( Fig. 1G ). In gastrodermal cells, 13 C was detected primarily in large osmiophilic lipid droplets ( 25 ), with a peak 13 C enrichment at 48 hours ( Fig. 1H ). Numerous gastrodermal crystalline granules (fig. S1), referred to as “yolk platelets” and often assumed to serve as protein storage ( 25 ), were found accumulating both translocated 13 C and 15 N ( Fig. 1I ). Data on adult colonies of P. damicornis exposed to similar pulse-chase experimental conditions (except for 100 μmol photons m −2 s −1 light intensity) and obtained with identical sample preparation and analytical methods ( 22 ) allowed for a direct comparison between the levels of translocation of compounds from dinoflagellates in planula and adult corals, respectively. Figure 2 illustrates the turnover and local translocation of photosynthetically derived 13 C and assimilated 15 N from dinoflagellate symbionts to adjacent host tissue (gastrodermis and epidermis) in both coral planulae and adult colonies. Figure 3A shows that the level of photosynthetic C fixation is similar, and the nitrate assimilation slightly higher, in dinoflagellates from adult colonies at the end of the labeling pulse (that is, at 6 hours). This demonstrates that the symbionts in the planulae and in adult corals have essentially the same capability to incorporate 13 C and 15 N from bicarbonate and nitrate, respectively. Fig. 3 Comparison of the trophic contribution of C1 Symbiodinium cells to P. damicornis type β host in coral planulae versus adult colonies. Data are shown as box-whisker plots, with black horizontal bars indicating average values, and are compared by means of Wilcoxon rank-sum test. 13 C and 15 N enrichments in dinoflagellates are shown at 6 hours, that is, at the end of the labeling pulse. 13 C and 15 N enrichments observed in the host tissue of planulae (P) or adult corals (A) are shown at 48 hours, during the chase. Data for adult corals are derived from a recent study ( 22 ). ( A ) Isotopic enrichment levels in dinoflagellates. ( B ) Isotopic enrichment levels in host coral epidermal and gastrodermal cells, as well as in gastrodermal lipid droplets (LD). NS, not significant. In Fig. 3B , the 13 C and 15 N enrichment levels in coral host tissue are compared between planulae and adult colonies at 48 hours into the pulse-chase experiment, reflecting the local translocation of compounds from the dinoflagellates. At this time point, the observed isotopic enrichments in the planula tissue generally have reached their maximum ( Fig. 1 , F to I), hence avoiding underestimation of translocation. From Figs. 2 and 3B , it is obvious that systematically less local translocation of both 13 C- and 15 N-enriched compounds took place from dinoflagellates to the adjacent planulae gastroderm and epiderm. Quantitatively, these differences amount to factors of ~9 and ~2 for 13 C and factors of ~6 and ~3 for 15 N in the epidermis and gastroderm, respectively. Lipid droplets in the gastroderm have been demonstrated to be the primary sink for translocated C-bearing photosynthates in adult corals ( 22 ). Figure 3B also shows that the 13 C-labeling levels in these lipid droplets were higher in adult corals by a factor of ~3.5. In conclusion, the local rate (at the scale of individual NanoSIMS images) of translocation of metabolic compounds from dinoflagellates to the coral host tissue was systematically much lower in planula compared with adult coral colonies. In further support of this finding, we note that, compared to adult P. damicornis corals, symbiosomal “extra-algal” lipid droplets (occurring outside the dinoflagellate but inside the symbiosome) were only rarely observed in the newly released coral planulae. These ultrastructures are thought to be involved in the extrusion of lipids from dinoflagellates toward the host tissue ( 26 – 29 ). The very low abundance of symbiosomal lipid droplets in planula larvae compared to adult corals is consistent with the hypothesis of a substantially lower rate of translocation of photosynthetic C from symbionts to host in this initial planktonic life stage of a reef-building coral. In a parallel 6-hour dual-isotopic pulse of [ 15 N]ammonium and [ 13 C]bicarbonate in light, we investigated the ability of newly released planulae to assimilate ammonium (fig. S2). This experiment also independently verified the observed time scale of photosynthetic 13 C accumulation in dinoflagellates presented above. With regard to ammonium, observations on adult symbiotic reef corals ( 23 , 24 , 30 ) have clearly demonstrated the preference of dinoflagellates for ammonium over nitrate assimilation and the ability of the host tissue for direct ammonium assimilation. Our data for planulae corroborate these findings. Dinoflagellate and planula host cells of both epidermis and gastrodermis rapidly (within 15 min) and simultaneously incorporated the 15 N tracer, albeit with a much higher efficiency for dinoflagellates compared to coral cells (factor of ~10; fig. S2). Additionally, throughout the pulse, hotspots of 15 N enrichment were observed in the epiderm tissue of [ 15 N]ammonium-labeled planulae, most of them colocalized with coral cell structures such as Golgi bodies and nucleoli (fig. S1). In the gastrodermal coral cells, these hotspots were abundant and frequently corresponded to crescent moon–shaped ultrastructures with an empty center (material likely lost during sample preparation) (fig. S1). Areas of high 15 N enrichment, due to ammonium assimilation within the planula host cells, could not be unambiguously correlated to prokaryotic morphotypes, although the potential involvement of bacteria in planula holobiont metabolism is likely ( 31 ). Overall, our results indicate that, in comparison with adult P. damicornis type β colonies, C1 Symbiodinium provides substantially less nutrition to the planula larvae. At the scale of the regions imaged with NanoSIMS, we have visualized the local translocation of isotopically labeled compounds into host cells in the immediate vicinity of the dinoflagellate cells. As shown in Figs. 2 and 3 , this local translocation was significantly lower in planula than in adults. However, to evaluate the total transfer of photosynthates and nitrogenous compounds to the coral, the density of symbionts in the host tissue must be considered. Indeed, a lower overall translocation from dinoflagellate symbionts to the host might be due to a lower bulk density of dinoflagellates and/or a lower rate of translocation from individual symbiont cells to their adjacent tissue layers. Therefore, the possible dinoflagellate density difference between a planula larvae and an adult coral was quantified and evaluated. The inner gastrodermal tissue was very thick in these newly released planulae, with lipids taking up the bulk of its volume ( Fig. 1 , A and B). The average Symbiodinium density of this planula gastrodermis was quantified to about one eighth of the density in the thin gastrodermis of an adult coral in the coenosarc region, that is, the tissue from which the corresponding NanoSIMS data in Figs. 2 and 3 were obtained. However, in the planula gastrodermis, the dinoflagellate population was strongly concentrated in a relatively narrow (<50 μm) layer located just below the mesoglea at the interface with the overlying epiderm ( Fig. 1 B). Here, the dinoflagellate density was in fact comparable to that of the thin gastroderm of the coenosarc area of adult colonies. In terms of bulk transfer of photosynthates, given that the average gastrodermal density of dinoflagellates was clearly lower in planulae than in adults, the metabolic input from the photosymbionts would be lower in planulae if the translocation took place at the same rate. However, in addition, our NanoSIMS imaging of isotopic enrichment around the dinoflagellate cells also revealed a significantly lower local translocation to the host planula tissue ( Figs. 2 and 3 ). Together, these results indicated that the lower level of trophic input from dinoflagellates to the host in the planulae, compared with adult corals, was due to the combination of a density effect and a much lower translocation rate from the photosymbionts to the host, imaged here for the first time with NanoSIMS. Further work is required to establish the generality of this conclusion by investigating other coral and Symbiodinium species. However, from this specific study, it appears that freshly released coral planulae rely principally on their endogenous, maternally derived energy reserves (lipids and proteins). At this early stage, planulae’s main benefit from hosting dinoflagellates seems to be primarily the transmission of Symbiodinium sp. populations. Nutritional metabolic fluxes between symbiotic partners seem to fully develop only at later life stages. A recent bulk-level isotopic study on 22- to 27-day-old P. damicornis planulae indicated that dinoflagellate endosymbionts translocate up to ~70% of photosynthetically fixed C (labeled using 14 C) to the coral host tissue, which is similar to the proportion observed in adult corals ( 9 ). Precisely establishing the developmental stage at which transition to adult metabolic interactions takes place in symbiotic corals, and how environmental changes might affect this process, is of keen interest to marine biologists and ecologists. More generally, the occurrence of ontogenetic variations in trophic exchanges between symbiotic partners presents a unique opportunity to investigate in detail the underlying fundamental regulatory mechanisms."
} | 4,471 |
33294801 | PMC7695907 | pmc | 1,578 | {
"abstract": "Anaerobic digestion was one of the first bioenergy strategies developed, yet the interactions of the microbial community that is responsible for the production of methane are still poorly understood. For example, it has only recently been recognized that the bacteria that oxidize organic waste components can forge electrical connections with methane-producing microbes through biologically produced, protein-based, conductive circuits. This direct interspecies electron transfer (DIET) is faster than interspecies electron exchange via diffusive electron carriers, such as H 2 . DIET is also more resilient to perturbations such as increases in organic load inputs or toxic compounds. However, with current digester practices DIET rarely predominates. Improvements in anaerobic digestion associated with the addition of electrically conductive materials have been attributed to increased DIET, but experimental verification has been lacking. This deficiency may soon be overcome with improved understanding of the diversity of microbes capable of DIET, which is leading to molecular tools for determining the extent of DIET. Here we review the microbiology of DIET, suggest molecular strategies for monitoring DIET in anaerobic digesters, and propose approaches for re-engineering digester design and practices to encourage DIET.",
"introduction": "Introduction Anaerobic digestion (the conversion of organic wastes to methane) is one of the few economically successful large-scale bioenergy strategies ( Appels et al., 2011 ; Holm-Nielsen et al., 2009 ; Mao et al., 2015 ). The three major factors limiting broader adoption of anaerobic digestion are slow rates of waste conversion to methane, low conversion efficiencies (50%–70% of theoretical methane yield for conventional digesters), and susceptibility to system disruption by toxins or system overloads ( Chen et al., 2008 ; De Clercq et al., 2016 ; Holm-Nielsen et al., 2009 ). Anaerobic digestion has been practiced for hundreds of years ( Meynell, 1982 ). Thus, the possibility of dramatically changing the performance of anaerobic digestion after all this time might have been considered slim. However, simple strategies for accelerating, stabilizing, and increasing the efficiency of anaerobic digestion has recently been documented for a broad diversity of organic wastes. As detailed below, a diversity of inexpensive electrically conductive materials promotes methane production from many organic substrates. The most likely explanation for conductive materials enhancing anaerobic digestion process is that they facilitate direct interspecies electron transfer (DIET) between the bacteria contributing to the degradation of organics and the methane-producing archaea. Digester process designs that favor DIET have also been discovered. The possibility of rewiring the electrical connections between microbes offers substantial new opportunities for re-engineering and optimizing anaerobic digestion. The purpose of this review is to summarize the evidence that conductive materials improve anaerobic digestion, to discuss how this is related to the microbiology of DIET, and to emphasize urgent research and engineering needs that could lead to further improvements in the function of DIET-based anaerobic digesters."
} | 816 |
27739431 | PMC5067608 | pmc | 1,579 | {
"abstract": "The Opalinus Clay formation will host geological nuclear waste repositories in Switzerland. It is expected that gas pressure will build-up due to hydrogen production from steel corrosion, jeopardizing the integrity of the engineered barriers. In an in situ experiment located in the Mont Terri Underground Rock Laboratory, we demonstrate that hydrogen is consumed by microorganisms, fuelling a microbial community. Metagenomic binning and metaproteomic analysis of this deep subsurface community reveals a carbon cycle driven by autotrophic hydrogen oxidizers belonging to novel genera. Necromass is then processed by fermenters, followed by complete oxidation to carbon dioxide by heterotrophic sulfate-reducing bacteria, which closes the cycle. This microbial metabolic web can be integrated in the design of geological repositories to reduce pressure build-up. This study shows that Opalinus Clay harbours the potential for chemolithoautotrophic-based system, and provides a model of microbial carbon cycle in deep subsurface environments where hydrogen and sulfate are present.",
"discussion": "Discussion Data indicated that two MAGs, belonging to families Desulfobulbaceae (c16a) and Rhodospirillaceae (c57), were likely responsible for primary production in this ecosystem: proteomic results for the Desulfobulbaceae MAG confirmed its autotrophic potential by providing evidence for all the proteins in the reductive acetyl-CoA pathway ( Fig. 6 , Supplementary Table 7 ); the ability of the Rhodospirillaceae MAG to fix carbon via the Calvin cycle was also evidenced by its proteome ( Supplementary Data 6 ). These two autotrophic microorganisms both utilize H 2 as an electron donor, as they both harbour group 1 [NiFe]-hydrogenases ( Supplementary Data 6 ), but they differ in their use of electron acceptors. The Desulfobulbaceae MAG is a sulfate-reducing bacterium and includes the entire dissimilatory sulfate-reducing pathway in its proteome ( Fig. 6 ). In contrast, in the Rhodospirillaceae MAG, a dissimilatory sulfite reductase was identified in the proteome ( Supplementary Data 6 ). Thus, this organism is likely to use intermediate valence sulfur species as terminal electron acceptors. Their presence can be explained by the introduction of O 2 during previous samplings, which oxidized sulfide (S(-II)) or Fe(II) to Fe(III), which in turn oxidized S(-II). In the absence of this artefact, the abundance of Rhodospirillaceae is expected to be lower. The five remaining microorganisms grow as heterotrophs in these conditions as surmised by the lack of detection of [NiFe]-type 1 hydrogenases from their proteome, while other proteins (for example, those pertaining to the acetyl-CoA pathway) are readily detected ( Fig. 5 , Supplementary Data 6 ) 34 . The Hyphomonas MAG (c22) is the only organism capable of degrading organic macromolecules, such as proteins and RNA. Based on the absence of a respiratory pathway and on the detection of proteins involved in acetate production, we propose that it ferments organic compounds derived from microbial necromass, producing acetate. Furthermore, the proteome of Peptococcaceae MAG c4a, an SRB, shows an abundance of aromatic compound degradation proteins (incomplete pathway of anaerobic toluene degradation), suggesting that it uses complex organic compounds derived from microbial necromass, in addition to ethanol, butyrate and formate, as electron donors and carbon sources ( Supplementary Data 6 ). Three MAGs (c8a, c12 and c23) represent Gram-positive SRB with proteomic evidence supporting utilization of acetate and other organic acids and/or ethanol as electron donors ( Supplementary Data 6 ). These last four heterotrophic SRB are expected to oxidize carbon to CO 2 using the oxidative acetyl-CoA pathway ( Supplementary Data 6 ). From careful biochemical pathway annotation of the seven MAGs harbouring sufficient proteomic data, and from the geochemical background, we inferred a putative carbon cycle ( Fig. 7 , Supplementary Data 6 ). The two autotrophic organisms fix CO 2 and produce biomass. The Hyphomonas MAG degrades microbial necromass and produces acetate. However, we observe no net accumulation of acetate in the water ( Supplementary Fig. 6 ), presumably due to acetate oxidation to CO 2 by the four heterotrophic SRB via the oxidative acetyl-CoA pathway. Other fermentation products (that is, ethanol, butyrate, formate) are also oxidized by these SRB, suggesting that the fermentation pathways of this system were not all identified. Desulfobulbaceae MAG c16a appears to be adapted to a wide range of H 2 concentrations. Indeed, this organism is abundant in the microbial community in the early anoxic phase, when H 2 input, delivered through the gas permeable membrane, was only limited by gas/water equilibrium ( Fig. 4 , Supplementary Fig. 2 ). Later, between days 56 and 77, the H 2 input decreased drastically ( Supplementary Fig. 2 ). As a result, the overall concentration of planktonic cells decreased but, as shown by 16S rRNA sequencing, Desulfobulbaceae MAG c16a remained the most abundant microorganism. These data suggest that this organism is able to thrive at H 2 concentrations ranging from saturated to non-detectable. A limitation of deep subsurface microbial sampling is the possibility of contamination. In order to address this possibility, we collected 15 additional porewater samples from 7 other boreholes across the Mont-Terri Underground Rock Laboratory ( Fig. 1 ) and carried out 16S rRNA analysis ( Supplementary Data 7 ). By comparing the OTUs with the 16S rRNA gene derived from genomes, we could detect OTUs corresponding to the two autotrophic bacteria (Desulfobulbaceae and Rhodospirillaceae MAGs) and to MAG Peptococcaceae c8a in all tested boreholes ( Supplementary Table 3 ), including that from borehole BHT-1 that was drilled under anoxic conditions using sterile precautions 35 . Thus, these three MAGs are surmised to be indigenous to Opalinus Clay rock. Average nucleotide analysis ( Supplementary Table 5 ) of these three genomes revealed that they are distinct from known genomes, constituting new genera, which supports an Opalinus Clay origin. These findings suggest that autotrophic hydrogen-oxidizing bacteria are extant in undisturbed Opalinus Clay, without H 2 amendment. In such conditions, these population could rely on two putative sources of H 2 that are the radiolysis of water 7 24 and fermentation pathways of autochthonous organic matter 5 24 . SRB, which are the most abundant metabolic group in the microbial community characterized here, are known to accelerate the rate of steel corrosion under anoxic conditions 36 . At first glance, it may seem that the activity of SRB would have a negative impact on the safety of deep geological repository, by promoting the corrosion of one of the engineered barriers, whose primary function is to isolate radionuclides from the environment. However, careful design may harness the ability of microorganisms to consume H 2 rapidly, reducing an overpressure that is otherwise expected in the repository 20 , while relegating sulfate reduction to an iron-rich porous medium that would sequester sulfide. For instance, by combining a barrier of bentonite, a swelling clay, around the canister with a higher permeability zone between the host rock and the bentonite, SRB may grow in the higher permeability zone where sulfide would precipitate, while relying on H 2 diffusing from the canister surface across the bentonite. This mechanism can maintain the integrity of the host-rock as well as minimize the impact of sulfide on canister corrosion. Thus, microbial activity can be integrated into the design of nuclear waste disposal, and can have a positive impact on the long-term safety case of the repository. Further investigations will require quantification of the rate of H 2 consumption in this system. This is important in order to ascertain whether it is greater than the H 2 production rate from anoxic steel corrosion. Furthermore, the rate of sulfate consumption is also of interest 37 , in order to determine whether it can be consumed faster than it can be replenished, via diffusion of porewater from Opalinus Clay. If sulfate can be depleted, methanogenic conditions could take hold. In that case, pressure in repositories might not decrease to the same extent, because methane is poorly soluble. The present study provides detailed insight into the metabolic interactions of microorganisms in a subsurface ecosystem where the oxidation of H 2 is the primary energy source. We are aware of the fact that it is based on conditions that are unrealistic for the undisturbed subsurface, due to the high concentrations of H 2 delivered in this in situ experiment. However, such conditions enable biomass build-up and thus metaproteomic analysis, which has, to our knowledge, never been performed for microbial systems in the deep subsurface. This is a significant step forward because, unlike metagenomic analysis, which is based on the presence of genes and thus, can only describe potential metabolic activities, metaproteomic analysis ascertains the presence of proteins and thus, active metabolism. Used jointly, these methods are powerful. Metagenomic binning extracts individual MAGs from a complex microbial community, while metaproteomics describe the metabolic activity of these MAGs. Together, they can paint a detailed picture of an active metabolic network at the scale of a microbial community. In this work, we describe a microbial system whose primary production is based on a chemolithoautrophic metabolism: a chemical reaction, H 2 oxidation coupled with SO 4 2− reduction, provides energy for microbial metabolism and for carbon fixation. The organic carbon generated is then available to the rest of the community, which can assimilate it for biomass build-up or oxidize it to gain energy. The consequence of the obvious lack of sunlight in deep subsurface environments is that carbon fixation depends on this type of metabolism. But this fact doesn't imply that this system is totally disconnected from sunlight. Indeed, unlike the model proposed by Pedersen 4 , the present one is based on the occurrence of sulfate ( Fig. 7b ). This means that it is ultimately connected to sunlight, because sulfate originates from sulfide oxidation on early Earth, after the onset of oxygenic photosynthesis 38 . The proposed carbon cycling model may be extant in many deep subsurface environments. Indeed, this carbon cycling is expected to take place in the subsurface when H 2 and SO 4 2− are present concomitantly. H 2 is a key metabolic compound for the deep subsurface 8 9 10 11 12 13 , and is produced in situ by serpentinization 6 and by the radiolysis of water 7 . Despite the low level of H 2 present in Opalinus Clay 24 , our work has suggested that autotrophic hydrogen-oxidizing SRB are extant in undisturbed Opalinus Clay, without H 2 amendment. Thus, it is conceivable that carbon cycling takes place within this rock formation at exceedingly low rates in macropores, echoing the very slow microbial metabolism identified in deep ocean sediments 39 ."
} | 2,798 |
37790447 | PMC10542533 | pmc | 1,580 | {
"abstract": "DNA origami nanodevices achieve programmable structure and tunable mechanical and dynamic properties by leveraging the sequence specific interactions of nucleic acids. Previous advances have also established DNA origami as a useful building block to make well-defined micron-scale structures through hierarchical self-assembly, but these efforts have largely leveraged the structural features of DNA origami. The tunable dynamic and mechanical properties also provide an opportunity to make assemblies with adaptive structure and properties. Here we report the integration of DNA origami hinge nanodevices and coiled-coil peptides into hybrid reconfigurable assemblies. With the same dynamic device and peptide interaction, we make multiple higher order assemblies by organizing clusters of peptides (i.e. patches) or arranging single peptides (i.e. patterns) on the surfaces of DNA origami to control the relative orientation of devices. We use coiled-coil interactions to construct circular and linear assemblies whose structure and mechanical properties can be modulated with DNA-based actuation. Actuation of linear assemblies leads to micron scale motions and ~ 2.5-10-fold increase in bending stiffness. Our results provide a foundation for stimulus responsive hybrid assemblies that can adapt their structure and properties in response to nucleic acid, peptide, protein, or other triggers.",
"conclusion": "Conclusion In this work, we established the use of coiled-coil peptide interactions in controlling higher-order assembly of DNA nanodevice conformations, as well as constructing reconfigurable DNA assemblies with dynamic control over structure and mechanical properties. We used the coiled coil peptides as an adhesive where specificity of interactions can be controlled by the location and geometry of peptide interactions. We took advantage of the site-specific addressability of DNA origami structures to organize individual, patches, or patterns of peptide interactions to direct coiled-coil interactions for actuation or assembly. This result in particular highlights that even with a single peptide-peptide interaction (consisting of two complementary coils) we could use spatial organization on the origami to drive a range of specific multivalent interactions. We demonstrated isolated coiled-coil interactions can actuate device conformation changes, and patches or patterns of peptides can enable controlled higher order assemblies. We formed linear assemblies of hinge devices with simple arrangement of peptide patches, while circular assemblies required a more complex pattern of peptides to avoid spurious interactions. Furthermore, our results suggest using coiled-coil interactions for higher order DNA origami materials assembly can mitigate non-specific aggregation, which is a common challenge when using DNA sticky ends for higher order assembly. Finally, we showed the structure and mechanical properties of these assemblies can be modulated by controlling the dynamic device conformation either before (for circular assemblies) or after (for linear assemblies) the higher order self-assembly. We showed a 2-3 fold increase in linear polymer bending stiffness with actuation, but our results indicate that further optimization could lead to 10-fold or more increases in stiffness with actuation. Our results build on recent efforts to make controllable assemblies of nanostructures( 43 , 44 ) . Many studies have demonstrated higher order assembly of static DNA origami structures( 19 , 20 , 45 ), and recent studies have extended to dynamic assemblies( 22 – 27 ) . Inspired by the development of peptide-DNA conjugates for DNA origami assembly ( 39 ), here we used coiled-coil peptides as a useful adhesive for higher order self-assembly of dynamic devices where orientational control, and hence assembly structure control, can be engineered by organizing peptides into patches or patterns. Recent interest and advances in the colloidal or patchy particle assembly of DNA origami structures( 46 , 47 ) can provide further guidance towards more complex and functional assemblies. In addition, we combined coiled-coil assembly with DNA strand displacement actuation in sequence, either “actuation-then-assembly” to modulate the structure of self-limiting circular assemblies, or “assembly-then-actuation” to modulate the structure and mechanical properties of linear assemblies. Prior work has demonstrated DNA origami assemblies with variable mechanical properties( 48 , 49 ); however, these were not actuated systems, but rather different folded versions of structures that combined into higher order assemblies. The responsive nature of our assemblies and the combination of peptide and nucleic acid interactions to achieve assembly and actuation suggest these dynamic assemblies could serve as a useful platform to design adaptive materials that change shape or properties in response to amino acid or nucleic acid-based triggers or other environment stimuli. In particular, the use of coiled-coils could be an attractive approach for interfacing with a variety of proteins ( 39 ).",
"introduction": "Introduction: Molecular self-assembly is a promising route to construct biomimetic or bioinspired materials that leverage the diverse properties and interactions of biomolecules ( 1 ). Nature produces many examples of self-assembled adaptive materials such as actin filaments that form bundles via local interactions with actin binding proteins, in order to modulate mechanical properties ( 2 – 4 ). Biomolecular nanotechnology provides a useful approach to mimic many of the structural, dynamic, and mechanical features of these systems. In particular, DNA nanodevices have been designed to exhibit programmed reconfigurations in response to a variety of triggers( 5 , 6 ), for example to actuate the application of forces to biomolecules( 7 ). Integrating these dynamic device functions into higher-order self-assembled architectures could provide an interesting approach to mimic the emergent properties of biomaterials such as adaptive structure and stiffness, large-scale shape changes, and stimulus-driven assembly or disassembly ( 8 – 10 ). Here we leverage the interaction properties of coiled-coil peptides and the structural and dynamic properties of DNA origami to make hybrid DNA-peptide constructs where reconfiguration of the DNA devices can regulate the structure and mechanical properties of higher order assemblies. DNA has emerged as one of most common materials for the versatile self-assembly of precise nanostructures and dynamic nanodevices( 11 – 14 ). In particular, DNA origami( 15 , 16 ) leverages base-pairing interactions between a long ssDNA scaffold and hundreds of short ssDNA staples to create nanostructures with complex shapes and programmable motion ( 17 , 18 ). Here we take advantage of these features to construct higher order dynamic assemblies. Prior work has established the hierarchical assembly of DNA origami nanostructures to scale up the overall dimensions, demonstrating methods to make micron-sized assemblies with precisely organized components ( 19 – 21 ). Recent examples have further demonstrated building dynamic properties into these higher order assemblies; for example: controlling growth/disassembly( 22 ); actuating changes in chirality( 23 ), cross-section( 24 ), bending( 25 ), or length( 26 ) in 1D assemblies; or changing shape in 2D assemblies( 27 , 28 ). Building on these prior efforts, here we aimed to integrate actuation and assembly to make reconfigurable materials where actuation of DNA origami devices can control higher order assembly structure and mechanical properties. Furthermore, as a step towards integrating the advantages of DNA-based assemblies with amino acid materials, we combined the structure and dynamic properties of DNA origami devices with the tunable interactions of peptides ( 29 , 30 ). Peptides have a number of useful properties such as biocompatibility( 31 ); well-established approaches for sequence design( 32 ) and synthesis ( 33 ), specific binding and self-assembly capabilities ( 34 ), and an existing basis for stimulus-responsive motifs( 35 ). Here, we leverage the specific binding interactions of coiled-coil peptides. Coiled-coil interactions ( 36 ), which consist of binding between two α -helical peptides driven by hydrophobic and charge-charge interactions, and are one of the most abundant protein assembly motifs found in nature. Previous research has identified peptide sequences that lead to coiled-coil interactions with well-understood design principles( 33 ). DNA-modified coiled-coils have been demonstrated as a good hybrid adhesive for building higher order structures, such as DNA origami filaments( 37 , 38 ), as well as to integrate DNA nanostructures with functional proteins bearing complementary fused coils( 39 ). Coiled-coils are also, in principle, reversible through an analogous mechanism to toehold-mediated strand displacement, through the addition of a fully complementary coil( 40 ). Expanding on this approach, here we employ coiled-coil interactions to construct assemblies that harness the reconfigurability of dynamic DNA origami devices. We demonstrate the ability to control the conformation of a dynamic DNA origami device with coiled-coil peptides. In addition, we used coiled-coil interactions as an adhesive to assemble multiple assembly configurations. To achieve these distinct configurations, we engineered specificity into the coiled-coil adhesion by patterning two peptides (which form a heterodimeric coiled coil) on the DNA origami surfaces to control the relative orientation of adjoining devices, enabling formation of distinct higher order assemblies from the same dynamic DNA origami building block (i.e. polymorphic assembly). Furthermore, we combined coiled-coil assembly with strand displacement actuation of dynamic DNA origami devices to modulate assembly structure and properties. Taken together, our results demonstrate hybrid DNA origami-peptide dynamic structures as useful building blocks for assemblies with polymorphic and reconfigurable structure and adaptive properties. Furthermore, the use of coiled-coils provides an entirely orthogonal interaction mode, which we found helped to mitigate aggregation that is a common challenge of higher order assembly with DNA sticky ends. In the future, this approach can also enable facile integration of functional proteins into the hierarchical nanostructures and allow these nanostructures to respond to protein cues to that modulate assembly, conformations, or properties.",
"discussion": "Results and discussion We implement a previously developed dynamic DNA origami hinge design with tunable mechanical properties as the basis for our hybrid assemblies( 41 ). The hinge arms are comprised of 20-helix bundles organized on an 8x3 square lattice cross-section with 4 internal helices removed from the middle layer ( Figure 1A ). In addition, there are ~ 8-nm protrusions sticking out past the vertex on the back end of the two arms. The arms are connected by eight ssDNA linkers. Four of these connections are short 2 nucleotide (nt) linkers that form a rotation axis at the hinge vertex, while the other four are long 70 nt ssDNA linkers used to modulate the hinge properties. Two of the four long ssDNA linkers span across the inner layer near the vertex, and the other two long linkers span across the outer layer of helices at the back end of the protrusion. Previous work has established versatile approaches to modulate the hinge mechanical properties (i.e. the flexibility and minimum energy angle). For this work we implemented the version referred to as nDFS.A ( 41 )( Supplemental Figure S1 ), which exhibits a flexible conformational distribution with most angles falling between 50-100° ( Supplemental Figure S2 ). We also implemented coiled-coil peptides that were previously utilized in the higher order assembly of static DNA origami nanostructures( 37 ). These peptides consist of 28 amino acid residues consisting of four 7-residue (heptad) sequences; the peptide with heptad repeat EIAALEK is referred to as referred to as EI, and the peptide with heptad repeat KIAALKE is referred to as KI ( Figure 1B ). The ends of both EI and KI incorporate azidolysine (azK) residues for conjugation to ssDNA handles by strain-promoted azide–alkyne cycloaddition (SPAAC) chemistry. These two ssDNA handles, with orthogonal sequences termed as A and B, are 14-nt long and modified with dibenzocyclooctyne (DBCO) at the 5’ end through an amine linker. We followed established protocols( 37 ) for the conjugation of ssDNA handle A to peptide EI (conjugate referred to as AEI) and conjugation of ssDNA handle B to peptide KI (conjugate referred to as BKI) (see methods for details). The handle A DNA oligo was conjugated on the C-terminus of the EI peptide, and handle B was conjugated on either the N- or C-terminus of the KI peptide leading to distinct binding configurations ( Figure 1B ) with different end-to-end distances between the DNA handles. The interaction where the DNA handles are on opposite ends (i.e. A on the C-terminus of EI and B on the N-terminus of KI), which we refer to as the N-terminal configuration, leads to a larger distance between the DNA handles, and the interaction where the handles are on the same end (i.e. A on the C-terminus of EI and B on C-terminus of KI) leads to a shorter distance between the DNA handles, which we refer to as the C-terminal configuration. We aimed to establish the utility of peptide-peptide interactions for the actuation and assembly of dynamic DNA origami devices. To demonstrate actuation, we incorporated two sets of overhangs, one with sequences complementary to the A handle (A*) on the inner face of the top arm and another with sequences complementary to the B handle (B*) on the inner face of the bottom arm. We incorporated the overhangs for AEI and BKI attachment 38 bp away from the hinge vertex ( Supplemental Figure S1 ). We chose to incorporate the C-terminal configuration of the coiled-coil interactions, which based on a rigid hinge model and length of conjugates would constrain the angle of the nanostructure to ~ 35°. Therefore, binding of the coiled-coil peptides should provide a clear conformation change from the free hinge angle distribution ( Figure 1C , top, Supplemental Figure S2 ). Addition of the AEI and BKI C DNA-peptide conjugates led to a large shift in the conformational distribution with most hinges exhibiting angles of ~ 20-50°, as revealed by TEM image analysis ( Figure 1C , bottom, Supplemental Figure S3 ), suggesting the coiled-coil interaction can effectively control the hinge conformation. We performed a mixed two-Gaussian model fit to the angle distribution data with one population exhibiting an angle of 29+−10° (i.e. average/peak ± standard deviation from Gaussian fit), corresponding to the actuated hinges, and a second population of hinges exhibiting an angle of 66+−19°, corresponding to hinges that remained unactuated. Based on these Gaussian fits we estimated an actuation efficiency of 74%. We also tested the coiled-coil actuation in the N-terminal binding configuration (AEI-BKI N ). As expected, these results did not cause a major change in the angle distribution because the larger end-to-end distance of the ends of the coiled-coil attached to DNA would lead to a ~ 70° angle. Nevertheless, a slight shift in the angle distribution still suggests some incorporation of the coiled-coil interaction ( Supplemental Figure S4 ). In addition, we also tested designs where the overhangs for binding conjugates was positioned much farther from the hinge vertex (133 bp away) where we observed a larger population of hinges with angular conformations below 50° suggesting effective actuation ( Supplemental Figure S5 ). We then studied the use of coiled-coil interactions to make higher order assemblies of dynamic DNA hinge nanodevices. For higher order assemblies, we started with dimerization by making multiple sets of hinge devices each with one set of overhangs: one with A* overhangs on the outer face of the top arm, one with A* overhangs on the inner face of the top arm, one with B* overhangs on the outer face of the bottom arm, and one with B* overhangs on the inner face of the bottom arm ( Supplemental Figure S6 ). We performed dimerization experiments by combining the outer A* and outer B* devices along with the AEI and BKI C conjugates (we used BKI C conjugates in all assembly experiments), which led to adhesion of two hinges in multiple possible configurations ( Figure 1D , left and middle). Interestingly, we observed 66% of hinge dimers in the same orientation (i.e. vertices pointing in the same direction, Figure 1D , left, Supplemental Figure S7 ). Since the pattern of peptides on the surface is symmetric, either orientation should maximize EI and KI pairing. However, the same orientation dimer configuration might be favored due to slight twisting of the arms, steric interactions of the frayed ends, or possible weak base-pairing interactions of the ssDNA scaffold loops on the vertex end. We also designed a separate version of hinges where the coiled-coil interactions adjoin the inner faces of the arms on two separate hinges ( Figure 1D , right, Supplemental Figure S7 ). Expanding on the capability of coiled-coil peptides to assemble dynamic devices, we made new designs for the construction of higher order multi-device assemblies using only this specified coiled-coil peptide pair (AEI-BKI C ) and a single hinge design. As a start, we first functionalized the outer side of one arm of the hinge with several A* overhangs and the other arm with B* overhangs, similar to the dimers, but in this case the AEI-BKI C complexes could lead to self-assembly of larger multi-device assemblies. Figure 2A shows a representative TEM image (additional TEM images in Supplemental Figure S8 ) of the resulting assemblies, which showed having all AEI on the top arm and all BKI on the bottom arm led to a roughly even mix of neighboring devices that had similar orientations (i.e. vertex pointing in the same direction) or opposing orientations (i.e. vertex pointing toward opposing directions). We observed 51+−6% of assembly interactions led to neighboring devices in the same orientation. While we observed some circular (i.e. several devices in a row with the same orientation) and linear (i.e. several devices in a row with opposing orientations) assembly, these arrangements only persisted for a few devices, leading to irregular higher order structures. This mixed assembly results are likely due to the entire arm being coated with the same peptide, meaning the assembly interactions can randomly orient in either direction while still maximizing the pairing. While the assembly is efficient, there is no control over orientation, and hence no control over the higher order assembly structure. To achieve uniform higher order assemblies, we engineered designs that could self-assemble into either controlled linear (i.e. all opposing orientation assembly interactions) or circular assemblies (i.e. all similar orientation assembly interactions). We took the approach of patterning A* and B* overhangs on the hinge arms to facilitate preferred binding in a specific orientation of neighboring hinges. We started by testing the two arrangements of overhangs shown in Figures 2B and 2C . Since the linear polymers require all opposing orientations, we arranged a simple pattern with a “patch” of B* overhangs nearer to the vertex and a patch of A* overhangs farther away from the vertex (both patches consist of 3 x 2 arrangement of overhangs) on the outer face of both arms. Hence, neighboring hinges would have to be in opposing orientations to form EI-KI interactions. TEM imaging revealed linear polymers were accurately formed with 99.9% of neighboring hinges exhibiting the correct opposing orientation ( Figure 2B , Supplemental Figure S9 ) and long linear assemblies many over a micron in length, with the longest observed consisting of 78 hinge structures ( Supplemental Figure S10 ). We also replaced the peptides to only DNA strands including same sticky ends in coiled-coil peptides to form self-assembled polymers, While this DNA sticky-end approach led to effective assembly, we observed more aggregation compared to the peptide assembly ( Supplemental Figure S11 ). These results suggest the use of coiled-coil peptide interactions for higher order assembly can help overcome aggregation, which is a common challenge of forming large assemblies with purely DNA-sticky end interactions. We tested two patch design approaches to facilitate a specific assembly of circular patterns. In the first approach, we reversed the pattern longitudinally in one arm of the hinge (i.e. the bottom arm was changed to have the A* patch near the vertex and B* patch away from the vertex). We found this case led to only 58+−5% of neighboring devices assembling in the same orientation, again leading to irregular higher order assemblies ( Figure 2C , Supplemental Figure S12 ). This lack of specificity in assembly likely arises because the top arm can assemble with either the bottom arm of a different structure (leading to the same orientation) or the top arm of that structure (leading to an opposing orientation). To improve control over the same-orientation assembly, we reasoned that a more asymmetric pattern would avoid these undesired “top arm-top arm” and “bottom arm-bottom arm” assembly interactions, leading to higher efficiency of forming circular patterns. Towards this end, we tested the pattern shown in Figure 2D . TEM imaging revealed this complex pattern yielded drastically improved control over neighboring devices assembling with the same orientation, leading to efficient formation of circular patterns. Since the hinge is flexible (i.e. can adopt a range of angles), circular assemblies were observed containing a varying number of hinges. Figure 2F shows examples of circular assemblies containing different numbers of hinge devices (additional TEM images in Supplemental Figure S13 ). The circular assembly containing five hinges was the most abundant at 66%. This is consistent with the average angle of the unactuated hinge, which was about 70°. We then tested our ability to control the number of hinges in circular assemblies by modulating the hinge angle. We introduced a closing DNA strand that actuates the hinge into a 41 ±13° angular conformation ( Supplemental Figure S14 ), and we assembled these actuated hinges into circular patterns using the same asymmetric patterned self-assembly process. TEM images ( Figure 3A , Supplemental Figure S15 ) revealed these constrained hinges led to an increase in the number of devices in the circular assemblies, with the majority of circles containing either 7x (37.9%) or 8x (32.0%) devices ( Figure3B ). Although the average hinge angle of 41° might suggest circular assemblies with 9 hinges would be likely, we only observed 7.4 % of circular assemblies containing 9 hinges. This suggests for smaller angles, the average angle does not completely govern the assembly process, likely due to the flexibility of the hinge and the thickness of the arms accounting for additional arc length subtended by each individual hinge. In addition, circular assemblies with a greater number of hinges have a higher probability of a defect (e.g. one opposing orientation assembly interaction), which will inhibit the formation of a fully closed circular assembly. With these circular assemblies, we showed that a priori actuation to modulate the hinge structure can tune the structure of the higher order assembly. We next used the linear polymers to demonstrate dynamic control over the structure and properties of the fully formed assemblies. We created linear polymers with hinge devices containing DNA overhangs on inner facing sides of the hinge arms, which allow for closing of the hinge by introducing an additional DNA strand that latches the two arms together by base-pairing overhangs on each arm ( Figure 3C , top). Experiments on isolated hinges showed that this actuation led to hinges with angular conformations of about 15+−4° ( Supplemental Figure S16 ). We observed a similar distribution of angles for hinges incorporated in these linear assemblies compared to isolated unactuated hinges and we found that actuating linear assemblies led to the angle distribution of hinges in the polymers shifting from 62+−5° for the free hinges to 20+−2° for the actuated hinges ( Figure 3D ). In addition to modulating the structure (i.e. changing end to end distance), the actuation led to assemblies that exhibited relatively less curvature ( Figure 3C , Supplemental Figure S17 ), indicating an increase in stiffness. To quantify this increase in stiffness, we measured an effective polymer persistence length for the linear assemblies with or without actuation. We manually identified and traced the hinges along the polymer, and fit a spline curve to represent the trajectory along the center of the hinge ( Figure 3E and Supplemental Figure S18 ). We calculated the variance of the transverse fluctuations from these trajectories, which can be correlated to the persistence length as previously described by Isambert et al ( 42 ). Fitting the transverse fluctuations of these trajectories resulted in a persistence length of 163±155 nm for the free hinge assemblies ( Figure 3F ). For the case where we added actuation strands, we observed incomplete actuation. The angle distribution ( Figure 3D ) for the hinges in the actuated polymers suggests an actuation efficiency of 72% (using a cutoff of 30°). The analysis of transverse fluctuations of these trajectories revealed a persistence length of 387±190 nm for the assemblies when we included hinges that remained open in the trajectory analysis (i.e. partially actuated hinge polymers, Figure 3F – E ). While these results show a clear increase in stiffness, we hypothesized that the unactuated hinges had a significant impact on the assembly properties. To quantify the properties of fully actuated regions, we traced sections of polymers where all hinges were actuated, which resulted in a persistence length of 1545±378 nm (i.e. full actuated, Figure 3F – E ). We interpret this result as indicative of the stiffness over a short distance where hinges are fully actuated, which likely also indicates the limiting behavior if the efficiency were optimized to 100% hinge actuation. To further explore these results, we simulated hinge polymer trajectories using the geometry of the hinges and experimentally measured angle distributions for the free hinge, partially actuated hinge (i.e. full angle distribution for actuated polymers in Figure 3E ), and fully actuated hinge (i.e. only selecting angles below 30o from the angle distribution for actuated polymers in Figure 3E ). Our simulation results ( Supplemental Figure S18 ) yielded persistence lengths of 95nm, 284nm, and 5141nm for the free, partially actuated, and fully actuated polymers. These results are consistent with our experimental results providing further support for the effects of actuation and actuation efficiency on the mechanical properties. Overall, these results illustrate that these dynamic DNA origami-peptide hybrid assemblies can be actuated to modulate both structure and mechanical properties."
} | 6,874 |
32836883 | PMC7428693 | pmc | 1,583 | {
"abstract": "Graphical abstract",
"conclusion": "6 Conclusions and future perspectives The review gives an overall idea about the fabrication and application of graphene based superhydrophobic surfaces. The effectiveness of superhydrophobic coatings and the advantages of graphene and its derivatives in the enhancement of superhydrophobicity of various metals as well as alloy substrates are depicted in a comprehensive manner. The possibility of surface engineering of graphene and its different forms for superhydrophobic coatings is in its preliminary stage and is a rapidly developing field of research. Even though the research on superhydrophobic materials is attaining wider attention, only limited works on graphene based superhydrophobic coatings are reported. Thus the functionalization of graphene and its derivatives can bring about a tremendous advancement in the area of superhydrophobic materials and consequently tune their properties for tailor made applications. Superhydrophobic reusable and recyclable facial masks having excellent antibacterial and virucidal properties are of top priority due to the outbreak of pandemic Covid-19. Excellent superhydrophobic masks capable of blocking respiration droplets and viruses can bring about a tremendous change in health care. The antibacterial, and mechanical robustness of functionalized and hybrid graphene based superhydrophobic materials can be exploited for making cost effective facial masks and PP kits. Apart from medical applications, graphene based superhydrophobic materials can also be utilized for anti-icing, sensing, pollution control and self-cleaning applications. Switchable superhydrophobic graphene based surfaces can also be utilized in the development of smart materials. Thus there lies a vast prospect for graphene based superhydrophobic surfaces in the development of value added products in future.",
"introduction": "1 Introduction Composite materials with superhydrophobic traits have got extensive consideration owing to their excellent characteristic properties such as self-cleaning, hindered corrosion [ 1 ], and application in water resistant electronic devices [ [2] , [3] , [4] ]. The excellent water repellent properties observed for several natural objects such as lotus leaf, butterfly wings, rice plants etc. motivated scientists towards developing materials having exceptional water repellency [ 5 ]. Superhydrophobic surfaces are those facets having a water contact angle of 150 or more [ 6 ] and these materials are prepared with sufficient surface abrasion and minimum surface energy [ 7 ]. Both industrial and domestic applications are possible with “superhydrophobic surfaces” which made them ranked 7th in journals of material science discipline between 2006 and 2010 [ 8 ]. Superhydrophobic nanocoatings extend the area of nanotechnology and superhydrophobicity into a new dimension which has an essential role in improving the properties of coatings. The technologies and materials in coating industry are advancing day by day to improvise the efficiency of coatings by the incorporation of cost effective and greener concepts. Undoubtedly, coating is one of the finest methods to alter the solid interface properties by paving a new protecting layer over the substrate via chemical or physical process. So the development of superhydrophobic nanocoatings are attaining more attraction due to its possible extended high end application [ 9 ]. The surface wetting performance can be generally classified into 4 categories hydrophilic, hydrophobic, superhydrophilic and superhydrophobic according to their water contact angle (WCA). Hydrophilic and hydrophobic have their WCA within the range of 10° < θ < 90° and 90° < θ < 150° respectively. Superhydrophilic and superhydrophobic domains are of considerable interest owing to the utmost surface wetting traits having WCA differing in the stretch of 0° < θ < 10°and 150° < θ < 180° respectively. The Superhydrophobic regime is important as it experiences a perfect non wetting capability with an exceptionally elevated water contact angle that result in the rolling of water droplets easily [ 10 ]. The work of Jiang et al. [ 11 ] on nanostructure based superhydrophobicity triggered the curiosity of scientific community and it was followed by several works on superhydrophobic nano materials. Wenzel model gives a fair explanation of the contact angle of a substrate which is in contact with a liquid. Cassie and Baxter further proposed a model which deals with heterogeneous surfaces comprising of two fractions, the former deals with solid liquid and latter with the liquid air interface. The composition of the surface together with its roughness regulate the surface dampening and the water contact angle of the surface [ 12 , 13 ]. The nano-micro hierarchical roughness plays an eminent role in manipulating the characteristics of superhydrophobic nanocoatings and they are classified as inorganic, organic and inorganic-organic materials [ 9 ]. Silica encompassed materials are the most common choices for superhydrophobic nanocoating area. In fact, they are hydrophilic, but through chemical treatment they obtain superhydrophobicity [ 14 , 15 ]. Carbonaceous materials such as carbon nanotubes, carbon nanofibers and fullerenes have gained focus in superhydrophobic nanocoatings in the recent years [ 9 ]. Inorganic and organic nanomaterials are also utilized as promising materials for enhancing the flexibility and molecular framework of these coatings [ [16] , [17] , [18] ]. Yuan et al. [ 17 ] developed superhydrophobic LDPE with lotus-leaf-like characteristics and contact angle in the range of 156 ° . A vast array of inorganic fillers could be employed for designing hybrid superhydrophobic nanocoatings. Qing et al. [ 19 ] developed ZnO/polystyrene nano composite coatings having a WCA of 158 ° . Graphene, a younger cohort material, is nothing but the allotrope of carbon isolated by basic mechanical exfoliation in the earliest years of 21st century [ 20 ]. It is a mono-atomic thin sheet of carbon arranged in a hexagonal honeycomb like crystalline lattice and it has attracted worldwide interest within no time because of fascinating properties arising from the two-dimensional structure and existence of sp2 bonded carbon atoms [ 21 ]. Graphene is believed to have a better future for the fabrication of various carbonaceous technical devices, owing to its superior conductivities at room temperature and inertness towards corrosion [ 20 ]. Graphene was isolated in 2004 and prior to that intercalated clays and fullerenes were used as coating materials for corrosion resistance [ 22 , 23 ]. For the last few years, application of graphene and graphene-related materials are topics of increasing research interests due to many outstanding features which make them suitable for a passive layer formation that protects metals from oxidation and corrosion [ [24] , [25] , [26] , [27] ]. The surface wettability of graphene has been discussed a lot and many efforts have been taken to control the same by modifying the surface of graphene [ 24 ]. In order to reduce surface energy and increase surface roughness, a lot of methods have been employed by means of layer-by-layer assemblies [ [28] , [29] , [30] ], spraying [ 31 ], electrospinning [ 32 , 33 ], spin coating [ 34 ], electrochemical reaction and deposition [ [35] , [36] , [37] , [38] ], chemical vapor deposition [ 39 ] etc. Though application of superhydrophobic nanocoatings is the main destination of all researches, the theory, materials and moulding has also fascinated the research community [ 9 ]. Self-cleaning is the most promising property of graphene coated superhydrophobic surfaces as water droplets can roll on the surface and take away the dirt sticking on the surface effectively. Numerous artificial superhydrophobic nanocoatings with self-cleaning traits have been synthesized by different methods and are applied in diverse domains based on their applications [ [40] , [41] , [42] , [43] , [44] , [45] ]. Graphene coated nano-composite materials can be effectively used in anti-icing and de-icing methods [ [46] , [47] , [48] , [49] ] and used in biosensors, biomedical implants and devices, food packaging, industrial and marine equipment [ [50] , [51] , [52] ]. Graphene is an oil repellent material and hence could be used for effective separation of oil and water [ 53 ]. It also imparts anti-corrosion property to a coating system [ 54 ]. Diverse varieties of graphene, mainly single layer/bilayer graphene films, graphene oxide (GO), graphene nanoplatelets and graphene nanoribbons can be synthesized by modern techniques [ 55 , 56 ]. Now a days, nanopellets of graphene are extensively used for the synthesis of composite materials via mixing with metals, ceramics and polymers to tailor the properties for various applications [ 57 ]."
} | 2,218 |
29075665 | PMC5656427 | pmc | 1,584 | {
"abstract": "Memristive devices help address the binding problem: Their memory supports a transient connectivity in oscillator networks.",
"introduction": "INTRODUCTION Consciousness and perception are, without doubt, one of the most fascinating functionalities of the human brain and result from massively parallel computing in a huge self-organizing dynamical neural network ( 1 , 2 ). Neural synchrony is an elegant concept that tries to explain the underlying computing scheme by using dynamical network behaviors ( 1 , 3 – 5 ). It assumes that information is encoded into coherent states via temporally correlated neural pattern activity ( 6 ). Thus, neural synchrony can cope with the “binding problem” by providing a dynamic functional relation between different descriptive attributes of the same object ( 3 , 7 – 10 ). First experimental evidences of these concepts have been reached from sensorimotor networks ( 11 , 12 ), whereas more recent studies have shown the universality of these concepts for the entire brain ( 4 , 6 ). In particular, their role in neural communication across spatially distributed brain areas through phase synchronization of the underlying neuronal firing activities was investigated. Although the underlying mechanisms of neuronal synchrony are currently under debate, it is still believed that this concept is an essential tessera explaining higher cognitive brain functions, in accordance to experimental and theoretical evidences ( 4 , 13 , 14 ). The aim of neuromorphic engineering is to emulate cognitive functionalities using artificial neural networks (ANNs) ( 15 ). Although machine learning approaches date back to the early beginning of serial, binary computing based on the von Neumann architecture ( 16 ), today′s approaches are only partially able to mimic cognitive functionalities. The biggest challenge in this context rises from the massive parallel working and highly complex interconnection of neural networks and requires a close cooperation between modeling and experiments. However, software-based neural network models have difficulty coping with the highly complex interconnection to provide a real-time and parallel computing scheme. The use of neuromorphic circuits might overcome these restrictions and has recently gained new momentum with the advent of memristive devices ( 16 , 17 ). Memristive devices are resistors with memory and have been proven to enable the emulation of synaptic functionality in a detailed and efficient manner ( 18 – 20 ). So far, important local biological synaptic mechanisms such as the Hebbian learning rule, including spike timing–dependent plasticity ( 18 , 20 , 21 ), short-term potentiation and long-term potentiation (LTP), and long-term depression (LTD) ( 22 – 30 ), have been realized. Recently, the potential of memristive devices to cause locally synchronous oscillations has been presented ( 31 ). Here, we show that memristive devices allow a new degree of freedom for the concept of neural synchrony: a local memory that supports a transient connectivity pattern in ANNs. In detail, we show that the use of memristive devices arranged in an oscillator network allows the integration and storage of coincident information in a manner where distinct information is combined and long-lasting associations are formed. By using a 4-inch wafer device technology, electrochemical metallization (ECM) cells with the layer sequence Al/TiO 2− x /Ag are fabricated and used for the coupling of self-sustained van der Pol oscillators ( 32 ). To illustrate the performance of the proposed computing paradigm, the temporal binding problem of a “bistable” object is investigated, in which selective attention to the same object forces the binding of different attributes. For this purpose, a context-dependent partial synchronization of the memristive network is experimentally realized using the inherent stochastic nature of the resistive switching devices. On the basis of the probability and distribution of the resistance switching process, a local plasticity model is proposed, which causes an autonomous phase and frequency locking of the participatory oscillators, that is, a transition from an asynchronous state to a synchronous state. We show evidence that the use of memristive devices allows the realization of active self-organized and transient ANNs, and opens a new pathway toward the realization of cognitive electronics.",
"discussion": "DISCUSSION We have shown that the proposed model of memristively coupled van der Pol oscillators allows the incorporation of plasticity into the concept of neural synchronization. For this purpose, the nonvolatility of memristive devices provides a long-lasting association of learned attributes. Higher-level assemblies, which might correspond to the temporal sensory input, are required for switching between the different representations. This is due to the fact that the brain is an active and dynamic system, which binds at any moment the environmental information from several sensory modalities to a coherent conscious perception. For this amazing computing process, three fundamental processing stages are necessary: selective attention, segregation, and integration ( 46 – 48 ). Selective attention acts as a kind of an effective input filter of the nerve system to extract the relevant information from a tremendous amount of permanently received environmental information by the sensory receptors. Only items that are deemed relevant to the existing task are considered. It is widely believed that, at the neural level, the strength of attention serves as the input to sensory neurons and is transacted into a firing rate of action potentials. Similar to this mechanism, the number of voltage pulses emulates the level of attention in our model: Depending on the number of applied pulses, the probability of a resistance change for a single memristive device increases ( Fig. 2A ). This allows us to cope with the initial processing step, where the outer world is being perceived rather than copied. Segregation stands for the necessity of encapsulating and composing the incoming information in distinct neuronal ensembles (motifs) to maintain their individual response profiles (for example, for color and shape of an object). To emulate the mechanism of segregation, we used van der Pol oscillators, which differ in their frequencies. This is of particular importance for the representation of different motifs, for example, in the case of the bistable image of the hippo shown in Fig. 1 . In particular, the frequency difference is important for the mechanism of desynchronization: By reverting the resistances of the memristive devices that were changed before, the particular oscillators return to their own frequencies. In our memristive circuit, this was obtained by a global reset of the individual memristive devices whenever the attention is changed. Alternatively, this can also be locally obtained by applying negative voltage pulses via S . Therefore, the stochasticity of the reset process, for example, can be used. It is important to mention that in the case that all oscillators have the same frequency and only differ in their phases, desynchronization process cannot be achieved. Thus, the presented model differs from the concept of phase synchronization, which is currently intensively discussed in terms of memory formation in the brain ( 4 ). Finally, integration means the temporal binding of the motifs and coincides with a partial loss of specialization. In this respect, the inherent stochasticity of memristive devices offers an elegant possibility of defining a local plasticity model that causes a partial phase and frequency locking of the network oscillators. It is worthwhile to compare our memristive model in terms of its biological realism, that is, how it reflects the general understanding of visual perceptions in the brain. This is connected to the fundamental question of how we see or, more precisely, perceive our environment. A widely accepted theory believes that vision is processed by three parallel pathways in the brain, namely, processing motion, depth/form, and color ( 47 ). This has been followed from the finding that cells in different areas of the visual cortex respond to different perceptual attributes of objects. In line with the image of the hippo shown in Fig. 1 , the processing of vision into coherent states is based on various features from different pathways, visual areas, and modalities. The memristive binding model presented might not cover this. In particular, the information flow from the high-level input (sensory input) to the local level (oscillator synchronization) was realized locally by S : Every coupling is individually addressed and the sensory input is directly linked to the individual activities of the particular aspects of the drawing (number of voltage pulses). However, compared to the complexity of the brain, in which the relevant information is selected from a myriad of information, our approach represents a strong simplification of that complexity. To account more effectively for the complexity of the problem, a global selection mechanism, which takes into consideration the higher-level down-streaming of information, is required. Nevertheless, the model has clear advantages in terms of its technical feasibility, which, in the future, may allow the realization of complex electrical circuits with cognitive functionality and the justification of the reductionist approach. In this respect, a reductionist model is important for the emulation because, during visual perception, the brain has to process several objects and control multiple actions concurrently. However, this requires a whole number of binding problems to solve and a dedicated interaction mechanism among them ( 7 , 47 ). Neural synchronization, in conjunction with a local memory, may support this communication mechanism, and it has been shown that synchronization is based on two major functions: neural communication and plasticity ( 4 ). Thus, a thorough consideration of memory processes seems to be important for the binding problem and is coped with the memristive model presented. Furthermore, by extending the proposed network with memristive circuit elements such as memcapacitors or meminductors ( 49 ), more complex network behavior might be feasible. In this respect, the memristive binding model presented here provides a new pathway toward the realization of cognitive electronics."
} | 2,621 |
35360182 | PMC8964061 | pmc | 1,585 | {
"abstract": "Liquid State Machines (LSMs) are computing reservoirs composed of recurrently connected Spiking Neural Networks which have attracted research interest for their modeling capacity of biological structures and as promising pattern recognition tools suitable for their implementation in neuromorphic processors, benefited from the modest use of computing resources in their training process. However, it has been difficult to optimize LSMs for solving complex tasks such as event-based computer vision and few implementations in large-scale neuromorphic processors have been attempted. In this work, we show that offline-trained LSMs implemented in the SpiNNaker neuromorphic processor are able to classify visual events, achieving state-of-the-art performance in the event-based N-MNIST dataset. The training of the readout layer is performed using a recent adaptation of back-propagation-through-time (BPTT) for SNNs, while the internal weights of the reservoir are kept static. Results show that mapping our LSM from a Deep Learning framework to SpiNNaker does not affect the performance of the classification task. Additionally, we show that weight quantization, which substantially reduces the memory footprint of the LSM, has a small impact on its performance.",
"introduction": "1. Introduction Neuromorphic computing research aims to enable the design of highly efficient devices capable of processing multi-scale and event-driven dynamic data, inspired by the ability of nervous systems in animals to coordinate actions with a vast stream of sensory information. At its core is the study of Spiking Neural Networks (SNNs), models that describe the dynamics and interactions of biological neurons, characterized by a spike time-encoding mechanism, event-based communication, and high parallelism. SNNs are being investigated in pattern recognition applications, and recent results show that they are able to match the performance of Deep Neural Networks in several computer vision and signal processing tasks (Tavanaei et al., 2019 ). Concurrently, efforts to deploy their time-encoding feature in hardware have resulted in the development of large-scale neuromorphic chips such as TrueNorth (DeBole et al., 2019 ), Loihi (Davies et al., 2018 ), and SpiNNaker (Furber et al., 2014 ). Several works show a reduction in the power consumption of neuromorphic computing systems as opposed to conventional systems (CPU, GPU) for specific pattern recognition tasks (Diehl et al., 2016a ; Amir et al., 2017 ; Liu et al., 2018 ) and optimization problems (Davies et al., 2021 ). This advantage would provide an edge of SNNs and neuromorphic computing in applications that require low power or high autonomy sensing and processing of data such as robotics, autonomous driving, and edge computing. In the spectrum of Deep Neural Network architectures, Recurrent Neural Networks (RNNs) are among the most used for sequential and temporal processing, showcasing a high performance in machine translation (Sutskever et al., 2014 ), image captioning (Karpathy and Fei-Fei, 2015 ), speech recognition (Graves and Schmidhuber, 2005 ), time series prediction (Sagheer and Kotb, 2019 ), etc. (Lipton et al., 2015 ). Furthermore, recurrent connectivity is prevalent in biological brain modules (Lukoševičius and Jaeger, 2009 ). This makes the design of Recurrent Spiking Neural Networks (RSNNs) and their implementation on neuromorphic hardware an interesting area to explore for the development of more efficient machine learning solutions. The Liquid State Machine (LSM) is one type of recurrently connected network of spiking neurons. Proposed by Maass et al. ( 2002 ), LSMs are randomly generated recurrent spiking neural networks whose internal connectivity parameters remain static during the training process, acting as a reservoir . This reservoir is excited by input signals and its state, a non-linear transformation of the input's history, is connected to a linear readout unit. The state of the reservoir can be seen as a mapping of the input data into a higher dimension where the prediction or classification task is easier to solve, similar to the kernel methods like Support Vector Machines. The main hypothesis regarding this kind of network is that, if set properly, they are able to represent spatiotemporal inputs in a higher dimensional space where non-linear combinations of frequencies resonate, providing useful information that makes the characterization of the input simpler to infer, while requiring a significantly less amount of computational resources compared to an RSNN trained in a standard way (Cramer et al., 2020 ), as the connectivity weights among layers of the network are not trained, except for the output or classification layer. While RSNNs seem an obvious design choice for neuromorphic computing platforms, very few works have attempted to implement large-scale Spiking RNNs in neurosynaptic processors. Diehl et al. ( 2016b ) implemented an Elman RNN for question classification in the IBM's TrueNorth using a “train-and-constrain” methodology including a 16-level weight quantization. The inputs were converted to spikes through a simple rate encoding. Shrestha et al. ( 2018 ) used a similar approach, that involves approximation techniques such as activation discretization, weight quantization, scaling, and rounding, to implement an LSTM for sequence classification tasks. In this article, we propose a method to train a Spiking RNN in a deep learning framework (Paszke et al., 2019 ), and to implement the trained model in a neuromorphic platform (Furber et al., 2014 ) for event classification. The model of choice is that of Liquid State Machines (LSMs). We use the Neuromorphic MNIST (N-MNIST) (Orchard et al., 2015 ) dataset to train and validate the results. The choice of an event-driven dataset instead of sequential datasets eliminates the need for spike conversion in our proposed method. Related work includes that by Tian et al. ( 2021 ), who proposed a method to train an LSM using a neural architecture search which achieved a 92.5% accuracy for the N-MNIST without a hardware implementation, and (Yang et al., 2020 ), who trained an LSM for the N-MNIST dataset with 93.1% accuracy and deployed it in a custom 32 nm ASIC. The best-reported accuracy for the N-MNIST dataset trained with a Deep SNN corresponds to the work by Samadzadeh et al. ( 2020 ), who achieved 99.6% using a Spiking CNN with Residual blocks and spatio-temporal backpropagation. For this and the aforementioned related works, the accuracy is calculated as the percentage of correctly classified inputs on a test set of 10,000 samples, which are not used in the training process. The main contributions of this work are: The design and implementation of an LSM for the SpiNNaker, a large-scale neuromorphic processor, which is widely used in neuromorphic computing research. State of the art results by an LSM for the N-MNIST dataset. Analysis of the impact of size and weight precision in the performance of the LSM in the SpiNNaker platform. Results of this work show a 94.43% accuracy for the best LSM, which outperforms the state-of-the-art.",
"discussion": "4. Discussion and Future Outlook In this article, we introduced a method to facilitate the inference of Recurrent Spiking Neural Networks on the SpiNNaker neuromorphic platform. This was validated by implementing a Liquid State Machine for an event-driven classification task, the N-MNIST, achieving the best-known accuracy results for such architecture and dataset. Additionally, we showed that the accuracy is not significantly affected when the simulated weights are constrained by quantization. Regarding the speed of this neuromorphic implementation, the wall-clock time required for a single inference depends on the time scale factor parameter used in SpiNNaker, which allows slowing down the simulations for a more reliable operation. Our simulations are designed so every inference takes 50 ms (25 ms for classifying the input and 25 ms for the inhibition period in preparation for the next input). However, in our best-reported results, a time scale factor of 5 was used, meaning a wall-clock time inference of 250 ms. This is far from ideal, and we encourage the community to find ways to implement recurrent connectivity in neuromorphic hardware which is robust to packet loss, so the inference can approach real-time. Considering the size of the networks implemented in this work, it is small for the potential of the SpiNNaker, which can simulate 255 LIF neurons per core, approximately 195K neurons in the 48-chip board. However, in our experiments we observe that the accuracy starts dropping beyond 10 neurons per core, limiting the maximum network sizes below 7,680 neurons. We hope that future works aiming for fast, accurate inference of large spiking neural networks in this platform build upon our work. Additionally, in future works, we would like to adapt our methodology to perform on-chip training and validate it in datasets with richer dynamical content. We will favor the use of biologically-feasible time-coded instead of rate-coded learning rules as it would reduce the spiking activity in the readout layer. We believe this work can be found valuable in the quest for building and implementing highly performing Spiking RNNs in neuromorphic processors which in turn would be a seed for future developments of energy-efficient multi-scale processing applications."
} | 2,364 |
27366961 | null | s2 | 1,586 | {
"abstract": "Biological conversion of natural gas to liquids (Bio-GTL) represents an immense economic opportunity. In nature, aerobic methanotrophic bacteria and anaerobic archaea are able to selectively oxidize methane using methane monooxygenase (MMO) and methyl coenzyme M reductase (MCR) enzymes. Although significant progress has been made toward genetically manipulating these organisms for biotechnological applications, the enzymes themselves are slow, complex, and not recombinantly tractable in traditional industrial hosts. With turnover numbers of 0.16-13 s(-1), these enzymes pose a considerable upstream problem in the biological production of fuels or chemicals from methane. Methane oxidation enzymes will need to be engineered to be faster to enable high volumetric productivities; however, efforts to do so and to engineer simpler enzymes have been minimally successful. Moreover, known methane-oxidizing enzymes have different expression levels, carbon and energy efficiencies, require auxiliary systems for biosynthesis and function, and vary considerably in terms of complexity and reductant requirements. The pros and cons of using each methane-oxidizing enzyme for Bio-GTL are considered in detail. The future for these enzymes is bright, but a renewed focus on studying them will be critical to the successful development of biological processes that utilize methane as a feedstock."
} | 348 |
40137260 | PMC11942833 | pmc | 1,587 | {
"abstract": "Arbuscular mycorrhizal fungi (AMF) are considered crucial for the survival of many endangered plant species. However, the dynamics of AMF communities in the roots and rhizosphere soil of Heptacodium miconioides , particularly along elevation gradients, remain underexplored. This study investigates AMF colonization, spore density, and community structure in the root and rhizosphere soil of H. miconioides across an elevation range from 306 to 1028 m a.s.l., employing high-throughput sequencing. Our results show that AMF colonization and spore density in H. miconioides increased with elevation. Glomus was the dominant genus in both root and rhizosphere samples. Elevation significantly influenced the AMF community structure and diversity in the root, with alpha diversity decreasing linearly with elevation. In contrast, no significant elevation-related changes were observed in the rhizosphere soil alpha diversity. The difference in AMF beta diversity between the root and rhizosphere soil was lowest at the highest elevation. Compared to the rhizosphere soil, the degree and degree centralization of AMF community co-occurrence networks in the root showed a significant increase at higher elevations. Variations in soil properties, particularly soil pH, available phosphorus, and total nitrogen levels strongly influenced AMF communities in rhizosphere soil, while nitrate nitrogen, available potassium, and acid phosphatase were correlated with AMF communities in the root. These findings highlight the impact of elevation on AMF communities in both root and rhizosphere soil, providing valuable insights for the habitat restoration and conservation efforts for this species.",
"conclusion": "5. Conclusions Our field investigation revealed a significant increase in AMF colonization rates and spore density in H. miconioides natural populations with the increase in the elevation gradient. The dominant AMF genera in both the root and rhizosphere soil included Glomus , Claroideoglomus , unclassified Glomeromycota , Scutellospora , Gigaspora , and Acaulospora , though their relative proportions varied with elevation. The root-associated AMF alpha diversity decreased linearly with increasing elevation, whereas the rhizosphere soil diversity remained largely unchanged. Beta diversity differences between the root and rhizosphere soil initially increased and then declined with elevation. Rhizosphere AMF communities were closely associated with soil pH, AP, and TN, while root-associated communities were significantly correlated with NO 3 − -N, AK, OM, acid phosphatase, and elevation. These findings provide valuable insights into the diversity and ecological preferences of the AMF species associated with H. miconioides . The characterization and identification of AMF species in the natural habitat of H. miconioides hold the potential for isolating highly effective AMF strains that have co-evolved with the species. Such strains could be harnessed as AMF inoculants to support H. miconioides conservation and habitat restoration efforts. These insights also offer a novel perspective for enhancing the conservation of other endangered plant species capable of forming AMF associations.",
"introduction": "1. Introduction Endangered plant species are vital components of natural ecosystems and global biodiversity [ 1 ]. However, these species are increasingly threatened by factors such as over-exploitation, habitat loss, invasive species, and environmental changes, including global warming and acid rain [ 2 ]. As a result, protecting endangered plants and enhancing the viability of their populations has become a critical conservation priority. Soil microorganisms are required to maintain the stability of an ecosystem [ 3 ], with arbuscular mycorrhizal fungi (AMF) being key symbionts for over 80% of terrestrial plants. AMF serve as a crucial link between aboveground and belowground ecosystems [ 4 ]. These fungi enhance mineral nutrient uptake, improve soil stability, and increase plant resilience to environmental stressors [ 5 ], while also influencing plant fitness and reproduction [ 6 ]. Recently, AMF’s role in the conservation of endangered plants has gained increasing attention. Numerous studies have demonstrated that rare and endangered plant species exhibit improved performance and greater mycorrhizal dependence in the presence of AMF [ 7 , 8 ]. For example, inoculating the endangered species Pterocarpus santalinus with AMF symbionts promoted seedling growth and facilitated successful field establishment [ 9 ]. Likewise, Wang et al. [ 10 ] found that AMF inoculation helped protect Zelkova serrata from acid rain stress by enhancing photosynthetic capacity, osmolyte regulation, and activating antioxidant enzymes. These findings underscore the potential of AMF to support the survival and environmental resilience of endangered plants, thereby offering a promising approach to their conservation. Mountain elevation gradients, with their varying environmental conditions, provide a useful model for studying soil microbial patterns, including AMF [ 11 ]. Elevation induces distinct changes in climate, seasonality, vegetation, and soil properties, all of which influence microbial communities [ 12 ]. While the relationship between AMF abundance and diversity along elevation gradients has been widely studied, the results remain inconclusive. For instance, some studies have reported a negative correlation between AMF colonization, spore density, and community diversity with elevation in Tibetan alpine grasslands [ 13 , 14 ]. In contrast, Vieira et al. [ 15 ] observed a higher Shannon diversity of AMF species at mid-to-high elevations in tropical mountains. Furthermore, Yu et al. [ 16 ] found that elevation positively correlated with the relative abundances of Ambispora and Glomus in the rhizosphere of Siraitia grosvenorii , significantly shaping AMF community composition. Other studies, such as Zhang et al. [ 17 ], suggested a bimodal pattern of AMF diversity with increasing elevation in Mt. Taibai, Qinling Mountains. These disparate findings highlight the need for further research to establish a comprehensive understanding of how AMF diversity responds to elevation in natural ecosystems. AMF in both the root and rhizosphere soil play distinct yet complementary roles in supporting plant growth and health. Previous studies have shown that functional groups of AMF in these two environments differ significantly in terms of biomass allocation [ 18 ]. For example, Rhizophagus species tend to allocate more biomass to the root, while Acaulospora species are more likely to allocate biomass to the rhizosphere soil. This differentiation suggests that AMF in the roots are specialized for nutrient exchange and stress tolerance, whereas AMF in the rhizosphere soil are more diverse, contributing to nutrient acquisition, soil structure, and microbial interactions [ 19 , 20 ]. Stevens et al. [ 21 ] demonstrated that the soil AMF community structure is primarily influenced by environmental factors, while root AMF communities are more strongly impacted by disturbances and host–plant interactions. Forest management practices, such as the mixed forest management of Juglans mandshurica , were reported to alter the AMF composition in the soil but had minimal effect on the root AMF community [ 22 ]. These findings suggest that the AMF species in roots are different from those in the rhizosphere soil. However, most studies to date were mainly carried out on farmland [ 23 ] and grassland ecosystems [ 24 ], with limited research on AMF diversity changes in both root and rhizosphere soils in mountainous ecosystems. Studying AMF distribution patterns along elevation gradients can provide valuable insights into the factors driving elevation-related changes in diversity. Heptacodium miconioides Rehd., a member of the monotypic genus Heptacodium in the Caprifoliaceae family, is an endemic and endangered deciduous tree. As a perennial species, H. miconioides occupies a unique position in the evolutionary history of Caprifoliaceae and holds significant scientific and ornamental value [ 25 ]. However, due to habitat fragmentation, low reproductive rates, poor environmental conditions, and human activities the natural populations of H. miconioides have been severely threatened [ 26 ]. It is classified as a second-class national protected plant in China and is listed as a vulnerable species on the IUCN Red List [ 27 , 28 ]. Previous studies have shown that H. miconioides forms a beneficial symbiotic relationship with AMF [ 29 ]. For instance, inoculation with Rhizophagus intraradices has been shown to enhance drought tolerance and promote seedling growth in H. miconioides [ 28 ]. Here, we used root and rhizosphere soil samples from H. miconioides across five elevation gradients in Dongbai Mountain to perform Illumina Miseq sequencing and analyze AMF diversity changes and community composition. The objective of this work was to achieve a deeper understanding of the diversity and structure of AMF communities in the root and rhizosphere soil associated with H. miconioides along elevation gradients. We aimed to: (1) compare the AMF community composition between the root and rhizosphere soil; (2) evaluate the response of AMF community diversity and distribution patterns to elevation gradients; and (3) assess the influence of edaphic factors and soil enzyme activity on AMF community structure in both the root and rhizosphere soils. Our results could provide insights into the role of AMF in the conservation, growth, and habitat restoration of H. miconioides and other endangered plant species.",
"discussion": "4. Discussion AMF play a crucial role in ecosystem stability and ecological restoration [ 4 , 5 ]. Many endangered plants form symbiotic relationships with AMF, often thriving in specialized habitats that harbor unique AMF species [ 43 ]. This study investigated the colonization and diversity of AMF in the root and rhizosphere soil of endangered H. miconioides across elevation gradients, identifying key environmental factors that shape AMF community structure. 4.1. Changes in AMF Colonization and Spore Density of H. miconioides Along Elevation Gradients AMF colonization and spore density are critical indicators of AMF growth and their symbiotic relationships with host plants [ 34 ]. Our previous research demonstrated that AMF inoculation successfully formed a robust symbiosis with H. miconioides roots under greenhouse conditions, achieving a colonization rate exceeding 80% [ 28 ]. In the current study, AMF colonization and spore density increased linearly with elevation, indicating a strengthening symbiotic relationship at higher elevations. This trend aligns with the natural distribution of H. miconioides , which is more abundant at higher elevations. The increased AMF colonization and spore density at higher elevations likely reflect AMF’s role in enhancing soil nutrient availability and promoting plant growth. However, these findings contrast with previous studies reporting either negative correlations between AMF colonization and elevation [ 13 , 44 ] or non-linear trends [ 45 ]. The discrepancy may be attributed to differences in elevation ranges, as previous studies examined gradients up to 1000 m, whereas this study focused on gradients of approximately 700 m. Variations in host plant species and the harsh environmental conditions of high-altitude ecosystems may also contribute to these differences [ 46 , 47 ]. Additionally, spore density in the rhizosphere soil also increased linearly with elevation, corroborating the findings of Coutinho et al. [ 48 ], who reported the highest spore density at 1100 m. However, other research on Chamaecyparis formosensis at elevations ranging from 1200 to 2500 m found no significant elevation effect on spore density [ 49 ]. Factors such as soil nutrient availability, host dependency, natural habitat, and spore ecological adaptations likely influence AMF spore distribution patterns [ 14 , 49 , 50 ]. Pearson’s correlation analysis revealed significant positive correlations between AMF spore density and WC, TN, catalase, and acid phosphatase levels, while a negative correlation was observed with pH. AMF spore density may be linked to acidic soils, which produce higher AMF spore counts but lower taxonomic diversity compared to slightly acidic soils [ 51 ]. Our results suggest that soil moisture, TN, and enzyme activities may play important roles in enhancing soil microbial reproduction, resulting in increased mycorrhizal sporulation [ 52 , 53 , 54 ]. These findings underscore the critical influence of soil properties on the rhizosphere spore density of H. miconioides along elevation gradients. 4.2. Comparison of AMF Communities in Root and Rhizosphere Soil of H. miconioides Along Elevation Gradients AMF occupy dual niches within the host roots and surrounding soil, playing a critical role in maintaining plant populations [ 43 , 55 ]. At the genus level, Glomus was more abundant in H. miconioides roots than in rhizosphere soil and consistently dominated AMF communities at all elevations. A similar dominance by Glomus or members of Glomeraceae has been observed in other endangered plants, including Ulmus chenmoui [ 52 ], Toona ciliata [ 56 ], and Tetraena mongolica [ 57 ]. The extensive colonization of Glomus within roots across diverse habitats enhances plant resistance and adaptability to environmental stress [ 58 ]. Nevertheless, other studies have identified Acaulosporaceae and Gigasporaceae as dominant AMF in the roots of endangered plants [ 59 ], suggesting that AMF dominance may depend on host specificity. Moreover, elevational differences also influenced the relative abundance of other AMF genera. Paraglomus and Claroideoglomus were predominantly detected at higher elevations, while Gigaspora was more common at lower elevations. Variations in water and nutrient availability likely shaped these distribution patterns. For instance, Gigasporaceae, known for producing extensive external hyphae, is well suited for nutrient acquisition in nutrient-poor soils [ 58 ]. The poor soil conditions at lower elevations in this study likely favored Gigaspora , whose well-developed external hyphae network supports nutrient absorption for host plants. Our study also found that the AMF community compositions in roots and rhizosphere soil differed significantly, consistent with previous findings from forest ecosystems [ 22 ]. Bonfim et al. [ 60 ] reported high AMF diversity in rhizosphere soil in a Brazilian Atlantic forest, with only Glomeraceae detected within roots. These compositional differences may be due to the selective preferences of plant root systems and the varying responses of AMF taxa to root and rhizosphere environments [ 22 ]. Root-associated AMF communities are primarily shaped by plant characteristics, whereas those in rhizosphere soil are influenced by external environmental factors. Moreover, the differences in beta diversity between root and rhizosphere AMF communities initially increased along elevation gradients but decreased at the highest elevation (1028 m), resembling trends observed in Taibai Mountain alpine meadows [ 42 ]. The shift from dispersion at medium elevations (low-nutrient conditions) to aggregation at high elevations (high-nutrient conditions) indicates that abiotic factors significantly influence AMF community structure. The reduced beta diversity differences at the highest elevation suggest that environmental conditions at high elevations foster more uniform AMF community assemblies between the roots and rhizosphere soil in H. miconioides . Furthermore, previous studies have shown that topological parameters in co-occurrence networks, such as clustering coefficient, average degree, and degree centralization, can reflect the interaction intensity between species [ 61 , 62 ]. In this study, the degree and degree centralization of the root AMF community co-occurrence networks significantly increased with the increasing elevation, while no significant difference in the rhizosphere soil was observed. These findings further suggest that the interaction intensities of the AMF community within H. miconioides roots are more pronounced at higher elevations compared to those at lower elevations. In addition to differences composition and distribution, alpha diversity patterns also varied between root and rhizosphere AMF communities along elevation gradients. Root AMF diversity significantly decreased with increasing elevation, aligning with previous studies reporting negative correlations between AMF diversity and elevation [ 13 , 14 ]. However, contrasting results from Brazilian forests, where AMF diversity increased at higher elevations [ 60 ], highlight the influence of regional environmental factors and soil characteristics. Despite the decline in root AMF diversity, no significant changes were observed in rhizosphere AMF diversity along elevation gradients. This discrepancy suggests that the reduced AMF-root symbiotic associations at high elevations are not due to the absence of symbiotic AMF in the rhizosphere but rather host plant selection against diverse fungal communities [ 63 ]. Interestingly, the Chao1 richness index for AMF communities remained stable in both root and rhizosphere samples across elevations. These findings indicate that neither the availability of AMF in rhizosphere soil nor the capacity of H. miconioides to establish and maintain mycorrhizal associations were significantly influenced by elevation gradients. 4.3. Relationship Between Environmental Factors and AMF Communities in Root and Rhizosphere Soil of H. miconioides The structure of the AMF community in the roots and rhizosphere soil of H. miconioides shows distinct relationships with environmental factors. Previous studies have demonstrated that soil properties, elevation, and environmental filtering significantly influence AMF diversity, leading to spatial variations in AMF communities at local scales [ 55 , 64 ]. Our findings indicate that the composition of rhizosphere soil AMF communities in H. miconioides is strongly associated with soil pH, AP, and TN levels. Soil pH plays a crucial role in shaping AMF community structure [ 65 ], likely affecting spore formation and development [ 56 ], nutrient availability [ 66 ], and the distribution of AMF genera [ 67 ]. In this study, while pH did not significantly influence root-associated AMF communities, it had a pronounced effect on the AMF composition in rhizosphere soil, suggesting that AMF communities in the rhizosphere are more sensitive to pH changes. Additionally, AP and TN levels were significantly correlated with AMF diversity in the rhizosphere soil, aligning with previous findings [ 57 ]. Changes in AP and TN likely influence AMF spore germination, mycelial growth, and the efficiency of mycorrhizal symbiosis [ 68 ]. The Mantel test further confirmed the significant impact of TN content on AMF community structure, supporting this viewpoint. In contrast, the root-associated AMF composition was significantly correlated with NO 3 − -N, AK, OM, acid phosphatase, and elevation. Correlation analysis revealed that OM content was positively associated with Claroideoglomus , while AK content correlated positively with Paraglomus . Nutrient enrichment, particularly increased OM levels, may enhance AMF reproduction, influencing community abundance and diversity [ 69 ]. AK is also known to support AMF colonization and the establishment of effective symbioses in certain plant species [ 70 ]. Acid phosphatase may have direct or indirect roles in soil and microbial-mediated phosphorus cycling, further influencing AMF abundance. Collectively, these findings suggest that variations in soil properties (pH, AP, TN, NO 3 − -N, AK, OM, and acid phosphatase) along elevation gradients significantly affect AMF genera, driving differences in the AMF composition between roots and rhizosphere soil. Our study underscores the ecological preferences of symbiotic AMF in response to elevation-related environmental changes within the natural distribution area of H. miconioides . Future research examining the influence of additional environmental factors, such as climate variables and vegetation types, could provide deeper insights into the mechanisms governing AMF community assembly and diversity along elevational gradients."
} | 5,133 |
25927230 | PMC4416025 | pmc | 1,588 | {
"abstract": "Hydrogen gas functions as a key component in the metabolism of a wide variety of microorganisms, often acting as either a fermentative end-product or an energy source. The number of organisms reported to utilize hydrogen continues to grow, contributing to and expanding our knowledge of biological hydrogen processes. Here we demonstrate that Volvox carteri f. nagariensis , a multicellular green alga with differentiated cells, evolves H 2 both when supplied with an abiotic electron donor and under physiological conditions. The genome of Volvox carteri contains two genes encoding putative [FeFe]-hydrogenases ( HYDA1 and HYDA2 ), and the transcripts for these genes accumulate under anaerobic conditions. The HYDA1 and HYDA2 gene products were cloned, expressed, and purified, and both are functional [FeFe]-hydrogenases. Additionally, within the genome the HYDA1 and HYDA2 genes cluster with two putative genes which encode hydrogenase maturation proteins. This gene cluster resembles operon-like structures found within bacterial genomes and may provide further insight into evolutionary relationships between bacterial and algal [FeFe]-hydrogenase genes.",
"introduction": "Introduction Hydrogen is an essential component in the metabolism of a variety of microorganisms [ 1 , 2 , 3 ]. In biology, the production of H 2 is predominantly catalyzed by two classes of enzymes, hydrogenases and nitrogenases [ 1 , 4 ], with hydrogenases also contributing to H 2 uptake [ 2 ]. Microbes are able to use these enzymes to catalyze the oxidation of H 2 and/or the reduction of protons during fermentation [ 2 , 5 , 6 , 7 ]. In addition, recent reports suggest that H 2 may also play a role in deacidification, enhancing the cell viability of certain microbes in harsh environments [ 8 , 9 , 10 ]. The complex role of H 2 in the life-cycle of autotrophic microorganisms, such as green algae, is linked to both photosynthetic and fermentative processes [ 11 , 12 ], and studies of H 2 metabolism in these microbes establish a basis for future metabolic and evolutionary studies of algal species which are currently uncharacterized in biohydrogen production [ 12 , 13 , 14 , 15 , 16 ]. Green algae both evolve and consume H 2 using [FeFe]-hydrogenases (EC#1.12.7.2), metalloproteins capable of catalyzing the reduction of protons as well as the oxidation of H 2 [ 5 , 6 , 17 , 18 ]. Electrons for H 2 production can either be channeled from photosynthetic water-splitting or obtained by the fermentation of carbon sources [ 6 , 19 ]. In a variety of green algae, hydrogenases are encoded by two gene paralogs ( HYDA1 and HYDA2 ) that have high sequence similarity. Although the respective physiological functions of each enzyme are still unclear [ 20 , 21 ], there is evidence suggesting that the HYDA1 gene product may contribute more to H 2 production in the light [ 22 ]. Maturation proteins are required to assemble the catalytic active site, and the corresponding genes ( HydE , HydF , and HydG in bacteria— HYDEF and HYDG in green algae) are ubiquitous in the genomes of organisms which utilize [FeFe]-hydrogenase [ 23 ]. Although ancestral forms of these genes in green algae were likely acquired by lateral gene transfer during evolution [ 24 , 25 , 26 , 27 ], hydrogenase gene clustering has not been noted in sequenced green algal genomes. Despite this lack of clustering, hydrogenase and maturation factor gene expression is tightly co-regulated. In addition, [FeFe]-hydrogenases are irreversibly-inactivated by the presence of O 2 , and expression of the HYD genes is induced under anaerobiosis [ 5 , 14 ]. The O 2 -sensitivity of [FeFe]-hydrogenases renders H 2 synthesis dependent on micro-aerobic conditions and thus limits production during light-driven oxygenic photosynthesis [ 15 , 28 , 29 ]. In Chlamydomonas reinhardtii , a model unicellular green alga, this inhibition may be overcome by sulfur deprivation, which limits O 2 production by photosystem II while still allowing electrons gained from photosystem I to be coupled to H 2 production via chloroplastidic ferredoxins [ 13 ]. In addition, anaerobic conditions are quickly established in the dark as respiration depletes O 2 , thereby allowing carbon stores generated during photosynthesis to be utilized for H 2 production [ 15 , 30 ]. Both of these methods allow for channeling of electrons to H 2 synthesis. \n Volvox carteri is a multicellular green alga that is separated from C . reinhardtii by approximately 220 million years of evolution [ 31 ]. V . carteri is composed of two cell types, gonidia and somatic, which are embedded within an extracellular matrix [ 32 , 33 ]. Consistent with the high degree of sequence similarity between the genomes of V . carteri and C . reinhardtii [ 31 ], these two organisms appear to share a number of similar metabolic processes [ 34 ]. V . carteri has been studied for >40 years [ 35 ] and H 2 production has previously been observed from a Volvox species [ 36 ]. Recently, two putative [FeFe]-hydrogenase genes were annotated in the genome of V . carteri [ 31 ], which may provide the organism with H 2 metabolism similar to that of C . reinhardtii . In this manuscript, we provide the first report of both in vitro and in vivo H 2 production in the multicellular green alga, V . carteri f. nagariensis and demonstrate that functional [FeFe]-hydrogenases are encoded by the annotated HYDA genes. Genes coding for the functional hydrogenases and associated maturation factors are arranged in a unique operon-like gene cluster within the V . carteri genome, providing additional evidence for an evolutionary relationship between bacterial and green algal [FeFe]-hydrogenases. Together, these data support a role for H 2 in the metabolism of V . carteri and provide a basis for further investigation of the ancestral acquisition of [FeFe]-hydrogenase genes in green algae.",
"discussion": "Discussion A variety of green algae have been characterized for their ability to couple energy derived from photosynthesis to the production of H 2 , especially the model organism C . reinhardtii [ 15 , 30 , 50 ]. Previous work has demonstrated that a species of Volvox is capable of H 2 production [ 36 ] to a similar extent as C . reinhardtii , an alga separated V . carteri by ~220 million years of evolution. The recently sequenced V . carteri genome identified two putative [FeFe]-hydrogenase genes [ 31 ], prompting us to investigate further the H 2 metabolism of this organism. To test the H 2 metabolism of V . carteri , algal cell cultures were acclimated to anaerobiosis, while the serum vials were wrapped with aluminum foil, thus eliminating physiological oxygen production and allowing residual oxygen to be removed via respiration. The anaerobic cells were then provided with an electron donor system and H 2 accumulation was measured. Under the conditions tested, V . carteri cultures evolved appreciable amounts of H 2 when supplied with an exogenous electron source (MV/Na 2 S 2 O 4 ) to drive H 2 production. Previous work has detailed differences in basic metabolism between somatic and gonidia cells, which may have implications related to anaerobic H 2 production [ 42 , 47 ]. To test for differences in H 2 metabolism between the two cell types, we separately measured H 2 evolution activities of isolated gonidia and somatic cells in the presence of an abiotic electron donor. Although the rates of H 2 evolution in each cell type appeared to be similar ( Fig 2B ), the H 2 production rates were normalized based on chlorophyll content. This complicates any direct comparison between gonidia and somatic cells because the amount of photosynthetic machinery does not directly correlate to cell number between the different cell types. Nevertheless, the ability of the isolated cells to generate hydrogen indicates that sufficient hydrogenase machinery is present in both cell types. Initially, V . carteri did not demonstrate physiological H 2 production under the conditions tested. Based on the rationale that cellular carbon sequestration was not sufficient to produce significant quantities of H 2 during fermentation, algal cultures were supplemented with sodium bicarbonate for 72 hours prior to anaerobic acclimation. In contrast to unsupplemented culture, the sodium-bicarbonate cultures produced demonstrable levels of H 2 , proving that V . carteri is capable of in vivo H 2 metabolism. The measured H 2 production reached a maximum of ~3 μmol H 2 /mg Chl at 10 hours, at which point accumulation ceased in the headspace. This is likely due to exhaustion of the fixed carbon storage and represents the maximum yield of H 2 under these experimental fermentative conditions. Under anaerobic conditions, transcript for the hydrogenase homologs HYDA1 and HYDA2 accumulated in V . carteri , and these homologs are likely responsible for the measured in vivo H 2 production. In support of this theory, the genes were heterologously-expressed in S . oneidensis MR-1 and the purified proteins demonstrated quantifiable in vitro H 2 evolution activity when supplied with an artificial electron donor. The purified proteins have similar K \n m values to one another, matching previous literature [ 48 ], although the specific activities measured for each enzyme were dissimilar. This discrepancy may be due to incomplete maturation of HYDA2 in the S . oneidensis MR-1 expression system or may arise from natural differences in activity, as previously observed by Adams et al. [ 49 ]. In addition to similar kinetic values, HYDA1 and HYDA2 also share common structural features with other characterized green algal [FeFe]-hydrogenases. For example, both proteins contain two short sequences not observed in non-algal hydrogenases and also lack the canonical iron-sulfur cluster-containing F-domains [ 14 ]. While examining the hydrogenase genes within the V . carteri genome, it was noted that the HYDA1 and HYDA2 genes are within close proximity to genes encoding maturation proteins, HYDEF and HYDG ( Fig 5 ). This level of gene clustering is unique to V . carteri among the three sequenced green algal genomes, although HYDEF and HYDG are proximal to each other in the genomes of both C . reinhardtii and C . variabilis ( S1 Fig ). Entire gene clusters can be transferred between organisms during DNA transfer events [ 51 , 52 , 53 ], and these events can also result in gene fusion, reminiscent of the green algal HYDEF fusion [ 54 , 55 ]. [FeFe]-hydrogenase gene clusters, similar to the V . carteri cluster, were also recently reported in the genomes of the photosynthetic heterokonts Nannochloropsis oceanica CCMP1779 [ 56 ] and Nannochloropsis gaditana CCMP526 [ 57 ]. Thus, the acquisition of [FeFe]-hydrogenase genes in heterokonts and green algae may share an evolutionary history, although the exact origin of the HYD genes is not known [ 27 , 58 , 59 ]. Together, the presence of these clusters may provide additional tools to investigate the evolutionary history of [FeFe]-hydrogenases. Using the phylogenetic analysis established by Meuser et al. [ 49 ] as a starting point, we analyzed the genomes of several bacterial species which contained [FeFe]-hydrogenase genes closely related to the V . carteri genes. Finding that these bacterial genomes did not contain hydrogenase gene clusters, we expanded our search to bacterial genomes not represented in the phylogram by Meuser et al. This search yielded three closely related thermophillic bacterial species ( Fervidobacterium nodosum , Fervidobacterium pennivorans , and Thermotoga thermarum ) that contained operon-like gene clusters (>22 kb span) within their genomes. It must be noted, however, that distinct differences exist between the V . carteri and bacterial gene clusters, such as the orientation of the genes and the fusion of the HYDE and HYDF genes in V . carteri . It is therefore intriguing that the relative positions of the genes are identical between V . carteri and bacterial clusters, although the bacterial genes, lacking introns, are shorter in length ( Table 2 and Fig 6 ). Despite these differences, the preserved order of the genes between the V . carteri and the bacterial genomes suggests that this clustering may be a remnant of horizontal gene transfer rather than a result of directed gene rearrangements for co-regulation. Examining the operon-like gene clusters between the thermophiles and V . carteri —and assuming direct gene transfer from bacteria to green algae—it is tempting to suggest that both HYDA genes in green algae were obtained during a single horizontal gene transfer event. In contrast, previous evidence from Meuser et al. would instead indicate that the presence of two HYDA genes in V . cateri result from gene duplication, as these genes are reciprocally monophyletic (Meuser et al. 2011). The presence of the gene cluster in V . carteri —with both putatively duplicate hydrogenase genes in close proximity to one another—is puzzling, especially as clustering of these genes is not observed in either C . reinhardtii or C . variabilis . It is interesting to note, however, that genes encoding phosphate acetyltransferase and acetate kinase are in close association with HYDA2 in both the V . carteri and C . reinhardtii genomes, albeit in different orders in the two species ( S1 Fig ). This clustering of related genes (acetate assimilation proteins are implicated in fermentative H 2 production [ 60 ]) observed in both species is intriguing, and additional green algal genomes containing hydrogenase genes need to be sequenced to explore the evolutionary history further. In conclusion, herein we provide the first evidence that V . carteri , a complex multicellular eukaryote, is capable of producing H 2 under physiological fermentative conditions. We demonstrate that the two [FeFe]-hydrogenase gene products catalyze H 2 evolution in vitro , suggesting a role for these two enzymes in V . carteri H 2 metabolism. A previously unreported gene cluster within the V . carteri genome encodes these hydrogenases, as well as the essential maturation machinery. This cluster may share a relationship to similar gene clusters found in thermophillic bacteria, providing new avenues in studying the evolutionary origins of [FeFe]-hydrogenases."
} | 3,622 |
22355753 | PMC3270498 | pmc | 1,589 | {
"abstract": "Climate change driven increases in intensity and frequency of both hot and cold extreme events contribute to coral reef decline by causing widespread coral bleaching and mortality. Here, we show that hot and cold temperature changes cause distinct physiological responses on different time scales in reef-building corals. We exposed the branching coral Acropora yongei in individual aquaria to a ± 5°C temperature change. Compared to heat-treated corals, cold-treated corals initially show greater declines in growth and increases in photosynthetic pressure . However, after 2–3 weeks, cold-treated corals acclimate and show improvements in physiological state. In contrast, heat did not initially harm photochemical efficiency, but after a delay, photosynthetic pressure increased rapidly and corals experienced severe bleaching and cessation of growth. These results suggest that short-term cold temperature is more damaging for branching corals than short-term warm temperature, whereas long-term elevated temperature is more harmful than long-term depressed temperature.",
"discussion": "Discussion This study investigated the effects of cooling and warming seawater on coral physiology, both of which are likely to become more frequent due to global climate change. The present study used temperature changes similar to the extreme changes experienced during the summer and winter of 2010 35 36 , but likely on somewhat faster time scales than occurring naturally on reefs. In contrast to most previous studies, which have focused on the effects of either cold temperature e.g. 21 27 28 or warm temperature e.g. 11 24 25 32 33 , the present study on a simultaneous cold and heat stress experiment combined with data collected during multiple time points helps elucidate the effects of temperature change on the physiology of corals and their endosymbionts. Both cold and heat stress negatively affected corals. Our experiment identified two critical time scales of physiological responses: during the initial phase (0–5 d), the decrease in temperature caused more stress to the coral holobiont, whereas during the final phase (6–20 d), the increase in temperature caused more stress to the coral holobiont. The acute effects of the cold treatment included declines in coral growth and endosymbiotic dinoflagellate density, and increases in photoprotective pigments. The decrease in effective quantum yield during the initial phase suggests that the treatment caused an immediate imbalance between the amount of light energy absorbed and processed through PSII. This imbalance is likely due to temperature dependant reduction of enzyme activities 37 , which decreased rates of photosynthetic reactions, caused a build up of excess light energy, and resulted in an increased need for photoprotection. The down-regulation of PSII photochemistry may have been photoprotective. The maximum quantum yield data provide evidence that the photosynthetic system recovered during the nighttime. Declines in photochemical efficiency and dinoflagellate density, combined with the general metabolic decrease at cold temperature, may have ultimately contributed to the large reductions in coral growth. Extreme rapid cold shock (4 hrs) has been reported to cause a reduction in dinoflagellate density in corals, and thus is consistent with our study 26 27 . The loss of dinoflagellates seems to occur from the release of intact dinoflagellate containing coral endoderm cells into the surrounding water, which was documented to occur under heat stress as well 26 . Short-term cold stress (≤18 h) in Montipora digitata affects maximum quantum yield, dinoflagellate density and chlorophyll a concentration, which could reflect the organism's ability for rapid photoacclimation since the responses were dependent on light intensity and magnitude of temperature change 28 . During the final phase of our experiment, the stabilization and improvement in coral and dinoflagellate physiological states suggest that cold-treated corals are able to acclimate to cooler temperatures and initiate recovery. During the final phase, the effective quantum yield and pressure over PSII stabilized, suggesting that the photosynthetic machinery was able to compensate by changing the concentration of proteins, pigments, and/or enzymes involved in photosynthesis, which could explain the observed increase in xanthophyll pool. The continued cold treatment did not cause sustained stress: in the final phase, carotene and xanthophyll cycling decreased, dinoflagellate density stabilized, and coral growth increased. Acute effects induced by the heat treatment were less severe than those induced by the cold treatment, however, chronic effects were more deleterious. Initially, the growth rate of heat-treated corals did decrease, although not as substantially as those of cold-treated corals; however the pressure over PSII remained unchanged until after 5 d. It is reported that rates of photosynthesis increase in short-term heat (<2 h) stress experiments on corals and on symbiotic dinoflagellates in culture, until temperatures of 31°C or 30°C, respectively 38 39 . The temperature ∼30°C seems to be a critical threshold from the photobiological standpoint, perhaps representing an inherent limit of PSII. Symbiotic dinoflagellates in culture have impaired photosynthesis above 30°C and photosynthesis ceased by 34–36°C 39 . Different clades of symbionts have varying degrees of susceptibility to thermal stress and discrete responses in culture 40 . Here, we propose that photodamage accumulated in the corals during our heat experiment, and that after 5 d, the photosynthetic system could no longer process the excess light energy. The decreased shading caused by the reduction in dinoflagellate density creates a higher local light field within the coral cells 41 . The combination of the locally increased light and inhibited repair processes of PSII 8 may have further stressed the remaining dinoflagellate population. Corresponding increases in photoprotective pigments after the initial phase were observed in heat-treated corals suggesting that the photosynthetic system was struggling to compensate for the rapidly accumulating stress. In the final phase of the heat treatment, dinoflagellate populations experienced a fast decline, and corals bleached and growth ceased. These results suggest that the heat-treated symbionts, which often become a source of oxidative stress with increased temperature 5 10 42 , were under considerable photostress causing rapid disruption of the coral-algal symbiosis. Obviously, bleached heat-treated corals were beyond typical acclimation responses 43 and clearly under severe irreversible stress. Similar to many other coral heat stress experiments 7 24 25 , decreases in photosynthetic yield and dinoflagellate density were also observed in our study; however, the direct comparison with the cold response analyzed in parallel at multiple time points allowed us to elucidate the dynamics of differential responses from the corals and their endosymbionts between treatments, which clearly provided evidence that the heat treatment was ultimately more harmful than the cold treatment for the corals. There has been one previous study on the effects of temperature on growth and mortality in Hawaiian corals 12 . Similar to the present study, reduced growth was observed in both heat and cold treatments. In contrast to the present study, those experiments found that heat causes faster stress and mortality (<2 d), but lower long-term (30 d) mortality than cold. Such discrepancy with our data suggests that species and locations might be important factors to understand corals' response to temperature change, and that some corals might live closer to their upper thermal limits while others closer to their lower one. Nonetheless, both studies conclude that both heat and cold conditions can be deleterious to corals, inducing stress through divergent physiological mechanisms over time. Although both cold and heat treatments had large negative effects on coral and dinoflagellate physiology, the treatments had distinct responses at different time scales. The present study indicates that transient decreases in seawater temperature can be very deleterious to corals, but that prolonged increases in seawater temperature will eventually be much more harmful. This result has serious implications for the future of coral reefs and their management, and suggests that in areas with cooler temperatures, corals and their symbionts may be able to acclimate to new environments and survive. However, reduced growth may also make the reefs more susceptible to sea level rise, another aspect of climate change 44 . Endosymbiotic dinoflagellates appear very sensitive to temperature changes and this research supports monitoring their photophysiology as an indicator of coral health 45 . Because of climate change, corals will experience more temperature anomalies that will not only cause physiological stress, but also have long-term repercussions on growth and fitness, ultimately affecting stability of coral reefs. Although temperature history can also influence coral physiological responses to current stressors, repeated and synergistic stressors may reduce coral resiliency 15 . Additional effects of abnormal temperatures such as coral diseases 44 and further stressors such as ocean acidification 15 increase the pressure on corals and multiple stresses provide less time for recovery. Nevertheless, extreme temperature events have been and will continue to be a major contributor to coral reef decline at a global and long-term scale."
} | 2,414 |
31636414 | null | s2 | 1,590 | {
"abstract": "In nature, DNA molecules carry the hereditary information. But DNA has physical and chemical properties that make it attractive for uses beyond heredity. In this Review, we discuss the potential of DNA for creating machines that are both encoded by and built from DNA molecules. We review the main methods of DNA nanostructure assembly, describe recent advances in building increasingly complex molecular structures and discuss strategies for creating machine-like nanostructures that can be actuated and move. We highlight opportunities for applications of custom DNA nanostructures as scientific tools to address challenges across biology, chemistry and engineering."
} | 167 |
26255308 | PMC4529744 | pmc | 1,591 | {
"abstract": "Metabolic engineering and synthetic biology are synergistically related fields for manipulating target pathways and designing microorganisms that can act as chemical factories. Saccharomyces cerevisiae ’s ideal bioprocessing traits make yeast a very attractive chemical factory for production of fuels, pharmaceuticals, nutraceuticals as well as a wide range of chemicals. However, future attempts of engineering S. cerevisiae ’s metabolism using synthetic biology need to move towards more integrative models that incorporate the high connectivity of metabolic pathways and regulatory processes and the interactions in genetic elements across those pathways and processes. To contribute in this direction, we have developed M etabolic E ngineering target S election and best S train I dentification tool (MESSI), a web server for predicting efficient chassis and regulatory components for yeast bio-based production. The server provides an integrative platform for users to analyse ready-to-use public high-throughput metabolomic data, which are transformed to metabolic pathway activities for identifying the most efficient S. cerevisiae strain for the production of a compound of interest. As input MESSI accepts metabolite KEGG IDs or pathway names. MESSI outputs a ranked list of S. cerevisiae strains based on aggregation algorithms. Furthermore, through a genome-wide association study of the metabolic pathway activities with the strains’ natural variation, MESSI prioritizes genes and small variants as potential regulatory points and promising metabolic engineering targets. Users can choose various parameters in the whole process such as (i) weight and expectation of each metabolic pathway activity in the final ranking of the strains, (ii) Weighted AddScore Fuse or Weighted Borda Fuse aggregation algorithm, (iii) type of variants to be included, (iv) variant sets in different biological levels. Database URL: \n http://sbb.hku.hk/MESSI/",
"conclusion": "Conclusions and future directions The ability to perform deep sequencing of industrially relevant microbial species at increasingly affordable costs can help to revolutionize microbial cell factory engineering in a similar way that revolutionized fields like human genetics and epigenetic studies. With this in mind, we developed MESSI, a S. cerevisiae web server, where we incorporated bioinformatics methods—that are being employed in NGS-based human genetics—for prioritizing genetic changes that need to be experimentally tested. The ultimate goal of MESSI is to provide a more solid and comprehensive basis for selecting the most promising host for desired phenotypes and discover which mutations would be expected to contribute most to that phenotype for metabolic engineering efforts. We believe that MESSI offers new opportunities for establishing links between genotype and phenotype in S. cerevisiae strains and can be efficiently used for searching genome-wide spaces for small variants and genes conferring phenotypic characteristics of interest. The first version of MESSI contains 21 S. cerevisiae strains for best strain selection and genotype-to-phenotype mapping. Even though the number of yeast strains is comparable with other studies in the literature ( 8 , 17 ) we intend to significantly expand the S. cerevisiae strain database to achieve higher confidence in the GWAS mapping and improve the prediction of regulatory points in the different metabolic pathways. Towards that objective we have initiated a collaborative effort to sequence the genomes and perform metabolomic profiling of >35 S. cerevisiae strains, including several industrial strains. To deal with the limitation of KEGG database and the ready-made definition of its pathways, a further direction of MESSI may be a comprehensive expansion of pathway databases (for instance, Reactome ( 34 ), SGD ( 22 ), etc. ). Furthermore, based on the better-defined pathways and the in-house strain set, we will work on an algorithm development to compute the optimized pathway expectation ( E ) and weights ( W ) automatically from the pathway topology, and then evaluate the method with downstream engineering in our strain set. Last but not least, we will continuously add more strain databases from different cultivation conditions, for instance, diverse carbon sources (ethanol is next in the pipeline) and cultivation conditions (batch cultivation, anaerobic, etc. ), to improve the practical value of MESSI in industrial bioengineering.",
"introduction": "Introduction The suitability of Saccharomyces cerevisiae for the production of a range of products, such as alcohols, acids, proteins and hydrocarbons as well as pharmaceutical and nutraceutical ingredients has been demonstrated numerous times. Its attractiveness as a cell factory is mainly attributed to the fast growth on relatively cheap carbon sources, the robustness and tolerance towards harsh industrial conditions (e.g. high osmotic stress and low pH) and the well-developed genetics ( 1 , 2 ). The continuous expansion of the genetic toolbox available for S. cerevisiae allowing manipulation of several genetic elements in a single round of transformation for strain development has placed yeast as the preferred host for bio-based production. Still, despite the several high-profile ongoing projects in both academia and industry for the use of S. cerevisiae to produce butanol, farnesene, stilbenes and alkaloids, to name just a few products ( 3 ), there is a clear need for the development of novel systemic approaches for the optimal—in terms of yield, productivity and final titer—functioning of the yeast metabolic network. Metabolic engineering is exactly those integrated and multidisciplinary approaches to regulate the performance of the metabolic network for the cost-effective biological manufacturing of industrially relevant products ( 4–6 ). The field has clearly revolutionized by the explosion of information regarding metabolic pathways, not only within the genome of the host organism but essentially all organisms, the availability of ‘omic’ data and systems level modelling of function, however the integration with synthetic biology is expected to offer great power in the design of platform strains. Even though there has been a lot of debate in the definition of the fields of metabolic engineering and synthetic biology in principle the two disciplines are synergistic but use fundamentally different approaches ( 6 ). Metabolic engineering is a top-down approach for defining which pathways and in which direction should be engineered for the development of novel microbial capabilities ( 7 ). On the other hand, synthetic biology, still regarded as a young discipline, tends to be seen as a bottom-up approach for improving the design of cell factories. Propelled by the significant decrease in DNA sequencing and synthesis cost, the improved understanding on genotype-to-phenotype relationships and standardization of DNA assembly procedures, synthetic biology provides the toolbox for constructing artificial elements to achieve particular functions. Applications of synthetic biology in yeast metabolic engineering are expected to increase dramatically in the future thus development of publicly available platforms that aim to capitalize on yeast’s natural diversity for assembling biological parts with the desired properties is of utmost importance. Following this trend we present M etabolic E ngineering target S election and best S train I dentification tool (MESSI), a web server for predicting efficient chassis and regulatory components for yeast bio-based production. MESSI uses publicly available metabolomic data from characterized S. cerevisiae strains for computing metabolic pathway activities and ranks the strains based on user-defined pathways of interest (single or multiple pathways). Furthermore utilizing the natural variation between the S. cerevisiae strains MESSI applies genome-wide association mapping for identifying putative genes and other genetic elements that correlate with the measured phenotype (metabolic pathway activity). MESSI is a user-friendly platform and the output generated is easy to interpret allowing the users to quickly select the most promising plug-and-play S. cerevisiae strain for a specific product. Candidate genes related with the pathway activity, e.g. regulatory role in controlling metabolic fluxes towards that product, are also provided."
} | 2,112 |
36236366 | PMC9570626 | pmc | 1,592 | {
"abstract": "Reinforcement learning (RL) trains an agent by maximizing the sum of a discounted reward. Since the discount factor has a critical effect on the learning performance of the RL agent, it is important to choose the discount factor properly. When uncertainties are involved in the training, the learning performance with a constant discount factor can be limited. For the purpose of obtaining acceptable learning performance consistently, this paper proposes an adaptive rule for the discount factor based on the advantage function. Additionally, how to use the advantage function in both on-policy and off-policy algorithms is presented. To demonstrate the performance of the proposed adaptive rule, it is applied to PPO (Proximal Policy Optimization) for Tetris in order to validate the on-policy case, and to SAC (Soft Actor-Critic) for the motion planning of a robot manipulator to validate the off-policy case. In both cases, the proposed method results in a better or similar performance compared with cases using the best constant discount factors found by exhaustive search. Hence, the proposed adaptive discount factor automatically finds a discount factor that leads to comparable training performance, and that can be applied to representative deep reinforcement learning problems.",
"conclusion": "6. Conclusions This paper proposed an adaptive discount factor for the environment with uncertainty. The discount factor has to be decided differently, according to the distribution of rewards given in the environment. A commonly used high discount factor places a high value on future rewards. However, depending on the environment, it can be risky to consider the distant future rewards too much. In this case, a high discount factor can impair the performance of the agent. In such an environment, a low discount factor can be an effective countermeasure. Generally, it is difficult to know what the appropriate discount factor is before training. Therefore, in this paper, we proposed an adaptive discount factor algorithm that can find a proper value of the discount factor automatically at the beginning of learning. The algorithm adjusts the discount factor according to the advantage function during training, such that the reinforcement learning agent shows good performance in the end. It is shown that the adaptive discount factor converges to the discount factor that is manually found via exhaustive search, and that leads to the best performance. Using Tetris and the path planning problem, we found that, depending on the environment, a slightly lower discount factor could yield a higher performance than a higher discount factor. Tetris has difficulty in predicting distant future rewards because of the next block that appears randomly. For the path planning problem, a high discount factor was suitable for an episodic task, but a low discount factor showed a high performance in a continuing task in which the goal point was randomly given. It is very cumbersome to find this from multiple test training. The proposed algorithm finds the discount factor by comparing the advantage functions created by different neural networks. In addition, in order to minimize the computational cost, it is performed quickly to search for the optimal discount factor at the beginning of learning. The proposed adaptive discount factor results consistently in a good performance for various environments. Future research includes how to enhance the performance of the value estimation when there is a nontrivial gap between the model and the real environment. In this paper, the algorithm is applied in response to the simulation problem in which uncertainty exists. However, in real environments, more unpredictable situations may arise. Therefore, we would like to consider ways to adjust the trade-off between uncertainty and the value of rewards in real environments.",
"introduction": "1. Introduction Reinforcement learning (RL) is a branch of machine learning in which an agent interacts with a given environment and learns the optimal policy to achieve the predefined goal. Deep RL is a method that uses neural networks to estimate the value function or the policy in reinforcement learning. In RL, an agent makes a decision, called action, at a state according to the policy, and the action is applied to the environment. Then, the agent obtains the next state and reward from the environment. In order to compute the next action, the agent maximizes the expectation of the sum of the discounted reward signals over a finite or infinite horizon. Since the future reward is unknown, the deep RL employs neural networks to estimate the expectation, called the value function, for the future [ 1 , 2 ]. In real-world problems, it may be difficult to predict the value function by receiving unseen reward signals or state information, due to unexpected situations [ 3 , 4 ]. These problems make it difficult to estimate the value in reinforcement learning. Since the value function is computed by the reward and discount factor, it is of utmost importance to decide them properly. In particular, the focus of this paper is placed on the discount factor, which is mostly constant in the existing literature. If the discount factor is large in an environment with high uncertainty, it may be risky to estimate the value function by considering the rewards in the distant future. Estimating the sum of rewards over a longer period of time can lead to unexpected situations due to the high uncertainty. For instance, agents can overestimate rewards in the distant future, or the convergence of the value function may not be guaranteed [ 5 , 6 , 7 , 8 ]. On the other hand, if the discount factor is too small, it can lead to a better generalization performance [ 9 , 10 , 11 ] but hinder the convergence speed of learning [ 12 ]. Furthermore, with the small discount factor, the reward only in the near future is taken into account in evaluating the value function, which can make short-sighted or aggressive actions. This observation motivates us to devise an adaptive rule to update the discount factor, rather than a constant. This paper especially focuses on developing an adaptive rule for the discount factor in the policy gradient algorithms that use advantage functions [ 13 , 14 ]. To this end, the initial low and upper bounds of the discount factor are defined in the proposed method, and the bounds are shrunk towards a higher advantage function as the training goes on. Since it is different to computing the advantage function in on-policy and off-policy algorithms, how to apply the proposed method to both on-policy and off-policy algorithms is presented. The discount factor by the proposed method converges to the optimal constant discount factor. Note that it is a nontrivial task to find the optimal constant discount factor when the MDP is high dimensional and the environment contains uncertainties. Moreover, the resulting training performance using the proposed method is comparable with that via the optimal constant discount factor. This means that the proposed adaptive method enables the agent to counteract the overestimation of an uncertain reward sum. To verify the performance of the proposed method, two environments are used: Tetris for the on-policy and robot motion planning for the off-policy algorithms. It is shown that the proposed adaptive discount factor outperforms the cases with constant discount factors when the environment has uncertainty. Furthermore, another adjustable algorithm in [ 15 ] gradually increases the discount factor, which means that the algorithm can get into trouble when the optimal discount factor is small. On the contrary, compared with the adjustable algorithm in [ 15 ], the proposed method can adjust the discount factor adaptively for any case, i.e., either a small or large optimal discount factor.",
"discussion": "5.3. Analysis and Discussion In view of the previous case study, it is confirmed that the adaptive discount factor is indeed effective. In many existing results on reinforcement learning, a large discount factor is usually used, such as 0.99. However, as seen from the case study, the high discount factor does not necessarily always guarantee a high performance. In the previous learning experiment, when a discount factor was randomly selected among values between 0.5 and 0.99 and training was performed, there was a case in which a higher performance was obtained with a value of lower than 0.99, as shown in Figure 8 and Figure 14 . On the other hand, Figure 12 showed the highest performance, with 0.99. The difference between these tasks is the existence of an uncertain risk or an unpredictable negative reward among distant future rewards. In the case of Tetris, the agent might obtain negative rewards according to future mistakes, but it is impossible to predict this because blocks are randomly determined. In the case of path planning, it is impossible to predict the failure and collision of path generation when generating a path to a new randomly generated goal point. These correspond to the example in Figure 3 . On the other hand, in the case of the path planning problem that is an episodic task, it is relatively easy to predict the success or failure of path creation for a fixed goal point. This case only matches Figure 4 , which is a sparse reward environment. Therefore, the discount factor of 0.99 showed the highest performance in this task. The adaptive discount factor algorithm closely finds the discount factor that can give the best performance in all cases. This saves the effort in finding a suitable discount factor. Additionally, as shown in Figure 9 , Figure 13 and Figure 15 , the adjustment of the discount factor is quickly terminated at the beginning of training. Since the adjusted discount factor is fixed and learning proceeds with the fixed value, it is not computational expensive. Another discount factor adjustment algorithm, called progressively increasing discount factor [ 15 ], gradually increases the discount rate to 0.99. It has been suggested that this method can achieve a higher performance than a fixed discount factor. However, the proposed adaptive discount factor outperforms in an environment requiring a low discount factor. This also suggests that a somewhat lower discount factor may be appropriate, depending on the environment."
} | 2,587 |
37126442 | PMC10165451 | pmc | 1,593 | {
"abstract": "Summary Standardized assays have greatly advanced the understanding of multicellular bacterial biofilms, but they lack cell-scale detail. Here, we present a fluorescence-based protocol that builds on past assays to reveal the cellular-scale arrangement within biofilms. We describe steps for growing biofilms on cover glass, followed by imaging and visualization of cellular arrangements in biofilms. We have applied this protocol to study Escherichia coli biofilms, though it could also be adapted to study biofilm formation in other species. For complete details on the use and execution of this protocol, please refer to Puri et al . (2023). 1"
} | 162 |
33500473 | PMC7838310 | pmc | 1,594 | {
"abstract": "The Scleractinian corals Orbicella annularis and O. faveolata have survived by acclimatizing to environmental changes in water depth and sea surface temperature (SST). However, the complex physiological mechanisms by which this is achieved remain only partially understood, limiting the accurate prediction of coral response to future climate change. This study quantitatively tracks spatial and temporal changes in Symbiodiniaceae and biomolecule (chromatophores, calmodulin, carbonic anhydrase and mucus) abundance that are essential to the processes of acclimatization and biomineralization. Decalcified tissues from intact healthy Orbicella biopsies, collected across water depths and seasonal SST changes on Curaçao, were analyzed with novel autofluorescence and immunofluorescence histology techniques that included the use of custom antibodies. O. annularis at 5 m water depth exhibited decreased Symbiodiniaceae and increased chromatophore abundances, while O. faveolata at 12 m water depth exhibited inverse relationships. Analysis of seasonal acclimatization of the O. faveolata holobiont in this study, combined with previous reports, suggests that biomolecules are differentially modulated during transition from cooler to warmer SST. Warmer SST was also accompanied by decreased mucus production and decreased Symbiodiniaceae abundance, which is compensated by increased photosynthetic activity enhanced calcification. These interacting processes have facilitated the remarkable resiliency of the corals through geological time.",
"introduction": "Introduction Tropical and subtropical coral reef ecosystems have long been recognized as sensitive indicators of global climate change and oceanic health 1 , 2 . Scleractinian corals thrive in nutrient-poor (oligotrophic) tropical and subtropical shallow seawater environments around the world. This is due in large part to symbiotic relationships between the host coral animal, unicellular, photosynthetic dinoflagellates belonging to the family Symbiodiniaceae 3 and other microorganisms, which together form a tightly integrated community collectively called the coral holobiont 4 – 9 . In order to preserve and better manage coral reef ecosystems, urgency is mounting to establish new analytical approaches capable of deciphering and monitoring the underlying mechanisms by which corals have successfully adapted and survived in the face of rapidly changing ancient and modern marine environmental conditions 10 – 16 . This work has included analyses of adaptations in coral tissue morphology and colony shape ( morphological plasticity ) as well as shifts in carbon translocation between living coral holobiont cells and the surrounding marine environment ( trophic plasticity) 17 – 27 . However, relatively little is known regarding how the three-dimensional (3D) μm-scale distribution and abundance of Symbiodiniaceae cells and biomolecules (chromatophores, calmodulin, carbonic anhydrase and mucus) vary within structurally intact coral tissue biopsies across changing bathymetry and seasonal SST. It is also not fully understood how these components might change seasonally to influence the formation of high density band (HDB) and low density band (LDB) skeletal layers. As a result, it remains uncertain how coral holobiont tissue cells and biomolecules successfully respond (acclimatize) to changes in water depth (WD) and seasonal sea surface temperature (SST) as is also reflected by overall skeletal structure. This is in large part because previous studies have often relied on techniques that either: (1) physically disrupt the original 3D context of coral holobiont tissue structure, which is destroyed when an air gun or water pick is used to remove and homogenize coral tissues from the skeleton 28 – 30 ; or (2) used fiber optic analyses on bulk coral tissue 31 . Exceptions include three previous studies that have completed analyses of multiple types of cell autofluorescence within the context of original, undisturbed coral tissue structure. For instance, Salih et al. 32 quantified bulk cellular tissue activity from fluorescent pigments and photo-protective chromatophores in multiple coral species on the Great Barrier Reef. Piggot et al. 33 quantitatively compared the two-dimensional (2D) abundance of Symbiodiniaceae and mucocyte cells in tissue-skeleton biopsies of O. annularis collected on Curaçao from controlled reef tract shading experiments and during seasonal changes in SST. Results identified correlations between seasonal SST and the abundance of Symbiodiniaceae and mucocyte in tissues, indicating that the O. annularis coral holobiont transitions from autotrophic to heterotrophic feeding strategies as SST increases. Sivaguru et al. 34 then qualitatively tracked shifts in the 3D distribution of Symbiodiniaceae and chromatophores in tissues of O. annularis and O. faveolata , also from Curaçao, during seasonal shifts in carbon translocation. The present study was conducted to directly test and advance hypotheses developed from these previous studies to investigate the specific cellular and biochemical changes corals make to acclimatize to increasing water depth and seasonal changes in SST. One hypothesis being tested is whether the 3D distribution and abundance of chromatophores can be modified by the coral itself to protect Symbiodiniaceae tissue distribution, abundance and function across bathymetric and seasonal gradients. Another is whether simultaneous changes in the 3D distribution and abundance of coral tissue biomolecules involved in skeletal precipitation might be associated with the formation of skeletal density banding. Next-generation two-photon laser scanning fluorescence microcopy was used to quantify shifts in the 3D distribution of Symbiodiniaceae, chromatophores biomolecules in tissues of O. annularis and O. faveolata . These analyses included: (1) spectral characterization of Symbiodiniaceae and chromatophores within their original structural context of coral tissue layer growth using single-photon and two-photon wavelengths of light; (2) 3D quantification of the distribution and abundance of Symbiodiniaceae and chromatophores throughout the entire volume of individual polyps in O. annularis (at 5 m WD) and O. faveolata (at 12 m WD); and (3) 2D quantification of cell distribution and density of Symbiodiniaceae (chlorophyll autofluorescence), chromatophores (autofluorescence), carbonic anhydrase (custom designed antibody), calmodulin (monoclonal antibody), and mucus (wheat germ agglutinin [WGA] stain) in O. faveolata at 12 m WD across seasons. Results have established a fundamentally new synthesis that better contextualizes water depth adaptions and acclimatization to seasonal variations in SST. This illustrates the physiological resiliency of corals during reciprocal host to symbiont carbon translocation and is reflected by coral skeletal density banding (CSDB) and their sustained survival in ever changing environments conditions through geological time.",
"discussion": "Results and discussion Symbiodiniaceae and chromatophore tissue distribution and abundance across bathymetry Symbiodiniaceae in O. annularis and O. faveolata exhibited a fluorescence emission peak from chlorophyll a at ~ 670 nm when excited with both 405 nm (blue light) and 780 nm (two-photon near infrared light) wavelengths (Supplementary Figs. S1 – S4 ). In corals, chlorophyll a is accompanied by chlorophyll c , and hence there is no discrimination made between them 70 . In both O. annularis and O. faveolata , Symbiodiniaceae cells were observed in the oral endoderm and are distributed throughout the entire polyp from the oral disk to the coenosarc, with the only exceptions being the most distal tips of the primary and secondary tentacles (Fig. 4 ; Supplementary Figs. S1 – S4 ; Movies 1, 2). In addition, Symbiodiniaceae were detected within the calicoblastic epithelium in both Orbicella species (Fig. 4 ; Supplementary Figs. S1 – S2 ; Movies 1, 2), which has not been previously observed in other coral species 42 , 54 . Qualitative two-photon laser scanning microscopy 3D projections of whole polyps indicate that the resulting autofluorescence emission intensity is significantly higher by 7–8% in the deeper water O. faveolata tissues than in the shallower water O. annularis tissues 34 (Fig. 4 ; Supplementary Figs. S1 , S2 ). Because the Symbiodiniaceae are in the oral endoderm in O. annularis and there is a significantly higher abundance of chromatophores in the oral ectoderm, 3D projections of Symbiodiniaceae emissions in O. annularis are attenuated and artificially appear darker (Fig. 4 ; Supplementary Figs. S1 , S2 ). However, the significantly lower abundance of chromatophores in O. faveolata do not attenuate the Symbiodiniaceae emissions (Fig. 4 ; Supplementary Fig. S1 ). As a result, the Symbiodiniaceae emissions of the entire polyp in O. annularis appear weaker with respect to the Symbiodiniaceae emissions observed in O. faveolata (Fig. 4 ; Supplementary Figs. S1 , S2 ). The highest abundance of Symbiodiniaceae in O. annularis occurs in the coenosarc (Fig. 4 A,C), while the highest abundance of Symbiodiniaceae in O. faveolata is in the folded polyp wall, coenosarc and tentacles overlying the secondary septa (Figs. 4 D,F, 5 ). Conversely, the lowest abundance of Symbiodiniaceae in O. annularis tissues is in the folded polyp wall tissues and tentacles overlying both the primary and secondary septa (Figs. 4 , 5 ; Supplementary Fig. S1 ), while the lowest abundance of Symbiodiniaceae in O. faveolata is in the tentacles overlying the primary septa (Figs. 4 and 5 ; Supplementary Fig. S1 ; Movies 1, 2). Chromatophores in both O. annularis and O. faveolata occur in the oral ectoderm throughout the entire polyp from the oral disk to the coenosarc and directly overlay Symbiodiniaceae that are within the oral endoderm (Fig. 4 ; Supplementary Figs. S1 , S2 ; Movies 1, 2). No chromatophores were observed in the present study within the calicoblastic epithelium, which is consistent with previous reports in other corals 54 , 71 . The highest abundance of chromatophores in O. annularis is in the tissue overlying primary and secondary septa, while in O. faveolata the highest abundance is in tentacles overlying the primary septa as well as the tentacle tips overlying the secondary septa (Figs. 4 , 5 ). Conversely, while the lowest abundance of chromatophores in O. annularis is in the coenosarc, the lowest abundance in O. faveolata is in the folded polyp wall tissues and tentacles overlying the secondary septa (Figs. 4 ; Supplementary Figs. S1 , S2 ). This inverse covariation of increasing chromatophore abundance accompanied by decreasing Symbiodiniaceae occurs in both O. annularis and O. faveolata (Figs. 4 , 5 ; Supplementary Figs. S1 , S2 ; Movies 1, 2). Quantitative comparison of the 3D two-photon imaging of O. annularis at 5 m WD with O. faveolata at 12 m WD (Fig. 5 ) indicate that there were no statistically significant differences in the abundance of Symbiodiniaceae in the tissues covering the primary and secondary septa (described in the statistical evaluation and analysis section; Fig. 5 A). In addition, the abundance of chromatophores in O. annularis is significantly higher in the tissues overlying the primary and secondary septa than those in O. faveolata (Fig. 5 ). Furthermore, the abundance of Symbiodiniaceae in whole polyps is 25% higher in O. faveolata tissues than in O. annularis tissues 34 (Fig. 4 ; Supplementary Figs. S1 , S4 , S11 ). Within both species, the tissues overlying the secondary septa generally exhibit a significantly higher abundance of Symbiodiniaceae than the tissues covering the primary septa (Fig. 5 ). Chromatophore abundance in whole polyps of O. annularis is 133% higher than in O. faveolata , while chromatophores are preferentially located in the tissues covering the primary septa in both species (Fig. 5 B). Conversely, O. faveolata exhibits no significant difference from O. annularis in the abundance of chromatophores in the tissues covering the primary and secondary septa (Fig. 5 B; Supplementary Figs. S4 , S11 ; Movies 3–6). Furthermore, within O. annularis the 3D two-photon microscopy volume measurement of chromatophores is 38% greater than the volume of Symbiodiniaceae, while in contrast within O. faveolata the volume of chromatophores is 56% lower than the volume of Symbiodiniaceae (Supplementary Figs. S4 , S11 ; Movies 3–6). Tracking these autofluorescence emissions within the intact original 3D tissue structure indicates that Symbiodiniaceae primarily occur in the oral endoderm and fewer are present in the calcioblastic epithelium, while the chromatophore emissions are exclusively from the oral ectoderm (Supplementary Figs. S4 , S11 ; Movies 3–6). Symbiodiniaceae and biomolecule distribution and abundance correlated with water depth, seasonal SST and CSDB Primary and secondary antibodies targeting carbonic anhydrase, calmodulin and mucus were compared between samples collected from the same colony of O. faveolata at 12 m WD in March 2008 (SST = 26.0 °C ± 0.03 °C at the end of the winter season) and May 2008 (SST = 27.0 °C ± 0.03 °C in the middle of the spring; Figs. 6 , 7 and 8 ; Supplementary Figs. S5 – S10 . This allowed comparison of their distribution and abundance within individual decalcified O. faveolata whole polyp tissue histology sections (Figs. 6 , 7 , 8 ). In addition, this permitted mapping within individual whole O. faveolata polyps (Fig. 7 A), in March 2008 (Fig. 7 B, 8 A,C, SI Figs. S5 – S7 ) and May 2008 (Fig. 7 B,D, SI Figs. S8 – S10 ), the distribution of individual biomolecules and their respective segregation or overlap within the intact tissue structure (oral ectoderm and oral endoderm). In contrast to previous models for general coral tissue structure 54 , these analyses indicate that O. faveolata does not possess an aboral endoderm (Fig. 7 ). Similarly, Symbiodiniaceae in O. faveolata were observed in both the oral endoderm and calicoblastic epithelium (Fig. 7 ). Polyps of O. faveolata collected in March 2008 exhibited higher abundances of Symbiodiniaceae and mucus, lower carbonic anhydrase and no significant differences in calmodulin compared to O. faveolata polyps collected in May 2008 (Figs. 6 , 7 , 8 ). These shifts in biomolecule abundance are graphically depicted by image tracings (Fig. 7 B, D) and are generally consistent with those presented in Piggot et al. 33 with respect to mucocytes in the oral ectoderm. However, quantification of images in the present study from the calicoblastic epithelium shows a decrease in the abundance of mucus and Symbiodiniaceae going from polyps collected in cooler March seawater to polyps collected in warmer May seawater (Fig. 8 ). These trends also correlate with increased abundance of carbonic anhydrase, while the calcium-binding protein calmodulin remains relatively constant (Fig. 8 ). Implications for water depth and seasonal acclimatization of the coral holobiont A comprehensive understanding of the underlying physiological and biochemical processes that govern seasonal acclimatization of the Orbicella holobiont is beyond the scope of the present study. However, as a step toward this eventual goal, multiple working hypotheses are presented in the following that combine the spatial and temporal distribution of Symbiodiniaceae cells and biomolecules documented in the present work (Figs. 4 , 5 , 6 , 7 , 8 A) together with reports from previous studies (e.g., 30 , 33 ; Fig. 8 C). Tissues in both O. annularis and O. faveolata contain seasonally variable, yet overall high abundances of Symbiodiniaceae cells (Figs. 4 , 5 , 6 , 7 , 8 A; 30 , 33 ). This is manifested as a cooler SST winter phenotype that transforms into a warmer SST summer phenotype (Fig. 8 C). This annual process of seasonal acclimatization of the coral holobiont includes: (1) a decrease in the tissue abundance of Symbiodiniaceae cells (Figs. 4 , 5 , 6 , 7 , 8 A; 30 , 33 ); (2) an increase in Symbiodiniaceae photosynthetic activity 30 ; (3) an increase in the tissue abundance of chromatophore cells (Figs. 4 , 5 , 6 , 7 , 8 A; SI Figs. S5 – S10 ); (4) a decrease in mucus production (Fig. 8 A; 33 ); (5) an increase in carbonic anhydrase production (Fig. 8 A); (6) no appreciable change in calmodulin (Fig. 8 A); and (7) an increase in calcification rate 30 ). Recent reports have demonstrated that lower Symbiodiniaceae abundances in the Orbicella summer phenotype are accompanied by higher rates of photosynthesis, both per symbiont and per total coral tissue area (Fig. 8 C; 30 ). As a result, symbiont photosynthetic activity increases in the summer phenotype despite significant declines in symbiont cell abundance (Fig. 8 C; 30 ), perhaps acting as a compensation mechanism for the decrease in the abundance of Symbiodiniaceae. As a result, trophic transitions away from Symbiodiniaceae-based autotrophy toward coral heterotrophic feeding is not obligatory during higher SST 30 . This trophic plasticity in O. annularis and O. faveolata 72 may therefore represent a fallback strategy for nutrient assimilation by the host coral whenever Symbiodiniaceae autotrophy is unavailable or insufficient. The inherent flexibility of trophic plasticity may be especially advantageous during the highly variable year to year changes in the timing, rate and magnitude of SST (Fig. 8 C) during the course of the life and survival of corals through geological time. Covariations observed between the distribution and abundance of Symbiodiniaceae and chromatophores imply that chromatophores may serve multiple functional purposes (Figs. 4 ; Supplementary Figs. S1 , S4 Movies 1–6). On the one hand, O. annularis and O. faveolata strategically position and increase the abundance of chromatophores in the oral ectoderm in response to changes in both SST and WD (Fig. 4 , SI Figs. S1 – S4 , and Movies 1–6). This implies that the green fluorescent proteins serve to photo-protect Symbiodiniaceae in the underlying oral endoderm 32 . On the other hand, there is also an inverse relationship between Symbiodiniaceae and chromatophore tissue abundance (Figs. 4 ; Supplementary Figs. S1 , S2 ; Movies 1, 2). This is exemplified by Symbiodiniaceae abundance being greatest in the coenosarc where chromatophore abundance is at its lowest (Fig. 4 ). Although no direct evidence was collected in the present study to further evaluate this possibility, these observations imply that chromatophores may serve other functional purposes for the coral holobiont in addition to photo-protection for the Symbiodiniaceae. Regarding the process of photoprotection, similar bulk coral polyp tissue Symbiodiniaceae and chromatophore emissions at varying depths has previously been reported from bulk tissue analyses of the reef building corals Acropora nobilis , Porites cylindrica and Montipora digitata on the Great Barrier Reef 32 . This effect is further accentuated in the shallow water O. annularis , where the abundance of chromatophores is significantly increased in response to increased PAR and ultra-violet radiation (UV) 4 , 73 . Furthermore, the distribution and abundance of Symbiodiniaceae, which is equivalent in both O. annularis and O. faveolata , is therefore likely to be strongly influenced in the environment by PAR 18 . Small changes in PAR occur across the small microscale topography of the polyp and surrounding tissues. For instance, while the highest elevation tentacle tissues at the top of the polyp are exposed to the highest PAR, the highest abundance concentration of Symbiodiniaceae occur in lower tissues to be protected from reactive oxygen species that cause photo-inhibition 1 , 74 – 76 . Qualitative and quantitative analyses of Symbiodiniaceae distribution and abundance in O. annularis and O. faveolata in the present study have identified (Figs. 4 , 5 ): (1) an ~ 50% decrease of Symbiodiniaceae in the uppermost folds of the tentacle tissues covering the primary septa; (2) a decrease in Symbiodiniaceae in the uppermost folds of the tentacle tissues covering the secondary septa; (3) an increase in Symbiodiniaceae abundance in the coenosarc. The exact mechanism by which specific wavelengths of light interact with the green fluorescent proteins in chromatophores, and how the lethal radiation is absorbed and not transmitted down to the Symbiodiniaceae layers is unknown. However, stereochemical changes in the configuration of amino acid residues have been proposed as a potential mechanism 77 , 78 . Taken together, this evidence indicates that Symbiodiniaceae photosynthesis and chromatophore photo-protection from UV light is most pronounced in shallow water O. annularis . In addition, the coenosarc and the primary and secondary tentacle tissues are the sites of the highest abundance and photosynthetic activity of Symbiodiniaceae in both O. annularis and O. faveolata (Fig. 4 ). Complex biotic and abiotic factors that combine to influence seasonal acclimatization also influence the crystalline structure and stratigraphy of coral skeletal growth 79 – 82 . However, precisely how coral skeletal density banding (CSDB) sequences, which are composed of macro- and micro-scale high density band (HDB) and low density band (LDB) layering, remains controversial (Fig. 8 , SI Figure; e.g., 7 , 83 . The successful functioning and survival of the coral holobiont and its impact on resulting CSDB depends on the simultaneous influence of complex intertwined environmental (e.g., SST, water depth, sedimentation, currents, nutrient and oxygen availability, and seafloor diagenesis) and biological factors (e.g., photo-pigmentation, electron transfer rates, CO 2 availability, host coral biomass 84 – 87 ). Especially influential for CSDB formation during seasonal acclimatization from winter to summer Orbicella phenotypes is how calcium anhydrase (CA) and calmodulin activity changes with SST, photosynthetically active radiation (PAR), rate of Symbiodiniaceae photosynthesis, and CO 2 availability 85 , 88 . However, because the total rate of Symbiodiniaceae photosynthesis increases as tissue cell abundance decreases when transitioning from the winter to summer phenotype (Fig. 8 C; 30 ), it is likely that a high aragonite saturation state is maintained in the calcifying space (mucus) between the calicoblastic epithelium throughout the year 54 , 89 – 92 . As a result, other yet to be identified processes, such as protein catalysis 93 , may also play a role. Throughout the seasonal acclimatization process of the coral holobiont, mutualism between Symbiodiniaceae and its Orbicella host depends on the exchange and fixation of carbon 94 , 95 . The coral provides its respired CO 2 as well as dissolved inorganic carbon (DIC) from seawater, which is then fixed during Symbiodiniaceae photosynthesis and translocated back to the coral as organic carbon nutrients. Remarkably, Symbiodiniaceae photosynthesis is capable of fulfilling as much as 95% of the metabolic requirements of the coral host when at maximum activity 96 . In order for Orbicella to continue to precipitate skeleton throughout the seasonal acclimatization process, the coral carbonic anhydrase (CA) converts HCO 3 - into CO 2 and thus sustains Symbiodiniaceae photosynthesis 95 , 97 , 98 . The CO 2 concentrating mechanisms of CA has a greater potential for fixing photosynthetic carbon at higher SST 94 , 95 , as reflected by the observed increase in CA abundance in the summer phenotype (Fig. 8 A,C). This will in turn shift the balance of carbon translocation between Symbiodiniaceae and the host coral and impact skeletal calcification 99 . These carbon translocation processes, production of fatty acids and other lipids by the host coral during feeding and by Symbiodiniaceae under variable PAR conditions, will also play a role in the formation of HDB and LDB layers (Fig. 8 B; 100 ). A custom developed antibody, which is made against Stylophora pistillata and universally conserved in scleractinian corals 65 , 101 – 104 , provided the required immunohistochemical specificity for O. faveolata CA abundance in the present study (Figs. 6 , 7 , 8 ; Supplementary Figs. S5 – S10 ). The observed change in CA is not statistically significant between March and May 2008, which is presumably due to the small 1 °C change in SST (Fig. 8 ). However, the mean intensities of CA from March to May imply a relatively increasing trend with increasing SST (Figs. 6 , 8 A), which is consistent with previous studies 94 , 95 . Quantification via micro CT imaging indicates that HDB layers are 16% more dense and less porous than the immediately above and below corresponding LDB skeletal layers (Fig. 8 B). Furthermore, this correlates closely with changes in the abundance of Symbiodiniaceae, mucus seasonal SST oscillations (Fig. 8 C). Calmodulin, a membrane-bound calcium binding protein that is utilized as an indicator for calcium availability, remained relatively unchanged in O. faveolata during both March and May 2008 (Fig. 8 A,C, SI Figs. S5 – S10 ). These results are consistent with previous studies that indicate calcium availability is a non-rate-limiting step in coral skeletogenesis and that it is actively transported across the calicoblastic epithelium through the light-activated Ca 2+ -ATPase pump 54 , 85 , 102 . Mucus, another component related to seasonal acclimatization, is composed of sialic-acid residues, polysaccharides (sugars) and secreted by mucocytes in the oral ectoderm, the oral endoderm and the calcifying space (Figs. 6 , 7 , SI Figs. S5 – S10 ). Previous studies have primarily focused on mucus produced in the oral ectoderm and oral endoderm that is used by the coral to form the protective coral surface microlayer 19 , 105 , 106 . In the present study, all mucus within the oral endoderm, oral ectoderm, calicoblastic epithelium and the calcifying space was labelled and quantified (Figs. 6 , 7 , 8 ; Supplementary Figures S5 – S10 ) using wheat germ agglutinin (WGA) targeted against sialic acid residues, which are primarily N-acetylglucosamine 19 , 54 , 71 , 93 , 105 – 111 . Mucus abundance increased from March to May 2008 and closely correlates with Symbiodiniaceae abundance (Fig. 8 ). It has been proposed that the secreted mucus closely associated with Symbiodiniaceae could be a by-product of photosynthesis in the oral endoderm and might serve either as energy storage or play a pivotal role in host-recognition of the endosymbiont 107 , 111 , 112 . In addition, mucus within the calcification space (Figs. 6 , 7 , 8 ), in coordination with mucocytes in the oral ectoderm and the oral endoderm, act in tandem as a targeted conduit for direct seawater influence on formation of encapsulated amorphous calcium carbonate (ACC) 113 . In conclusion, shallower 5 m WD O. annularis biopsies exhibited a decrease in the abundance of Symbiodiniaceae cells and an increase the abundance of chromatophores. Conversely, deeper O. faveolata 12 m WD exhibited inverse relationships of increasing Symbiodiniaceae and decreasing chromatophores. Furthermore, inverse covariations observed between the distribution and abundance of Symbiodiniaceae and chromatophores imply that chromatophores may serve multiple functional purposes in addition to photo-protection. Seasonal acclimatization of the O. faveolata holobiont observed in the present study and previous reports, suggests that biomolecules (chromatophores, calmodulin, carbonic anhydrase and mucus) are differentially produced during acclimatization from cooler to warmer SST. Decreased Symbiodiniaceae abundance in summer SST phenotypes are accompanied by higher rates of photosynthesis, which compensates for the decrease in Symbiodiniaceae. This implies that trophic plasticity toward coral heterotrophic feeding, while not required during higher SST, remains a fallback metabolism available throughout the year when environmental stress reduces Symbiodiniaceae autotrophic capacity. The transition to warmer SST was also accompanied by decreased mucus production, decreased Symbiodiniaceae abundance and increased photosynthetic activity that combines to enhance calcification. These complex interacting processes that facilitate coral acclimatization to changing water depth and sea surface temperature have made coral holobiont resiliency a hallmark of coral reefs ecosystems through geological time."
} | 7,185 |
37382581 | PMC10358715 | pmc | 1,595 | {
"abstract": "We present the extremophilic bacterium Methylacidiphilum fumariolicum SolV as a platform for the recovery of rare earth elements (REE). Strain SolV is able to selectively extract the light REE from artificial industrial waste sources, natural REE-containing and post-mining waters. Upscaling, different media composition and accumulation over several cycles were successfully implemented, underlining the potential for bio-recovery of REE."
} | 110 |
37850721 | PMC10586088 | pmc | 1,596 | {
"abstract": "ABSTRACT The next milestone of synthetic biology research relies on the development of customized microbes for specific industrial purposes. Metabolic pathways of an organism, for example, depict its chemical repertoire and its genetic makeup. If genes controlling such pathways can be identified, scientists can decide to enhance or rewrite them for different purposes depending on the organism and the desired metabolites. The lignocellulosic biorefinery has achieved good progress over the past few years with potential impact on global bioeconomy. This principle aims to produce different bio-based products like biochemical(s) or biofuel(s) from plant biomass under microbial actions. Meanwhile, yeasts have proven very useful for different biotechnological applications. Hence, their potentials in genetic/metabolic engineering can be fully explored for lignocellulosic biorefineries. For instance, the secretion of enzymes above the natural limit (aided by genetic engineering) would speed-up the down-line processes in lignocellulosic biorefineries and the cost. Thus, the next milestone would greatly require the development of synthetic yeasts with much more efficient metabolic capacities to achieve basic requirements for particular biorefinery. This review gave comprehensive overview of lignocellulosic biomaterials and their importance in bioeconomy. Many researchers have demonstrated the engineering of several ligninolytic enzymes in heterologous yeast hosts. However, there are still many factors needing to be well understood like the secretion time, titter value, thermal stability, pH tolerance, and reactivity of the recombinant enzymes. Here, we give a detailed account of the potentials of engineered yeasts being discussed, as well as the constraints associated with their development and applications.",
"conclusion": "8. Conclusion Application of microbes in industrial biotechnology process is a key factor shaping biorefinery and bioeconomy. However, challenges remain with adequate strain characterization, growth-culture dynamics, and toxicity issues especially with metabolites generated from biomass utilization. A particular example is ‘methylotrophy/methalotrophy,’ which is the utilization of reduced single carbon (C1) substrate (like methylamine and methanol) as sole carbon and energy source. The production of formaldehyde, a single carbon toxic hydrocarbon during methy/methalotrophy growth/transition has been a major problem. This problem can be easily bypassed with engineered yeast strains. Here, the metabolic oxidation of C1 and regeneration of pentose like in XuMp metabolism which is the main constraint in bacteria, can be enhanced using genetic engineering tools. Nevertheless, biorefinery requires choosing of suitable microbes for specific bioprocess while the basic mechanisms underlining their metabolic actions should be well understood and predictable. Native microbes are not as efficient as synthetic organisms for some important fermentation process. Many synthetic yeasts have been designed and are claimed to show the desired biomass utilization, and production capacity with desired time, quality, and quantity. However, many of such yeasts have not been utilized industrially for mass production. This review has highlighted the importance and several successes recorded in laboratory-scale experiments. It established that laboratory successes do not automatically translate to success in their application at industrial scales, and advocates that the science community still need more convincing facts when it comes to the release of engineered strains either into the environment or applying them in real time outside the laboratory scale. As highlighted in this review article, the basic research question on the use of engineered yeasts should be on their metabolic requirements, their performance under different carbon and energy sources, and their response to different cultural conditions. It is important to determine which synthetic design would be the best for particular bioprocesses and how efficiently they can process carbon substrates to products. On the question of which synthetic design to use, the advancement in molecular and synthetic biology tools has made it simple or rather easy to achieve many things. The utilization of peroxisomal signal peptides, and genetic engineering tools like the CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated proteins) have helped considerably. However, the growth of engineered yeasts as pointed out in this article, can be improved by using different carbon and nitrogen sources for solid state fermentation of LCBs. In addition, the use of in silico metabolic modeling, 13C-metabolic flux analysis, and some systems biotechnology pipeline tools have reduced this constraint. The goal of methylotrophy although still need more extensive research but holds the greatly innovative use of C1 feedstock for the enhancement of diversified process. Taken together, we conclude that the adoption of engineered yeasts in biorefinery would establish a twin benefit of a sustainable global economy and a reduced climate change associated with the nonrenewable fossil resources.",
"introduction": "1. Introduction All sectors that are contributing to global economy, be it energy, agriculture, food, or industry, are facing regulatory challenges to safeguard their energy generation, usage, and their rippling effects on the environment in terms of climate change. Modern society is mostly reliant on energy sources that contribute to greenhouse gas emission, which in turn negatively affects the environment [ 1 , 2 ]. Nevertheless, improvement of living standards requires sustainable energy for meeting the world’s ever-increasing energy consumption. Hence, the quest for an energy generation/industrialization that would be environmentally friendly, renewable, and sustainable. This requires transitioning from the use of fossil-based to renewable biological resources that do not contribute to the greenhouse effect. Indeed, the attention of governments, industries, and energy sectors across the globe is now shifting to the application of renewable natural resources for energy generation and refineries with more public acceptance resulting in a boom for bio-refineries over the past few years with interesting development in renewable energy technologies and hence bioeconomy [ 3 , 4 ]. Bio-industrialization and bio-energy are thought to contribute to the reduction in the emission of greenhouse gases, create new job opportunities, and enhance regional development and energy supply chains [ 5 ]. For example, the European Union (EU) set up a low carbon economy goal, for long-term environmental and economical sustainability, to be achieved by 2050. As such, the bioeconomy has taking new shape over the past few decades. Vital bio-based compounds; biofuels and bioenergy have contributed immensely to many nations’ wealth. It is expected that in the future a larger percentage of chemicals produced globally would be derived from biological sources [ 6 ]. The bioeconomy relies on a collection of industrial processes and products that make use of microorganisms that break down woody plants to produce biofuels and bioproducts. Microorganisms, specifically white-rot fungi, play a number of roles in many biotechnological applications including biorefineries [ 7 ] and the degradation of lignocellulosic materials with successive product utilization to give rise to a value-added biofuel and other bio-goods. In fact, there has been an upsurge in the application of bio-based materials, such as the plant’s lignocellulosic biomass/biomaterials (LCBs), in biorefineries. The lignocellulose feed-stocks including barley straw, corn stover, wheat straw, corn fiber, switch-grass, and many other plant-derived materials have been successfully used as substrates for the production of various viable chemicals and biofuels [ 8 , 9 ]. The high levels of lignin, cellulose, hemicellulose, and sugars make them suitable for microbial growths since they are good substrates for the fermentation process that is very important for biorefineries [ 10 , 11 ]. The conversion of LCBs to valuable products often follows this sequence of processes: (i) biomass pretreatment, (ii) enzymatic hydrolysis, (iii) microbial fermentation, and (iv) purification (also called downstream process). Many innovative technologies have been developed to enhance the first and second stages of biomass conversion to enhance and hasten the downstream process. With the advancement of modern biotechnological techniques, the application of synthetically/genetically engineered organisms for enhanced bio-refineries of plant biomass has been viewed with great potentials [ 12 ]. Through synthetic biology and genome engineering tools, it is now possible to develop synthetic enzymes, like those expressed in white-rot yeasts [ 13 ], that efficiently break down lignin and other components of cellulosic materials. Genetically engineered yeast strains host enzymes that confer properties such as enhanced tolerance, increased metabolic flexibility, and fermentation efficiency [ 14 , 15 ]. The conversion of LCB into bioproducts by synthetic yeasts with enhanced enzyme properties has led to more efficient lignocellulose biorefineries (LB) [ 16–18 ]. However, the majority of these laudable findings on the engineering of yeast strains for LB are limited to the laboratory and not many of these new strains have been exploited industrially. The optimization of microbial whole-cell biocatalyst has contributed to the success in biorefineries. Many yeast cells have become essential components in food industries, be it in bread, wine, beer, or beverage making. Also, yeasts have proven valuable in many biotechnological applications and play vital roles in the overall biorefineries [ 7 ]. The conversion of LCBs by microbes that are more tolerant to hash environment, with wide substrate specificity and well-structured metabolic mechanism is now desired for achieving more efficient LB [ 19 ]. This is possible through synthetic biology and genome engineering tools applied to yeasts that are the most suited for this purpose due to their genetic diversity [ 12 ]. Great success has been made in biofuel and biodiesel production by increasing the metabolic flexibility and fermentation efficiency of many yeast strains like the popular the baker’s yeast ( Saccharomyces cerevisiae ) [ 14–18 ]. S. cerevisiae is now being employed in different bioprocesses for the generation of food, biochemicals/biofuels, while other yeasts have also been exploited. Candida guilliermondii , for instance, has been used for the synthesis of xylitol; Yarrowia lipolytica for lipid production; and Ashbya gossypii for generating riboflavin (Vitamin B2) [ 20–22 ]. This review aims to discuss in depth lignocellulose and its biorefinery that have led to the synthesis of many important bio-based products. We also highlight the roles of lignocellulolytic enzymes in biorefineries and the various metabolic engineering strategies of white-rot yeasts. Hence, we advocate the exploitation of new biotechnological tools for the creation of new synthetic white-rot yeasts. The ability to use them on a large scale industrially might be a primary driver of a sustainable biorefinery and bioeconomy."
} | 2,845 |
36134157 | PMC9419774 | pmc | 1,599 | {
"abstract": "Memristive devices are widely recognized as promising hardware implementations of neuromorphic computing. Herein, a flexible and transparent memristive synapse based on polyvinylpyrrolidone (PVP)/N-doped carbon quantum dot (NCQD) nanocomposites through regulating the NCQD doping concentration is reported. In situ Kelvin probe force microscopy showed that the trapping/detrapping of space charge can account for the memristive mechanism of the device. Diverse synaptic functions, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), and the transition from short-term plasticity (STP) to long-term plasticity (LTP), are emulated, enabling the PVP–NCQD hybrid system to be a valuable candidate for the design of novel artificial neural architectures. In addition, the synaptic device showed excellent flexibility against mechanical strain after repeated bending tests. This work provides a new approach to develop flexible and transparent organic artificial synapses for future wearable neuromorphic computing systems.",
"conclusion": "Conclusion We have experimentally demonstrated a flexible and transparent memristive synapse by inserting NCQDs into PVP. By regulating the NCQD doping concentration, the memristive behavior changes from D-RS to A-RS. The gradual memristive switching is attributed to trapping/detrapping of space charges and formation/rupture of multiple CPs, which is verified by in situ KPFM. Several essential synaptic functions, such as EPSC, PPF, STDP, and a transition from STP to LTP, were demonstrated in the device. In addition, the memristive device can be prepared on a flexible substrate, endowing it with excellent flexibility. This work provides a feasible method for enabling organic memristors to closely simulate synapses, which will broaden the scope for integrating neuromorphic computing systems with novel environmentally friendly and flexible features.",
"introduction": "Introduction Brain-inspired neuromorphic computing has attracted great attention as innovative technology owing to its ability to perform intelligent and energy-efficient computation. 1–3 Electronic synaptic devices are considered to be the key step for hardware implementation of neuromorphic computation. Among these devices, the emerging memristor is considered to be a promising candidate for synaptic emulation owing to its functional and structural resemblance to the biological synapse. 4–8 Memristors with conductance states incrementally modulated by an external electric field have demonstrated capability for emulating diverse important synaptic functions observed in biology. 9–12 Moreover, the simple metal/resistive switching (RS) layer/metal sandwich structure can contribute to facilitate implementation of high-density neural networks. 13–15 In comparison with their inorganic counterparts, organic–inorganic hybrid systems have tremendous advantages in fabrication of memristive synapses owing to their cost-effective, environmentally friendly, and biocompatible properties. 16–18 Polymers are usually used as the active matrix, and low-dimensional inorganic materials, including nanodots, 19,20 nanotubes, 21,22 and nanosheets, 23,24 are introduced into the polymer as charge trapping centers to trigger RS. In many cases, organic–inorganic hybrid based memristors exhibit digital-type RS (D-RS) between the low-resistance state (LRS) and high-resistance state (HRS) for memory storage applications. For example, Ding et al. reported a Ti 3 C 2 nanosheet/polyvinylpyrrolidone (PVP) based memristor with configurable multistate nonvolatile memory behavior. 25 Liu developed flexible nonvolatile rewritable memory devices based on MoS 2 –PVP nanocomposites. 26 However, analog-type RS (A-RS) with continuous resistance-state variation, which is urgently needed for biorealistic emulation of synaptic functions, has seldom been reported in organic–inorganic hybrid systems. Owing to the ubiquitous charge trapping effects, the RS behavior is primarily associated with the characteristic of the electron-trapping centers. Therefore, searching for suitable nanomaterial-doped polymers is beneficial for guiding development of A-RS based memristive synapses with excellent performance and environmental benignity. As a novel zero-dimensional carbon nanomaterial, nitrogen-doped carbon quantum dots (NCQDs) possess unique properties, such as small sizes, excellent biocompatibility, excellent aqueous solubility, and low toxicity, and they are considered to be promising materials for a number of biological and optoelectronic applications. 27–30 Doping CQDs with electron-rich N atoms is considered to be a valid strategy to modulate their intrinsic properties and improve their fluorescent properties and quantum yield. 31,32 More importantly, the polar groups and defects on the surface of NCQDs can regulate generation and recombination of electron–hole pairs, 33–35 suggesting the potential feasibility of doping polymers with NCQDs to achieve closely biorealistic artificial synapses based on the trapping-assisted hopping effect. Herein, we report a flexible and transparent organic memristive synapse with A-RS behavior in NCQD–PVP hybrid composite films. By modulating the doped NCQD concentration, the filled trapping centers under high NCQD concentration promote formation of multiple conductive paths (CPs), leading to a gradual change in the device conductivity. Advanced synaptic functions, such as excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), and transformation from short-term plasticity (STP) to long-term plasticity (LTP), are efficiently emulated. In addition, the transparent organic memristive synapse on a poly(ethylene terephthalate) (PET) substrate shows excellent flexibility under a bending test, providing a potential approach for development of green electronics and wearable neuromorphic computing systems.",
"discussion": "Results and discussion The NCQDs were prepared by a simple hydrothermal treatment process using citric acid and urea as the carbon and nitrogen source, respectively ( Fig. 1(a) ). The detailed preparation schemes of the NCQDs are provided in Fig. S1 in the ESI † and the Experimental section. The surface morphology of the as-prepared NCQDs was characterized by transmission electron microscopy (TEM) and atomic force microscopy (AFM). A TEM image ( Fig. 1(b) ) showed good dispersion of the NCQDs in water owing to their hydrophilic nature. The particle size distribution of the pristine NCQDs was in the range from 2 to 5 nm (inset of Fig. 1(b) ), suggesting the uniform size and nearly spherical shape of the NCQDs. The NCQDs had an average size of 3.5 nm ( Fig. 1(c) ). To analyze the chemical composition, the X-ray photoelectron spectroscopy (XPS) of the NCQDs was performed. The XPS survey scan of the NCQDs is shown in Fig. 1(d) , in which the apparent peaks are located at 284.2, 403.3, and 535.1 eV, corresponding to C 1s of sp 2 C, N 1s of the doped N, and O 1s of the oxygen-containing functional groups, respectively. 31–33 The structure of the NCQDs was further characterized by Fourier transform infrared (FT-IR) spectroscopy ( Fig. 1(e) ). The absorption bands of the NCQD surface revealed the presence of polar groups, including –OH, 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, C–N (C N), and –NH. 31–33 The emission fluorescence spectrum of the NCQDs is shown in Fig. 1(f) , which is consistent with the phenomenon shown in the photograph in Fig. 1(a) , that is, the NCQDs showed bright blue fluorescence under 325 nm ultraviolet light. 31–33 The ultraviolet-visible absorption spectrum of the synthesized NCQDs is shown in Fig. 1(g) , in which the absorption band peak is located in the 250–280 nm region, showing that the main absorption of the NCQDs is in the ultraviolet region. 31–33 Fig. 1 NCQD characteristics. (a) Detailed preparation scheme of the NCQDs. (b) TEM image. (c) AFM image. (d) XPS spectrum. (e) FT-IR spectrum. (f) Fluorescence emission spectrum under 325 nm laser irradiation. (g) Ultraviolet-visible absorption spectrum of the NCQDs on a flexible PET/ITO substrate. In this work, the NCQDs were inserted into PVP to prepare flexible and transparent artificial synapses. The motivation for developing a highly efficient PVP–NCQD based memristor with biorealistic synaptic functions is shown in Fig. 2(a) . The structure and ion transport process of the flexible Al/PVP–NCQD/indium tin oxide (ITO) memristor on a PET substrate are analogous to the ion channel and Ca 2+ dynamics of biosynapses. The Al/PVP–NCQD/ITO sandwich structure was prepared in crossbar arrays, as shown in the optical microscope image in Fig. 2(b) . In the cross-sectional scanning electron microscopy (SEM) image ( Fig. 2(c) ), a PVP–NCQD layer with a thickness of 50 nm can be clearly observed between the Al and ITO electrodes, demonstrating the two terminal structure of the device. The surface morphology of the PVP–NCQD film was investigated by AFM (Fig. S2, ESI † ). The root mean square roughness value ( R q ) was 0.7 nm, confirming the flatness of the as-prepared film. In addition, the PVP–NCQD film was highly transparent, with transmittance of between 90% and 95% in the visible spectrum ( Fig. 2(d) ). A photograph of the memristive synapse is shown in Fig. 2(e) . The device was highly flexible. The data from the mechanical flexibility tests will be discussed later. Fig. 2 Flexible and transparent Al/PVP–NCQD/ITO memristive synapse. (a) Illustrations of the biological synapse (left) and flexible Al/PVP–NCQD/ITO memristor (right). (b) Optical microscope image of the memristor crossbar array. (c) SEM image of the cross-section of the Al/PVP–NCQD/ITO memristor crossbar array. (d) Ultraviolet-visible transmission spectrum of the PVP–NCQD film. The inset shows a photograph of the transparent memristive synapse. (e) Photograph of the memristive device fabricated on a flexible PET substrate under bending. To investigate the memristive characteristic of the device, a bias voltage was applied to the Al top electrode and the ITO bottom electrode was grounded. The RS behavior of the device greatly depended on the doping concentration of NCQDs in the nanocomposite. No obvious resistive switching (RS) behavior is appeared in the devices without inserting NCQDs into PVP film beacase there are no charge trapping centers that enhance the localized electric field to aggregate electrons. 17,36 (see Fig. S3, ESI † ). The typical D-RS behavior after the formation process with a NCQD concentration of 10 wt% is shown in Fig. 3(a) , in which an abrupt transition between the HRS and LRS was triggered by a set (0 to 2 V)/reset (0 to −2 V) voltage sweep. The device could be repeatedly switched by 50 consecutive RS cycles ( Fig. 3(b) ). 37,38 When the doping concentration of the NCQDs was increased from 10 to 30 wt% (see Fig. S4, ESI † ), both the set and forming voltage greatly decreased, similar to most previous reports. 37 It is interesting to note that the transition from D-RS to gradual memristive switching (A-RS) occurred when the doping concentration of the NCQDs was increased from 10 to 40 wt%. Continuous switching of the device (A-RS type) with consecutive positive and negative voltage sweeps (0 to 2 V/0 to −2 V) is shown in Fig. 3(c) and (d) , respectively. The insets in Fig. 3(c) and (d) show the variation of the current read at 0.2 V with the cycle number, in which the uptrend (downtrend) is observed after applying positive (negative) sweeps. In addition, the devices with higher concentration exhibit poor A-RS performance duo to the existence of large leakage current in composite film, 17,39 suggesting the NCQDs concentration of 40% is the optimal condition to achieve excellent A-RS performance (see Fig. S5, ESI † ). Here, to simulate similar information processing to synapses, the memristor was stimulated in the pulse mode. The device conductance continuously strengthened/weakened with 100 consecutive positive/negative voltage pulses ( Fig. 3(e) and (f) ), which can simulate the synaptic potentiation/depression process when treating the device conductance as the synapse weight. 9,10 Fig. 3 A-RS memristive behavior realized by modulating the doping NCQD concentration in the Al/PVP–NCQD/ITO memristor. (a) I – V characteristics of the memristor with 10 wt% NCQD doping concentration. (b) Statistical values of the HRS/LRS under repeated RS measurement. (c) and (d) I – V characteristics of the memristor with 40 wt% NCQD doping concentration. (e) and (f) Potentiation and depression of the current caused by repeated stimulation of 100 positive pulses (+2 V, 10 ms) and 100 negative pulses (−2 V, 10 ms). To better understand the conduction mechanism of the devices, the log–log current–voltage ( I – V ) curves of PVP–NCQD based memristors with different NCQD concentrations (10 and 40 wt%) are shown in Fig. 4(a) and (b) . The log–log plot of the direct current sweep exhibited a slope of near one (blue lines) for positive bias in both D-RS and A-RS, indicating typical trap-controlled space-charge limited conduction (SCLC). For the HRS under positive bias, the charge transport behavior consisted of three regions ( Fig. 4(a) and (b) ). In the low bias region, the transport followed Ohm's law (slope ≈ 1, J ohm ∼ V ). The J – V equation is as follows: 40–42 1 where J ohm is the transport current, q is the electronic charge, n 0 is the concentration of the free charge carriers in thermal equilibrium, ε is the dielectric constant, μ is the electronic mobility, V is the applied voltage, and d is the thickness of the thin film. With increasing applied voltage, the slope was around 2, suggesting that the conduction in the trap-filled limited region followed Child's law ( J child ∼ V 2 ). The J – V equation is as follows: 40–42 2 Fig. 4 Memristive switching mechanism of D-RS and A-RS in the Al/PVP–NCQD/ITO memristor. (a) and (b) I – V curves in log–log scale in the positive voltage sweeping region of the device with D-RS and A-RS behavior. The charge trapping capability of the PVP–NCQD film was detected by KPFM. Surface potential images of selected cross-sections (c) before and (d) after the charge injection process and the related cross-section data of the surface potential. Schematic diagrams of the mechanisms of (e) D-RS and (f) A-RS. For the LRS, the conduction mechanism was well fitted by ohmic conduction (slope ≈ 1), which is consistent with formation of a CP in the RS layer. Furthermore, Kelvin probe force microscopy (KPFM) was performed to clearly demonstrate the charge trapping. To form the writing operation for a charge carrier injection process, +5 V tip bias was added to the written square region of about 2 μm × 2 μm ( Fig. 4(c)i and (d)i ). For surface potential measurement, a 5 μm × 5 μm area was then measured by KPFM. The section data of the surface potential were collected and plotted before and after the charge injection process ( Fig. 4(c)ii and (d)ii ). The potential difference between the electrical writing region and nonwriting region was ∼250 mV. The potential in the writing region was clearly lower than that in the non-operation region, demonstrating trapping of electrons in the composite films. 42,43 One possible switching model is proposed based on the above results ( Fig. 4(e) and (f) ). The mechanism of RS can be described by a charge trapping assisted hopping conduction model. For the D-RS of the low NCQD concentration (10 wt%) in Fig. 4(e) , the charge carriers are injected from the ITO electrode and trapped in the NCQDs under positive bias. Owing to low NCQD concentration, formation of one dominant CP inside the PVP–NCQD nanocomposites resulted in transition from the HRS to the LRS. For the device with high NCQD concentration (40 wt%) in Fig. 4(f) , the filled trapping centers under high NCQD concentration tended to form multiple CPs owing to abundant and concentrated trapped defects. The device conductance can be continuously adjusted by controlling the number of these multiple CPs, which is similar to a model involving modulation of the number of CPs leading to a gradual change of the device conductivity of multiple CPs. 17,42,43 Such A-RS behavior in the PVP–NCQD based memristor enabled close emulation of synaptic learning functions. The current response of the memristive device under a single spike (2 V, 1 μs) is shown in Fig. 5(a) . The single spike triggered an abrupt increase in the current followed by decay to the initial state, which is similar to the excitatory postsynaptic current (EPSC) of the biological synapse. 10,44,45 Importantly, the EPSC opens the temporal range for relating the next spike. One typical correlation between spikes is the paired-pulse facilitation (PPF) function, which represents the temporal correlation between two neighboring spikes and is related to the Ca 2+ dynamics in biology. 46,47 Herein, PPF behavior that continuously enhanced the EPSC by using two successive spikes (2 V, 1 μs) was achieved in the A-RS memristor, as shown in Fig. 5(b) . The peak value of the EPSC induced by the second spike I 2 was higher than that of the first spike I 1 , resulting in the PPF effect. The PPF effect weakened with increasing interval time, as shown in Fig. 5(c) . Fig. 5 Synaptic learning functions closely emulated in the Al/PVP–NCQD/ITO memristor. Short-term synaptic functions of (a) EPSC, (b) PPF, and (c) PPF versus the relative spike timing. A transition from STP to LTP appears through the (d) pulse amplitude and (e) pulse frequency. (f) Relative changes of the synaptic weight (Δ G ) with spike timing (Δ t ), showing the typical STDP function of the memristive synapses. Generally, synaptic plasticity can be divided into short-term plasticity (STP) and long-term plasticity (LTP) depending on the memory retention. 9,48 LTP is activated by a permanent change in the synaptic weight. It is believed to be the synaptic mechanism of learning and memory storage, and a transition from STP to LTP appears through repeated rehearsal events. 9,48 To measure such a transition, pulses with different amplitudes (1–3 V) were applied to the device. Memory retention was enhanced with increasing pulse amplitude ( Fig. 5(d) ), demonstrating the transition from STP to LTP. Similarly, memory retention was also enhanced when the pulse frequency was increased from 0.313 to 5 MHz (or the pulse width was increased from 0.2 to 5 μs) ( Fig. 5(e) and S6, ESI † ). In neuromorphic systems, the spike-timing-dependent plasticity (STDP) learning rules are one typical type of LTP, which is one of the essential learning laws for emulating synaptic functions. 49,50 By adjusting the timing between the pre- and postsynaptic spikes, the synaptic weight can be modulated in two ways. To induce LTP, the presynaptic spike needs to precede the postsynaptic spike (Δ t > 0), whereas the opposite timing of the pre- and postsynaptic spikes (Δ t < 0) induces LTD. To biorealistically implement STDP, a pair of temporally correlated spikes was applied to the top electrode and bottom electrode of the memristor as pre- and postsynaptic spikes, respectively (see Fig. S7 in the ESI † for the detailed trigger signal design). The synaptic weight (Δ G ) plotted against the relative timing (Δ t ) is shown in Fig. 5(f) . The change of conductance Δ G as a function of Δ t can be expressed by the exponential fitting equation: 9,10,49,50 3 where A + and A − are the amplitude parameter for LTP and LTD, respectively. τ + and τ − are the time constant for LTP and LTD, respectively. Smaller Δ t resulted in a greater conductance change in the A-RS memristor, which is similar to the biological synapse. 9,10,49,50 All of the above results indicated that essential synaptic functions can be emulated in the Al/PVP–NCQD/ITO device, which satisfies the basic requirements for artificial synapses. The PVP–NCQD based memristive synapse also possesses the feature of flexibility for future wearable applications owing to the organic polymer RS layer. To verify the flexibility feature, the Al/PVP–NCQD/ITO device was prepared on PET. A continuous bending test was then performed using a force gauge (Mark-10) and a highly configurable motorized stand (ESM303) (the bending equipment is shown in Fig. S8 † ). Fifty consecutive positive/negative pulses (2 V/−2 V, 10 ms) were applied to the bent synaptic device. The statistical values of G max / G min of potentiation/depression under different bending numbers (0–250 times) are shown in Fig. 6(a) and (b) . There was no significant change in the potentiation/depression behavior with the number of bending cycles, other than acceptable switching fluctuation, suggesting that the memristive device possessed excellent flexibility against mechanical strain. The above results demonstrated that no obvious degradation was induced by mechanical bending, indicating the excellent bending stability of the device and potential application to flexible and wearable electronics. Fig. 6 Flexibility characteristic of the Al/PVP–NCQD/ITO synaptic device. (a) Pulse trains (50 positive and 50 negative pulses, 2 V, 10 ms) applied to the device after different bending numbers N . (b) Statistical values of G max / G min under different bending numbers."
} | 5,473 |
35756965 | PMC9194759 | pmc | 1,600 | {
"abstract": "Bacterial communities form biofilms on various surfaces by synthesizing a cohesive and protective extracellular matrix, and these biofilms protect microorganisms against harsh environmental conditions. Bacillus subtilis is a widely used experimental species, and its biofilms are used as representative models of beneficial biofilms. Specifically, B. subtilis biofilms are known to be rich in extracellular polymeric substances (EPS) and other biopolymers such as DNA and proteins like the amyloid protein TasA and the hydrophobic protein BslA. These materials, which form an interconnected, cohesive, three-dimensional polymer network, provide the mechanical stability of biofilms and mediate their adherence to surfaces among other functional contributions. Here, we explored how genetically-encoded components specifically contribute to regulate the growth status, mechanical properties, and antibiotic resistance of B. subtilis biofilms, thereby establishing a solid empirical basis for understanding how various genetic engineering efforts are likely to affect the structure and function of biofilms. We noted discrete contributions to biofilm morphology, mechanical properties, and survival from major biofilm components such as EPS, TasA and BslA. For example, EPS plays an important role in maintaining the stability of the mechanical properties and the antibiotic resistance of biofilms, whereas BslA has a significant impact on the resolution that can be obtained for printing applications. This work provides a deeper understanding of the internal interactions of biofilm components through systematic genetic manipulations. It thus not only broadens the application prospects of beneficial biofilms, but also serves as the basis of future strategies for targeting and effectively removing harmful biofilms.",
"introduction": "1 Introduction Bacterial biofilms are bacteria that are embedded in an extracellular matrix of polysaccharides, proteins, nucleic acids, and lipids that provides structural rigidity, protection, and regulation of gene expression and permeability regulation [ [1] , [2] , [3] ]. The biofilm is a three-dimensional spatial organization in which molecules interact and communicate with one another, with a tightly packed, heterogeneous structure [ 4 ]. Such biofilms are a major problem in both industry and healthcare [ 5 , 6 ]. Bacillus subtilis is a non-pathogenic Gram-positive bacterium that can form architecturally complex biofilms and is widely used as a model strain for biofilm studies [ 7 , 8 ]. The substrate of B. subtilis is similar to other biofilms in that it provides structural rigidity, protects the embedded cells from environmental insults and ensures their presence and propagation [ 9 ]. Biofilms are dynamic and can adapt to changing environments [ 10 ]. The field of B. subtilis biofilm research is rapidly evolving, and some advances have been made in exploring the unique physicochemical properties of biofilms. This includes studies of biofilm wrinkling [ 11 , 12 ], adhesion properties [ [13] , [14] , [15] ], and antibiotic resistance [ [16] , [17] , [18] , [19] ], as well as mechanical properties [ 20 , 21 ]. To expand the uses of biofilms, additional research into the mechanical properties of bacterial biofilms is required. B. subtilis biofilms are comprised primarily of extracellular polymeric substances (EPS) and the amyloid protein TasA and the hydrophobic protein BslA ( Fig. 1 ). EPS contributes to the formation of biofilm structure, helps biofilm to adhere to the surface and provides mechanical stability to the biofilm [ [22] , [23] , [24] ]. TasA polymerizes into highly stable amyloid fibers, which serve as the biofilm's structural “backbone” and are involved in the formation of the biofilm matrix [ [25] , [26] , [27] , [28] , [29] ]. BslA forms a hydrophobic layer on the surface of the biofilm, which may contribute to its resistance to antimicrobials and disinfectants [ 30 , 31 ]. The morphology, internal structure, and mechanical stability of biofilms are heavily influenced by the polymers that comprise them. In this study, we investigated the effect of EPS, TasA and BslA, the main components of B. subtilis biofilms, on the mechanical properties. We genetically engineered various mutant strains to test their growth state, mechanical properties, antibiotic tolerance, and printing ability. Biofilms are cross-linked polymer gel composites that can be three-dimensional (3D) printed as bioinks [ 32 ]. Previous study used engineered B. subtilis biofilms to demonstrate a programmable and printable platform of living functional materials [ 33 ]. Based on this research, we can select host bacteria that are more suitable for printing and use functional 3D printed biofilms to construct living biofilm-derived materials, which may have broad applications in the future. Fig. 1 Bacillus subtilis biofilms formation. ( a ) Digital camera image of the wild-type B. subtilis biofilm. The mature biofilm exhibits a complex network of intertwined wrinkles and ridges and is highly hydrophobic. ( b ) A schematic of wild-type B. subtilis biofilm. The biofilm matrix composition is complex and can contain self-produced molecules, including TasA fibers, EPS, BslA and bacteria. Fig. 1",
"discussion": "4 Discussion Previous efforts have attempted to explore B. subtilis biofilm formation. Focusing on the physical properties of B. subtilis biofilms as in the case of this study, many of these mathematical and experimental investigations analyzed the influence of three key biofilm matrix components on the biofilm colonies. Kesel et al. quantitatively investigated the surface roughness, stiffness and the bulk viscoelasticity of biofilms and demonstrated the importance of specific biofilm matrix components for the distinct physical properties and biofilm growth of B. subtilis biofilms [ 40 , 41 ]. Benigar et al. reported on the structure and dynamics of biologically important model polymer mixtures that mimic the extracellular polymeric matrix in native biofilm of Bacillus subtilis [ 42 ] . In addition, a combined experimental and computational approach had been applied to investigate potential benefits arising from division of labor during biofilm matrix production [ 43 ]. Previous studies suggested that there is an internal force within the biofilm that helps it to shape its structure, improves the mechanical resistance, and facilitates its invasion and self-repair [ 44 ]. Yannarell et al. explored a striking dual-species biofilm [ 45 ]. Klotz et al. quantified the impact of specific biofilm matrix components on biofilm erosion behavior [ 46 ]. Despite those advances, it still lacks a whole picture regarding how specific biofilm components affect the structures, mechanical and biological properties of biofilms. In this study, using B. subtilis as a model system, we looked into how specific biofilm components affect macroscopic and microscopic structures, as well as properties like stiffness and printability. We were mainly interested in how biofilm-relevant genes influence the physical and growth properties of B. subtilis during biofilm formation. Understanding the basic components underlying the mechanical properties of biofilms, as well as the effects of various external stresses affected the biofilms, can be a valuable strategy for gaining structural insights. The application of a combination of genotype and phenotype approaches, as well as quantitatively determining the influence of its individual constituents on biofilm structure, mechanics, and permeability, might be the most promising way to achieve the goal of dissecting the relationship between the molecular composition of a biofilm and its properties. A thorough understanding of biofilm compositions should enable us to control biofilm formation in the future, potentially easing biofilm removal. Biofilms with specific properties might be used as engineered living materials (ELMs) for biomedical applications [ 47 ]. The altered matrix gene expression patterns in ΔtasA suggested that a higher proportion of TasA might be required for stable pellicle production. The outward expansion during biofilm growth is limited by the surface layer protein BslA encapsulating the biofilm, and the area covered by the biofilm is slightly increased in the presence of EPS. In the case of EPS, it can simply increase the biomass and thus increase the total coverage of the biofilm. The knockout strain showed weaker mechanical properties compared to the wild strain, indicating that the material properties of the biofilm may vary depending on the important components of the biofilm. The information regarding how biofilm components affect the properties of biofilms can be used a reference for combating bacterial biofilms on medical devices or industrial surfaces in the future. In addition, living materials with unprecedented functionalities can be created using the freedom of shape provided by this printing technique and the inherent performance of biofilm [ [48] , [49] , [50] ]."
} | 2,269 |
24688710 | PMC3962205 | pmc | 1,601 | {
"abstract": "Biomimetic design of new materials uses nature as antetype, learning from billions of years of evolution. This work emphasizes the mechanical and tribological properties of skin, combining both hardness and wear resistance of its surface (the stratum corneum) with high elasticity of the bulk (epidermis, dermis, hypodermis). The key for combination of such opposite properties is wrinkling, being consequence of intrinsic stresses in the bulk (soft tissue): Tribological contact to counterparts below the stress threshold for tissue trauma occurs on the thick hard stratum corneum layer pads, while tensile loads smooth out wrinkles in between these pads. Similar mechanism offers high tribological resistance to hard films on soft, flexible polymers, which is shown for diamond-like carbon (DLC) and titanium nitride thin films on ultrasoft polyurethane and harder polycarbonate substrates. The choice of these two compared substrate materials will show that ultra-soft substrate materials are decisive for the distinct tribological material. Hierarchical wrinkled structures of films on these substrates are due to high intrinsic compressive stress, which evolves during high energetic film growth. Incremental relaxation of these stresses occurs by compound deformation of film and elastic substrate surface, appearing in hierarchical nano-wrinkles. Nano-wrinkled topographies enable high elastic deformability of thin hard films, while overstressing results in zigzag film fracture along larger hierarchical wrinkle structures. Tribologically, these fracture mechanisms are highly important for ploughing and sliding of sharp and flat counterparts on hard-coated ultra-soft substrates like polyurethane. Concentration of polyurethane deformation under the applied normal loads occurs below these zigzag cracks. Unloading closes these cracks again. Even cyclic testing do not lead to film delamination and retain low friction behavior, if the adhesion to the substrate is high and the initial friction coefficient of the film against the sliding counterpart low, e.g. found for DLC.",
"conclusion": "4. Conclusions In the current work, we broadened our bio-inspired research of structure formation and mechanical properties of nano-wrinkled thin hard films on polymers towards the tribological behavior under ploughing and sliding conditions: Based on the mechanical behavior of human skin under compressive and tensile loads and the results of tribological tests, we established a biomimetic material model for hard surfaces with low friction coefficients on soft, highly elastically deformable substrates. Sliding in such contacts occur on pads (cohesive parts of the film with high adhesion to the soft surface), which are divided by cracks. For thin films, deposited under high energetic conditions, these cracks run zigzag along the largest hierarchical (nano-)wrinkle structures, which are formed by relaxation of compressive intrinsic growth stresses by a compound deformation of elastic substrate and hard film during film growth. Additional elasticity in such wrinkled surface may be provided by smoothing of wrinkles under tensile loading. This mechanism is similar to deformation of human skin, consisting similarly of a hard layer (stratum corneum) on a soft bulk: Thick stratum corneum regions, which are similar to the described pads of films, on which the tribological contact to counterparts occur, are separated by wrinkles, which provide the elasticity by smoothing out and slightly stressing the epidermis below. In conclusion, this bio-inspired material concept is on the way towards many technical applications for tribological protection of ultra-soft polymers.",
"introduction": "1. Introduction Skin is the heaviest organ of all animals (e.g. human: ∼16% of body weight) being designed by nature as a three-layer, semi-dense barrier of the organism to the surrounding. It bridges brilliantly the demands of flexibility (adaptability to the underlying surface) and hardness (tribological resistance) by wrinkled and partly fractured surfaces. Additionally, high local pressure sensitivity is enabled by such a grooved structure, found for less regularly structure for human skin but for highly ordered structures for a wide variety of animal skins (e.g. on tree fog toe pads by hexagonal arrays of epithelial cells) [ 1 – 3 ]. Human skin is composed of a hard layer (stratum corneum) on a soft, compliant substrate (epidermis with lucidum, granulosum, spinosum, germinativum, papillary and reticular dermis and hypodermis): The 10 to 25 µm thick stratum corneum layer with an elastic modulus between 50 and 400 MPa (depending on indentation depth) is built of 10 to 20 layers of non-viable, keratinized corneocyte cells [ 4 – 7 ]. Mechanically, stratum corneum is described of corneocytes being “bricks”, which are bound together by 0.1–0.3 µm thin lipid-rich “mortar” (intercellular lipids and degraded desmosomal protein junctions) [ 6 ]. The compliant skin layers below are together 1 to 4 mm thick and have an elastic moduli < 1 MPa [ 8 ]. Skin topography is widely influenced by wrinkles, which form due to permanent intrinsic tensile stresses in the substrate (reticular dermis). Three hierarchical wrinkle structures with 70 – 200 µm (primary lines, “Langer's lines”), 20 – 70 µm (secondary lines) and <10 µm depth are found, covering the whole skin and enabling simultaneously surface hardness, tribological resistance, and flexibility [ 9 ]. Generally, wrinkle depth and density is adapted to the required deformability and tribological resistance: The thicker the skin and the larger its demanded deformability, the deeper and more dense wrinkles are. While the larger wrinkles are forming lines (e.g. Langer lines), the smallest wrinkle structure separates groups of corneocytes. Under mechanical forces, the skin surface can extend without loading the cells by reversible smoothing of wrinkles. In a stress–strain curve, this results in a toe region [ 10 , 11 ]. The direction for the higher extensibility is perpendicular to the direction of the primary line wrinkles with ∼40% elongation in the toe region compared to ∼20% along the wrinkles. As a general consequence, the stratum corneum hardly experiences elongation stresses but only unfolds under typical cyclic loading in vivo . Further straining of skin leads to straightening and alignment of the fibrous component in dermis, being visible by a linear region in the stress–strain curve [ 12 , 13 ]. Focusing on the fracture of stratum corneum, plastic deformation starts after 10% extension with irreversible elongation [ 14 ]. In dry stratum corneum, cracking occurs at the end of this phase of low slope in the stress–strain curve, while a strain hardening phase occurs in hydrated stratum corneum, which is characterized by higher fiber mobility and differences in the intercellular lipid composition. This final rupture is always extracellular and most likely at the desmosomes [ 15 ]. Wu et al. [ 16 ] found 0.7 MPa peak stress for fracture of dry stratum corneum, which decreasing at higher skin hydration. At higher strains, stresses decayed due to continuing deformation of viable epidermis and dermis. The surface morphology of the fractured skin surface reveals a separation of stratum corneum islands (compare to [ 16 ]): Channeling cracks form around these islands through the whole thickness of stratum corneum. Predominantly, these extracellular channeling cracks follow the third-order wrinkles around groups of corneocytes. Under tensile stresses, this channeling process does not arrest until it encounters another channel or an edge, creating a connected channel network [ 17 ]. Sources of channel networks at higher stresses are surface cracks starting from a flaw even at lower stresses, based on fracture mechanics based cracking models. Tribologically, the stack of connected layers, varying in elasticity, shear strength and continuity (e.g. by wrinkles), show at minimal lubrication conditions (without any sweat, lipids, etc.), friction coefficients of 0.6 against paper, 1.6 against polyethylene or 2.6 against polycarbonate [ 18 ]. Such high coefficients of friction are due to high skin viscoelasticity [ 19 , 20 ]. Lubricated conditions and filling of grooves with liquids (water, sweat, lipids) may change adhesion by capillary forces. Higher normal forces general decrease friction coefficients [ 21 ]. Friction generally leads to shear stresses, under which the stack of skin layers (except the topmost hard stratum corneum) behaves like a viscous fluid [ 22 ]. As described above and shown for tribological contact in [ 21 ], wrinkles elastically compact under compressive loading in front of a slider and smooth out under tensile loading behind the slider. Cyclic tribological loading under such conditions can result in layer-by-layer wear of the 10 to 20 anucleated corneocyte layers in the stratum corneum, whereby layer delamination is found along the cornified proteins acting as glue between these cells. If the occurring tribological tensile strains are too high and elastic compliance of skin is exhausted, plastic deformation (tissue trauma) in the subsurface layers may occur. Tensile fracture behind the moving counterbody can results in intercellular fracture along larger wrinkles. Transcellular failure is rather implausible. Applying such a biomimetic concept for obtaining both flexibility and hardness for engineering materials gathers increasing interest, e.g. for the tribological protection of soft materials like polymers and elastomers. Generally, these materials are distinguished candidates for low weight design in mechanical engineering, but lack on mechanical surface strength and consequently tribological resistance and generally possess high friction coefficients. Their high elasticity causes large surface deflections during compressive loading, e.g. found in sliding contacts: Hard, stiff, and sharp counterparts (like e.g. mineral grains) are deeply incising, ploughing, and scratching the polymer surface. Mechanically, material is piled up in front of the moving counterpart as well as on the sides of the residual groove for polymers with high plasticity and low strain hardening [ 23 ]. Contrary, highly elastic polymers lead to sinking-in in front of the sliding counterpart. Viscoelastic and viscoplastic effects reduce the size of this groove time-dependently after tribological loading (scratching). Due to a close correlation to polymer strain hardening, the minimal groove size is found for polymer materials being hard and elastic at the same time [ 23 ]. Nevertheless, too high plastic strain finally results in material failure – e.g. microcracking, fatigue, and detachment of wear particles [ 24 ]. Coating deposition on polymers with materials of higher tribological resistance like hard films has high potential to optimize scratch and tribological resistance, following the mentioned principle for combined hard and elastic materials. The surface hardness increases, while the viscoelastic behavior of the polymer bulk is preserved. Nevertheless, hard films have poor elasticity and struggle with high deflection of soft polymer substrates. Highest tensile stress and strain levels close to the surfaces frequently exceed elastic and plastic deformability of hard films. Films immediately fail by cracking and / or delamination, if the film thickness is insufficient thick for load support. Cohesive fracture of hard films starts from the interface to the soft material and run towards the film surface. Further cohesive film cracks form in the bulged region around the indenter contact and run in opposite direction. The extent of such cohesive film as well as for subsequent adhesive fracture at the interface to the polymer depends on the film material (mechanical properties) and the film adhesion to the substrate (type of chemical and physical bonding). High friction between the sliding counterpart and the film hasten failure by residual shear stresses [ 25 – 28 ]. To overcome these limitations for thin films, we started biometically inspired research based on deformation of human skin. In former works, we found, that wrinkles are main element in topography formation of thin films on polymers, if they are deposited by physical vapor deposition (PVD) or plasma-activated chemical vapor deposition (PACVD) techniques under low temperature (room temperature) and high energetic conditions (high content of ions or kinetic particles in plasma) [ 29 – 31 ]. The influence of high energetic conditions was intensively studied by the authors for a variety of thin films (titanium, titanium nitride (TiN), precious metals, diamond-like carbon (a-C:H), etc.) on different polymers (polycarbonate (PC), thermoplastic polyurethane (PU), polyamide, polyimide, etc.). Such conditions generally lead to high intrinsic compressive film stresses due to high densities of lattice defects (deposited contaminations, ultra-fine grain size). Briefly, wrinkles occur by relaxation of these intrinsic stresses by a common deformation of the substrate surface zone and the thin film, whereby similarities in the mechanics of wrinkle formation (intrinsic stresses) as well as in the deformation behavior were found between skin and thin films [ 32 ]. Generally, a hierarchical wrinkle structure forms due to stiffening of the surface during deposition: Higher deformation resistance for intrinsic stress relaxation leads to the introduction of additional wrinkle structures of much larger wavelength. Initially occurring wrinkles are on sub-micrometer scale (“nano-wrinkles”), later formed hierarchical overstructures on micrometer scale [ 31 ]. In comparison to human skin, these hierarchically formed wrinkles on coated polymers have about 2 to 3 orders of magnitude lower size [ 32 ]. Under low strains, wrinkles can smooth out elastically [ 33 ]. Higher strains result in fracture under tensile stresses, whereby the cracks run zigzag on the micrometer scale along wrinkle grooves (the areas of lowest strength) and follow rather perpendicular to the tensile stress direction on the millimeter scale [ 32 ]. Deformation (stretching) is focused locally in the polymer below these zigzag crack bands. Film fragments between the crack bands are rather unstressed as well as the substrate surface below these fragments. Doubling the strain leads to a higher density of crack bands, while unloading closes the cracks. Adhesion of all these inorganic films on the polymers is guaranteed by a gradient interface (pseudodiffusion interface), formed by implantation of high-energetic metal atoms (up to 100's eV ionic and/or kinetic energy) in the polymer substrate during the initial phase of deposition. These metal atoms are found in up to 150 nm depth in X-ray photoelectron spectroscopy studies (shown in [ 29 ]), binding there to oxygen atoms [ 34 ] but also (revealed by Fourier-transformed infrared spectroscopy) to atoms in polymer chains [ 32 ]. Goal of this work is to figure out the influence of wrinkling on tribology (friction and wear resistance) of these compounds, whereby we will not lose track of the biomimetic comparison. Tribological conditions of scratching (ploughing) of sharp counterparts and sliding of smooth balls under low loads (mN) are applied in these investigations, going far beyond the state-of-the-science of tribology on (ultra-)thin, wrinkled hard films on (ultra-)soft substrates: Kim et al. [ 35 ] showed wrinkling influences on tribology for ultra-thin, hydrogen containing amorphous carbon (diamond-like carbon, DLC, a-C:H) coated surfaces on soft polydimethylsiloxane polymer. Low friction was found for higher film thickness (>200 nm), while high friction and strong stick-slip effects occurred for thin films (<150 nm), which is explained by elastic deformation of the soft substrate under the normal load. Nevertheless, sliding of the steel ball on the nanostructures generally lead under the applied high loads and high sliding velocities to higher friction compared to similar films deposited on silicon wafer without wrinkles. Wear was described to be based on layer-by-layer mode, finally smoothing the wear track and reducing friction. Nevertheless, this work arises questions about the influence of the substrate elasticity (or material), the thin film material and its friction properties as well as of the contact pressure on the tribological conditions, which will be addressed in this work. In our bio-inspired material design, we will proof the biomimetic concept of adaptability of soft materials with hard surfaces to the counterpart by fracture resulting in “pads” for ploughing (scratching) loading conditions and will investigate low- and high-cycle tribological sliding fatigue modes in dependency of the loading condition, substrate, film, film thickness, and load. Therefore, we have chosen model systems based on very thin hard films (20 - 100 nm) of titanium nitride (TiN) and a-C:H on ultra-soft, highly viscoelastic thermoplastic polyurethane (PU) in comparison to much less elastic, harder polycarbonate (PC) substrates. Because these materials were deposited differently to our previous results, their wrinkling topography and formation mechanisms are initially explained. For studying low as well as high cycle tribological fatigue, a wide range of loads (contact pressures) and differently sharp indenters (diamond tips, sapphire balls) were applied.",
"discussion": "3. Results and discussion 3.1 Surface topography, wetting and film adhesion The surface topographies ( Fig. 1 ) for both TiN and a-C:H films on PU and PC polymers are formed by intrinsic stress induced self-assembling by nano-wrinkling, as described briefly in the introduction section and detailed in former works [ 30 – 32 ]. In contrast, films on stiff silicon wafers are flat and fully reproduce the substrate surface (see AFM images in Fig. 1d ). The topographical features on the polymers are vermicular-like wrinkles with narrow distribution of sizes, which cover the whole surface with random orientation and high density. As visible for ultra-soft PU by comparison of the AFM images for 20 and 100 nm a-C:H films ( Fig. 1c , d and e ), the nano-wrinkled surface topography is formed hierarchically: Wrinkling occurs step-wise at distinct film thicknesses by mechanical instability in order to release intrinsic compressive growth stresses [ 31 ]. Consequently, the small wrinkle topography found for 20 nm films is also present on the thicker 100 nm films. Nevertheless, stiffening of the surface and reasonably higher deformation resistance for intrinsic stress relaxation leads to the introduction of additional wrinkle structures of much larger wavelength. As shown in Fig. 1e , slight indications (height differences) for the introduction of such superstructures are even present in the 20 nm thin a-C:H film. Similar feature of two different sizes of visible wrinkles is also evident for the 100 nm TiN film on PU. Figure 1 AFM images of surface topographies of uncoated substrates (a: PU, f: PC) and for wrinkle structures formed by coating with (b, g) 100 nm thin TiN films, (c, d, h) 100 nm a-C:H, and (e, i) 20 nm a-C:H. Hierarchical superstructures are marked with dotted lines. For harder and stiffer PC, no formation of larger hierarchical structures is visible. Wrinkling starts above 20 nm film thickness and formed wrinkle structures at 100 nm film thickness are quite comparable to that found for 20 nm on PU (compare Fig. 1c , e and 1h , i ). As described in former works [Xxx], introduction of additional larger wrinkle structures requires very soft (“ultra-soft”), easily deformable substrate materials like PU: The about one order of magnitude lower elasticity of the PC substrate shifts the introduction of larger wrinkle structures to higher film thickness than 100 nm. Generally, all nano-wrinkled surfaces are coated with dense films and film porosity is very low: Fig. 2a shows the microstructure of a 50 nm thin TiN film on PU with nanocrystalline structure ( Fig. 2b ). Cracks are missing in the strongly bend wrinkle, which is obviously formed by widening of the cone shaped crystallites during growth. Intercolumnar boundaries are the mechanically weak paths for film fracture (with possible nano-porosity), being aligned nearly perpendicular to the substrate surface (see arrows in Fig. 2a ). Fracture along these weak paths is shown in Fig. 2c for a 50 nm TiN film, which was fragmented during too much straining in thin foil preparation by Microtom cutting. In contrast, a-C:H films are fully amorphous without any visible crystallite structures. Nevertheless, wrinkle formation is based on similar stress relaxation mechanisms for a-C:H too. Figure 2 HR-TEM images of 50 nm thin TiN thin films on PU: (a) Cross-section image with arrows indicating the intercolumnar areas. (b) Small angle electron diffraction pattern. (c) Top-view image with indicated intercolumnar crack. Wrinkling effects are visible by comparing the roughness values in Table 1 too: While on Si all films are perfectly smooth, roughening by a factor of 9 is found for 16x16 µm 2 large investigated areas of 100 nm thick TiN and a-C:H films on PU, while only an increase by factor 3.5 in the roughness arises by wrinkling on PC substrates. Other effects of film growth, like formation of large grains or columns, can be excluded due to a generally higher trend for formation of such features on perfectly smooth silicon at the applied low temperatures [Lackner habil]. On the lower scale (AFM scan size 2x2 µm 2 ) the hierarchical formation of wrinkles is proved by much lower RMS roughness (e.g. found for 100 nm TiN on PU).\n Table 1 Root-mean-square (RMS) roughness of thin films deposited on PC, PU, and silicon substrates, measured by AFM and given in dependency of the scanned area size (statistics of 3 measurements). RMS roughness [nm] \n \n Substrate PC PU Si AFM scan size [µm 2 ] 16x16 2x2 16x16 2x2 2x2 Uncoated 6.2 ± 0.5 0.6 ± 0.1 8.2 ± 0.4 1.4 ± 0.1 0.1 ± 0.1 100.2 nm TiN 21.9 ± 1.3 18.2 ± 0.7 72.8 ± 7.9 47.8 ± 0.1 0.3 ± 0.1 20.3 nm a-C:H 12.7 ± 0.8 24.4 ± 0.8 0.5 ± 0.1 100.7 nm a-C:H 20.2 ± 1.1 73.6 ± 6.2 0.1 ± 0.1 3.2 Scratching and ploughing of hard films on polymers with low loads and sharp indenters Scratch testing with progressive loads was applied in order to obtain information of forces, being necessary for cohesive and adhesive film failure on PC and PU substrates under strong ploughing conditions. During scratching, the sharp ball-shaped tip of the indenter is ploughing the surface and forming a groove due to elastic and plastic deformation, if the load bearing capacity is exceeded. The applied measurement test procedures, which include a pre- and post-scan to subtract surface roughness, enable the calculation of the residual plastic deformation (ɛ pl ) after scratching as well as the total elastic and plastic deformation during scratching (ɛ el + ɛ pl ), as shown in Fig. 3 . Figure 3 Dependency of the residual plastic deformation (ɛ pl ) after scratching as well as the total elastic and plastic deformation during scratching (ɛ el + ɛ pl ) on distance and load during scratching with progressive loads. Critical loads (L c1 and L c2 , average values of 5 scratches) are roughly indicated (for their definitions see text). Film types: (a, d) 100 nm TiN, (b, e) 20 nm a-C:H, (c, f) 100 nm a-C:H. Substrate types: (a-c) PC, (d-f) PU. 3.2.1 Scratching of coated PC For PC, we found independently of the type of the applied coating the well-known material response in scratching for such film-substrate material systems ( Fig. 3a – c and Fig. 4 ): After exceeding the load-bearing capacity of the surface with (visco-)elastic, reversible contact between the diamond tip and the coated PC, plastic substrate deformation lead to permanent deformation of the compound ( Fig. 4a ): This is correlated with a break in the ɛ pl curves ( Fig. 3a – c ) and first fracture of the film at the track edge parallel to the scratch direction ( Fig. 4a ). This event is also referred to L c1 in this work due to the difficulty of determination of the onset of film fracture in these material systems by the very low stored elastic energy, being released at these fracture events. Physically, this assumption for L c1 definition is justified by the loss of load bearing capacity of the surface. The higher L c1 for 100 nm thin a-C:H stands for higher ultimate strength and toughness compared to TiN, most probably due to the amorphous a-C:H vs. the nano-crystalline, nano-columnar TiN structure (grain size < 10 nm) (see above). Additionally, the elastic modulus for a-C:H is much lower (see chapter 2.2): Based on scratch test modelling by [ 43 ] for coated steels, more flexible a-C:H films can decrease the tensile stress levels below the indenter by 70% compared to TiN. Angular cracks outside the scratch track edge in the strongly bent surface zone, which are typical for higher loads and less sharp indenters [ 25 , 44 – 48 ], are missing in the applied test protocol with mN loads. The onset of transverse semi-circular cracking in the track ( Fig. 4b ), generally being the next step in scratch failure, was microscopically found at lower loads for 100 nm TiN films (2.2 mN) than for 100 nm a-C:H films (2.7 mN), but isn't visible in the graphs in Fig. 3 . Adhesive fracture of films on the substrate surface occurs on the scratch track edges by delamination ( Fig. 4c ). Starting at critical loads L c2 ( Fig. 3 ), film peeling (delamination) is continuously spreading at rising loads. The initial slope of the depth-load curve (ɛ el + ɛ pl ) suddenly decreases after film delamination at L c2 = 2.3-2.5 mN loads for 100 nm TiN and 20 nm a-C:H, but ∼6 mN load for 100 nm a-C:H film. The position of peeling during scratching can be linked with the peak plastic deformation of the substrate at an angle of about 45° from the plane of symmetry in the plane of the film [ 49 ]. Figure 4 Typical failure modes of hard coatings on polymer substrates in scratch testing with progressive loads in the mN range and very sharp indenter (3 µm tip radius), shown for a 100 nm a-C:H film on PC. (a) Start of plastic deformation of the PC substrate and cohesive film fracture in the scratch track close to the edge (L c1 ). (b) Start of cohesive film fracture by transverse semi-circular cracks in the scratch track. (c) Start of adhesive fracture (chipping at edge of scratch track) between film and substrate (L c2 ). 3.2.2 Scratching of coated PU While the explained behavior during scratching of films on PC is not surprisingly, the higher elasticity of PU drastically changes the occurring material deformation mechanisms towards the biomimetic materials concept: Pressing the sharp diamond indenter on the coated PU, extensive elastic substrate deformation occurs independently of the deposited film, as visible in the curves for total deformation (ɛ el + ɛ pl ) in Fig. 3d – f . Consequently, both the ball-shaped tip as well as the indenter cone surface penetrates the PU, whereby the penetration depth is about 80 times higher than for PC. Under such conditions, cohesive film fracture starts under very low critical loads. Very low dissipated energies prevent experimental measurement of critical loads even by the NST device. On the contrary, the high (visco-)elasticity of PU prevents the visibility of a pronounced, permanently plastically deformed scratch track after scratching ( Fig. 5 ). Instead of a broad track with nearly parallel edges (as shown for coated PC above), a thin zigzag line evidences the performed scratching on PU at low normal loads ( Fig. 5a ). Higher loads result in a branched network of zigzag lines ( Fig. 5b ). Hence, after the scratch contact the reversible (visco-)elastic deformation of the PU substrate closes the crack edges rather completely. The plastic deformation depth of the scratch track ɛ pl ( Fig. 3d – f ), measured immediately after scratching, is <0.25 µm at 1 mN loads. Comparing this values to >12 µm total elastoplastic deformation (ɛ el + ɛ pl ) during scratching alludes to the very high PU volume, which is being (visco-)elastically squeezed to an uparched fold around the indenter. Nevertheless, the 100 nm a-C:H film in Fig. 5b shows no evidences for film delamination around the zigzag cracks even at higher loads, while such slight tendency to delamination was found for TiN films. This seems to have similar reasons as the lower L c1 value of this film on PC substrates, especially the lower toughness and the higher elastic modulus of TiN as discussed above. Figure 5 Images of scratches in 100 nm a-C:H coated PU at (a) ∼0.5 mN and (b) ∼2 mN. Scratching was performed bottom up with a diamond indenter with 1 µm tip radius. Image (a) was taken in the region of first visible fracture inside the scratch track and (b) at a position, where the crack network branched in the highly visco-elastically deformed region around the scratch without any film delamination. Overview inserts, taken by light microscopy, show clearly the crack path by the dark contrast, while charging of edges in SEM improved the visibility of the zigzag crack path following valleys of the wrinkled structure. Explaining the deformation mechanism for coated PU under these loading conditions in Fig. 5 requires the comparison of the micron-scale topographical relief, shown in Fig. 1c , d : The zigzag scratch fracture at low loads ( Fig. 5a ) follows the hierarchically largest wrinkle size on the surface, whereby the crack path follows the local mechanically weakest pathways in the wrinkle valleys. Likewise, the branched path of cracks at higher loads ( Fig. 5b ) runs along valleys too. Mechanically, the wrinkle valleys possess highest stress concentration in tensile straining of the surface, being present in all areas being bent and drawn during scratching. Even in less distinct hierarchical superstructures, found for 20 nm a-C:H, similar mechanisms crack paths are found. Similar fracture mode of thin hard films on PU substrate materials were found by the authors in in-situ SEM investigations of linear tensile straining [ 32 ]: Briefly explained, uniaxial tensile load breaks TiN films on PU to segments. Cracks are generally located in the wrinkle valleys. Tensile strain is concentrated in the PU substrate below these cracks, while the substrate surface below the segments is rather unstressed. Reason therefore is the cohesion strength. Rising the uniaxial tensile stresses consequently increases stress and strain in PU bulk. Strain hardening in the elongated PU below the crack spread surface strains to larger PU volume and, thus, also to the PU surface beneath the film segments. Partition of segments is then caused by cohesive film fracture after locally exceeding film cohesion strength. The formed zigzag crack path again runs along wrinkle valleys of the largest hierarchical superstructure. Reasonably, the mechanism of zigzag fracture on a nano-wrinkled, coated soft polymer is similar for both uniaxial straining and scratch testing. The mechanism is elastically reversible and cracks close almost entirely, if no film fragments are clamped between the crack edges. Finally, the initial surface topography is restored, similar to a self-healing process. Even cyclic loading is possible [ 32 ], being important for the cyclic tribological loading of such materials, as discussed below. Finally, this fracture mode is widely biomimetically comparable to skin deformation. 3.3 Tribological fatigue by sliding of smooth large counterparts in dependency of contact pressure Based on the knowledge of compound failure mechanisms under ploughing conditions in single-pass scratch testing, tribological sliding experiments with high cycle numbers were performed with smooth, large Al 2 O 3 ball counterparts (2.5 mm radius) and higher loads. Such conditions decrease the contact pressure down to elastically sustainable stresses for both PC and PU. Nevertheless, the occurring surface strains of >2% and >25%, respectively, are mechanically critical for the films on the polymer. Finally, maximum stresses are shifted deeper inside the polymer bulk under these contact conditions: Under assumption of the Hertzian theory, they are located in about 50% of the indenter radius below the indenter. Friction curves, given in Fig. 6 for coated PC and 7 for coated PU, reflect the reduced tendency to adhesive film failure and lower wear, enabling high cycle numbers at low friction coefficients in linear sliding tests, especially for a-C:H films. Figure 6 Friction coefficients in dependency of the contact pressure and the contact cycles in linear sliding of an Al 2 O 3 ball with 2.5 mm radius for (a) 100 nm thick TiN, (b) 100 nm a-C:H and (c) 20 nm a-C:H films on PC substrates. While scratching conditions with deep penetration of the indenter lead to general high friction coefficients (0.2-0.45 at 2 mN to 0.45-0.55 at 10 mN with the higher values for TiN), sliding conditions lower initial friction at the first pass (0.2-0.4 for TiN and 0.1-0.2 for a-C:H) for both coated PC and PU. Although the friction coefficients are on similar level, the friction and wear mechanisms are very dependent on the substrate material and implicated by the substrate elasticity, illustrating biomimetic material design possibilities too. 3.3.1 Sliding on coated PC For PC substrate with lower elasticity, we found tribological effects similar to sliding on coated rigid substrates: Briefly explained, after the run-in period, either immediately increasing friction for TiN films ( Fig. 6a ) or decreasing friction for a-C:H films ( Fig. 6b , c ) is evident. The slight decrease of friction and standard deviation of friction for a-C:H films is due to surface smoothing (ironing) by the sliding counterpart. The lower the load (contact pressure), the longer the period of continuous atom-scale material transfer from a-C:H film roughness tips to valleys is. The obtained friction coefficients of 0.1 to 0.2 are similar to that on rigid and smooth Si wafers. After >1000 contact cycles friction increases for a-C:H, which is based on similar mechanisms, being decisive for rising friction on TiN at much lower cycle numbers: Films are partly delaminating, the polymer surface is bared and rough wear track surfaces are formed. Such roughness is indicator for material failure below the interface in the PC. The tribological fatigue is load dependent, occurring at 20 mN load (16 MPa contact pressure, Fig. 7d ) after about 100 contacts, but for 50 mN (21.5 MPa contact pressure) immediately after the first contact cycle. After film delamination – occurring most probably at or below the film-PC interface – friction coefficients are similar to the contact of Al 2 O 3 to uncoated PC (0.3 – 0.4). As mentioned, friction for a-C:H films rise slowly, being indicator for mild fatigue: The wear track after 2000 contact cycles ( Fig. 7e , f ) shows some cohesive film cracks due to the cyclic (visco-)elastic PC substrate deformation, but any large adhesive fracture around the cracks is missing both for 20 and 100 nm film thickness. More pronounced fracture of the 100 nm film indicates its lower flexibility too. The initially mild wear is based on a continuous layer-by-layer removal, supported by continuously rising scratching (ploughing) by loosened particles. This is indicated by parallel sliding lines, which finally bare the PC substrate in the whole contact region. Main impact on the different behavior of TiN and a-C:H films emanates from both the material structure (nanocrystalline vs. amorphous growth) and the adhesion forces to the Al 2 O 3 counterpart: If frictional forces are kept low (e.g. at for a-C:H and the lower normal load conditions for TiN), the shear load in the film, at the interface and below the polymer surface is low. Lower von-Mises stresses are accompanied by lower front and transverse pile-up regions during sliding, as simulated by Kral and Konvopoulos [ 50 ]. Finally, this delays fatigue mechanisms and provide high-cycle low-friction sliding. Figure 7 Light microscopy images of the wear track after 2000 sliding cycles of an Al 2 O 3 ball with 5 mm diameter at 20 mN load (4 MPa contact pressure for PU and 16 MPa for PC) on coated (a-c) PU and (d-f) PC substrates. Film types: (a, d) 100 nm thin TiN, (b, e) 20 nm a-C:H, and (c, f) 100 nm a-C:H. 3.3.2 Sliding on coated PU The tribological behavior of coated PU substrates is significantly different ( Fig. 8 ): Friction coefficients are generally low (< 0.15) for both TiN and a-C:H films over the whole cycle number, but significantly influenced by the film thickness and the applied loads (contact pressure). Higher friction emerges for the 20 nm a-C:H thin film, although it is smoother than the 100 nm film and has a more homogenous nano-wrinkled topography with only slight hierarchical wrinkle superstructure. Apparently, this results from higher real contact area by less load-bearing capacity and better adaptation to the counterpart curvature, indicated by a denser network of smaller cracks in Fig. 7b (20 nm a-C:H) compared to Fig. 7c (100 nm a-C:H). As described above, the deformation of the substrate is concentrated below these zigzag cracks, which run along wrinkle “valleys” too. TiN films are segmented to smaller fragments and have a higher density of cracks compared to a-C:H. Nevertheless, the friction coefficient is rather similar. Missing delamination by shear cracking of the substrate, as found above for PC, enables for TiN coated PU substrates similar low friction behavior than found for a-C:H. Figure 8 Friction coefficients in dependency of the contact pressure and the contact cycles in linear sliding of an Al 2 O 3 ball with 2.5 mm radius for (a) 100 nm thick TiN, (b) 100 nm a-C:H and (c) 20 nm a-C:H films on PU substrates. The comparison of friction coefficients from coated and bare (e.g. after 5000 contact cycles on TiN) or uncoated PU surfaces, shows the huge impact of even thin films and the basis for our bioinspired material design: Uncoated PU shows under similar test conditions in dry sliding generally high friction coefficients > 0.6: They are mainly due to contributions of both adhesion on molecular level [ 51 , 52 ] and internal damping and energy loss in the viscoelastic body of the elastomer (hysteretic friction) [ 51 , 53 ], while viscous and cohesion (tearing) components are assumed to be very small [ 51 , 54 ]. Thin films on the PU surface lower both the adhesion component by the different material combination in the contact and the hysteretic friction by slightly improved load bearing capacity, which is proved by higher friction for thinner a-C:H films. 3.4 Aspects of bioinspired material development for low-friction surfaces on highly elastic materials It's obvious, that under the applied normal loads both fracture of the films to small segments (“pads”) on especially ultra-soft PU as well as high film adhesion on the substrate surface are mandatory for the described tribological behavior. The contact between the counterpart and the surface – both under ploughing and sliding conditions described above – occurs on these pads, while any visco-elastic deformation is found to be concentrated in the PU surface beneath these cracks in between the pads. To minimize shear loading below the pads, low friction on their surface is mandatory. Low friction reduces front and transverse pile-up regions around the moving counterpart too and, hence, reduce tribological strains. Amorphous a-C:H films are good candidates for such materials, because their cohesive film strength is high and their elastic modulus low too. The function of the pads, on which the tribological contact occurs, is similar to that of the thick stratum corneum regions: For low-load conditions without any tissue trauma, sliding on human skin generally occurs on these hard stratum corneum regions, which are separated from one-another by wrinkles. Load-dependent adaptation to the counterpart curvature occurs by elastic bending, which is concentrated for low loads and small topographical features of the counterpart in the smallest wrinkle structure around groups of corneocytes, for higher loads and curvatures in the secondary and primary lines [ 21 ]. Hierarchically formed wrinkles on coated PU, separating these pads with cohesive films, have similar function: They provide elasticity of the surface, whereby the hierarchical wrinkling structure contributes to adaptation to the counterpart surface too: As described in [ 33 ], high elastic deformability (>7%) is provided by smoothing out the smallest wrinkle structure in very thin films (like the 20 nm a-C:H film). However, this influence is hard to be experimentally accessed for tribological contacts. Higher required elasticity of the PU surface is provided by the shown zigzag fracture along hierarchical superstructures, which form in thicker films. For stiffer, harder PC, the phenomenon is much less pronounced, if observing the fracture in the wear track shown in Fig. 7f (and e): Fracture of the highly adhesive films and formation of segmented wear tracks provide the required cyclic viscoelastic deformability during sliding. Wear on both coated PU and PC occurs mainly in layer-by-layer mode and without delamination of larger particles. This confirms for the applied test conditions high fatigue resistance of the segmented films in the wear track. Finally, we found similar effects for increasing surface deformability during tribological contact for carbon-fibre reinforced epoxy composites too."
} | 10,428 |
28879232 | PMC5580877 | pmc | 1,602 | {
"abstract": "Spectral conversion of light enhances algal photosynthesis and enables technologies for sustainable energy and food production.",
"introduction": "INTRODUCTION Global climate change and impending resource scarcity increasingly make the transition to a bio-based economy a matter of urgency ( 1 ). Phytoplankton, which is composed of photosynthetic cyanobacteria and microalgae, is essential for global carbon recycling and also for sustaining the marine food chain ( 2 ). Diatoms, which differ from green algae and plants with respect to accessory pigments and light absorption properties, constitute a major group of microalgae and account for 40% of total organic carbon produced annually in marine ecosystems ( 3 ). Diatoms are also considered as one of the promising resources for the sustainable production of foods and feeds, bioactive pharmaceuticals, and biofuels ( 4 – 7 ). Recently, more genetic and genome engineering tools have been developed for the production of various commodities from diatoms, including bioactive natural products, food supplements, specialty chemicals, and biofuels ( 8 – 13 ). Emerging genetic engineering tools make diatoms good candidates as production platforms for biotechnology ( 8 , 11 , 13 ). Intensive cultivation is needed to produce algae-derived bioactive compounds and fine chemicals in both outdoor and indoor culture systems ( 14 ). For large-scale production using diatoms, energy conversion efficiency is a determining factor for economic feasibility and potential of enclosed photobioreactors (PBRs) also plays a vital role of open ponds ( 15 , 16 ). Economic viability is based mostly on algal biomass production, in which photosynthetic efficiency is essential for enhancing the productivity of cultures ( 17 , 18 ). Currently, the photosynthetic capabilities of algal systems are still relatively low ( 19 ), a factor that increases the overall energy costs and reduces the efficiency of cultivation and harvesting processes with a high environmental burden ( 16 ). For instance, a recent comprehensive analysis indicated that more fossil energy was consumed than bioenergy produced in most scenarios using a theoretical open raceway pond facility for outdoor cultivation of microalgae ( 20 ). Among many factors that affect algal growth rates, photolimitation from unfavorable light-dark cycles caused by insufficient mixing and photoinhibition due to oversaturation of light on the surface, both of which exist simultaneously in dense cultures of microalgae, act to limit photosynthetic efficiency and reduce overall biomass productivity ( 21 ). In diatoms, a key mechanism for the abatement of photoinduced stress is nonphotochemical quenching (NPQ), in which energy-dependent quenching (qE) is the most important part of NPQ in diatoms, because there is no state-transition quenching (qT) and photoinhibitory quenching (qI) is little ( 22 ). NPQ takes place in the light-harvesting complex (LHC) antennae of photosystem II (PSII), where the excess energy of absorbed light is dissipated as heat ( 22 ). Therefore, much effort is needed to understand and optimize the light-harvesting system and process to make the prospect of developing diatom cell factory a feasible one. Under natural light conditions, high-energy blue light usually gets wasted and dissipated as heat if excess light energy is provided on the surface of high-density cultures. However, wasting of light energy may be reduced if the light spectrum can be recompositioned by efficiently converting part of the blue portion of the spectrum to green, which can be harvested by accessory pigments. In diatoms, the carotenoid fucoxanthin absorbs light in the blue-green to yellow-green region of visible spectrum in vivo and transfers the energy to the photosynthetic reaction center ( 23 ). This light recompositioning in PBRs may also mitigate photoinhibition through the improvement of light distribution internally because the converted green light may penetrate deeper into dense diatom cultures due to a lower absorbance of the green spectral region in comparison with blue light ( 23 ). Here, we present an approach to improve the photosynthetic efficiency in diatom cells through intracellular spectral recompositioning (ISR) of incoming light and demonstrate that quantum yields of photosynthesis, as well as biomass production, increased substantially by integrating fluorescent protein components into the cells and enhancing the light absorption and redistribution in the PBRs.",
"discussion": "DISCUSSION Improving photosynthesis is of considerable interest for biotechnological applications, aquaculture, and agriculture ( 34 ). However, for the production of biofuels, biomass, and bioactive compounds from microalgae, one of the major limitations is low photosynthetic efficiency at full sunlight or high light intensities for dense cultures ( 21 ). Researchers in the field have proposed some solutions to address the issue of low photosynthetic efficiency by applying flashing lights, redesigning vertical PBRs, and introducing rapid mixing in the cultivation systems for potential production at industrial scales ( 35 ). Here, we implemented a novel strategy, that is, the ISR, which was able to significantly increase photosynthesis efficiencies in a diatom species. We also demonstrated how ISR could boost light energy utilization in photosynthesis in diatoms and revealed the potential mechanism underlying the enhancement process so that the low-efficiency problem in photosynthetic processes may be addressed at a systems level. Photosynthetic efficiencies in microalgae cultures are much lower than their theoretical maxima due to imbalances between the fast rate of light capture and the much slower rate of subsequent photosynthetic electron transfer and carbon fixation ( 36 ). A significant amount of the excess light energy is dissipated as heat or chlorophyll fluorescence that cannot be used by algal cells for photosynthesis ( 36 ). In a dense culture of algal suspensions, the algal cells at the surface, or the side exposed to the light sources, receive more light energy than they can use for the downstream carbon dioxide reduction; the excess energy is lost through heat dissipation and chlorophyll fluorescence ( 21 ). The algae below the surface or on the side opposite to the light sources in a PBR cannot obtain sufficient light energy due to mutual shading in algal cells ( 21 ). Approaches have been proposed to partially address these concurrent photoinhibition and photolimitation issues in dense cultures, such as genetic engineering techniques, to reduce the size of the light-harvesting antennae to improve light penetration and algal growth in laboratory-scale PBRs ( 18 ). With the development of ISR of light approach, we demonstrated that biomass production of microalgae could be improved substantially not only in flat-panel PBRs under the blue-red light regime but also, as evidenced by our pond simulator experiments, in open pond cultivations under high-intensity white light. In addition to the enhancement of photosynthesis with flashing lights using advanced LEDs ( 17 , 37 , 38 ), design of PBRs through a spectral shifting of incoming lights has also improved algae growth and productivity ( 27 ). A previous study selected and used fluorescent dyes to modify the spectrum of the available light source for growth enhancement via a double tubular bioreactor system with a dye outside and algae inside ( 27 ). A more recent study showed a novel design concept by using a photoluminescent material as a backlight converter in bioreactors, and this arrangement improved the energy-harvesting efficiency in green algae ( 39 ). The photoluminescent spectral conversion inspired by solar photovoltaic systems has been widely applied in PBR redesign ( 15 , 40 – 42 ). Theoretically, direct manipulation of algae using fluorescent components may be the most efficient way for light capture and harvesting in PBRs ( 43 ) because it can also generate an internal light source in addition to spectral optimization and the growth performance may be enhanced by a factor of 2 ( 27 ). For green microalgae, engineering of PSII reaction centers in the model species Chlamydomonas reinhardtii has achieved optimal photosynthetic efficiencies under different solar intensities ( 12 ). In contrast, the ISR strategy developed in this study optimizes the incoming light spectrum internally to enhance energy efficiency by integrating fluorescent protein components genetically. The ISR approach can use existing infrastructure, such as existing PBRs and open pond systems, so that it offers more flexibility for applications and requires no PBR modification and, hence, no increase in CAPEX for its implementation for scaling-up in comparison with PBR reflector strategy. Among the four LHCX genes, LHCX1 gene was abundant and found maximally expressed under nonstressful light conditions ( 44 ), which suggested the LHCX1 protein as a likely NPQ effector that modulates the excess energy dissipation and enables efficient photoprotection in diatoms in response to different environments ( 44 ), whereas the LHCSR3 protein (LHC stress-related protein 3) has been well studied and demonstrated as an essential NPQ effector protein of the high-light response in C. reinhardtii ( 45 ). It has also been reported that high-light exposure induced overexpression of some LHC proteins, which promote NPQ for photoprotection in diatoms ( 22 ). For example, LHCX2 , the nucleus-encoded gene for one of the antenna proteins, responds to light stress and is up-regulated under high-light conditions, promoting higher qE for photoprotection ( 22 ). The LHCX genes have been studied extensively for their potential roles in photoprotection in short-term response to high-light exposure or light quality shift ( 22 , 44 ). However, our study showed a modest increase only at the mRNA level for LHCX1 , LHCX3 , and LHCX4 genes and their uncovered quasi–steady-state expression profile under light stress conditions. Transcriptomic analysis together with LHCX protein level analysis showed different changes in transcript and protein levels and suggest that LHCX proteins may be posttranslationally modified ( 22 , 32 , 33 ), which is also consistent with the rapid induction that we observe in NPQ level and shift in the quantum yield ( Figs. 3 and 4 ). Together, these results reflect the complex regulation of photoprotection in diatoms due to multiple-layer players ( 33 ). In principle, the ISR strategy may be used chemically through the incorporation of fluorophores or biogenically through the expression of fluorescent proteins in diatoms. Direct incorporation of fluorescent dyes chemically into the diatom culture appears simple, but the dyes and their decomposed derivatives may have adverse effects on diatoms in the long run. In addition, the costs of introducing chemical fluorophores may be prohibitive in long-term, large-scale cultivation due to potential instability of fluorescent dyes in diatom cultures and scale of the usage. Furthermore, if the biomass is to be used for the production of nutraceutical or bioactive compounds for human and animal consumptions, inclusion of chemical fluorophores is not likely going to be an acceptable practice. Biogenic employment of ISR addresses these issues about fluorescent dyes by genetically engineering diatom cells with fluorescent proteins as controllable components due to GFPs having been widely used as labeling and imaging tools in algae and developed with relatively high stability ( 11 ). For ISR development, the chloroplast-localized eGFP expression may be the preferred design strategy as a close association between eGFP and the fucoxanthin-containing LHCs (LHCXs) can facilitate more efficient light energy transfers. However, we obtained higher expression levels from the cytoplasmically targeted eGFP constructs, and strong eGFP expression and its associated highly stable fluorescence are prerequisites for successful light conversion and transfer in the eGFP transformants. Further, it has been reported that the native fluorescent proteins, such as GFP, produced in corals may also play a significant role in light response and modulation of internal light environments for algal symbionts ( 46 ) through light spectral recomponsitioning, speculating that the ISR strategy developed in this study is analogous to a natural one evolved in these marine organisms. Although the ISR approach developed in this study takes advantage of fucoxanthin that is present only in brown algae and diatoms, other algal groups, such as red algae (Rhodophyta), that use phycobiliproteins as accessory pigments also efficiently absorb and transfer green light energy to chlorophyll a ( 47 ). Furthermore, β-carotene, which is a common carotenoid species present in all major algal groups, such as green algae, red algae, and diatoms as well as in higher plants, can also absorb green light and transfer energy to chlorophyll, albeit at a lower energy transfer efficiency ( 48 , 49 ). In addition to GFPs, red fluorescent proteins (RFPs) that absorb green light and emit red fluorescence can be applied in different algal production systems using the ISR strategy developed here. RFPs with a high fluorescence quantum yield and appropriate emission spectrum can be used to maximize the light utilization in algae because chlorophyll a efficiently recaptures red light/fluorescence. We foresee the described approach to help in the development of superior diatoms and other algal strains for large-scale cultivation, in addition to shedding light on managing light stress in diatoms."
} | 3,423 |
29018197 | PMC5635113 | pmc | 1,603 | {
"abstract": "Bacterial adaptation is accelerated by the acquisition of novel traits through horizontal gene transfer, but the integration of these genes affects genome organization. We found that transferred genes are concentrated in only ~1% of the chromosomal regions (hotspots) in 80 bacterial species. This concentration increases with genome size and with the rate of transfer. Hotspots diversify by rapid gene turnover; their chromosomal distribution depends on local contexts (neighboring core genes), and content in mobile genetic elements. Hotspots concentrate most changes in gene repertoires, reduce the trade-off between genome diversification and organization, and should be treasure troves of strain-specific adaptive genes. Most mobile genetic elements and antibiotic resistance genes are in hotspots, but many hotspots lack recognizable mobile genetic elements and exhibit frequent homologous recombination at flanking core genes. Overrepresentation of hotspots with fewer mobile genetic elements in naturally transformable bacteria suggests that homologous recombination and horizontal gene transfer are tightly linked in genome evolution.",
"introduction": "Introduction The gene repertoires of bacterial species are often very diverse, which is central to bacterial adaption to changing environments, new ecological niches, and co-evolving eukaryotic hosts 1 . Novel genes arise in bacterial genomes mostly by horizontal gene transfer (HGT) 2 , a pervasive evolutionary process that spreads genes between, eventually very distant, bacterial lineages 3 . It is commonly thought that the majority of genes acquired by HGT are neutral or deleterious and thus rapidly lost 4 . Yet, HGT is also responsible for the acquisition of many adaptive traits, including antibiotic resistance in nosocomials 5 . Hence, genome diversification is shaped by the balancing processes of gene acquisition and loss 6 , moderated by positive selection on some genes, and purifying selection on many others 7 . Chromosomes are organized to favor the interactions of DNA with the cellular machinery 8 . For example, most bacterial genes are co-transcribed in operons, leading to strong and highly conserved genetic linkage between neighboring genes 9 . At a more global level, early-replicating regions are enriched in highly expressed genes in fast-growing bacteria to enjoy replication-associated gene dosage, creating a negative gradient of expression along the axis from the origin (ori) to the terminus (ter) of replication (ori->ter) 10 , 11 . These organizational traits can be disrupted by the integration of novel genetic information. At a local level, new genes rarely integrate within an operon and, instead, they tend to be incorporated at its edges, where they are less likely to affect gene expression 12 . At the genome level, the frequency of integration of prophages in the genome of Escherichia coli increases along the ori->ter axis 13 . The results of these studies suggest that the fitness effects of HGT in terms of chromosome organization depend on the specific site of integration. In prokaryotes, HGT takes place by three main mechanisms: natural transformation, conjugation, and transduction. Mobile genetic elements (MGEs) play a key role in HGT because they are responsible for the latter two processes, respectively by the activity of conjugative elements and phages 14 . Integrative conjugative elements (ICEs) and prophages are large genetic elements that may account for a significant fraction of the bacterial genome 15 , 16 , and bring to the chromosome many genes in a single event of integration. For example, some strains of E. coli have up to 18 prophages 17 , and Mesorhizobium loti encodes one ~500 kb ICE 18 . The integration of these large MGEs changes the chromosome size and may split adaptive genetic structures such as operons. This might contribute to explain why most integrative MGEs use site-specific recombinases (integrases) that target very specific sites in the chromosome 19 . Integrases and MGEs have co-evolved with the host genome to decrease the fitness cost of their integration 13 . MGEs carrying similar integrases tend to integrate at the same sites in the chromosome, leading to regions with unexpectedly high frequency of MGEs at homologous regions. This concentration of MGEs in few sites has been frequently described 20 , 21 , especially in relation to the presence of neighboring tRNA and tmRNA genes 22 . Yet, a previous work described the existence of regions with high rates of diversification in E. coli (hotspots), some of which lacked recognizable integrases 23 . In particular, the genes flanking two hotspots were associated with high rates of homologous recombination ( rfb and leuX ). In Streptococcus pneumoniae , the chromosomal genes flanking MGEs also showed higher rates of homologous recombination 24 , 25 . In this species, it was suggested that integration of MGEs close to core genes under selection for diversification could be adaptive by facilitating the transfer and subsequent recombination of the latter 26 . Here, we define and identify hotspots in a large and diverse panel of bacterial species and show how they reflect the mechanisms driving genome diversification by HGT.",
"discussion": "Discussion Our study showed high concentration of HTgenes in a small number of locations in the chromosomes of many bacterial species. These hotspots include most MGE-related genes, fitting previous observations that the latter co-evolved with the host to use integrases targeting specific locations in the chromosome that minimize the fitness cost of chromosomal integration. For example, many temperate phages integrate tRNA genes without disrupting their function 32 . The concentration of most self-mobilizable MGEs at few loci might be thought sufficient to justify the existence of hotspots, but we found that few hotspots had identifiable prophages or conjugative elements and that most lacked integrases. These puzzling results could be caused by failure to identify MGEs, but our methods were shown to be highly accurate at identifying conjugative elements and prophages 13 , 33 , or by the presence of many radically novel integrase-lacking MGEs in these model microbial species, which would be very surprising. Hotspots also contain degenerate MGEs that we have failed to identify. Yet, inactivated elements are not expected to drive the observed rapid genetic turnover of these regions. Our results suggest that an MGE-independent mechanism, double homologous recombination at the flanking core genes, contributes to hotspot diversification. The mechanism only requires housekeeping recombination functions and exogenous DNA with homology to the flanking core genes. This last condition is easy to fulfill, because these genes are present in all genomes of the species (and usually in closely related species). In agreement with our hypothesis, we showed that naturally transformable species had more hotspots, and fewer MAPs in hotspots, than the others. There are other mechanisms of transfer that can bring homologous sequences without MAPs in non-transformable bacteria, including generalized transduction, gene transfer agents, or DNA-carrying vesicles 34 . Their role in hotspot diversification remains to be explored. Many HTgenes are not adaptive (or even deleterious) and are rapidly lost by genetic drift (or purifying selection) 6 , 7 , 35 . Nevertheless, regions of high concentration of HTgenes must also include adaptive genes, as shown here for ARGs. In these circumstances, the high genetic turnover at hotspots might seem paradoxical, because it may lead to their loss. Actually, even adaptive genes can be lost with little fitness cost under certain circumstances. Genes under diversifying selection, such as defense systems, may be adaptive for short periods of time and subsequently lost (or replaced by analogous genes) 36 . Some costly genes may be adaptive in only very specific conditions, such as ARGs 37 , and become deleterious for the cell fitness upon environmental change. Finally, some genes under frequency-dependent selection, such as toxins 38 , may stop being adaptive when their frequency changes in the population. Genetic drift, purifying, diversifying, and frequency-dependent selection can thus contribute to the rapid turnover of HTgenes. As a consequence of their high genetic turnover, hotspots are expected to be enriched in genes of specific adaptive value. Hotspots may affect bacterial fitness not only by the genes they contain, but also by the way they drive genome diversification. According to the chromosome-curing model 39 , hotspots may facilitate the elimination of elements with deleterious fitness effects, such as certain MGEs, by double recombination at the flanking core genes. This fits our observation that core genes flanking hotspots endure higher rates of homologous recombination. As a response to chromosome curing, natural selection is expected to favor MGEs that inactivate genes encoding recombination and repair proteins 39 . Interestingly, we also found that hotspots tend to be flanked by recombination and repair core genes. Although these genes seem intact, at least they respect the constraints that we imposed for their classification as core genes, their expression may be affected by HTgenes in the neighboring hotspot. For example, excision of a MGE in Vibrio spendidus 12B01 from a mutS gene downregulates the expression of the latter leading to a hypermutator phenotype 40 . Several selective effects can contribute to explain the very different number of hotspots per species, which were strongly correlated with the number of HTgenes and weakly with genome size (itself also correlated with the rate of HGT 27 ). The first association may explain why species with little genetic diversity, such as B. anthracis and mycobacteria, have few hotspots in spite of their large genome size. It is also possible that our statistical tests lack power when species have few HTgenes. Some ecological determinants also affect the number of HTgenes, and their concentration in the genome. For example, sexually isolated species with few MGEs, such as obligatory endosymbionts, are expected to have few hotspots. Many of these species may also inefficiently select for hotspots because they have low effective population sizes. Conversely, the highest number of hotspots was found in facultative pathogens with very diverse gene repertoires, including E. coli , Pseudomonas spp ., and Bacillus cereus . A rigorous statistical assessment of the ecological traits affecting the organization of HTgenes will require the analysis of a larger panel of species representative of the different prokaryotic lifestyles. Overall, our results suggest that hotspots are the result of the interplay of several recombination mechanisms and natural selection, presumably because they minimize disruption of genome organization by circumscribing gene flux to a small number of permissive chromosomal locations. For example, the increase in prophage-containing hotspots along the ori-> ter axis suggests co-evolution between these elements and the host to remove prophages from early replicating regions that are also rich in highly expressed genes in fast growing bacteria 13 . Interestingly, the spatial distribution of the remaining hotspots does not show similar patterns, which can be due to the lower fitness costs associated with their excision. Further work is needed to understand if there are other organizational traits that constrain the distribution of hotspots in the chromosome, and in particular in those devoid of recognizable MGEs. Knowing these traits might facilitate large-scale genetic engineering and should lead to a better understanding of the evolutionary interactions between horizontal gene transfer and genome organization. Finally, our study focused on the dynamics of hotspots and how they contribute to genome diversification, but left unanswered the questions related to their origin and fate. Previous studies identified common prophage hotspots between E. coli and Salmonella enterica 13 . Hence, we will have to study taxonomical units broader than the species level to unravel their origin. As for their fate, long-term adaptive HTgenes may become fixed in the population, explaining the patterns of nestedness of certain hotspots, and leading eventually to the split of the hotspot into two new (eventually hot) spots."
} | 3,120 |
26039595 | PMC4454141 | pmc | 1,604 | {
"abstract": "Recent experimental and observational data have revealed that the internal structures of collective animal groups are not fixed in time. Rather, individuals can produce noise continuously within their group. These individuals’ movements on the inside of the group, which appear to collapse the global order and information transfer, can enable interactions with various neighbors. In this study, we show that noise generated inherently in a school of ayus ( Plecoglossus altivelis ) is characterized by various power-law behaviors. First, we show that individual fish move faster than Brownian walkers with respect to the center of the mass of the school as a super-diffusive behavior, as seen in starling flocks. Second, we assess neighbor shuffling by measuring the duration of pair-wise contact and find that this distribution obeys the power law. Finally, we show that an individual’s movement in the center of a mass reference frame displays a Lévy walk pattern. Our findings suggest that inherent noise (i.e., movements and changes in the relations between neighbors in a directed group) is dynamically self-organized in both time and space. In particular, Lévy walk in schools can be regarded as a well-balanced movement to facilitate dynamic collective motion and information transfer throughout the group.",
"discussion": "Discussion We conducted three investigations on the inherent noise in schools of ayus that show polarized and milling patterns, obtaining individual temporal coordinates. First, we calculated the mean-square displacement in the center of the mass reference frame, as seen in starling flocks, and observed that there were super-diffusive behaviors in polarized schools with exponents of α > 1. This result indicates that fish in schools diffuse faster than Brownian motion. Although model simulations 30 have predicted two-dimensional super-diffusion with an exponent of α = 4/3, diffusion in real schools occurs faster than predicted, except for schools with 10 individuals. Moreover, we observed the trend that the larger the school size is, the higher the diffusion exponent will be. This relation may be caused by an effect of the area in which the fish can travel, i.e., on the domain covered by a school. If a fish moves beyond the domain, it would be separated from its school. We might, therefore, consider that the smaller the school size is, the more constraints there will be on an individual’s diffusion and that the exponent would reach a certain value in larger schools. Note that schooling fish inevitably contact with the boundary of the tank. Although it seems that mean velocity does not change due to the school size ( Table 1 ), the larger the size is, the larger the boundary effect might become. Therefore, there is also a possibility that the diffusive exponent changes due to the boundary effect. The polarized and milling patterns are known as emergent collective ordered states in fish schools, which co-exist for the same individual behaviors 10 31 . One can discriminate these two self-organized patterns by using two order parameters: polarization parameters and rotation parameters. This condition raises the question as to what type of properties would be commonly observed in both polarized and milling states. For these two states, we calculated pair-wise contact duration, which allows us to quantify neighbor shuffling because it measures how long individuals interact with neighbors. We found that the distributions of contact durations in polarized schools showed a power-law behavior, as have been observed in various human communities. This result indicates the absence of a characteristic scale with respect to how long individuals interact with neighbors. Similarly, we observed the distribution with a power-law behavior in contact duration in the milling school. Therefore, this property of neighbor changing with respect to time is considered to be common both in polarized and milling patterns in fish schools. Note that the way we quantified neighbor shuffling here is different from the method employed by Cavagna and others for starling flocks. They defined neighbors as a number of individuals nearest to a focal individual, whereas we defined neighbors as individuals in the neighborhood of the focal individual within detection range r d . In other words, whereas Cavagna and others used topological neighborhoods in their studies, we used metric neighborhoods to estimate neighbor shuffling 32 . It seems that if individuals behave ideally, there is no inherent noise and hence no position changing in the collective group. The results discussed above, however, indicate that individuals exhibit super-diffusive behavior within the group, leaving neighbors with no characteristic time scale. Such inherent noise, which might be expected to be detrimental for collectivity, plays an important role in facilitating interactions with various neighbors and thereby robust collective motion and information transfer. Is there a balance between excessive movement that is detrimental to the maintenance of the group and movement that is too slight to contribute to collectivity? In considering this question, our results show that fish movement lengths in schools follow a truncated power-law distribution, i.e., a Lévy walk. In a study on foraging strategy, a Lévy walk with the power-law exponent μ ranging from 1 < μ ≤ 3 was considered to be important in a natural environment in which resources are unpredictably distributed 18 . A Lévy walk with μ = 2 indicates optimal searching behavior in such an environment. For the exponent μ ≈ 1, movement patterns are close to ballistic motion. This movement is useful to a foraging animal that is exploration foraging if resources are homogeneously distributed far from an animal’s location. For μ > 3, the walk is approximated as Brownian motion. This motion is applicable for exploitation foraging if resources are abundantly distributed near an animal’s location. A Lévy walk with the exponent μ ranging from 1 < μ ≤ 3, therefore, indicates a foraging pattern that balances exploitation and exploration foraging. We can paraphrase these explanations regarding individual movements within a group with an analogy. If the step lengths of individuals in the center of the reference frame follow the power-law distribution with the exponent μ ≈1, they might move with much longer step lengths that might be detrimental to collective motion and information transfer through the group. If μ > 3, individuals might stay in a local region within the group, and it would be difficult for individuals’ movements within the group to contribute to dynamic collective behavior. A Lévy walk with exponent μ ranging from 1 < μ ≤ 3 was observed in schooling ayus, which can be regarded as a well-balanced movement to facilitate dynamic collective motion and information transfer throughout the group. Moreover, we observed that the exponent μ was ranging around two (from 1.86 to 2.33). Although this is the same value found for optimal foraging under certain circumstances 26 , there is no food in a school. What can be the resource? We consider that each individual searches “communication” among other individuals; new communication is explored, and familiar communication among neighbors is exploited. Our discovery sheds light on the underlying causes of Lévy walk that is not only search for foods but also communication. Note again that our results suggest that (i) even though individuals show cohesive schooling behavior with high polarity, (ii) each individual movement relative to the center of the mass of the group displays Lévy walk pattern. It has been reported that anomalous foraging patterns including Lévy walk can emerge from collective foraging dynamics, such as leader-follower and/or fission-fusion dynamics 33 34 . These dynamics may partially explain the Lévy walk within the group especially with respect to (ii) as Lévy walk that emerges from inter-individuals interaction. However, more powerful model of collective behavior must be required to understand both (i) and (ii) at the same time."
} | 2,034 |
32210238 | PMC7093453 | pmc | 1,606 | {
"abstract": "Natural photosynthesis can be divided between the chlorophyll-containing plants, algae and cyanobacteria that make up the oxygenic phototrophs and a diversity of bacteriochlorophyll-containing bacteria that make up the anoxygenic phototrophs. Photosynthetic light harvesting and reaction centre proteins from both kingdoms have been exploited for solar energy conversion, solar fuel synthesis and sensing technologies, but the energy harvesting abilities of these devices are limited by each protein’s individual palette of pigments. In this work we demonstrate a range of genetically-encoded, self-assembling photosystems in which recombinant plant light harvesting complexes are covalently locked with reaction centres from a purple photosynthetic bacterium, producing macromolecular chimeras that display mechanisms of polychromatic solar energy harvesting and conversion. Our findings illustrate the power of a synthetic biology approach in which bottom-up construction of photosystems using naturally diverse but mechanistically complementary components can be achieved in a predictable fashion through the encoding of adaptable, plug-and-play covalent interfaces.",
"introduction": "Introduction Our everyday experience of photosynthesis is dominated by the blue/red-absorbing pigment chlorophyll, a magnesium tetrapyrrole that acts as both a harvester of solar energy and a carrier of electrons and holes. Variants of this versatile molecule, principally chlorophyll a and chlorophyll b , are found in the plants, algae and cyanobacteria that make up the oxygenic phototrophs. Less well-known are the anoxygenic phototrophs, bacteria that use electron donors other than water and have one or more variants of bacteriochlorophyll as their principal photosynthetic pigment. Although these bacteria are less obvious in our environment, oxygen-tolerant species are widespread in oceanic surface waters where they make a sizeable contribution to global solar energy conversion 1 . A few species, including the bacteriochlorophyll a -containing Rhodobacter ( Rba .) sphaeroides , have played major roles in our understanding of excitation energy transfer in light-harvesting “antenna” complexes (LHCs) 2 – 4 and charge separation in photochemical reaction centres (RCs) 5 , 6 . Improving the performance of photosynthesis and finding new ways to exploit natural solar energy conversion have become important research topics 7 , 8 , and there is growing interest in the use of photosynthetic proteins as environmentally benign components in biohybrid devices for solar energy conversion 9 – 14 . Photoexcitation of a RC in such a device triggers intra-protein charge separation, producing a potential difference between opposite “poles” of the protein that drives subsequent electron transfer to create a photocurrent and photovoltage. In addition to solar energy conversion per se, proposed applications of photoprotein devices have included biosensing, light/UV sensing, touch sensing and solar fuel synthesis 9 – 16 . Photosynthetic proteins are attractive as device components because they are environmentally sustainable and benign, they achieve solar energy conversion with a very high quantum efficiency (charges separated per photon absorbed) and they can be adapted to purpose through protein engineering. However, a limitation is their selective use of available solar energy 7 , 8 , a consequence of their particular palette of light-harvesting pigments (Fig. 1a ). This can be evidenced in devices through the recording of action spectra of external quantum efficiency (EQE—the number of charges transferred per incident photon), which exhibit peaks and troughs that correspond to the absorbance spectra of the particular light-harvesting pigments that are coupled to charge separation in the device 12 , 17 – 21 . Fig. 1 Component absorbance, emission and mechanism. a Thylakoid membranes from oxygenic phototrophs such as pea and chromatophore membranes from anoxygenic phototrophs such as Rba. sphaeroides have complementary absorbance spectra due to differences in the electronic structures of the macrocycle π electron systems of chlorophyll and bacteriochlorophyll (see also Supplementary Fig. 1 ). b The major plant light-harvesting complex LHCII harvests solar energy in regions where absorbance by Rba. sphaeroides RCs is weak, notably around 650 nm, and its emission spectrum overlaps the absorbance spectrum of the RC between 640 and 800 nm. c The red-enhanced emission spectrum of heterodimeric plant LHCI has a stronger overlap with the absorbance spectrum of the Rba. sphaeroides RC, particularly the coincident absorbance bands of the bacteriopheophytins (H A /H B ). d Architecture of the RC cofactors and the route of four-step charge separation which oxidises P870 and reduces Q B . The bacteriochlorophylls (orange carbons) and bacteriopheophytins (yellow carbons) give rise to the absorbance bands labelled in c . Further descriptions of pigment-protein structures and their sources are given in Supplementary Fig. 1 . One option for the expansion of a protein’s light-harvesting capacity is to attach to it chromophores such as synthetic dyes 22 – 24 or emissive nanoparticles 25 – 27 . Drawbacks of this approach are that synthetic dyes are often expensive and prone to photobleaching 26 , while fluorescent nanoparticles can be toxic and achieving well-controlled assembly of protein–nanoparticle conjugates is challenging 28 . More akin to the present study is a report of a fusion protein between a single Yellow Fluorescent Protein (YFP) and the purple bacterial RC, which has the effect of somewhat enhancing light harvesting in a region where RC absorbance is weak by adding a single chromophore 29 . A striking observation is the complementary nature of the absorbance spectra of chlorophyll and bacteriochlorophyll photosystems (Fig. 1a ). This is enabled by the somewhat different electronic structures of their principal pigments (Supplementary Fig. 1a ) and facilitates the occupancy of complementary ecological niches by oxygenic and anoxygenic phototrophs. Chlorophyll absorbs most strongly in the blue and red whereas the absorbance of bacteriochlorophyll is shifted to the near-ultraviolet and near-infrared. The absorbance spectra of plant and bacterial carotenoids between 400 and 600 nm are also somewhat complementary (Fig. 1a ). Thus, anoxygenic phototrophs harvest parts of the solar spectrum which oxygenic phototrophs do not absorb well, and vice versa. Following nature’s lead, here we present the use of genetic encoding to achieve in vitro self-assembly, from diverse components (Fig. 1b, c ), of photoprotein “chimeras” that display polychromatic solar energy harvesting and conversion. The components are the Rba. sphaeroides RC 5 , 6 and the LHCII 30 – 33 and heterodimeric LHCI 34 – 38 proteins from Arabidopsis ( A .) thaliana (Supplementary Fig. 1b–e ). Highly specific and programmable self-assembly is achieved through adaptation of these components with the constituents of a two-component protein interface domain (Supplementary Fig. 1f ) that covalently locks together two photosynthetic membrane proteins that have no natural propensity to associate in a specific and/or controllable manner. The resulting macromolecular, adaptable chimeric photosystems have defined compositions, and display solar energy conversion across the near-UV, visible and near-IR.",
"discussion": "Discussion The data establish that it is possible to genetically encode in vitro self-assembly of a hybrid chlorophyll/bacteriochlorophyll solar energy conversion system using a highly specific split-interface domain. To our knowledge such combinations of chlorin and bacteriochlorin pigments are not used for light harvesting in nature, although in green sulfur bacteria the multiple BChl a light harvesting and electron transfer cofactors of the RC are supplemented by four molecules of Chl a that are used electron acceptors during charge separation 49 . In a similar vein, in the related heliobacterial RC the multiple BChl g (an isomer of Chl a ) cofactors are supplemented by two molecules of 8 1 -hydroxychlorophyll a that also act as electron transfer acceptors 50 . Hence some organisms have evolved to supplement bacteriochlorin cofactors with chlorins to achieve charge separation, but not to expand solar energy harvesting in the way demonstrated here. The SpyCatcher/Tag system provided a versatile means of constructing self-assembling hybrid photosystems. LHCII could be modified with SpyTag at either its N- or C-terminus, and by also using heterodimeric LHCI proteins that were either singly or doubly SpyTag modified the oligomeric state of the chimeras could be varied between heterodimers (RC#LHCII and LHCII#RC), heterotrimers (LHCI#RC) and heterotetramers (RC#LHCI#RC). The SpyCatcher/Tag linking domain produced predictable and stable products due to its very high partner specificity and the autocatalytic formation of a locking covalent bond. This binding reaction, which under the present conditions was found to have a half-time of between 10 and 90 min, was irreversible, relatively insensitive to reaction conditions and was free from side products (i.e. a failed reaction did not lead to depletion of reactants). The assembly strategy used, using E. coli and Rba. sphaeroides as separate bacterial factories for the synthesis of protein components that could be assembled in vitro, avoided the need to re-engineer a host organism to be able to produce both chlorophyll and bacteriochlorophyll (and different types of carotenoid). This methodology therefore provides a route for the bottom-up redesign of a photosystem in vitro despite the challenges of working with large, multi-component integral membrane complexes. The mechanism of solar energy conversion operating in the chimeras, based on the well-understood photophysical properties of the component proteins, is summarised in Fig. 5 . Energy captured by the pigment systems of LHCII or LHCI will be passed to the RC in a downhill manner, exciting the primary electron donor bacteriochlorophylls (P870*) and initiating charge separation to form P870 + Q B − . Energy harvested by the chlorophyll b (or carotenoid—not shown) pigments of either LHC is passed to the lower energy chlorophyll a . Inter-protein energy transfer is likely to involve a sub-set of red-shifted chlorophyll a in either LHC, and entry of energy into the RC is likely to occur principally via the bacteriopheophytin cofactors (H A/B ) as their absorbance has the greatest spectral overlap with LHC emission (Fig. 1b, c ). Fig. 5 Solar energy conversion in chimeras. Energy flow within LHCII or LHCI is from higher energy chlorophyll b to lower energy chlorophyll a . LHCI also exhibits a red-shifted emissive state with mixed excitonic/charge-transfer (CT) character. Excited state energy entering the RC via the bacteriopheophytins (H A/B ) migrates to the P870 bacteriochlorophylls via the monomeric bacteriochlorophylls (B A/B ), initiating charge separation to form P870 + Q B − . Energy harvested by the carotenoid pigments of LHCII or LHCI (not shown) would transferred to the RC via their chlorophylls through fast internal relaxation 61 . As evident from comparing Fig. 1c with Fig. 1b , LHCI exhibits a red-enhanced fluorescence that produces an ~80% stronger spectral overlap with RC absorbance (factor J in Supplementary Table 1 ) compared to LHCII. Despite this, the efficiency of ET in the LHCI#RC chimera was not significantly higher than that in either the LHCII#RC or RC#LHCII chimera. This is likely due to the reconstituted LHCI heterodimers being in a partially quenched state 48 , 51 that reportedly reduces their quantum yield to only 29% of that of LHCII 36 , so counteracting the potential benefits of an enhanced spectral overlap. In agreement with this our estimates of quantum yield were 30% for LHCI-Td and 28% for Td-LHCI-Td (Supplementary Table 1 ). In future work it might be possible to partially overcome this through SpyTag modification of LHCI in a native organism, as the quantum yield of purified native LHCI has been reported to be ~64% that of LHCII, more than double that of recombinant LHCI 36 . Estimates of ET efficiency in RC#LHCI#RC chimeras in solution were consistently higher than those for the LHCI#RC chimera (parameters E P870 and E FL in Table 1 ), consistent with the presence of two ET acceptors in the former. Estimates of the ET efficiency to the second RC added to Lhca1 in RC#LHCI#RC, made using Eq. ( 5 ), yielded values that were either 50% or 69% of that for transfer to the first RC attached to Lhca4. This is consistent with the presence of a relatively low-energy red-form chlorophyll a dimer in the Lhca4 subunit (Supplementary Fig. 1d ) that is responsible for the red-enhancement of the LHCI emission spectrum 36 , 38 , 47 , 48 , and which may have produced more efficient ET to the RC attached to Lhca4 than that attached to Lhca1. To conclude, this work shows that genetically adapting two diverse photosynthetic membrane proteins with the components of an extramembrane interface domain enables in vitro self-assembly of a chimeric photosystem in which UV/near-IR solar energy conversion by a bacteriochlorophyll-based RC is augmented by visible light capture by chlorophyll-based LHCs. This approach inspired by a concept of synthetic biology, to adapt naturally incompatible biological modules to interface in a standardised way through genetic encoding, creates covalently stabilised macromolecular photosystems that are predictable and programmable. In addition to providing photosynthetic structures and energy transfer pathways to explore, these polychromatic photosystems constitute interesting materials for biohybrid devices that in recent years have expanded in application beyond photoelectrochemical solar energy conversion to fuel molecule synthesis, energy storage, biosensing, touch sensing and photodetection. Finally, the demonstrated flexibility with which RCs and LHCs could be interfaced opens the possibility of constructing more elaborate, self-assembling chimeric photosystems that employ multiple orthogonal linking modules 52 , 53 and a wider range of photosynthetic and redox proteins that, despite being separated by billions of years of evolution, can be adapted for future solar energy conversion through genetic programming."
} | 3,617 |
26710855 | PMC4758253 | pmc | 1,607 | {
"abstract": "Organisms respond to environmental variation partly through changes in gene expression, which underlie both homeostatic and acclimatory responses to environmental stress. In some cases, so many genes change in expression in response to different influences that understanding expression patterns for all these individual genes becomes difficult. To reduce this problem, we use a systems genetics approach to show that variation in the expression of thousands of genes of reef-building corals can be explained as variation in the expression of a small number of coexpressed “modules.” Modules were often enriched for specific cellular functions and varied predictably among individuals, experimental treatments, and physiological state. We describe two transcriptional modules for which expression levels immediately after heat stress predict bleaching a day later. One of these early “bleaching modules” is enriched for sequence-specific DNA-binding proteins, particularly E26 transformation-specific (ETS)-family transcription factors. The other module is enriched for extracellular matrix proteins. These classes of bleaching response genes are clear in the modular gene expression analysis we conduct but are much more difficult to discern in single gene analyses. Furthermore, the ETS-family module shows repeated differences in expression among coral colonies grown in the same common garden environment, suggesting a heritable genetic or epigenetic basis for these expression polymorphisms. This finding suggests that these corals harbor high levels of gene-network variation, which could facilitate rapid evolution in the face of environmental change.",
"introduction": "Introduction The persistence of populations in the face of environmental variation in space and time is in large part determined by the ability of individual organisms to match parts of their phenotypes to their environments ( Moran 1992 ; Kawecki and Ebert 2004 ; Marshall et al. 2010 ). When environments change, a variety of mechanisms play major roles in phenotype environment matches. Migration of species to more suitable habitat in response to environmental change has been observed in many changing ecosystems ( Chen et al. 2011 ). Local adaptation and phenotypic plasticity have also been observed to play a major role in the regional persistence of populations faced with environmental change ( Kozlowski and Pallardy 2002 ; Stillman 2003 ; Leimu and Fischer 2008 ; Hereford 2009 ; Sanford and Kelly 2011 ). Strong natural selection coupled with standing genetic variation may allow rapid adaptation in the face of environmental change ( Barrett and Schluter 2008 ; Chevin et al. 2010 ; Pespeni et al. 2013 ; Orr and Unckless 2014 ). Plastic responses to environmental change may allow populations to persist long enough to adapt to changing conditions, provided that enough standing variation in adaptive traits exists at the limits of acclimation ( Ghalambor et al. 2007 ). As widespread climate change increases in strength, maintenance of ecosystem function will in large part depend on the ability of ecologically important species to maintain a sufficiently close phenotypic match to their changing environment through some combination of these mechanisms ( Barrett and Schluter 2008 ; Chevin et al. 2010 ; Somero 2010 ; Orr and Unckless 2014 ). A substantial proportion of phenotypic variation both between and within species is generated by variation in gene expression ( Wray et al. 2003 ). Populations in which variation in gene expression can be related to environmental history and stress tolerance can provide a mechanistic physiological framework for exploring the potential adaptive and acclimatory responses of populations to environmental change. For example, both constitutive and plastic variation in gene expression across environmental gradients has been repeatedly linked to population-level responses to environmental stress in widespread plant species ( Swindell et al. 2007 ; Lasky et al. 2015 ). Yet, experimental studies of natural populations often identify transcriptional changes at thousands of genes as a function of season, diurnal timing, acclimation state, diet, acute environmental stress, or a host of other factors ( Oleksiak et al. 2002 ; Jaenisch and Bird 2003 ). It is often difficult to determine which of these gene expression differences are most important in generating differences in organismal phenotypes, or when differences in the expression of individual genes are pivotal to organismal physiology. For example, we recently showed that thousands of genes show altered expression in reef corals in response to experimental heat stress ( Seneca and Palumbi 2015 ) Determining the roles of these genes as coral reefs are faced with rapid environmental change is a huge challenge. A great deal of recent progress in analyzing complex gene expression variation has come from studies using a systems genetics approach, which identifies groups of coexpressed genes with correlated responses across samples with genetic and environmental differences. These expression modules are thought to represent physiological and developmental units and have been shown to correlate with differences in physiological or morphological phenotypes. Coexpressed sets of genes can be characterized by examining them for enrichment of particular gene classes, and by testing them for associations with genetic or environmental influences. Overall, systems genetics approaches greatly simplify subsequent gene expression analysis and better reflect the underlying cellular physiology captured in gene expression studies. By reducing variation in transcripts from thousands of genes to a small number of functionally distinct transcriptional modules made up of sets of coexpressed genes, these approaches simplify hypothesis testing and reduce the number of independent statistical tests applied to highly intercorrelated individual gene expression profiles ( Civelek and Lusis 2014 ). Here, we characterize variation in gene expression patterns in the context of heat tolerance by reef-building corals, which suffer a breakdown of their coral–dinoflagellate symbioses in response to small increases in temperature. Corals show high levels of gene expression variation between environments, and some populations of the same species are more stress tolerant than others ( Oliver and Palumbi 2011 ; Barshis et al. 2013 ). Previous work has described conspecific populations with different levels of heat tolerance; transplants between them show that about half of the between-population phenotypic difference is due to acclimation between microclimates and half is due to constitutive differences between populations ( Palumbi et al. 2014 ). Other work has shown that latitudinal differences in bleaching resilience are heritable in crossbreeding experiments and are related to differences in the expression of a large set of “tolerance-associated genes” ( Dixon et al. 2015 ). In this study, we take advantage of transplanted clones to characterize gene expression variation in natural populations of corals in two different common gardens experiencing different thermal regimes. We previously described an experiment in which 3 replicates of each of 20 colonies of the tabletop coral Acropora hyacinthus were reciprocally transplanted between adjacent back-reef lagoons with different temperature profiles ( Seneca and Palumbi 2015 ). After a year, we measured gene expression profiles for each colony acclimated to each transplant site under normal environmental conditions, and after a standard heat stress that mimics strong coral bleaching effects. Previous analyses found that thousands of genes showed altered expression 5 and 20 h after heat stress ( Seneca and Palumbi 2015 ). Here, we show that these thousands of genes cluster into just a few coexpressed transcriptional modules with distinct functional enrichments. These modules show coordinated responses to environmental perturbations but also show consistent differences in expression between individual coral colonies. One such module includes a set of tightly coexpressed sequence-specific DNA-binding proteins including several transcription factors that are correlated with both colony-level and acclimatory variation in bleaching susceptibility. In addition, the existence of high levels of standing variation in induction of “bleaching modules” from individual to individual suggests that bleaching-related gene networks may harbor enough standing genetic variation to be capable of rapid evolution in response to environmental change.",
"discussion": "Discussion Coral gene expression variation across thousands of genes can be grouped into differences in the expression of a relatively small number of modular coexpression clusters. The largest module includes genes seen regularly in coral bleaching studies, including proteins involved in apoptosis. Different stress-responsive modules include clusters of genes with more specific cellular functions, including extracellular matrix proteins and transcription factors. These two gene classes were especially common in Modules 10 and 12, respectively. Expression of genes in these modules is highly correlated with colony bleaching that occurred the day after we measured their expression. Thus, change in expression in these modules anticipates coral bleaching. How changes in these gene expression patterns serve to modulate bleaching is unknown. Transcription factors that dominate Module 12 and increase with bleaching might be responsible for myriad downstream gene expression shifts. Extracellular matrix genes that dominate Module 10 and decrease with bleaching might reduce the connection among coral gastrodermal cells in order to facilitate exocytosis of the symbiont. They might also alter cytoskeletal anchoring of the symbiont within the coral host cell to allow expulsion. Discovery of these modules provides better understanding of the cellular processes of bleaching, and suggests potential bioassays of bleaching likelihood that enhances previous studies of single gene expression patterns. For example, assaying the expression level of the Module 12 ETS-family transcription factors in wild corals experiencing heat anomalies could potentially give insight into the stress level of those corals, and allow wildlife managers to assess how close they are to inducing a bleaching response. Acclimation and Adaptation via Changes in Modular Gene Expression Patterns Our data suggest that both acclimatory and genetic variation in bleaching resilience in this species is mediated in part by differences in the induction of bleaching-related gene networks. For most modules, expression varies widely among colonies, suggesting a genetic or persistent epigenetic effect on overall expression. However, because we followed individual coral colonies after they were fragmented and transplanted to more and less thermally stressful microclimates, we could also compare gene expression in the same genotype across different environments. Several modules showed roughly equal contributions of colony differences and acclimation on their expression ( supplementary table S1 , Supplementary Material online). This is similar to the expression patterns reported by Palumbi et al. (2014) based on single gene analyses. In the present case, the analysis suggests that acclimation is in part accomplished not by independent changes in gene expression among many genes one at a time, but through regulation of coexpressed gene modules. Like many other modules, Module 12 showed both colony-level and acclimatory differences in eigengene expression. However, the other early bleaching module, Module 10, was an exception to this pattern. Module 10 did not show strong colony or acclimatory effects, but it did show a weak colony by treatment by environment effect. Although these estimated colony and environmental interactions account for very little of the variation in expression in this module (about 1%; supplementary table S1 , Supplementary Material online), nevertheless expression of Module 10 genes was negatively associated with bleaching. One possible explanation for this is that Module 10 responds to some environmental factors that vary strongly within pools and not much between them. Depth and current flow are possible candidates, but further work is needed to ascertain the nature of control of the extracellular matrix proteins and other components of Module 10. The Transcriptional Response of Corals to Heat Stress The genes that we identified as responding to heat stress in this experiment are broadly similar to the genes reported by other studies of coral responses to heat stress. Apoptosis genes have frequently been associated with the coral heat stress response ( Desalvo et al. 2008 ; Voolstra et al. 2009 ; Bellantuono et al. 2012 ). In addition, growth, cell division, and metabolism are also often implicated in transcriptomic studies of coral environmental stress responses ( Desalvo et al. 2008 ; Portune et al. 2010 ; Kenkel et al. 2013 ; Bay and Palumbi 2015 ). A growing number of studies have found that variation in the expression of oxidative stress and extracellular matrix genes may be particularly associated with bleaching resilience in coral hosts ( Barshis et al. 2013 ; Dixon et al. 2015 ; Seneca and Palumbi 2015 ). In this experiment, we previously identified genes involved in innate immunity, apoptosis, extracellular matrix formation, and cytoskeletal processes to be highly enriched among stress responsive genes ( Seneca and Palumbi 2015 ). Where the present systems genetic analysis differs from other single gene approaches is in the way it takes advantage of variation between many different coral colonies in their responses to a common stress, using this variation to group many genes into distinct transcriptional modules. We use this modular structure to discover distinct functional enrichments, environmental responses, and relationships to physiology (in this case, bleaching outcomes) for these distinct sets of genes. Using this approach, we find that the most abundant classes of stress responsive genes (e.g., apoptosis genes) are not the most strongly related to variation in bleaching outcomes. Instead, the expression of other, less abundant classes of genes (e.g., ETS-family transcription factors) is strongly related to differences in coral bleaching within a single population. Other experiments have identified these genes as responsive to heat stress in coral ( Desalvo et al. 2008 ; Polato et al. 2013 ), but where single gene analyses often overlook the importance of less abundant classes of genes, our systems genetic analysis shows these genes to occupy a place of potentially pivotal importance in coral bleaching gene networks. System Genetics Applied to Gene Expression in the Wild Systems genetics approaches have made great progress in identifying gene regulatory networks involved in generating complex phenotypes. They have been particularly useful in better understanding heritable disease, and have revealed underlying causes of physiological and developmental variation between strains of Drosophila and mammals ( Ayroles et al. 2009 ; Brawand et al. 2011 ). Here we demonstrate the application of these methods to questions about physiological and developmental variation in wild populations of ecologically and economically important nonmodel organisms ( Filteau et al. 2013 ). By identifying physiological and developmental units in the form of transcriptional modules first, and characterizing their functional enrichments and relationships to genetic, environmental, and physiological factors later, we can gain insights into the potential roles of genes with variable expression and can better understand the regulatory architecture of complex traits like coral bleaching resilience. The presence of a module made up of highly expressed transposon-related transcripts with highly variable expression between samples was an unexpected source of variation in this population. The activation of silenced transposons by environmental stress has been observed across diverse array of organisms, including corals ( Grandbastien 1998 ; Capy et al. 2000 ; de la Vega et al. 2007 ; Beauregard et al. 2008 ; Desalvo et al. 2008 ). This activation can disrupt cellular function, but the mutagenic effects of transposition can also generate novel genetic variation, including novel stress-induced gene expression patterns, potentially facilitating adaptation to stressful environments ( Beauregard et al. 2008 ). A deeper understanding of the causes and consequences of transposon activation in coral populations might clarify the relative importance of different effects of transposon activation in shaping population responses to environmental change."
} | 4,218 |
27150504 | PMC4906156 | pmc | 1,608 | {
"abstract": "Life arose in a world without oxygen and the first organisms were anaerobes. Here we investigate the gene repertoire of the prokaryote common ancestor, estimating which genes it contained and to which lineages of modern prokaryotes it was most similar in terms of gene content. Using a phylogenetic approach we found that among trees for all 8779 protein families shared between 134 archaea and 1847 bacterial genomes, only 1045 have sequences from at least two bacterial and two archaeal groups and retain the ancestral archaeal–bacterial split. Among those, the genes shared by anaerobes were identified as candidate genes for the prokaryote common ancestor, which lived in anaerobic environments. We find that these anaerobic prokaryote common ancestor genes are today most frequently distributed among methanogens and clostridia, strict anaerobes that live from low free energy changes near the thermodynamic limit of life. The anaerobic families encompass genes for bifunctional acetyl-CoA-synthase/CO-dehydrogenase, heterodisulfide reductase subunits C and A, ferredoxins, and several subunits of the Mrp-antiporter/hydrogenase family, in addition to numerous S-adenosyl methionine (SAM) dependent methyltransferases. The data indicate a major role for methyl groups in the metabolism of the prokaryote common ancestor. The data furthermore indicate that the prokaryote ancestor possessed a rotor stator ATP synthase, but lacked cytochromes and quinones as well as identifiable redox-dependent ion pumping complexes. The prokaryote ancestor did possess, however, an Mrp-type H + /Na + antiporter complex, capable of transducing geochemical pH gradients into biologically more stable Na + -gradients. The findings implicate a hydrothermal, autotrophic, and methyl-dependent origin of life. This article is part of a Special Issue entitled ‘EBEC 2016: 19th European Bioenergetics Conference, Riva del Garda, Italy, July 2–6, 2016’, edited by Prof. Paolo Bernardi.",
"conclusion": "4 Conclusions The overall picture of core physiology in Luca that we infer from genome sequences is almost indistinguishable from that in Fig. 1 c of Lane and Martin [59] that was obtained from comparative physiology, bioenergetics, and theory. That two completely independent approaches converge on the same set of proteins and functions in early bioenergetics is noteworthy. It indicates that a version of the hydrothermal vent theory focusing on the acetyl-CoA pathway, methyl synthesis, acetogens and methanogens [66] possesses an element of robustness in that it interfaces well with the physiology of anaerobic prokaryotes [15] , [98] , with thermodynamics [3] , [4] , with findings from geochemistry about serpentinization and hydrothermal vents [71] , [97] , and as we have shown here, with comparative genomics. Even ribosomal phylogenies tend to agree with the predictions of the model in that newer metagenomic indicate a greater antiquity for methanogen-related metabolism within the archaea than previously assumed [30] , and newer phylogenetic data put methanogenesis at the root of the archaeal domain [67] , [87] . In contrast to today's oxidized and strongly oxidizing environment, the highly reducing hydrothermal setting on a young, metal-rich, anaerobic Earth that we have in mind as Luca's residence [106] offers very favorable thermodynamic conditions for the synthesis of Luca's building blocks [3] , [5] , [71] , [97] . When we look at Luca as an anaerobe and as the common ancestor of prokaryotes, we obtain a picture of its genome that resembles clostridial acetogens and methanogens. With regard to the most primitive forms of microbial physiology, microbiologists reached the same conclusion 45 years ago [26] , namely that methanogens and acetogens probably represent the most ancient lineages [36] . We required 2000 genomes and powerful computers for our conclusions, while Decker et al. just thought about it. Evidently, just thinking about things can be a source of scientific progress. The following are the supplementary data related to this article. Supplementary Table A1 Oxygen requirements of the organisms present in the 1045 protein families. Supplementary Table A1 Supplementary Table A2 Functional annotation. Supplementary Table A2",
"introduction": "1 Introduction One of the more intriguing enterprises in comparative genomics is to infer the nature of the last universal common ancestor, also called Luca, on the basis of gene content [46] , [57] , [73] , [6] , [74] , [75] , [81] , [112] . The standard approach to the problem is to generate a reference tree – sometimes called a backbone tree or species tree – and then to plot the distribution of gene families, usually the COGs, or clusters of orthologous groups [110] onto the leaves of the tree and then to infer presence and absence patterns along the inner branches and nodes of the tree, right down to its root, the presence and absence patterns at the root giving an estimate of Luca's gene content [6] , [27] , [46] , [73] , [74] , [75] , [81] , [112] . The models that one assumes for gene gain and loss have a considerable impact on the inferred genome of Luca [23] , [24] , [46] , [73] , [100] as does the selected reference tree [31] and the genome collection of the study. In early investigations of Luca gene content, Luca was considered as the last common ancestor of bacteria, archaea and eukaryotes [115] . More recent findings have eukaryotic ribosomes branching within the archaea, rather than as their sister [22] , [87] , [108] , [114] , such that in the more modern “two domain” trees, Luca is the last common ancestor of prokaryotes. To stress that, one could introduce the term last prokaryotic common ancestor, or Lpca. But new terms for established concepts are seldom helpful, and Luca means different things to different people anyway. Here we stick to the term Luca, but we use it here to mean the last common ancestor of prokaryotes, which in our view of early evolution was not a free living cell, but rather was an entity that had the genetic code, that had proteins, that had ribosomes and an ATPase [106] , that could make DNA as a stable repository for retrievable information, but probably could not replicate DNA as chromosomes [51] , [53] , and that – we posit – probably was contained within naturally forming inorganic compartments as chemical confines of a geological structure like a hydrothermal vent, which supplied the reduced carbon and continuous chemical disequilibrium (energy supply) that Luca needed to get organized in the first place [65] , [66] , [59] , [107] . But irrespective of where and how it arose, newer phylogenetic data indicate that eukaryotes need to be excluded when it comes to estimating the gene content of Luca. Excluding eukaryotes has an immense effect on Luca gene content estimation. This is because current views and current data have it that eukaryotes arose from a symbiosis of two prokaryotes, the bacterial ancestor of mitochondria and its archaeal host [1] , [22] , [54] , [58] , [64] , [90] , and there are only about 2585 gene families that eukaryotes share widely with prokaryotes [55] . By including eukaryotes in Luca gene content estimation, one would be excluding all enzymes specific to anaerobic chemolithoautotrophy, all enzymes specific to anoxygenic photosynthesis, all enzymes specific to sulfate reduction, and all enzymes specific to all biochemical pathways that eukaryotes do not possess, which comprises the vast majority of genes distributed across prokaryotic genomes. Eukaryotes possess only a very, very small sample of prokaryotic energy metabolic diversity [120] and an even smaller sample of prokaryotic gene diversity in general [55] . It is thus important to estimate Luca gene content based upon all prokaryotic genes, not just the narrow sample of genes that eukaryotes inherited from prokaryotes at eukaryotic (and plastid) origin [55] . Removing the restriction that the inclusion of eukaryotes introduces into Luca gene content estimation is easy, one just excludes eukaryotic gene from the set to be considered for Luca inference. Far more problematic, however, is the issue of lateral gene transfer. This is because – even in studies that exclude eukaryotes from Luca inference – many studies score genes as present in Luca if the genes are present in several archaea and one (or more) bacterium, or present in several bacteria and one (or more) archaeon [9] . But such genes could easily be transdomain lateral gene transfers and not holdovers from Luca at all. In haloarchaea alone, there are more than 1000 well-documented cases of genes that were acquired from bacteria via transdomain lateral gene transfers [76] . In a broader sample of archaeal lineages, Nelson-Sathi et al. [77] identified more than 6000 cases of transdomain lateral gene transfers. In prokaryotes, LGT is not only frequent [10] , [45] , [50] , [61] but it also played an important role in prokaryote lineage diversification [77] . Transdomain LGT generates gene distribution patterns that complicate the inference of Luca's gene content. In an insightful paper, Kannan et al. [46] clearly outlined the problems that LGT introduces regarding Luca: If a gene family was invented relatively late in evolution, in a particular bacterial lineage, and then transferred across broad taxonomic boundaries (for example from bacteria to archaea or vice versa), then its phylogenetic distribution would erroneously mimic presence in Luca. If not recognized as LGTs, such genes lead to a vastly (and artefactually) inflated Luca genome content. If such interdomain LGT is widespread, reconstructing Luca's gene content becomes tedious. How to identify transdomain LGTs so as to remove their inflating effects upon Luca gene content estimation? We have a suggestion. We recently reported a clustering and phylogenetic analysis of over 6 million genes from 1891 prokaryotic genomes, focusing on genes shared by archaea and bacteria [77] . We found that interdomain LGTs from bacteria to archaea vastly outnumbered gene transfers in the other direction and that gene acquisitions from bacteria correspond to the origin of several major archaeal groups [77] . In that study, 4705 protein families showed extensive interdomain LGT, and another 4397 protein families were identified where archaea and bacteria are monophyletic in the corresponding phylogenetic tree. Gene presence in archaea and bacteria, in addition to monophyly of archaea and bacteria, is the minimal condition that should be fulfilled for genes that were present in Luca but not subject to interdomain LGT. Among the 4397 protein families reported in which archaea and bacteria are monophyletic, 3347 cases represent fairly obvious interdomain LGTs in that the genes are widespread in bacteria but present in only one archaeal lineage [77] . The remaining 1045 genes show archaea and bacteria to be monophyletic but show no obvious signs of interdomain LGT. This set of genes is, in principle, a candidate list for genes present in Luca but not transferred between domains since the divergence of bacteria and archaea. These 1045 genes are therefore of interest and compose our starting point for the functional analysis of how the primordial ancestor of bacteria and archaea made a living. Yet there still might be some gene families among those 1045 that, despite bi-domain presence and domain monophyly, were subject to interdomain LGTs that went undetected in our earlier report. For example, oxygen dependent enzymes can hardly have existed in Luca because life arose in a world without oxygen [42] , [56] , [67] , but they might have been passed around promiscuously after the advent of oxygenated environments. Because Luca had to be an anaerobe (oxygen being a biological product), we can introduce one more criterion for Luca presence: oxygen dependent enzymes and pathways cannot have been present in Luca, such that enzymes and pathways specific to, or typically found among, aerobes (but not in anaerobes) can be excluded from Luca's gene set. To gain insights on the primordial metabolism of the common ancestor of bacteria and archaea before its diversification into the bacterial and archaea lineages, we set out here to identify protein families that span the archaeal–bacterial division, that were not subject to interdomain LGT, and that are preferentially found within the genomes of anaerobes. Of course, we cannot exclude the existence of other proteins in Luca, such as the ones widely shared by aerobic and anaerobic organisms, or some of the ones whose evolutionary history involved interdomain LGT events.",
"discussion": "3 Results and discussion 3.1 Universal (or nearly so) genes Genes that are widely distributed across prokaryotic domains were either present in Luca before the divergence of bacteria and archaea [27] , [52] or were subject to interdomain LGT [46] . We previously clustered 134 archaeal genomes into 25,762 protein families and identified their corresponding homologs among 1847 bacterial genomes [77] . In that dataset, the genomes span, according to prokaryote systematics, 13 archaeal and 23 bacterial higher taxonomic groups respectively, which roughly correspond to phyla (or class) and are designated for convenience henceforth as phyla here. If we search for nearly universal protein families using the criteria of i) presence in at least 22 bacterial phyla (missing in only one phylum) and at least 12 archaeal phyla (missing in only one phylum) and ii) monophyly of the domains within the corresponding maximum likelihood tree, we end up with a set of 27 nearly-universal protein families ( Fig. 1 a), corresponding to the familiar set of 30–35 “core” genes for (mostly ribosomal) proteins that are now commonly used to infer lineage relationships in place of rRNA alone [20] , [21] , [22] , [39] , [108] , [114] . If we allow for some gene loss during evolution (or rapid sequence divergence in some lineages) and thus opt for a less stringent distribution criterion, and furthermore relax the criterion for domain monophyly, our extended protein set consists of 109 protein families that include 9 aminoacyl-tRNA synthetase families, whose complex evolutionary history is well known [116] , [117] , several enzymes involved in amino acid biosynthesis and ATP synthase subunits ( Fig. 1 b, Table 1 ). This extended, or nearly universal, set corresponds very closely in content to the 102 nearly universal trees, or “nuts”, reported by Puigbò et al. [84] ), in that sense we could independently reproduce (109 genes) their nearly universal tree set (102 genes). The core and the extended core thus indicate the (obvious) presence of ribosomes in Luca [33] , [74] , an ion-gradient-dependent energy harvesting machinery [59] , and the presence of some amino acid biosynthesis. However, neither the core nor the expanded core (or nearly universal set) deliver information regarding the type of carbon and energy metabolism of primordial cells, because microbial metabolism has diversified within and across lineages during 3.5 billion years of microbial evolution. But the size of both the core (~ 30 genes) and the extended core (~ 100 genes) indicates that the clusters that we are using [77] deliver universal and nearly universal gene family distributions that correspond very well with what others have found independently using smaller genome samples and different methods [39] , [84] . From this point on, we will focus on the remaining non-universal protein families that might have been present in Luca. 3.2 Distinction between recent and ancient protein families In a previous study [77] we identified 4397 protein families that retained the monophyly of archaea and bacteria in maximum likelihood trees ( Fig. 2 a). However 3347 of those correspond to protein families in which either i) several bacteria and only one archaeal lineage or ii) several archaea and only one bacterial lineage group is represented. We further include in this group, for thoroughness, five protein families in which the archaeal representatives belong to Desulfurococcales and Fervidicoccus fontis Kam984 (here it is grouped with Desulfurococcales). The narrow phylogenetic distributions of these 3352 protein families (present in only one group in one of the domains) indicate that they correspond to interdomain LGT events that occurred after the prokaryotic domains had already diversified into major lineages. As such, they contain information about LGT frequencies, which is not our focus here, but do not contain direct clues about early metabolism and were excluded from our present analysis. When we exclude those interdomain LGTs, following the suggestion of Kannan et al. [46] , what remains is a set of 1045 protein families, containing sequences from at least two archaeal groups and at least two bacterial groups are present in the families and where the domains are monophyletic in phylogenetic trees. Fig. 2 b shows which archaeal lineages and which bacterial lineages harbor these Luca genome candidates. Their patterns of gene sharing are not randomly distributed among either archaea or bacteria, rather they are preferentially distributed in pairs of lineages (taxa) from each domain. These taxon pairs are boxed and labeled with numbers in Fig. 2 b: 1) clostridial and methanogenic lineages, 2) actinobacterial and Sulfolobales lineages, and 3) deltaproteobacterial and methanogen lineages. The boxed taxon pairs identify archaeal and bacterial lineages that share Luca candidate genes, that is, genes that are i) present in archaea and bacteria, but ii) not present in all bacteria and archaea (which we expect for Luca's genes, because Luca's habitat was different from today's), iii) where the domains do not interleave in the 1045 maximum likelihood trees, and iv) where the gene family is present in more than one archaeal lineage and more than one bacterial lineage (that is, lineage specific interdomain LGTs have been filtered out). Within the Luca candidate genes that identify the lineage pairs 1–3, the COG categories “energy production and conversion” and “carbohydrate metabolism” are among the most prominently represented ( Table 1 ). 3.3 Ancient means anaerobic However, even for these Luca candidate gene protein families, it is still possible that domain monophyly stems from lineage specific interdomain LGT and subsequent within domain transfer. This mechanism of distribution could apply both to ancient genes present in Luca and to later lineage specific inventions and/or later environment specific genes, for example oxygenic environments. Here we seek to identify ancient proteins. Because Luca arose in anaerobic environments [26] , [67] , proteins that arose in, or are typical for, oxygenated environments cannot be ancient, hence we would like to exclude them from the Luca candidate gene set. If we understand the literature of geochemists correctly, nobody can say for sure at the moment when the first oxygen arose [62] , but we can be reasonably sure that it was present in the atmosphere roughly 2.5 billion years ago [42] , [99] and accumulated in the oceans roughly 600 million years ago [62] , [109] . A few might disagree [79] and argue for the presence of O 2 since the early Archaean. Yet despite some uncertainty about when oxygen arose, we can be relatively sure that Luca arose in a world without appreciable amounts of oxygen [56] , [67] , because oxygen is a product of cyanobacterial photosynthesis involving two photosystems, which is a highly derived form of microbial physiology, having arisen after anoxygenic photosynthesis, cytochrome dependent respirations, fermentations and autotrophy [26] , [67] , [96] . Thus, in the search for a list of bona fide Luca candidate genes, the next pruning step is to look for the protein families shared only by anaerobic organisms, meaning that we filter aerobes and proteins typical of aerobes from the data. For this, we have to ascertain the oxygen tolerance or oxygen requirements of the 1981 organisms within our dataset. How to do this in the absence of specific growth information for each genome, and taking facultative aerobes into account? Microbial physiology can help. To reduce O 2 to water, prokaryotes use two evolutionary unrelated membrane complexes, the bd oxygen reductase and the heme–copper oxygen reductases (HCOs, also known as the complex IV or cytochrome c oxidase). While the bd oxygen reductase is generally associated with oxygen detoxification or very low oxygen environments [11] , the HCOs are incorporated in prokaryotic electron transfer chains that are much more diverse than the canonical mitochondrial one, but, as in the case of eukaryotes, that also promote the establishment of an electrochemical cation gradient across the membrane to feed the universal ATP synthase [82] , [29] . Since organisms that express bd oxygen reductases usually also possess heme–copper oxygen reductases [11] , one way to assess the oxygen requirements of the organisms present in our dataset is simply to look for the presence of HCOs in their genome. This is not trivial, though, because HCOs are related (structurally and sequence-wise) with nitric oxide reductases (NORs), with which they share the same general structural core of subunit I, the presence of a low-spin heme and a similar catalytic center composed of a high-spin heme and a metal ion — copper in the case of HCOs and iron in the case of NORs [18] , [25] , [105] , [29] . However, instead of reducing oxygen to water, NORs perform the two-electron reduction of NO to N 2 O and are not related with aerobic respiration. Thus, if we can effectively distinguish between HCOs (aerobic) and NORs (anaerobic) we can distinguish at the genome level between organisms that regularly deal with O 2 (aerobes, facultative aerobes, having HCOs) and those that shun it (anaerobes, lacking HCO while possessing NOR, or lacking both). For this we used tools developed elsewhere [105] and adapted to this specific problem (see Methods ). In the present genome sample, ~ 67% (1332 out of 1981) of the genomes contain one or more HCOs, revealing the adaptation to oxic habitats, while ~ 33% (649 out of 1981) of the genomes are devoid of HCOs ( Table 2 ). Our method sorted 18 organisms (genomes) that only contain NORs into the anaerobic category, which is important, because of the possible existence of NO dependent chemistry in early earth [12] and the presence of NORs at the onset of bioenergetic processes as argued by some [78] , [29] . NORs are divided into two main groups, according to the nature of their electron donor. Thus, cNORs represent the enzymes that use soluble electron donors such as cytochrome c, HiPIPs or cupredoxins and qNORs represent the enzymes which oxidize quinols [29] . qNORs have representatives in the two prokaryotic domains although their presence in Archaea can be both attributed exclusively to two interdomain LGT events, one to Crenarchaeota and one to halobacteria [14] , [38] or vertical inherence, multiple losses (except in some Crenarchaeota organisms), and an additional LGT of bacterial qNORs to halobacteria [29] . Regarding cNORs, only one sequence has been identified so far within Archaea [29] and the phylogenies of this subfamily, as the authors recognize, are prone to change over time. By the HCO criterion, aerobes are present in three archaeal phyla in our sample (Thaumarchaeota, Crenarchaeota, and Euryarchaeota). HCOs are widely distributed among crenarchaeotes, being present in all 16 members of the Sulfolobales sampled, among Thermoproteales (6 out of 13 Pyrobaculum species) and one member of the Desulfurococcales. By contrast, among the euryarchaeotic genomes sampled, only Halobacteriales and one member of the Thermoplasmatales ( Picrophilus torridus DSM9790) contain genes coding for HCO ( Table 2 ). The existence of functional HCOs in halobacteria is well documented [121] , [122] as is the identification of large influx of gene transfers from bacteria to the halophiles [89] and to the halophile common ancestor [76] , [118] , that transformed an ancient methanogen into an oxygen-respiring heterotroph. Interestingly, a strictly anaerobic, acetate-oxidizing S 0 -reducing haloarchaeon has been sequenced [104] , showing that aerobic respiration is not anymore a universal feature of the haloarchaea and underscoring ongoing metabolic diversification and gene loss within haloarchaea. Within bacteria, HCOs are present in genomes from 19 out of the 23 bacterial groups, although with different densities of distribution. While aerobic (and facultative aerobic) organisms dominate the proteobacterial, cyanobacterial and actinobacterial lineages, the majority of the bacterial genomes sampled belonging to the fusobacteria, thermotogae, aquificae, negativicutes, tenericutes, chlamydiae and spirochaetes groups do not contain detectable HCOs. Having a genomic proxy for oxygen tolerance among organisms (genome lineages) within the present sample allows us to classify the domain pairs as anaerobes, oxygen tolerant, or mixed ( Fig. 2 c, Table 2 ). Of the three most frequent domain pairs, pair 1 contains methanogen (anaerobes) or anaerobic lineages derived from methanogens together with clostridia (anaerobes), pair 2 contains Sulfolobales (aerotolerant) and Actinobacteria (aerotolerant), while pair 3 contains deltaproteobacterial (mixed aerotolerant and anaerobes) and methanogen (anaerobes) or anaerobic lineages derived from methanogens. The pairs also allow us to separate the protein families into aerobic (recent), anaerobic (ancient) or mixed protein families. 3.4 Who is new (aerotolerant), who is old (anaerobic)? HCOs allow us to sort genomes into categories of aerotolerant (having HCOs) or not (lacking HCOs). But the exercise here is to classify protein families as being typical for aerobes or anaerobes. In a simple scheme, we could just say that proteins encoded in genomes labeled as anaerobic should correspond to anaerobic protein families and those families having members that occur in aerotolerant genomes (having HCOs) should be excluded from the LucaGC set. But that criterion is too strict and will falsely exclude many anaerobic protein families, because i) they might be present in facultative anaerobes, and ii) they might have been subject to recent LGTs into aerotolerant species but have not yet been lost. This presents a difficult problem that readily lends itself to years of parameter space exploration, recurrent adjustments, and getting bogged down in endless minutiae of calculations. We took a pragmatic, albeit in some aspects somewhat arbitrary, approach. For a protein family to qualify for designation as an anaerobic family, we chose as our first (arbitrary) criterion that least 90% of all genes present in the family belong to organisms devoid of HCOs. To ensure a similar representation of archaea and bacteria in terms of anaerobic organisms, we included an additional filtering to consider only the protein families where at least 85% of the archaeal organisms represented are anaerobic and at least 85% of the bacterial organisms are anaerobic. This has the effect of excluding protein families whose high anaerobic score is due to an overrepresentation of anaerobic organism from one domain versus the other. Conversely, the criteria of at least 85% of archaeal organisms and 85% of bacterial organisms represented containing HCO were used to identify protein families as aerotolerant. What about the other protein families beyond these thresholds? Could they also be Luca candidate genes? Some might. We recognize that we might be excluding both “aerotolerant” and “anaerobic” protein families from these groups, this initial approach to filtering LGTs can probably be improved upon in future applications. Using these criteria, 79 protein families were classified as “aerotolerant”. The majority of the taxonomic distribution of the Luca candidate genes that identify aerotolerant taxon pairs identify Sulfolobales, Halobacteriales, Actinobacteria and Proteobacteria ( Fig. 3 b). In terms of functional annotation, these Luca candidate genes consist of mainly cytochrome and copper-containing related proteins, dioxygenases, and some NADH- and FAD-dependent oxidoreductases ( Fig 3 d). That is, the Luca candidate genes that link these aerobe-containing interdomain taxon pairs are inventions that arose after the cyanobacterial innovation of oxygenic photosynthesis [2] . Their distributions are not likely the result of ancient vertical inheritance, as Luca arose long before oxygen did. Perhaps as a slight disappointment, but also not entirely as a surprise, 904 of the 1045 families were classified as “mixed” with respect to occurrence within aerotolerant lineages. This has to do with LGTs and facultative anaerobes and these families deserve further inspection in future studies. What, on the bottom line, did this investigation uncover? We found two things, which we will summarize in the final two sections. 1) Perhaps more important than its role as a terminal acceptor, oxygen is an outstanding oxidant that microbes learned to use widely in many pathways. 2) At the anaerobic core of genes that reflect vertical inheritance from the prokaryotic common ancestor, we find evidence for antiporter-dependent ion gradient conversion, ATP synthase ion-gradient harnessing, FeS-cluster dependent soluble electron transport, and methyl-group dependent metabolism. 3.5 What did O 2 do for metabolism? The transition from an anoxic world to the emergence of oxic environments promoted overall modifications in the environment redox potential [26] , [48] , changing the metal availability and leading to the invention of new proteins and folds [49] , [43] that allowed the expansion of the existing metabolic pathways to include oxygen-dependent reactions [19] , [43] , [88] , [99] . Moreover, the irreversibility of oxygen consuming reactions promoted a positive selection pressure for the transfer and maintenance of oxygen related metabolism throughout prokaryotic organisms. Modern genomes harbor many O 2 -dependent reactions distributed across 11 metabolic pathway categories ( Fig. 4 ). In addition to its availability as a terminal acceptor for pre-existing (anaerobic) respiratory chains, O 2 allowed several energetically demanding reactions to occur more readily, for example the oxidation and cleavage of aromatic compounds by various dioxygenases [35] . Many existing biosynthetic pathways were also affected, as for example, the O 2 independent (more ancient) and alternative O 2 -dependent (derived) biosynthetic pathways for heme [13] , [34] , [40] , cobalamine [91] , [92] , and chlorophyll [80] to name a few. About half of the O 2 -dependent enzymes that we identified in the current sample of 1821 genomes are involved in the degradation of isoprenoids and xenobiotics. Without question, O 2 expanded the realm of anabolic and catabolic pathways across genomes [88] , but it did not alter the nature of the basic building blocks of life, nor did it fundamentally alter their biosynthesis. In the main, oxygen facilitated the oxidation of the building blocks of life, opening up new pathways of heterotrophic growth. A similar metabolic transition occurred at the origin of heterotrophy, as the first heterotrophs arose from autotrophs by learning to glean energy from the anaerobic oxidation of amino acids and bases at low H 2 partial pressures [96] . Earth is now different from when life arose, the main difference for microbes being that today there are oxic habitats [42] , [99] . Some have argued that O 2 or high potential acceptors like O 2 (for example NO), must have existed at life's origin, the argument being that the free energy changes associated with H 2 -dependent CO 2 reduction were not sufficient to get life started [94] . O 2 is indeed a strong terminal acceptor for electron transfer phosphorylation (hence valuable once life had already evolved respiratory chains). But is it too often overlooked that the use of oxygen comes at a huge cost. How so? As it relates to organic compounds like amino acids and nucleic acid bases – the substance of life – it is a curious but significant observation that life with oxygen is energetically far more expensive than life without oxygen [5] . The energetic costs to synthesize the cellular building blocks under oxic conditions are on average 12.9 times higher than under anoxic conditions [60] . This is because the reactions that generate the basic building blocks of cells (amino acids in the main) are thermodynamically favorable in anoxic environments but thermodynamically unfavorable in oxidizing environments [60] , [70] . The reason is that modest H 2 partial pressures (≤ 10 − 4 –10 − 3 atm, [111] ) favor the reduction of CO 2 to organic compounds (which is why acetogens and methanogens can grow) while low H 2 partial pressures favor the slow oxidation of amino acids and bases back to ammonium and CO 2 \n [96] , whereas O 2 allows fast and highly exergonic oxidations of even unfermentable organics, which is why wood, coal and oil burn well, releasing heat in the process. 3.6 The ancient metabolic core: small, but strictly anaerobic Only 62 protein families that trace to the prokaryote common ancestor fulfilled the criteria set here to be designated as “anaerobic” families. Even though 62 is not a genome-sized number, it is still greater than ~ 30, the usual list of suspect genes used for the reconstruction of deep phylogeny, and importantly, our list of 62 does not include either the core (~ 30) or the extended core (~ 100). The 27 universal and the 102 nearly-universal protein families were filtered out from the ancient anaerobic core due to their presence in both aerobic and anaerobic organisms. At this point we have to make a distinction between prokaryotic interdomain monophyletic families (that is, vertical inherence from Luca) and universal proteins, whose wide distribution with or without interdomain LGT events (e.g. ATP synthases), were present in Luca. The 62 anaerobic protein families identified above are the only remaining markers for ancient metabolism we can still retrieve from extant genomic data using this procedure. Of course, we cannot exclude the existence of other proteins in Luca, such as the ones widely shared by aerobic and anaerobic organisms, or some whose evolutionary history involved interdomain LGT events. But these are the ones we identify with this procedure. Yet these are new insights into Luca's gene complement. Furthermore, the functional classification of these 62 ancient anaerobic proteins goes beyond the traditionally identified informational genes, covering a number of functional categories not previously known about Luca ( Table 1 ). The Luca candidate genes shared by anaerobes are the genes that we sought to identify at the outset of this study. The protein families that i) still retain the ancient archaeal–bacterial division in phylogenies, and ii) that are preferentially encoded in the genomes of anaerobes, identify mainly methanogens and clostridia ( Fig. 3 a). Here one might ask “identify them as what?” Methanogens (Methanomicrobia and Methanococci) and clostridia (Clostridiales) are the most frequent pairs within the 62 protein families that preserve the archaeal–bacterial split, hence were not obviously distributed via LGT, and that only rarely (if ever) occur in genomes that harbor HCOs. These protein families are typical of anaerobes, whereby Luca had to be an anaerobe. Put another way, if we acknowledge that Luca was an anaerobe, there are only 62 protein families that we can trace as probable vertical inheritances from Luca (as opposed to LGTs) that are typical of strict anaerobes in that they rarely, if ever, occur in genomes of aerotolerant (facultative anaerobic) prokaryotes, and these 62 proteins are mainly shared by clostridia and methanogens. One might object that this observation results from the high number of clostridial and methanogenic organisms devoid of HCOs within our dataset. However, both archaea and bacteria contain other taxonomic groups without HCOs, but that have far fewer Luca candidate genes than clostridia and methanogens. The functional annotations of the anaerobic Luca candidate genes reveal numerous methyl transferases, subunits of the acetyl-CoA synthase complex, the soluble heterodisulfide reductase subunits C and A, several SAM-dependent methyltransferases and ferredoxin ( Supplemental Table A2 ), in addition to several subunits of the H + /Na + -antiporter MRP/hydrogenases and related complexes within this group ( Fig. 3 c). The Mrp antiporter is surprising and particularly interesting. Based purely on comparative physiology and theoretical considerations, it was recently proposed that a crucial step in bioenergetic evolution at an early stage prior to the emergence of free-living cells, was the advent of an Mrp-type H + /Na + antiporter that could convert geochemical pH gradients into biologically more useful Na + gradients [59] . This antiporter, the kind common to Ech and FeNi hydrogenases [63] , was suggested to have been the first step en route to replacing geochemical ion gradients with biologically derived ones, to generate Na + gradients that could be more readily harnessed by Luca's rotor–stator ATPase, yet prior to the invention of redox-chemistry-based (electron-transport-based) ion pumping systems [59] . Later work on simulated gradients provided support for that view [103] . Here, in a completely independent genome based approach, the Mrp-type antiporter suddenly turns up among 62 genes in the ancient anaerobic core. This is surprising, but was also predicted on the basis of bioenergetics by the theory that life arose at an alkaline hydrothermal vent. The present data also indicate the absence of redox-based ion-pumping systems in Luca ( Table 3 ), as previously suggested [66] , [59] , [106] , [107] . The 27 universal and the 102 nearly-universal protein families were filtered out from the ancient anaerobic core because they are present in both aerobic and anaerobic organisms. If Luca could synthesize ATP with the help of proteins that were able to harness and transduce a geochemical pH gradient, where did the biochemical energy come from that allowed Luca to perform protein synthesis in the absence of chemiosmotic harnessing? It remains true that there are only two ways in which cells conserve energy in the form of ATP: chemiosmotic coupling and substrate level phosphorylation (SLP) [98] . Before the origin of chemiosmotic coupling, SLP would thus have been the only option. In essence, there have been only two viable suggestions for how the first cells might have harnessed energy via SLP: a geochemical supply of carbon monoxide [32] or a geochemical supply of methyl groups [66] , [68] — on the early Earth, oxidations of organics from space will not support energy metabolism [96] . Both proposals focus on the reactions catalyzed by bifunctional acetyl-CoA synthase/carbon monoxide dehydrogenase (CODH/ACS), which condenses a Ni-bound methyl group with Ni-bound carbon monoxide (carbonyl) to generate a Ni-bound acetyl group that is removed from the enzyme via thiolysis to generate a thioester, which is cleaved by phosphate to yield acetyl phosphate, which phosphorylates ADP via SLP [85] , [106] . Consistent with both proposals [32] , [66] CODH/ACS is contained within the anaerobic core ( Table 3 ). Additional evidence for the existence of abiotic synthesis within alkaline vents systems comes from laboratory experiments showing that the direct reduction of CO 2 by H 2 in the presence of pH gradients or electric potential can lead to the formation of formaldehyde, acetate and pyruvate [41] , [93] , [102] . Though an excellent source of energy and electrons, carbon monoxide has a very restricted role in biochemistry outside of energy metabolism [86] . Studies of abiotic synthesis under the far-from-equilibrium electrochemical vent conditions will help to clarify the presence or absence of CO under these conditions [41] . Methyl groups, on the other hand, have a very central and general role in microbial metabolism, in particular in S-adenosyl methionine (SAM) and in radical SAM enzymes, which have a myriad of functions in biosyntheses, in particular in cofactor biosyntheses [101] , [119] . Of the 62 proteins in the ancient core, six (~ 10%) are SAM-dependent enzymes, making these the most common class of proteins in the ancient core behind “unknown function”. Clearly, the ancient core points to a central involvement of methyl groups in ancient metabolism. This is very much in line with the predications from the theory that life arose at hydrothermal vents: “… the biochemical system proposed [] would remain strictly dependent upon geochemically provided methyl groups up until the advent of (protein dependent) chemiosmotic harnessing ” ( [66] , p. 1912). Recent geochemical studies have uncovered evidence for disequilibrium of one carbon species in the Von Damm hydrothermal field, indicating that there are distinct kinetic barriers to methane synthesis in some hydrothermal systems [72] , such that a model for the origin of life that requires a geochemical source of chemically accessible methyl moieties is not asking for too much. That methyl groups have such a prominent place in the ancient anaerobic core is interesting and possibly significant. If vent conditions allowed methyl synthesis, then the synthesis of thioesters and acetyl phosphate would also, in principle, be possible [41] , [93] , [102] . Other proteins in the ancient anaerobic core, besides Mrp subunits, ATP synthase subunits, CODH/ACS subunits and SAM dependent enzymes include heterodisulfide reductase, an electron bifurcating enzyme essential to energy metabolism in methanogens [47] , ferredoxin, flavodoxin, an iron (II) transport protein, and a thiocytidine biosynthesis protein involved tRNA modification. Four proteins in the list are membrane transport proteins, which would indicate that Luca existed in an environment where hydrophobic layers corresponding to the thickness of a lipid bilayer existed. That makes sense because neither the Mrp antiporter nor the rotor stator ATP synthase can function without a membrane, though the membrane need not be genetically encoded [16] , as the synthesis of hydrophobic compounds at vents is expected from thermodynamics [3] and is observed in some modern hydrothermal systems [83] , [97] . Glutamine synthase, the enzyme that introduces nitrogen into metabolism, is in the list, as are CoA-ligases and NADPH dependent flavin reductases, transducers of one electron to two electron transport. The latter, together with ferredoxin, flavodoxin, electron bifurcating enzymes and radical SAM enzymes point to a major role of one electron transport in Luca's metabolism. But where would the reduced nitrogen come from? For life to get started, reduced nitrogen had to be (at least locally) available, and both transition metal catalysts [28] and hydrothermal vent conditions [12] could have been a local source of ammonium. Moreover, the presence of a nitrogenase accessory protein in the 62 anaerobic families list can be an indication for the existence of an ancient nitrogenase in Luca, as previously proposed [69] . Thus, if we i) remove interdomain LGTs as Kannan et al. [46] suggested, ii) identify lineages that have the most Luca candidate genes, and iii) distinguish between anaerobic Luca candidate genes in anaerobic lineages (might be ancient) and aerotolerant Luca candidate genes in aerotolerant lineages (cannot be ancient) we are left with the result in Fig. 3 a and c, namely that Luca looks most similar to modern bacterial and archaeal lineages that harbor anaerobic chemolithoautotrophs: clostridial-type acetogens and methanogens. This is noteworthy in the respect that acetogens and methanogens are the groups of organisms that stand in the foreground of theories for the origin of life that are based in microbial physiology and that connect well to geochemistry [66] , [8] , [59] , [106] . The idea that life started at hydrothermal vents has been around for 30 years [7] , it continued to be further developed. In our inference, we have ended up with something that is half-alive, a curious but necessary intermediate in the transition from non-living to living things."
} | 11,192 |
33320632 | null | s2 | 1,609 | {
"abstract": "Silk fibroin is a natural polymer with a unique repetitive structure that translates to extraordinary properties in terms of processability and mechanical properties. The "
} | 42 |
27386574 | PMC4928977 | pmc | 1,610 | {
"abstract": "Oleophobic surfaces capable of power-free self-transportation of oil droplets are designed.",
"conclusion": "CONCLUSION We have demonstrated directional self-transportation of oil droplets on oleophobic surfaces composed of radially arrayed undercut stripes. Moreover, three modes of droplet motion are observed and the underlying mechanism is explained. The oil droplet self-transportation capability of the surfaces can be optimized by reducing the stripe intersection angles or by increasing the stripe width. The radially arrayed undercut microstripe patterns allow alignment of oil droplets to a central point. This property could be used for power-free and self-aligned liquid transportation for various analytical devices because many chemical and biological samples have surface tensions much lower than that of pure water. Alternatively, it could be used to eliminate oil or ink accumulation and contamination in fluid devices by transporting stray droplets away from peripheral areas, for example, as an anti-clogging solution for ink-jet printheads.",
"introduction": "INTRODUCTION Many natural surfaces have directional liquid wetting and transportation properties, which allow cactuses ( 1 ) and spider silk ( 2 ) to collect fresh water from fog, and birds to shed water droplets off their feathers ( 3 ). On artificial surfaces, dynamic manipulation of droplets in a directional manner enables intelligent fluidic devices that hold diverse applications, including heat management ( 4 ), ink-jet printing ( 5 ), fluid diode ( 6 , 7 ), water harvesting ( 8 , 9 ), emulsion separation ( 10 ), and microanalysis ( 11 – 13 ). Asymmetric chemical modification ( 4 , 14 – 16 ) and geometrical patterning ( 17 – 19 ) are two general strategies for anisotropic liquid manipulation and even enable self-transportation of water droplets, which is highly useful for exploiting power-free fluidic devices that can operate in the absence of external energy input. Although significant advancements have been achieved for water droplet self-transportation ( 14 – 19 ), self-transportation of oils and other liquids with lower surface tensions remains a major challenge. Chemically patterned surfaces formed through the reactive formation of a molecular gradient usually show very limited contact angle difference at the front and back edges of oil droplets because of their much lower surface tension compared to water, resulting in insufficient driving force to overcome the hysteresis effect. On the other hand, oils usually tend to impregnate the texture on surfaces patterned with a geometrical gradient, making self-transportation infeasible. Lorenceau and Quéré ( 20 ) reported self-movement of oil droplets impaled on conical copper wires. However, because the copper wires were oleophilic, the droplet was in fact moving on an oil film, which means continuous loss of liquid during transport. Although Sumino and co-workers ( 21 ) demonstrated oil droplet self-running on a surface, the whole process involved continuous desorption and re-adsorption of surfactants and contamination of the oil droplet itself. Moreover, this droplet self-running occurred in the submerged oil-water system, which limits its applicability. Here, we report the self-transportation of oil droplets in ambient air on highly oleophobic, microtextured surfaces. In addition, we identify three distinct modes of droplet motion on the fabricated surfaces and explain through modeling the underlying mechanism. We further reveal how to optimize oil self-transportation capability through tuning the texture parameters.",
"discussion": "DISCUSSION To understand the above oil droplet motion behaviors, we calculated the contact angle θ of oil on the patterned surfaces according to the Cassie-Baxter model ( 26 , 29 ) cos θ = r f f 1 cos θ 0 − f 2 = r f f 1 cos θ 0 − ( 1 − f 1 ) (1) where f 1 is the area fraction of the projected solid-liquid interface, r f is the effective roughness of the wetted solid-liquid interface area (that is, the ratio of wetted area and its projection area), and θ 0 is the contact angle on a planar surface with identical surface chemistry to the structured surface. In our experiments, the oil contact angle on a flat surface with a TiO 2 layer and a fluoropolymer coating is 55° (fig. S2). Because θ 0 is less than 90°, the solid-oil-air contact line stops at the lower edge of the sidewall ( Fig. 1C ). In such a configuration, f 1 is R / L , and r f f 1 is calculated to be R / L + 2 h /Φ L . The apparent contact angle θ is thus determined: cosθ = ( R / L + 2 h /Φ L )cosθ 0 − (1 − R / L ), where R ≤ L ≤ 4 mm. Because R = D /Φ, we can get θ = { arccos [ ( D Φ L + 2 h Φ L ) cos θ 0 − ( 1 − D Φ L ) ] ( D / Φ ≤ L ≤ 4 mm ) θ 0 ( 0 ≤ L ≤ D / Φ ) (2) Function curves showing the relationship between θ and L are plotted in Fig. 4A , which can be classified into three groups (indicated by different colors), corresponding to three types of droplet behaviors. The surfaces in the green group (A4, A5, B3, B4, and C5) show relatively lower contact angles than the other two groups of samples, and more importantly, these surfaces have steeper wettability gradients than the other two groups at the outer area of the patterns (2 mm < L < 4 mm). On the basis of an interfacial free energy argument, the driving force for droplet motion on gradient surfaces scales as F d = −π r 2 γ d (cos θ)/ dL , where r is the base radius of the droplet ( 30 ). Thus, − d (cos θ)/ dL can be used as a characteristic parameter to evaluate the driving force of different surfaces. On the patterned area, we can get − d ( cos θ ) d L = D ( 1 + cos θ 0 ) + 2 h cos θ 0 L 2 Φ (3) The − d (cos θ)/ dL versus L curves of different patterns are plotted in Fig. 4B . The green group of samples exhibits the largest − d (cos θ)/ dL , which thus provides sufficient driving force to overcome the hysteresis effect. This explains why only the green group can initiate the inward self-motion of oil droplets. Moreover, the relatively small contact angles on the green group of samples give a larger base radius r for droplets, which also benefits droplet motion because the driving force F d ∝ r 2 , whereas the hysteresis force F h ∝ r ( 31 ). It is worth mentioning that the liquid transport is limited to the radial length of the stripes, and meanwhile, the stripes cannot be too long such that the droplet size is well above the maximum separation between two neighboring stripes. For the blue group of samples in Fig. 4 , the less steep wettability gradient is insufficient to drive the motion of oil droplets, which thus remain pinned on the surfaces. Fig. 4 Mechanistic understanding. ( A to C ) Calculated oil contact angle θ (A), wettability gradient − d (cos θ)/ dL (B), and breakthrough pressure P c / P ref (C) curves as functions of L on the 12 patterned surfaces. Different curve colors correspond to the three modes of droplet motion. The red curves in (A) are shown in dashed lines because oil droplets tend to collapse into the Wenzel state in this regime. For the red group of samples in Fig. 4 (A1, A2, B1, and C1), the upward suspension force provided by the undercut structure turns out to be insufficient for creating a robust solid-air composite interface to support oil droplets, and consequently, the oil droplets readily undergo a transition to the Wenzel state ( 32 ); that is, the liquid penetrates the texture. The breakthrough pressure P c for oil to penetrate the texture can be quantified as P c = 2 γ sin θ 0 L Φ − D (4) Tuteja et al. proposed that P ref = 2γ/ l cap can be used to estimate the minimum pressure of a droplet exerted on a highly hydrophobic/oleophobic surface, where l cap = γ / ρ g is the capillary length of the liquid (~1.9 mm for hexadecane) ( 22 , 33 ). Hence, we can use P c / P ref as a dimensionless parameter to evaluate the robustness of different patterns to support Cassie state oil droplets. The red group of samples is located at the bottom right regime of Fig. 3A , that is, the area with the largest Φ and smallest D values, which thus give much smaller P c / P ref values compared to the other two groups ( Fig. 4C ). For example, at the outmost location ( L = 4 mm), A1 only gives a P c / P ref of around 4.6, whereas A5 has a P c / P ref of as high as 105. This explains why oil droplets collapse easily into the Wenzel state on the red group of samples. Moreover, when an oil droplet collapses, its outer side collapses more easily than the inner side because the outer side contacts microstripes with a larger interspace, which means less P c to prevent collapse occurrence (fig. S4). Therefore, the collapse initiates from the outer side, and such asymmetric collapse results in the outward spreading of oil droplets. Finally, we discuss the parameter requirements for optimizing the oil droplet self-transportation ability of the patterns. The key is to maximize the driving force F d and the breakthrough pressure P c to avoid droplet pinning or collapse events. From Eq. 3, we obtain F d ∝ f ( D , Φ) = [ D (1 + cosθ 0 ) + 2 h cosθ 0 ]/Φ. As f ( D , Φ) decreases with Φ and increases with D (fig. S5), F d can thus be optimized by reducing Φ and increasing D . From Eq. 4, it is clear that P c also decreases with Φ and increases with D . Therefore, we can conclude that the pattern design can be optimized by simultaneously minimizing Φ and maximizing D . This also explains why the green regime in Fig. 3A is located at the top left corner where both Φ and D are optimized so that oil droplets can readily move inward, whereas the red regime is at the bottom right corner where the lowest P c is obtained and droplets tend to collapse into the Wenzel state, and the blue regime is located in between where droplets do not move or collapse. In practical fabrication, Φ is restricted by the machining precision of the microfabrication/nanofabrication technique and cannot be infinitely reduced, and D cannot exceed the product of pattern radius and Φ. On the basis of the theoretical consideration presented above, samples A4, A5, B3, B4, and C3 (marked green in Figs. 3 and 4 ) were identified as the most promising. Every sample in this group was able to sustain the Cassie state of droplets of various surface tensions down to a surface tension of 27 mN/m and spontaneously move them along the radial gradient. This capability is demonstrated in fig. S6 and movie S2 for sample B3, which had the lowest driving force ( Fig. 4B ) and the lowest breakthrough pressure ( Fig. 4C ) of the samples in the green group. On the other hand, sample A5, which has the highest driving force and breakthrough pressure, can drive an even broader range of liquids. Even an ethanol droplet (99.5%, γ ~22.4 mN/m) can move spontaneously on such a pattern (fig. S7 and movie S3). We also investigated the effect of pattern parameters and liquid type on droplet speed. For different patterns in the green group, the transport speed of a 3-μl hexadecane droplet changes from 2.98 to 2.40 cm/s (see table S2), which is caused by their difference in wettability gradient ( Fig. 4B ). In contrast, the effect of liquid type is less pronounced, and the speed shows a slight increase with decreased liquid surface tension, attributed to the increased droplet contact radius (see table S3)."
} | 2,843 |
26692897 | PMC4676187 | pmc | 1,611 | {
"abstract": "Synthesis gas (syngas) is a gas mixture consisting mainly of H 2 , CO, and CO 2 and can be derived from different sources, including renewable materials like lignocellulose. The fermentation of syngas to certain biofuels, using acetogenic bacteria, has attracted more and more interest over the last years. However, this technology is limited by two things: (1) the lack of complete knowledge of the energy metabolism of acetogenic bacteria, and (2) the lack of sophisticated genetic tools for the modification of acetogens. In this review, we discuss the bioenergetic constraints for the conversion of syngas to different biofuels. We will mainly focus on Acetobacterium woodii , which is the best understood acetogen in terms of energy conservation. Syngas fermentation with Clostridium autoethanogenum will also be discussed, since this organism is well suited to convert syngas to certain products and already used in large-scale industrial processes.",
"conclusion": "Conclusions From our calculations, it is obvious that with H 2 as electron donor, synthesis of most of the products by the pathways discussed here has a negative energy balance. This would lead to the synthesis of unwanted by-products like acetate. Modifications of the pathways can improve the energy yield, which in some cases makes the production energy-positive (ethanol, butanol). With CO as electron source, synthesis of most products goes along with the synthesis of ATP and would, in theory, allow a complete conversion. However, CO oxidation goes along with production of CO 2 which could be circumvented by analogous oxidation of H 2 . Simultaneous consumption of H 2 and CO, however, will be limited by CO-sensitive hydrogenases. By an elaborate selection of the employed organism and implementation of certain enzymes by metabolic engineering, in theory, a 100 % conversion of synthesis gas into many biofuels is feasible."
} | 473 |
35187859 | PMC9009134 | pmc | 1,612 | {
"abstract": "Abstract Flexible triboelectric nanogenerators (TENGs) have attracted increasing interest since their advent in 2012. In comparison with other flexible electrodes, hydrogels possess transparency, stretchability, biocompatibility, and tunable ionic conductivity, which together provide great potential as current collectors in TENGs for wearable applications. The development of hydrogel‐based TENGs (H‐TENGs) is currently a burgeoning field but research efforts have lagged behind those of other common flexible TENGs. In order to spur research and development of this important area, a comprehensive review that summarizes recent advances and challenges of H‐TENGs will be very useful to researchers and engineers in this emerging field. Herein, the advantages and types of hydrogels as soft ionic conductors in TENGs are presented, followed by detailed descriptions of the advanced functions, enhanced output performance, as well as flexible and wearable applications of H‐TENGs. Finally, the challenges and prospects of H‐TENGs are discussed.",
"introduction": "1 Introduction Nowadays, the growing popularity of next‐generation portable and wearable electronics has spurred increasing demands for power sources. [ \n \n 1 \n , \n 2 \n , \n 3 \n , \n 4 \n , \n 5 \n \n ] However, the rigid/complicated configuration, large size, and environmentally unfriendly properties of most traditional power supplies no longer satisfy the critical requirements of wearable electronics. Therefore, researchers have made numerous efforts to develop soft and green power sources for wearable applications in recent years. [ \n \n 6 \n , \n 7 \n , \n 8 \n , \n 9 \n \n ] Among them, flexible triboelectric nanogenerators (TENGs) have aroused interest because of their simple structure and fabrication, light weight, high output, and low cost. [ \n \n 10 \n , \n 11 \n , \n 12 \n , \n 13 \n , \n 14 \n , \n 15 \n \n ] \n In addition to triboelectric materials, electrodes are also important to the integration of TENGs in flexible and wearable applications. Typically, there are four types of electrodes in flexible TENGs: i) metal sheets; ii) carbon sheets; iii) conductive polymer films; and iv) hydrogel films. [ \n \n 16 \n , \n 17 \n , \n 18 \n , \n 19 \n , \n 20 \n \n ] In spite of the high conductivity, metal sheets are usually not suitable for flexible and wearable applications because of the limited flexibility and stretchability. Similar drawbacks also plague carbon sheets that show lower conductivity despite a lower cost. Although conductive polymer films can be stretched if they are fabricated on flexible substrates, the synthesis is usually complicated and the conductivity is poor. In contrast, hydrogels possess unique advantages such as high transparency, stretchability, biocompatibility, as well as small environmental impact. [ \n \n 21 \n , \n 22 \n , \n 23 \n , \n 24 \n \n ] More importantly, compared to the electronic conductivity exhibited by the metal sheets/carbon sheets/conductive polymer films, hydrogels have ionic conductivity. This property allows fine‐tuning and optimization of the resistance and charge‐carrier density, selection of chemical ionic species utilized in the material, as well as integration of biological and electronic systems. [ \n \n 25 \n , \n 26 \n \n ] Consequently, hydrogels have possessed a large potential to improve the performance and integration of soft electrodes/conductors in TENGs for wearable and biomedical electronics. In 2017, Xu et al. [ \n \n 27 \n \n ] reported the first hydrogel‐based TENG (H‐TENG) consisting of a polyvinyl alcohol (PVA) hydrogel as the conductor and polydimethylsiloxane (PDMS) as the triboelectric layer. The PVA‐based H‐TENG worked as a self‐powered human motion sensor because it can harvest biomechanical energy from stretching, bending, and twisting. In the same year, Pu et al. [ \n \n 28 \n \n ] reported the first ionic hydrogel (polyacrylamide (PAM)/LiCl) that possessed high stretchability and transparency as the current collector. The skin‐like PAM/LiCl H‐TENG boosting human motion energy harvesting and touch sensing has great potential in artificial skins, wearable electronics, and soft robots. Since then, H‐TENGs have garnered more attention leading to the rapid development of the output performance and long‐term stability, which benefits the extended applications. Up until now, there have been several reviews highlighting the development of hydrogels as soft conductors, but they mainly focus on the properties and applications to sensing devices such as biosensors, force/strain sensors, and gas sensors. [ \n \n 26 \n , \n 29 \n , \n 30 \n , \n 31 \n , \n 32 \n , \n 33 \n \n ] A comprehensive review focusing on hydrogels as ionic conductors in flexible TENGs with a detailed discussion on the achievements and challenges is still lacking. In this review, we summarize the recent progress and current status of hydrogels as current collectors in TENGs. Specifically, the advantages of using hydrogels as ionic conductors are discussed. By classifying the types of hydrogels in flexible TENGs with emphasis on conductive additives, the advanced functions and outputs ( Figure \n \n 1 \n ) to improve the long‐term stability of H‐TENGs are highlighted. Last but not least, wearable applications of H‐TENGs are presented, and finally, the challenges and opportunities for future research and development of H‐TENGs are discussed. Figure 1 Development of H‐TENGs: advanced functions, enhanced outputs, and flexible and wearable applications."
} | 1,368 |
37744148 | PMC10512229 | pmc | 1,613 | {
"abstract": "About 90% of all land plants form mycorrhiza to facilitate the acquisition of essential nutrients such as phosphorus, nitrogen, and sometimes carbon. Based on the morphology of the interaction and the identity of the interacting plants and fungi, four major mycorrhizal types have been distinguished: arbuscular mycorrhiza (AM), ectomycorrhizal (EcM), ericoid mycorrhiza, and orchid mycorrhiza. Although most plants are assumed to form only one type of mycorrhiza, some species simultaneously form associations with two mycorrhizal types within a single root system. However, the dual-mycorrhizal status of many species is under discussion and in some plant species the simultaneous association with two mycorrhizal types varies in space or time or depends on the ecological context. Here, we assessed the mycorrhizal communities associating with common hawthorn ( Crataegus monogyna ), a small tree that commonly associates with AM fungi, and investigated the potential factors that underlie variation in mycorrhizal community composition. Histological staining of C. monogyna roots showed the presence of a Hartig net and hyphal sheaths in and around the roots, demonstrating the capacity of C. monogyna to form EcM. Meta-barcoding of soil and root samples of C. monogyna collected in AM-dominated grassland vegetation and in mixed AM + EcM forest vegetation showed a much higher number of EcM sequences and OTUs in root and soil samples from mixed AM + EcM vegetation than in samples from pure AM vegetation. We conclude that C. monogyna is able to form both AM and EcM, but that the extent to which it does depends on the environmental context, i.e., the mycorrhizal type of the surrounding vegetation.",
"conclusion": "Conclusion Overall, we conclude that C. monogyna is a tree species that is capable of forming associations with fungi that form ectomycorrhizal and arbuscular mycorrhiza. While the species consistently associated with AM, its association with EcM depended on the surrounding vegetation and EcM colonization of the roots is rather low, suggesting C. monogyna is not capable of independently supporting EcMF. Whether it can be considered a dual-mycorrhizal plant species thus depends on how an EcM plant is defined: based on morphology (the presence of EcM structures) or functionality (the mutualistic association with EcMF). Further research is needed to determine whether other species of genus Crataegus have the same properties and whether C. monogyna and/or its mycorrhizal partners experience benefits from dual colonization of the roots.",
"introduction": "Introduction With more than 90% of the land plants worldwide forming mycorrhiza, this is the ecologically most important mutualistic association between fungi and plants (Smith and Read, 2008 ; van der Heijden et al., 2015 ; Brundrett and Tedersoo, 2018 ). In return for carbohydrates, the fungus increases nutrient and water uptake by the plant and can provide protection against pests and pathogens (Marx, 1972 ; Smith and Read, 2008 ; Cameron et al., 2013 ). Depending on the morphology of the roots and the taxonomic plant and fungal groups involved, four main types of mycorrhiza can be distinguished: arbuscular mycorrhiza (AM), ectomycorrhizal (EcM), ericoid mycorrhiza and orchid mycorrhiza (van der Heijden et al., 2015 ; Brundrett and Tedersoo, 2018 ). Although most plants are assumed to form only one type of mycorrhiza (Soudzilovskaia et al., 2020 ), some species are known to form associations with fungi of more than one mycorrhizal type (Teste et al., 2020 ). These plants are referred to as dual-mycorrhizal plants and generally form AM and EcM (Teste et al., 2020 ). With 71% of all land plants consistently (and 7% inconsistently) associating with fungi of the phylum Glomeromycota, AM is the most widespread type of mycorrhiza (Brundrett and Tedersoo, 2018 ). This mycorrhizal type can be further subdivided in two morphological types: the Arum -type, characterized by the presence of arbuscules and intercellular hyphae, and the Paris -type, characterized by the presence of intracellular hyphal coils (Dickson et al., 2007 ). About 8,500 plant species are known to form associations with ectomycorrhizal fungi (EcMF), (Brundrett and Tedersoo, 2018 ). While AM are formed by one monophyletic group of fungi, the EcM lifestyle independently evolved multiple times within the fungal kingdom and can be found in various groups within the Ascomycota and Basidiomycota (Martin et al., 2016 ). In EcM, the fungus typically does not penetrate the plant cells but forms a dense, labyrinthine structure of hyphae between the epidermal and cortical plant root cells, called the Hartig net, and encloses the plant root with a hyphal sheath (Brundrett and Tedersoo, 2018 ). AM and EcM do not only differ morphologically, but also in their capabilities to take up nutrients, e.g., the mobilization of N and P from organic substrates (Read and Perez-Moreno, 2003 ). Dual-mycorrhizal plants vary in the extent to which they are colonized by and depend on either of the two mycorrhizal types. In some species, both types can be found simultaneously within the same root system, while in others the presence of either type will depend on the life history stage of the host plant and local environmental factors such as soil conditions or the surrounding vegetation (Teste et al., 2020 ). In their recently proposed classification of dual-mycorrhizal plants, Teste et al. ( 2020 ) call the former context-free dual-mycorrhizal plants and the latter temporally dependent or spatially dependent dual-mycorrhizal plants. In temporally dependent dual-mycorrhizal plants, mycorrhizal type dominance varies between life history stages of a species, while in spatially dependent dual-mycorrhizal plants mycorrhizal type dominance varies between habitats or regions (Teste et al., 2020 ). Due to the eco-physiological differences between the two mycorrhizal types, the type that will be most beneficial largely depends on local habitat characteristics (Read and Perez-Moreno, 2003 ). Surrounding vegetation is known to affect mycorrhizal community composition, by affecting local abiotic conditions, by providing the inoculum from which the roots are colonized or through competitive interactions between mycorrhizal types. Grünfeld et al. ( 2020 ), for example, showed that roots of AM forest herbs were more extensively colonized by arbuscular mycorrhizal fungi (AMF) in forest stands with a high cover of AM trees than in stands with a low cover of AM trees. McHugh and Gehring ( 2006 ) found that the presence of AM shrub negatively affected EcM colonization in Pinus edulis . How surrounding vegetation affects the interaction between EcMF and AMF within the same, dual-mycorrhizal plant species is however far less studied. The major goal of this study was to gain a better understanding of how surrounding vegetation can affect the mycorrhizal communities of individual plants by testing the hypothesis that the mycorrhizal communities of the temperate tree Crataegus monogyna Jacq. strongly differ depending on the surrounding vegetation. Species from the genera Alnus, Eucalyptus, Populus and Salix are widely accepted to be dual-mycorrhizal, but for many other plant species the mycorrhizal status is not clear and under discussion (Brundrett and Tedersoo, 2020 ; Teste et al., 2020 ). This is caused by differences in definitions for the various mycorrhizal types, incorrect assignments of a certain mycorrhizal type and errors accumulating in databases [see Brundrett and Tedersoo ( 2020 ) and Teste et al. ( 2020 ) for more details]. One example of such a disputed plant species is C. monogyna , which is considered purely AM according to some sources (Brundrett and Tedersoo, 2020 ; Soudzilovskaia et al., 2020 ) and dual-mycorrhizal according to others (Trappe, 1962 ; Harley and Harley, 1987 ; Maremmani et al., 2003 ; Bueno et al., 2017 ; Teste et al., 2020 ). While its AM status is not under discussion, it is its EcM status that needs confirmation. More specifically, we assessed the dual-mycorrhizal nature of C. monogyna by microscopically examining roots for ectomycorrhizal diagnostic features (i.e., the presence of a Hartig net and hyphal sheath), and tested whether the mycorrhizal type of the surrounding vegetation (AM dominated grassland or mixed AM and EcM forest edge) affected the AMF and EcMF communities in the roots of C. monogyna .",
"discussion": "Discussion Is Crataegus monogyna a Dual-Mycorrhizal Plant? The presence of a Hartig net and hyphal sheath in and around the roots of C. monogyna indicates that this species is capable of forming ectomycorrhizal (EcM). Some saprotrophic fungi have been found to show affinity for roots and to form mantle-like structures (Smith et al., 2017 ), but metabarcoding of the root-associated fungal communities demonstrated the presence of typical EcMF taxa in C. monogyna roots. However, root colonization levels were low and EcM structures were only found in samples from the mixed AM + EcM vegetation, suggesting that C. monogyna is not able to independently support EcM fungi. Whether C. monogyna can be considered a dual-mycorrhizal species consequently depends on the definition of an EcM plant: whether it is a species capable of forming EcM structures or a species capable of supporting EcMF (Teste et al., 2020 ). These results also raise the question whether other species of the genus Crataegus are able to form ectomycorrhizal. Although closely related species often share mycorrhizal types or nutritional strategies, this is less often the case in this type of flexible mycorrhizal associations where the mycorrhizal type depends on environmental circumstances (Gerz et al., 2018 ). Simply attributing the same mycorrhizal status to all other Crataegus species will thus probably result in misclassification errors (Bueno et al., 2019 ). On the other hand, it is likely that Crataegus species that are more typically found in forests, such as C. laevigata or C. mollis , also form ectomycorrhizal and it would thus be interesting to search for EcM structures in these species. Variation in Mycorrhizal Type Dominance Is Dependent on Vegetation Type Surrounding vegetation is known to affect mycorrhizal root colonization and mycorrhizal community composition, both in AMF (Hausmann and Hawkes, 2009 ; Grünfeld et al., 2020 ) and EcMF (Dickie et al., 2004 ; Hubert and Gehring, 2008 ). This effect is mostly attributed to the increased availability of inoculum with increasing presence of plants of a certain mycorrhizal type and to host preferences of mycorrhizal fungi (Ishida et al., 2007 ). Here, we found a much lower number of EcMF sequences and OTUs in the soil of grasslands, indicating a much lower EcMF inoculum availability. In contrast, higher OTU diversity and sequence numbers were found in samples collected along forest edges. EcM structures were also only found in root samples collected in the forest edge. AMF OTU richness, hyphal and arbuscular root colonization, on the other hand, were higher in samples from grassland than from the forest edge. These results indicate that the mycorrhizal type of the surrounding vegetation can have a pronounced effect on the presence of a mycorrhizal type. This has already been observed in tree seedlings, especially after disturbances. For example, Dickie et al. ( 2001 ) showed that Quercus rubra seedlings planted near Acer (AM) stumps in a logged forest stand had higher AMF root colonization rates than seedlings planted near Quercus stumps, which had the highest EcMF colonization rate. In another study, AMF were more frequently encountered on Pinus muricata seedlings that established in AM-dominated scrub than in EcM-dominated forest after wildfire (Horton et al., 1998 ). Colonization of these seedlings by EcMF took longer, but once these fungi had colonized the roots, they were more diverse in the EcM-dominated forest than in the AM-dominated scrub where EcMF inoculum availability was much lower. C. monogyna roots were extensively colonized by AMF, forming both the Arum -type and the Paris -type. Both morphological types have been found in the Rosaceae family before but not simultaneously in the same species (Dickson et al., 2007 ). But the co-occurrence of the two types is known occur in other plant species (Kubota et al., 2005 ; Salomón et al., 2014 ). While the presence of AM in C. monogyna roots is standard, the low colonization rates by EcM structures suggest it is optional. To what extent C. monogyna and its mycorrhizal partners benefit from the dual colonization remains unknown. Flexibility in mycorrhizal associations has been found to correlate with niche breadth (Gerz et al., 2018 ). It is possible that optional association with EcMF increases the niche C. monogyna can occupy, e.g., through increased flexibility throughout ecosystem development. C. monogyna can facilitate the natural succession from grassland to forest by increasing seedling survival of late-successional, shade-tolerant tree species (Gómez-Aparicio et al., 2004 ). It is also possible that associating with EcMF increases the flexibility of C. monogyna to cope with changes in soil properties (e.g., soil temperature, litter type), soil microbial communities and/or surrounding vegetation during forest succession (Teste et al., 2020 ). Effects of Local Soil Conditions? Although the dominant mycorrhizal type is known to affect soil conditions (Tedersoo and Bahram, 2019 ), no significant difference in soil conditions was found between samples collected from AM dominated grassland and from mixed AM + EcM forest edge. This result indicates that the observed differences in EcM presence can be attributed to differences in inoculum availability and are not the result of differences in soil pH or nutrient availability as a cause. Both EcMF and AMF communities were affected by local soil conditions (respectively soil pH and moisture and soil pH and plant-available phosphorus). This is in line with other studies that have shown that abiotic conditions are important in structuring mycorrhizal communities (Boeraeve et al., 2018 , 2019 ; van der Linde et al., 2018 ; Van Geel et al., 2018 ). Our results further showed that EcMF and AMF community composition did not significantly differ between soil and root samples, suggesting that C. monogyna associates with a random selection of whatever is present in the soil surrounding its root system. Although EcM plants generally associate with a broad range of EcMF, most EcMF show at least some host specificity toward or preference for a particular host plant and EcM plants thus often differ in their EcMF communities, even when growing together (Bruns et al., 2002 ; Ishida et al., 2007 ; Lang et al., 2011 ). In contrast, AMF are considered to have a low host specificity, but some studies have found moderate host selectivity in both grasslands and forests (Öpik et al., 2009 ; Sepp et al., 2019 ). The fact that no differences in mycorrhizal community compositions were found between root and soil samples could be an indication that EcMF colonization of C. monogyna roots is due to opportunistic behavior of the tree, the EcMF or both."
} | 3,826 |
39560151 | PMC11727241 | pmc | 1,614 | {
"abstract": "Abstract The memristor has recently demonstrated considerable potential in the field of large‐scale data information processing. Metal halide perovskites (MHPs) have emerged as the leading contenders for memristors due to their sensitive optoelectronic response, low power consumption, and ability to be prepared at low temperatures. This work presents a comprehensive enumeration and analysis of the predominant research advancements in mechanisms of resistance switch (RS) behaviors in MHPs‐based memristors, along with a summary of useful characterization techniques. The impact of diverse optimization techniques on the functionality of perovskite memristors is examined and synthesized. Additionally, the potential of MHPs memristors in data processing, physical encryption devices, artificial synapses, and brain‐like computing advancement of MHPs memristors is evaluated. This review can prove a valuable reference point for the future development of perovskite memristors applications. In conclusion, the current challenges and prospects of MHPs‐based memristors are discussed in order to provide insights into potential avenues for the development of next‐generation information storage technologies and biomimetic applications.",
"conclusion": "3.6 Conclusion This section presents a summary of the effect of optimization strategies on the performance of memristors, with a focus on passivation of perovskite memristors, adjustment of the crystal structure, modification of the material dimensions and doping. Since the majority of perovskite memristor instability stems from the external water and oxygen erosion of the perovskite film through the GBs, enhancing device stability could be improved by affecting the crystallite size through passivation, reducing the material dimension to isolate water and oxygen with hydrophobic organic cations, or by preparing a single‐crystal perovskite. To achieve a large on/off ratio and better endurance, adjusting the energy level to obtain a suitable Schottky barrier could be considered. Furthermore, stable migration paths and low high‐resistance currents could also be obtained by dimensionally restricting the carrier transport direction and migration rate. Since each of these optimization approaches possesses distinctive characteristics and some are mutually reinforcing, it is difficult to accurately delineate the impact of these improvement strategies on memristor performance.\n\n5 Prospects and Conclusions In summary, for the demonstration of the RS mechanism of perovskite memristors, the most representative is the in‐situ PL technique, which visualizes the formation and breakage of conductive channels inside the device in a real‐time manner. Since perovskite materials have a wide range of applications, we analyze the advantages of perovskite‐based memristors and conclude that low‐dimensional perovskites currently exhibit superior memristor performance in response to recent reports. Subsequently, the applications of perovskite memristors are discussed, and due to their unique optical properties, the most promising and potential applications should be the applications in optical synapses and visual neural networks. Then, we provide an overview of the current challenges of perovskite memristors, including RS mechanisms, device stability, fabrication integration techniques, and neural synapses. Although in‐situ PL techniques are currently available, more in‐situ techniques will further advance the study of mechanisms. Electrochemical impedance tests and principal calculations will also lead to a deeper understanding of the RS mechanism. Long‐term stability is an important challenge for commercialization, and since perovskites are inherently environmentally sensitive, the development of perovskite memristors with excellent stability remains a focus of current research. At the same time, efforts to develop perovskite memristors with high integration, multifunctional devices compatible with traditional CMOS processes are also conducive to subsequent commercialization. In particular, the development of perovskite memristors with very low power consumption, excellent stability, and excellent light absorption is still needed in optical neuromorphic computing. The applications of MHPs memristors in information storage and encryption technology, logic operation, biomimetic synapses, neural network computing, and other fields show remarkable potential in recent years. In this review, we summarize the RS mechanism, performance optimization strategies, and the status of multi‐disciplinary applications of MHPs memristors, as well as discuss their major research advances. With the development of characterization techniques, the understanding of the RS mechanism of memristors is gradually becoming clearer. Many reports have been published on the characterization of CFs of MHPs memristors with TEM, EDS, AFM, etc., and a recent one utilizing the in‐situ PL technique is even more remarkable owing to its ability to dynamically observe the formation and breakage of CFs. The low‐dimensional perovskite materials (1D and 0D perovskite) are presently demonstrating superior performance in the field of memristors, which show more competitive performance compared to other perovskite memristors, such as lower high‐resistance current, larger switching window, faster switching speed, and more stable performance. Furthermore, MHPs‐based memristors show significant potential at application prospects in information storage, data processing, flexible wearable devices, logic operations, biological synapses, and brain‐like neural morphology. Notably, the unique optical properties of perovskite materials make it easier to realize optical storage capabilities and optical synapse cells, which makes them highly promising for the realization of light‐based visual neural networks. However, the current application of MHPs memristors is still in the early stage of research, and there is still a long way to construct a complete system architecture. To that end, we will discuss the main challenges and our views on future research trends for MHPs memristors. 5.1 RS Mechanisms So far, the RS mechanism of MHPs memristors reported mainly includes CFs model, interface RS mechanism, and charge capture/de‐capture model. However, these mechanisms have not been clearly distinguished, in particular, for the mechanism of multiple filaments competition/coexistence in carbon fiber models, there is no more direct characterization method to support this claim. In addition, the memristor may have multiple working mechanisms, which need further research and proof. The understanding of the mechanisms of MHPs memristors is conducive to the selection of memristor materials and the optimization of performance. At present, a variety of characterization methods have been used to study the mechanism of perovskite memristors. A work in 2024 showed the significant role of in situ PL technique in demonstrating the fracture of perovskite memristor filament formation. [ \n \n 114 \n \n ] This suggests that in situ techniques can visualize the formation and destruction of conductive channels inside perovskite memristors. This study shows that the formation and destruction of conductive channels inside perovskite memristors can be visualized using in‐situ testing techniques. More in situ characterization techniques are expected to investigate the mechanism of resistance variation in perovskite memristors. Although there have been some reports of using electrochemical methods such as impedance mapping to study the ion motion inside MHPs memristors, [ \n \n 129 \n , \n 264 \n \n ] due to the complex ion‐electron system inside MHPs, a lot of work is still needed to gradually quantify and analyze the ion‐electron motion. Similarly, there are still few reports on first principal calculations of perovskite memristors, and more research on theoretical calculations of the mechanisms of perovskite memristors is still needed. 5.2 Long‐Term Reliability For most commercial products, the retention time is expected to be at least 10 years, regardless of whether the device is operating or standby. [ \n \n 265 \n \n ] Endurance provides an intuitive measure for the service life and working efficiency of memristors, even reaching 10 12 for oxide‐based memristors. [ \n \n 266 \n \n ] However, the MHPs memristor is still far from these standards. The long‐term reliability of the MHPs memristor still needs to be improved. Moreover, due to the instability of MHPs itself in high temperature and humidity environments, how to adopt effective strategies to enhance the environmental reliability of the device is an urgent issue to be solved. Due to the different crystal structures derived from different materials, as well as the different preparation methods, it is difficult to determine which material has the best memristor performance. However, for the time being, a lower‐dimensional approach does result in superior memristor performance. As the lower‐dimensional perovskite material has a quantum confinement effect, which limits carrier transport and results in a larger switching ratio, the device also has excellent environmental stability due to the presence of large organic hydrophobic cations. Although researchers have reported numerous strategies that could be beneficial to improve the environmental reliability of devices, these methods introduce cumbersome preparation processes with higher costs, which are not conducive to the commercial production of memristors. Thus, it is necessary to develop the MHPs memristor with novel structures and modification strategies. It is worth noting that the sensitive response of MHPs materials to illumination will be expected to achieve low‐power optoelectronic devices, which is a significant advantage to satisfy the practical demands, and requires continued efforts to be explored. 5.3 Challenges of Integration Device MHPs memristor arrays have shown excellent performance in many applications. Nonetheless, the integrated density of these MHPs memristor arrays is low. Highly integrated devices with a small spatial volume and the ability to collect and process larger amounts of data need to be further studied. This requires researchers to continuously optimize the device fabrication process, explore and solve the problems of crosstalk between devices and structural damage by Joule heat in the case of high‐density integration. Meanwhile, there are only a few researches that simply integrate MHPs memristors with other types of devices (such as solar cells and detectors, etc.) to realize multifunctional applications, but most of the work is still based on the memristor itself and has not been integrated with other devices. Moreover, the fabrication of MHPs memristors differs greatly from the traditional CMOS process, which could be an obstacle to multifunctional integrated chips. With the deepening of the knowledge and research of memristors in science and industry, we boldly predict that the multifunctional integrated memristor devices will also become one of the research hotspots. Especially for the application of bionic synapses, it may lead to more abundant bionic devices. Integrating MHPs memristors with other types of electronic devices or directly constructing optical integrated devices‐based perovskites will greatly promote and expand the development of the memristor field. 5.4 Synaptic Devices and Visual Neural Networks Although more applications of memristors are being explored, such as reservoir computing, encryption devices, etc., the most prominent feature of perovskite material is its unique light‐absorbing ability. The future trend of perovskite memristors is focused on optical devices. One of the most popular applications is to realize optical synapses and thus achieve a comprehensive simulation of biological vision. To simulate biological synapses, the first step is to reduce the power consumption of the device to the human brain level of ≈1–100 fJ per spike or even lower. [ \n \n 267 \n \n ] At the same time, the synaptic device should have excellent consistency and stability for accurate signaling. Many groups are already investigating the possibility of developing biomimetic vision systems based on MHPs memristors. [ \n \n 79 \n , \n 250 \n \n ] The biological eye is composed of trillions of visual nerve cells, and some organisms are capable of capturing higher‐frequency flashes of information. In order to obtain a wider range of visual information and the ability to capture information about moving objects, it is necessary for vision devices to have a very high degree of integration, fast response to optical signals, and the ability to tolerate a large number of writes and erasures. We believe that with the endless efforts of researchers, the RS mechanism of MHPs memristors will be analyzed more thoroughly and its reliability will be improved. Furthermore, as more interdisciplinary researchers establish communications and collaboration, the MHPs memristors will also be more thoroughly researched for application in multiple fields. Perhaps in the foreseeable future (the development blueprint is illustrated in Figure \n 11 \n ), brain computing, intelligent robots, electronic prosthetic, and so on in science fiction will appear in front of us. Figure 11 The development process and prospects of perovskites memristors.",
"introduction": "1 Introduction Edge devices, cloud computing, and the Internet of Things (IoTs) are continuously advancing our lives towards greater intelligence. [ \n \n 1 \n , \n 2 \n , \n 3 \n \n ] Nevertheless, enormous amounts of data necessitate more powerful computers for efficient processing. As chip feature sizes constantly approach their physical limit, it becomes increasingly difficult to enhance the processing power of computers, which prompts researchers to explore alternative devices and computer architectures. [ \n \n 4 \n , \n 5 \n , \n 6 \n , \n 7 \n \n ] The introduction of the memristor, a fourth passive device, has shown promise in overcoming the issue of data transfer in conventional computing systems. [ \n \n 8 \n , \n 9 \n , \n 10 \n \n ] This issue, known as the “memory wall,” arises from the separation of computing and storage modules in the von Neumann system. [ \n \n 11 \n , \n 12 \n , \n 13 \n \n ] Several reports have exhibited that memristors enable in‐memory computing with low power consumption, fast response, and higher integration, [ \n \n 14 \n , \n 15 \n \n ] which prompted researchers to investigate the application of memristors in the information field and achieve significant breakthroughs. Due to the extensive data transfer between portable devices, data leakage has become a significant problem in the field of information security. Unlike software encryption, the use of encryption methods such as Physical Unclonable Functions (PUFs) contributes to the prevention of hacking. [ \n \n 16 \n , \n 17 \n \n ] In particular, since the random characteristics of the device cannot be easily simulated, the memristor‐based hardware security primitives could effectively increase the decryption time and provide information security. [ \n \n 18 \n , \n 19 \n , \n 20 \n \n ] In addition to the random property that allows them to act as an encrypted source, extensive studies have demonstrated the abilities of memristors to exhibit nonlinearities and to respond to variations in the amplitude and frequency of input signals, which is similar to the capacity of neurons to tune synaptic transmission by potential differences generated by variations in the concentrations of neurotransmitters (K + and Ga + , etc.). [ \n \n 21 \n , \n 22 \n , \n 23 \n , \n 24 \n , \n 25 \n , \n 26 \n , \n 27 \n , \n 28 \n \n ] This similarity allows the memristor to mimic the various biological forms of synaptic plasticity and, moreover, to mimic the memory‐learning behavior of the brain. Therefore, the memristor has emerged as a leading candidate for the realization of bionic devices and brain‐like computation. Additionally, memristors have garnered significant attention in the field of artificial neural networks (ANNs). [ \n \n 29 \n , \n 30 \n , \n 31 \n , \n 32 \n , \n 33 \n , \n 34 \n \n ] In recent years, there has been a significant amount of research conducted on memristor‐based neural networks, resulting in notable advancements in device materials, algorithm optimization, and recognition and classification. [ \n \n 35 \n , \n 36 \n , \n 37 \n , \n 38 \n , \n 39 \n \n ] However, the majority of these achievements have been limited to software‐based simulations, and the development of a fully hardware‐implemented memristor‐based neural network remains a significant technological challenge. Fortunately, the team led by Huaqiang Wu at Tsinghua University has successfully developed the world's first memristor chip capable of on‐chip learning. This achievement, made possible by the fully hardware‐implemented memristor convolutional neural network (CNN) technology, confirms the viability of using memristors in neural networks. [ \n \n 40 \n \n ] Furthermore, it encourages other researchers to investigate various device materials and explore wider applications in the field of memristor research. Various materials, including oxide materials, sulfide materials, organic polymers, and perovskite, have exhibited the RS phenomenon. [ \n \n 41 \n , \n 42 \n , \n 43 \n , \n 44 \n , \n 45 \n , \n 46 \n \n ] Regrettably, the implementation of memristors has been hindered by constraints such as the inadequate environmental stability of organic materials, the intricate process of sulfur compounds, and the elevated preparation temperature of oxide materials. [ \n \n 45 \n , \n 47 \n \n ] Metal halide perovskites (MHPs) have recently attracted considerable interest due to their superior flexibility, structural adjustability, low power consumption, and excellent photoelectric response. [ \n \n 48 \n , \n 49 \n , \n 50 \n , \n 51 \n , \n 52 \n , \n 53 \n \n ] MHPs are characterized by the molecular formula ABX 3 , where the A represents an organic or inorganic monovalent cation such as MA + (asmethylammonium), or FA + (formamidine) located at the apex angle of the cubic lattice. Site is occupied by a divalent cation—either Pb 2+ or Sn 2+ , positioned at the center of the cubic lattice. Meanwhile, the X represents the halogen ions (Cl − , Br − , or I − ) that are located at the face‐centered positions. The substitution of organic methylamine with an inorganic ion, such as cesium or rubidium, results in the formation of a fully inorganic perovskite. Moreover, the perovskite structure permits the substitution of both the B and X sites, thereby illustrating the structural adaptability of MHP materials. Due to their numerous advantages, MHPs have emerged as highly promising candidates for a variety of optoelectronic applications, including solar cells, photodetectors, light‐emitting diodes (LEDs), and field‐effect transistors (FETs). [ \n \n 54 \n , \n 55 \n , \n 56 \n , \n 57 \n , \n 58 \n , \n 59 \n , \n 60 \n , \n 61 \n , \n 62 \n \n ] Additionally, research has indicated that MHP materials are well‐suited for memristors. [ \n \n 63 \n \n ] In particular, the combination of perovskite materials' photosensitivity with memristors allows for the creation of a novel class of perovskite‐based photoelectric memristors, with potential applications that extend beyond the capabilities of conventional oxide memristors. MHPs memristors have attracted great attention as new application areas of perovskite materials after the first report of MAPbI 3‐x Cl x ‐based memristors by Yoo et al. in 2015. [ \n \n 64 \n \n ] Later, in 2017, Kim et al. [ \n \n 65 \n \n ] and Zhu et al. [ \n \n 66 \n \n ] initially described the ionic vacancy migration motion inside MHPs, which provided an explanation of the resistive mechanism of perovskite memristors. Subsequently, additional research on perovskite memristors was conducted, including investigations into lead‐free perovskite, [ \n \n 67 \n , \n 68 \n \n ] inorganic perovskite, [ \n \n 69 \n \n ] perovskite quantum dots, [ \n \n 70 \n , \n 71 \n \n ] perovskite nanocrystals [ \n \n 72 \n , \n 73 \n \n ] and other perovskite memristors with varying compositions, dimensions, and structures, as well as studies on the optimization of perovskite memristor performance through passivation [ \n \n 74 \n , \n 75 \n \n ] and doping. [ \n \n 76 \n \n ] And in 2016, Xu et al. [ \n \n 77 \n \n ] simulated synaptic plasticity with perovskite memristors which pioneered the exploration of artificial synaptic devices based on MHPs memristors. Numerous applications of MHPs memristors in neuromorphic computing have been reported subsequently. In 2020, Zhu et al. demonstrated a memristor‐based reservoir computation (RC) system that enables effective neural signal analysis with high spatiotemporal accuracy and potentially closed‐loop feedback control. [ \n \n 78 \n \n ] In the same year, Gu et al. [ \n \n 79 \n \n ] prepared hemispherical retinas based on high‐density perovskite nanowire arrays prepared by gas‐phase method. This work not only fabricated highly integrated optoelectronic devices on non‐planar substrates, but also inspired subsequent research on artificial bionic devices. Subsequently additional perovskite‐based synaptic devices and neural system computations have been successively reported. Furthermore, additional research has been conducted on perovskite‐based synaptic devices and neural system computations. For example, further studies on PVK‐based RC system by Yang et al. [ \n \n 80 \n \n ] in 2022, and studies on two forms of perovskite memristors, drift and diffusion, reported by John et al. [ \n \n 81 \n \n ] and Wang et al. [ \n \n 82 \n \n ] in 2022 and 2023, respectively. Given the nearly decade‐long history of perovskite memory resistor development, there is a substantial body of literature comprising review articles on perovskite memristors, offering diverse perspectives on this topic. Fang et al. provided a summary of the development of lead‐based to lead‐free perovskite memristors from the perspective of the toxicity and stability of perovskites, [ \n \n 47 \n \n ] and Gogoi et al. provided an overview of the development and application perspectives of flexible perovskite‐based memristors. [ \n \n 83 \n \n ] They summarized and analyzed from the perspective of device characterization. Additionally, there are also review articles that describe things from the perspective of material classification. For instance, Guan et al. and Liu et al. summarized the applications of low‐dimensional perovskite materials and nanostructured perovskite materials in the field of memristors, respectively. [ \n \n 84 \n , \n 85 \n \n ] Additionally, there are also reviews that focus on applications, such as overviews on synaptic devices, [ \n \n 86 \n \n ] neural system computation, [ \n \n 87 \n \n ] RC, [ \n \n 88 \n \n ] artificial intelligence, [ \n \n 89 \n \n ] etc. Unlike these published review articles, in our review, we start from the point of view of optimizing device performance, from the device working mechanism, material selection, device structure, and different memristor application fields are more comprehensive summary and discussion. Firstly, the conduction mechanisms based on MHPs memristors were discussed, including the CFs model, interface‐type RS, and charge trapping/detrapping. Despite the mechanism have been discussed in the reported reviews of perovskite memristors, we aim to provide a comprehensive overview of the mechanism of perovskite memristors, with an emphasis on the equipment utilized in these reports for demonstrating the mechanism, which has not yet been overviewed in the previous reviews. Moreover, we then provide an overview of the optimization strategies of researchers on perovskite memristors in recent years. Similarly, numerous reviews of perovskite memristors with excellent performance, such as the previously mentioned reviews from a low‐dimensional material perspective or from the perspective of device parameters, [ \n \n 90 \n \n ] but a comprehensive overview encompassing diverse optimization strategies focus on enhancing the performance of MHPs‐based memristors remains absent. We analyze and demonstrate how optimization strategies such as interface passivation, crystal modulation, dimensional modulation, and doping influence various aspects of the performance of MHPs memristor including storage window, endurance, retention time, switching speed, power consumption, etc. In part four, we present and discuss in detail the potential applications of MHPs memristors such as information computing, information security, bionic synapses, and ANNs from multiple perspectives. In contrast to traditional Complementary‐Metal‐Oxide‐Semiconductor (CMOS) logic gates, a solitary memristor is capable of performing logic operations, such as “and” and “or”. Furthermore, the random character of the resistance fluctuation in the MHPs memristor may also be exploited in the domain of information security. We provide a comprehensive analysis of MHPs memory resistors simulating different synaptic behaviors, focusing on their potential application in bionic synapses. Finally, we discuss the utilization of MHPs synaptic devices in brain‐like computational neural networks and bionic visual neural networks. Finally, we present an overview of the potential obstacles and prospects for the future advancement of MHP‐based memristors."
} | 6,347 |
24379809 | PMC3863755 | pmc | 1,615 | {
"abstract": "Microorganisms capable of reducing or oxidizing structural iron (Fe) in Fe-bearing phyllosilicate minerals were enriched and isolated from a subsurface redox transition zone at the Hanford 300 Area site in eastern Washington, USA. Both conventional and in situ “i-chip” enrichment strategies were employed. One Fe(III)-reducing Geobacter ( G. bremensis strain R1, Deltaproteobacteria ) and six Fe(II) phyllosilicate-oxidizing isolates from the Alphaproteobacteria ( Bradyrhizobium japonicum strains 22, is5, and in8p8), Betaproteobacteria ( Cupriavidus necator strain A5-1, Dechloromonas agitata strain is5), and Actinobacteria ( Nocardioides sp. strain in31) were recovered. The G. bremensis isolate grew by oxidizing acetate with the oxidized form of NAu-2 smectite as the electron acceptor. The Fe(II)-oxidizers grew by oxidation of chemically reduced smectite as the energy source with nitrate as the electron acceptor. The Bradyrhizobium isolates could also carry out aerobic oxidation of biotite. This is the first report of the recovery of a Fe(II)-oxidizing Nocardioides , and to date only one other Fe(II)-oxidizing Bradyrhizobium is known. The 16S rRNA gene sequences of the isolates were similar to ones found in clone libraries from Hanford 300 sediments and groundwater, suggesting that such organisms may be present and active in situ . Whole genome sequencing of the isolates is underway, the results of which will enable comparative genomic analysis of mechanisms of extracellular phyllosilicate Fe redox metabolism, and facilitate development of techniques to detect the presence and expression of genes associated with microbial phyllosilicate Fe redox cycling in sediments.",
"conclusion": "Conclusion A culturing campaign successfully recovered Fe-phyllosilicate redox cycling organisms from sediments and groundwater in the vicinity of a distinct redox transition in the Hanford 300 Area subsurface. The recovered organisms are phylogenetically related to organisms detected in 16S rRNA gene libraries for Hanford 300 Area sediments. Hence, the isolates represent appropriate targets for further physiological and genomic studies of Fe-phyllosilicate redox metabolism relevant to the Hanford subsurface. To this end, each of the Fe(III)-reducing and Fe(II)-oxidizing isolates described above are currently undergoing whole genome sequencing through the U.S. Department of Energy's Joint Genome Institute (JGI) Microbial Isolates sequencing program. The results of this project will expand significantly our knowledge of the diversity of lithotrophic Fe(II) oxidation metabolism. Of particular interest is the mechanism(s) by which the Fe(II)-oxidizing taxa utilize Fe(II) in insoluble Fe-silicate minerals such as smectite and biotite. These minerals are virtually insoluble at neutral pH, which means that the organisms must possess specific machinery to extract electrons from the mineral surface. The emerging picture of how neutral pH Fe(II) oxidizers and Fe(III) reducers may utilize analogous strategies to carry-out extracellular electron transfer (Hartshorne et al., 2009 ; Bird et al., 2011 ; Liu et al., 2012 ; Roden, 2012 ) will be informed and expanded by the sequencing project. Development of genome-enabled techniques to detect the presence and expression of genes associated with solid-phase Fe(II) oxidation will eventually provide direct insight into the influence of enzymatic Fe(II) oxidation on biogeochemical processes in Hanford 300 Area and other subsurface environments. Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.",
"introduction": "Introduction Subsurface sediments and groundwater at the 300 Area of the Hanford Site in southeastern Washington State are contaminated with large quantities of radioactive waste generated during Cold War Era nuclear weapons production. Process wastewater infiltrated through the ca. 10-m-thick vadose zone below the disposal facilities, leading to a groundwater uranium (U) plume that has persisted for decades (Peterson and Connelly, 2001 ; Christensen et al., 2004 ). Other subsurface environments at Hanford are similarly contaminated with large quantities of technetium (Tc)-99 (Zachara et al., 2007 ). The valence state of U and Tc is a crucial factor determining their mobility in the subsurface. Both U and Tc are typically present as soluble anionic species under oxic conditions, but can be converted to insoluble UO 2 (uraninite) and TcO 2 (technetium dioxide) phases through both biological and abiotic processes under anoxic conditions (Borch et al., 2010 ). Iron (Fe) bearing mineral phases are likely to play a central role in controlling the U and Tc stability, e.g., through (1) oxide- or phyllosilicate-associated Fe(III) serving as an electron acceptor for dissimilatory metal-reducing bacterial (DMRB) that are capable of simultaneous enzymatic Fe(III) and U(VI) or Tc(VI) reduction (e.g., Jeon et al., 2004 ; Burke et al., 2005 ); or (2) Fe(II)-bearing phases, potentially biogenic in origin, serving as abiotic reductants for U(VI) or Tc(VI) (e.g., Fredrickson et al., 2004 ; Jeon et al., 2005 ; Lee et al., in press ). The upper, unconfined Hanford 300 Area sediments are comprised of relatively unweathered, Pleistocene-age glacial deposits of the Hanford formation. These sediments are dominated by basaltic and granitic fragments with interspersed silt and clay-sized phyllosilicates (chlorites and ferruginous biedellites as well as some smectite) (Zachara et al., 2007 ). The older, Miocene-Pliocene-age Ringold Formation that underlies the Hanford formation contains more weathered sediments (Lindsey and Gaylord, 1990 ), which are dominated by dioctahedral smectite with traces of chlorite, kaolinite, illite, quartz, cristabolite, and feldspar (Peretyazhko et al., 2012 ). Oxic-anoxic transition zones are observed in fine-grained Ringold Formation sediments, which are likely the result of (at least in part) microbially-driven processes. In particular, there is a distinct redox transition near the top of the Ringold formation (Lin et al., 2012a ; Peretyazhko et al., 2012 ), below which a variety of DMRB taxa have been identified using molecular methods (Lin et al., 2012a ). Wet-chemical and spectroscopic analyses suggest that the transition from oxic to anoxic layers involves significant, presumably microbially-catalyzed, reduction of Fe(III) in phyllosilicates (Peretyazhko et al., 2012 ). Fe(II)-bearing phyllosilicates in Ringold sediments are potent reductants for Tc(VI) (Fredrickson et al., 2004 ; Peretyazhko et al., 2012 ), and hence the redox transition zone in the upper Ringold represents a potentially important barrier toward vertical Tc migration. Whether or not microbial activity plays a role in the oxidative transformation of Fe(II)-bearing phases (e.g., reduced phyllosilicates) in the vicinity of the redox transition is unknown. The purpose of this study was to isolate and identify microorganisms associated with Fe-phyllosilicate redox metabolism in Ringold formation clays and Hanford 300 Area groundwater. While the capacity for DMRB to reduce structural Fe(III) in phyllosilicates is well-established, much less is known about the potential for microbially-catalyzed oxidation of Fe(II)-bearing phyllosilicates (Dong et al., 2009 ). A key goal of the current work thus was to evaluate, using culture-based approaches, whether or not Hanford 300 Area sediment contains organisms that are capable of enzymatic oxidation of structural Fe(II) in clays and other Fe-silicate minerals. We also screened for the presence of Fe(III) phyllosilicate-reducing organisms. Information on the phylogenetic and physiological properties of Fe phyllosilicate redox cycling bacteria would be useful for developing tools to track the presence, abundance, and activity of Fe redox cycling organisms in the environment. In particular, such tools would be valuable for determining the role that such organisms may play in controlling the coupled redox speciation of Fe and metal/radionuclide contaminants such as U and Tc in subsurface sediments at Hanford and other U.S. DOE sites.",
"discussion": "Results and discussion Enrichment and isolation A large fraction (70–100%) of the 480 colonies transferred from i-chips targeting Fe(III) reducers resulted in the recovery of positive second-generation enrichments capable of utilizing either soluble Fe(III)-NTA, oxidized NAu-2 smectite, or a mixture of nitrate and fumarate as an electron acceptor (Table 2 ). Although none of the i-chip-derived Fe(III)-reducing enrichments were brought into pure culture, a Fe(III) phyllosilicate-reducing isolate designated strain R1 was recovered from conventional enrichments initiated with natural oxidized Ringold sediment (obtained from just above the redox transition zone) as the electron acceptor (see Table 4 ). The presence of active smectite reduction activity in enrichments (and the pure culture) was evidenced by a distinct color change in the mineral suspension (Figure A3A ). The Fe(III)-reducing isolate is 98.9% similar in 16S rRNA gene sequence to Geobacter bremensis (Straub et al., 1998 ; Straub and Buchholz-Cleven, 2001 ), and is therefore referred to hereafter as G. bremensis strain R1. G. bremensis was originally isolated from freshwater ditch sediments in Germany (Straub et al., 1998 ; Straub and Buchholz-Cleven, 2001 ), and belongs to the “ Geobacter subsurface clade 1” within the Geobacteraceae (Lovley et al., 2011 ). Table 2 Microbial recovery from i-chips A1 and A3 (see Figure A1 ) targeting Fe(III)-reducing microorganisms . Medium i-chip Colonies transferred Cultures recovered % recovery a Electron donor(s) Electron acceptors(s) Acetate + hydrogen Fe(III)-NTA A1 100 71 71 A2 100 96 96 Acetate + hydrogen Smectite A1 100 97 97 A2 100 100 100 Acetate + pyruvate + malate + hydrogen Nitrate + fumarate A1 30 29 97 A2 50 39 78 a Recovery was calculated as % of positive cultures on specific medium . The fractional recovery of lithotrophic Fe(II)-oxidizing enrichments from i-chip colony transfers (480 total) was much lower (7–10%) than in the case of Fe(III)-reducers, and was also much lower than that achieved when parallel i-chip colonies were transferred into heterotrophic medium with nitrate plus fumarate as electron acceptors (Table 3 ). Nevertheless, a variety of Fe(II)-oxidizing isolates were ultimately recovered from i-chip as well as conventional sediment enrichments, using both lithoautotrophic and mixotrophic isolation strategies (Table 4 ). The presence of aerobic and nitrate-reducing mineral oxidation activity was evidenced by a distinct color change in the biotite and reduced smectite suspensions, respectively (Figures A3B,C ). Three strains of Bradyrhizobium japonicum (one isolated lithoautotrophically and two isolated mixotrophically) and one each strain of and Cupriavidus necator , Dechloromonas agitata , and Nocardioides sp. (all isolated on mixotrophic medium) were chosen for further study. B. japonicum , C. necator , and D. agitata have been previously identified as nitrate-reducing Fe(II)-oxidizers (Chaudhuri et al., 2001 ; Shelobolina et al., 2012a ), whereas to our knowledge this is a first report of a Fe(II)-oxidizing Nocardioides species. Table 3 Microbial recovery from i-chips B1 and B3 (see Figure A1 ) targeting Fe(II)-oxidizing microorganisms . Medium i-chip Colonies transferred Cultures recovered % recovery a Electron donor(s) Electron acceptors(s) Chemically reduced NAu2 smectite Nitrate B1 100 7 7 B3 100 10 10 Biotite Oxygen B1 100 3 3 B3 100 9 9 Acetate + pyruvate + malate + hydrogen Nitrate + fumarate B1 20 12 60 B3 20 10 50 a Recovery was calculated as % of positive cultures on specific medium . Table 4 Fe redox cycling microorganisms isolated from Hanford 300 sediments . Strain designation (genbank accession number), Fe redox metabolism Original source Initial enrichment Isolated on No. of related isolates recovered Identification (closest cultured bacterium, % identity) Related 16S rRNA gene detected in sediment? Geobacter bremensis R1 (KF800712), Fe(III) reduction Enrichment, oxidized Ringold sed + acetate/H 2 Smectite + acetate/H 2 HFO/acetate roll tubes 4 Geobacter bremensis TMJ1 T , 98.9% Yes b Geobacter bemidjiensis , 97.6% Bradyrhizobium japonicum 22 (KF800709), Fe(II) oxidation i-chip, biotite Biotite + O 2 FeCl 2 /O 2 10+ Bradyrhzobium liaoningense 2281 T , 99.4% Yes a Bradyrhizobium japonicum is5 (KF800707), Fe(II) oxidation i-chip, biotite Biotite + O 2 Heterotrophic plates Bradyrhizobium japonicum USDA 6 T , 99.4% Bradyrhizobium japonicum in8p8 (KF800708), Fe(II) oxidation Enrichment, reduced Ringold sed + NO − 3 Biotite + NO − 3 Heterotrophic plates Cupriavidus necator A5-1 (KF800713), Fe(II) oxidation i-chip, biotite Biotite + O 2 Heterotrophic plates 3 Cupriavidus necator ATCC 43291 T , 98.6% “ Ralstonia eutropha ” H16, 98.4% Yes a Dechloromonas agitata is5 (KF800710), Fe(II) oxidation Enrichment, biotite + NO − 3 Fe(II)-NTA + NO − 3 Fe(II)-NTA/acetate/NO − 3 roll tubes 2 Dechloromonas agitata CKB T , 99.6% No Nocardioides sp. in31 (KF800711), Fe(II) oxidation Enrichment, biotite + NO − 3 Fe(II)-NTA + NO − 3 Fe(II)-NTA/acetate/NO − 3 Roll tubes 1 Nocardioides pyridinolyticus OS4 T , 97.9% Yes a The % values indicate the degree of similarity in 16S rRNA gene sequence . a 16S rRNA gene clone libraries (Lin et al., 2012b ) . b Quantitative PCR with Geobacter-specific primers (Lin et al., 2012a ) . Fe redox metabolism of the isolates Fe(III) reducer A growth experiment with Geobacter bremensis strain R1 using oxidized NAu-2 smectite as the electron acceptor and acetate as the electron donor showed direct coupling of cell growth to Fe(II) production and acetate consumption (Figure 1 ). The quantity of acetate consumed was approximately equal to the value of 0.2 mM expected for reduction of ca. 1.6 mmol L −1 of Fe(III). Approximately 14% of the total Fe(III) content of the smectite was reduced, comparable to values obtained in other microbial Fe(III) phyllosilicate reduction studies (Kostka et al., 1999 ; Shelobolina et al., 2003 ; Jaisi et al., 2005 ; Komlos et al., 2008 ; Mohanty et al., 2008 ). The isolate can also grow with amorphous Fe(III) oxide or fumarate as the electron acceptor (data not shown). Figure 1 Growth of Geobacter bremensis strain R1 with NAu-2 smectite as the electron acceptor and acetate as the electron donor . Data represent mean ± SD of triplicate cultures. Fe(II) oxidizers Bradyrhizobium japonicum strain 22 was derived from aerobic biotite enrichment cultures initiated with reduced Ringold sediment, and isolated under chemolithoautotrophic conditions via dilution to extinction in FeCl 2 /O 2 medium. Subsequent studies confirmed that strain 22 was capable of repeated chemolithoautotrophic growth with soluble Fe(II) as the sole electron donor and oxygen as the electron acceptor (Figure 2A ). The cell yield in these experiments was approximately 5 × 10 7 cells per μmol Fe(II) oxidized, assuming that most of the Fe(II) oxidation took place biologically, which is typically the case in non-mixed, diffusion-controlled Fe(II) oxidation experiments such as those employed here (Sobolev and Roden, 2001 ; Roden et al., 2004 ). This cell yield is comparable (within a factor of 2–3) to that observed for other neutral-pH chemolithoautotrophic Fe(II) oxidizing bacteria (Neubauer et al., 2002 ; Sobolev and Roden, 2004 ). Strain 22 could also aerobically oxidize structural Fe(II) in biotite (Figure 2B ), and repeatedly oxidized structural Fe(II) in reduced NAu-2 smectite with nitrate as the electron acceptor (Figure 2C ). The extent of biotite oxidation (ca. 5% of total mineral Fe(II) content) was similar to that observed during growth of the chemolithoautotrophic Fe(II)-oxidizing, nitrate-reducing “Straub culture” with biotite as the energy source (Shelobolina et al., 2012b ), and the extent of reduced smectite oxidation (ca. 40%) was comparable to that observed for the other Fe(II)-oxidizing isolates described here (see Figure 3 ). Figure 2 Fe(II) oxidation by Bradyrizobium sp. strain 22: (A) Repeated growth in aerobic FeCl2 medium; (B) aerobic oxidation of biotite (inoculum grown previously several times in aerobic biotite medium); (C) repeated growth in reduced NAu-2/nitrate medium . Data in panel A show the mean ± SD of five replicate cultures; data in panel (B) represent the results from a single culture that had been transferred several times in identical medium before conducting this experiment; data in panel (C) show the mean ± SD of triplicate cultures. Figure 3 Growth of mixotrophic Fe(II)-oxidizing isolates on reduced NAu-2 smectite with nitrate as the electron acceptor. (A) \n Bradyrhizobium sp. strain in8p8; (B) \n Bradyrhizobium sp. strain bis5; (C) \n Cupriavidu necator strain A5; (D) \n Dechloromonas agitata strain dis5; (E) \n Nocardioides sp. strain in31; (F) sterile control. Data represent the mean ± SD of triplicate cultures. Symbols: •, Fe(II); ▴, nitrate; ▵, nitrite; ▿, cells. Strains of B. japonicum are known to be capable of autotrophic growth with H 2 as the electron donor (Neal et al., 1983 ; Franck et al., 2008 ). In addition, B. japonicum strain USDA110 is capable of chemolithotrophic growth with thiosulfate as the sole electron donor (Masuda et al., 2010 ). Although Fe(II)-phyllosilicate oxidizing strains of Bradyrhizobium were recently isolated from a clay-rich subsoil in Wisconsin (Shelobolina et al., 2012a ), the NAu-2 smectite employed in that as well as the present study was not completely free of associated organics. Thus, our studies with FeCl 2 /O 2 medium represent the first demonstration of the ability of B. japonicum to grow via Fe(II) oxidation under fully chemolithoautotrophic conditions. The inferred capacity for CO 2 fixation was confirmed through preliminary whole genome sequencing of strain 22 genomic DNA at the University of Wisconsin Biotechnology Center. The Illumina sequence was assembled de novo using the CLC Genomics Workbench, and annotated through RAST (Version 4.0). The annotated genome revealed the presence of the entire Calvin-Benson CO 2 fixation subsystem (see Figure A4 ). All of the organisms isolated as mixotrophs ( B. japonicum strains is5 and in8p8, D. agitata strain is5, C. necator strain A5-1, and Nocardioides sp. strain in31) grew in medium with chemically reduced NAu-2 smectite as the electron donor and nitrate as the electron acceptor (Figure 3 ). Each strain was grown twice in the reduced smectite/nitrate medium prior to conducting the growth experiments shown in the Figure 3 . Growth generally ceased after 15–36 days, when there was still 0.5 M HCl-extractable Fe(II) present in the medium. The incomplete oxidation of structural Fe(II) in smectite has been observed previously in abiotic (Shen and Stucki, 1994 ; Yang et al., 2012 ) and biotic oxidation studies (Shelobolina et al., 2012a ); a possible explanation for this phenomenon is that collapse of smectite layers during Fe(III) reduction makes a portion of the structural Fe(II) inaccessible to subsequent abiotic or enzymatic attack (Stucki, 2011 ). There was modest accumulation of nitrite (ca. 0.5–1.5 mM) during Fe(II) oxidation, and thus abiotic reaction of nitrite with reduced NAu-2 smectite could have contributed to the observed Fe(II) oxidation activity. However, recent studies of NAu-2 oxidation by organisms isolated from clay-rich subsoils showed that the kinetics of this abiotic reaction are such that enzymatic oxidation is the predominant mechanism for nitrate-driven smectite oxidation (Shelobolina et al., 2012a ). This conclusion is supported by the cell yields in these experiments, which varied from 1.5–5 × 10 7 cells per upmuol Fe(II) oxidized, well within the range observed for growth of B. japonicum strain 22 and other aerobic Fe(II) oxidizers in FeCl 2 /O 2 medium (see above), as well as nitrate-dependent growth of the chemolithoautrophic Fe(II)-oxidizing “Straub culture” with aqueous or solid-phase Fe(II) as the sole energy source (Blöthe and Roden, 2009 ; Shelobolina et al., 2012b ). Relevance of the isolates to in situ phyllosilicate Fe redox metabolism 16S rRNA gene sequences of the Fe(III)-reducing and Fe(II)-oxidizing isolates were compared to sequences contained in the Greengenes (Desantis et al., 2006 ) database, which was augmented with the Hanford 300 Area subsurface sediment full-length 16S rRNA gene clone library database from Lin et al. ( 2012b ), as well as a 16S rRNA gene 454 pyrosequence amplicon database for Hanford 300 Area of groundwater bacteria (Lin et al., 2012c ). A bootstrap-supported neighbor-joining tree (Figure 4 ) was created based on evolutionary distances computed using the Kimura 2-parameter method in MEGA (Tamura et al., 2007 ). All of the isolates were related, at least to the genus level, to taxa identified in conventional and pyrosequencing libraries of 16S rRNA genes from the Hanford 300 Area subsurface. Thus, our enrichment and isolation studies successfully recovered organisms related to those previously identified by culture-independent approaches. Figure 4 Neighbor-joining tree of16S rRNA gene sequences for the isolates (circles) with the presence of the nearest reference strains and the Hanford sediment (solid triangles) and groundwater (open triangles) clones . Bootstrap values less than 50% were not shown. Numbers in parenthesis indicate the depth of the Hanford sediment sample in meter and the percentage of this clone/sequence in each clone library at the specific depth or the range of their relative abundance in groundwater pyrosequencing library. The recovery of a Fe(III) phyllosilicate-reducing Geobacter isolate from Ringold formation sediments was not unexpected given that Fe(III) phyllosilicates contribute a significant portion of Fe(III) in Ringold oxidized sediment (Peretyazhko et al., 2012 ), and that multiple species of Geobacteraceae are known to reduce structural Fe(III) in phyllosilicates (Shelobolina et al., 2007 ). Organisms from the Geobacteraceae were shown to be present in relatively high abundance (as indicated by qPCR analysis of 16S rRNA genes) in the vicinity of the redox transition in the upper Ringold formation (Lin et al., 2012a ). The facile recovery of active Fe(III) phyllosilicate-reducing enrichments from i-chip colony transfers (Table 2 ) is likewise consistent with the presence of a Fe(III)-reducing community in the vicinity of the redox transition in Ringold sediments. In contrast to these findings, recent studies of the potential for Fe(III) reduction in Ringold formation sediments (from both above and below the redox transition) with and without added organic carbon (0.9 mM acetate, 0.6 mM lactate, and 0.3 mM glucose) yielded negative results (Lee et al., 2012 ). There is no obvious reason for this discrepancy, as our initial enrichment culturing showed substantial reduction (14–21%) of oxidized Ringold sediment by native Fe(III)-reducing populations. In addition, studies of the potential for phyllosilicate Fe redox cycling in Ringold sediment employing a pure culture of G. sulfurreducens have verified that Fe(III) phases in oxidized Ringold sediment are available for microbial reduction (Shelobolina et al., unpublished data), and recent microcosm experiments have demonstrated the potential for reduction of Fe(III) phases in Ringold sediment from just below the redox transition (Percak-Dennett and Roden, unpublished data). It seems possible that heterogeneities in sediment subsamples used in different experiments could account for the lack of Fe(III) reduction in Ringold sediments reported by Lee et al. ( 2012 ). We recovered a suite of lithotrophic organisms capable of oxidizing structural Fe(II) in smectite with nitrate (or, in some cases, biotite with O 2 ) as the electron acceptor from Hanford 300 Area sediments and groundwater, all of which have been detected in previous molecular surveys. In particular, Bradyrhizobium -related taxa constituted a significant fraction (up to 5%) of 16S rRNA gene sequences in clone libraries from sediments above the redox transition (see Figure 4 ). Does this imply that such organisms are playing an active role in Fe silicate mineral redox cycling in Hanford sediments? Although the isolates reported here were not screened for their ability to oxidize native reduced Fe(II) phases present in Ringold formation sediments, experiments with the chemolithoautotrophic Fe(II)-oxidizing, nitrate-reducing “Straub culture” [which is capable of oxidizing structural Fe(II) in both biotite and smectite; Shelobolina et al. ( 2012b ); Xiong ( 2013 )] indicate that such phases are in fact susceptible to partial enzymatic oxidation. Thus, it seems feasible that Fe(II)-oxidizing lithotrophs could gain energy from oxidation of the large quantities of structural Fe(II) present in reduced Ringold sediments. Recent sediment microcosm experiments with reduced Ringold sediments have demonstrated the potential for partial biologically-mediated oxidation of solid-phase Fe(II) with nitrate as the electron acceptor (Percak-Dennett and Roden, unpublished data)."
} | 6,366 |
25502908 | PMC4986457 | pmc | 1,616 | {
"abstract": "Methanogens are methane-producing archaea that plays a key role in the global carbon cycle. To date, the evolutionary history of methanogens and closely related nonmethanogen species remains unresolved among studies conducted upon different genetic markers, attributing to horizontal gene transfers (HGTs). With an effort to decipher both congruent and conflicting evolutionary events, reconstruction of coevolved gene clusters and hierarchical structure in the archaeal methanogen phylogenetic forest, comprehensive evolution, and network analyses were performed upon 3,694 gene families from 41 methanogens and 33 closely related archaea. Our results show that 1) greater than 50% of genes are in topological dissonance with others; 2) the prevalent interorder HGTs, even for core genes, in methanogen genomes led to their scrambled phylogenetic relationships; 3) most methanogenesis-related genes have experienced at least one HGT; 4) greater than 20% of the genes in methanogen genomes were transferred horizontally from other archaea, with genes involved in cell-wall synthesis and defense system having been transferred most frequently; 5) the coevolution network contains seven statistically robust modules, wherein the central module has the highest average node strength and comprises a majority of the core genes; 6) different coevolutionary module genes boomed in different time and evolutionary lineage, constructing diversified pan-genome structures; 7) the modularized evolution is also closely related to the vertical evolution signals and the HGT rate of the genes. Overall, this study presented a modularized phylogenetic forest that describes a combination of complicated vertical and nonvertical evolutionary processes for methanogenic archaeal species.",
"conclusion": "Conclusions This study revealed the intrinsic reasons of conflicting phylogenetic signals in previous literatures. The modularized evolutionary pattern in the phylogenetic forest of methanogen-related species would deepen our understanding of the mode and processes of Archaeal origin and evolution, which pave the way for in-depth evolutionary and functional genomics studies in the future. At the same time, the frequently observed horizontally transferred genes between methanogens with COG category M (cell wall/membrane/envelope biogenesis) and V (defense mechanisms) would inspire further investigation to the factors rendering the unique life style and biochemical traits of methanogens.",
"introduction": "Introduction The biosynthesis of methane is a ubiquitous, defining characteristic of methanogens ( Ferry 1994 ). Via the process of methanogenesis, these methanogens play key roles in carbon cycle by producing 900 million tons of methane annually, contributing to 16% of total emission of global warming gases ( Schlesinger 1997 ; Elizabeth and Dina 2006 ; Hedderich and Whitman 2006 ). In terms of ecology, these strict anaerobes find residence in sediment, wetland, rice paddy, as well as anthropogenic sites such as anaerobic digesters and biogas plants ( Liu and Whitman 2008 ). Phylogenetically speaking, methanogens are classified under six taxonomic orders in the Archaea domain: Methanopyrales , Methanococcales , Methanocellales , Methanobacteriales , Methanomicrobiales , and Methanosarcinales ( Garcia et al. 2000 ; Liu and Whitman 2008 ; Sakai et al. 2008 ). These methanogens are distinguished from other archeons by the possession of a unique complex biochemistry for methane synthesis as part of their energy metabolism, forming a nonmonophyletic cluster juxtaposed with nonmethanogenic taxa ( Deppenmeier 2002 ). Herewith, the interesting taxonomic position of methanogens intrigued both ecologists and phylogenists regarding its evolutionary history ( Reeve et al. 1997 ; Bapteste et al. 2005 ; Luo et al. 2009 ). With the use of different genetic markers, contradicting phylogenetic relationships between methanogens and closely related nonmethanogenic species are yielded, which indicate pervasive events of horizontal gene transfers (HGTs). One unresolved classification resides in the ambiguous relationship among Methanomicrobiales , Methanosarcinales , Methanocellales , and a closely related nonmethanogenic order Halobacteriales , which remains debated to date ( Boone et al. 1993 ; Garcia et al. 2000 ; Bapteste et al. 2005 ; Brochier et al. 2005 ; Wright 2006 ; Yarza et al. 2008 ; Kelly et al. 2011 ; Nelson-Sathi et al. 2012 ). The disputable phylogenetic position of Methanopyrales serves as another example—this order was considered to be distantly related to all other methanogen lineages ( Burggraf et al. 1991 ; Rivera and Lake 1996 ; Brochier et al. 2004 ), whereas other reports proposed a close phylogenetic linkage between Methanopyrales , Methanococcales , and Methanobacteriales ( Nolling et al. 1996 ; Slesarev et al. 2002 ; Brochier et al. 2004 ; Bapteste et al. 2005 ; Gao and Gupta 2007 ; Luo et al. 2009 ). The major cause of the incongruence in methanogen genealogy is the rampant episodes of HGT in prokaryotic evolution, distributing genes across prokaryotes with distant genetic relationship ( Dagan and Martin 2006 ; Fraser et al. 2007 ; Brilli et al. 2008 ; Dagan et al. 2008 ; Boucher and Bapteste 2009 ; Norman et al. 2009 ; Schliep et al. 2011 ; Treangen and Rocha 2011 ). Such cellular mechanisms created species-independent evolutionary modules, whereby a majority of the genes possess distinct evolutionary history, and only around 1% (or less) of the gene trees share an identical topology with the species tree ( Dagan and Martin 2006 ; Bapteste et al. 2008 ). As a result, the clarification of phylogenetic relationship in methanogens requires a robust classification based on the complexity of the whole phylogenetic forest, composed of trees from all orthologous gene families. With the development of comparative genomics in studying multiple stains within single species, the concept of “pan-genome” has been proposed to allow an inclusion of both “core genome” (genes shared by all strains) and “dispensable genome” (genes shared by specific strains) ( Medini et al. 2005 ). The concepts of core-genome and pan-genome have been extended to metagenomes and higher taxonomic level ( Lawrence and Hendrickson 2005 ; Segata and Huttenhower 2011 ; Droge and McHardy 2012 ). According to the complexity hypothesis, informational genes (involved in transcription, translation, and related critical pathway) are usually necessary and less prone to be transferred horizontally. Meanwhile, operational genes involved in housekeeping, are commonly horizontally transferred ( Jain et al. 1999 ). To allow an inclusion of these new concepts, various approaches combining both vertical and horizontal evolution analyses have been developed recently ( Huson and Bryant 2006 ; Leigh et al. 2008 ; Schliep et al. 2011 ). Among these methods, phylogenetic networks have promising trends in describing both vertical and nonvertical evolution in given gene sets, although the results may not be interpreted easily ( Huson and Bryant 2006 ). In service of this, Leigh et al. (2008) proposed a hierarchical clustering method which identifies the congruently evolved gene families and reconstructs the super-matrix tree for each cluster. Concurrently, Schliep et al. (2011) adopted clanistics analysis on prokaryotic phylogenetic forest to reveal the prevalent incongruence among the phylogenetic trees, potential HGTs, and evolutionary pattern related functional traits. Over the past decades, the network analysis approach has been widely applied in biological evolution and ecology studies ( Proulx et al. 2005 ). With the rapid expansion in biological data, especially in the fields of genomics, transcriptomics, proteomics, and other “omics,” it is anticipated that biological network analyses would become relied upon heavily. Previous studies have illustrated that clustering genes based on their topology incongruence levels is very useful in identifying modules with identical or similar evolutionary history, and in filtering vertically inherited genes (coherently evolved core genes) before the construction of phylogenomic trees ( Susko et al. 2006 ; Puigbo et al. 2009 , 2012 ; Leigh et al. 2011 ). Albeit likelihood-based clustering methods are more statistically robust than topology-distance based methods in principle, the analyses on real data set often yield an excessive number of clusters ( Leigh et al. 2008 ; Leigh et al. 2011 ). A notable example would be the formation of over ten clusters in 102 nearly universal trees in the work of Leigh et al. ( 2008 , 2011 ). This kind of decomposition based on likelihood method in the phylogenetic forest is indeed helpful in studying the intricate phylogenetic incongruence or system and random errors in phylogeny reconstruction. Nonetheless, blurred cluster methods, such as fuzzy clustering, are more useful discovering vertical and nonvertical evolutionary processes and revealing major modules of genes sharing similar evolutionary history. For this purpose, a pioneering work using boot-split distance-based classical multidimensional scaling analysis was developed by Puigbo et al. (2009) . Overall, the development of distance-based clustering methods for coevolved genes recognition remains an incomplete avenue. To derive an accurate inference of methanogen phylogeny, it is necessary to employ in-depth evolutionary analyses with sophisticated approaches that prevent these limitations and plight. In this study, comprehensive evolutionary and network analyses were performed in an effort to reveal congruent and conflicting evolutionary signals, coevolved gene clusters, and global and local features in the archaeal methanogen phylogenetic forest. Although both congruent and incongruent evolutionary signals between genes were revealed in the incongruence tests analysis of the whole phylogenetic forest, the clanistics analysis discovered a scrambled evolutionary relationship among methanogens and certain methanogenesis-adaptive genes. Furthermore, a comprehensive network analysis based on topology similarity was implemented, in order to elucidate the hierarchical structure and global and local features in the archaeal methanogenic phylogenetic forest. In addition, the combined analyses of HGT, origin of the gene families and simulated evolution of genes families, revealed ubiquitous vertical inheritance evolution, as well as the variable phylogenetic depth (origins time) of gene families and variable HGT rate. Altogether, the results contribute to the modularized phylogenetic forest for methanogens and related species.",
"discussion": "Results and Discussion Phylogenomic Trees To resolve the phylogeny between methanogens and closely related organisms, species/phylogenomic trees based on 13 prokaryotic core genes (one and only one copy in 101 prokaryotic genomes) and 92 archaeal core genes (one and only one copy in 74 archaeal genomes) were reconstructed using ML and Bayesian methods. Previous results revealed inconsistent interrelationship among Methanomicrobiales , Methanosarcinales , Methanocellales , and Halobacteriales ( Bapteste et al. 2005 ; Brochier et al. 2005 ; Yarza et al. 2008 ; Kelly et al. 2011 ). Such intricate relationships have also been shown in our results. First, the position of Methanocellales is uncertain in phylogenies using different markers. Methanocellales is closer to Halobacteriales in tree I and II in figure 1 a with high confidence level (bootstrap or Bayesian posterior probability >90%); but in tree III and IV, it is closer to Methanosarcinales , again with high confidence level. Second, Methanomicrobiales is closer to Methanosarcinales than to Halobacteriales in tree II with high confidence level, whereas tree I, III, and IV support that Methanomicrobiales is closer to Halobacteriales . Therefore, the taxonomic class Methanomicrobia (containing orders Methanomicrobiales , Methanosarcinales , and Methanocellales ) is not consistently monophyletic due to the ambiguous positions of Methanocellales and Methanomicrobiales among different species trees. Although the statistically best species trees III and IV (see the following part for the criteria) show that Methanocellales is closer to Methanosarcinales ; and Methanomicrobiales is closer to Halobacteriales , the hypothesis is not supported with high confidence level (with bootstraps values below 50% in tree III). This suggests that different regions in 92 archaeal core genes might not have exactly the same evolutionary history in these lineages. Besides these, the four phylogenomic trees also show that Methanopyrales is either closer to Methanobacteriales based on prokaryotic cores genes (in tree I and II) or closer to the common ancestor of Methanococcales and Methanobacteriales (in tree III and IV). This reveals that although the accurate phylogenetic position of Methanopyrales is still vague in prokaryotic or archaeal core gene trees, the monophyletic group composed of Methanococcales , Methanobacteriales , and Methanopyrales is consistent in all phylogenomic trees. In addition, statistical tests with AU test ( Shimodaira 2002 ) were used to compare topological consistency in trees in figure 1 a to identify the best tree. Five additional trees were also constructed for comparisons, and they are 1) three trees of 16S rRNA using neighbor joining (NJ), ML, and Bayesian methods; and 2) two trees using NJ method on above two core gene sets (See supplementary fig. S1 , Supplementary Material online, for the full description of the nine trees). First, the likelihood was recalculated on archaeal core gene set (92 genes) with nine alternative topologies in figure 1 a and supplementary figure S1 , Supplementary Material online. The result shows that ML tree (tree III in fig. 1 a or supplementary fig. S1 e , Supplementary Material online) and Bayesian trees (tree IV in fig. 1 a or supplementary fig. S1 f , Supplementary Material online) based on archaeal core gene set, 1) have the highest likelihood; 2) are statistically no different (AU-test, Bonferroni P -value > 0.01) with each other; 3) fit the data significantly better than other seven topologies (AU-test, Bonferroni P -value < 1e-10); and 4) over 91% of the individual genes trees of the archaeal core genes is congruent (AU-test, Bonferroni P -value > 0.01) with tree III and IV. The same tests were performed on prokaryotic core gene sets (13 genes) and results show that the topology in tree I–IV (in fig. 1 a ) are statistically no different (AU-test, Bonferroni P -value > 0.01) and significantly better than five other alternative topologies (AU test, Bonferroni P -value < 1e-10). To check whether the empirical substitution models for amino acid have significant influence on the topology estimation in the phylogenomic tree, substitution model JTT and LG were also implemented in RAxML and Phylobayes. These trees show concordant topologies with original trees using the WAG model (Bonferroni adjusted P -value > 0.01 in AU test). Lastly, nine supertrees using Clann ( Creevey and McInerney 2005 ) based on both prokaryotic, archaeal core gene sets or multiple copy universal genes (species-specific duplicated genes) using Bayesian method. Various substitution models (WAG, LG, or JTT) were evaluated with the same procedure. The topologies in these supertrees did not fit the data better than tree III or IV (AU test, Bonferroni P -value < 1e-10). We also applied the AU-test on the reference topologies from 92 archaeal core genes to the whole phylogenetic forest (3,694 trees). The result shows that found that tree III and IV (in fig. 1 a ) are still best fit trees. To evaluate the influence of an arbitrarily chosen outgroup ( Crenarchaeota ) to the root position of the phylogenomic trees, we adopted other distantly related archaeal (e.g., Nanoarchaeota ) or bacterial genomes to root these phylogenies. The result shows that over 95% of the interorder relationships among six orders of methanogens, and the closely related Halobacteriales are still the same for the same data set using identical reconstruction methods. Incongruence among Gene Trees To decipher the consistent and conflicting evolutionary signals in the phylogenetic forest, incongruence levels among 250 randomly selected genes were measured via the AU test ( Shimodaira 2002 ). The result is illustrated using heatmap depictions (the upper triangular matrix in fig. 1 ). The results show a surprising pattern in that 1) 55% (51 out of 92) of the archaeal core genes are in topological discordance with either archaeal core genes or other genes; 2) nearly core genes (represented in at least half of the methanogen genomes) show higher (56%) topology congruency with all genes than with archaeal core genes; and 3) other genes show very distinct evolutionary processes and few of them have congruent topologies. Our further regression analysis revealed that the AU test P -value has a negative correlation with taxa number ( supplementary fig. S2 a , Supplementary Material online) for methanogen ortholog families, suggesting that taxa number has a nonnegligible impact on the congruence results with AU tests. Additional correlation analysis based simulation data (see Materials and Methods for more details), which have totally congruent topology and same taxonomy composition as real data. This confirmed that with the increase of taxa number, the proportion of false rejection of congruent topology would get increased ( supplementary fig. S2 b , Supplementary Material online). To correct this family size effect, the number of taxa for each comparison was limited to at most 20 (taxa were selected randomly when total congruent taxa between the two genes exceeded 20). The new incongruence test result shows in lower triangular matrix of fig. 2 that 1) the core genes have more consistent topologies than nearly core genes and other genes, indicating strong vertical inherited signals within the core gene trees; 2) the nearly core genes show moderately congruent topologies with all genes, suggesting that their vertical inherited signal were disrupted by episodes of HGT, duplication/loss, or other nonvertical evolutionary processes; 3) most genes (other genes) are incongruent with others overall, implying that HGTs and other nonvertical evolutionary process were rampant. A similar pattern could be observed if the size of the gene family was limited to 10. All these results provide evidence to the fact that the archaeal methanogenic core genes have a much stronger consistency regarding the vertical evolutionary signals than other genes, notwithstanding the ubiquitous HGT episodes. The strong evolutionary signal of vertical inheritance among core genes is also supported by phylogenomic tree results, as a high confidence level encompassed most branches for tree I–IV ( fig. 1 a – d ).\n F ig . 2.— Summary of properties in coevolutionary network. ( a ) Distribution of weighted node degree (strength). The straight line denotes the fit power-law distribution and the red circles denote the fit log-normal distribution. ( b ) Distribution of unweighted node degree. ( c ) Module definition using hierarchical clustering in the coevolution network. Seven major modules were defined with colors: dark orange, yellow, blue, black, red, brown, and pink. Gray color denotes the unclassified genes. ( d ) Visualization of the network based on force-directed layout. All nodes are colored according to the module classification in figure 2 c . It should be noted that the discrepancies between trees usually arise due to two fundamental reasons: real biological incongruences and estimation errors. Real biological incongruences could be attributed to complex nontree-like evolution, such as HGT (most abundant nonvertical evolutionary processes), gene conversion, and other nonvertical evolutionary process, etc. ( Morrison 2011 ; Puigbo et al. 2012 ). On the other hand, estimation errors are usually caused by 1) inappropriate data, including insufficient (random errors), low-quality or missing data, laboratory artifact, and inclusion of overly divergent outgroup; 2) misspecified ortholog or paralog relationships; 3) systematic errors caused by improper models or parameters in phylogeny reconstruction; 4) artifacts in phylogeny reconstruction, such as long branch attraction caused by rapid substitution rates. Attributed by these factors, an example would be the inconsistency of the phylogenetic position of Methanopyrus kandleri based on different transcription and ribosomal genes ( Brochier et al. 2004 ). Another example is the convoluted phylogenetic relationship within the rapid diverging of class II methanogen groups and Halobacterials ( Brochier-Armanet et al. 2011 ). To circumvent these issues, we exhausted almost all available complete genomes and applied a thorough evaluation of ortholog families in this study. These procedures effectively prevented the detrimental problems, pertaining missing data, insufficient information, or misspecified orthologs. Furthermore, we also evaluated different reconstruction methods (ML, Bayesian, and NJ) with various substitution models (WAG, JTT, and LG). The result shows that the overall incongruence patterns are extremely similar to those described in figure 1 . Nonetheless, some local topologies remained difficult to be recovered due to unusual evolutionary processes in certain trees (even in some phylogenomic tree). Clanistics Analysis Recently, an innovative evolutionary analytical method named clanistics analysis was developed. This method analyzes the intricate evolutionary units from thousands of unrooted gene trees to reveal the evolutionary patterns and ecological relationships ( Lapointe et al. 2010 ). When the unrooted phylogenetic tree is dissected into clans and slices according to native and intruder categories, useful insights on the evolutionary history could be unveiled by recurring patterns. In this study, clanistics analysis was first performed on 3,694 gene trees using ten taxonomic order categories. The result shows that genes could be classified into three groups according to their coherent patterns in ten taxonomy categories (patterns A–E are shown in supplementary fig. S3 , Supplementary Material online, and their biological meanings are illustrated in supplementary materials , Supplementary Material online). These groups are I) 107 universal vertically evolved genes (with at least six patterns of A1 or C and without any nonvertical patterns); II) 1,359 genes with at least one potential nonvertical evolutionary event among taxonomic orders (with at least one pattern of B1, B2, D1, D2 or E); and III) 2,228 nearly lineage-specific genes (with at most five patterns of A1 or C and without any other nonvertical patterns). Among the 107 genes in group I, 31 were previously defined as archaeal core genes. COG functional enrichment analysis using Fisher’s exact test reveals that categories J (translation, ribosomal structure and biogenesis) and K (transcription) of group I genes are significantly overrepresented when compared with all 3,694 genes (Bonferroni adjusted P -value < 0.01). For group III genes (lineage-specific genes), 95% (2,113 out of 2,228) contains no more than two taxonomic orders, suggesting that these gene families are much younger than core genes. Functional enrichment analysis suggests that none of the functional categories is significantly overrepresented or underrepresented in group III. For group II genes, COG functional categories L (replication, recombination and repair) and M (cell wall/membrane/envelope biogenesis) are the only two significantly enriched groups (Bonferroni adjusted P -value < 0.01). This observation reveals that the potential interorder HGTs in methanogens are significantly related to the genes involving in the integration of alien gene into host genomes. To reveal the phylogenetic relationship among six methanogen orders, clanistics analysis was performed on the whole phylogenetic forest (3,694 trees) with additional taxonomy categories (see Materials and Methods for more details). The result shows that most trees (57%) displays adjacent relationship among the class I orders of Methanococcales , Methanobacteriales , and Methanopyrales (pattern A1 and C). In contrast, most of the other trees reveal that nonmethanogen species (mainly Thermococcales , Thermoplasmatales , or Archaeoglobales ) is adjacent to at least one class I methanogen order. These results illustrate that although average phylogenetic signals support the monophyly structure of class I methanogen in the most of the phylogenomic trees ( fig. 1 a ), evolutionary history for individual gene is variable and inconsistent. We further investigated the relationship between class II methanogen and nonmethanogenic order Halobacteriales. The result shows that Methanomicrobiales is closer to Halobacteriales (supported by 46% of genes) than to Methanosarcinales (supported by 23% of genes). One attractive feature of clanistics analysis is that the potential phenotypic/environmental adapted genes could be identified from the coherent patterns in a phylogenetic forest ( Schliep et al. 2011 ). A gene cluster is defined as potential phenotype adaptive/related one when it displays 1) at least one perfectly coherent phenotypic pattern (pattern A1) and 2) no coherent taxonomic pattern could be observed. Our analysis for the coherent patterns in methanogenesis phenotypes (hydrogenotrophic, acetoclastic, and methylotrophic) reveals that 114 genes are affiliated with hydrogenotrophic functionalities, 1 with acetoclastic, and 4 with methylotrophic. Further functional enrichment analysis shows that genes of COG category M (Cell wall/membrane/envelope biogenesis) are overrepresented significantly (Bonferroni adjusted P -value <0.05) for hydrogenotrophic phenotype adaptation. It is interesting to note that genes in COG category M are not only related to hydrogenotrophic phenotype adaptation, but are also overrepresented in genes which experienced HGT (coherent group II genes defined above). Among all 119 methanogenesis adaptation-related genes, we noticed one interesting gene belongs to COG3276 group, which encodes a translation factor involved in selenoprotein biosynthesis, a process that is directly related to methanogenesis. We also observed a series of genes (COG0348, COG1032, COG0535, COG1242) related to ferredoxin or Fe-S redox reactions. These genes are potential participants in numerous cellular redox reactions, including methanogenesis. These methanogenesis-related genes would provide useful information for a better understanding of the formation and adaptation of methanogenesis pathways. A Coevolution Network in Archaeal Methanogen Phylogenetic Forest In this study, a comprehensive network analysis based on topology similarity was implemented. The aim is to decompose the hierarchical structure, as well as the global and local features in the archaeal methanogenic phylogenetic forest. Pairwised phylogenetic topology similarities among gene trees were calculated to construct the adjacent matrix in the weighted coevolutionary network by RPHD, a modified version of Penny and Hendy’s topology distance ( Penny and Hendy 1985 ) (see Materials and Methods for more details). First, to reveal the basic characteristic of the coevolutionary network, node strength distribution was calculated and shown in figure 2 a . The distribution (blue points in fig. 2 a ) follows neither power-law distribution nor log-normal distribution using Pearson χ 2 test ( P -value < 0.001). Previous studies showed that in weighted networks, the log–log frequency distribution of the node strength follows a power-law distribution (Pareto distributions) or log-normal distribution, and both patterns reflect a specific processes of network growth ( Barabási et al. 1999 ; Redner 2005 ; Bhattacharya et al. 2008 ). In this coevolutionary network, tails are heavier in both raw node strength distribution (histogram in left-bottom corner of fig. 2 a ) and log–log transformation (blue points in fig. 2 a ) than that in best fitting log-normal and power-law distribution. In addition, the same pattern of heavy tail can still be observed when the unweighted node strength was adopted ( fig. 2 b ). To reveal the potential causes for the heavy tail pattern in the node strength distribution, topology similarities and node strengths of simulated data sets with varying HGT rates were calculated (see Materials and Methods for more details). The five simulated data sets share the same taxonomy distribution and gene size as the experimental data, with HGT rates ranging from 0 (fully congruent) to infinite (fully incongruent). With the increasing HGT rate in each family ( fig. 3 ), it is observed that the node strength distributions display a clear transition from strong fat tail (fully congruent) to weak fat tail (sporadic HGT), then to nonfat tail (fully incongruent). The result suggests that the ubiquitous vertical inheritance signals in phylogenetic tree could be denoted by the heavy tail pattern in node strength distribution.\n F ig . 3.— Distribution of node strength for simulated data with different HGT rates. Five simulation data sets have same taxonomy distribution and gene size as the real data but different number of HGT events, from 0 (totally congruent) to 2, 4, 8, and infinite (totally incongruent between random topologies). Network Properties and Functional Bias in Coevolutionary Modules The major objectives of this network analysis are to identify coevolved gene clusters (modules) within the phylogenetic forest, and to identify the potential driving force behind the complex coevolutionary relationships. In this study, the network modules were identified using hierarchical clustering and dynamic branch cut methods ( Langfelder et al. 2008 ) based on TOM, a smoothed out matrix from original adjacency matrix ( Dong and Horvath 2007 ; Yip and Horvath 2007 ; Langfelder and Horvath 2008 ). Seven coevolution modules were detected and shown in figure 2 c . To justify the module classification, the coevolution network was visualized with force-directed layout, in which all nodes were treated as intrinsically repelling beads, and edges acted as springs to pull beads together ( fig. 2 d ). The network structure as displayed by force field layout coincides well with the module definition ( fig. 2 c ), as modules are located in distinct regions while the members are tightly clustered within modules. The reproducibly analysis for these seven modules (see supplementary materials , Supplementary Material online) revealed that there modules are statistically robust. Besides the node strength and modularity, we also adopted various measures to investigate the characteristics and roles of different modules in the coevolution network. These measurements, including intramodular connectivity, closeness, betweenness, and local clustering coefficients, reveal that genes in dark orange and yellow modules have high intramodular connection and play key role in the coevolutionary network. More detailed description and discussion about these network measures can be found in the supplementary materials , Supplementary Material online. Here, we also performed the enrichment analysis based on the functional annotation of all coevolution modules against KEGG ( supplementary table S5 , Supplementary Material online) and COG databases ( supplementary fig. S6 , Supplementary Material online). The COG functional categories H (coenzyme transport and metabolism), J (translation, ribosomal structure, and biogenesis), and F (nucleotide transport and metabolism) were found to be overrepresented in the dark orange module ( P -value < 0.01 with χ 2 test). We also discovered translation-related genes to be overrepresented according to KEGG annotation. Genes in the dark orange module are mainly core genes, which are the indispensable part of all taxa being studied. This suggests that there exists strong selective pressure on these core functions to maintain their consistent (and vertical) evolutionary signals. Annotation in the yellow module is rather eye catching. As for the genes in yellow module, vital functions (e.g., DNA replication, methane metabolism, etc.) of methanogens were overrepresented ( P -value < 0.01 with χ 2 test) according to KEGG annotation. Given the intimate relationship between yellow and dark orange modules, there is a possibility that the yellow module gene families participate in vital biological processes of methanogens and have experienced similar evolutionary force as genes in dark orange module. In addition, an overrepresentation of glutamine and peptidoglycan metabolism pathways was identified in the red module, whereas transporters pathways were identified in the pink module. Meanwhile, it is worthwhile to note that methane metabolism and ribosome pathway genes were found underrepresented in the black module. A similar trend was observed in COG-based functional annotation, wherein functional categories I (lipid transport and metabolism), G (carbohydrate metabolism and transport), V (defense mechanisms), T (signal transduction mechanisms), and M (cell wall/membrane/envelope biogenesis) were overrepresented in the black, blue, brown, pink, and red modules, respectively. Hence, it was of our interest to identify the driving force behind the modularized coevolution in the phylogenetic forest. Herewith, coevolution is a well-established concept in host–pathogen interaction ( Buckling and Rainey 2002 ; Gagneux 2012 ). In brief, such events could be interpreted as the evolution of certain genes under a common evolutionary driving force. However, coevolutionary events have not been well studied in correlation to biological functions. From our results, there appears to be a trend for gene families of similar or related biological functions to experience common evolutionary pressures. It was hence postulated that such biological function-biased pressures could be one of the factors leading to the modularized evolution of the gene families. Patchy Taxonomic Structure and Origin of Different Coevolutionary Modules To investigate the composition of the coevolutionary modules, the taxonomy abundance distributions were calculated. The taxonomy abundance evaluates the proportion of genes from certain taxonomy in given gene families. For example, 20% of Methanomicrobia means 20% of OTUs in the tested gene families belongs to Methanomicrobia . The results show that all modules have distinct taxonomy composition at either class ( fig. 4 a ) or family level (data not shown). Almost all the modules have distinct dominant OTUs at the taxonomic class level, except for the dark orange module. Because the dark orange module has very similar composition with all genes, their genes are expected in almost all species and have similar taxonomy composition as the whole gene pool. On the other hand, the pink and brown modules both have similar dominant OTUs of Methanomicrobia at taxonomy class level, whereas their compositions are quite different at taxonomy family level. The intriguing taxonomic distinction in coevolutionary modules raises the question of whether these modules are defined by a similar taxonomy or a similar evolutionary history (topology). To answer this question, we randomized the tree topology with the same taxonomy composition. The results show that although genes from the same module defined in the real data tend to be closer with each other due to similar taxonomy composition ( supplementary fig. S7 a , Supplementary Material online), no similar and statistical robust modules can be identified in the whole network using the same procedures ( supplementary fig. S7 b , Supplementary Material online). Hence, it is reassured that the coevolutionary modules represent the reassembly of genes with similar topologies, instead of similar taxonomy compositions.\n F ig . 4.— Difference of taxonomy composition and phylogenetic depth among modules. ( a ) Distribution of taxonomy abundance in class level. Module names followed the definition in figure 2 . ( b ) Relationship between cumulative gene number and phylogenetic depth. x axis of phylogenetic depth reflects the relative time of origin for the gene families. To investigate potential causes of the patchy structure and distinct taxonomic compositions among the coevolutionary modules, the phylogenetic depth of family birth (relative time of family origin) was calculated for each family. The result shows that with the exception of the dark orange module, all other modules genes bloomed at different time periods ( fig. 4 b ). Most of the genes in dark orange module originate in the common ancestor of 74 archaeal species. This result provides one possible explanation for the origin of modularized evolution in the phylogenetic forest that the origin of genes burst out in different time period and in distinct evolutionary lineages. Assuming that different gene families originate from different evolutionary lineages, distinct patchy taxonomic structures among different modules, leading to the pan-genome structures, can be reasonably explained. Quantitative Characterization of HGTs in the Phylogenetic Forest To further investigate 1) the trends and barriers of HGT quantitatively in archaeal methanogen-related genomes; 2) their influence on the scrambled relationships among methanogen orders; and 3) the contribution of HGT (from archaeal species) to the current genome contents, we employed a recently developed method with explicit evolutionary model. The model incorporates gene birth, speciation, duplication, loss, and horizontal transfer ( David and Alm 2011 ). The distribution of total HGT events in each family (shown in supplementary fig. S5 , Supplementary Material online) shows that only around 37% of gene families (ortholog clusters) are not affected by HGT, and 63% of gene families experienced at least one HGT during the entire evolutionary history. This HGT frequency (number of HGT events in the family) is similar to a previous estimation by Kloesges et al. (2011) in a study of proteobacteria . In the whole phylogenetic forest, 48% of gene families experienced only 1–2 times HGT events. This suggests that although most gene families experienced HGT in their whole life period (since origin), the HGT frequency in each gene family remains low. To correct the effect of family size on HGT measurement, the HGT frequency was further normalized with family size by defining HGT rate = HGT frequency/family size. The distributions of normalized HGT rate for seven modules (shown in fig. 5 a ) reveal that genes in dark orange module have the lowest normalized HGT rate, followed by the yellow module; and other module genes have much higher HGT frequencies. For the 45 genes directly involved in methanogenesis pathways, their normalized HGT rate (mean: 0.097 and median: 0.083) is very similar to that of other genes (mean: 0.099 and median: 0.071). For the 119 genes related to methanogenesis pathway, they have a slightly higher average HGT rate (mean: 0.129 and median: 0.125) than other genes. One interesting phenomenon is that there is a significant negative correlation ( P -value <2e-16) between normalized HGT rate and global node strength in the coevolution network. The correlation coefficient square ( R 2 ) is not very high (0.118) due to the influence of some extreme HGT rate outliers (value of 0). If the correlation is calculated base on genes with at least one HGT event, the negative correlation becomes much stronger ( R 2 = 0.352) (shown in fig. 5 b ). The results from this correlation analysis suggest that besides the time of origin of gene families, HGTs could also influence the ortholog cluster relationships and the formation of modules in the coevolutionary network. Genes in the central part of the network have much lower HGT rate, whereas genes in the peripheral part of the network could experience more frequent HGTs. This reveals that composite evolutionary processes may contribute to the modularized evolution in the archaeal methanogen phylogenetic forest.\n F ig . 5.— Characteristics of normalized HGT rate in coevolutionary network. ( a ) Normalized HGT rate in each coevolution module. ( b ) Linear regression between normalized HGT rate and global node strength in coevolution network. Density of the dots reflects the density of the genes. Furthermore, to evaluate the influence of HGT on current genomes in terms of different proteome size, the HGT recipient rate (the ratio of the number of horizontally transferred genes to the total number of genes tested in each genome) was calculated. This rate measures the long-term influence of HGT from archaeal species on the proteomes in each genome. The result shows that the median HGT recipient rate ranges from 0.23 to 0.42 for different taxonomy orders, suggesting at least 20% of the genes in archaeal genomes were transferred from other archaeal genomes. In addition, most of the nonmethanogen species (taxonomy order Thermococcales , Halobacteriales , Thermoplasmatales , and Archaeoglobales ) acquired fewer foreign genes than methanogen species. One exception is Methanopyrales , which has the second lowest HGT recipient rate (∼0.23). Finally, to reveal the tendency and barriers of HGT in the phylogenetic forest, the HGT frequency of donor-recipient pairs was calculated. The result shows that 52% of HGTs are intraorder events and 42% are interorder events. The remaining 6% occurred between more distantly related groups. Genes in the genome of Halobacteriales , Methanomicrobiales , and Methanosarcinales have the highest cumulative rate as HGT donors. The results in figure 6 show that Archaeoglobales-Archaeoglobales and Methanomicrobiales – Methanomicrobiales are the most frequent HGT paths (donor-recipient). One nonmethanogen order Thermococcales has a high HGT rate as donor but a low HGT rate as recipient. This order mainly interacts with other nonmethanogen species, suggesting its special role as a “generous” genetic material reservoir. Methanomicrobiales is regarded as the most active order involved in HGTs either as donor or recipient. Among the 15 most frequent HGT paths (marked with stars in fig. 6 ), seven are related to Methanomicrobiales . In contrast, four paths in the ten least frequent donor-recipient paths involve Halobacteriales ( Methanococcales – Halobacteriales , Halobacteriales – Methanococcales , Methanomicrobiales – Halobacteriales , and Methanobacteriales – Halobacteriales ). These patterns imply that the scrambled relationship between class II methanogen and related species was caused mainly by intermethanogens HGTs in class II, instead of HGTs between methanogens and related nonmethanogen species ( Halobacteriale ).\n F ig . 6.— Heatmap of donor-recipient frequency among different taxonomy orders. Darker color refers to lower frequency transfer path and lighter color refers to higher frequency transfer path. The top 15 highest and 10 lowest frequent paths were marked with stars and circles, respectively. Compared with recipient, the donor of HGT is much harder to be detected because the extinct, undiscovered, un-sampled or unsequenced genomes will make the reference species tree based estimation miss the biological direct donor ( Ge et al. 2005 ). In this study, we integrated almost all available complete methanogen-related archaeal genomes to ensure the accuracy of the deduced the archaeal donor-recipient HGT relationship. To evaluate the influence of reference species tree and accuracy of phylogenetic tree over HGT detection, species trees I–III ( fig. 1 a ) were tested with multiple substitution models (JTT, WAG, or LG). The results showed that all the elements aforementioned (e.g., HGT rates, HGT recipient rates, and HGT path frequency) were reproduced perfectly, with less than 5% variation in value when compared with the original estimated values. In summary, the robustness of the patterns was confirmed. Furthermore, HGT rates and donor-recipient relationships were evaluated in methanogens and related archaeal species in this study, providing a reflection of the HGT tendencies (e.g., HGT recipient rate and frequent HGT paths, etc.) among the archaeal species herein studied. Previous studies revealed that when bacteria species were taken into consideration, about one-third of genes in Methanosarcina mazei could have been transferred from bacteria horizontally ( Deppenmeier et al. 2002 ). In addition, Nelson-Sathi et al. (2012) discovered that over 70% gene families (containing at least one bacteria homolog) in Halobacterials have acquired genes from bacteria. As a result, the effort to quantify HGTs in all major methanogen-related archaeal lineages in our study provides complementary and useful information regarding more recent gene flow histories within major archaeal lineages. From a functional genomic perspective, we found categories of J (Translation, ribosomal structure, and biogenesis) and K (Transcription) have significantly (Bonferroni adjusted P -value < 0.01 in Mann–Whitney U test) lower HGT rates than other categories. This is consistent with complexity hypothesis ( Jain et al. 1999 ). In contrast, the COG categories of M (cell wall/membrane/envelope biogenesis), T (signal transduction mechanisms), and V (defense mechanisms) have significantly higher HGT rates than other categories. Among the genes in COG V category (defense mechanisms) with HGT, COG1131 (ABC-type multidrug transport system) were most frequently observed. These evolutionary patterns in methanogen-related species are concordant with some previous finding that some HGTs could strengthen the defense systems in some prokaryotic species ( Godde and Bickerton 2006 ). In summary, highly frequent and specific HGT paths among methanogen orders may lead to their scrambled phylogenetic relationships; and the pattern of modularized evolution in the phylogenetic forest is also related to HGTs bias in these gene families."
} | 11,606 |
27074334 | null | s2 | 1,617 | {
"abstract": "As coral bleaching events become more frequent and intense, our ability to predict and mitigate future events depends upon our capacity to interpret patterns within previous episodes. Responses to thermal stress vary among coral species; however the diversity of coral assemblages, environmental conditions, assessment protocols, and severity criteria applied in the global effort to document bleaching patterns creates challenges for the development of a systemic metric of taxon-specific response. Here, we describe and validate a novel framework to standardize bleaching response records and estimate their measurement uncertainties. Taxon-specific bleaching and mortality records (2036) of 374 coral taxa (during 1982-2006) at 316 sites were standardized to average percent tissue area affected and a taxon-specific bleaching response index (taxon-BRI) was calculated by averaging taxon-specific response over all sites where a taxon was present. Differential bleaching among corals was widely variable (mean taxon-BRI = 25.06 ± 18.44%, ±SE). Coral response may differ because holobionts are biologically different (intrinsic factors), they were exposed to different environmental conditions (extrinsic factors), or inconsistencies in reporting (measurement uncertainty). We found that both extrinsic and intrinsic factors have comparable influence within a given site and event (60% and 40% of bleaching response variance of all records explained, respectively). However, when responses of individual taxa are averaged across sites to obtain taxon-BRI, differential response was primarily driven by intrinsic differences among taxa (65% of taxon-BRI variance explained), not conditions across sites (6% explained), nor measurement uncertainty (29% explained). Thus, taxon-BRI is a robust metric of intrinsic susceptibility of coral taxa. Taxon-BRI provides a broadly applicable framework for standardization and error estimation for disparate historical records and collection of novel data, allowing for unprecedented accuracy in parameterization of mechanistic and predictive models and conservation plans."
} | 528 |
19747389 | PMC2758838 | pmc | 1,618 | {
"abstract": "Background Mesophotic corals (light-dependent corals in the deepest half of the photic zone at depths of 30 - 150 m) provide a unique opportunity to study the limits of the interactions between corals and endosymbiotic dinoflagellates in the genus Symbiodinium . We sampled Leptoseris spp. in Hawaii via manned submersibles across a depth range of 67 - 100 m. Both the host and Symbiodinium communities were genotyped, using a non-coding region of the mitochondrial ND5 intron (NAD5) and the nuclear ribosomal internal transcribed spacer region 2 (ITS2), respectively. Results Coral colonies harbored endosymbiotic communities dominated by previously identified shallow water Symbiodinium ITS2 types (C1_ AF333515, C1c_ AY239364, C27_ AY239379, and C1b_ AY239363) and exhibited genetic variability at mitochondrial NAD5. Conclusion This is one of the first studies to examine genetic diversity in corals and their endosymbiotic dinoflagellates sampled at the limits of the depth and light gradients for hermatypic corals. The results reveal that these corals associate with generalist endosymbiont types commonly found in shallow water corals and implies that the composition of the Symbiodinium community (based on ITS2) alone is not responsible for the dominance and broad depth distribution of Leptoseris spp. The level of genetic diversity detected in the coral NAD5 suggests that there is undescribed taxonomic diversity in the genus Leptoseris from Hawaii.",
"conclusion": "Conclusion This study was a natural first step to exploring the biological traits that allow Leptoseris spp. to persist and dominate at mesophotic depths (i.e. 67 to 100 m depth). We found common shallow-water Symbiodinium types at depths not previously recorded for these endosymbionts. We also found genetic variability at mitochondrial NAD5, which suggests undescribed taxonomic diversity in Leptoseris . Mesophotic coral communities are found beyond the limits of traditional SCUBA diving and as a result, their ecology is poorly understood [ 6 , 7 ]. An understanding of the mechanism(s) by which reef corals adjust to extremes in the environment and the limits inherent to these mechanisms provides insights into the future responses of deep and shallow reef communities to environmental change. Our study indicates that a specialist symbiont is not a prerequisite for existence at environmental extremes.",
"discussion": "Discussion Leptoseris corals are some of the deepest-dwelling zooxanthellate corals in the world [ 7 ] and the biological attributes that underpin the ability of this genus to thrive across such a large depth range (as deep at 165 m [ 5 ]) are central to our understanding of limits of the coral-endosymbiont interaction. Intracellular photosynthetic dinoflagellate symbionts of the genus Symbiodinium are pivotal to the success of corals as a group and are known to be taxonomically and physiologically diverse [ 12 ]. We thus hypothesized that the Symbiodinium communities hosted by Leptoseris spp. in the mesophotic zone might be highly specialized to this environment and that this would be apparent as a pattern in the distribution of Symbiodinium types hosted by Leptoseris spp. over a depth gradient. Surprisingly, our data do not support this hypothesis; Leptoseris spp. sampled at 67 m and deeper, host Symbiodinium types commonly found in shallow-water corals across the Pacific [ 21 ]. Although endosymbiont diversity will vary by host species, this finding contradicts studies examining endosymbiont diversity in corals sampled across shallower depth gradients (≤ 40 m) that observed partitioning of Symbiodinium communities at the level of clade [ 16 ] and ITS2 types [ 17 , 22 ] by depth, and therefore, evidence for depth-based ecological function in symbionts. We found Symbiodinium ITS2 types C1 and C1c in mesophotic zooxanthellate corals. ITS2 type C1 has also been found in two Leptoseris incrustans colonies sampled in Hawaii between 10-20 m depth [ 21 ]. The generalist C1 Symbiodinium types are widely distributed both geographically and environmentally [ 21 ]. Solar radiation is a major determinant of photosynthesis, and therefore influences the amount of carbon translocated to the host, and the phototrophic contribution to the animal [ 23 ]. When light declines with depth, without photoacclimation, carbon fixation rates and the amount of translocated carbon declines [ 23 ]. Leptoseris fragilis in the Red Sea exhibit large changes in photosynthetic pigment concentrations with changes in depth [ 24 ]. Our results from Leptoseris spp. in Hawaii suggest that this may be a capacity of generalist Symbiodinium types such as C1 and C1c. However, confirming whether these abundant, generalist types have the ability to photoacclimate across the depth range under consideration here will require pigment studies and endosymbiont density counts and will be an important component of future research on the deep water corals in Hawaii. Despite the focus on Symbiodinium and its ability to photoacclimate, the coral host can also influence photoacclimation. Research on Leptoseris fragilis in the Red Sea has shown possible photoadaptations in host light-harvesting systems that may enhance photosynthetic performance [ 25 ]. These include fluorescent pigments that may convert light at depth to wavelengths useable for photosynthesis [ 4 ], plate-like growth forms, and morphological adaptations like conical knobs that may serve as coral \"light traps\" [ 24 ]. Leptoseris in Hawaii also possess these three adaptations, but their influence on photosynthetic performance in Leptoseris spp. in Hawaii has yet to be directly demonstrated. Furthermore, Leptoseris could differ from shallow corals in its reliance on phototrophic carbon because this coral could obtain more nutrients from feeding [ 26 ], or have reduced needs for nutrients with slower growth rates and lower metabolism [ 27 ]. For example, L. fragilis has trophic adaptations that may be responsible for minimizing their dependence on photosynthetically fixed carbon [ 28 ]. L. fragilis has a perforated gastrovascular cavity, resulting in a flow-through system where microscopic particulate organic material such as detritus, bacteria, and plankton can accumulate [ 28 ]. As light decreases with depth, greater reliance on feeding heterotrophically (rather than on phototrophy) may enable these corals to survive. However, to date, no studies have examined the relative contribution of photosynthetically fixed carbon to the daily energy budget of corals at these depths in Hawaii; such studies are critical to a more comprehensive understanding of Leptoseris's spp. broad depth distribution. Given the known slow rate of evolution in coral mitochondrial DNA [ 29 , 30 ], the six distinct coral haplotypes we found likely represent multiple species and highlight unrecognized diversity in this coral genus. In a previous study using the NAD5 marker, Concepcion et al. (2006)[ 29 ] found no variation between species within the genus Acropora and Pocillopora , but for the genus Porites , P. asteroides and P. compressa differed by two indels, and P. porites and P. compressa by four single bp changes. Interestingly, we found different Symbiodinium communities associated with different mitochondrial NAD5 haplotypes. Four of the coral mitochondrial types (N2, N4, N5, and N6) were dominated by C1 and C1c endosymbionts, however, N1 (n = 4 colonies) and N3 (n = 1 colony) showed different patterns of symbiont association (with C1, C1_v1a, and C1_v1b and with C27, respectively)."
} | 1,903 |
21957432 | PMC3161195 | pmc | 1,620 | {
"abstract": "Evolutionary neural networks, or neuroevolution, appear to be a promising way to build versatile adaptive systems, combining evolution and learning. One of the most challenging problems of neuroevolution is finding a scalable and robust genetic representation, which would allow to effectively grow increasingly complex networks for increasingly complex tasks. In this paper we propose a novel developmental encoding for networks, featuring scalability, modularity, regularity and hierarchy. The encoding allows to represent structural regularities of networks and build them from encapsulated and possibly reused subnetworks. These capabilities are demonstrated on several test problems. In particular for parity and symmetry problems we evolve solutions, which are fully general with respect to the number of inputs. We also evolve scalable and modular weightless recurrent networks capable of autonomous learning in a simple generic classification task. The encoding is very flexible and we demonstrate this by evolving networks capable of learning via neuromodulation. Finally, we evolve modular solutions to the retina problem, for which another well known neuroevolution method—HyperNEAT—was previously shown to fail. The proposed encoding outperformed HyperNEAT and Cellular Encoding also in another experiment, in which certain connectivity patterns must be discovered between layers. Therefore we conclude the proposed encoding is an interesting and competitive approach to evolve networks.",
"conclusion": "Summary and conclusions A novel developmental encoding for evolving networks has been proposed, called Developmental Symbolic Encoding. In this encoding, the genotype is a tree of routines, which in turn consist of lists of instructions saying how to develop the network. The network grows primarily by means of node divisions and connection arrangements, which is roughly how biological neural networks develop. DSE combines some concepts of CE and HyperNEAT. Much as CE, it can grow networks by means of node divisions, and features an explicit genetic modularity and hierarchy, conducing reuse of code and network modules. Much as HyperNEAT it can establish connectivity patterns between groups of nodes, and exploit some geometric-like relationships between them, enabling evolution of highly regular network topologies. These two ways of growing networks are combined in a coherent genetic representation, optimistically allowing to get best of both encodings while solving problems. The encoding exhibits scalability—it can represent network phenotypes compactly, with the genotype growing slower than phenotype along the problem size. In other words, it is capable of capturing some regularities of network solutions, and thus regularities hidden in problems. The encoding has been also demonstrated to feature modularity and code reuse, where a single piece of genetic code, namely routine, generates multiple copies of subnetwork in the final network. Modularity and the evolution of modular solutions is supported by the fact, that routines are easily transferable between individuals and populations. This in turn opens an interesting further research on parallel multiple task solving, in which a number of populations solves a number of different, but related tasks, while possibly taking advantage of communication. The scalability of DSE has been demonstrated in symmetry and parity problems. Evolved solutions for these problems were fully general with respect to the number of inputs, i.e. networks were able to automatically scale themselves up to the size of the problem during development, while being encoded by a fixed genotype. Certainly only for some problems such a perfect scalability can be achieved. Modularity, in turn, has been demonstrated in a more difficult variant of the parity problem and also in a classification problem. These problems required a capability to discover and exploit useful modules or subnetworks. DSE is also complete and closed, which means it can represent any recurrent network topology and any genotype represents some valid network. Also flexibility of the encoding has been shown, which allows to employ arbitrary node and connection types, including weightless, weighted, plastic and modulated connections. It is possible to employ nodes and connections having evolvable transfer and learning functions; restrict the space of topologies to feed-forward only; or impose any calculable constraints on the network, by including appropriate terms in a fitness function. Much as CE and HyperNEAT, DSE is a complex method, involving many parameters and unspecified algorithmic details. There are endless options to modify the way things are done, or extend the encoding by new elements, such as instructions and genetic operators. From clarifying and simplifying the encoding, to introducing explicit learning algorithms, to extending the network model by non-aggregatory transfer functions. Further research over DSE is wide open. Although it is difficult to reliably compare such (relatively) complex methods as HyperNEAT, CE and DSE, two experiments involving these encodings have been conducted. In the first one, DSE outperformed its relatives in evolving some predefined target connectivity patterns. Likewise in the second experiment, it gave the best results, delivering modular solutions to the retina problem. Thus we conclude DSE is a competitive neuroevolution method worth further development and trying in practice.",
"introduction": "Introduction As computers gain computing power, practical potential of evolutionary computation does grow as well. Evolutionary synthesis of intelligent agents, hardware or software, is a field of continuously growing interest. Agents can be evolved to solve virtually any reproducible problem for which a fitness function can be defined, and the most interesting results might be expected in domains, where little human expertise and no robust methods exist so far. One promising approach to evolve intelligent agents is to combine learning and evolution in the evolutionary artificial neural networks (EANNs) or neuroevolution framework. Evolutionary algorithm (EA) can be used to optimize network topology, weights, transfer functions or learning rules. While plenty of EANN systems has been proposed so far [see e.g. 7 , 33 ], most of them addressed only one or two selected aspects of network architecture. Less common are attempts to capture most of the architecture in the representation. And even more difficult is to find encodings designed with scalability in mind—property conducing evolution of complex, somehow regular networks. The motivation behind using evolution to generate networks is probably well known to Evolutionary Intelligence journal reader. Nonetheless it might be worth brief recapitulation here. First of all, the same EA can be used across many different problem domains, while requiring little knowledge about them. The very flexible definition of the fitness criterion allows to generate networks having an arbitrary performance measure optimized—be it accuracy, efficiency, robustness or any combination of these. Likewise, user-specified design constraints can be easily imposed, such as input-output interface or types and numbers of nodes and connections. EANNs can be optimized along a single or multiple dimensions, either implicitly or explicitly, by using an appropriate multi-objective EA [ 1 ]. However, there are also disadvantages and limitations of the evolutionary approach—primarily unbounded computing power demands and difficulties in the analysis of evolved solutions. The main challenge in pursuit to evolve complex networks efficiently is their genetic representation. In this paper we propose a novel encoding for networks, called Developmental Symbolic Encoding (DSE). As the name suggests, the encoding is developmental, which means networks are grown according to some genetic recipe. The genome is a tree of routines, which in turn consist of lists of instructions. Genetic program grows the network by dividing nodes and layers and by connecting them in a more or less patterned manner. To this extent the encoding incorporates some concepts of two related neuroevolution methods—Cellular Encoding [ 9 ] and HyperNEAT [ 27 , 30 ]. The encoding proposed allows to grow modular and regular networks in a scalable way. In a broad sense, scalability means a capability to solve varying size, and thus also large-scale, problems efficiently. This in turn implicates a capability to capture regularities inherent in these problems. Scalability manifests itself as a slower growth of the genotype as compared to the phenotype of network solution—a sign that some regularity of the problem has been reflected in the network and captured in its genotype. One of the most important elements influencing scalability is a capability to produce modular networks, i.e. networks consisting of structurally localized and functionally encapsulated subnetworks. From a topological perspective, a module is a set of nodes densely connected internally and sparsely connected to other nodes. Modularity is an important feature because it facilitates code reuse and exchange of useful modules between networks. That is why a significant amount of research has been devoted to modularity in evolutionary computation, as e.g. in Genetic Programming (GP) [see e.g. 22 , ch. 6.1]. The two features of DSE, scalability and modularity, are demonstrated experimentally. In Sect. 4.1 we evolve perfectly scalable solutions to parity and symmetry problems. In Sect. 4.2 we demonstrate modular capabilities on the parity problem. In Sect. 4.3 we evolve modular networks capable to learn autonomously in a generic classification task and manifesting scalability in solving the task for increasing number of inputs. Proposed encoding is very flexible in that it can generate whole array of networks—weighed and weightless, recurrent or feed-forward, employing arbitrary transfer functions as well as connections types. Depending on a variant of the evaluation algorithm, they can learn autonomously, through plastic connections with local learning rules, employ neuromodulation, and even backpropagation. It is possible to evolve learning rules for connections and transfer functions for nodes are also evolvable in principle; which is by itself an interesting subject for investigation [see 20 ]. In theory, the encoding can express any recurrent network, thus allowing to evolve networks equivalent to any Turing machine and solve any computable problem. Yet, even with all these features, it remains difficult to estimate practical utility of the encoding. Due to its complexity, it has been tested on a few specific problems so far. It is also not straightforward to compare it with other neuroevolution methods. In Sect. 5 , however, we manage to compare DSE with HyperNEAT and Cellular Encoding on two problems and the results show DSE is very competitive. Unlike its relatives, DSE succeeded in delivering modular solutions to the retina problem. It also outperformed the two other encodings in a task similar to the bit mirroring problem [ 3 ], in which regular patterns of connectivity must be discovered between two layers of nodes. In Sect. 2 we briefly describe several notable approaches to evolve networks. In Sect. 3 we describe an adopted computational model of the network and the encoding itself. In Sect. 4 we examine the concepts of scalability and modularity of genetic representation and demonstrate these two features in DSE; we also demonstrate some flexibility of DSE with an example of network using neuromodulation to learn. Experiments are presented in Sect. 5 and finally we conclude the paper in Sect. 6 ."
} | 2,941 |
35335443 | PMC8955187 | pmc | 1,622 | {
"abstract": "The triboelectric nanogenerator (TENG) has emerged as a novel energy technology that converts mechanical energy from surrounding environments to electricity. The TENG fabricated from environmentally friendly materials would encourage the development of next-generation energy technologies that are green and sustainable. In the present work, a green triboelectric material has been fabricated from natural rubber (NR) filled with activated carbon (AC) derived from human hair. It is found that the TENG fabricated from an NR-AC composite as a tribopositive material and a poly-tetrafluoroethylene (PTFE) sheet as a tribonegative one generates the highest peak-to-peak output voltage of 89.6 V, highest peak-to-peak output current of 6.9 µA, and can deliver the maximum power density of 242 mW/m 2 . The finding of this work presents a potential solution for the development of a green and sustainable energy source.",
"conclusion": "4. Conclusions The biodegradable NR-ACH composite was synthesized and used to fabricate a TENG to convert mechanical energy into electricity. The addition of ACH was found to improve the electrical output performance of the TENG due to the high surface areas of the porous structures of ACH filler materials, which also acted as charge trapping sites to intensify triboelectric charges generated during electrification events. The NR-ACH TENG with the optimum ACH concentration of 0.6% generated the highest electrical power density of 242 mW/m 2 , which was almost three times larger than that of the unmodified NR TENG. The generated electrical power was able to charge the commercial capacitors to power small electronic devices. In addition, the NR-ACH TENG, with a single electrode configuration, was able to detect the movement of the human body, which could be applied to a motion-sensing application.",
"introduction": "1. Introduction The triboelectric nanogenerator (TENG) is emerging as an energy-harvesting device that converts mechanical energy into electricity based on a combination of the effects of contact electrification and electrostatic induction [ 1 ]. Mechanical energy is one of the most abundant forms of energy that exists in many different forms in our living environment. To harvest these mechanical energies, the concept of environmental friendliness is regarded as one of the most important aspects for the development of a clean and sustainable energy source. The commonly used materials for the TENG fabrication are polymers, such as poly-dimethylsiloxane (PDMS) [ 2 , 3 ], poly-vinylidenefluoride (PVDF) [ 4 , 5 ], poly-tetrafluoroethylene (PTFE) (or Teflon) [ 6 , 7 ], polyimides (or Kapton) [ 8 , 9 ], and polymethyl methacrylate (PMMA) [ 10 , 11 ]. Most of them are synthetic polymers [ 12 ], which have high costs and non-degradable environments. Many efforts have been made to develop biodegradable and environmentally friendly triboelectric materials. These include plant-based materials, such as wood [ 13 ], leaves [ 14 ], and cellulose [ 15 ] and animal-based degradable materials, such as chitosan [ 16 ], silk fibroin [ 17 ], and gelatin [ 18 ]. Natural rubber (NR) is a natural polymer, and its chemical structure is cis-1,4-polyisoprene, which is typically extracted from the tree Hevea brasiliensis [ 19 ]. Natural rubber latex has been widely used as raw material for manufacturing a wide range of industrial products [ 20 ]. The majority of NR products are utilized in kinetic environments that involve motions and vibrations. In this respect, NR is a crucial candidate for biodegradable triboelectric materials to harvest large-scale mechanical energy. Moreover, NR has the feasibility to form composite materials by adding nanoparticles or filler materials and to modify its internal and surface structure to intensify triboelectric charges in order to boost the energy conversion performance of the TENG. Recently, there were a few studies on NR-based TENGs, including silica-based rubber compounds for harvesting mechanical energy from car tires [ 21 ], stretchable rubber-based TENGs as self-powered body motion sensors [ 22 ], and NR nanocomposite TENGs for energy-harvesting applications [ 23 , 24 ]. Regarding the realization of the practical uses, many approaches have been proposed to enhance the electrical output power of TENGs. Extensive studies have been focusing on the promotion of triboelectric charge quantities in triboelectric materials. This can be done by increasing the surface areas and charge retention abilities or capacitances of the triboelectric materials [ 25 , 26 ]. There are many different ways to modify triboelectric materials for enhancing the power output of the TENG, including surface modification, such as plasma etching [ 27 ], micro/nano-patterning [ 28 , 29 ], soft lithography [ 30 ], and internal structure modification into porous or sponge structures [ 31 , 32 , 33 ]. Porous-structured materials have been employed to improve the TENG performance. This contributes to the increased electrification in the internal structure, which promotes triboelectric charge generation and accumulation [ 34 , 35 ]. Activated carbon (AC) is a carbonaceous material with a high porosity and surface area, which can be derived from natural carbon sources, such as plants, animals, and minerals [ 36 ]. Human hair is a bio-waste with a high carbon content [ 37 ], which is attractive to be used as a starting material for producing activated carbon. With AC’s high specific surface area, ACs derived from human hair (ACH) were found in a variety of applications, such as electrode materials for super-capacitors [ 38 ] and batteries [ 39 ], gas adsorption [ 40 ], and wastewater treatment [ 41 ]. In this work, AC derived from human hair was introduced as a filler material for NR, which was employed as a triboelectric material for TENG. This work was the first report on using human bio-waste and natural products to fabricate a biodegradable TENG with a high energy-conversion performance. The effect of ACH filler content in an NR-ACH composite on TENG performance was investigated. The microstructural characterizations of ACH and NR-ACH composites were performed to explain their contribution to the enhancement of energy conversion performance. In addition, the energy-harvesting applications of the fabricated TENG to charge a capacitor and to power a small electronic device and motion sensing application were demonstrated."
} | 1,601 |
24253402 | PMC3834362 | pmc | 1,623 | {
"abstract": "This study explores how contact angle hysteresis and titling angle relate with stickiness on superhydrophobic surfaces. The result indicates that contact angle hysteresis could not be mentioned as a proper factor to evaluate the surface stickiness. By analyzing the system pinning force of droplet placed on a titled surface, we concluded that both solid fraction and surface geometric factor are the critical factors determining the surface stickiness.",
"discussion": "Discussion To well explain the previously mentioned results and get an advanced understanding of stickiness on superhydrophobic surfaces, we need to address one question first: how the surface with larger CAH has smaller pinning force? Actually, according to equation (1) , the pinning force is determined by the difference of cosine values other than CAH. The cosine values decrease with the increasing contact angles. The contact angles on our samples are in the range from 90° to 180°. For a certain CAH value in this range, the difference of cosine values and the corresponding pinning force will decrease with the increasing contact angles. Furthermore, although the measured CAH values increase with the decreasing solid fractions, the difference of cosine values on the smallest solid fraction point is still much lower than others. Actually, as can be observed in figure 3(b) , difference of cosine values at the smallest solid fraction point is only ∼1/30 of the one at the largest solid fraction point. Therefore, it is reasonable to observe the coexistence of lower pinning force and larger CAH in Figure 3 (a) . In the following section, we would like to discuss more on this topic in this communication. The above mentioned pinning force is just the one applied on per unit length of the apparent droplet boundary. To evaluate the surface stickiness precisely, it is critical to measure the total pinning force applied on the whole system. According to previous studies 23 , the system pinning force can be expressed by the relation: Where m is the mass of the liquid droplet, g is gravitational acceleration, θ T represents TA. Figure 4 (a) shows the TA values on each surface, together with the system pinning force, with respect to the liquid-solid contact area fraction. For all the samples, the TA simply demonstrates the same changing tendency with system pinning force. McCarthy et al have commented on the importance of the TCL and its pinning effect on surface stickiness 21 . Meanwhile, they challenged the reasonableness of adopting solid fraction as the critical influence factor on the stickiness 22 . However, for their own theory, there is no direct correlation between the surface structures and surface stickiness could be revealed. To explore the working mechanism and also the critical influence factors of this relation, we further considered the system pinning force when droplet scanning over a solid surface. As indicated in Figure 4 (b) , the droplet will not scan until the driving force by surface tension forces is large enough to challenge this pinning force, therefore this system pinning force can also be expressed as: By combining Eq. (2) and Eq. (3), we could build a new relation between titling angle and TCL: Note that L could not be simply treated as the width of the drop viewed along the sliding direction. L is a geometric factor depends on the pattern types, determines the effective TCL length along the droplet boundary, on which the surface tension forces are effectively applied. Based on this relation, the observed increase in TA with increasing solid fraction arises from two main factors. Firstly, the droplet contact angle decreases with increasing solid fraction, which would cause the footprint area of droplet to increase. At the same time, the parameter L in Eq.(4) is increased. Secondly, as discussed in previous section, the difference of cosine values increases while the contact angle decreases ( Figure 3 (b) ). Therefore, the parameter of solid fraction not only affected the effective TCL length L along the droplet boundary, but also influenced the pinning effect on per unit length of the apparent droplet boundary. Meanwhile, however, as mentioned in previous section, neither the effective TCL length L nor the pinning effect on per unit length of the apparent droplet boundary could be totally determined by solid fraction, they are also closely related with the surface pattern types. We believe that different surface geometries will definitely induce totally different pinning effects, even though these surfaces have the same solid fraction. Our point is well in line with the experimental results of Zhang 17 . All these results and discussion indicate that the surface stickiness should be determined by overall considering the solid fraction and also the surface geometric factor. The more detailed working mechanism determining the value of L should be of interests to those researchers in this field and be investigated further. By investigating the relation between CAH, TA and pinning force, this study reveals the proper influence factors and the related working mechanism of the pinning phenomena on superhydrophobic surfaces. The pinning force which prevents TCL moving smoothly and forming the thermodynamically stable contact angle is found not directly related with CAH. However, we found the surface stickiness depends on the morphology of the surface structures, which includes the parameters of solid fraction and surface geometric factor. This new insight paves the way to better understand the outstanding problem of hysteresis, pinning. Not only for the theory development, is the surface stickiness also of significant importance in the microfluidics applications, such as friction reduction."
} | 1,439 |
34755087 | PMC8564057 | pmc | 1,625 | {
"abstract": "Summary Skin-like electronics are developing rapidly to realize a variety of applications such as wearable sensing and soft robotics. Hydrogels, as soft biomaterials, have been studied intensively for skin-like electronic utilities due to their unique features such as softness, wetness, biocompatibility and ionic sensing capability. These features could potentially blur the gap between soft biological systems and hard artificial machines. However, the development of skin-like hydrogel devices is still in its infancy and faces challenges including limited functionality, low ambient stability, poor surface adhesion, and relatively high power consumption (as ionic sensors). This review aims to summarize current development of skin-inspired hydrogel devices to address these challenges. We first conduct an overview of hydrogels and existing strategies to increase their toughness and conductivity. Next, we describe current approaches to leverage hydrogel devices with advanced merits including anti-dehydration, anti-freezing, and adhesion. Thereafter, we highlight state-of-the-art skin-like hydrogel devices for applications including wearable electronics, soft robotics, and energy harvesting. Finally, we conclude and outline the future trends.",
"conclusion": "Concluding remarks and future perspectives Hydrogels represent an important class of materials possessing broadly tunable physical and chemical properties. Efforts devoted to engineering hydrogel devices with skin-like properties and functionalities have expanded their applications in different areas such as wearable sensing, soft sensors and actuators, and stretchable electronics. Over the past decade, substantial progress has been made to develop hydrogel materials with high mechanical toughness, stretchability, and/or conductivity. In addition, the development of nature-inspired hydrogel devices with enhanced merits (e.g., anti-dehydration, anti-freezing and bio-adhesion) has flourished recently. This review simmarized the recent hydrogel materials development and device application in wearable sensing, soft robotics, and energy harvesting. Despite the exciting advances, there are several challenges to be further addressed. Multimodal sensation Currently, skin-inspired ionic hydrogel sensors are mainly designed to have single or dual operation modality ( Liu, 2020 ). However, the human skin possesses a multimodal sensation capability. Therefore, new designs of hydrogel materials and structures are worth exploring to realize new types of multimodal sensors. For example, in biological systems, Na + or K + ion channels can efficiently discriminate Na + or K + from other alkali cations and even from each other, while traditional ionic hydrogel sensors have never been able to sense those differences. Existing stretchable hydrogel sensors were focused on measuring biophysical signals; the realization of multimodal (biophysical and biochemical) sensing capabilities would extend the functionalities of wearables and robots. Sensing of touch, pressure, deformation, temperature and humidity and even detecting the presence of chemical and biological markers in the environment would be useful for a wide range of applications ( Li and Mooney, 2016 ; Mostafalu et al., 2018 ; Trombino and Cassano, 2020 ; Wang et al., 2018a ). New fabrication strategies Currently, skin-inspired hydrogel devices are usually fabricated directly from bulk hydrogel and the patterning and integrationmainly rely on manual assembly. Miniaturization and scalable fabrication of hydrogel devices will enrich the functionality of a single device and reduce the device-to-device variation. Most proof-of-concept ionic hydrogel devices featured millimeter thickness ( Yang and Suo, 2018 ). Fabrication of thinner hydrogel films with good breathability, durability, stretchability, and biocompatibility improve the user’s comfort and reduce/avoid irritation over long-term use. For example, 3D printing of hydrogel has allowed the fabrication of strain sensors ( Tian et al., 2017 ) and the patterning of highly conductive and soft hydrogel microstructure with high resolution (∼30 μm) for bioelectronic neural recording ( Yuk et al., 2020 ). 3D printing is also promising to highly integrate multimodal sensation into a single soft robot ( Truby et al., 2018 ). In addition, existing microfabrication techniques such as soft lithography could be possible for constructing micrometer-sized hydrogel arrays for building a large number of sensing modules with different modality. Improved Intelligence based on hydrogel ionic computing and big data The ultimate goal of ionotronics is to develop an integrated intelligent system, which consists of ionic sensing, ionic wiring, ionic memory/computing, and ionic decision/actuation components. Today, stretchable hydrogel ionic sensing, wiring, and actuation have been demonstrated ( Yang and Suo, 2018 ) while stretchable hydrogel ionic computing has rarely been reported to realize seamless signal transduction from input to output ends within a closed loop. The gap of stretchable hydrogel memory/computing may be filled in the near future considering the recently developed stretchable ionic diodes ( Kim et al., 2020a ; Lee et al., 2019 ; Wang et al., 2019b ; Ying et al., 2020a ). Stretchable hydrogel ionic diode, as the counterpart of semiconductive electronic diode, could be a basic component to construct stretchable transistors and more integrated ionotronic systems. On the other hand, massive datasets would be collected during long-term wearing of highly integrated hydrogel sensors for personalized healthcare. Artificial intelligence can be integrated to enhance the data processing and analysis for personalized health monitoring and disease prediction/prognosis. Full automation is another opportunity for soft robotics equipped with multimodal hydrogel sensors. Advanced, big data-driven control methods could facilitate closed-loop controlled operations of soft robots, through real-time processing of feedback signals from highly integrated and distributed soft hydrogel sensors ( Liu et al., 2020d ; Wang et al., 2020b ). Stretchable power-storage devices The development of stretchable energy supply systems is another challenge to achieve more stable and longer lasting ionic hydrogel systems for wearables and soft robotics. The trend to integrate multiple hydrogel sensor arrays into wearable/robotic platforms will increase the power consumption during continuous monitoring as well as large existing actuation power consumption. Even though the development of self-powered devices [e.g., integration of stretchable solar cells ( El-Atab et al., 2019 ), stretchable TENG-based sensing and energy harvesting ( Pu et al., 2017 ), stretchable ionic diode-based sensing and energy harvesting ( Kim et al., 2020a ; Ying et al., 2020a ) and biofuel-powered e-skin ( Yu et al., 2020c )] suggests promising solutions to solve this issue, their power generation efficiency remains problematic. Therefore, the development of wearable and stretchable power-storage devices with large capacities is important for future wearable and soft robot designs.",
"introduction": "Introduction The skin is the largest organ of human body and serves as the first physical, thermal, and hygroscopic barrier between the external environment and the body’s internal components. Importantly, the skin contains the largest amount of sensing receptors to perceive various environmental stimuli that humans encounter, such as pressure, strain, humidity, temperature, and pain ( Figure 1 A). To date, tremendous skin-inspired flexible and stretchable devices have been developed based on our understanding of the human skin’s sensing functions ( Benight et al., 2013 ; Cheng et al., 2019 ; Kim et al, 2011a , 2011b , 2016a ; Lei and Wu, 2018 ; Liang et al., 2013 ; Lipomi et al., 2011 ; Oh et al., 2016 ; Pelrine et al., 2000 ; Sekitani et al., 2008 , 2009 ; Sun et al, 2006 , 2014 ; Ying et al., 2020a , 2020c , 2021b , 2021c ; Yu et al., 2020d ; Zang et al., 2013 ). These advances have revolutionized wearable electronics and other related fields. Current skin-like wearable devices have already been used for personal health monitoring [e.g., detection of glucose, uric acid, lactose, heart rate, blood pressure, ion levels, stress level, strain, tactile, temperature, humidity ( Trung and Lee, 2016 ; Xu et al., 2020 ; Yu et al., 2020b )], for communication between humans and devices [e.g., human–machine interfaces ( Wang et al., 2018a )], and for wearable robotic assistance [e.g., exosuits and artificial prosthetics ( Mengüç et al., 2014 ; Zhao et al., 2016 )]. On the other hand, next-generation soft robotics requires a variety of stretchable sensors to be ‘worn’ on soft-bodied robots for sensing and perception during interaction with their surroundings, where the sensing capability of skin-like electronics will be highly useful to improve the soft robot designs ( Shih et al., 2020 ). Because of the inherent material match and functional complementarity between the skin-like devices and soft robots, there have been significant efforts to develop skin-like stretchable and wearable sensors for integration with soft robotic systems. Biological ( Justus et al., 2019 ), optical ( Larson et al., 2016 ; Zhao et al., 2016 ), strain ( Kim et al., 2020c ), and tactile ( Booth et al., 2018 ; Thuruthel et al., 2019 ) sensing capabilities have been embedded into soft robots to enable them to interact with their users and the environment more intelligently. Figure 1 Skin-like hydrogel devices (A) Schematic of the human skin that resists physical deformation due to the elastin fiber and collagen in the dermis layer, maintains the body temperature due to the fat cells in the hypodermis layer, holds water due to the hygroscopic substance (i.e., pyrrolidone carboxylic acid), and transports ionic signal directionally within sensory neurons. (B) Main features of hydrogels (e.g., toughness, ionic conductivity, anti-dehydration, anti-freezing, adhesive, and self-powering) desired for practical use as wearable sensors, soft robotics and energy harvesting devices. Among various materials for constructing skin-like devices, stretchable and tough ionic hydrogel is one of the most suitable candidates. Hydrogel mimics multiple properties and functions of biological systems, such as their superior softness and hydration, excellent material biocompatibility, and unique ionic sensing functions ( Bao et al., 2020 ; Lee et al., 2018a ; Sheng et al., 2019 ; Yang and Suo, 2018 ). In addition, their tunable mechanical properties with on-demand design of toughness, stretchability, and elasticity can accommodate the diverse mechanical properties of substrates the hydrogel devices will be mounted on (e.g., cloth, skin, soft robot body, and tissue). Thus, hydrogels could potentially reduce the mechanical, electrical, and/or biological mismatches between soft-robotic/human bodies and traditional electrical counterparts ( Yuk et al., 2019a ). So far, a variety of hydrogel-based, skin-like devices have been developed for applications including wearable sensing, soft robotic sensing, and energy harvesting ( Figure 1 B). These devices are capable of transducing touch, pressure, deformation, humidity, and temperature inputs into changes of electrical signals (e.g., capacitance, resistance, open circuit voltage [OCV], and short circuit current [SCC]), thus mimicking the sensing functions of the natural skin. The development of hydrogel devices, however, is still in the early stage and facing many challenges. For example, most existing hydrogel devices have limited functionality to sense only one stimulus. As a sensing component, they usually require external power supplies. In addition, hydrogel devices are not stable during open-air operations, and cannot maintain mechanical deformability and electrical conductivity in cold environments. Moreover, conventional hydrogels usually have poor adhesive capability and cannot ensure firm adherence to substrates of different materials (e.g., human skins, fabric clothes, and elastomers), limiting the fidelity of signals acquisition during wearable and soft robotic sensing under various conditions (e.g., dry and wet surfaces, sweaty skin, subzero temperature, and dynamic deformation and movement). Therefore, in this review we primarily aim to summarize the current strategies that address these challenges for those real-world applications. There have been several reviews on hydrogel materials and their device applications, which focus their toughening mechanism ( Chen et al., 2015 ; Gong, 2010 ; Peak et al., 2013 ; Zhao, 2014 ), enhanced environment adaptability ( Zhou et al., 2019 ), adhesive mechanism ( Peak et al., 2013 ; Yang et al., 2020 ), bioelectronics ( Yuk et al., 2019a ), ionotronics ( Yang and Suo, 2018 ), and soft machines ( Liu et al., 2020d ). This review will put an emphasis on the state-of-the-art skin-like ionic hydrogel devices with specific features for emerging application areas such as wearable electronics, soft robotics, and energy harvesting. We start with a brief introduction to the fundamentals of hydrogel and strategies to increase its toughness and conductivity. Thereafter, we review recent advances of ionically conductive hydrogel devices with advanced merits such as anti-dehydration, anti-freezing, and adhesion. Then, we summarize state-of-the-art applications of skin-like hydrogel devices for wearable electronics, soft robotics, and some other important applications such as energy harvesting. Finally, we conclude with a perspective discussion on the remaining challenges and opportunities, and also propose a number of future directions in the field. We hope this review will bring new insights on how to design new types of hydrogel devices for seamless merging of humans, wearables, and robots."
} | 3,486 |
30778064 | PMC6379389 | pmc | 1,626 | {
"abstract": "Replicating nacre’s multiscale architecture represents a promising approach to design artificial materials with outstanding rigidity and toughness. It is highly desirable yet challenging to incorporate self-healing and shape-programming capabilities into nacre-mimetic composites due to their rigidity and high filler content. Here, we report such a composite obtained by infiltrating a thermally switchable Diels-Alder network polymer into a lamellar scaffold of alumina. The chemical bond switchability and the physical confinement by the filler endows the composite with sufficient molecular mobility without compromising its thermal dimension stability. Consequently, our composite is capable of self-healing internal damages. Additionally, in contrast to the intractable planar shape of other artificial nacres, precise control of the polymer chain dynamics allows the shape of our composite to be programmed permanently via plasticity and temporarily via shape memory effect. Our approach paves a new way for designing durable multifunctional bioinspired structural materials.",
"introduction": "Introduction Biological structural materials are usually strong, tough, and lightweight owing to their elegant and complex architectures at multiple length scales 1 , 2 . In particular, the nacreous layer of mollusks, composed of alternating layers of calcium carbonate platelets and biopolymer, exhibits an extraordinary mechanical behavior 3 , 4 . In the last decade, nacre-mimetic composites (artificial nacres) have been developed using various approaches including magnetic field-assisted additive manufacturing 5 , layer-by-layer assembly 6 – 8 , spray-casting 9 , ice templating and ceramic sintering 10 – 12 , matrix-directed mineralization 13 , and evaporation-induced self-assembly 14 . Unfortunately, these artificial nacres are incapable of recovering mechanical damages, a prerequisite for their durability. In addition, while natural nacre exists in intricate forms including spiral and ladle shapes, artificial mimics are limited to simple flat geometries due to the fabrication methods involved 6 – 14 . Smart nacre with self-healing capability and shape-programmability is highly desirable for practical applications 1 – 3 , but this demand has not been met. Self-healing and shape-programmability are commonplace for soft polymers but are rarely realized in rigid structural materials 15 – 25 . Combining these attributes into a nacreous architecture is challenging: the complex multi-scale structure prohibits the inclusion of encapsulated agents for extrinsic healing 24 ; the high loading of rigid fillers restricts the molecular mobility of the soft polymer matrix, a key enabler for intrinsic self-healing and shape adaptability. In principle, the use of dynamic polymer network 25 – 27 as the matrix represents a potentially attractive approach to design such a smart nacre. However, harnessing the related properties in a nacreous composite requires reconsideration of molecular mobility from a different perspective due to the physical confinement by the heavily loaded inorganic fillers. We report hereafter our successful attempt in this direction. Specifically, we are able to incorporate self-healing and shape-programmability into a nacre-mimicking composites by using a dynamic network polymer with high thermal switchability as the polymer matrix. In addition to its capability of healing internal damages, the macroscopic shape of this smart nacre can be programmed in two different ways (permanently via plasticity and temporarily via shape memory effect), in sharp contrast to the intractable planar shape of known artificial nacres. Our study highlights the possibility of harnessing the rich designability of soft polymers in a predominantly inorganic structural material system. The simplicity in the composite fabrication and its scalability imply that various filler systems with functions beyond structural are possible. Therefore, we believe that our approach paves a way for designing durable multifunctional bioinspired structural materials for various practical applications.",
"discussion": "Discussion In summary, we demonstrate that dynamic covalent polymer network can be utilized as the polymer matrix to enable the fabrication of a smart nacre with intrinsic self-healing and versatile shape-programming capacities. More generally, the study highlights the possibility of harnessing the rich designability of soft polymers in a predominantly inorganic structural material system. Additionally, the simplicity in the composite fabrication and its scalability imply that various filler systems with functions beyond structural are possible. Overall, the study points to a future direction in designing bioinspired structural material systems with multi-functions beyond those typically associated with a single biological material."
} | 1,210 |
37744860 | PMC10515177 | pmc | 1,628 | {
"abstract": "Laser processing is a simple way to obtain hydrophobic\nor even\nsuperhydrophobic properties of metal surfaces. However, preparation\nof superhydrophilic surfaces by this method, the properties of which\ndo not change under the influence of various factors, remains a difficult\ntask. In this work, we show that with increasing laser power, the\ndegree of oxidation of the treated metal surface also increases. As\na result, highly oxidized samples showed highly stable superhydrophilic\nproperties. A Janus membrane fabricated from a stainless steel mesh\nwith asymmetric hydrophilic-hydrophobic wettability demonstrated stable\nwater diode properties. In addition, it was found that during the\nexamination of sample surfaces by Raman spectroscopy, organic compounds\nadsorbed on the hydrophobic surface were decomposed by the laser of\nthe spectrometer, which imposes limitations on the laser power when\nusing this method in characterizing hydrophobic surfaces of metals\nfabricated by laser processing.",
"conclusion": "4 Conclusions This study demonstrated\none way to obtain long-lasting hydrophilic\nmetal surfaces prepared by laser treatment without additional chemical\ncoating. It was shown that an increase in laser power contributed\nto the creation of a highly developed nanostructured surface with\nits simultaneous strong oxidation, as a result of which a passivating\noxide layer was formed on the surface. As a result of this amorphous\noxide layer, the acquired high hydrophilicity of metal samples remained\nunchanged for a long time, without showing any symptom of transition\nto a hydrophobic state, which was usually observed in metals after\ntheir laser treatment. This method has been tested on both aluminum\nplates and stainless steel meshes. The Janus membrane with asymmetric\nwettability, fabricated from the mesh, continued to demonstrate the\nproperties of a water diode even after 3 months of monitoring. In\naddition, organic molecules adsorbed on surfaces could be detected\nusing Raman spectroscopy (instead of the commonly used XPS method).\nHowever, this method required a reduction in the laser power of the\nspectrometer, since during the measurement, these hydrocarbons degraded\nunder laser irradiation.",
"introduction": "1 Introduction The interaction of liquids\nwith solid surfaces is one of the essential\nproperties of a material, depending on which this material can be\nused in specific applications. The ability of a liquid to maintain\ncontact with a solid medium is determined by the intermolecular interaction\nand depends on both the degree of surface roughness and its chemical\ncomposition. 1 − 3 Therefore, the properties of both the solid and liquid\nsurfaces are critical at the interface to determine the final wetting\nbehavior. It is important to understand how to fabricate the desired\ntopography and impose favorable chemistry on engineering metal substrates\nfor desired wettability. It has been widely demonstrated that the\nwettability state of a solid surface can be changed by micro/nanostructuring\nof the surface. 4 − 6 Fabrication techniques such as thermal oxidation,\nchemical vapor deposition, laser structuring, and plasma spray were\nwidely used to create wetting properties based on two primary factors,\nand those are surface chemistry and surface micro-/nanostructures. 7 − 11 Laser processing of metal surfaces is a reliable technique to produce\nsuperhydrophilic or superhydrophobic materials, depending on the surface\nenergy of the material. 12 − 15 Furthermore, a femtosecond laser with its good consistency,\nhigh precision, and negligible heat-affected zone is a one-step solution\nfor creating multifunctional surfaces. Modification of the surface\nmorphology under ultrafast laser irradiation is mainly carried out\nby ablation of a localized area with a focused beam, which makes it\npossible to change both the chemical composition of the material and\nthe surface microstructure. 16 − 19 Various metallic materials, such as aluminum,\nstainless steel,\ncopper, titanium, and others, have been used as substrates to prepare\nsuperhydrophobic or superhydrophilic surfaces. Superhydrophobic surfaces\nof metals attracted lot of attention and research efforts in recent\nyears due to potential applications in many fields, such as self-cleaning,\nanti-icing, anticorrosion, or enhanced heat transfer. 20 − 23 On the other hand, hydrophilic metallic materials with good adhesion\nsurfaces are considered important applications for both scientific\nfindings and practical applications, such as cell-based biosensors,\ncell–cell communication, coating fabrication, and water-assisted\nflow generation. 24 − 26 In addition, Janus membranes with asymmetric hydrophobic/hydrophilic\nsides make them promising in many applications such as liquid manipulation,\nhighly efficient separation of oil and water, water harvesting, and\nswitchable ion transport. 27 − 31 Freshly laser-processed samples are usually superhydrophilic\ndue\nto the enhanced surface roughness and the formation of metal oxide\nsites created during treatment. 32 − 34 When oxygen molecules interact\nwith the treated metal surfaces, polar compounds are formed, as a\nresult of which the surface energy increases, allowing attracting\nthe water droplets. However, it was found that the laser-textured\nmetals transfer from a hydrophilic to hydrophobic state when samples\nare left in ambient air. This process occurs due to the chemisorption\nof nonpolar hydrocarbons contained in the air and does not require\nany additional effort or chemicals. 35 The\ntransition, in which the laser-treated metal surface acquires hydrophobic\nor superhydrophobic properties, could last from several days to several\nweeks. On the other hand, storing laser-processed metals in a vacuum\nsignificantly reduced this transition time (up to several hours). 32 One of the reasons for this phenomenon is the\nvery low water vapor content inside the vacuum chamber, which avoids\npassivation of OH centers by hydrogen-bound water molecules. Another\nis the presence of organic contaminants in the vacuum chamber, which\nmay come from the lubrication system of the vacuum pumps. Consequently,\nthe chemisorption of hydrocarbons occurs much more efficiently in\nthe chamber than in atmospheric conditions. Thus, the superhydrophobic\nsurfaces of metals can be fabricated\nby laser processing and subsequent storage of samples in a vacuum\nchamber. However, obtaining long-term stable superhydrophilic properties\nof laser-treated metals without additional treatment or coating remains\na challenge. The stable hydrophilicity of metals within 30 days after\ntheir laser treatment has been shown in several studies. 36 , 37 However, these results are not entirely correct, since the samples\nwere stored in air, where the time of transition from a hydrophilic\nto a hydrophobic state is largely determined by the concentration\nof hydrocarbons in the air. In the present work, laser-treated\nsurfaces of aluminum plates\nwere tested for their ability to retain their hydrophilic properties\nduring long-term storage in the environment or in a vacuum chamber.\nIt was found that the main factor determining the stability of the\nhydrophilic state was the oxidation of the structured surface created\nby the laser. An increase in the degree of oxidation of the samples,\ndepending on the power of the laser used, improved the ability of\naluminum to retain its obtained hydrophilicity from the transition\nto a hydrophobic state. To confirm the role of oxygen, the properties\nof samples prepared in air and in an argon medium were compared. Based\non the results obtained, a Janus membrane with asymmetric hydrophobic–hydrophilic\nwettability of the sides was fabricated from a stainless steel mesh.\nThis membrane demonstrated stable water diode properties, in which\nwater could only pass through the mesh in one direction. In addition,\nit was found that, under certain measurement conditions, the XPS method\ncommonly used to analyze the composition of the surface of samples\ncould be replaced by the simpler method of Raman spectroscopy. It\nwas found that the intensity of the peaks in the Raman spectra, attributed\nto organic compounds adsorbed on the hydrophobic surface, decreases\nwith time due to the degradation of these compounds upon the interaction\nwith the laser used with the Raman spectrometer. Thus, a necessary\ncondition to properly analyze the chemical composition of hydrophobic–hydrophilic\nsurfaces of laser-treated metals by Raman spectroscopy is to decrease\nthe power of the spectrometer’s laser.",
"discussion": "3 Results and Discussion Laser ablation\nmakes changes in the surface morphology and chemical\ncomposition, which strongly depend on the laser power. Figure 2 shows the SEM images and EDX\nspectra of the surfaces structured by femtosecond pulses at 2, 4,\nand 8 W laser power in air. An increase in the laser radiation power\nleads to a significant change in the morphology of the metal surface\nboth in area and in depth, reaching ∼100 μm in thickness\nat 8 W. At the same time, the atomic weight fraction of O increased\nfrom ∼17% for the sample treated at 2 W to ∼45% for\nsamples treated at 8 W, indicating oxidation of the Al surface after\nthe femtosecond laser treatment. Figure 2 SEM images (resolution scales 20 and 5\nμm) and EDX spectra\nof aluminum samples processed at a laser power of 2 W (a), 4 W (b),\nand 8 W (c) in air. Just after laser treatment, the original wettability\nof samples\n(WCA ∼ 80°) acquired pronounced superhydrophilic properties\nwith the WCA less than 5°. The sample treated with a laser power\nof 8 W showed superhydrophilic behavior, in which a drop of water,\nafter contact with the treated surface, quickly spread out in a horizontal\ndirection (see Movie S1 in the Supporting\nInformation). This superhydrophilicity of the surfaces was attributed\nto the enhanced surface roughness and accumulation of a large number\nof polar oxide sites created on the treated surface, which have a\nstrong affinity for hydroxylation. 32 − 34 It is well known\nthat laser-treated hydrophilic metallic surfaces\nbecome hydrophobic or super-hydrophobic when exposed to air for a\nlong period of time. 35 The primary reason\nfor this wetting transition is related to absorption of organic compounds\nfrom the air. This is a very slow process, and in ambient conditions,\nit can take from a few days to several weeks. However, it has been\nfound that vacuum storage of laser-treated metal samples greatly accelerates\nthe wettability transformation from hydrophilic to hydrophobic/superhydrophobic,\ntaking only a few hours. 17 , 23 , 32 The adsorption of hydrocarbons under vacuum takes place in a more\nefficient way than in air due to a lower amount of water molecules,\nwhich passivate OH sites on the surface. Thus, the accelerated wettability\ntransition of samples kept under vacuum was basically due to the adsorption\nof hydrocarbon molecules inside the vacuum chamber. The main source\nof organic contaminants is primarily the vacuum pumps, which usually\nuse mineral oils and additives that can flow through the pumping line\ninto the chamber and get adsorbed onto metal surfaces. The typical\nvalues of the WCA obtained from samples stored in the\nvacuum chamber for 12 h are shown in Figure 3 . As can be seen, the samples processed at\na laser power of 2 and 4 W were successfully converted to hydrophobic\nstates with a WCA of about 130°. The samples processed at 8 W\nalmost did not change their superhydrophilicity, only slightly increasing\nthe WCA to 4°. It should be noted that these samples remained\nsuperhydrophilic even after 3 months. Figure 3 Typical photographs of WCA measurements\nof aluminum samples processed\nat a laser power of 2 W (a), 4 W (b), and 8 W (c) after storing in\na vacuum chamber. The main differences between the samples processed\nat low (2 and\n4 W) and high (8 W) laser powers were the morphology and degree of\noxidation of their surfaces. Therefore, in order to find out the influence\nof these two factors on the wettability properties of the samples,\none sample was prepared using 8 W laser power in an inert argon gas\nmedium. As can be seen from Figure 4 , the samples prepared in air and Ar environments have\na similar nanostructural morphology, indicating that the growth of\nstructures does not depend on the processing medium. However, due\nto the lack of oxygen during laser treatment, the surface remains\npractically nonoxidized. An insignificant amount of oxygen observed\nin the EDX spectrum can be caused by both natural oxidation and the\npresence of hydroxyl groups on the surface when the sample comes into\ncontact with air. At the same time, the sample treated in an argon\natmosphere became highly hydrophobic after storage in a vacuum, which\nclearly indicates the main role of oxidation in the transformation\nof wettability from a hydrophilic state to a hydrophobic one. Figure 4 SEM images\n(resolution scales 200 and 5 μm), EDX spectra,\nand photographs for WCA measurements of the aluminum samples processed\nat a laser power of 8 W in air (a) and Ar (b) medium. The WCA measurements\nwere carried out after the samples were stored in a vacuum for 12\nh. Laser processing of metals in air leads to the\nformation nano/microstructures\nof metal oxides on the treated surface. Metal oxides have a higher\nsurface energy and tend to be hydrophilic because their electronic\nstructure favors the formation of hydrogen bonds. 15 Therefore, immediately after laser processing, the surface\nusually demonstrates superhydrophilic behavior. For example, the process\nof laser ablation of aluminum leads to the formation of Al 3+ and O –2 , which subsequently create a passivating\nAl 2 O 3 layer on the surface. Since aluminum atoms\non the surface are electron-deficient, this leads to the formation\nof a hydrogen bond with interfacial water molecules, promoting the\nheterolytic dissociative adsorption of water molecules. 32 Therefore, surfaces after laser treatment behave\nas superhydrophilic. However, hydroxyl groups on the surface can act\nas effective adsorption or reaction sites. It has been established\nthat the presence of hydroxyl groups on the surface of oxides plays\na significant role in the chemisorption of organic molecules, for\nexample, airborne hydrocarbons, which can include short nonpolar or\nhydrophobic molecules and thus decrease the surface energy and transform\nthe wetting property from hydrophilic to hydrophobic. 32 Increasing the laser power leads to both the creation\nof a more\ndeveloped nanostructured surface and its greater oxidation. The adsorption\nof nondissociated water molecules on the surface of highly oxidized\nmetals can significantly slow down the adsorption of hydrocarbons\ndue to the formation of a passive layer on reactive sites. We attribute\nthis to be the main reason for the stable hydrophilicity of the samples. The XRD patterns of the samples treated in Ar and air environments\nare shown in Figure 5 . For comparison, the spectrum of untreated Al was also added. The\nfour strong diffraction peaks were observed in the spectra at 38,\n44, 65, and 78 degrees, which correspond to (111), (200), (220), and\n(311) crystallographic orientation of aluminum (JCPDS No. 89-4037).\nAs can be seen, after laser-processing the samples, the intensity\nof the diffraction peak in the plane (111) increased significantly\n(for greater clarity, this peak is shown separately in the inset of\nthe figure). This increase in intensity may mean that the microstructure\nobtained on the sample surface is mainly formed from crystals with\nthe (111) orientation. A decrease in the intensity of this peak observed\nin samples treated in air may be associated with the oxidation of\nthese crystals. On the other hand, the absence of peaks corresponding\nto aluminum oxide in the spectra suggests their amorphous nature. Figure 5 XRD patterns\ncollected from untreated and laser-treated in air\nand Ar environments at a laser power of 8 W. Since XPS is a popular and powerful method for\nstudying the surface\ncomposition, it has usually been used to analyze hydrophobic and hydrophilic\nsurfaces of laser-treated samples. At the same time, only a few studies\nwere found in the literature where another method of surface analysis,\nsuch as Raman spectroscopy, was used for these purposes. Perhaps,\nas it was found in this work, this was due to the degradation of organic\ncomponents on the surface during their interaction with the laser\nused in the spectrometer. When using a laser with energies above 10\nmJ, the difference in the signals from hydrophilic and hydrophobic\nsamples was negligible. However, the difference in spectra was clearly\nobserved at a laser energy of 2 mJ, as shown in Figure 6 . The broad band at ∼800 and ∼1800\ncm –1 could be assigned to the asymmetric COO–\ndeformation of the carboxylate groups of organic molecules adsorbed\non the surface after storage in a vacuum chamber. 38 Figure 6 (a) Raman spectra taken from Al samples treated in air (superhydrophilic)\nand Ar (highly hydrophobic) after storage in a vacuum for 12 h. (b)\nDependence of the spectrum of the sample processed in the Ar medium\non the time of laser irradiation of the spectrometer. These peaks degraded under laser irradiation ( Figure 6 b). After the first\nmeasurement,\nthe sample remained under the laser and was measured again. After\n10 and 20 min of irradiation, the initial intensity of the 800 cm –1 band was decreased by 1.7 and 2.9 times, respectively.\nThis degradation of the signal could be caused by desorption of organic\nmolecules from the surface due to the heating effect. As was shown,\nthe hydrophobicity of samples obtained as a result of vacuum storage\ncould be converted back to hydrophilicity by annealing them in air\nat a temperature of 250 °C, as a result of which organic components\nwere removed from the surface of the samples. 39 Thus, the absence of this signal at a laser power of more than 10\nmJ could be caused by rapid heating of the samples. Based on\nthe above results on the important role of the oxidation\nstate in obtaining a permanent hydrophilicity of the metal surface,\nwe applied this method to fabricate a Janus membrane using a stainless\nsteel mesh. To produce a hydrophobic/hydrophilic asymmetric structure,\none side of the mesh was processed by the laser with a power of 8\nW. Figure 7 shows SEM\nimages, EDX spectrum, and WCA of the laser-treated side of the mesh.\nAs in the case of an aluminum plate, the formation of laser-induced\nstructures on the surface of the mesh wires was accompanied by their\noxidation. As expected, after laser treatment, the wettability of\nthe sample changed to hydrophilic with a WCA of ∼58° (versus\n120° for the untreated mesh). Due to the porous mesh geometry,\na drop of water cannot completely spread over its surface. Figure 7 SEM images\n(resolution scales 200 and 5 μm), EDX spectra,\nand photographs for WCA measurements of the stainless steel mesh processed\nat a laser power of 8 W in air. The asymmetric wettability of the Janus membrane\nsurfaces (hydrophilic\non the treated side and hydrophobic on the untreated side) provides\nthe behavior of the water diode in this system. As shown in Figure 8 a (see also Movie S2 in the Supporting Information), the\npassage of water droplets through the mesh was blocked by the bottom\nhydrophobic side, as a result of which they accumulated on the hydrophilic\nside. In contrast, when water droplets from the upper hydrophobic\nside were in contact with the hydrophobic part of the mesh, they could\neasily pass through the mesh, thereby demonstrating the properties\nof a water diode ( Figure 8 b and Movie S3 in the Supporting\nInformation). This anisotropic water transport property arose from\na cross-sectional wettability gradient created in the mesh by laser\ntreatment. 40 As can be seen from Figure 8 c,d, almost the entire\nsurface of the untreated side of the mesh wires was covered with deposited\nmicro/nano oxide droplets. These droplets create a wettability gradient\nalong the perimeter of the wire, starting with superhydrophilic on\nthe treated side and gradually turning into hydrophobic on the untreated\nside. Depending on the direction of the wettability gradient along\nthe wires, there is a difference in the critical pressure of the water\nbreakthrough, which is decisive when water passes through the mesh. 41 Figure 8 Asymmetric water transport of the mesh with different\nwettability\nsurfaces. (a) Accumulation of drops on the hydrophilic side. (b) Passing\nof drops through the mesh from the hydrophobic side. SEM images of\nwires from the untreated side of the mesh (resolution scales (c) 20\nand (d) 5 μm). Finally, the mesh was placed in a vacuum chamber\nfor 24 h to check\nthe consistency of its properties. As expected, the wettability of\nthe treated side remained hydrophilic and the Janus membrane continued\nto demonstrate its asymmetric water transport properties."
} | 5,212 |
34982700 | null | s2 | 1,629 | {
"abstract": "The reservoir computing networks (RCNs) have been successfully employed as a tool in learning and complex decision-making tasks. Despite their efficiency and low training cost, practical applications of RCNs rely heavily on empirical design. In this article, we develop an algorithm to design RCNs using the realization theory of linear dynamical systems. In particular, we introduce the notion of α -stable realization and provide an efficient approach to prune the size of a linear RCN without deteriorating the training accuracy. Furthermore, we derive a necessary and sufficient condition on the irreducibility of the number of hidden nodes in linear RCNs based on the concepts of controllability and observability from systems theory. Leveraging the linear RCN design, we provide a tractable procedure to realize RCNs with nonlinear activation functions. We present numerical experiments on forecasting time-delay systems and chaotic systems to validate the proposed RCN design methods and demonstrate their efficacy."
} | 255 |
34995513 | null | s2 | 1,630 | {
"abstract": "Contrary to multicellular organisms that display segmentation during development, communities of unicellular organisms are believed to be devoid of such sophisticated patterning. Unexpectedly, we find that the gene expression underlying the nitrogen stress response of a developing Bacillus subtilis biofilm becomes organized into a ring-like pattern. Mathematical modeling and genetic probing of the underlying circuit indicate that this patterning is generated by a clock and wavefront mechanism, similar to that driving vertebrate somitogenesis. We experimentally validated this hypothesis by showing that predicted nutrient conditions can even lead to multiple concentric rings, resembling segments. We additionally confirmed that this patterning mechanism is driven by cell-autonomous oscillations. Importantly, we show that the clock and wavefront process also spatially patterns sporulation within the biofilm. Together, these findings reveal a biofilm segmentation clock that organizes cellular differentiation in space and time, thereby challenging the paradigm that such patterning mechanisms are exclusive to plant and animal development."
} | 287 |
36131816 | PMC9418559 | pmc | 1,631 | {
"abstract": "Hierarchical structures in nature provide unique functions for living organisms that can inspire technology. Nanoscale hierarchical structured surfaces are essential to realize the dual functions of non-wetting and transparency for applications such as cover glasses and windows; however, these structures are challenging to fabricate. In this study, nano-hierarchical structured glass surfaces were fabricated using multi-step colloidal lithography and etching to obtain tunable morphology. Nanostructured surfaces of mono-pillar structures of diameter 120 and 350 nm and hierarchical-pillar structures of their combinations exhibited superhydrophobicity after perfluoropolyether coating. In particular, the hierarchical nanosurfaces showed excellent non-wetting properties with the apparent, advancing, and receding water contact angles exceeding 177° and contact angle hysteresis below 1°. Water bouncing behaviors – contact time, spreading diameter, and shape of the bouncing motion were also evaluated according to the Weber number to examine the robustness of superhydrophobicity. Hierarchical nanosurfaces showed larger spreading diameters than mono-nanosurfaces with 14 bounces, indicating minimal energy loss from friction, as can be explained by the effective slip length. Furthermore, the nano-hierarchical structures exhibited better transmittance for wide angles of incidence up to 70° than mono-nanostructures owing to their reduced scattering area and multi-periodicity.",
"conclusion": "Conclusions Inspired by the lotus leaf and moth-eye, nanoscale hierarchical pillars were fabricated via multiple step colloidal lithography with 120 and 350 nm polystyrene nanoparticles. First, the smaller nanopillars were fabricated, and then the larger nanoparticles were used as a mask in the subsequent etching process. Upon repeating these steps, hierarchical structures can be obtained regardless of size. Four fabricated nanosurfaces, mono-pillared S 120 and S 350 , and hierarchical-pillared S NH1 and S NH2 , exhibited superhydrophobicity. Notably, S NH2 and S NH1 have water contact angles of 177° and a sliding angle of 1°. S NH2 with a 120 nm morphology hierarchical surface exhibited the best antireflective effect among the samples by decreasing the scattering area, in particular, when the angle of incidence increased. Therefore, S NH2 has a transmittance of 80% at an incidence angle of 70°. The water bouncing behavior of the samples demonstrated the superiority of the non-wetting behavior of the surfaces, showing the advantages of hierarchy. The values of the maximum capillary pressures were calculated as 23.97 MPa for 120 nm morphology-based samples; S 120 , S NH1 , and S NH2 . Furthermore, the impacted droplets for the Weber number above 290 were splashed and the critical velocity of droplet breakup was obtained as 2.97 m s −1 for all samples. Furthermore, the impacted droplets on S NH2 had the highest number of bounces, 14 times at Weber number 6, and largest maximum spreading diameter of 42.30 mm among the samples. These observations can be explained by the effective slip length. In particular, these results provide a valuable contribution by maintaining the transparency overcoming the hierarchy in microstructures. Therefore, the presented nanoscale hierarchical surface displays outstanding potential for superhydrophobicity and wide angular transmittance, demonstrating extreme water bouncing behavior. Hierarchy in nature results in unique multifunctionality, and multiple step colloidal lithography can potentially be used to formulate such structures, specifically at the nanoscale.",
"introduction": "1. Introduction Hierarchical structures are effective in realizing smart biological behavior in nature by implementing or reinforcing various surface functionalities, such as self-cleaning, antireflection, antibiofouling, water harvesting, structural color, and drag reduction. 1,2 These amazing properties enabled by hierarchical structures can be observed in the lotus leaf, 3–6 pitcher plant, 7–11 moth eye, 12–16 butterfly wings, 17 cicada wings, 18 Namib desert beetle's back, 19–21 and shark skin. 22–25 In particular, the introduction of a hierarchical nanostructure on transparent surfaces can lead to significant performance enhancement for the developing applications such as the cover glass of photovoltaics, building and car windows and lenses of optical sensors. 26–28 However, the implementation of nanoscale hierarchical structures on transparent surfaces is challenging because of the current lack of controllable and cost-effective nanofabrication techniques and lack of knowledge of the role of nano-hierarchies in wetting and optical properties. The superhydrophobic surface of lotus leaves has a micro/nano hierarchical structured wax layer that exhibits a high contact angle and a low sliding angle. The microbump- and nanopillar-based hierarchical structures enhance the wetting properties compared to the hydrophobic chemical surface by reducing the contact area and the loss of the kinetic energy of the water droplets at the interface between water and the structures. 19,20 The antireflective surface of the moth eye has ordered nanopillar structures, creating an interface at which the refractive index changes gradually. Consequently, the nanopillar structures largely suppress Fresnel reflection for a broad range of wavelengths as well as wide angles of incidence compared to optical coatings based on the quarter-wavelength principle. 29–31 Several studies have attempted to induce the combination of superhydrophobic and antireflective functional surfaces by fabricating the nanostructures on a transparent substrate. Park et al. demonstrated a nanotextured fused silica wafer using a multiple coating layer and subsequent etching steps. The subwavelength-tapered conical structures with high aspect ratios and large packing densities resulted in superhydrophobicity and omnidirectional transparency. 32 Furthermore, Lin et al. reported transparent superhydrophobic glass surfaces with nanopatterns fabricated using a femtosecond laser with exceptional water repellency and thermostability. 33 However, these surfaces were not successful in exhibiting improved non-wetting with dynamic droplets and transparency for wide angles because they utilized simple mono-structures unlike the hierarchical structures in nature. Therefore, hierarchical nanoscale surface development is required to understand extreme non-wetting properties and superior angular antireflective effects for the practical application of nanoscale dual roughness. To date, the fabrication of hierarchical structures has been limited for the micro–nano hierarchy and has been performed via expensive, complex, and time-consuming methods such as electron beam lithography, nanoimprint lithography, photolithography, and hybrid approaches with laser or plasma treatment; 34 while the hierarchical structure at the nanoscale has not been well reported owing to the delicate complicated multistep process and restriction on the preparation of nanomasks. Colloidal lithography, also called nanosphere lithography, offers great advantages due to its high throughput and low cost. 35 Recently, Xu et al. reported the fabrication of periodic three-dimensional hierarchical silicon nanotubes using the selected and repeated etching of polystyrene nanospheres and deposited nickel. 36 Fang et al. produced silicon metastructures using a multi-step etching of a polystyrene nanosphere layer. 37 Although hierarchically aligned nanostructures have been demonstrated, the reported fabrication process is very delicate, and shows critical dependence on the etching conditions because only one size of polystyrene nanospheres was used as a mask. In this study, we used a simple nanofabrication process to demonstrate a nanoscale hierarchical pillar-structured surface with tunable morphology and a two-tier solid fraction of low values. The manufactured hierarchical nanosurface was investigated to understand its wetting behavior and transmittance. Effectively, superhydrophobicity via water bouncing behavior after a low surface tension chemical coating as well as broadband antireflection up to an incident angle of 70° were observed, demonstrating the excellent properties of the hierarchical nanosurface. The proposed nanofabrication method and the resulting hierarchical nanostructures imply that the bioinspired nanopillar surfaces provide high-performance functionality and can be expanded for various practical applications such as solar cells, windows, lenses, exterior materials of home appliances, and bio-devices.",
"discussion": "Results and discussion The fabrication process for hierarchical nanostructures involves repeating basic colloidal lithography and reactive ion etching (RIE). 38 The process for fabricating hierarchical nanopillar surfaces via multiple step colloidal lithography (MS-CL) is illustrated schematically in Fig. 1(a) , and the resulting surfaces of each step are shown in the associated scanning electron microscopy (SEM) images in Fig. 1(b)–(e) . Polystyrene nanoparticles (PS NPs) were employed as a mask to achieve the target morphology with the desired superhydrophobicity and low reflectivity for self-cleaning and transparency. The success of the surface patterning was determined by controlling the self-assembly of PS NPs and manipulating their monolayer packing. The floating method was used to pack particles on the surface, 39 enabling hexagonal packing of PS NPs at the air/water interface driven by surface tension and gravity, as shown in Fig. 1(b) and (d) . We used commercial colloidal PS NPs having the sizes of 120 ± 7 and 350 ± 14 nm. This determined the 1st and 2nd base diameters of the nanopillars on the surface. Thereafter, anisotropic quartz glass etching was successively conducted in a mixture of CF 4 , H 2 , and O 2 gases, as shown in Fig. 1(c) . The condition are described in detail in ref. 40 . Finally, the remaining polystyrene nanoparticles on the structures were removed using O 2 plasma. Furthermore, to fabricate nanoscale hierarchical structures, the colloidal lithography of larger NPs than the 1st NPs was repeated using the same process. During etching, the prepared1st nanopillars located below the 2nd NPs were protected from reactive ion etching, resulting in the fabrication of a hierarchically structured surface, as shown in Fig. 1(e) . In summary, quartz glass was etched with a mask of monolayered 120 nm PS NPs at first and thereafter with 350 nm PS NPs. MS-CL can be repeated continuously with increasing NPs for multistage hierarchical structures. In addition, we could control the number of short nanopillars in the nanoscale hybrid structure by adjusting the size reduction time during the 2nd RIE process. Fig. 1 (a) Fabrication sequence for the hierarchical nanopillar array on quartz glass using two-step colloidal lithography with reactive ion etching method. 120 and 350 nm diameter polystyrene nanoparticles were used as a mask. The SEM images of (b) self-assembled monolayer of 120 nm PS NPs, (c) the surface after 1st RIE process using 120 nm PS NPs, (d) self-assembled monolayer of 350 nm PS NPs on top of the nanopillar surface shown in (c), and (e) the surface after 2nd RIE process using 350 nm PS NPs. The scale bar is 500 nm. To obtain the dual functions of robust superhydrophobicity and omnidirectional broadband antireflective properties with hierarchical nanostructures, two types of samples were prepared with a combination of 120 and 350 nm NPs. Fig. 2 shows the SEM images of the mono-structured nanopillars, S 120 and S 350 , and hierarchical nanopillars, S NH1 and S NH2 . The morphology of the nanopillars was determined according to the size reduction and etching times. As the size reduction time of the PS NP is increased, sharp tip-shaped nanopillars are obtained. The height of the nanopillars was linearly related to the etching time. Among the several etching processes used, S 120 resulted in truncated cone-shaped nanopillars with a height of 517 ± 21 nm, top diameter of 82 ± 6 nm top, and bottom diameter of 120 ± 7 nm, as shown in Fig. 2(a) ; S 350 also led to truncated cone-shaped nanopillars with a height of 678 ± 28 nm, top diameter of 203 ± 13 nm, and bottom diameter of 350 ± 14 nm, as shown in Fig. 2(b) . The maximum gap between the top of the nanopillars was 40 ± 5 nm for S 120 and 151 ± 7 nm for S 350 . By contrast, the hierarchically structured samples had two different shapes of nanopillars by modulating the size reduction time of the 2nd NPs as 30 and 45 s for S NH1 and S NH2 , respectively. The reason for the difference between the short and long pillars is that the long pillars were not etched by the 350 nm PS NP mask in the 2nd RIE process. As a result, hierarchically structured samples such as S NH1 and S NH2 showed dissimilar morphologies based on the change in size of the 350 nm PS NP mask owing to the varied size reduction times in the 2nd RIE process. Therefore, while S NH1 has three to five grouped pillar structures, S NH2 has two to three grouped pillar structures. Furthermore, S NH1 led to long pillars with a height of 682 ± 12 nm, top diameter of 79 ± 2 nm, and maximum gap of 154 ± 3 nm, and short pillars with a height of 272 ± 18 nm height, top diameter of 93 ± 5 nm, and maximum gap of 40 ± 3 nm, as shown in Fig. 2(c) . However, S NH2 shows the long pillars with a height of 682 ± 21 nm, top diameter of 78 ± 2 nm, and maximum gap of 520 ± 12 nm, as well as the short pillars with a height of 269 ± 14 nm, top diameter of 103 ± 9 nm, and maximum gap of 40 ± 6 nm, as shown in Fig. 2(d) . Then, based on the maximum gaps of the samples, the contact area fraction ( Φ C ) of the nanostructured samples was determined. Φ C of S 120 and S 350 were 0.42 and 0.29, respectively. By contrast, the hierarchically structured samples have two Φ C depending on the pillar height because the long pillars have a larger maximum gap than the short pillars. While the contact area fraction of S NH1 was 0.09 for long pillars and 0.54 for short pillars, the contact area fraction of S NH2 was 0.08 and 0.66 for long and short pillars, respectively, as listed in Table 2 . Fig. 2 Slightly tilted top SEM images of nanopillar array on quartz glass fabricated with colloidal lithography using (a) 120 nm PS NPs (S 120 ), (b) 350 nm PS NPs (S 350 ), (c) 120 and 350 nm PS NPs (S NH1 ) with 20 s size reduction time in the 2nd RIE process and (d) 120 and 350 nm PS NPs (S NH2 ) with 40 s size reduction time in the 2nd RIE process. The detailed fabrication conditions and morphology dimensions of nanopillar structures are described in the text. Water contact angles and contact area fraction of S 120 , S 350 , S NH1 , and S NH2 . L and S indicate long pillars and short pillars, respectively Sample Contact angle (CA) (°) Advancing CA (°) Receding CA (°) CA hysteresis (°) Contact area fraction ( Φ C ) S 120 170.6 ± 1.4 177.4 ± 2 163.5 ± 1.9 13.9 ± 2.1 0.42 S 350 172.4 ± 2 173.2 ± 1.1 160.6 ± 4 12.7 ± 1.3 0.29 S NH1 177.4 ± 2.0 177.8 ± 1 177.1 ± 0.1 0.7 ± 0.8 0.09 (L), 0.54 (S) S NH2 177.8 ± 0.9 177.9 ± 0.1 177.2 ± 1.0 0.7 ± 0.1 0.08 (L), 0.66 (S) The water contact angle (CA) was measured to investigate the superhydrophobicity of the hierarchy. Because the physical structure and chemical coating determine the surface wettability, the samples were coated with perfluoropolyether (PFPE) via a simple dipping method. In the contact angle measurements, the water droplet did not adhere on the surface as shown in ESI Movie S1. † After observing that the water droplet hardly attached to the surface, the water contact angle values were measured in Table 2 . The static, advancing, and receding contact angles of all samples were >160°, suggesting that all cavities were filled with air. Furthermore, the surfaces with large cavity area fractions have a higher contact angle and lower hysteresis angle than the surfaces with small cavity area fractions. The contact angle hysteresis of S NH1 and S NH2 was 0.7°, which is 10° lower than the contact angle hysteresis of S 120 and S 350 . Based on these measured CAs, nanoscale hierarchically structured samples showed excellent water repellency, and a droplet could slide easily even with a small slope (∼1°) on S NH1 and S NH2 . The optical properties of the nanopillar-structured surfaces were investigated considering their wide angular transmittance. The antireflection of nanostructures for photovoltaics or optical devices provides improved transmission and visual clarity in particular for a wide angle of incidence (AOI). According to Boden et al. , the reflection minima of the reflectance spectrum of a nanopillar array are related to the diameter of the nanopillar depending on the wavelength. 35 Moreover, because the height of the nanopillar affects the interference between the reflected light from its top and bottom, the wavelengths that cause the lowest reflection can be adjusted depending on the height. 40 Therefore, the formation of nanoscale hierarchical structures with different diameters or heights can be an effective strategy to improve the antireflection effect. 41,42 Furthermore, the benefits achieved by the periodic arrangement of nanostructures, such as low scattering loss in the short wavelength range, can be maintained. 43 Considering these aspects, the fabricated nano-hierarchical structure offers great advantages in achieving high antireflectivity performance owing to its controllability. \n Fig. 3(a) shows photographs of the quartz glass under light exposure. All samples exhibit an antireflective effect, so that the letters below the glasses are clearly observed. However, the visibility varies slightly with different characteristics according to the nanopillar morphologies. Therefore, the transmittance was measured using a spectrophotometer in the configuration shown in Fig. 3(b) . Although the transmittance of S NH1 is almost identical to that of S 350 in the short-wavelength range, a broadband enhancement compared to S 350 is observed at normal incidence, as shown in Fig. 3(c) . S NH2 that has an increased number of short pillars on the top, exhibited a significant enhancement in the transmittance for the short-wavelength range compared to S NH1 and S 350 . This implies that reducing the number of short nanopillars is effective in diminishing size-dependent incoherent backward scattering. In addition, S NH2 also exhibited slightly better properties than S 120 at long wavelengths owing to the existence of a 350 nm size morphology. When the AOI was 70°, S NH2 still demonstrated the best antireflective effect, as shown in Fig. 3(d) . Moreover, the improvement in transmittance due to the nanoscale hierarchical structure becomes more pronounced as the AOI increases. For example, S NH1 has an identical transmittance in the short-wavelength range to that of S 350 for normal incidence, while the transmittance of S NH1 for an AOI of 70° is higher than that of S 350 . This better angular antireflective effect of the nano-hierarchical structure can be explained by the decrease in the scattering area. In the case of normal incidence, the difference in the area that produced light scattering between S NH1 and S 350 was negligible, allowing a similar transmittance to be observed over the short-wavelength range. However, for an AOI of 70°, S NH1 provides a more reduced scattering area than S 350 ; thus, its transmittance increased. In Fig. 3(e) , the values of the average weighted transmittance (AWT) of the samples used in this work are presented as a function of AOIs to show a broadband improvement of the high-angle antireflective effect owing to nanoscale hierarchical structuring. The values of AWT were obtained using ∫ T ( λ ) I ( λ ) AM1.5G d λ /∫ I ( λ ) AM1.5G d λ , where T ( λ ) is the measured transmittance and I ( λ ) AM1.5G is the standard terrestrial solar irradiance. 44 Because S NH2 has a better angular antireflective effect than S NH1 owing to its effective design that avoids undesirable scattering, only S NH2 was compared with the bare quartz and the other mono-structures as shown in Fig. 3(e) . S 350 exhibited a lower performance in angular antireflective effects than the other samples due to significant scattering loss in the short-wavelength region. However, S 350 exhibits good antireflective properties in the infrared range because the size of the nanostructure corresponds to the subwavelength scale. Although S NH2 appears to have little difference in AWT values compared to S 120 , S NH2 consistently showed improved AWT values over S 120 for 50–70° AOIs, as shown in the inset of Fig. 3(e) . In particular, when the AOI was 70°, S NH2 showed a 0.6% improvement in AWT compared to S 120 . This means that S NH2 clearly shows a larger scattering than S 120 for the short-wavelength region, but compensates for the scattering loss by broadband transmittance improvements using dual periodicity. Consequently, S NH2 exhibits a superior high-angle antireflective performance than S 120 for broadband wavelengths. Therefore, considering the results of the above investigations on the angular antireflective effect, it is concluded that the reduced scattering area and the dual periodicity are the primary merits of the nano-hierarchical structure to achieve a better antireflective effect for the broadband wavelengths and wide AOIs. Fig. 3 Angle of incidence (AOI) dependent optical properties of nanopillar structured glasses. (a) Photographic images of nanostructured glasses, (b) measurement setup of AOI dependent transmittance, (c and d) specular transmittance of nano-textured glasses at AOI = 5° and 70° respectively, (e) average weighted transmittance (AWT) values of samples as a function of AOIs for 380–1000 nm wavelengths (the inset represents the AWT values of each sample for 40–70° of AOIs in order to show high angle AR effect of nano-hierarchical structure numerically). Because the aim of this work is to demonstrate the enhanced high angle antireflective effect via effective nanoscale hierarchical structuring, the results for S NH2 are only compared with the other mono-structures and the bare quartz substrate. The non-wetting property of the samples was evaluated in depth by analyzing water bouncing behavior to understand the aspect of the hierarchy. The superhydrophobic wetting state is categorized by the Wenzel state or the Cassie–Baxter state. In the Wenzel state, a water droplet follows surface asperity, 45 wherein the water droplet is strongly pinned onto the contact area, specifically at the edges of the structures. By contrast, in the Cassie–Baxter state, because a water droplet sits on the top of the surface structures, the surface area in contact with the solid surface is small. 45 This generates the liquid–gas interface where the adhesive force is absent, forming on a gas layer below the droplet. In addition, a water droplet in the Cassie–Baxter state can be easily removed from the surface because it has a smaller adhesive area than that in the Wenzel state. The droplet impact experiment allows for different spreading and bouncing motions according to the extent of non-wetting and mostly induces the wetting transition from the Cassie–Baxter to the Wenzel state. Therefore, the investigation of impact behavior is useful for determining the non-wetting ability, particularly for superhydrophobic surfaces with high contact angles. When an impacted droplet is in the Wenzel state, the droplet is pinned on the surface or bounces off, leaving small droplets on the surface. Alternatively, when an impacted droplet is in the Cassie–Baxter state, the structural gap creates capillary pressure that pushes water out with fast contact time and without a splash. Table 1 summarizes the spreading motion with various impact velocities and Weber numbers (We = ρv 2 D / σ ) of the impacting droplets for different impact heights, calculated hammer pressures ( P WH ), and dynamic pressures ( P D ). 46 In the definition of Weber numbers, ρ is the droplet density, v is the droplet impact velocity, D is the droplet diameter, and σ is the liquid surface tension. When the droplet is impacted, pressures are balanced on the hierarchical nanosurface, P WH , P D , and capillary pressure ( P C ), as described in Fig. 4(a) . P WH is generated by compression waves during the droplet impact, and P D is caused by kinetic energy. To prevent the penetration of water into the gap, P C is created by the surface tension between the structures on the surface during impact. The two pressures applied at the direction normal to the surface by impact are P WH and P D and are expressed as 1 P WH ≈ 0.2 ρcv , 2 P D = 0.5 ρv 2 , where ρ is the density of water, c is the speed of sound in water (∼1490 m s −1 ), and v is the impact velocity. P C is created by the surface tension between the structures of the surface during impact as 47 3 P C = −2 σ cos θ / g s , where σ is the surface tension of the liquid, θ is the contact angle, and g s is the gap between the nanostructures. In this study, owing to the colloidal lithography of the hexagonally packed PS beads as a mask, g s can be expressed using the structure diameter ( D ) and the contact area fraction ( Φ C ) as 4 Fig. 4 (a) Schematics of pressure balance of dynamic pressure, water hammer pressure, and capillary pressure. (b) Water bouncing behavior according to impact velocity. Then, g s and P C are 5 6 Varanasi et al. showed the wetting state by balancing the water hammer pressure, dynamic pressure, and capillary pressure. 48 When the pressure condition was P WH > P D > P C , the impacted droplet was in a Wenzel state, whereas when the pressure condition was P WH > P C > P D , the impacted droplet was in a partial pinning state. Furthermore, when the pressure condition was P C > P WH > P D , the impacted droplet was in a Cassie–Baxter state. Because all surfaces have truncated conical nanostructures as a result of colloidal lithography, the capillary pressure varies depending on the height of the structure. Unlike S 120 and S 350 , S NH1 and S NH2 have two different capillary pressures and maximum capillary pressures because they have truncated conical structures of different heights. The values of maximum capillary pressures were 8.08 MPa for S 350 and 23.97 MPa for S 120 , S NH1 , and S NH2 , respectively. Based on the calculated pressure values of P WH (0.12–1.47 MPa), P D (0.12–1.47 kPa), and the maximum P C (8.08–23.97 MPa), it is inferred that the impacted water droplets were in the Cassie–Baxter state. As shown in Fig. 4(b) and Table 1 , the first spreading motion of impacted droplets were of three typical types; flat disk, wavy shape, and splash motion depending on the impact velocity ( i.e. , Weber number), regardless of the sample. The movie of droplet impact motion on S NH2 at different dropped heights of 80, 230, and 450 mm ( i.e. Weber number; 48, 140, and 290) is available in ESI Movie S2. † For a Weber number <48, the spreading shape was flat disk. When the Weber number was 140, the spreading shape changed from a flat disk to a wavy shape and for a Weber number ≥290, the spreading motions on all surfaces were wavy, and the impacted droplets then splashed. The critical velocity of the droplet breakup was 2.97 m s −1 . This behavior proves that all of the nanostructured samples showed excellent superhydrophobicity. The detailed image sequences of Weber numbers 48, 140, 290, and 416 at 4 ms intervals up to 16 ms are displayed for the analysis of the impact behavior in Fig. 5(a)–(d) . Water droplets show the representative motion of impact, spreading, recoiling, and rebound. The spreading motion and diameter of the samples show different behaviors according to the Weber number and surface wetting property in 4 ms images, and all droplets recoiled and rebounded at 8 ms. Furthermore, from the images, it is observed that the extent of rebounding was slightly different, in particular, for the splash behavior at high Weber numbers of 290 and 416. Depending on the Weber number, not only the spreading motion but also the spreading diameter changed. Fig. 5(e)–(h) show the spreading diameters and contact time of the impacted droplets depending on the Weber number. As the Weber number increased, the maximum spreading diameter increased, and the contact time, which is the time required for the bouncing process, increased owing to the increase in contact area where the friction force acts. Among the surfaces, S NH2 had shorter contact times than the others. In addition, for Weber number above 290, the impacted droplets splashed and separated into small droplets and then rebounded from the surfaces with changed mass. On the S NH1 and S NH2 surfaces, larger spreading diameters of the impacted droplets were observed than those on S 120 and S 350 . Fig. 5 Time sequence images of water droplet impact on the surface of (a) S 120 , (b) S 350 , (c) S NH1 , and (d) S NH2 . The scale bar is 2 mm. The ratio of the spreading diameter ( S ) to initial droplet diameter ( S 0 , 2.542 mm) over time on (e) S 120 , (f) S 350 , (g) S NH1 , and (h) S NH2 . The excellent performance of the hierarchical nanostructures of S NH1 and S NH2 was investigated in terms of the maximum spreading diameter, number of bounces, and effective slip length, as shown in Fig. 6 . Fig. 6(a) and (b) show the comparison of the maximum spreading diameter and the number of bounces of each sample according to the Weber number, respectively. The maximum spreading diameter decreased in the order of S NH2 , S NH1 , S 350 , and S 120 . When the weber number was 881, the value of maximum spreading diameter ( S M ) is 42.30, 42.22, 34.54, and 34.15 mm for S NH2 , S NH1 , S 350 , and S 120 , respectively. The maximum spreading diameter of S NH1 and S NH2 is 1.2 times larger than that of S 120 and S 350 . Furthermore, the impacted droplets on S NH2 had the highest number of bounces; in particular, when the Weber number was 6, the water droplets bounced 14 times on S NH2 , 12 times on S NH1 , 6 times on S 350 , and 6 times on S 120 as shown in ESI Movie S3. † Furthermore, as the Weber number increased, the number of bounces decreased owing to severe splash behavior. These behaviors clearly indicate that nanoscale hierarchical structures contribute to non-wetting properties, even with dynamic water droplets. The superior bouncing property of nano-hierarchical surfaces can be explained by the slip length as an indicator to elucidate the bouncing behavior on superhydrophobic surfaces. The spreading diameter of the samples is affected by friction during spreading and recoiling as the energy loss of the impacted droplet is minimized with a low friction interaction. Friction force is the force generated by the no-slip boundary condition in the liquid–solid interaction. 49 On a smooth surface, the friction force is defined as 7 F f = Aηγ s , where A is the area of the spreading water, η is the shear viscosity of water, and γ s is the shear rate on the solid patch. Owing to the shear rate, the velocity of the fluid, called the slip velocity, V , varies along the z -axis. Fig. 6(c) illustrates that the slip velocity varies depending on the boundary conditions such as no slip, partial slip, and preface slip. The boundary condition is expressed as slip length b . Considering the slip velocity and slip length, the friction force is given by 8 Fig. 6 a) Ratio of the maximum spreading diameter ( S M ) to initial droplet diameter ( S 0 , 2.542 mm) on all surfaces. (b) The number of bounces of 8.6 μl droplet on the surfaces when Weber number is ≤140. (c) The schematics of slip velocity and slip length according to boundary condition; no slip, partial slip, and perfect slip. (d) The schematics of effective slip length due to the heterogeneity of boundary conditions on structures. (e) The calculated effective slip length of the surfaces. The spreading and recoil process on the superhydrophobic surface with the Cassie–Baxter state has two boundary conditions owing to the contact and gas areas. During the spreading and recoiling of water, the area of the pillars induces a no-slip boundary condition, whereas the gas area induces a perfect slip boundary condition on the liquid–vapor interface. The friction force on a superhydrophobic surface is defined as 9 F f = AΦCη \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"9.538462pt\" height=\"16.000000pt\" viewBox=\"0 0 9.538462 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(0.013462,-0.013462)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M240 960 l0 -80 80 0 80 0 0 80 0 80 -80 0 -80 0 0 -80z M80 760 l0 -40 40 0 40 0 0 -40 0 -40 40 0 40 0 0 -160 0 -160 -40 0 -40 0 0 -80 0 -80 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 80 0 80 0 0 40 0 40 40 0 40 0 0 80 0 80 40 0 40 0 0 120 0 120 40 0 40 0 0 40 0 40 40 0 40 0 0 120 0 120 -40 0 -40 0 0 -120 0 -120 -40 0 -40 0 0 80 0 80 -40 0 -40 0 0 40 0 40 -80 0 -80 0 0 -40z\"/></g></svg>\n\n s , where s is the average shear rate of the solid patch. To estimate s , the velocity profile in the liquid is influenced by the solid zones only in a region of size 0.5 D . 10 s ∼2 U / D , where U is the velocity of the fluid in the free-slip zones. Therefore, F f ≅ 2 A ( Φ C ) ηU / D . The heterogeneity of the boundary conditions is expressed by the effective slip length ( b eff ), which is defined by F f = AηV / b eff 48 as shown in Fig. 6(d) . 11 using eqn (4) , 12 The previous result shows that, in the case of patterned no-slip dots, 1/π is appropriate as a prefactor, α , of the predicted relationship. 50 13 The results calculated using the equation are shown in Fig. 6(e) and ESI Table S1 † with values of minimum and maximum capillary pressure ( P C ). The effective slip length of S NH2 was found to be the longest. Therefore, the friction force on S NH2 is smaller than that of the others and provides the highest number of bounces among the reported numbers. Consequentially, based on the several analyses of bouncing behavior, it is suggested that the contact time, spreading diameter, and spreading shape of the bouncing behavior do not precisely correlate with the excellence of the hierarchical structures, while the effective slip length distinguishes between the superhydrophobic samples. To date, the reports on the water impact behavior, critical velocity, number of bounces, and slip length, for understanding superhydrophobicity have been scarce. Table 3 summarizes the various superhydrophobic surfaces with different wetting characteristics. Based on the analysis of these results, nanohierarchy provides excellent superhydrophobicity, resulting in a high critical velocity and a large number of bounces with two-tier morphology for large capillary pressure and long effective slip length. Because the mechanical robustness of the nanopillar structured surfaces was confirmed against finger rubbing, 65,66 these functional surfaces is promising in the practical applications. Water contact angle, critical velocity, number of bouncing, and calculated slip length of various surfaces as reported Structure CA (°) Critical velocity (m s −1 ) Number of bouncing Slip length (nm) Nano-hierarchical structure This study 178 2.97 14 800 Flat 51 120 25–100 Rough surface 52 120 50–350 Porous structure 53 130 1.14 Rough surface 54 120–150 2–16 Hierarchical structure 55 154 1.41 Micro structure 56 156 2 Micro structure 57 157 1.34 Rough surface 58 160 2.1 Hierarchical structure 59 164 1.97 Hierarchical structure 60 164 2.95 Nano structure 61 165 0–1500 Hierarchical structure 62 166 2.24 Porous structure 63 169 7 Porous structure 64 175 14"
} | 9,029 |
24915445 | PMC4051588 | pmc | 1,632 | {
"abstract": "Elizabeth Sattely, Anne Osbourn, and colleagues discuss in this Essay four long-standing challenges in plant metabolic engineering: to create plants that provide their own nitrogen, have improved nutrient content, function better as biofuels, and have increased photosynthetic efficiency.",
"conclusion": "Conclusion The long-standing nature of these challenges highlights two needs: First, this field would benefit tremendously from increased funding, especially from federal agencies that have not traditionally invested in plant biology. The likely impact of plant metabolic engineering on the future of fuel and food suggests that funding agencies focused on human health and energy security should consider plant metabolic engineering a priority. Initiatives that encourage the training of plant metabolic engineers—people who understand basic plant biology and fundamental principles of engineering—are especially critical to the future success of the field. Second, the design-build-test cycle in plant engineering needs to be accelerated. Three classes of technologies will be of particular importance: (i) Transcriptomic [48] and metabolomic [49] analyses are capable of rapidly generating functional data that inform engineering efforts, especially in non-model hosts. Computational analyses that glean insights from these data to predict genes of importance for, e.g., nitrogen utilization, will be particularly enabling. (ii) Genome editing tools will enable multiple changes to be made simultaneously to a broad range of model- and non-model plants. Recent advances in using TALENs [50] and CRISPR/Cas9 [51] to engineer plant genomes hold unusual promise in launching far more ambitious efforts to systematically engineer plants. (iii) A larger synthetic biology parts list specific to plants that includes tissue-specific promoters, transporters, multi-gene expression constructs, and biosynthetic enzymes (for example, see [52] ). Taken together, these technologies would enable the manipulation of plant metabolism at an unprecedented level, and promise to translate basic knowledge of plant metabolism into tangible benefits for agriculture.",
"introduction": "Introduction In their native form, plants constitute a remarkable feat of metabolic engineering. Not only does their energy derive entirely from the sun and their carbon from CO 2 , but they can defend themselves from pests and predators without the benefit of mobility; they participate in complex symbioses, in part by tailoring the composition of their epi- and endophytic microbial communities, and they can survive extremes of temperature and nutrient and water availability. What more could we ask of plants? A great deal, it turns out. Both conventional breeding and modern metabolic engineering have been used to boost productivity and to enhance fitness (for example, by increased resistance to pests, herbicides, and climatic extremes) [1] . In addition, new areas of application have been introduced that would have seemed like science fiction only a few decades ago, including the use of plants to produce vaccines, bioplastics, and derivatives of complex natural product drugs [2] – [4] . Many of these more recent engineering goals could not have been accomplished by fine-tuning endogenous host metabolism; instead, they required the installation of new metabolic pathways from other plants or bacteria. Adding new nodes to a plant metabolic network is a difficult task that will benefit from advances in targeted genome modification, tissue-, cell-, and organelle-specific gene expression, and the controlled expression of multi-gene pathways [5] – [7] . In this essay, we highlight recent progress in, and the near-term potential of, four long-standing grand challenges in plant metabolic engineering: two deal with important applications in food and energy, while the remaining two are of general utility in improving plant fitness, and in principle would be useful for improving plants as a chassis for other metabolic engineering efforts. Nature never intended plants to be grown as crops on an industrial scale, nor did plants evolve solely for human nourishment. Plants are not naturally inclined to give up their structural oligosaccharides in ready-to-eat form in the service of providing green energy. Although each of these challenges has been recognized for decades and important advances have been made [8] – [10] , solutions to them still lie far beyond our current capabilities. Nevertheless, the technologies developed to meet them will have myriad uses long before the problems themselves are solved. Techniques for using synthetic biology to make multiple deletions, additions, and other edits to plant genomes stand out as a particularly important set of enabling technologies for the challenges described below [11] . Finally, while we focus primarily on the technical aspects involved in developing these engineering efforts, we recognize that addressing societal acceptance, economic considerations, environmental impact, and long-term sustainability are also of critical importance for their successful implementation."
} | 1,277 |
36129892 | PMC9491537 | pmc | 1,633 | {
"abstract": "Equipped with a novel isolator-housed metabolic cage system, a study in PLOS Biology assessed how the metabolism of mice harboring a defined minimal microbial community (OligoMM12) differs from that of germ-free and conventionally colonized mice."
} | 62 |
36299727 | PMC9588965 | pmc | 1,635 | {
"abstract": "Microalgae have high lipid accumulation capacity, high growth rate and high photosynthetic efficiency which are considered as one of the most promising alternative sustainable feedstocks for producing lipid-based biofuels. However, commercialization feasibility of microalgal biofuel production is still conditioned to the high production cost. Enhancement of lipid accumulation in microalgae play a significant role in boosting the economics of biofuel production based on microalgal lipid. The major challenge of enhancing microalgal lipid accumulation lies in overcoming the trade-off between microalgal cell growth and lipid accumulation. Substantial approaches including genetic modifications of microalgal strains by metabolic engineering and process regulations of microalgae cultivation by integrating multiple optimization strategies widely applied in industrial microbiology have been investigated. In the present review, we critically discuss recent trends in the application of multiple molecular strategies to construct high performance microalgal strains by metabolic engineering and synergistic strategies of process optimization and stress operation to enhance microalgal lipid accumulation for biofuel production. Additionally, this review aims to emphasize the opportunities and challenges regarding scaled application of the strategic integration and its viability to make microalgal biofuel production a commercial reality in the near future.",
"conclusion": "Concluding remarks and future perspectives Microalgae have been drawing tremendous attention as a promising emerging feedstock for the production of lipid-based biofuels. Economical and commercial application of microalgal biofuel production is subject to the enhancement of lipid accumulation on the basis of overcoming the conflicts between microalgal cell growth and lipid accumulation. Extensive efforts have been made on improving microalgal lipid accumulation including genetic modifications of microalgal strains by metabolic engineering and process regulations of microalgae cultivation by integrating multiple optimization strategies widely applied in industrial microbiology ( Table 1 ). In future, in-depth understanding of the microalgal lipid metabolic network is essential for the construction of high-performance microalgal strains through metabolic engineering and molecular modification. Emerging omics techniques, including metabolomics, proteomics, and lipidomics, have been exhibiting great potential for further identifying and understanding of the microalgal lipid biosynthetic pathways by cooperating with genetic engineering ( Arora et al., 2018 ; Rawat et al., 2021 ). Systematic optimization strategies integrating various biomass improvement strategies with nutrient and environmental stress operation during the staged cultivation mode should be developed for the maximization of microalgal lipid accumulation. More assessment of these synergistic strategies applying in large-scale microalgal lipid production with economic feasibility are still required. These advancements for enhancing microalgal lipid accumulation are certainly making biofuel production based on microalgae a reality for commercial application in the near future. Table 1 Selected species of microalgae accumulate lipid for biofuel production. Microalgal species Lipid content References \n Phaeodactylum tricornutum \n 60.6% dry cell weight (DCW) \n Jung et al. (2019) \n Chlamydomonas sp. 59.4% of DCW \n Ho et al. (2014) \n \n Chlorella protothecoides \n 58% of DCW \n Ghidossi et al. (2017) \n \n Phaeodactylum tricornutum \n 57.5% of DCW \n Zou et al. (2018) \n \n Nannochloropsis oculata \n 56% of DCW \n Ra et al. (2016) \n \n Phaeodactylum tricornutum \n 55.7% of DCW \n Xue et al. (2017) \n Chlorella sp. 53.5% of DCW \n Feng et al. (2020) \n \n Scenedesmus obliquus \n 49.4% of DCW \n de Jaeger et al. (2014) \n \n Scenedesmus obtusus \n 47.7% of DCW \n Xia et al. (2013) \n \n Nannochloropsis oceanica \n 42.9% of DCW \n Chen et al. (2017) \n \n Chlorella vulgaris \n 39% of DCW \n Laraib et al. (2021) \n \n Chlorella sorokiniana \n 32% of DCW \n Zhu and Huang (2017)",
"introduction": "Introduction Biofuel is a form of energy which captures solar energy as chemical energy in the bonds of biologically produced materials ( Srivastava et al., 2020 ). As one of the most important study aspects in exploitation and application of the renewable energy, biofuel plays a significant role in dealing with the increasing demand of energy and the deteriorating environmental pollution problems ( Medipally et al., 2015 ; Ong et al., 2020 ; Peng et al., 2020 ). Compared with others, lipid-based biofuels have been attracting extensive attention due to the higher energy density, better infrastructure compatibility and greater application flexibility ( Wang et al., 2022 ). Unicellular microalgae are photoautotrophic organisms which grow like photosynthetic plants while lacking the complex cell structures of higher plants ( Slade and Bauen, 2013 ). Microalgae have been considered as one of the most promising alternative sustainable feedstocks for producing lipid-based biofuels due to their higher lipid accumulation capacity, higher growth rate and higher photosynthetic efficiency compared to the traditional plants ( Chisti, 2007 ; Chu, 2017 ; Anto et al., 2020 ; Wang et al., 2020 ). In addition, microalgae provide proteins that can be used as feed source for animals ( Amorim et al., 2021 ). Some of them can also produce high value biologically active compounds like some antioxidant pigments ( Markou and Nerantzis, 2013 ). Microalgae have been displayed greater sustainable and commercial advantages as feedstock for biofuels production ( Harun et al., 2010 ; Saranya and Shanthakumar, 2021 ). Microalgae could offer great prospect for biofuel exploitation. However, the process is still not carbon neutral and commercially viable because of the high production cost ( Behera et al., 2021 ; Brar et al., 2021 ). Enhancing microalgal lipid accumulation could improve the economic feasibility of the biofuel production. Several recent reviews have summarized genetic and metabolic engineering approaches and/or cultivation regulating strategies for enhancing microalgal lipid accumulation or productivity, but a very few discussed these strategies all together for achieving high lipid production with more focus on the trade-off between microalgal cell growth and lipid accumulation ( Chu, 2017 ; Sun et al., 2019 ; Khan and Fu, 2020 ; Shokravi et al., 2020 ; Brar et al., 2021 ). The focus of this review is thus to highlight the advancements and emerging approaches towards achieving enhancement of microalgal lipid accumulation for biofuel production on the basis of the trade-off between microalgal cell growth and lipid accumulation. The scope of present work covers genetic manipulations of microalgal strains and optimizations of microalgal cultivation systems, along with their challenges."
} | 1,732 |
28386026 | null | s2 | 1,636 | {
"abstract": "Bacteria within communities can interact to organize their behavior. It has been unclear whether such interactions can extend beyond a single community to coordinate the behavior of distant populations. We discovered that two "
} | 56 |
22679527 | PMC3368814 | pmc | 1,637 | {
"abstract": "A DNA capsule fitted with aptamer controlled target sensing has been “woven” using a 7308-base single-stranded DNA “thread” and 196 staple oligonucleotides. The capsule enables logic-gated molecular cargo delivery to targeted cell surfaces."
} | 60 |
39429886 | PMC11487609 | pmc | 1,639 | {
"abstract": "Abstract With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co‐occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four‐module process for metabolic interaction analysis, namely: (I) Prepare genome‐scale metabolic models; (II) Infer pairwise interactions of genome‐scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome‐assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance‐adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross‐feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome‐assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.",
"introduction": "INTRODUCTION Microbial ecology research has benefited from advancements in sequencing techniques, ranging from marker gene‐based amplicon sequencing to shotgun metagenomics and sophisticated analysis methods, including taxonomic identification, diversity analysis, and microbial co‐occurrence networks [ 1 ]. While these methods, especially microbial ecological network analysis or microbial interaction networks, provide valuable insights, they often fall short of revealing the underlying mechanisms governing microbial interactions due to their reliance on statistical inference and limited capacity to capture functional details [ 2 , 3 ]. However, with the rapid advancement in metagenomic data analysis, approaches based on functional traits or metabolic profiling now allow for a deeper understanding of microbial ecological networks. This progress has paved the way for new bioinformatic tools that enable the development of various modeling methods capable of predicting or simulating metabolite exchange or metabolic complementarity between microbes [ 4 ]. These methods rely on the information encoded within microbial genomes (e.g., reference genomes, metagenome‐assembled genomes, or single‐amplified genomes) and their genome‐scale metabolic models (GSMMs) [ 5 ]. The level of detail used varies, with some methods focusing solely on the types of reactions and metabolites, while others consider factors like microbial growth rates. Specifically, PhyloMint focuses on the types of metabolites in pairwise genomes, SMETANA (species metabolic interaction analysis) focuses on the overlap and exchange of metabolic resources in communities (can be more than two species, higher order interactions), and metabolic distances are calculated by simulating metabolic fluxes in the metabolic models on growth or energy production. Although these bioinformatic tools can construct and analyze GSMMs, they generally require specialized software or programming languages such as Python, MATLAB, or command‐line interfaces. This poses a significant barrier for researchers without expertise in these areas, hindering large‐scale and specialized data analysis processes. In addition, there were no critical methods or criteria to detect the microbial interactions characterized by the massive amount of modeling‐derived data (primarily numerical), where the random matrix theory (RMT) approach can effectively address the need. To meet these challenges, we integrated several user‐friendly tools into the well‐established integrated Network Analysis Pipeline (iNAP) [ 6 ], now upgraded to iNAP 2.0. These tools include CarveMe for automated GSMM construction [ 7 ], PhyloMint [ 8 ], SMETANA [ 5 ], and Cobrapy [ 9 ] for predicting metabolic exchanges between microbes (Figure 1 ). To put it in a nutshell, the iNAP 2.0 update offers significant improvements over its predecessor, equipping genome‐scale metabolic modeling and metabolic interaction as new weapons to decipher microbial ecological networks of statistical correlations. Figure 1 An overview of the schematic design and implemented tools of metabolic interaction network analysis of iNAP 2.0. Users can start from genome or protein sequence to reconstruct genome‐scale metabolic models and proceed to multiple analyses. iNAP, integrated Network Analysis Pipeline. Workflow overview and implementation The metabolic modeling pipeline added in iNAP 2.0 allows users to start the analysis from genome sequences (Box 1 , Figure 2 ). The pipeline leverages Prokka for predicting coding sequences [ 11 ] and CarveMe for the automated construction of GSMMs. Alternatively, users can manually curate and reconstruct GSMMs using tools like ModelSEED [ 12 ], or directly import prebuilt models from databases like the Virtual Metabolic Human (VMH) [ 13 ]. For analysis of metabolic interactions between microbes, iNAP 2.0 offers three methods: PhyloMint (competition/complementarity index), SMETANA score, and metabolic distance based on pFBA. Notably, the PhyloMint index highlights potentially transferable metabolites considered during calculations [ 8 ]. Different from the arbitrary threshold determination for the metabolic interaction index, iNAP 2.0 provides the RMT method, which could find a fair threshold of statistical significance, to construct and analyze the topological properties of the resulting metabolic interaction network. Box 1: Data set formats for input files This box gives the input file format required for genome‐based metabolic modeling analysis. Zipped genome sets (.zip). The zipped genome set contains all genome sequence files (.fasta/.fa) to be analyzed. Ensure all sequence files are directly compressed rather than stored in folders and then compressed. Each sequence file name should be unique, not starting with numbers, and not contain spaces, hyphens, or other special characters (underscore is recommended). If genome sets are planned for SMETANA analysis, the number of genome files should not exceed 300 due to SMETANA's high consumption of computational resources. Prokka predicted protein sequences (.zip). The predicted protein set contains all protein sequence files (.faa) corresponding to the genomes. This file can be obtained using the Prokka tool or protein sequence files already obtained or downloaded from reference databases. Compression and naming requirements are the same as above. Growth medium for gap‐filling (.txt/.tabular). When using CarveMe (gap filling) in Step 2‐B, users can upload customized media in addition to the default five media. The media description file should contain four columns: medium, description, compound, and name. Note that compound names and IDs must be consistent with the BiGG database ( http://bigg.ucsd.edu/ , [ 10 ]). Figure 2 The workflow of metabolic modeling analysis and construction of complementarity/competition metabolic network. PTM, potentially transferable metabolite; K‐S test, Kolmogorov–Smirnov test; MENs, molecular ecological networks. Inheriting the user‐friendly Galaxy framework [ 14 ] from its predecessor, iNAP 2.0 provides a detailed protocol outlining the analysis steps. This protocol describes the method parameters selection and results of each modeling method in an easy‐to‐follow order, along with potential avenues for further exploration. Section I: Prepare GSMMs According to the requirements for the input file format (Box 1 ), users need to upload the zipped genome sets and start from Step 1, or upload the zipped protein sequence sets and start from Step 2‐A/B. For a better startup, two zipped demo genome sets are stored in: User Panel—Shared Data—Data Libraries—Metabolic Modeling Demo Datasets. Demo data set 1 is a seven‐strain microbiome genome set (a SIHUMI, simplified human microbiome) [ 15 ]. The reference genomes were downloaded from the NCBI Reference Sequence Database (RefSeq). Demo data set 2 is a genome set of 100 metagenome‐assembled genomes (MAGs) from a hot spring habitat [ 16 ]. 1. Prokka . iNAP 2.0 utilizes Prokka with default settings for genome annotation [ 11 ]. Alternatively, users can employ tools like Prodigal [ 17 ] or EGGNOG‐mapper [ 18 , 19 ] for this step. The output is a compressed protein sequence file (Box 1 ). 2‐A. CarveMe . iNAP 2.0 offers CarveMe, a fast and automated tool for building GSMMs [ 7 ]. This step takes the zipped protein sequence file output from Step 1 or zipped predownloaded annotated protein sets as input. The output format (sbml‐fbc2) ensures compatibility with most constraint‐based modeling tools. In the GSMM reconstructed by CarveMe, the upper and lower bounds of the reaction flux (ready for FBA) directly call the default values in Cobra ( cobra_default_ub, cobra_default_lb ), which are 1000 and −1000 mmol/gDW/h. The output file of this step is a zipped metabolic model file (XML format). 2‐B. CarveMe (gap filling) . When CarveMe is used to construct a GSMM, the program determines the scores of various reactions based on the functional profiles. It performs mixed integer linear programming (MILP) to determine which reactions are supposed to be included in the final model. Therefore, microbial models in natural environments might lack certain reactions due to limitations in binning or annotation (Box 2 ). We recommend using the gap‐filling function to correct the model derived from metagenome‐assembled genomes (MAGs) of environmental metagenomes. CarveMe provides five predefined media compositions for gap filling (default: Lysogeny broth, LB), allowing growth simulation of the model on corresponding media. Users can also create and utilize custom media compositions, such as dietary components for gut microbiome models. It is important to note that defining media for environmental microbiomes can be challenging due to the difficulty of culturing most microorganisms and the limitations of rich media. Using rich media for gap‐filling can ease over‐gap‐filling. We also recommend using some tools, for example, CHESHIRE (CHEbyshev Spectral HyperlInk pREdictor), based on deep learning methods to assist gap filling for refining metabolic models [ 20 ]. The input and output files are the same as in Step 2‐A. Users are suggested to declare the media composition used for gap‐filling in their results to guarantee the reproducibility of the modeling. Box 2: Note on GSMM reconstruction quality While GSMM reconstruction is a powerful tool, it is not without its limitations. Several factors can influence the model quality, and it is important to acknowledge that our pipeline does not support GSMM reconstruction quality evaluation. However, the credibility of GSMMs can be enhanced through the following strategies: \n 1. Utilizing high‐quality genomes: The reliability of a GSMM is closely tied to the quality of the genomes used. Genomes vary in quality, from complete reference genomes to MAGs. Whenever possible, whole reference genomes should be prioritized for GSMM reconstruction due to their completeness. However, given that many microbes cannot be isolated and cultured under laboratory conditions, MAGs serve as a valuable alternative. Recent studies, such as those by Giordano et al. [ 21 ] and Hsieh et al. [ 22 ], have demonstrated the successful use of MAGs in GSMM reconstruction, underscoring their credibility. Nevertheless, it is crucial to emphasize that the quality of MAGs, particularly their completeness, significantly impacts modeling accuracy. Therefore, stricter criteria should be applied when refining MAGs for GSMM reconstruction. 2. Choosing reliable model builders: The choice of GSMM reconstruction tools can also affect model quality. Mendoza et al. conducted a systematic assessment of GSMM tools, offering users four key criteria: findability, accessibility, interoperability, and reusability [ 23 ]. Based on these criteria, tools like CarveMe were highlighted for their ability to generate GSMMs with high reaction set similarity to manually curated models. In this protocol, we encourage users to explore various model builders. However, researchers should remain vigilant about the potential for false results, particularly when interpreting outcomes at the gene level. \n Section II: Infer pairwise interactions of GSMMs iNAP 2.0 offers three methods to quantify metabolic interactions between pairwise GSMMs: PhyloMint, SMETANA, and pFBA‐based metabolic distance. Like species abundance‐based co‐occurrence networks, these metabolic interaction indices can be filtered and constructed as microbial metabolic networks. This section explains the principles and interpretations of each method. Method 1: PhyloMint PhyloMint predicts metabolic indices of competition and complementarity ( MI \n competition and MI \n complementarity ) between genomes using their GSMMs. Each GSMM first predicts a seed set of metabolites, the minimal subset of compounds that cannot be synthesized endogenously (defined as a strongly connected component, SCC). Then, the competition and complementarity indices between pairwise models A and B are calculated using the formula given in [ 8 ]:\n \n MI competition ( A , B ) = ∑ C ( SeedSet A ∩ SeedSet B ) ∑ C ( SeedSet A ) , \n \n \n MI complementarity ( A , B ) = | SeedSet A ∩ ¬ SeedSet B | | SeedSet A ∩ ( SeedSet B ∪ ¬ SeedSet B ) | , \n where C is the inverse of the seed set size, and ¬SeedSet \n B \n is the nonseed set of B. According to the calculation formula, MI \n competition and MI \n complementarity are asymmetric. 3. PhyloMint . The program calculates MI \n complementarity / MI \n competition between n input GSMMs (output from Step 2‐A/B), resulting in n \n 2 sets of indices calculated. These indices show how many models A and B share in metabolic functions and how well A can use substances released by B (Table 1 ). The parameter MaxCC indicates the maximum number of members in an SCC (recommended set to default: 5). The result of PhyloMint is stored in a four‐column table, showing genome pairs and their MI \n competition and MI \n complementarity . Table 1 Brief introduction of three approaches of metabolic interaction modeling and their representing index explanation. Method Index Meaning Range Symmetry Reference PhyloMint \n MI \n competition \n The baseline metabolic overlap between two GSMMs. [0,1] Asymmetric [ 8 ] \n MI \n complementarity \n The potential for one GSMM to utilize the other's potential metabolite output. [0,1] Asymmetric SMETANA MIP (Metabolic interaction potential) The difference in minimal nutritional requirements when a community allows metabolite exchange and when it does not at all. Positive integers Symmetric [ 5 , 24 ] MRO (Metabolic resource overlap) The maximum possible overlap between the minimal nutritional requirements of all member species. [0,1] Symmetric SCS (Species coupling score) The dependency of one species in the presence of the others to survive. [0,1] Symmetric MUS (Metabolite uptake score) The dependency of one species in the specific metabolite given by the others. [0,1] Symmetric MPS (Metabolite production score) The ability of one species to produce a specific metabolite. Binary Symmetric SMETANA score The certainty on a cross‐feeding interaction. [0,1] Symmetric Metabolic distance \n MD \n The dissimilarity between vectors of reaction fluxes of two GSMMs. [0, ∞) Symmetric [ 25 ] Abbreviation: GSMM, genome‐scale metabolic model. John Wiley & Sons, Ltd. 4. PhyloMint PTM . MI \n complementarity index, as noted earlier, represents A's potential to utilize metabolic substances from B. Potentially transferable metabolites (PTMs) are defined as the intersection of A's seed set and B's nonseed set, as per [ 8 ]. This step takes in the zipped model set (output of Step 2‐A/B) and PhyloMint result (output of Step 3) as inputs and outputs these substances in a tabular file, displaying the donor and receptor of each compound along with their nomenclature and full index in the BiGG database. Note that if your GSMMs are obtained elsewhere instead of generated by CarveMe, the file extension might be in SBML format (Systems Biology Markup Language). Remember to change the extension setting in this step. 5. Create PhyloMint Matrix . This step converts the result generated by Step 4 from a tabular form to a matrix form for network threshold determination. In particular, we provide two processing modes for the asymmetric MI index: (1) Keep the original value, that is, directly convert the results of Step 4 into an asymmetric matrix, and then the metabolic interaction network constructed using this matrix will be directed; (2) Select the larger value in the pairwise index of each pair of models to represent the interaction strength between the models, so that the generated matrix is symmetric, and the metabolic interaction network constructed using it will be undirected, which we set to default and recommend for better using the RMT‐based method for network threshold determination. Method 2: SMETANA SMETANA is a tool to quantify competition and complementation within communities by calculating metabolic resource overlap and minimum growth requirement metabolites in the community [ 5 ]. SMETANA allows the simulation of communities composed of more than or equal to two GSMMs, so the program (Step 7 and 8) requires a table to indicate which GSMMs belong to a community. On the iNAP 2.0 platform, the limit of models for SMETANA analysis is 300. 6. Create Community List . To calculate the interaction between pairwise GSMMs, iNAP 2.0 provides the required table of all pairwise GSMMs in a given model set (output of Step 2‐A/B) by default, which will be used as input for Steps 7(‐A/B) and 8(‐A/B). 7‐A. SMETANA Global . For a given community, SMETANA defines two indices, metabolic resource overlap (MRO) and metabolic interaction potential (MIP), to represent the competition and complementarity levels of the community, respectively. The specific definition and calculation of the indices have been previously described in detail [ 5 ]. The indices can be calculated using the global mode of SMETANA. Users should input the zipped genome sets and the community list obtained in Step 6 to generate the MIP/MRO values of the pairwise models in the community. 7‐B. Iterative SMETANA Global . SMETANA has been confirmed by its developers to have a drawback: the results of each run may vary slightly. This inconsistency is due to the solution pool feature of the CPLEX solver. [Correction added on 27 September 2024, after first online publication: In the preceding sentence, the word “consistency” was changed to “inconsistency”.] The developers recommend running multiple runs and calculating the average index value to represent the final value. To achieve this, the program is designed to run 2‐10 runs of the same input as Step 7‐A and output the average results. 8‐A. SMETANA Detailed . In addition to using MRO/MIP to quantify the metabolic interactions of the community, SMETANA also provides a detailed mode to calculate a series of indices to quantify further the interspecies interactions: SCS (species coupling score), metabolite uptake score (MUS), and metabolite production score (MPS). These three indices are combined and recorded as the SMETANA score to represent the sum of interspecies dependencies in the community. The definitions and meanings of all indices are detailed in Zelezniak et al. [ 5 ] and Table 1 . The input request is consistent with Step 7‐A. 8‐B. Iterative SMETANA Detailed . The reason for iterative calculation is the same as Step 7‐B. 9. Create SMETANA Matrix . This step converts the SMETANA MIP/MRO results from a tabular to two matrices for network threshold determination. It requires the community list (output from Step 6) and the SMETANA global mode results (output from Step 7‐A/B) as input files. Method 3: Metabolic distance Previous studies have suggested that metabolic distance (metabolic dissimilarity) is vital in forming and determining synergistic interactions in microbial communities [ 25 , 26 ]. 10. Metabolic distance . This program helps to calculate the pairwise metabolic distances of GSMMs according to the method described by Giri et al. [ 25 ]. The input for this step is the zipped model sets (output of Step 2‐A/B). Specifically, the program first conducts FBA on each model with the biomass reaction as the objective function to optimize (maximize) the biomass reaction flux, which is typically used to represent the growth rate reaction. Optimizing other objectives, such as ATP yield, could lead to different metabolic strategies. By default, iNAP 2.0 fills in “Growth”, representing the growth rate reaction in GSMMs generated by CarveMe. Then, the optimized biomass reaction flux is fixed, and a parsimonious FBA (pFBA) is conducted to minimize the sum of absolute fluxes in each model while constraining the objective function (e.g., biomass production) to the optimal value obtained from the initial FBA. Subsequently, the reactions whose flux is not zero in at least one model are selected as representatives, the flux vectors of the models are generated, and the Euclidean distances between them are calculated as proxies of metabolic distances. This step outputs the results in three forms: two distance matrixes (original Euclidean distance and standardized Euclidean distance) and a three‐column table of the original Euclidean distance (model A/model B/Euclidean distance). One should inspect the original Euclidean distance first. If the matrix contains many values that differ by order of magnitude (this is likely to happen when models built with different bounds are input simultaneously, which we do not recommend), using the standardized Euclidean distance matrix should be considered. The standardized Euclidean distance is defined as the Euclidean distance calculated on the standardized data. The standard data is calculated according to this formula: Standard value = (Original value – Mean value)/Standard deviation [ 27 ]. Section III: Construct metabolic interaction networks Different metabolic interaction indices have different numerical forms and value ranges. iNAP 2.0 provides various methods to determine network thresholds, including RMT‐based approaches. 11. RMT (cutoff, Chi‐square test) . This step can use the adjacency matrix representing the interaction strength generated by PhyloMint (Step 5) and metabolic distance (Step 10) as input. Since the values of SMETANA indices do not meet the requirements of the RMT method, it is recommended to use the Z‐score method (See Step 13). Currently, this step only accepts symmetric matrices as input, and the values in the matrix should be normalized between 0 and 1. 12. RMT (cutoff, Kolmogorov–Smirnov test) . This step and Step 11 use the RMT‐based method to determine the network threshold. The χ \n 2 test is utilized in Step 11, and the Kolmogorov–Smirnov test is used in this step. Compared with the χ \n 2 test, the Kolmogorov–Smirnov test is expected to give a more relaxed threshold, which may be more practical when dealing with values like the MI \n competition and MI \n complementarity indices. 13. Z‐score outlier detection . iNAP 2.0 provides a Z‐score outlier detection method for the adjacent matrix to filter interactions for constructing the networks. This method has been used to build a network based on the PhyloMint MI \n competition and MI \n complementarity indices [ 8 ]. However, we recommend using this method for SMETANA indices (e.g., MIP) and metabolic distance (before normalization) because they might not fit the requirement of the RMT approach. The input for this step is the adjacency matrix representing the interaction strength generated by PhyloMint (Step 5), SMETANA Global (Step 7‐A/B), and metabolic distance (Step 10). We provide standard and modified Z‐score formulas for outlier detection:\n \n Z standard = ( x o b s − μ ) σ , \n \n \n Z modified = 0.6745 ( x obs − x ~ ) MAD , \n where x \n obs is the observed value, µ , σ , x ~ and MAD are the mean, standard deviation, median and median absolute deviation of the data set. There are different reports on the filtering criteria for outliers: the absolute values of Z‐score greater than 3.5 [ 28 ] and 2.698 [ 29 ] are more commonly used. 14. Construct Network Adjacent Matrix . After selecting an appropriate method to determine the threshold for network filtering (selected by Steps 11, 12, or 13), the original adjacent matrix and the selected threshold are input into this step, which generates the adjacent matrix representing the final metabolic interaction network. Section IV: Analyze metabolic interaction networks All the tools that have been introduced in iNAP can be used for the analysis of metabolic interaction networks, such as global network properties, individual nodes’ centrality , module separation, and module hubs . Below, some new tools or analysis tools that are specific to metabolic interaction networks are introduced in detail. 15. Global network properties and individual nodes’ centrality . This step requires the adjacent matrix of metabolic interactions from Step 14 as input. Two result tabular files are (1) A global property file including many parameters such as average node degree, average clustering coefficient, and network density, which fully describe the properties of the metabolic interaction network; (2) A node attribute file including the properties of all nodes, such as degree, betweenness, stress, and so on. 16. Module separation and module hubs . This step also requires the adjacent matrix of metabolic interactions from Step 14 as input. It calculates the network modularity and distributes the nodes to different modules. Based on the module, each node's within‐module connectivity ( z \n \n i \n ) and among‐module connectivity ( P \n \n i \n ) will be computed. Based on the z \n \n i \n ‐ P \n \n i \n distribution, the keystone nodes or hub nodes can be assigned, such as module hubs, connectors, and network hubs. Details can be found on the help page of this tool. 17. Integrate node attributes . The node centrality (output of Step 15), the node within‐ and among‐module connectivity ( z \n \n i \n ‐ P \n \n i \n value and roles in the network, output of Step 16), and the taxonomic annotation of the node (optional and uploaded by users, tabular‐separated. txt file with first column as node IDs, referred to example file in iNAP 2.0) are essential components of the microbial network information. This step can merge the above three files for subsequent analysis or as an annotation file for visualization. 18. Network intersection . This step compares the adjacent matrices of two networks and finds the subnetworks composed of their common edges. It then outputs the intersected network with an adjacent matrix and edge list of the subnetwork. 19. Visualize the PTM network . This step uses the output of Step 5 as input to transform the potentially transferable metabolites in the specific network into a directed bipartite microbe‐metabolite network (Figure 3A ). All metabolite annotations stored in the input are integrated and output in a node attribute table. Figure 3 Visualization of the bipartite network generated by PhyloMint PTM and metabolic distance heatmap. (A) Microbe‐metabolite (potentially transferable metabolite) bipartite network output by iNAP 2.0. (B) A heatmap of metabolic distance generated by iNAP 2.0. The results were obtained from demo data set 1 [ 15 ]. iNAP, integrated Network Analysis Pipeline; PTM, potentially transferable metabolite. 20. Metabolic distance heatmap . This step generates a heatmap using the metabolic distance matrix (output of Step 10), which allows for the significant identification of distinctive metabolic profiles in microbial consortiums (Figure 3B ). Troubleshooting The advice for troubleshooting is summarized in Table 2 . Table 2 Troubleshooting table. [Correction added on 27 September 2024, after first online publication: In the third row of “Error message/Problem encountered” column, the number “500” was revised to “300”.] Step Error message/Problem encountered Possible reason Solutions All An error occurred while updating information with the server. The server is overloaded or under maintenance. Wait patiently. 1, 2, 3 Error opening file: No such file or directory. \n \n 1. Wrong extension; 2. The input compressed file contains a folder. \n \n \n \n 1. Correct the extension; 2. Make sure all sequence files are directly zipped. \n \n 1, 2, 3 Error: Sequence file number exceeds the limit of 300. Metabolic modeling may consume a lot of computing resources, and iNAP2.0 allows processing up to 300 genomes/models. Reduce the number of genomes or consider using other computing resources such as HPC. 8, 9, 10, 11 The program takes forever. SMETANA is a very time‐consuming program. Reduce the number of model pairs. Abbreviation: SMETANA, species metabolic interaction analysis. John Wiley & Sons, Ltd."
} | 7,486 |
27272242 | null | s2 | 1,640 | {
"abstract": "Mutualism is ubiquitous in nature and plays an integral role in most communities. To predict the eco-evolutionary dynamics of mutualism it is critical to extend classic pair-wise analysis to include additional species. We investigated the effect of adding a third species to a pair-wise mutualism in a spatially structured environment. We tested the hypotheses that selection for costly excretions in a focal population (i) decreases when an exploiter is added (ii) increases when a third mutualist is added relative to the pair-wise scenario. We assayed the selection acting on Salmonella enterica when it exchanges methionine for carbon in an obligate mutualism with an auxotrophic Escherichia coli. A third bacterium, Methylobacterium extorquens, was then added and acted either as an exploiter of the carbon or third obligate mutualist depending on the nitrogen source. In the tripartite mutualism M. extorquens provided nitrogen to the other species. Contrary to our expectations, adding an exploiter increased selection for methionine excretion in S. enterica. Conversely, selection for cooperation was lower in the tripartite mutualism relative to the pair-wise system. Genome-scale metabolic models helped identify the mechanisms underlying these changes in selection. Our results highlight the utility of connecting metabolic mechanisms and eco-evolutionary dynamics."
} | 344 |
30505048 | null | s2 | 1,641 | {
"abstract": "Tropical reefs are shifting from coral to macroalgal dominance, with macroalgae suppressing coral recovery, potentially via effects on coral microbiomes. Understanding how macroalgae affect corals and their microbiomes requires comparing algae- versus coral-dominated reefs without confounding aspects of time and geography. We compared survival, settlement, and post-settlement survival of larvae, as well as the microbiomes of larvae and adults, of the Pacific coral "
} | 117 |
30210458 | PMC6119820 | pmc | 1,643 | {
"abstract": "Metalliferous mine tailings have a negative impact on the soil environment near mining areas and render cultivable lands infertile. Phytoremediation involving the synergism of legume and rhizobia provides a useful technique in tackling this issue with cost-effective, environmentally friendly, and easy-to-use features under adverse soil conditions. Leucaena leucocephala has been found to build symbiotic relationships with native rhizobia in the iron-vanadium-titanium oxide (V-Ti magnetite) mine tailing soil. Rhizobia YH1, isolated from the root nodules of L. leucocephala , was classified as Sinorhizobium saheli according to similarity and phylogenetic analyses of 16S rRNA, housekeeping and nitrogen fixation genes. Besides nitrogen fixation, S. saheli YH1 also showed capabilities to produce indole-acetic acid (IAA) (166.77 ± 2.03 mg l −1 ) and solubilize phosphate (104.41 ± 7.48 mg l −1 ). Pot culture experiments showed that strain YH1 increased the biomass, plant height and root length of L. leucocephala by 67.2, 39.5 and 27.2% respectively. There was also an average increase in plant N (10.0%), P (112.2%) and K (25.0%) contents compared to inoculation-free control. The inoculation of YH1 not only reduced the uptake of all metals by L. leucocephala in the mine tailings, but also resulted in decreased uptake of Cd by up to 79.9% and Mn by up to 67.6% for plants grown in soils contaminated with Cd/Mn. It was concluded that S. saheli YH1 possessed multiple beneficial effects on L. leucocephala grown in metalliferous soils. Our findings highlight the role of S. saheli YH1 in improving plant health of L. leucocephala by reducing metal uptake by plants grown in heavy metal-polluted soils. We also suggest the idea of using L. leucocephala - S. saheli association for phytoremediation and revegetation of V-Ti mine tailings and soils polluted with Cd or Mn.",
"conclusion": "Conclusions In conclusion, metalliferous V-Ti magnetite tailings from Panzhihua region harbor a PGP-positive rhizobia species, which was identified as Sinorhizobium saheli YH1. This strain exhibited IAA-producing and phosphate-solubilizing activities and was tolerant to high amounts of Cd and Mn. It also improved plant height, root length, and biomass yield for L. Leucocephala grown in both V-Ti tailings and soils amended with Cd/Mn. In particular, strain YH1 demonstrated abilities to nodulate the plant and reduce the uptake of heavy metals for the plant in the tailings and Cd- / Mn-supplemented soils. Our results thus provide further understanding of the efficiency of S. saheli YH1 in promoting plant health under heavy metal-ridden soil environments and suggest that it could be potentially used as an inoculum for the phytoremediation of metal-contaminated soils.",
"introduction": "Introduction Industrial activities, e.g., mining and smelting, are a major source of water and soil pollutions and threaten human health through accumulative effects along food chains (Wuana and Okieimen, 2011 ). Residents living in proximity to mining areas are continually exposed to hazardous substances released from the factories. Large swathes of cultivable land have been laid to waste as a result of mine tailings being continuously dumped in huge volumes into reservoir-like ponds. Chronic damages to the surrounding soil environment are caused through leaching effects by rainfall. Vanadium-titanium (V-Ti) magnetite mine tailings contain elevated amounts (>2,000 mg kg −1 ) of manganese (Yu et al., 2014 ), which is considered to be a major metal pollutant in soil and aquatic environments (Li et al., 2014 ). Cadmium is one of the main heavy metal pollutants that have high cytotoxicity and usually associate with anthropogenic activities such as mining and metal smelting, causing severe contamination to agricultural soils near the vicinity of mines (Liu et al., 2013 ; Zheng et al., 2018 ). It is necessary to give a satisfactory solution to this environmental hazard. Plant growth-promoting rhizobacteria (PGPR) are noted for their capabilities to colonize roots of legumes and at the same time confer beneficial effects on the hosts by alleviating deleterious abiotic stresses (Rajkumar et al., 2010 ). They have been deemed as a promising approach to the remediation of polluted soils for the rhizobia-legume associations not only promote plant growth but also raise soil nitrogen level, leading to an increased crop yield (Bashan and Holguin, 1998 ). Several rhizobial species that can form mutualism with legumes are primarily found in genera Azorhizobium, Bradyrhizobium, Mesorhizobium, Rhizobium, and Sinorhizobium (Hayat et al., 2010 ). Symbiotic nitrogen fixers in the rhizosphere colonize root systems through sophisticated mechanisms at both cellular and molecular levels. Legume-rhizobia associations can lead to enhanced plant growth either by biological nitrogen fixation (Sanginga et al., 1988 ), indole-3-acetic acid (IAA) production, siderophore secretion, and phosphorus solubilization or by a combination of all the above mentioned features (Khan et al., 2009 ). It was reported that the symbiosis of Sinorhizobium meliloti CCNWSX0020 and Medicago lupulina exhibited phytostabilizing effects for Cu by boosting plant growth and metal uptake from Cu-spiked soil while decreasing the translocation of Cu in the plant (Kong et al., 2015 ). In another report, the biofuel legume Pongamia pinnata was used for the phytoremediation of V-Ti magnetite mine tailings in partnership with Bradyrhizobium liaoningense and both plant growth and metal uptake were significantly increased under multi-metallic conditions (Yu et al., 2016 ). Leucaena leucocephala first came under notice due to its water holding capacity in a hot and dry climate. Besides, it has strong tolerance and adaptability to drought and therefore is of great importance for agriculture and forestry (Shelton and Brewbaker, 1998 ). L. leucocephala was introduced to Panzhihua city, a major industrial hub in southern Sichuan Province 30 years ago as a pioneer species for afforestation and has ever since been widely cultivated along the hot and arid valleys of Golden Sand River, upper Yangtze (Xu et al., 2013a ). A previous study found that it could serve as a pioneer for the revegetation of lead-zinc and tin mine tailings, indicating its potential to thrive under metal-contaminated environments (Li, 2006 ). In another case, a variant of L. leucocephala was discovered to be capable of taking up and metabolizing organic pollutants such as ethylene dibromide and trichloroethylene even without the synergism of rhizobia, which again proves that it could be a potential candidate for the remediation of polluted soils (Doty et al., 2003 ). Although many other similar studies for the remediation of heavy metal-contaminated sites have been reported of late years, the information on remediation effects of L. leucocephala and its associated rhizobia is still scanty. In the present study, we aim to establish the rhizobia-plant association and explore the feasibility of using it as a novel means for the phytoremediation and revegetation of heavy metal-polluted soils. In this work, we report PGP effects of Sinorhizobium saheli YH1 on and reduced metal uptake for L. leucocephala in the phytoremediation of both original mine tailings and soils supplemented with Cd or Mn. Our results propound the idea of utilizing the symbiotic system of L. leucocephala and S. saheli YH1 as an alternative to the alleviation of environmental hazards including Cd, Mn, and other metals.",
"discussion": "Discussion Rhizobia identification L. leucocephala -associated sinorhizobia in this region have been previously identified as nearest neighbors to S. americanum, S. fredii, S. kummerowiae, S. meliloti, S. mexicanus, S. saheli , and S. xinjiangense (Xu et al., 2013a , b ). The existence of various toxic metals tends to exercise a natural selection process, through which metal-tolerant species are favored (Rajkumar et al., 2009 ). As was shown by our results, L. leucocephala was still able to grow vigorously and produce nitrogen-fixing nodules regardless of the presence of elevated amounts of toxic metals and the infertility in the V-Ti mine tailings, indicating the successful establishment of synergism between rhizobia and the host. Both 16 rRNA and MLSA results identified strain YH1 as the closest neighbor to Sinorhizobium saheli (99.0% similarity), which is widely reported as a beneficial rhizobium to colonize L . leucocephala (Wang et al., 2006 ; Ardley, 2017 ). It is well known that the dinitrogenase reductase enzyme encoding nifH is accountable for the formation of root nodules with nitrogen-fixing capability (Laguerre et al., 2001 ). In our work, the symbiotic gene nifH of Sinorhizobium strain YH1 was clustered nearer to Neorhizobium huautlense (formerly known as Rhizobium huautlense ) CCBAU 65798 with 99.6% homogeny than to Sinorhizobium saheli which only had 97.1% similarity. This could be explained by the fact that N. huautlense was also found to be a PGPR microbe that could reduce the accumulation of Cd by plant (Chen et al., 2016 ). In addition, a horizontal gene transfer could help further explain this phenomenon. It may be conjectured that the functional gene nifH of Rhizobium huautlense was accidentally obtained by YH1 due to close co-existence of the both species in the same region, as was proposed by other researchers before (Andrews et al., 2018 ). Pot culture experiment in tailings Studies on the in-situ remediation of V-Ti tailings-polluted sites by the association of natively grown L. leucocephala and rhizobia are scarce. Mine tailings in Panzhihua region differ from other mine wastes due to their excessive amounts of extractable iron, titanium, and vanadium among other toxic metals. L. leucocephala is not deemed as a hyper-accumulator as both biological concentration and translocation factors for most metals of interest were no more than 1.0. In spite of the higher concentrations of Fe and Ti in the tailings, the plants did not tend to absorb them as much as Cu and Ni, which are considered toxic to the plant at a lower dose. Phytoremediation of soils using legume-rhizobia associations can be generalized under two categories: mobilization, being the enhancement of metal uptake by the plants, owing to the production of various mobilizing agents such as biosurfactants, organic acids, siderophores and through biomethylation and redox effects (Ullah et al., 2015 ); immobilization or stabilization, the process in which the bioavailability of heavy metals in the rhizosphere is reduced due to on-root sorption and precipitation effects by root exudates and microbial metabolites (Salt et al., 1995 ; Wong, 2003 ). In this study, it may be concluded that L. leucocephala -YH1 symbiotic system, which led to 8.4% reduction of plant uptake for Mn and 65.5% for Cd in tailings, has the best reduction effect on Cd. This is again confirmed by vermiculite pot experiment where higher reduction rates on cadmium ranging from 59.7 to 79.4% were found. Consequently, the significant reduction ( P < 0.05) of metal uptake except for Cd, as shown in the inoculated group probably implies the positive effects of the strain YH1 on the host which exhibit the immobilization feature in the root system. Among a few leguminous tree species, L. leucocephala is able to tolerate higher concentrations of toxic metals compared to non-legumes and is less likely to succumb to multi-metal contaminated substrates (Chan et al., 1999 ). In addition, there is evidence showing the predominant status of this tree species in the topsoil of Pb/Zn mine tailings and the potential of using it as a phytoremediation tool (Zhang et al., 2001 ). The growth state of plant-YH1 consortium demonstrated that, in contrast to control group, plants infected with strain YH1 showed successful nodulation and that biomass yield, plant height, root lengths and NPK contents were significantly elevated. This is indicative of the effectiveness of this inoculum and suggestive of the normal functioning of the symbiotic genes in it. It is widely reported that a number of PGPRs capable of increasing plant yield and improving soil conditions can be used in phytoremediation: these include Achromobacter, Acinetobacter, Actinobacteria, Azotobacter, Bacillus, Flavobacterium, Ochrobactrum, Pseudomonas, Rhizobium , and Bradyrhizobium (Reichman, 2007 ; Wani et al., 2008 ; Khan et al., 2009 ). For a long time, arbuscular mycorrhizal fungi (AMF) have been especially noted for improving phytoremediation by attenuating various metal stresses to the host apart from improving plant growth, whose mechanisms are frequently alluded to those of bacterial PGP strains (Lins et al., 2006 ). Discoveries of phytostabilization using rhizobia-legume systems came under notice when a number of rhizobial strains were reported to be both PGP-positive and capable of reducing metal uptake for the host. In a separate study, Bradyrhizobium sp. ( vigna ) RM8, isolated from green gram in metal contaminated sites, tolerant to high levels of nickel and zinc, active in promoting plant growth, was found to be able to cut nickel and zinc intake by the host while alleviating the toxic stresses (Wani et al., 2007 ). Another instance of phyto-immobilization was recorded by Dary et al. ( 2010 ), in which, an effective nitrogen fixer Bradyrhizobium sp. 750 reduced the accumulation of Cd, Cu, and Pb by Lupinus luteus in a field experiment on a multi-metal polluted site. There appears to be more metal enhancers than reducers and the former are often coupled with the ability of siderophore production (Glick, 2010 ), which may be attributed to the fact that siderophores as chelating agents make insoluble metal compounds bioavailable and thereby facilitating metal uptake. However, it works both ways, as established by Dimkpa et al. ( 2008 ), in which the accumulation of nickel in cowpea plants was lowered with the help of Ni-binding hydroxamate siderophores produced by Streptomyces acidiscabies , and this may prove that siderophores play a dual role in determining the uptake pattern regarding their various types while other more dominant factors may also have to be taken into account (Ma et al., 2011 ). Phosphorus solubilization is another indispensable trait for soil microbes in the immobilization of heavy metals. Free metal ions can be readily precipitated as metal-P complexes of various mineral phases such, taking cadmium as an example, as Cd 5 H 2 (PO 4 ) 4 ·4H 2 O, Cd(H 2 PO 4 ) 2 , Cd 3 (PO 4 ) 2 and amorphous cadmium phosphates at higher pH values (Sharma and Archana, 2016 ), which can be deposited on the surfaces of both roots and microbes in the rhizosphere resulting in reduced metal bioavailability and a reduction in both biological concentration and translocation effects (Park et al., 2011 ). Indole-3-acetic acid is a phytohormone which has been widely regarded as an index for assessing the effectiveness of the promotion of cell elongation in plant tissues (Nadeem et al., 2015 ). The production of IAA by strain YH1 is higher (>100 μg ml −1 ) than most rhizobial stains previously reported and can be considered as an IAA-overproducer (Chiboub et al., 2016 ; Yu et al., 2017 ). It is observed that negative impact of metal accumulation inside plant tissues could be mitigated by the application of IAA (Nadeem et al., 2015 ). In our study of microbial phosphorus solubilization, strain YH1 was found to be more competent than most strains isolated from infertile and polluted soils, which among other PGP traits, further confirmed the effectiveness of strain YH1 to be potentially utilized in the phytoremediation of heavy metal contaminated soils. As of today, there is limited information on the remediation of soils involving members from Sinorhizobium . It was revealed that some strains of Sinorhizobium meliloti helped with the uptake of Cd, Cu and Zn by Medicago plants with high translocation effects (Fan et al., 2011 ; Ghnaya et al., 2015 ; Zribi et al., 2015 ). Most interestingly, it was found that a symbiotic PGPR strain may help increase the uptake by one plant species while cause the decrease by another, as in the case of Bradyrhizobium sp. YL-6, where it boosted Cd uptake by Lolium multiflorum while reduced Cd uptake by Glycine max (Guo and Chi, 2013 ). Pot culture in Cd/Mn soils Our work exhibited that the plants achieved greater biomass yield in both Cd and Mn tainted soils. In this experiment, the cross influence of other metals was minimized by using single metal-spiked vermiculite as substrate. Treatments with YH1 inoculum were all indicative of the successful formation of synergism with the microbe. By comparing plant dry weight between different treatments and the tolerance of YH1 to Cd and Mn, it is obvious that Cd appears to exercise more negative effects on both plant and rhizobia, and this may be explained by the fact that Cd has both higher microbial toxicity and phytotoxicity than Mn (Lambers et al., 2015 ; Ullah et al., 2015 ) and that Mn content is way higher in the tailings from which this strain was isolated. Biomass yield and growth parameters of leucaena plants were both reduced under the stress of cadmium even at a low concentration of 25 ppm as revealed by a previous study (Shafiq et al., 2010 ). Both Cd and Mn can stunt root growth and have damaging effects to leaves (Khan et al., 2011 ). It has been well explained that, several sophisticated microbial mechanisms conspire to curb the metal bioavailability to plants, which include biosorption onto the outer wall, intracellular sequestration, and complexation by certain biogenic anions (Gadd, 2004 ). Root is where the toxic metals exert direct influence on the plant. Changes in this microbial-rhizospheric niche may alter the composition and patterns of exudation, which can further lead to damage to the root-hair cells. The significant increase in root length against the increasing amount of Cd, is probably attributed to the reduced metal stress caused by the immobilization effects. There existed a great difference regarding the overall uptake of these two separately added metals, in which Mn uptake was more than three times the amounts of Cd in both groups and the translocation for Mn was more than 10 times that for Cd. Manganese exhibits extreme toxicity to plant cells in excessive amounts (>500 mg kg −1 in content) and is positively linked to soil acidity and a lack of other exchangeable metal ions such as Ca, Mg, and Fe in the rhizosphere (de Varennes et al., 2001 ). The reduction of Mn uptake by plants can be more complicated as common microorganisms are usually not directly involved in this process except for manganese oxidizing bacteria, which can increase Mn availability through the release of low weight bacteriogenic acids as IAA (Millaleo et al., 2010 ). Therefore, it may be conjectured that Mn resistant strain YH1 helped alleviate manganese uptake through indirect mechanisms by altering the plant exudation patterns, as suggested in studies on AMF-plant interactions (Nogueira and Cardoso, 2003 ), where plant exudates were changed under the influence of microbial activities resulting in an immobilization effect and reduced metal uptake by the plant. Cadmium is a toxic metal with high mobility in both plant tissues and soils, the uptake of it by plants is in rise with the increase of its background concentration in both inoculated group and control. It is apparent that in metal-spiked soil, the uptake of Cd is also drastically reduced with the inoculation of YH1 especially under higher contents, and the plant translocation factor for Cd was also decreased with the inoculation of YH1, which is consistent with the tailings experiment and further indicates the metal-immobilizing effects on the legume. Nodulation under Cd or Mn stress The decreasing trend in nodule yield under both metal stresses against the increasing levels of soil metal contents from 5 to 20 mg kg −1 was reversed in the treatment with 35 mg kg −1 Mn, which may be accounted for by the fact that manganese is less toxic to plants and its microbial symbionts. Manganese is an indispensable trace element constituting the reactive centers of various enzymes and is more than 20 times the amounts of the other metal pollutants in the V-Ti tailings. It should be noted that excessive ingestion of Mn can also cause toxicity to both plants and bacteria (Zornoza et al., 2010 ). Investigations by predecessors discovered that although Mn exists in plants in fairly large amounts, its toxicity can still affect the bacterial growth and legume-rhizobia associations (de Varennes et al., 2001 ; Hayes et al., 2012 ). However, our results confirmed that Cd appears to be more toxic than Mn, since the nodule number was lower than in plants treated with Mn at the same amounts of 5 and 35 mg kg −1 ."
} | 5,264 |
30555446 | PMC6284035 | pmc | 1,644 | {
"abstract": "Ammonia inhibition is an important reason for reactor failures and economic losses in anaerobic digestion. Its impact on acetic acid degradation is well-studied, while its effect on propionic and butyric acid degradation has received little attention and is consequently not considered in the Anaerobic Digestion Model No. 1 (ADM1). To compare ammonia inhibition of the degradation of these three volatile fatty acids (VFAs), we fed a mixture of them as sole carbon source to three continuous stirred tank reactors (CSTRs) and increased ammonium bicarbonate concentrations in the influent from 52 to 277 mM. The use of this synthetic substrate allowed for the determination of degradation efficiencies for the individual acids. While butyric acid degradation was hardly affected by the increase of ammonia concentration, propionic acid degradation turned out to be even more inhibited than acetic acid degradation with degradation efficiencies dropping to 31 and 65% for propionic and acetic acid, respectively. The inhibited reactors acclimatized and approximated pre-disturbance degradation efficiencies toward the end of the experiment, which was accompanied by strong microbial community shifts, as observed by amplicon sequencing of 16S rRNA genes and terminal restriction fragment length polymorphism (T-RFLP) of mcrA genes. The acetoclastic methanogen Methanosaeta was completely replaced by Methanosarcina . The propionic acid degrading genus Syntrophobacter was replaced by yet unknown propionic acid degraders. The butyric acid degrading genus Syntrophomonas and hydrogenotrophic Methanomicrobiaceae were hardly affected. We hypothesized that the ammonia sensitivity of the initially dominating taxa Methanosaeta and Syntrophobacter led to a stronger inhibition of the acetic and propionic acid degradation compared to butyric acid degradation and hydrogenotrophic methanogenesis, which were facilitated by the ammonia tolerant taxa Syntrophomonas and Methanomicrobiaceae . We implemented this hypothesis into a multi-taxa extension of ADM1, which was able to simulate the dynamics of both microbial community composition and VFA concentration in the experiment. It is thus plausible that the effect of ammonia on VFA degradation strongly depends on the ammonia sensitivity of the dominating taxa, for syntrophic propionate degraders as much as for acetoclastic methanogens.",
"introduction": "Introduction Biogas production is an important renewable energy source and organic waste treatment technology (Plugge, 2017 ). For nitrogen-rich organic waste, the accumulation of ammonia can become a major problem. Ammonia inhibition has been held responsible for heavy economic losses and even reactor failures (Rajagopal et al., 2013 ). Suggested solutions to ammonia inhibition are based on the direct removal of ammonia from the reactor, the prevention of high ammonia concentrations by dilution or co-digestion with nitrogen-poor substrates (e.g., maize silage), or by adaptation of the microbial community (Krakat et al., 2017 ). Bioaugmentation (Fotidis et al., 2014 ) or support media (Poirier et al., 2017 ) have been suggested to speed up this adaptation. Several, partly contradicting theories have been presented on ammonia inhibition in anaerobic digestion. A major controversy is whether acetoclastic or hydrogenotrophic methanogens are more strongly inhibited, with experimental evidence for both cases (Krakat et al., 2017 ). Furthermore, the shift toward more syntrophic acetate oxidation (SAO) instead of acetoclastic methanogenesis at elevated ammonia concentrations has received much attention (Schnürer and Nordberg, 2008 ; Werner et al., 2014 ; Luo et al., 2016 ). Commonly, free ammonia is thought responsible for ammonia inhibition because it can diffuse into the cells (Rajagopal et al., 2013 ), but also the ammonia ion is thought to cause inhibition (Astals et al., 2018 ). The concentration of free ammonia depends on total ammonia nitrogen (TAN) concentration, pH, and temperature. Several underlying and partly connected mechanisms of free ammonia inhibition after diffusion into a cell have been put forward and summarized by Krakat et al. ( 2017 ): proton imbalance, change of the intracellular pH, increase in maintenance energy requirement, and inhibition of specific enzymatic reactions. Interestingly, these mechanisms have been solely discussed as explanations for ammonia inhibition in the context of methanogenesis. However, they might also apply to other functional groups, for example proton-reducing bacteria degrading propionic or butyric acid. Propionic acid and in fewer cases and less in strength also butyric acid accumulations have been observed repeatedly in the context of ammonia inhibition (Li et al., 2017b ; Yirong et al., 2017 ; Peng et al., 2018 ; Yang et al., 2018 ). Nevertheless, the inhibition of syntrophic propionic and butyric acid oxidizing bacteria is often neglected in mechanistic descriptions of ammonia inhibition. For example, in the Anaerobic Digestion Model No. 1 (ADM1), only the inhibition of acetoclastic methanogens is included (Batstone et al., 2002 ). In a more recent adaptation of ADM1, ammonia inhibition was implemented for syntrophic acetic acid oxidizing bacteria (Wett et al., 2014 ) but still not for syntrophic propionic and butyric acid oxidizers. Quantitative descriptions of the inhibition of volatile fatty acid (VFA) degradation are difficult in complex systems because these acids are simultaneously produced and consumed. While there are several studies on ammonia inhibition using acetic acid as sole carbon source (Steinhaus et al., 2007 ; Hao et al., 2015 ; Westerholm et al., 2017 ), there is only one study on the impact of ammonia inhibition on an open, methanogenic culture fermenting propionic acid as sole carbon source (Li et al., 2017a ). In their study, methanogenesis from propionic acid was strongly inhibited, but it could not be concluded if propionic acid oxidizing bacteria are directly inhibited by ammonia or indirectly inhibited by accumulation of hydrogen via an inhibition of the hydrogenotrophic methanogens as suggested earlier (Wiegant and Zeeman, 1986 ). There are no studies on ammonia inhibition using butyric acid as sole carbon source. The goal of our study was to compare ammonia inhibition of acetic acid degradation with that of propionic and butyric acid degradation. Therefore, we used a synthetic mixture of acetic, propionic, and butyric acid as substrate amended with micronutrients in three continuous stirred tank reactors (CSTRs), and followed VFA degradation efficiencies and microbial community dynamics over time. Deliberate ammonia inhibition was induced by increasing ammonium bicarbonate concentration in the substrate in two reactors while the third reactor remained unchanged as a control. Furthermore, we added HCl to one of the reactors to reduce the pH and thus the share of free ammonia, which has been shown previously to successfully alleviate ammonia inhibition (Strik et al., 2006 ). Microbial community compositions were analyzed over the course of the experiment using 16S rRNA gene amplicon sequencing for bacteria and terminal restriction fragment length polymorphism (T-RFLP) profiling of mcrA genes for methanogenic archaea. At the end of the experiment, the functional resilience of the microbial communities was studied by deliberate disturbances in the form of pulse feedings. Finally, ADM1 was amended by second populations for acetic and propionic acid degradation, respectively, to model the effect of microbial community changes on the VFA concentration.",
"discussion": "Discussion Apart from a lower pH, the addition of HCl to the influent of R NH3, HCl starting on day 36 led only to minor differences in VFA concentrations, acclimatization time and microbial community composition compared to R NH3 . Apparently, at the time of HCl addition, both reactors were already successfully acclimatizing to ammonia inhibition. Furthermore, both reactors were overall little affected by the disturbances. Therefore, both reactors are discussed in the following with the focus on their common response to ammonia inhibition. The VFA accumulations during the start-up of R NH3 and R NH3, HCl seemed to have no decisive effects on the overall experiments since both reactors recovered before the ammonia inhibition was induced. Strong ammonia inhibition of both acetic and propionic acid degradation The inhibition of VFAs other than acetic acid are neglected in ADM1 (Batstone et al., 2002 ). However, looking at VFA degradation efficiencies (Figure 3 ), it became clear that propionic acid degradation was even more inhibited than acetic acid degradation. We also observed a stronger or similarly strong inhibition of propionic acid degradation compared to acetic acid degradation in several additional experiments we conducted (see Supplementary Material A ). This suggests that ammonia inhibition of propionic acid degradation should receive more attention and be included in anaerobic digestion models. Calculating VFA degradation efficiencies is only possible in synthetic systems like ours and not for more complex substrates such as manure, because the amounts of individual VFAs produced in acidogenesis from complex substrates are hard to quantify and thus, the VFA degradation efficiencies cannot be calculated. Therefore, the impact of ammonia inhibition on propionic acid degradation might have appeared weaker than it actually was in many studies because lower propionic acid concentrations than acetic acid concentrations were reached. Concerning the mechanism of ammonia inhibition of propionic acid degradation, an indirect inhibition mechanism was suggested by Wiegant and Zeeman ( 1986 ) who argued that a strong ammonia inhibition of hydrogenotrophic methanogenesis can lead to accumulation of hydrogen, which inhibits propionic acid degradation by increasing the Gibbs energy change of catabolism to near or above zero (Figure 7A ). However, this indirect route seemed not to play a major role in our experiment since we did not observe any hydrogen accumulation. Furthermore, butyric acid degradation was almost not inhibited in our experiment, which would have been the case if hydrogen accumulated. Therefore, we assumed direct ammonia inhibition of propionic acid degradation as the working hypothesis for our study. Based on our observations, acetic and propionic acid degradation were strongly inhibited in our experiment while butyric acid degradation and hydrogen conversion were hardly affected (Figure 7B ). Figure 7 Inhibition scheme of acetogenesis and methanogenesis following (A) Wiegant and Zeeman ( 1986 ) and (B) our working hypothesis. Hydrogen “inhibition” means that increased hydrogen partial pressures increase the Gibbs free energy change of catabolism of propionic and butyric acid oxidation. C 2 H 4 O 2 , C 3 H 6 O 2 , C 4 H 8 O 2 , CO 2 , H 2 , CH 4 , NH 3 , and NH 4 + are the molecular formulas of acetic, propionic, and butyric acid, as well as carbon dioxide, hydrogen, methane, free ammonia and ammonium cation, respectively. Relationship of VFA degradation rates and microbial community composition After the increase in ammonia concentration started, it took about 5 HRTs until VFA concentrations reached values similar to the start of the experiment in R NH3 and R NH3, HCl . These increases and decreases in VFA concentrations were accompanied by strong shifts in the microbial community. The propionic acid oxidizing Syntrophobacter was washed out of the reactors after ammonia inhibition started. Also in several other experiments we conducted at elevated ammonia concentrations, Syntrophobacter was washed out (see Supplementary Materials A,C ). The absence of Syntrophobacter at high ammonia concentrations was also observed in a mesophilic reactor treating household waste at TAN concentrations of 386–414 mM (Westerholm et al., 2015 ), supporting our observation that Syntrophobacter is an ammonia sensitive taxon. The only other known syntrophic propionic acid degrading genus in our experiments was Pelotomaculum , which was not competitive with a relative abundance of about 0.1% independent of the ammonia concentration. Since propionic acid oxidation continued after these two taxa reached negligible relative abundances, one or several yet unknown ammonia tolerant propionic acid oxidizing taxa must be among the detected bacteria. The OTUs that increased in relative abundance after the ammonia inhibition such as Aminobacterium and Lutispora are possible candidates; however, they could grow on butyric acid or decaying biomass as well. Other studies support the ammonia tolerance of these two taxa. Lutispora was found to correlate with the recovery of biogas production in an ammonia inhibited biogas reactor digesting wastewater treatment plant sludge (Chen et al., 2018 ). Aminobacterium increased in abundance after an increase in ammonia concentration in an anaerobic digester treating chicken manure and feathers with wood shavings (Belostotskiy et al., 2015 ). The butyric acid degrading Syntrophomonas tolerated the increase in ammonia concentration in our experiment. A tolerance to TAN of up to 6.5 g L −1 was observed earlier (Lee et al., 2018 ). Poirier et al. ( 2017 ) even observed relative abundances to total bacteria of 22% for ammonia concentrations as high as 25 g L −1 , supporting our observation that Syntrophomonas is ammonia tolerant. The bacterial taxa Thermovirga and Blvii28 wastewater sludge group became abundant with up to 30% relative abundance to total bacteria each during the first 21 days. It is unlikely that they were mainly propionic acid degraders in our reactors because of their high abundance compared to the low share of propionic acid in the feed and it is unlikely that they were mainly acetic acid degraders because at the low free ammonia concentrations during the first 21 days in all reactors (<0.028 g L −1 ), acetoclastic methanogenesis commonly dominates over SAO (Luo et al., 2016 ). Therefore, they were most likely syntrophic butyric acid degraders. Since both taxa were washed out after the increase in ammonia concentration in our experiment as well as in an experiment by Lee et al. ( 2018 ), they likely are ammonia sensitive. Concerning acetoclastic methanogens, Methanosaeta dominated all CSTRs at the start of the experiments when ammonia and acetic acid concentrations were low. Its advantage over Methanosarcina at low acetic acid concentrations can be explained by its higher substrate affinity and minimum concentration threshold for acetic acid (Jetten et al., 1992 ). After the increase in ammonia concentration, Methanosaeta was washed out during the ammonia inhibition and replaced by Methanosarcina . The sensitivity of Methanosaeta to ammonia has been shown in a pure culture study (Steinhaus et al., 2007 ). Methanosarcina is known to be more tolerant against ammonia inhibition than Methanosaeta (De Vrieze et al., 2012 ). This tolerance has been connected with their ability to form aggregates (De Vrieze et al., 2012 ). Aggregates with diameters up to 1 mm were also observed in our experiment. After Methanosarcina completely dominated the acetoclastic methanogens, acetic acid degradation efficiencies recovered up to almost 100% toward the end of the experiment, showing that the applied ammonia concentrations were not inhibitory for Methanosarcina . The hydrogenotrophic methanogens of the family Methanomicrobiaceae were also tolerant to the increase in ammonia concentration and remained abundant until the end of the experiment. We observed this tolerance in several other experiments on ammonia inhibition that we conducted (see Supplementary Material A ). Methanomicrobiaceae have been found previously to be tolerant to TAN concentrations of about 3 g L −1 in pure culture (Schnürer et al., 1999 ; Nettmann et al., 2010 ; Wang et al., 2015 ) and mixed culture studies (Angenent et al., 2002 ; Westerholm et al., 2011 , 2012 ). Theoretically, it is possible that all acetic acid was converted via SAO and hydrogenotrophic methanogenesis after day 21. In that case, Methanosarcina would be hydrogenotrophic instead of acetoclastic as assumed above. However, this appears to be unlikely. First, a switch to SAO should have led to an increase in relative abundance of bacteria compared to methanogens that we did not observe. Second, genera known for SAO were observed only in negligible abundances in our reactors. Five species capable of SAO have been cultured belonging to the genera Pseudothermotoga, Thermacetogenium, Clostridium, Syntrophaceticus , and Tepidanaerobacter (Müller et al., 2016 ). From them, only Clostridium and Syntrophaceticus were detected with maximum relative abundances of 0.2% to total bacteria. Third, anaerobic digesters dominated by SAO have been observed to be dominated by Methanoculleus or Methanobrevibacter instead of Methanosarcina (Luo et al., 2016 ). Finding acetoclastic Methanosarcina dominant in ammonia inhibited biogas plants is not unusual. Luo et al. ( 2016 ) observed the dominance of acetoclastic methanogenesis in industrial biogas plants with up to 0.46 g L −1 of free ammonia which is almost double the maximum free ammonia concentration of 0.25 g L −1 that we observed. Schnürer and Nordberg ( 2008 ) observed a dominance of acetoclastic methanogenesis over SAO at free ammonia nitrogen concentrations of up to about 0.3 g L −1 in lab-scale digesters. Therefore, we assumed in our ADM1 simulations that Methanosarcina replaces Methanosaeta as an acetoclastic methanogen. Still, it cannot be excluded that Methanosarcina additionally took up hydrogen and that some SAO occurred in our reactors. Implementation of taxon-specific ammonia inhibition in ADM1 The relationship between microbial community dynamics and ammonia as well as VFA concentrations could be successfully reproduced with our extended ADM1 model. However, major changes to the original ADM1 structure were necessary. First of all, inhibition of propionic acid degradation was not implemented in the original ADM1 structure. However, we could show in our experiment that propionic acid degradation was even stronger inhibited than acetic acid degradation, and therefore, the addition of an ammonia inhibition term for propionic acid degradation was necessary. Secondly, simulations based on the original ADM1 structure did not (and in principle cannot) lead to a process recovery after the increase in VFA concentrations following ammonia inhibition because only one taxon of each functional group is part of the original structure. For example, only one taxon is capable of acetoclastic methanogenesis. As a consequence, our simulations using the original ADM1 structure could only lead to no VFA accumulation, to a constant elevated VFA concentration, or process breakdown. However, our experimental data clearly showed an increase in VFA concentration due to ammonia inhibition, followed by a decrease in VFA concentration toward the end of the experiment after the microbial community adapted to the new conditions. Therefore, the addition of at least a second taxon of acetic and propionic acid degraders was necessary in ADM1 to reflect the reactor performance dynamics as a consequence of the microbial community dynamics. When fitting the simulations to the experimental results, we only changed the ammonia inhibition constants of X ac,1 , X ac,2 , X pro,1 , and X pro2 , and the initial concentration and half saturation constants of X ac,1 and X pro,1 compared to the benchmark model parameter values by Rosen and Jeppsson ( 2006 ). The fitting resulted in higher substrate affinities (lower half saturation constants) for the ammonia sensitive populations (X ac,2 and X pro,2 ) compared to the ammonia tolerant populations (X ac,1 and X pro,1 ). This reflects the higher substrate affinities previously observed for Methanosaeta compared to Methanosarcina (Jetten et al., 1992 ; Straub et al., 2006 ). However, the advantage of Syntrophobacter over its competitors is not known. Other advantages than substrate affinity are possible, such as higher maximum substrate uptake rates, higher specific growth yields or a combination thereof. A high sensitivity for the parameters K I,nh3,ac,2 , K S,ac,1 , K I,nh3,pro,2 , K S,ac,2 and the initial concentrations of X ac,1 and X pro,1 was found (see Sensitivity Analysis in Supplementary Material A ). By contrast, increasing the values of the ammonia inhibition constants of the ammonia resistant taxa (K I,nh3,ac,1 and K I,nh3,pro,1 ), i.e., decreasing the ammonia sensitivity of these populations, did not change the simulation results. This indicates that higher ammonia concentrations are necessary to unambiguously determine these parameter values (see Supplementary Material A , Figure S6 ). Hydrogenotrophic methanogenesis and butyric acid oxidation were not changed in ADM1 because their performance was hardly impacted by the increase in ammonia concentration in our experiment. While our model helps to illustrate the relationship between microbial community dynamics and VFA accumulation, extending it for the use as predictive model remains a challenge, in particular regarding parameter identification. In communities with competing taxa, the quantification of taxon-specific substrate uptake rates is a major obstacle. Fitting these rates based on gross consumption likely leads to non-unique solutions and a loss of generality and predictive power. A predictive model would require the physiological characterization of all relevant taxa in pure cultures or defined co-cultures on defined media. Isolating prokaryotes was not always successful in the past (Pelletier et al., 2008 ). However, the advent of metaomics techniques gives hope that suitable cultivation conditions can be inferred more easily in the future (Overmann et al., 2017 ). Success in cultivation would be rewarded with high benefits: A predictive multi-taxa ADM1 model could be an essential resource in gaining deterministic control over the microbial community composition, for example by changing process parameters and/or bioaugmentation, in order to increase the productivity of biogas plants suffering from ammonia and other inhibitions. In conclusion, ammonia inhibition is a major challenge for the biogas process, resulting in economic losses and even process failures. Both our experimental and simulation results showed the importance of ammonia-sensitive taxa, such as Methanosaeta and Syntrophobacter , and ammonia-tolerant taxa, such as Methanomicrobiaceae and Syntrophomonas , for understanding the reactor performance as a result of microbial community dynamics of anaerobic digesters impacted by high ammonia concentrations."
} | 5,737 |
34113807 | PMC8169794 | pmc | 1,645 | {
"abstract": "Summary Controllable IR-reflection systems can be applied to displays, adaptive military camouflages, thermal managements, and many other fields. However, current reported controllable IR-reflection systems suffer from utilizing rigid materials, complicated devices, or high working temperature/voltage, which are not suitable for their widespread applications toward soft systems. Herein, inspired by cephalopods, we demonstrate a facile and scalable method for adaptive IR reflection based on a Janus rubber film, which is composed of aluminum-coated microsheets (AMSs)/rubber composite top and a rubber only bottom. Expansion of the Janus rubber film causes random arrangement of AMSs to stay planar, resulting in the change from IR scattering to concentrated IR reflection. By fixing the Janus rubber films upon the arranged tubes, as-prepared arrays could display complex and changeable patterns by selectively pumping tubes. Being facile and of general validity, our strategies broaden the scope of future controllable IR reflecting applications for environmental IR camouflages and displays.",
"introduction": "Introduction A continuous-range tuning of infrared (IR) features upon matters can enable innovative technologies and applications, such as building insulations ( Granqvist et al., 2017 ; Hu et al., 2018 ), energy-conserving windows ( Ke et al., 2018 ; Lee et al., 2019 ; Liu et al., 2016 ), protective clothing ( Cai et al., 2017 ; Jiang et al., 2016 ; Peng et al., 2020 ), and adaptive IR camouflages for military and commercial purposes ( Yu et al., 2014 ; Chandrasekhar et al., 2003 ; Zhu et al., 2020 ; Gu et al., 2020 ). Two main mechanisms are now established, including the modulation of IR emittance and the control of IR reflection ( Li et al., 2020a ). The former mechanism usually utilizes microfluidics-based or thermoelectric systems by injecting hot/cool liquids or special thermoelectric materials, respectively ( Morin et al., 2014 ; Hong et al., 2020 ). However, they suffer from slow response time, complex preparation processes, and changing of their real temperatures. The latter one bases on metals, metal oxides, graphenes, conductive polymers, and metastructures to modulate the IR reflection without changing their own temperatures ( Li et al., 2020b ; Mandal et al., 2018 ; Chandrasekhar et al., 2002 ). Commonly, their working mechanisms are divided into three types, including thermochromic, electrochromic, and mechanochromic devices. IR thermochromic materials ( Dai et al., 2013 ; Ji et al., 2018 ), such as VO 2 and its composites, can exhibit considerable IR reflection changes because of their thermally induced phase transitions yet limited by fixed working temperature ( Xu et al., 2018a ). IR electrochromic devices ( Xu et al., 2018b , 2020 ; Leung et al., 2019 ) are able to tune the IR reflection by electrochemical redox reactions or metal electrodeposition but struggle with the comparatively low IR emittance tunabilities and narrow IR modulation ranges. Mechanochromic devices combining metal thin films and elastic polymers can rapidly modulate their IR reflections by forming cracks or wrinkles under mechanical force; however, they are restricted by narrow IR modulation ranges and hysteresis during cycling ( Tian et al., 2017 ; Chandrasekhar et al., 2002 ; Salihoglu et al., 2018 ). Notably, above-mentioned approaches are not scalable manufactured, high temperature/voltage dependent, and commonly rigid, which might not be suitable for practical applications. Thus, a facile (simple materials and setup) and easy-to-scale-up method to engineer the adaptive IR reflection for soft systems (such as soft robots) is urgently needed. Learning from nature is a powerful resource to design new material systems ( Golnaz and Marco, 2018 ; Wang et al., 2014 ). Cephalopods are the king of adaptive camouflage in visible and IR range ( Mathger et al., 2009 ; Mathger and Hanlon, 2007 ; Yang et al., 2021 ). Their skin surfaces contain numerous chromatophores and structural reflectors, which can tune the interaction between the light and the skins through the muscle contraction and expansion ( Phan et al., 2016 ). Inspired by cephalopods, herein we demonstrate a facile and scalable method for adaptive IR reflection based on a Janus rubber film, which is composed of an AMS/rubber composite top and a rubber-only bottom. The apparent IR reflection was dramatically changed following the expansion/recovery of the Janus rubber film. In addition, such Janus rubber films were easy to construct a pixilated device that can control IR reflection of each pixel upon selected actuation, exhibiting changeable IR patterns for environmental camouflages and displays. Our adaptive-IR-reflection system simultaneously possesses simple actuation mechanism, room working temperature, controllable response (depends on pumping system), stability to repeated cycling (100 cycles), amenability to patterning and multiplexing, and straightforward manufacturability.",
"discussion": "Discussion We have designed a Janus rubber film to control IR reflection and conceptualized and validated a new strategy for a facile, rapid, and on-demand IR reflecting device through blowing the Janus rubber film. Several advantages can be found in this approach. First and the most important one, our method utilizes low-cost and even commercial products, rather than special synthetic materials, to realize adaptive IR reflection. Second, the arrangement of the tubes can serve as pixel arrays to display complex patterns by selective pumping of desired tubes. Future development of this study focuses on the optimization of the propulsion setup for minimization. This blowing-assisted strategy involving adaptive IR reflection shows great potential for displays and camouflages in the civil and military applications. Limitations of study The limitations of our study are relatively low resolution of the assembled devices and the immature pumping system. First, in this study, the diameter of the assembled tubes is relatively large for high resolution of IR camouflage and display. A smaller diameter of the tube would be necessary for a high-resolution IR reflection. Second, our pneumatic pumping system is not programmable and automatic controlled. For better and quicker IR camouflage and display, a more sophisticated pumping and releasing control system would be designed."
} | 1,598 |
35190599 | PMC8861010 | pmc | 1,646 | {
"abstract": "Although numerous dinoflagellate species (Family Symbiodiniaceae) are present in coral reef environments, Acropora corals tend to select a single species, Symbiodinium microadriaticum, in early life stages, even though this species is rarely found in mature colonies. In order to identify molecular mechanisms involved in initial contact with native symbionts, we analyzed transcriptomic responses of Acropora tenuis larvae at 1, 3, 6, 12, and 24 h after their first contact with S. microadriaticum , as well as with non-native symbionts, including the non-symbiotic S. natans and the occasional symbiont, S. tridacnidorum . Some gene expression changes were detected in larvae inoculated with non-native symbionts at 1 h post-inoculation, but those returned to baseline levels afterward. In contrast, when larvae were exposed to native symbionts, we found that the number of differentially expressed genes gradually increased in relation to inoculation time. As a specific response to native symbionts, upregulation of pattern recognition receptor-like and transporter genes, and suppression of cellular function genes related to immunity and apoptosis, were exclusively observed. These findings indicate that coral larvae recognize differences between symbionts, and when the appropriate symbionts infect, they coordinate gene expression to establish stable mutualism.",
"introduction": "Introduction Symbioses are ubiquitous in nature and are intricately involved in adaptation, ecology, and evolution of most life forms 1 , 2 . Cnidarians, such as reef-building corals, are associated with endosymbiotic dinoflagellates of the family Symbiodiniaceae 3 , 4 . Coral reefs, structurally dependent upon reef-building corals and their symbionts, are the most biologically diverse shallow-water marine ecosystems 5 . Most coral species (~ 71%) acquire algal symbionts directly from the ocean in each generation 6 . The scleractinian coral genus, Acropora , the most common and widespread in the Indo-Pacific 7 , harbors Symbiodinium or Durusdinium in its early life stages 8 , 9 while mature colonies generally harbor Cladocopium 10 , 11 . In addition, more than half of Acropora recruits (~ 70%) at Ishigaki Island, Okinawa Prefecture, Japan, harbor Symbiodinium , even though numerous other genera/species of Symbiodiniaceae, including Cladocopium , are common in the water column 9 . Host-symbiont specificity can also be extended to the species level, with S. microadriaticum predominating (~ 97%) among the Symbiodinium taxa in Acropora recruits 12 , indicating that S. microadriaticum is a native symbiont in early life stages of Acropora in Okinawa. For recognition of beneficial symbionts or harmful pathogens, pattern recognition receptors (PRRs) on surfaces of host cells and microbe-associated molecular patterns (MAMPs) on surfaces of symbionts are thought to be important 13 . In cnidarians, the PRR-MAMP system is also crucial to establish symbiotic relationships 3 . Cell surfaces of symbiotic dinoflagellates are populated with glycoconjugates, with some glycan motifs similar among species and others unique to each species 14 . Various lectins, which recognize glycans, have been isolated from corals, suggesting that these are involved in recognition of specific symbiotic partners of corals 3 , 15 – 17 . After recognition of symbiotic algae, downstream cellular signaling pathways, such as the innate immune system, were modulated to initiate symbiosis 18 . For example, stimulation of the Toll-like receptor (TLR) signaling pathway affects the stability of symbiosis between the sea anemone, Exaiptasia diaphana , and microalgae 19 . In corals, several studies involving bleaching treatments of mature colonies have suggested the importance of immunity and apoptosis for their symbioses 20 – 24 . However, cellular mechanisms that occur in corals and symbionts during initial contact are still unidentified. Although several studies have examined transcriptomic responses of coral larvae to symbiotic dinoflagellates during initial contact 25 – 28 , no studies have used their native algal symbionts in early coral life stages. We recently developed an Acropora larval system as a model to study symbiont selection and recognition by host corals 29 . Using this system, we previously documented transcriptomic responses of A. tenuis during symbiosis establishment with its native symbiont, S. microadriaticum (Smic) 30 . To study molecular responses that occur in coral larvae during initial contact with native symbionts, we analyzed the transcriptome of A. tenuis larvae at 1, 3, 6, 12, and 24 h post-inoculation (hpi) with symbionts. In addition, in order to highlight gene expression changes exclusive to native symbionts, we also investigated transcriptomic responses of A. tenuis larvae exposed to a closely related, non-symbiotic Symbiodinium taxon S. natans , (herein Snat), and an occasionally symbiotic Symbiodinium, S. tridacnidorum (herein Stri).",
"discussion": "Discussion A previous study reported that A. digitifera larvae immediately changed the expression level of 1,073 genes after exposure (4 hpi) to a non-native symbiont ( Breviolum minutum ), but that no genes were differentially expressed later (at 12 and 24 hpi) 26 . Consistent with the previous study, A. tenuis larvae responded to non-native symbionts immediately after inoculation, but expression levels of DEG soon returned to baseline levels (Table 1 ), suggesting that initial recognition of Symbiodinium occurred within 1 h. In contrast, A. tenuis larvae gradually responded during initial contact with native symbionts (Table 1 ). Interestingly, when A. tenuis larvae were exposed to Cladocopium , a native symbiont of adult corals, the number of DEGs did not increase with infection time 27 , which is different from the results of this study. These differences were probably caused by an infection with symbionts that should not have co-existed in the early life stages in nature. For example, Yuyama et al. 34 reported that all inoculated Cladocopium in A. tenuis polyps were abnormal in shape, suggesting that Cladocopium may be unsuitable for host corals in early life stages, as the majority of Acropora larvae favor Symbiodinium or Durusdinium in nature 8 , 12 . Symbiotic dinoflagellates possess glycan ligands on their cell surfaces, such as mannose, glucose, and galactose, which are recognized as MAMPs by host corals 15 – 17 , and lectins that recognize the glycan ligands have been reported from various corals 17 , 35 – 40 . Although continuous gene expression of these lectins should be crucial during initial contact with symbionts, expression of some of them was upregulated when coral larvae were exposed to symbionts 26 , 30 . In this study, two genes with lectin-related domains were significantly upregulated only when A. tenuis larvae were inoculated with native symbionts (Fig. 4 ). Interestingly, no genes with lectin-related domains were reportedly differentially expressed when A. tenuis was exposed to Cladocopium 27 . Considering the specific upregulation of genes with lectin domains to native symbionts in early life stages, these two genes may help to recognize appropriate symbionts in specific life stages. Dinoflagellates produce diverse photosynthetic products, such as carbohydrates and amino acids 41 , 42 , and metabolic exchanges between hosts and symbionts are well known 4 . Upregulation of solute carrier (SLC) transporters, which transport sugars and amino acids, in host corals under daylight 43 and several days after exposure to native symbionts 30 have been reported. These SLC transporters are thought to be the major pathway for metabolic exchanges between host corals and symbionts. Although Mohamed et al. 27 reported upregulation of transporters ( S23A2 and S26A6 ) by 72 hpi with Cladocopium , no genes with potential to transport sugars or amino acids were included among DEGs of host corals in that study. In contrast, SLC2A12 -like gene, which may transport sugars, was upregulated at 24 hpi in this study (Supplementary Fig, S4 ). Furthermore, this gene was also upregulated at 4 d post- S. microadriaticum inoculation 30 , suggesting that nutrient exchange with native symbionts occurs as early as 24 hpi. Three NLRC4 -like genes and one MFHAS1 -like gene were specifically downregulated in Smic-inoculated larvae (Fig. 3 ). NLRC4 is a member of the nucleotide oligomerization domain-like receptor (NLR) family 44 , and coral-specific expansion of this group has been reported 45 . NLR can activate several innate immune pathways, including the NFkB and MAPK pathways 44 . MFHAS1 is a leucine-rich repeat-containing protein and has the potential to modulate the TLR signaling pathway in human macrophages 46 . Although we could not detect downregulation of downstream genes in bulk RNA-seq, these results suggest the occurrence of immune-suppression in Smic-inoculated larvae. On the other hand, an MYD88 -like gene was significantly downregulated in larvae inoculated with S. tridacnidorum , which is an occasional symbiont in early life stages of Acropora 12 . MYD88 is a critical adapter protein downstream of all TLR signaling in mammals 47 , suggesting that immune-suppression may also occur in Stri-inoculated larvae. The importance of immune suppression during symbiosis establishment has been suggested in sea anemones (reviewed in Mansfield and Gilmore 18 ), and recently it was experimentally demonstrated in Aiptasia 19 , suggesting that immune suppression is conserved and essential for cnidarians during initial contact with their symbionts. Apoptosis is a highly conserved programmed cell death mechanism in metazoans 48 , 49 and has previously been suggested as a possible pathway in the breakdown of symbiosis under stress in corals 22 – 24 . Although the possible role of apoptosis in maintenance of a stable symbiotic relationship has not been experimentally addressed, its association during initial contact with symbiotic algae has been suggested, since some apoptosis-related genes were up- and downregulated 25 , 50 . Hence, it is thought that apoptosis may contribute to the dynamic equilibrium between host and symbiont cell growth and proliferation 50 . However, another hypothesis has also been proposed by Dunn and Weis 51 . When caspase activity that causes apoptosis was inhibited, larvae of the coral, Fungia scutaria , were successfully colonized with a symbiont that is normally unable to colonize; therefore, apoptosis contributes to selection of compatible symbionts after phagocytic uptake 51 . Consistent with this hypothesis, 11 genes involved in apoptosis were exclusively downregulated in larvae inoculated with native symbionts in this study (Fig. 3 ), indicating that suppression of apoptosis may be conserved among corals as a selection mechanism after phagocytic uptake of symbionts. In addition to suppression of genes involved in immunity and apoptosis, most DEGs (89.2%) were downregulated at 24 hpi, and functional annotation revealed that many of these encoded transcription and translation, cell proliferation, and immune responses (Table 2 ), indicating that overall downregulation of cellular functions occurs during initial contact with native symbionts. Although we were unable to detect it in larval transcriptome data until 24 hpi, metabolic suppression of amino acids, sugars, and lipids has been reported in A. tenuis larvae at 4–12 dpi 30 ; thus, suppression of genes involved in transcription and translation may be related to metabolic suppression. Symbiodinium is one of the dominant algal symbionts in early life stages of Acropora corals at Ishigaki Island, Okinawa Prefecture, Japan (until they are at least 14 d old) 12 and the southern Great Barrier Reef, Australia (until they are at least 83 d old) 52 . One reason for this may be that Symbiodinium are highly infectious to corals at this stage 8 , 52 , 53 . However, another possible reason for this may be that Symbiodinium tolerates higher solar irradiance and thermal stress 54 – 57 . Despite these advantages, adult colonies of Acropora in those locations are mainly associated with Cladocopium 10 , 11 , 52 . Perhaps this is because Symbiodinium has a lower carbon fixation rate than Cladocopium , which is required to form calcium carbonate skeletons 58 . The genus Symbiodinium includes species with characteristics ranging from symbiotic to opportunistic or free living 59 . Symbiosis between Acropora and Symbiodinium differs even among closely related species 29 . Smic (AJIS2-C2) isolated from an Acropora recruit is taken up by A. tenuis planula larvae more than other Symbiodinium 29 . Our transcriptomic data suggest that Smic modulates the immune system of host corals and exchanges metabolites with the host within 24 h (Fig. 5 ), indicating that highly infectious Smic is a suitable symbiotic partner for Acropora in early life stages. Figure 5 Schematic time series summary of possible intercellular events occurring in Acropora tenuis larvae during initial contact with native symbionts. Sentences with a dot indicate possible cellular events, and genes associated with them are shown nearby. A brown dotted line indicates a symbiosome (the organelle in which a symbiont resides). Red or blue text indicates significantly (FDR < 0.05) up- or down-regulated genes, respectively. PRRs indicate pattern recognition receptors. TF indicates transcription factor. In summary, our data show clear transcriptomic differences in coral larvae in response to native and non-native symbionts, indicating that A. tenuis larvae recognize different Symbiodinium strains within 1 hpi. When A. tenuis larvae contact native symbionts, symbiont recognition, circadian cycle changes, cell volume homeostasis, and endocytic uptake occur within 12 hpi (Fig. 5 ), and then metabolic suppression, immune and apoptosis suppression, circadian cycle changes, and nutrient uptake are induced by 24 hpi (Fig. 5 ). This study highlights not only the importance of immune-response suppression and apoptosis suppression during initial contact with native symbionts, but also the relevance of cellular mechanisms, such as circadian cycle changes and nutrient uptake, during the period from initial contact to symbiosis establishment. Although RNA-seq techniques have become more feasible than in the previous decade, it is still difficult to capture minute gene expression changes with bulk RNA-seq, because only a small percentage of the volume of a coral larva contains cells with algae. Tissue-specific RNA-seq 19 , 60 , single cell RNA-seq 61 , or coral cell lines 62 may reveal more comprehensive molecular responses of coral symbioses in the future."
} | 3,711 |
40055539 | PMC11961364 | pmc | 1,647 | {
"abstract": "Many biological tissues are mechanically strong and stiff but can still heal from damage. By contrast, synthetic hydrogels have not shown comparable combinations of properties, as current stiffening approaches inevitably suppress the required chain/bond dynamics for self-healing. Here we show a stiff and self-healing hydrogel with a modulus of 50 MPa and tensile strength up to 4.2 MPa by polymer entanglements in co-planar nanoconfinement. This is realized by polymerizing a highly concentrated monomer solution within a scaffold of fully delaminated synthetic hectorite nanosheets, shear oriented into a macroscopic monodomain. The resultant physical gels show self-healing efficiency up to 100% despite the high modulus, and high adhesion shear strength on a broad range of substrates. This nanoconfinement approach allows the incorporation of novel functionalities by embedding colloidal materials such as MXenes and can be generalized to other polymers and solvents to fabricate stiff and self-healing gels for soft robotics, additive manufacturing and biomedical applications.",
"conclusion": "Conclusion We have demonstrated a general strategy to fabricate strong, stiff and self-healing hydrogels based on highly entangled polymers in the monodomain of co-planar nanoconfinement. The confinement is imposed between hectorite nanosheets, which spontaneously form nematic liquid crystallinity (that is, co-planar alignment) on one-dimensional dissolution. The high-AR hectorite ensures controllable uniform interlayer spacings around 100 nm, which can be further shear oriented into a macroscopic unidirectional monodomain. Once the confinement approaches the dimension of the highly entangled PAAm chains, a dramatic increase in the Young modulus of the hydrogel up to 50 MPa is observed, which is one order of magnitude higher than a non-confined hydrogel, whereas the UTS value reaches up to 4.2 MPa. Despite the high modulus, the hydrogels possess excellent self-healing properties, with 33% recovery of UTS in the end-to-end geometry and almost 100% in the side-by-side geometry. In particular, the hydrogel also shows strong binding to various substrates such as glass and metals, showing an adhesive strength of up to 0.49 MPa. The unique properties of the nanoconfined hydrogel allow the robust assembly of complex three-dimensional shapes, showing potential for additive manufacturing. In addition, the nanoconfinement effect can be extended to other types of monomer and solvent, such as organohydrogels with outstanding mechanical and adhesive properties. The incorporation of functionalities is demonstrated by an MXene-doped nanoconfined Hec-PAAm hydrogel with thermal camouflage and EMI-shielding capabilities. The nanoconfinement strategy, thus, allows high stiffness in a self-healing hydrogel comparable to biological tissues like skins and opens new avenues to engineer soft-matter properties and to design complex shapes, relevant for applications like artificial skin and soft robotics."
} | 749 |
35079598 | null | s2 | 1,648 | {
"abstract": "Agent-based models of 'flocking' and 'schooling' have shown that a weighted average of neighbor velocities, with weights that decay gradually with distance, yields emergent collective motion. Weighted averaging thus offers a potential mechanism of self-organization that recruits an increasing, but self-limiting, number of individuals into collective motion. Previously, we identified and modeled such a 'soft metric' neighborhood of interaction in human crowds that decays exponentially to zero at a distance of 4-5m. Here we investigate the limits of weighted averaging in humans and find that it is surprisingly robust: pedestrians align with the mean heading direction in their neighborhood, despite high levels of noise and diverging motions in the crowd, as predicted by the model. In three Virtual Reality experiments, participants were immersed in a crowd of virtual humans in a mobile head-mounted display and were instructed to walk with the crowd. By perturbing the heading (walking direction) of virtual neighbors and measuring the participant's trajectory, we probed the limits of weighted averaging. (1) In the 'Noisy Neighbors' experiment, the neighbor headings were randomized (range 0-90°) about the crowd's mean direction (±10° or ±20°, left or right); (2) in the 'Splitting Crowd' experiment, the crowd split into two groups (heading difference = 10-40°) and the proportion of the crowd in one group was varied (50-84%); (3) in the 'Coherent Subgroup' experiment, a perturbed subgroup varied in its coherence (heading SD = 0-2°) about a mean direction (±10° or ±20°) within a noisy crowd (heading range = 180°), and the proportion of the crowd in the subgroup was varied. In each scenario, the results were predicted by the weighted averaging model, and attraction strength (turning rate) increased with the participant's deviation from the mean heading direction, not with group coherence. However, the results indicate that humans ignore highly discrepant headings (45-90°). These findings reveal that weighted averaging in humans is highly robust and generates a common heading direction that acts as a positive feedback to recruit more individuals into collective motion, in a self-reinforcing cascade. Therefore, this 'soft' metric neighborhood serves as a mechanism of self-organization in human crowds."
} | 582 |
32356962 | null | s2 | 1,649 | {
"abstract": "Many bacteria use membrane-diffusible small molecule quorum signals to coordinate gene transcription in response to changes in cell density, known as quorum sensing (QS). Among these, acyl-homoserine lactones (AHL) are widely distributed in "
} | 60 |
35515474 | PMC9054071 | pmc | 1,650 | {
"abstract": "Creating a robust omniphobic surface that repels various liquids would have broad technological implications for areas ranging from biomedical devices and fuel transport to architecture. The present omniphobic surfaces still have the problems of complex fabrication methods, high cost, and being environmentally harmful. To address these challenges, here we report a novel process to design a non-fluorinated, long-term slippery omniphobic surface of candle soot nanoparticles with a silicone binder that cures at room temperature. The porosity, nanoscale roughness, strong affinity of the substrate with the silicone lubricant, and retention of lubricant after curing of the binder play an important role in its stability and low ice adhesion strength at sub-zero temperature. The developed surface exhibits damage resistant slippery properties, repellency to several liquids with different surface tensions including blood, delay in freezing point along with ultra-low ice adhesion strength (2 kPa) and maintains it even below 7 kPa under harsh environmental conditions; 90 frosting/defrosting cycles at −90 °C; 2 months under an ice layer; 2 months at 60 °C; 9 days flow in acidic/basic water and exposure to super-cold water. In addition, this novel technique is cheap, easy to fabricate, environmentally benign and suitable for large-scale applications.",
"conclusion": "4. Conclusion In conclusion, we have demonstrated a new approach for fabricating a simple, low cost, scalable and environmentally friendly SLIPS surface which relies on silicon-oil-infused nanostructure of porous soot particles deposited on binder RTV-1. The developed surface shows repellency to several liquids with different surface tensions including blood and damage resistant slippery property due to strong affinity of silicone oil with soot particles along with RTV-1 silicone binder. The stable and defect-free lubricating interface shows delay in freezing point along with ultra-low ice adhesion strength (2 kPa) and maintains it below 7 kPa after harsh environmental conditions. The slippery soot surface also exhibited low ice adhesion strength at sub-zero temperature. We believe this new development is also suitable for large scale applications such as anti-fouling and drag reduction.",
"introduction": "1. Introduction Non-wetting surfaces are designed with suitable roughness and chemical composition, and are beneficial in a wide variety of commercial applications. 1 The non-wetting properties of the lotus leaf originate due to the presence of air pockets within the texture. Lotus leaf inspired surfaces continue to show non-wetting properties as long as the air pockets remain stable. 2 Maintaining stable air pockets, however, is challenging: the air pockets can lose the stability upon physical damage to the texture, can be distorted by wetting pressures, 3 and can be displaced by low surface tension liquids. 4 Besides, frost nuclei, or condensation which is formed at the nanoscale in the texture, can totally change the wetting properties and render the textured surface highly wetting. 5,6 Slippery liquid-infused porous surfaces (SLIPSs) is a different approach to attain non-wetting properties. 7 SLIPSs involves surfaces having pockets of a lubricating liquid other than that of air as in the case of superhydrophobic surfaces. 8 SLIPSs are highly in demand in the fields of materials science and chemistry due to their wide applications such as in anti-fouling, oil–water separation, bubble and droplet manipulation, and anti-icing surfaces. 9–12 Ice build-up on surfaces has become a serious issue in household appliances, outdoor public and in several industries including transportation, wind turbines, power plants, and telecommunications. 13 Different icephobic techniques have been applied to protect the surfaces from ice formation. The “first-generation” of icephobic strategies involved the use of infrared or electro-thermal heating, low freezing point agents (glycols, salts), mechanical forces (vibration, ice-plowing, electromagnetic impulse). These strategies are expensive, environmentally harmful and high energy consuming. The “second-generation” of icephobic strategies involve the surface engineering inspired by nature. 14 In recent research the lotus leaf-inspired (superhydrophobic) 15,16 and Nepenthes pitcher plant-inspired SLIPSs (slippery liquid-infused porous surfaces) are being used for icephobic purpose. 17–19 Superhydrophobic surfaces prevent the wetting and ice formation through small contact area due to the presence of air between the water and the substrate. 20–23 However, the inherent drawbacks of superhydrophobic surfaces are their poor stability under high pressure of condensed water droplets, hard to repair after damage and the presence of a high-energy interface (solid–liquid). This interface increases the ice adhesion strength 5,24,25 and promotes the heterogeneous ice nucleation on these surfaces. 26,27 The SLIPSs are used due to the smooth nature of the infused liquid. 7,28 The infused lubricant forms a stable lubricating film in the porous structure of the substrate by replacing the unreliable trapped air. The infused liquid's smooth nature in SLIPS increases the mobility of water droplets and decreases the ice adhesion value at the interface. 29–31 However, SLIPS does not show durability due to the depletion of infused-liquid under high shear flows and during several icing–deicing cycles. 32,33 Inspired by ice skating, Wang et al. developed the anti-icing SLIPSs with a self-lubricating layer of water from moisture and melted ice. 34,35 In another strategy, Beemer et al. and He et al. used the PDMS gels for super-low ice adhesion value (6 kPa) and explained the crack initiator mechanism on soft material. 36,37 Irajizad and his coworkers introduced the concept of stress-localization to develop durable icephobic surfaces with ultra-low ice adhesion value (1 kPa). 38 Organogels were also developed by swelling the PDMS network with lubricants for anti-icing applications. 39–41 Yu et al. synthesized a stable organogel and then lubricated with amphiphilic lubricating oil for low ice adhesion strength. 42 Golovin and his coworkers designed the elastomers by lowering their cross-link density and shear modulus for ultra-low ice adhesion. They inserted the miscible polymeric chains to introduce the interfacial slippage. 43,44 Recently, the same author introduced the low interfacial toughness material and removed the accreted ice due to gravity. The interfacial toughness depends on the bonding, high-stress region between the substrate and ice. 45 Recently, we fabricated a durable superhydrophobic candle soot coating which showed 20 kPa ice adhesion strength at room temperature but it displayed high ice adhesion at −20 °C and its Cassie–Baxter state was damaged after one day dip under the ice. 16 However, the challenge still remains to design a facile fabrication of slippery surface which not only shows stability after long-term dip under ice, low ice adhesion strength even at −150 °C but also repel several liquids with different surface tensions including blood. Here, we develop a new approach to fabricate a stable, slippery, and environment-friendly omniphobic surface. We used the RTV-1 silicone as a binder; the candle soot particles were deposited on it in order to create porosity, nanoscale roughness, and large surface area for lubricant retention. The surface showed long-term stability due to the strong affinity and same chemical composition of lubricant with the binder RTV-1. The developed surface also exhibited, repellency to several liquids with different surface tensions including blood, delay in freezing point along with ultra-low ice adhesion value (2 kPa) and maintains it even below 7 kPa under harsh environmental conditions; 90 frosting/defrosting cycles at −90 °C; 2 months under the ice layer; 2 months at 60 °C; 9 days flow in acidic/basic water and exposure to super-cold water. Besides, this novel technique is easy to fabricate, cheap, environment-friendly, and appropriate for large-scale applications.",
"discussion": "3. Results and discussions 3.1 Design principle of SLIPSs Before laying to the fabrication method, we discuss the design principle of SLIPS (slippery liquid infused porous surfaces). The stable slippery surface must fulfill the following criteria (i) lubricating oil and probe liquid must be immiscible; (ii) lubricant must be wet and wick into the solid substrate (surface energy of solid substrate must be higher than the surface tension of lubricating oil, ought to mismatch the probe liquid; if intrinsic surface energy of solid substrate does not match with lubricating oil, the chemical composition of solid substrate could be modified). (iii) The solid substrate must have a higher affinity for the lubricating oil over the probe liquid. (iv) The solid substrate should preferably have nanoscale roughness in order to provide the large surface area for the retention and the strong adhesion of lubricating oil. 18,46 Our slippery soot coating follows all the principles as conspicuously evident in the Video S1, ESI. † 3.2 Fabrication and surface properties of slippery soot surface Although the soot particles show great water repellency, they do not have any physiochemical interaction with the substrate which confirms its poor adhesive property. 47 To address this issue, a silicone binder (RTV-1, PDMS component) is used to develop its adhesive behavior with the substrate. The RTV-1 (room temperature vulcanizable) is used as a low modulus hydrophobic binder. The base material RTV-1 is viscoelastic, resistant to high temperature, readily curable and non-deformable. The binder RTV-1 was spin-coated (4000 rpm, thickness 90 μm) on the substrate. Then, the RTV-1 coated substrate was brought above the candle flame and we selected the topmost part of the candle flame (3.2 cm above the wick of candle, supplementary Fig. S1 † ) in order to deposit the soot particles with smaller size ≤20 nm. The soot deposition continued for one minute with constant to and fro motion of RTV-1 coated substrate in order to get a uniform layer of soot particles. RTV-1 coated substrate turned black with sustainable thin film of rubber owing to it's non-deformable and resistant to high temperature as shown in Fig. 1 and S2, ESI. † Fig. S3a, ESI, † shows the porous morphology and fragile structure of the soot particles without binder (RTV-1). The soot particles deposited into the binder RTV-1 are shown in SEM image of Fig. S3b, ESI. † The collected spherical soot particles have average size of 20 nm. The results of AFM show that the soot deposited substrate has average root mean square roughness ( R rms ) of 168 nm as shown in Fig. S3c and d, ESI. † The prepared soot coated superhydrophobic surface was covered by applying the droplets of dimethyl silicone oil. The silicone oil gets completely penetrated into the soot coated substrate due to its porosity, suitable surface energy, and nanoscale roughness (cause increase in surface area). The porosity and nanoscale roughness of soot particles ( Fig. 2 ) enhances the adhesion and immobilization of lubricant due to the capillarity. 48 Fig. 2 Schematic shows the fabrication of stable SLIPS due to the capillarity of porous soot particles. The nitrogen adsorption–desorption isotherms in Fig. S5 † shows the surface area up to 156.66 m 2 g −1 and the total pore volume up to 0.5144 cm 3 g −1 with 13.8 nm pore diameter. In addition, the magnified SEM images and other structural parameters of the porous soot particles have also been shown in Fig. S4 and Table S1 † respectively. Thus, these small size interconnected pores enhanced the complete spreading, and firmly holding of lubricant for the good slippery behavior of soot particles. In addition, the adhesive base material RTV-1 silicone and the lubricant silicone oil show strong affinity with each other due to their identical chemical composition. The lubricant was applied only for 15 minutes, then the substrate was stood vertically to remove the excess lubricating oil. Finally, the substrate was allowed to stand until its complete dryness and curing of binder RTV-1 at room temperature. The soot coated RTV-1 network swelled and proved to be a good reservoir for the retention of silicon oil as shown in Fig. 3 . Further chemistry about the RTV-1 binder and its curing at room temperature has been discussed in Scheme S1, ESI. † Fig. 3 Schematic shows the candle soot slippery surface (CS-SLIPS) as a good reservoir and lock in place of silicone oil. The smooth nature of the developed slippery surface of soot is manifest in Fig. 4 by AFM and SEM results. The interaction of water with superhydrophobic and slippery surfaces of soot particles can be seen in ESI Video S2. † It shows that the position of trapped air in superhydrophobic surface (shiny silver mirror) has been replaced with lubricating oil to make it slippery in nature. Fig. 4 (a) 2-D AFM image of slippery soot surface (b) 3-D AFM image of slippery soot surface (c) SEM image of slippery soot surface (d) Magnified SEM image of slippery soot surface. The wettability results of the uncoated (hydrophilic) and coated (hydrophobic to slippery) surfaces are shown in ESI Fig. S6. † The contact angle hysteresis (CAH) of water droplet (10 μL) was also measured on the prepared samples as available in Fig. 5 . The low CAH = 3° value of slippery soot surface also indicates its smooth nature without the pinning points. Fig. 5 Contact angle hysteresis of water on sample surfaces. The slippery performance of the prepared surface is also determined with the help of sliding angles (SA) and contact angles (CA) of various liquid droplets having different surface tensions. The Fig. 6 shows that the value of contact angles of different liquid droplets rise with the increase of surface tension for 10 μL droplet volume. The contact angles of 10 μL droplets of formamide, dimethylsulfoxide (DMSO), ethylene glycol, glycerol, and water are 70 ± 2.0°, 73 ± 2°, 79 ± 2°, 85 ± 2°, and 118 ± 2° respectively. The sliding angles increase with the rise of surface tensions of various liquid droplets for 10 μL droplet volume. The sliding angles of 10 μL droplet volume of formamide (DMF), dimethylsulfoxide (DMSO), ethylene glycol, glycerol, and water are 2.4 ± 0.5°, 4.2 ± 0.5°, 4.5 ± 0.7°, 5.5 ± 0.6°, and 6 ± 0.5°, respectively as shown in Fig. 6b . Fig. 6c shows the photos of contact angles and sliding angles of various liquid droplets for 10 μL droplet volume on the soot slippery surface. These photos clearly show difference of wetting of behavior of various liquid droplets having different surface tension. Fig. 6 (a) Contact angle of various liquid droplets (10 μL) on the soot slippery surface. (b) Sliding angles of various liquid droplets (10 μL) on the soot slippery surface. (c) Contact angle (CA) and Sliding angle (SA) photos of various liquid droplets on the soot slippery surface. The omniphobic nature of soot slippery surface provide a straightforward solution for blood repellency, resistance to fouling ( Fig. 7 ) and repellency of liquids with different surface tension. The slippery behavior of the above mentioned liquids with different surface tensions including coffee, milk and blood can be shown in the Video S3. † Fig. 7 Photos (a and b) represent the blood repellency and its contact angle with soot slippery surface. 3.3 Delay in freezing point on candle soot slippery surface The delay in the crystallization of water droplets was determined through differential scanning calorimeter (DSC, Q200 system, TA instruments) analysis by decreasing the temperature incrementally and the complete freezing phenomenon of a single water droplet was observed with the help of goniometer (CAM 200 OCA) at −20 °C. 42,49 The DSC results reveal that the candle soot slippery (CS-SLIPS) crucible shows small heat flow (released due to water crystallization) as compare to superhydrophobic (CS + RTV-1) and blank hydrophilic crucible. The small heat flow is attributed to the insulating thin layer of lubricant oil. The ultra-smooth interface of oil-infused soot particles lowers the crystallization rate of water droplet due to the reduced ice nucleation sites as compared to the superhydrophobic surface. Since the blank crucible is hydrophilic in nature and has more contact area with water droplet, therefore the water droplet of hydrophilic crucible shows large heat flow (171 W g −1 ) and crystalizes at −9.9 °C (high temperature) as compared to superhydrophobic (CS + RTV-1, −12.8 °C) and slippery oil-infused candle soot surface (CS-SLIPS, −13.3 °C) as shown in Fig. 8 . Thanks to the reduced ice nucleation sites of oil-infused soot particles and their slippery property for delaying the crystallization of water (3.4 °C). Fig. 8 DSC curves of super-cooled water droplet on blank crucible (hydrophilic), crucibles with candle soot (superhydrophobic) and crucibles with candle soot slips (slippery). The complete freezing phenomenon of a single water droplet (7 μL) was also observed on hydrophilic (glass), candle soot deposited glass (superhydrophobic) and slippery oil-infused soot surface at −20 ± 2 °C. The transparent center of water droplet gets disappeared and a sharp peak of ice appeared after the completion of freezing process. The freezing delay of water droplet on the prepared samples was observed in this order: slippery surface (CS-SLIPS) > superhydrophobic surface (CS + RTV-1) > hydrophilic surface (glass). The slippery surface of soot particles (CS-SLIPS) showed six times more freezing delay of water as compared to hydrophilic glass as shown in Fig. 9 . The delay in freezing point and crystallization of water affirm that the slippery surface of soot particles prevents the ice formation by removing the water droplet before its freezing. The freezing delay of water droplet is credited to the slippery interface due to lack of pinning points and heterogeneities. The candle soot slippery interface is suitable for homogenous nucleation of water according to the classical nucleation theory. 50 Eqn (1) and (2) represents the relation between the homogenous nucleation barrier (Δ G homo ) and heterogeneous nucleation barrier (Δ G Heter ). The value of their cofactor S ( θ ) was determined by using the following eqn (3) which depends on contact angle θ . 1 2 3 Here r indicates nucleation radius, σ IL represents interfacial energy between ice nucleus and liquid water, Δ G V is driving force for solidification and θ is contact angle of ice nucleus on slippery soot coating as shown in Fig. 9 . The value of the contact angle in our case is 120° and which results the Δ G Heter ≈ 0.84Δ G homo . The candle soot slippery surface promotes the homogenous nucleation 51,52 of water instead of heterogeneous nucleation due to its chemically homogenous and smooth interface. 53 Fig. 9 Photographs show the delay in the freezing process of water droplet (7 μL) on hydrophilic glass, superhydrophobic soot surface (CS + RTV-1) and slippery soot surface (CS-SLIPS). In short, when the temperature of the soot coated superhydrophobic surface decreases, the trapped air (Cassie–Baxter state) in between the soot particles and the water droplet is removed with the passage of time then soot particles provide nucleation site for the water molecules to form a stable ice nucleus in short time. But in case of slippery liquid infused surface SLIPS, the possible nucleation sites of soot particles are reduced by applying silicone oil. 28 The solid–liquid interface is converted into liquid–liquid interface (SLIPS) which helps to slow the homogenous nucleation as compared to superhydrophobic surface. Thus, SLIPS surface of soot particles provides a chemically homogeneous interface and drastically reduces the possible nucleation sites which results delay in freezing point of water. 3.4 Ice adhesion strength The icephobic surfaces show ice adhesion τ ice < 100 kPa. 43,45 In our case, the soot slippery surface showed extremely low ice adhesion (3 ± 1 kPa) as compared to the superhydrophobic (20 ± 5 kPa) and hydrophilic glass (615 ± 20 kPa) as shown in Fig. 10 . A real icephobic surface can also remove the ice under gravity action, small vibration and wind forces. We have performed different tests at different temperatures (25 °C to −90 °C) and observed the ice fall under the action of gravity, vibration, air pressure, and small force of tweezers. For this purpose, the ice pieces of different sizes were made on the slippery soot surface then their fall was observed under the action of gravity. This shows that a large and a small piece of ice can be easily removed from the developed slippery surface as evident in the ESI Video S4. † We also observed the ice removal at −20 °C on slippery soot surface due to wind force as shown in ESI Video S5. † This test shows that the ice can be easily removed from the slippery soot surface at low temperature. Finally, we observed the ice removal with the help of small force (tweezers) at different temperatures (25 °C to −90 °C). This test also confirms the easy removal of ice from the slippery soot surface even at −90 °C as shown in the ESI Video S6. † Fig. 10 Ice adhesion strength on sample surfaces. 3.5 Stability of slippery soot surface The poor stability of slippery surfaces is a big problem that hinders its use for industrial applications. The leakage and evaporation of lubricant can destroy its slippery and icephobic behavior. To address this issue, we have developed a long-life slippery surface of soot particles with the durable binder (RTV-1, cured at room temperature and swelled in the presence of lubricant). The capillarity effect of porous soot particles and chemical affinity of the lubricant with base material RTV-1 (binder) played an important role in the longevity of the surface. In order to determine the stability of the developed slippery surface tests viz. icing/deicing cycles; frosting/defrosting cycles; super-cold water-impact; liquid nitrogen/water cycles; dip under ice; thermal stability at 60 °C and acidic/basic water flow were performed. The slippery soot surface maintained its slippery property (SA, 10 ± 1°) with ice adhesion value (3 ± 0.3 kPa) after 100 icings/deicing and 120 frostings/defrosting cycles at −20 °C as shown in Fig. 12 and 13 . In order to convince the readers about this novel surface, 90 times frosting/defrosting test was performed at −90 °C. At the end, little increase in sliding angle 15 ± 1° (decrease in contact angle, 110 ± 2°, Fig. 14 ) and increase in ice adhesion value (3.5 ± 0.3 kPa) was observed as compared to initial state values of the slippery soot surface as shown in the Fig. 12 and 13 respectively. Fig. 11 Candle soot slips (CS-SLIPS) maintain its slippery behavior after 2 months dip under the ice. Fig. 12 Schematic shows the sliding angle of candle soot slippery surface (CS-SLIPS) after different stability tests. Fig. 13 Schematic shows the ice adhesion strength of candle soot slippery surface (CS-SLIPS) after different stability tests. Fig. 14 Contact angle of water on candle soot slippery surface after different mechanical tests. We also determined the stability of slippery soot surface under drastic conditions (∼−150 °C substrate temperature) 100 liquid nitrogen/water cycles. The developed slippery surface was put into the liquid nitrogen for 50 seconds then quickly dipped it into the water in order to make a thin ice layer. A gentle vibration or a weak shear force is enough to remove the thin layer of ice before its melting as shown in ESI Video S7. † Finally, increase in sliding angle 15 ± 2° (decrease in contact angle 112 ± 2° Fig. 14 ) and increase in ice adhesion value (4 ± 0.3 kPa) was observed as compared to initial state values of the slippery soot surface as shown in Fig. 12 and 13 respectively. The slippery behavior of super-cold water (−10 °C) was also observed on the developed slippery soot surface. First, we dropped small super-cold water droplets then 800 mL beaker of super-cold water with certain height was dropped on to the slippery soot surface as shown in ESI Video S8. † This test shows that the surface maintains its slippery and hydrophobic property even after the impact of super-cold water and little increase in sliding angle 14 ± 2° (decrease in contact angle 108 ± 2°, Fig. 14 ) and increase in ice adhesion value (3.5 ± 0.2 kPa) was observed as compared to initial state value of the slippery soot surface as shown in Fig. 12 and 13 respectively. The stability of developed surface was observed by dipping it completely under the ice for 2 months at −10 °C as shown in Fig. 11 . After 2 months, the surface was placed at room temperature for some time then the thick layer of ice was allowed to fall off under the gravity action shown in ESI Video S9 † and little increase in sliding angle 12 ± 1° (decrease in contact angle 116 ± 2° Fig. 14 ) and increase in ice adhesion value (4 ± 0.2 kPa) was noted after 2 months as compared to initial state value of the slippery soot surface as shown in Fig. 12 and 13 respectively. Similarly the thermal stability of the developed surface was observed by putting it in an oven at 60° for 2 months and increase in sliding angle 12 ± 1° (decrease in contact angle 156° Fig. 14 ) and increase in ice adhesion value (3.5 ± 0.2 kPa) was noted as compared to initial state value of the slippery soot surface as shown in the Fig. 12 and 13 respectively. In order to observe the stability of the developed surface in flowing water, the surface was dipped in three different beakers (200 mL pure water, mild acidic water, mild basic water) for 9 days with gentle water flow and the water was changed after every 24 hours respectively. The contact angle, sliding angle and mass loss was noted every time. Finally, the change in a mass loss was negligible (Fig. S7, ESI † ) due to the very strong affinity of lubricant with soot particles and curable binder RTV-1. The increase in sliding angle 21 ± 3° (decrease in contact angle 102 ± 2°, Fig. 14 ) and increase in ice adhesion value (5 ± 0.2 kPa) was observed as compared to initial state value of the slippery soot surface as shown in Fig. 12 and 13 respectively. The increase in sliding angle after 9 days water flow in mild acidic/basic conditions indicates that the depletion of lubricating layer (dimethyl silicone oil) starts. If the same experiment is performed for longer periods under higher water stirring, then the complete loss of lubricating layer may occur resulting the loss of slippery property. This problem can be solved by increasing the thickness of acid/base resistant RTV silicone rubber (binder, lubricant reservoir) as well as by using high viscosity and cross-linking silicone lubricant. Generally slippery liquid infused surfaces have contact angle of water above 100°. In our case the developed slippery surface of candle soot maintained its contact angle above 100° after different tests performed in harsh conditions as shown in Fig. 14 . The reason behind this is the strong affinity of silicone oil with low energy soot particles. We know that the liquid repellent property of the surfaces is destroyed in harsh conditions and do not show damage resistant slippery property. But in our case, the fluidic nature of low viscosity silicone oil and its strong affinity with the porous soot particles along with RTV binder helps to make a damage resistant slippery surface. The possible mechanism of the developed damage resistant slippery surface is the replenishing of the lubricating layer after physical damages. The lubricant in the porous soot and RTV rubber can flow freely towards the damaged area due to the surface-energy-driven capillary action, 7 and refills the physical voids spontaneously. Thus we can say that the firmly locked silicone oil into the nanopores of soot particles forms a defect-free lubricating interface that eliminates the pinning of liquid applied to its surface. This leads to form a robust damage resistant slippery surface. 54 The Fig. 15 shows the stability of the slippery soot surface after tape peel with 200 g weight, pressing with finger, and applying many cuts in different directions on the surface. The SEM images of the slippery soot surface before and after applying physical damages have been shown in the Fig. 16 . In addition, we used the polarizing microscope (NikON, ECLIPSE E600W, Japan) to observe the clear difference in the slippery soot surface before and after applying physical damages. The resulting images have been shown in the ESI S8 and S9. † The stability of the developed damage resistant slippery surface can be clearly seen in Videos S10 and S11. † The slippery surface of candle soot maintained its stability after different tests performed in harsh conditions as shown at the end of ESI. † Moreover, the stability can also be increased by increasing the thickness of the coating as well as using high viscosity and cross-linking silicone oil. Fig. 15 (a) Mechanical stability of the candle soot slippery surface after subjection to pressing, tape peel, and touching. (b) Applying physical cuts in different directions on slippery soot surface (c) shows the stable slippery surface of soot particles in the presence of physical cuts. Fig. 16 (a–c) SEM images shows the mechanical stability of the candle soot slippery surface after subjection to pressing, tape peel, touching, and knife cuts. (d) SEM image of candle soot surface after applying cut without lubricant."
} | 7,421 |
36638177 | PMC9839323 | pmc | 1,651 | {
"abstract": "All dielectric materials including ceramics, semiconductors, biomaterials, and polymers have the property of flexoelectricity, which opens a fertile avenue to sensing, actuation, and energy harvesting by a broad range of materials. However, the flexoelectricity of solids is weak at the macroscale. Here, we achieve an ultrahigh flexoelectric effect via a composite foam based on PDMS and CCTO nanoparticles. The mass- and deformability-specific flexoelectricity of the foam exceeds 10,000 times that of the solid matrix under compression, yielding a density-specific equivalent piezoelectric coefficient 120 times that of PZT. The flexoelectricity output remains stable in 1,000,000 deformation cycles, and a portable sample can power LEDs and charge mobile phones and Bluetooth headsets. Our work provides a route to exploiting flexible and light-weight materials with highly sensitive omnidirectional electromechanical coupling that have applications in sensing, actuation, and scalable energy harvesting.",
"introduction": "INTRODUCTION Flexoelectricity refers to generation of electricity by materials when subjected to nonuniform mechanical strains such as bending and twisting ( 1 – 5 ). Compared to piezoelectricity, flexoelectricity is a universal effect not limited by crystalline symmetry, polarization, and depolarization temperature, and is thus allowed in all dielectric materials including soft matters such as polymers ( 6 – 9 ), biomaterials ( 10 – 13 ), and liquid crystals ( 2 , 14 , 15 ), as well as ceramics and semiconductors ( 16 ) and perovskites under high temperatures ( 17 ). The flexoelectric effect finds wide applications in flexoelectronics ( 18 ), sensing and actuating ( 19 – 21 ), photoflexoelectricity ( 22 ), data storage ( 23 ), and energy harvesting ( 24 – 26 ). Photocurrent of ferroelectric oxides can be enhanced by two orders of magnitude by the flexoelectric effect corresponding to a giant strain gradient as high as 10 7 /m ( 27 ), and the bulk photovoltaic coefficient of MoS 2 sheets is enhanced by orders of magnitude compared to that of most noncentrosymmetric materials under a strain gradient of 10 6 /m ( 28 ). Photoconductance of BiFeO 3 films can be effectively mediated by strain gradients ( 29 , 30 ). However, the flexoelectric effect is size dependent and is weak at the macroscale, as is evident from the small flexoelectric coefficients in the range of 10 −5 to 10 −3 C/m for ferroelectrics and ceramics and 10 −10 to 10 −8 C/m for polymers ( 31 ). Thus, hereto, the effect has been mostly investigated and used at nano- to microscales where local giant strain gradients can be realized by various ways such as indentation ( 32 – 34 ), lattice mismatch ( 27 , 30 , 35 – 38 ), and bending of nano/microscale beams ( 26 , 39 – 42 ). In this work, we achieve an ultrahigh flexoelectric effect through design of a composite foam based on polydimethylsiloxane (PDMS) and a twisted foam structure ( Fig. 1 ). The mass- and deformability-specific flexoelectric response of the structure is more than 10,000 times that of the solid truncated pyramid PDMS material under compression. The foam structure can generate electricity under omnidirectional deformation and thus can also be used as a piezoelectric material. In this sense, the equivalent piezoelectric coefficient (1522 pC/N) is comparable to the high-performance piezoelectric ceramic Pb(Mg 1/3 Nb 2/3 )O 3 -PbTiO 3 (PMN-PT, 1510 pC/N), yielding a 120 times higher density-specific equivalent piezoelectric coefficient than lead-zirconate-titanate (PZT). By this design, we generate an output of electricity at the microampere scale with a simple portable device. We term this device a “strain-gradient electric generator.” By storing energy generated by the foam samples, we have successfully lit red light-emitting diode (LED) lights and charged mobile phones and Bluetooth headsets. The output remains stable over 1 million deformation cycles. Fig. 1. Fabrication and characterization of twisted composite foam. Schematic plots of precured PDMS and n-CCTO particle mixture ( A ) and composite foam with ligaments consisting of PDMS matrix and n-CCTO particles ( B ). ( C ) Photo and SEM images of the foam structure. ( D ) Twisted PDMS-CCTO composite foam and configuration of electrodes.",
"discussion": "DISCUSSION In summary, we have realized practical flexoelectricity harvesting at the macroscopic scale via a polymeric composite foam with an ultrahigh flexoelectric effect. The mass- and deformability-specific flexoelectric output is 10,000 times that of the pure solid polymer under unidirectional compression. The foam can also be used as a light-weight and sensitive piezoelectric material with its ultrahigh equivalent piezoelectric coefficient. The electric output remains stable over 1,000,000 cyclic deformations. Owing to its omnidirectional mechanoelectric coupling (the flexoelectric effects under macroscopic bending are demonstrated in the “Flexoelectric effects of composite foam beams under macroscopic bending” section of the Supplementary Materials, shown in fig. S4), the foam adapts to various deformation modes such as body motion, tides, winds, and vibrations to convert various mechanical energies into electricity without complicated mechanisms and abrasion caused by moving parts. Other dielectric materials such as degradable polymers and even natural materials also exhibit the flexoelectric effect. They can make biodegradable or biocompatible omnidirectional electromechanical sensors and generators. Therefore, this work breaks the limitation of low flexoelectric effect of solids at the macroscale and paves a way for wide applications of flexoelectricity."
} | 1,419 |
33574827 | PMC7870699 | pmc | 1,653 | {
"abstract": "Studies in natural ecosystems show that adaptation of arbuscular mycorrhizal (AM) fungi and other microbial plant symbionts to local environmental conditions can help ameliorate stress and optimize plant fitness. This local adaptation arises from the process of multilevel selection, which is the simultaneous selection of a hierarchy of groups. Studies of multilevel selection in natural ecosystems may inform the creation of sustainable agroecosystems through developing strategies to effectively manage crop microbiomes including AM symbioses. Field experiments show that the species composition of AM fungal communities varies across environmental gradients, and that the biomass of AM fungi and their benefits for plants generally diminish when fertilization and irrigation eliminate nutrient and water limitations. Furthermore, pathogen protection by mycorrhizas is only important in environments prone to plant damage due to pathogens. Consequently, certain agricultural practices may inadvertently select for less beneficial root symbioses because the conventional agricultural practices of fertilization, irrigation, and use of pesticides can make these symbioses superfluous for optimizing crop performance. The purpose of this paper is to examine how multilevel selection influences the flow of matter, energy, and genetic information through mycorrhizal microbiomes in natural and agricultural ecosystems, and propose testable hypotheses about how mycorrhizae may be actively managed to increase agricultural sustainability.",
"introduction": "Introduction Although the term “mycorrhiza” is often equated with a root inhabiting fungus, technically, a mycorrhiza is not a fungus, but rather the symbiosis between a fungus and a plant root ( Frank, 1885 ; Trappe, 2005 ). Acknowledging this fact immediately expands our perspective of mycorrhizae to include not only fungi, but also their complex interactions with plant hosts. The purpose of this essay is to expand this perspective even further and envision mycorrhizae as complex adaptive systems in which matter, energy and information move through a hierarchy of interconnected components ( Figure 1 ). Asymmetrical trading partnerships between plant hosts and arbuscular mycorrhizal (AM) fungi drive mycorrhizal systems: most plants can survive and – depending on the environment – possibly thrive in the absence of the symbiosis, while AM fungi are obligate symbionts and require a living plant host for survival. Most wild plants rely on mycorrhizae for normal nutrition, drought tolerance, and pathogen protection ( Smith and Read, 2008 ). Although nearly all crops form AM symbioses, their value in production agriculture is debated ( Ryan and Graham, 2018 ; Rillig et al., 2019 ). Envisioning mycorrhizae as constantly evolving symbiotic systems helps explain the reasons for this debate. This essay explores the hypothesis that local adaptation of mycorrhizal systems arises through multilevel selection, and that current agricultural practices uncouple critical feedbacks so that the mutualistic properties of mycorrhizas may diminish over time. An evolutionary framework can guide the design of experiments that test strategies to recouple feedbacks among plants, AM fungi and their associated microbiome so that the benefits of mycorrhizae can be harnessed in the development of sustainable agroecosystems. Figure 1 Mycorrhizae are symbiotic associations between plant roots and fungi, and their phenotype is determined by interactions among plant and fungal genotypes and the environment. A hierarchy of environmental factors determines mycorrhizal phenotype including abiotic conditions such as climate and soil properties and biotic factors such as communities of plant competitors, animal herbivores, and microbial antagonists and mutualists. Genotypes of plant and fungal partners can be characterized by cooperative traits that strengthen the mutualism and selfish traits that weaken the mutualism. Figure modified from Johnson et al. (1997) ."
} | 1,002 |
33447820 | PMC7798468 | pmc | 1,654 | {
"abstract": "Interspecies interactions in bacterial biofilms have important impacts on the composition and function of communities in natural and engineered systems. To investigate these interactions, synthetic communities provide experimentally tractable systems. Biofilms grown on agar-surfaces have been used for investigating the eco-evolutionary and biophysical forces that determine community composition and spatial distribution of bacteria. Prior studies have used genetically identical bacterial strains and strains with specific mutations, that express different fluorescent proteins, to investigate intraspecies interactions. Here, we investigated interspecies interactions and, specifically, determined the community composition and spatial distribution in synthetic communities of Pseudomonas aeruginosa , Pseudomonas protegens and Klebsiella pneumoniae . Using quantitative microscopic imaging, we found that interspecies interactions in multispecies colonies were influenced by type IV pilus mediated motility, extracellular matrix secretion, environmental parameters, and these effects were also influenced by the specific partner in the dual species combinations. These results indicate that the patterns observable in mixed species colonies can be used to understand the mechanisms that drive interspecies interactions, which are dependent on the interplay between specific species’ physiology and environmental conditions.",
"conclusion": "Conclusions Interactions between bacteria are key for determining the composition and function of communities. Here we used colonies to investigate intra- and inter-species interactions. Using the members of our three species model community, we showed that they differ in how they interact with members of their own species due to their physiological traits: TFP motility in P. aeruginosa allowed populations to mix whereas ECM in K. pneumoniae caused straight borders between population sectors. Using mutants deficient in these traits, we showed that their impact depended on the agar concentration. These physiological traits were also important when interspecies interactions were examined. The motility of P. aeruginosa enabled it to outcompete P. protegens and to facilitate increased colonization area by K. pneumoniae . Importantly, the spatial distribution of species, observed for dual-species colonies did not resemble any patterns previously seen for experimental or simulated monospecies and dual-strain colonies. These experiments show that interspecies interactions differ substantially from intraspecies interactions and that co-culture colonies are a powerful way to investigate how bacterial physiology determines these interactions.",
"introduction": "Introduction It is now widely accepted that bacteria form biofilms as an adaptive strategy that facilitates growth and protects them from environmental stresses [ [1] , [2] , [3] ]. The genetic systems, adherence mechanisms and consequences of biofilm formation have also been well studied for a number of model bacterial species [ [4] , [5] , [6] ]. While such studies have greatly improved our understanding of biofilms and their function, most studies focus on a single species in isolation, whereas in nature, biofilms exist as communities of different species [ 7 ], driving the need to study multi-species biofilms. The spatial structure and dense growth of biofilms facilitates interactions between these different species, which in turn strongly influence the growth and survival of bacteria in natural and engineered communities [ 8 , 9 ]. Interactions between community members vary considerably; some interactions facilitate growth and survival, while others inhibit growth or even result in the death of one species [ 10 ]. Both faciliatory and inhibitory interactions can be mediated by secreted products, e.g. metabolite exchange [ 11 ] and antibiotic production [ 12 ] or by contact-dependent mechanisms, e.g. adhesion [ 13 , 14 ] and Type VI secretion mediated killing [ 15 ]. Biofilms also create spatial structure, which can enable the genetic division of labour to improve pellicle formation [ 16 ] and influences the evolution [ 17 ], formation [ 18 ] and outcome [ 19 ] of interspecies interactions. Furthermore, spatial structure directly enables metabolite exchange for cross-feeding as well as influencing access to essential nutrients and oxygen [ 20 ]. Using a synthetic, three-species community, we have shown that co-culture biofilms exhibit increased growth by particular members and enhanced stress tolerance for the entire community [ 21 ]. In this community, we observe close association between Klebsiella pneumoniae and Pseudomonas aeruginosa , while P. protegens is more randomly distributed. However, the genetic dissection of the factors driving the organization and function of this community is limited by the long experimental time frames associated with growing biofilms in flow cells. Therefore, we sought for a higher throughput experimental system to investigate factors hypothesized to be involved in interspecies interactions. Colonies grown on a nutrient surface are an efficient system for manipulating and visualizing the spatial distribution of different microorganisms [ 22 ]. For example, in colonies, various spatiotemporal aspects of microbial interactions have been characterized including the co-localization of mutually auxotrophic strains of Saccharomyces cerevisiae [ 23 , 24 ], the vertical separation of differently sized cells of Escherichia coli [ 25 ], the sequential range expansion of nitrate reducing Pseudomonas stutzeri [ 26 ], the spatial separation patterns of strains of Vibrio cholerae [ 27 ] or Bacillus subtilis [ 28 ] that differ in the quantity of extracellular matrix produced, and the proportion of different antibiotic resistant/sensitive strains of P. aeruginosa [ 29 ]. All of these studies have focused on different strains or mutants of a single species (intraspecies interactions), differing only in the fluorescent marker they express and targeted deletion of specific genes. However, it is also clear that this approach has considerable potential for investigating interactions between different species and addresses our need for increased throughput. We previously showed that our model biofilm community comprised of P. aeruginosa PAO1, P. protegens Pf-5 (formerly P. fluorescens [ 30 , 31 ]) and K. pneumoniae KP-1 exhibits properties not observed in biofilms of the individual species or liquid cultures. These include an overall increase in total biofilm biomass, sharing of resistance to sodium dodecyl sulfate allowing the sensitive species, P. protegens , to survive [ 21 ], and reduced production of morphotypic variants for all species [ 32 ]. We hypothesized that many of the factors that are important for monospecies biofilm formation would play important roles in mixed species community interactions as well. For example, we previously showed that changes in matrix production by P. aeruginosa altered the biofilm community composition in flow cells [ 33 ]. In this study, we adapted the colony biofilm approach to investigate the mechanisms that drive positive and negative interactions in dual-species biofilms. We determined whether these interactions were beneficial or detrimental by quantifying the area colonized by each species and qualifying where each species was found. We showed that each pairing of species resulted in different spatial distributions. To determine the cause of these differences, we tested the effects of biofilm related traits, specifically the type IV pilus of P. aeruginosa and matrix production by K. pneumoniae, and showed how they influence interspecies interactions and spatial distribution in an environment-dependent fashion. This work demonstrates that co-culturing in colony biofilms is a useful tool for determining the mechanisms and outcomes of interactions between bacterial community members.",
"discussion": "Discussion Colonies highlight differences in intraspecies interactions Our model community of P. aeruginosa, P. protegens and K. pneumoniae has been shown to reproducibly establish biofilms with differential abundances of the three bacteria, it displays shared defence mechanisms and reduced production of natural genetic variants, suggesting strong selection pressure against those variants in the community. To begin to unravel the mechanisms involved in these interactions, here we have adapted a colony biofilm community system as a higher throughput approach to test a matrix of combinations of mutants, species interactions and physiological conditions. For each of the three species from our model community, intraspecific competition resulted in species-specific patterns of sector formation. Separation of mixed colonies into sectors has been attributed to genetic drift at the leading edge of the colony, as despite having equal fitness, stochastic effects result in new territory being colonized by either one strain or the other, but not both [ 45 ]. Here, based on quantitative and qualitative assessment, we show that the shape of the boundaries between sectors depends on the bacterium, which likely reflects their individual traits. The separation of wild-type K. pneumoniae strains into sectors with straight boundaries resembled that of Saccharomyces cerevisiae [ 24 ], whereas the jagged boundaries of P. protegens were more similar to those of P. stutzeri [ 26 ], simulated rod-shaped cells [ 46 ] or E. coli [ 45 ]. In the case of E. coli , the formation of fractal boundaries between sectors has been explained by the anisotropic forces of cell division and growth causing unlinked chains of rod-shaped cells to buckle [ 39 ]. Thus, similar buckling likely causes the formation of jagged edges between boundaries of the wild-type P. protegens strains. The jagged boundaries visible between sectors within K. pneumoniae NMV colonies ( Supplementary Figure 4 ) indicate that its secreted extracellular matrix causes the straight boundaries between sectors observed in the wild-type. Colonies of P. aeruginosa grown on nutrient rich LB medium were previously shown to be well mixed [ 47 ], similar to our observations here using minimal medium. The observation that pilB mutants segregate into clear sectors [ 47 ] and the resemblance of videos of the edge of P. aeruginosa colonies ( Supplementary Video 1 ) to those showing TFP motility [ 48 ] indicates that this motility likely enabled the mixing of the P. aeruginosa wild-type strains. Interspecies interactions differ from intraspecies interactions Previous studies have focused on colonies of either identical, but differently labeled strains of the same species, or strains with specific genetic modifications, whereas here, we investigated interactions between different species. While the patterns for our single strain data resemble previous work, it is clear that patterns of species distribution in mixed species colonies differ markedly. Thus, the data presented here show how the different physiological traits of the three studied bacteria determine community composition and spatial distribution in colonies. We have assessed the three pairwise interactions tested here using the notation of Momeni et al. [ 23 ], where A [~,~] B indicates a neutral interaction and A[↑,↓]B indicates a relationship where A benefits and B is negatively affected ( Table 2 ). The spatial distribution patterns of P. aeruginosa with P. protegens , and P. protegens with K. pneumoniae are similar as one strain expanded outward faster and surrounded the other. Although the area covered by the inner strains was not reduced compared to when they were grown alone, both interactions negatively affect the inner strain as it no longer had equal access to space/nutrients. For the first pair, the area covered by the outer strain, P. aeruginosa, was significantly decreased, indicating that both strains experienced negative outcomes from the interaction, so P. aeruginosa [↓,↓] P. protegens . For the second pair, P. protegens was the outer strain but its area was not reduced so, P. protegens [~,↓] K. pneumoniae . In the third case, when P. aeruginosa was paired with K. pneumoniae , P. aeruginosa did not differ in the amount of area covered but the area of K. pneumoniae was significantly increased, so, P. aeruginosa [~,↑] K. pneumoniae . The results therefore suggest that differences in expansion rate may be a key factor in the development of the patterns observed here. Thus, future experiments that collect images every couple of hours, could enable a detailed quantification of expansion rates for single and mixed species colonies. Consistent with our previous work comparing planktonic and biofilm growth modes, these results differ from our observations in planktonic culture where P. protegens outgrew the other two species by between 10-100 fold and P. aeruginosa and K. pneumoniae equally reduced each other’s growth by ~100 fold [ 21 ]. The interactions between K. pneumoniae and the two Pseudomonas species were starkly different. When it was grown with P. aeruginosa it colonized more than twice as much area than it could alone, indicating a strong benefit from being co-cultured. P. fluorescens Pf0-1 has been observed to rapidly evolve a division of labour where two different mutant strains are able to make a faster expanding colony than either the wild-type or each strain individually [ 49 ]. In those mixed colonies, one strain produced the force for colony expansion through cell division while the other produced an extracellular polymer which acts as a lubricant at the expanding edge of the colony. In our work, K. pneumoniae may have been taking advantage of the extracellular DNA [ 41 ] or rhamnolipid surfactants [ 50 ] produced by P. aeruginosa to enable colony expansion. Non-motile E. coli was observed to take advantage of motile Acinetobacter baylyi, forming intricately branched floral patterns [ 51 ]. These patterns were qualitatively dissimilar to those we observed, indicating that the mechanism by which K. pneumoniae takes advantage of P. aeruginosa motility is also likely different. This strong effect of P. aeruginosa on K. pneumoniae is consistent with our observations in flow cell biofilms where despite making up only a small (1–5%) proportion of the 3-species community, P. aeruginosa influences the relative proportions of both other bacteria [ 21 ]. Conversely, P. protegens did not assist K. pneumoniae but was found around the outside of K. pneumoniae and these two strains were also more mixed at 48 h than monospecies colonies of each species. In strains of E. coli engineered to have equal growth rates but different cell shapes, small cells were found to reside on top of the colonies and larger cells were below, at the agar surface, where they maintained access to nutrients obtained from an agar surface [ 25 ]. K. pneumoniae cells are larger than Pseudomonas which may have allowed P. protegens to overgrow it and prevent K. pneumoniae from expanding further. In the third case, P. aeruginosa was also found around the outside of P. protegens , but these species were not mixed. This indicates that P. protegens could not take advantage of P. aeruginosa ’s motility and these species mutually exclude each other in space. Based on the observations here, future developments of this experimental system could also explore the three-dimensional distribution of the strains. For example, Liu et al. (2017) showed that species in community biofilms organised themselves spatially to optimise fitness that this spatial organisation was facilitated by the other community members [ 20 ]. P. aeruginosa twitching motility is important for interactions Twitching motility by P. aeruginosa has been best studied in interstitial biofilms where cells are sandwiched between an agar surface and a glass coverslip [ 41 , 44 , 48 , 52 ]. The colonies studied here are qualitatively similar, although the expanding front does not form as intricate lattices. Without a functional TFP, the SCV could only colonize about 20% of the area of the wild-type. When paired with wild-type P. aeruginosa or P. protegens , the SCV was outcompeted for space, indicating that TFP motility is a competitive trait. It also appears to be a commensal trait as the area colonized by K. pneumoniae was not increased when paired with the P. aeruginosa SCV, whereas it was increased three-fold when paired with TFP motile, wild-type P. aeruginosa . TFP motility can also be abrogated by deleting the gene for pilin subunits, pilA [ 52 ]. It will be interesting to contrast our observations of a hyper-piliated pilT mutant with a non-piliated pilA mutant. In flow-cell biofilms, a pilA mutant of P. aeruginosa was less competitive with Agrobacterium tumefaciens [ 53 ], but conversely, outcompeted Staphylococcus aureus [ 54 ]. Motility, as a competitive trait, has been suggested to allow strains better access nutrients, to cover other organisms or to disrupt their biofilms [ 55 ], which is supported by our results. Even though P. aeruginosa was able to cover K. pneumoniae with and without a functional TFP, only the motile wild-type P. aeruginosa had a commensal relationship with K. pneumoniae , indicating that motility can also be a commensal trait. K. pneumoniae secreted matrix is important for interactions Self-secreted extracellular matrix is a hallmark of biofilms that influences the spatial positioning and interactions between cells within a biofilm [ 22 ]. Matrix secretion allows producing cells to better access nutrients in colonies of P. fluorescens [ 56 ] and for simulated cells under flow conditions [ 57 ], by excluding other cells. In flow-cell biofilms, the K. pneumoniae NMV outcompeted its isogenic wild-type strain, but was less fit when grown with P. aeruginosa and P. protegens [ 32 ]. In colony biofilms, the K. pneumoniae NMV colonized the same total area as the wild-type strain when alone, but did not mix when the two were co-cultured. Furthermore, the NMV did not colonize more area when paired with P. aeruginosa and was outcompeted by P. protegens , resulting in less area covered compared to the NMV when cultured alone. This indicates that the biofilm matrix normally produced by K. pneumoniae KP-1 improves interspecies, but not [ 58 ] intraspecies, competition. The mutual exclusion observed between the wild-type and NMV also indicates that the NMV cannot take advantage of the wild-type, similarly to exclusion by B. subtilis [ 59 ] and V. cholerae [ 60 ]. When co-cultured, Pantoea agglomerans and B. subtilis form colonies with structural properties not observed in either single species alone, even with a B. subtilis mutant that does not produce EPS, indicating that it could share the exopolysaccharide being produced by P. agglomerans [ 58 ]. Our results indicate that the K. pneumoniae NMV can not similarly make use of Pseudomonas matrix components. Agar concentration changes inter-strain interaction outcomes It is well understood that agar concentration influences bacterial motility [ 61 ]. It also affects the ability of biofilms to extract nutrients as it determines the osmotic pressure of an environment [ 27 ]. Here we showed that lowering the agar concentration from 1.5% to 0.6% increased the area colonized by the TFP motility deficient P. aeruginosa SCV, though it was still less than the wild-type. However, in co-culture it colonized as much area as the wild-type and was not encircled by the parental wild-type ( Table 2 ). Conversely, the K. pneumoniae NMV was less effective at colonization compared to the wild-type on 0.6% but not 1.5% agar, but was still able to compete with the wild-type when co-cultured in both situations. For the P. aeruginosa SCV, lowering the agar concentration likely enabled flagella-based motility that could partially compensate for the defect in twitching motility. Motility is important for colonization of roots [ 62 ], the gastrointestinal tract of Zebra fish [ 63 ] and for the persistence of uropathogenic E. coli [ 64 ]. Here, we showed that motility influenced intraspecies competition but the outcome depended on environmental conditions. Previously, we observed that the P. aeruginosa SCV completely outcompeted wild-type P. aeruginosa in flow cell biofilms [ 32 ]. The increased attachment provided by hyper-piliation of the SCV [ 44 , 65 ] likely provides this benefit in flow cells, whereas the lack of motility was a detriment in colonies. This highlights the differences in environmental pressures between the culturing methods. Hyper-piliation also leads to aggregation [ 66 ], which may explain how the P. aeruginosa wild-type and SCV separated into sectors with straight boundaries on 0.6% agar. This appeared to be similar to the differently tagged strains of K. pneumoniae , indicating that this community morphology may not exclusively be caused by matrix secretion. For K. pneumoniae (which is non-motile), lowering the agar concentration allowed the wild-type to colonize more area, which was similar to how a rugose strain of V. cholerae that hypersecretes extracellular matrix (ECM)formed larger colonies on lower concentrations of agar [ 27 ]. In V. cholerae , it was demonstrated that matrix secretion generates an osmotic pressure gradient between the agar and the colony, allowing the colony to expand by physical swelling and also drawing more nutrients out of the agar. It is likely that this is a general mechanism attributable to the biofilm matrix. In this context, the wild-type K. pneumoniae would be similar to the rugose V. cholerae , producing larger amounts of ECM, while the K. pneumoniae NMV and wild-type V. cholerae are analogous in their relatively lower amount of ECM production. The community morphology of dual strain colonies of ECM secretors and non-secretors differed between the two species. In V. cholerae , the hyper-secretor was encircled by the non-secretor at both 1.5 and 0.6% agar, but colonized far more area at 0.6% as the non-secretor was pushed to the outside of the colony. Conversely, the K. pneumoniae NMV was not displaced and even prevented spreading by wild-type K. pneumoniae ( Fig. 5 , D). Similar to V. cholerae , the K. pneumoniae NMV did not benefit from the matrix secreted by wild-type K. pneumoniae as its area was not increased in co-culture. In B. subtilis , it was also observed that ECM-producing cells outcompete non-secretors [ 28 ], and to a higher degree when the humidity is higher (which is similar to lower agar concentration). Additionally, osmotic pressure generated by the ECM enhances colony spreading in this species [ 67 ]. Thus, matrix production appears to be a general strategy of bacteria that increases competitiveness in colonies."
} | 5,751 |
39391734 | PMC11466649 | pmc | 1,655 | {
"abstract": "Summary Nitrogen (N) is the most limiting nutrient in agroecosystems, and its indiscriminate application is at the center of the environmental challenges facing agriculture. To solve this dilemma, crops’ nitrogen use efficiency (NUE) needs to increase – in other words, more of the applied nitrogen needs to reach humans. Microbes are the key to cracking this problem. Microbes use nitrogen as an energy source, an electron acceptor, or incorporate it in their biomass. These activities change the form and availability of nitrogen for crops’ uptake, impacting its NUE, yields and produce quality. Plants (and microbes) have, however, evolved many mechanisms to compete for soil nitrogen. Understanding and harnessing these competitive mechanisms would enable us to tip the nitrogen balance to the advantage of crops. We will review these competitive mechanisms and highlight some approaches that were applied to reduce microbial competition for N in an agricultural context.",
"conclusion": "Conclusion Clearly, soil microbes have a disproportionate effect on the yield, quality, and NUE of crops. Here, we suggest refocusing the nitrogen fertilization paradigm on the competing soil microbes as they offer the potential to solve many of the issues related to low crop NUE. Using microbes to stimulate nitrogen release from soil organic matter while steering nitrogen transformations toward more advantageous forms for plants has the potential to drastically reduce the application of nitrogen fertilizers while increasing crop quality and yields, thereby increasing the profit margins for farmers and reducing the environmental footprint of agriculture.",
"introduction": "Introduction Nitrogen is the most limiting nutrient for crops growth, and this is further intensified by microbial competition resulting in less than 15% of the N applied in the field ending up being consumed by humans. 1 About half of the losses occur when fertilizer is applied to the soil, 1 mostly because microbes use nitrogen as an energy source and electron acceptor (dissimilatory processes) or for their own growth (assimilatory processes) ( Figure 1 ). Microbial dissimilatory processes – such as nitrification and denitrification – change the oxidation state of inorganic nitrogen and thereby its mobility and its state in soil, resulting in most of the applied N leaching into freshwater systems, where it can cause eutrophication, or being released back to the atmosphere, often in the form of the potent greenhouse gas (GHG) nitrous oxide. In addition to dissimilatory processes, microbes assimilate applied N for their own growth, immobilizing it in their biomass. As for the N already present in the soil – the soil organic nitrogen (SON) – which provides 50–90% of the N that reaches crops, microbes need to first depolymerize it – to release monomers such as amino acids – or mineralize it to ammonia so that plants can use it. Microbial activities, as influenced by their interactions with plants, other soil organisms, and their environment, will therefore determine the form and quantity of nitrogen that will reach plants. First, this will directly impact crops yields and quality, because depending on the form of nitrogen available plants will need to invest different amounts of energy to absorb it and transform it into nitrogen-containing polymers such as proteins and DNA. Second, it will also influence nitrogen use efficiency (NUE) – the amount of applied N fertilizer that reaches the crop – as some microbial activities result in losses from the soil or immobilization in microbial biomass. Plants have therefore evolved mechanisms to counter or tweak N-related microbial activities in their rhizosphere. Microbial competition is clearly an important ecological process for plant N nutrition, but more emphasis has been put on mutualistic interactions for increased N provision to plants. Figure 1 Overview of the nitrogen cycling in soils Only key microbial steps mentioned in the text are shown for simplicity. Dotted arrows show plant uptake: ammonium, nitrate, and various organic nitrogen compounds. Created with BioRender.com . Some microbes can increase plant N uptake through various strategies: fixing nitrogen, 2 , 3 transporting nitrogen to the roots, depolymerizing SON, 4 enhancing root length and density (by producing or degrading phytohormones), 5 and increasing the influx rates of the plant’s nitrogen transporters. 6 , 7 While in theory these microbial strategies benefit plants, the outcome will depend on the biotic and abiotic environment in which they occur. For instance, under N-limiting conditions mycorrhizal fungi colonization can reduce plant N uptake because the fungi will compete for N. 8 , 9 , 10 Another limitation, is that even though N-fixing isolates have been shown to colonize the roots of maize ( Zea mays ) 11 and wheat ( Triticum aestivum ), 12 mutualistic N-fixation is not significant for these important cereal crops. In contrast, most crops have evolved mechanisms, such as nitrification inhibition and rhizosphere priming, to inhibit or steer microbial N-related activities. Could these competitive mechanisms be harnessed to improve crops yields, quality, and NUE? Although developing new crop varieties with enhanced NUE and using slow-release nitrogen fertilizers has shown some promise, these solutions do not consider explicitly the microbial competition for N. We need to better understand the mechanisms behind this competition to increase crops’ NUE, yields and nutritional quality while reducing fertilizer inputs and GHG emissions. In this short review, we will detail several examples of plant-microbe and microbe-microbe competition for nitrogen, namely amino acid uptake, rhizosphere priming, predation, fungal-bacterial competition, biological de/nitrification inhibition, and root architecture modification ( Table 1 ). We will also discuss how these mechanisms could be harnessed to tip the competition for N in the favor of crops and increase their yields, quality, and NUE. Table 1 Summary of the competitive processes adopted by plants, microbes and viruses for soil N N forms Processes Involved organisms Organismal interactions/strategy Reference Organic N Amino acids uptake Plants vs. bacteria and fungi Competition for amino acids via different transporters. Geisseler et al., 13 ; Fischer et al., 14 ; Jack et al., 15 ; Gournas et al., 16 ; Padan, 17 ; Hosie et al., 18 ; Víglaš and Olejníková. 19 Rhizosphere priming Plants vs. microbes Plants and microbes compete for SON. Plants exude C-rich compounds to stimulate microbes to mineralize organic N to ammonia and nitrate. Kuzyakov and Xu, 20 ; Zhu et al., 21 ; Pausch et al., 22 ; Jiang et al., 23 ; Zhu et al., 24 ; Lu et al., 25 ; and Yin et al. 26 Predation Predator protists vs. microbes Predatory protists accelerate microbial turnover. Organic N fertilization increases protists abundance (such as nematodes) which could lead to shifts in microbial community composition and nutrient cycling. Geisen et al., 27 ; Hu, and Qi., 28 ; and Qi et al. 29 Viruses vs. bacteria and archaea Lytic viruses cause microbial lysis which releases bioavailable N, such as DNA and proteins into the soil. Kuzyakov and Mason-Jones., 30 ; Jover et al. 31 SON depolymerization Bacteria vs. fungi Bacteria and fungi compete for organic N and nutrients by producing extracellular enzymes, adopting growth strategies (filamentous hyphae for fungi and biofilms for bacteria), and releasing various compounds, such as organic acids, volatile organic compounds, bactericides and fungicides. Bahram and Netherway, 32 ; Enggrob et al., 33 ; Jilling et al., 34 ; Hamlet and Plowright, 35 ; Palmieri et al., 36 ; and Li et al. 37 Inorganic N Biological nitrification inhibition (BNI) Plants vs. nitrifiers (AOA and AOB) Plants produce and exude BNIs in the rhizosphere, which inhibit or suppress nitrification by targeting AMO and/or HAO enzymes, giving them time to assimilate N in ammoniacal form. Li et al., 38 ; Subbarao et al., 39 ; and Subbarao et al. 40 Inorganic N immobilization Heterotrophic microbes vs. nitrifiers (AOA and AOB) Heterotrophic microbes immobilize inorganic N via intermembrane proteins and transform it into protein, creating microbial biomass. Kleiner, 41 ; Jansson and Persson 42 Biological denitrification inhibition (BDI) Plants vs. denitrifiers Plants root extracts can inhibit denitrifiers activity and reduce denitrification process in soil. BDI occurs by allosteric inhibition of the nitrate reductase through procyanidins modifications to the cell membrane. Bardon et al., 43 ; Bardon et al. 44 Dissimilatory reduction of nitrate to ammonium (DNRA) Denitrifiers vs. DNRA microorganisms Denitrifiers compete with DNRA microorganisms which also use nitrate as an electron acceptor and can inhibit their activities. Rütting et al., 45 ; Putz et al. 46 Organic and inorganic N Root traits modifications Plants vs. microbes Plants compete for N by modifying their root system architecture, which is regulated by many phytohormones. Root traits influence microbial diversity and certain microbes modify root traits. Putz et al., 47 ; King et al., 48 ; Pervaiz et al., 49 ; López-Bucio et al., 50 ; Molina-Favero et al., 51 ; Schenkel et al., 52 ; Schroeder et al., 53 ; Kiba et al., 54 ; Sharma et al., 55 ; and Li et al. 56"
} | 2,332 |
37124007 | PMC10142452 | pmc | 1,657 | {
"abstract": "This research explores a novel two-terminal heterostructure of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO memristor, which exhibited essential biological synaptic functions. These synaptic functions play a critical role in emulating biological neural systems and overcoming the limitations of traditional computing architectures. By repeating a fixed pulse train, in this study, we realized a few crucial neural functions toward weight modulation, such as nonlinear conductance changes and potentiation/depression characteristics, which aid the transition of short-term memory to long-term memory. However, we also employed multilevel switching, which provides easily accessible multilevel (4-states, 2-bit) states, for high-density data storage capability along with endurance (10 2 pulse cycles for each state) in our proposed device. In terms of synaptic plasticity, the device performed well by controlling the pulse voltage and pulse width during excitatory post-synaptic current (EPSC) measurements. The spike-time-dependent plasticity (STDP) highlights their outstanding functional properties, indicating that the device can be used in artificial biological synapse applications. The artificial neural network with Pt/Cu 2 Se/Sb 2 Se 3 /FTO achieved a significant accuracy of 73% in the simulated Modified National Institute of Standards and Technology database (MNIST) pattern. The conduction mechanism of resistive switching and the artificial synaptic phenomena could be attributed to the transfer of Se 2− ions and selenium vacancies. The neuromorphic characteristics of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO devices demonstrate their potential as futuristic synaptic devices.",
"conclusion": "4. Conclusions A memristive device with a multilayer Pt/Cu 2 Se/Sb 2 Se 3 /FTO structure was fabricated by thermal evaporation and the DC sputtering technique. This device illustrated that resistance modulation behaves like analog bipolar resistive switching. It exhibited various synaptic functions that emulate the human biological neural system. The device resistance switched from HRS to LRS and LRS to HRS over 10 3 repetitive pulse cycles during the endurance test. The device demonstrated stable endurance in the two resistance states with a resistance ratio of 4 after extending the first 10 3 pulse cycles. By using repetitive pulse cycles, it was successfully possible to produce the crucial functions of a synapse, which include a nonlinear change in conductance, multilevel switching, potentiation/depression, and the transition of short-term memory to long-term memory. EPSC and STDP have also been demonstrated in the device to implement HLR for futuristic neuromorphic computing applications. The proposed Pt/Cu 2 Se/Sb 2 Se 3 /FTO device showed a simulated MNIST pattern recognition accuracy of about 73%, indicating considerable potential in memristor-based neuromorphic systems. These findings might be useful for neuromorphic computing.",
"introduction": "1. Introduction The human brain is smarter than the neuromorphic computers built thus far. The human brain is composed of a complex neural network that can work together to perform complicated processes, such as face recognition, speech recognition, visual system, pressure and light sense. 1,2 The human biological system has enormous parallel neural connections and synapses to perform all these tasks. In order to replicate it, researchers are trying to develop the concept of neuromorphic computing. Many electronic devices using hardware and software have been built to realise artificial synapses for neuromorphic computing to emulate the human biological neural system. However, they have some limitations at high density and suffer issues in data processing. 3 In the era of big data storage, high-speed and high-performance devices are required for neuromorphic computing systems to manage the volume of information processing and align with the Internet of Things. However, neuromorphic computing approaches that depend on nonlinear and parallel data processing have become topics of tremendous interest. 4–6 Recently, complementary metal-oxide-semiconductor (CMOS)-based neural networks have been implemented through machine learning. However, they require expensive hardware and high computation power due to the less efficacy of the traditional von Neumann architecture. 7 Therefore, conventional technology faces a number of hurdles in computing efficacy, which may cause slowing down the speed of data processing. The idea of neuromorphic computing systems has been researched to alleviate the bottlenecks in the von Neumann architecture and enhance the processing capability of the computer. 8–10 CMOS technology-based neural networks are inefficient, consume huge amounts of energy, and require many transistors to implement the neuron and synapse functions. 2,5,7,11 However, the human brain efficiently processes real-time unstructured signals such as self-learning, parallel connection, fast data processing, and power efficiency. Therefore, a new memristor system, i.e. , resistive random-access memory (RRAM) has been developed in recent studies. It is closest to the human biological synapse due to its nonlinear transfer characteristics and can substitute the CMOS technology. 12 It has two terminal sandwiched structures, which are deemed promising next-generation memory and logic system candidates. 10,13,14 In such sophisticated bionic electronic systems, RRAM is still crucial to achieving low energy consumption, high computing power, and ultrahigh storage density. 15,16 To date, various resistive switching-based synaptic devices built using different materials and bilayer structures have been studied. 12 Typically, the memory strength (long-term and short-term memory) is decided according to the synaptic weight difference between the pre-and post-synaptic neurons after receiving an action potential. 2,17 Resistive switching has been investigated in various chalcogenides materials, such as In 2 Se 3 , 18 In 2 Te 5 , 19 Sb 2 Se 3 (ref. 20 ) and GeSe; 21 however, in our proposed device, we achieved more characteristics of the human brain for a neuromorphic computing system. In this study, we report the multilayer structure of Pt/Cu 2 Se/Sb 2 Se 3 /FTO for the first time. The selenium ions (Se 2− ) are relatively immobile in the Cu 2 Se thin film, while the copper ions (Cu 2+ ) have strong mobility and superionic behaviour. However, Cu 2+ follows an alternative approach to performing resistive switching than copper (Cu) deposited on the top electrode. 22 Sb 2 Se 3 also has decent carrier mobility, 23 which is important for the resistive switching characteristics of the device. Cu 2 Se is a superionic p-type semiconductor material with an energy bandgap between 1.2–2.3 eV. 24 To reach our goal, we selected Cu 2 Se and Sb 2 Se 3 multilayers to form an artificial synapse that can perform excellent resistive switching with bio-inspired characteristics. Additionally, we demonstrate the multilayer structure of Pt/Cu 2 Se/Sb 2 Se 3 /FTO as a biocompatible artificial synapse for the first time, to the best of our knowledge. Here, FTO stands for fluorine-doped tin oxide used as the substrate. Various synaptic functions have been investigated to mimic the human biological neural system. Furthermore, the device exhibited excellent analog resistive switching characteristics for the transition from short-term memory to long-term memory. We also demonstrate the various functions of the synaptic device toward weight modulation, including nonlinear changes in conductance, multilevel switching and potentiation/depression characteristics. With regard to synaptic plasticity, EPSC, STDP and MNIST patterns were well performed by the device. This work paves a way for memory transition from short-term to long-term memory and also synaptic plasticity, due to which it can be deemed the next-generation brain-inspired computing system.",
"discussion": "3. Results and discussion \n Fig. 1(a) illustrates the human biological neural system, wherein the pre-synaptic neuron is connected with the post-synaptic neuron through the synaptic connection. 27 The neurotransmitters are released from the pre-synaptic neuron when an electrical signal strikes the axon and is received by the neuroreceptors on the dendrites of the post-synaptic neuron, as shown in the magnified area of Fig. 1(a) . 28–30 The human biological synapse is the place where the concentration of neurotransmitters controls the synaptic weight, based on which the memory is stored. 30,31 The human biological synapse can be emulated by the RRAM device, in which the active layers of Cu 2 Se and Sb 2 Se 3 can control the conductance between the top and bottom electrodes. In our study, the Pt/Cu 2 Se/Sb 2 Se 3 /FTO-based electronic synapse was fabricated, as illustrated in Fig. 1(b) . Fig. 1 (a) The human biological neural system consists of pre-synaptic neurons and post-synaptic neurons interconnected through synapses, as clearly depicted in the magnified image. (b) The schematic of the electronic multilayer synapse structure (Pt/Cu 2 Se/Sb 2 Se 3 /FTO) that works like a human biological synapse. \n Fig. 2(a) presents the energy dispersive X-ray spectroscopy (EDS) data, which confirms the elemental composition of Cu, Sb and Se. Fig. 2(b) is the cross-sectional scanning electron microscopic (SEM) image, demonstrating the multilayer deposition of the Cu 2 Se and Sb 2 Se 3 thin films on the FTO substrate. The distinct layers in the thin film device could be distinguished clearly, as shown in the inset of Fig. 2(b) ; however, the possible interdiffusion of layers across the interface cannot be ruled out. Fig. 2(c) shows the SEM images of the synthesised thin film, and the calculated average grain size of the deposited thin film was ∼250 nm (±20 nm). It was observed that the thin film was deposited uniformly and homogeneously on the FTO substrate, as shown in the inset of Fig. 2(c) . The atomic force microscopy (AFM) analysis ( Fig. 2(d) ) confirmed that the calculated root means square surface roughness of the thermally deposited thin film structure was 8 nm. Fig. 2 (a) The elemental composition of Cu 2 Se and Sb 2 Se 3 by using EDS. (b) Cross-sectional SEM image of the multilayer structure of Cu 2 Se and Sb 2 Se 3 . In the inset, the magnified images of the Cu 2 Se and Sb 2 Se 3 layers can be distinguished easily. (c) The top-view SEM image of the multilayer structure. The inset image shows that the film was deposited uniformly. (d) The AFM image used to calculate the surface roughness. To emulate the functionality of the biological synapse, low- and high-resistance states must be acquired. Therefore, the electrical measurement of the artificial synaptic device (Pt/Cu 2 Se/Sb 2 Se 3 /FTO) was carried out with a dual sweep of the DC voltage. We swept the voltage from −3.5 V to 0 V to 4.5 V; initially, the device remained in the low-resistance state (LRS), and the current was in the range of 10 −2 A, as depicted in region (1) of Fig. 3(a) . At low voltages, the resistance state of the device did not change, as depicted in region (2). When the DC voltage reached around 3 V, the current started to decrease gradually, and the corresponding voltage is referred to as the RESET voltage ( V RESET ), as depicted in region (3). Beyond 3 V, the resistance state completely transformed from the LRS to a high-resistance state (HRS) gradually. On the other hand, while decreasing voltage sweep, the cyclic current changed linearly in the I – V graph over a linear scale (not shown), which indicates ohmic behaviour between the device and contacts, as depicted in regions (2) and (4). Subsequently, the polarity of the applied voltage was changed and swept from 4.5 V to 0 V to −3.5 V; the HRS state did not change, as shown in regions 4 and 5, leading to memory formation in the form of resistance because it creates a memory window between the two states at the same voltage, as depicted in Fig. 3(a) . Beyond −2 V, again the state of HRS gradually started to switch to the LRS corresponding to the SET voltage ( V SET ), which is the significance of analog bipolar resistive switching. It is evident that the device exhibits bipolar resistive switching behaviour. The reproducibility of the device during SET and RESET operations was also observed and showed utmost identical behaviour to other similar devices. The device V SET (HRS to LRS) exhibited good consistency for switching resistance; however, V RESET (LRS to HRS) spans a voltage window within acceptable limits, and it may be the result of improper formation of some conducting filaments during V SET , and they ruptured in the early stage of positive voltage sweep as shown in the ESI (S1 † ). We measured the bipolar resistive switching in various other devices that show the same utmost behaviour. Fig. 3 Electrical characteristics: (a) the I – V characteristics of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device, indicating analog behaviour during the continuous cycles of SET and RESET. (b) The gradual increase and decrease in conductance with the application of repeated fixed voltage sweeps to SET and RESET the device. In the inset, a magnified area under the red circle shows the change in conductance during RESET. (c) The endurance test of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device was conducted under DC conditions. The inset shows the initial 10 3 pulse cycle, indicating that the device reached stability after 600 cycle. The inset of figure (c) presents the stability of the HRS and LRS states of the device when the cycles were further extended by up to 10 3 cycle under the same conditions. (d) The probability distribution of HRS and LRS evidences that the LRS state is more stable after 600 cycle, but HRS fluctuates. In the inset, the error bar chart is used to check the dispersion of the LRS and HRS states. The conductance of the analog device increased (decreased) gradually whenever it switched from HRS to LRS and LRS to HRS under consecutive repeated voltage sweep cycles of 0 V to −1 V and 0 V to 1 V, as depicted in Fig. 3(b) . These results influence the transition of short-term memory to long-term memory. The inset of Fig. 3(b) shows a magnified area covered under the red circle, which indicates that the conductance decreased gradually, approaching the long-term forgetting memory. However, to test for its analog resistive switching and possible application as a neural network, we checked the conductance of the devices at very low applied voltages far below the voltage of any type of sharp resistive switching. Gradually increased (decreased) of the conductance in the resistive switching device is analogous to the respective synaptic potentiation and depression behaviours. To test the DC endurance of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device, we applied 10 3 write-and-read pulse cycles. The device was switched from HRS to LRS using a −3.5 V write pulse with a pulse width of 100 ms and from LRS to HRS using a 4.5 V write pulse with a pulse width of 100 ms. In both cases, a 0.2 V read pulse with a 100 ms pulse width was used to read the data. The HRS state showed degradation up to 600 cycles, as shown in the inset of Fig. 3(c) . The instability at the starting phase of endurance measurements may have occurred due to the breakdown of some weakly formed conductive filament of selenium vacancies (V Se ). To illustrate the stability concern in both the resistance states, we extended the cycles further by 10 3 pulse cycles, and both states (HRS and LRS) remained stable in comparison with the measured states of the preceding 10 3 pulse cycles, as illustrated in Fig. 3(c) . In addition, it could be seen that the resistance OFF/ON ratio between HRS and LRS was ≈4. The obtained ratio is small, but the device is performing the switching process necessary for neuromorphic computing, which may help in the coming decades. For validation, we checked the endurance cycles in other devices and found similar behaviour, as shown in ESI (S2 † ). The graph in Fig. 3(d) was plotted to check the cumulative probability distribution of the LRS and HRS resistance values, of which HRS exhibited scattering to a certain extent, but the LRS state was comparatively less scattered; and this can be appropriate for memory applications if further research can be done. Hypothetically, we can say that this behaviour may be due to the improper formation of conducting filaments inside both of the multilayer cells. The statistics of the inset in Fig. 3(d) show less dispersive LRS values plotted in the error bar chart. A new device must initially undergo some stabilisation cycles before it can be used in new applications. Nevertheless, these results may be acceptable for valuable outcomes in future studies. For high-density data storage, there is one possible way for the realization of multi-bit data storage in RRAM. It was carried out by controlling the intermediate resistance states by varying the SET and RESET voltages. Initially, the device under test (DUT) was brought to HRS with a high-amplitude positive voltage sweep. In fact, the multi-resistance state of the device was switched from HRS to LRS using a series of consecutive voltage sweep cycles, including 0 V to −2 V, −2.25 V, −2.5 V, −3 V, and −3.25 V, and the different current levels were observed. Similarly, to switch from LRS to HRS, we performed a series of consecutive voltage sweep cycles, including 0 V to 4 V, 5 V, 5.5 V, 5.75 V, 6.25 V and 6.5 V, and the varied current levels were obtained. The different current levels after every cycle in the negative and positive sweep cycles in Fig. 4(a) confirm multilevel switching in the same device. Fig. 4 (a) The endurance of the multilevel switching characteristics of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device during SET operation. (b) The possible combinations of the multi-bit resistance states (00, 01, 10, and 11) and each resistance state measured for 100 pulse cycles. A total of 400 pulse cycle were run to achieve multiple states. (c) The applied pulse patterns to achieve multilevel switching of the device. The possible combinations of the multi-bit resistance states, which can be considered as 00, 01, 10, and 11 for data writing and erasing, were measured in the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device. Fig. 4(b) clearly describes the multiple states that are reached during bipolar resistive switching. To achieve these multi-states of 00, 01, 10, and 11, we ran 400 exciton pulse cycles (100 pulse cycles for individual states). Initially, the device had to be RESET to the 00 state by applying 5.5 V and the 01, 10, and 11 states by applying −1 V, −2 V and −3 V, respectively. The data of these multi-states were read at 0.5 V after an interval time of 100 ms between the write and read pulses, as shown in the pulse pattern ( Fig. 4(c) ). For high-density memory storage, multi-states are essential aspects of synaptic devices. The conductance of the distinct resistive states could be achieved by controlling the stop voltage during the SET and RESET processes. These multi-resistance states occurred without any set compliance current. They could be modulated only by varying the pulse amplitude, suggesting that Pt/Cu 2 Se/Sb 2 Se 3 /FTO is a non-compliance-worthy artificial synaptic structure for neuromorphic computing applications. Therefore, this can be a potential multilevel storage memory device for next-generation neuromorphic computing systems. To mimic the complex biological neural function of the human brain, the device must emulate the variations in synaptic weight, which is the most important factor in a synaptic device. 5 Besides the voltage sweep, we emulated the long-term potentiation (LTP) and long-term depression (LTD) characteristics of human biological synapses. Here, we investigated the neuromorphic characteristics of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO artificial synaptic device in terms of LTP and LTD. Initially, the device was in the RESET state, but after applying the constant voltage write-pulse train of −1.5 V with a pulse width of 100 ms for potentiation and 1.5 V with a pulse width of 100 ms for depression, the conductance of the device changed gradually, as shown in Fig. 5(a) . The conductance of the device was measured up to 200 cycles each for potentiation and depression. The conductance of the device was measured by applying a 0.2 V read pulse after every write pulse. The pulse delay between the write and read pulses was 100 ms. The applied pulse pattern, including the pulse amplitude and pulse width of the write and read pulses to get the potentiation and depression curve, is well illustrated in Fig. 5(b) and (c) . The relationship of conductance versus the number of pulse cycles was consistent for 2000 pulse cycles. In response to the pulse train, the measured LTP and LTD were similar to the LTP and LTD of a human biological synapse. LTP and LTD usually occur after the rehearsal of short-term potentiation and short-term depression; this process is known as learning, which is fundamental for the adaptation of living beings. 32 The term learning is related to the fact that if something is continuously learned over a long period, the probability of remembering would be higher, which clears the transition of short-term to long-term memory. Here, short-term stands for fast forgetting rates (fast decay of conductance) of the memory for the first few cycles, as we can see in the conductance curve. Besides, according to the Hebbian learning rule, synapses enhance post-neuron excitability, but excessive excitability is not required, meaning the synaptic weight of potentiation must be saturated. 33 In the proposed device, the conductance approaches the saturation level after the rehearsal of some applied pulses. This excitation phenomenon is related to long learning memory. 34 The gradual increase and decrease of LTP and LTD can be used for neuromorphic computing applications. 35 Fig. 5 (a) The potentiation and depression characteristics of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device. (b) The applied 200 SET train pulses (−1.5 V, 100 ms) demonstrate potentiation. (c) The applied 200 RESET train pulses (1.5 V, 100 ms) demonstrate depression characteristics. A total of 2000 pulse cycles were applied to demonstrate the reproducibility of potentiation and depression. (d) EPSC decay after the application of a single short pulse with varying pulse amplitudes (−2 V, −2.5 V and −3 V) and a fixed 100 ms pulse width. Inset image: the pattern of pulses with different amplitudes and a fixed pulse width. (e) The EPSC decay rate after the application of variable pulse widths (100 ms, 250 ms and 500 ms) and a fixed −3 V pulse amplitude. Inset image: the pattern of pulses with different pulse widths and a fixed pulse amplitude. When an action potential strikes the axon, neurotransmitters are released from the pre-synaptic neuron to the post-synaptic neuron, due to which the signal is transmitted between two neurons via a synapse, as discussed in Fig. 1(a) . In neuroscience terms, the generated action potential is transmitted from the pre- to post-synaptic neuron via a synapse, likely producing an excitatory post-synaptic current (EPSC) that likely triggers a post-synaptic neuron to generate an action current. 36,37 Short-term plasticity, which is considered the physiological basis of the necessary computation, illustrates that the synaptic response rapidly degrades on a time scale of a few seconds, while long-term plasticity response, which imparts the capacity for learning and memory, often lasts for minutes to hours. 38 In the proposed multilayer device, the top and bottom metal electrodes work like the pre-synaptic and post-synaptic neurons in the biological neural system. 39 Fig. 5(d) illustrates the EPSC characteristics when a single short voltage pulse of different amplitudes was applied (−2 V, −2.5 V, and −3 V). Initially, a −2 V/100 ms write pulse was applied, as depicted in the inset of Fig. 5(d) ; the EPSC current increased sharply and then decayed gradually, showing an exponential trend over 60 seconds from its peak value, which is essentially the synaptic dynamic function. The EPSC current data were read continuously by applying a read pulse of 0.2 V with a 100 ms pulse width. These read pulses were applied after every 1 second. At −2.5 V and −3 V, the EPSC current amplitudes were higher than the previous ones, and the decay rate for a few seconds was low compared with the previous EPSC current level, leading to long-term plasticity. For memory consolidation, the transition of short-term plasticity to long-term plasticity could be realized by increasing the pulse amplitude, as depicted in Fig. 5(d) , which shows higher amplitudes lead to stronger synaptic weight modulation. In induced EPSC, pulse sharpness from the pre-synaptic neuron also plays an essential role in transferring the electrical signal from the pre-synaptic neuron to the post-synaptic neuron. Furthermore, the amplitude of −3 V was fixed and varying pulse widths (100 ms, 250 ms, and 500 ms) were employed to study the effect on the current and decay rate of the synaptic device. Among these different applied pulses, the device behaved like a human brain in terms of the transition of short-term plasticity to long-term plasticity at the 100 ms pulse width. Whereas, at broader pulse signals, it did not show proper biological activities. The pattern of the applied pulse sharpness is shown in the inset of Fig. 5(e) . To train the neural network, the Hebbian learning rule (HLR) is fundamental and is very important to validate the execution of artificial synaptic devices. To implement the HLR, we must demonstrate STDP. 2,40,41 STDP is the essential characteristic of synaptic plasticity and is associated with two different stimuli signals in the presence of time. On the other hand, the weight of an artificial synaptic device in STDP can be altered by pre-and post-synaptic stimulation. 3 In order to emulate the STDP rule, a pulse pair signal with a pre-synaptic and post-synaptic spike amplitude of V − / V + = −3 V/4 V was implemented to the top and bottom electrodes, respectively. The applied pre-and post-synaptic pulse designs are shown in (I) and (III) quadrants of Fig. 6 . It can be seen that when the relative time difference decreased, more significant synaptic weight changes would occur in both cases (Δ t > 0 and Δ t < 0). Here, Δ t (Δ t = t post-spike − t pre-spike ) is the relative time difference, which defines the time interval between the end spike of a pre-synaptic neuron and the initial spike of the post-synaptic neuron, as illustrated in (I) and (III) quadrants of Fig. 6 . Between the pre-and post-synaptic spike pairs, an interval of 5 seconds is long enough to ignore the effect of V − and V + according to the decay time of EPSC. 3,42 For STDP, the percentage synaptic weight change (Δ w %) for Δ t was calculated by using the equation Δ w % = (( G after − G before )/ G before ) × 100, where G after and G before are the conductance values after the current stimulation and previous stimulation, respectively. When the pre-synaptic spike pairs reach before the post-synaptic spike pairs, Δ w enhanced with decreasing Δ t . On the contrary, when the post-synaptic spike pairs reach before the pre-synaptic spike pairs, Δ w decreased along with an increase in Δ t . In the (II) quadrant, when Δ t < 0, a positive synaptic weight change occurs, which indicates synaptic potentiation, and in the (IV) quadrant, when Δ t > 0, a negative synaptic weight change occurs, which indicates device depression. The relationship between the relative change in synaptic weight and the spike time difference is consistent with the STDP biological synapse. For the STDP learning rule, Δ w is established by defining the function of exponential fitting. 43,44 1 where Δ w , Δ t , A ± and τ ± represent the relative weight change, relative pre- and post-synaptic spike time differences, scaling factor, and time constant, respectively. The variation in Δ w % versus Δ t was fitted (red line) by using eqn (1) , as depicted in the (II) and (IV) quadrants of Fig. 6 . The forgetting function of the device is described by the values of τ + and τ − , which were calculated from the fitting data as 72 ms and −185 ms, respectively. This reflection can be attributed to the trend of intrinsic decay of EPSC. When Δ t was smaller between two spikes, Δ w enhanced significantly. This confirmed that the STDP behaviour of Pt/Cu 2 Se/Sb 2 Se 3 /FTO is consistent with the biological synapse, and therefore, it has succeeded in emulating the Hebbian STDP learning rule. Fig. 6 In quadrants (II) and (IV), the synaptic weight change (Δ w %) STDP is presented as a function of the relative time change (Δ t ) between the pre-and post-synaptic paired pulse propagation. In quadrants (I) and (III), schematic representations of the pre-synaptic and post-synaptic applied paired pulses are given. Additionally, the handwritten numerical digit pattern recognition accuracy of the device was tested using MNIST. The corresponding LTP/LTD data of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device were plotted as normalized conductance values, as shown in Fig. 7(a) and (b) . The obtained normalized conductance dataset was fitted to the following equations. 45 2 3 Here, G + and G − are the normalized conductance values of LTP and LTD, respectively. v + and v − are the nonlinearity coefficients of LTP and LTD, respectively. P and P max are the pulse number, and the maximum number of pulses applied in the LTP and LTD measurements, respectively. For the as-prepared device, the obtained nonlinearity coefficients were 6.54 and −6.54, respectively. The inset of Fig. 7(a) shows the pulse applied for LTP/LTD. Fig. 7 The MNIST pattern recognition simulation of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO devices. (a) The conductance plot was obtained in the LTP and LTD operations. (b) The calculated normalized conductance of the devices with curve fitting. (c) The depiction of the employed synapse neural network comprising an input layer of 400 neurons, a hidden layer of 100 neurons and an output layer of 10 neurons for MNIST pattern recognition. (d) The simulated accuracy curves of the ideal and Pt/Cu 2 Se/Sb 2 Se 3 /FTO devices on the MNIST dataset. The synapse neural network architecture employed for the simulation has been depicted in Fig. 7(c) . The simulation of image classification was performed using the MNIST dataset consisting of a database of handwritten digits with ten classes. The accuracy plots of the ideal device and the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device were plotted for the comparison, as shown in Fig. 7(d) . The calculated accuracy plot over 100 epochs indicated that the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device exhibited a max accuracy of ∼73%, which is slightly lower than that of the ideal RRAM device (∼94.5%). Due to the increased nonlinearity (∼6.54), the accuracy of the Pt/Cu 2 Se/Sb 2 Se 3 /FTO devices appears significantly lower than that of the ideal device. However, the accuracy plot saturated nearly 15 epochs. An extensive comparison of many chalcogenide materials is presented in Table 1 . In order to assess the performance of the proposed RRAM device, the resistive switching phenomenon has been viewed in a variety of ways. However, each of the materials listed below has a distinct behaviour. In the Pt/Cu 2 Se/Sb 2 Se 3 /FTO device, V SET and V RESET are a little bit higher compared to other cited materials, but it shows interesting characteristics such as multilevel switching and high endurance. The calculated MNIST pattern shows slightly lower accuracy than Ag–Ge 30 Se 70 and may increase upon exploring more possibilities through research. Sb 2 Se 3 is sensitive to optical illumination, which is advantageous for the optical tunability aspect of synaptic plasticity. There has been a great deal of research into these materials. The accuracy can be enhanced if more research is done in the future. Comparison of the different electrical parameters of chalcogenide materials S. no Materials \n V \n SET (V) \n V \n RESET (V) Endurance cycles Switching Multilevel MNIST pattern Ref. 1 Ag/Sb 2 Se 3 /W 5 −5 — Analog No No \n 46 \n 2 ReSe 2 /graphene 3.5 −4 250 Digital No No \n 47 \n 3 SnSe/SrTiO 3 2.5 −0.6 — Digital Yes No \n 48 \n 3 Al/Cu2Se/Pt 0.4 −0.3 — Analog No No \n 49 \n 4 Ag/Sb 2 Te 3 /Ag −1 1 10 2 Analog Yes No \n 50 \n 6 Au/MoS 2 /Au ≈2.9 ≈−2 — Analog Yes No \n 51 \n 7 TiN/GeSbTe/TiN — — 70 Analog Yes No \n 52 \n 8 Ag–Ge 30 Se 70 ≈0.15 ≈−0.1 — Analog Yes 79%, 87% \n 53 \n 9 Pt/Cu 2 Se/Sb 2 Se 3 /FTO −3.5 4.5 10 3 Analog Yes 73% \n This device \n The possible fundamental mechanism of resistive switching in the as-prepared device is proposed. It may involve the migration of ions and vacancies inside the Pt/Cu 2 Se/Sb 2 Se 3 /FTO thin film structure. In the absence of an external power supply, there is no movement of ions and vacancies, as shown in Fig. 8(a) . When a negative voltage is applied to the top electrode (Pt) and a positive terminal is connected to the bottom electrode (FTO), V Se is formed 54,55 and moves towards the top electrode, whereas Se 2− starts moving towards the bottom electrode in both cells, as shown in Fig. 8(b) . These V Se form thin conducting filaments through which floods of electrons move from Pt to the FTO electrode, causing the device to switch from the HRS state to the LRS state, as illustrated in Fig. 8(b) . Conversely, when the polarity of the top and bottom electrodes is reversed, the conducting filaments are destroyed due to the recombination of V Se and Se 2− ions in the upper and lower electrolyte cells, as demonstrated in Fig. 8(c) . This causes the device to switch back from LRS to HRS. Therefore, the primary mechanisms behind the Pt/Cu 2 Se/Sb 2 Se 3 /FTO synaptic devices are based on Se 2− ionic movement, V Se , filament formation and destruction. Fig. 8 A schematic representation of the internal mechanism: (a) in the absence of an external power supply, no movement of ions and vacancies happens. (b) When the negative and positive terminals are connected respectively to the top and bottom electrodes, the migration of V Se and Se 2− starts in the upper cell (Cu 2 Se) and lower cell (Sb 2 Se 3 ). The migration of V Se forms the conducting filament during SET operation in both cells. (c) When the negative and positive terminals are connected respectively to the bottom and top electrodes, V Se recombines with Se 2− ions in both cells during RESET operation."
} | 8,670 |
29997336 | PMC6071141 | pmc | 1,659 | {
"abstract": "Regenerated silk (RS) is a protein-based “biopolymer” that enables the design of new materials; here, we called “bionic” the process of regenerated silk production by a fermentation-assisted method. Based on yeast’s fermentation, here we produced a living hybrid composite made of regenerated silk nanofibrils and a single-cell fungi, the Saccharomyces cerevisiae yeast extract, by fermentation of such microorganisms at room temperature in a dissolution bath of silkworm silk fibers. The fermentation-based processing enhances the beta-sheet content of the RS, corresponding to a reduction in water permeability and CO 2 diffusion through RS/yeast thin films enabling the fabrication of a mechanically robust film that enhances food storage durability. Finally, a transfer print method, which consists of transferring RS and RS/yeast film layers onto a self-adherent paraffin substrate, was used for the realization of heat-responsive wrinkles by exploiting the high thermal expansion of the paraffin substrate that regulates the applied strain, resulting in a switchable coating morphology from the wrinkle-free state to a wrinkled state if the food temperature overcomes a designed threshold. We envision that such efficient and smart coatings can be applied for the realization of smart packaging that, through such a temperature-sensing mechanism, can be used to control food storage conditions.",
"conclusion": "4. Conclusions Here, we described how the activation of the metabolic activity of microorganisms with regenerated silk increases the crystalline content of RS fibroin, reducing the water permeability by ≈30% and increasing the shelf-life of perishable food over a period of 7 days. Then, we reported a method which consists of transfer printing the prepared freestanding RS and RS/yeast layer films onto a self-adherent Parafilm substrate. By increasing the applied strain (ε) via thermal heating of the high thermal expansion paraffin-based substrate, we observed an increase of the wrinkles’ wavenumbers in the RS/yeast skin layer due to a high mechanical mismatch between the film layer and the paraffin substrate. This approach can be used in the real design of smart coatings by controlling the mechanical properties as the designing parameter for tuning the wrinkle morphologies. Moreover, the proposed method for coupling two different systems could be considered as a valid alternative for laminating two or more flexible layers without a bonding agent. Such solventless laminates could be much more environmentally friendly and result in a low-cost product for high-speed production lines.",
"introduction": "1. Introduction Living microorganisms have long been used in food preservation [ 1 , 2 ]; such microorganisms form living surfaces that provide an attractive platform for the development of functional materials. At present, biotech companies uses fungi to produce valuable products [ 3 ]; thus, combining the fermentation mechanism of some microorganisms with biomaterials could give rise to bionic composites with novel properties. Among such novel products, the development of innovative packaging solutions to increase the shelf-life of fresh fruits by slowing down their metabolism so they remain fresh and appetizing for longer, and sensors to monitor if perishable food in the cold chain is maintained in the desired temperature range to prevent the growth of pathogens and spoilage microorganisms, remains a challenge. To date, biodegradable polymers (i.e., polylactic acid or polylactide (PLA), polyglycolic acid or polyglycolide (PGA), poly-caprolactone (PCL), and polyhydroxybutyrate (PHB)) have been explored for food packaging due to mechanical robustness of the matrix with hydrophobic chain groups that allow for low permeability to oxygen and water vapours [ 4 ]. However, their biocompatibility, lamination without solvent-based adhesives, and sensing properties are still challenging aspects for their utilization in smart packaging applications. Concerning the sensing issue, the spontaneous generation of wrinkles induced by the buckling of a thin skin due to thermal contraction of the underlying substrate may be used as smart sensors for monitoring temperature changes in a food cold chain. In this regard, micro/nanoscale surface patterns obtained by coupling a stiff skin to a soft substrate have been used to create reversible patterns that are responsive to temperature and provide a unique surface morphology to sense temperature changes [ 5 , 6 , 7 ]. From the material point of view, silk fibroin is an ideal candidate for packaging applications since it is a biocompatible structural protein that can be processed to obtain films which recently have been used as biodegradable and edible sensors to monitor food degradation [ 8 , 9 , 10 ]. The polymorphism of silk fibroin (i.e., random coil, silk I, and silk II structures) can be also tailored by controlling the content of β-sheet crystals to enable the correct gas exchange and water vapour permeability through silk-based membranes [ 11 , 12 , 13 ]. Among the different fabrication methods, transfer printing is the most known method for interfacing silk on soft substrates [ 14 ]. In this regard, the hidden strength and stiffness of natural honeycomb walls constructed from recycled silk and wax secreted by worker bees [ 15 , 16 , 17 ] is reminiscent of modern fiber-reinforced composite laminates. Taking inspiration from the honeybee comb cell wall, a self-adhesive and soft thermoplastic paraffin wax can be used to stick a regenerated silk (RS) film to produce a bilayer system [ 18 ]. Being the paraffin wax a material with a high thermal expansion coefficient, wrinkles occur to minimize the total energy of such a bilayer system when the compressive strain, caused by the thermal expansion coefficients and rigidities, mismatches between the skin layer and the substrate induced by thermal stimulus. Inspired by our previous work where the metabolic activity of living microorganisms was used as an engineered platform for the fabrication of advanced carbon-based materials [ 19 ], here we extend this approach, with emphasis on the fermentation process used for centuries in wine- and bread-making, to produce bionic composites which integrate regenerated silk nanofibrils that from the geometrical point of view and in terms of mechanical properties are similar to carbon nanotubes. The resulting reduced volume fraction of nanofibrils within the film could make the fermented hybrid composite more resistant to fracture with self-repairing properties exploiting the microorganisms’ growth process that allows for the intracellular transport of nanomaterials, and the CO 2 bubbles produced during fermentation could be used to produce porous architectures for biomedical applications. Here, it was observed that once Saccharomyces cerevisiae yeast cells were fermented by nutrient addition into a silk fibroin solution, the regenerated silk shows a higher content of beta-sheet structures. Moreover, the microorganism growth increased the cell density and reduced the porosity of the RS membrane, limiting the exchange of water and gas diffusion. As conceptual proof, we demonstrated as an example that the deposition of such a living coating on fruits helps the preservation of their shelf-life. Finally, we demonstrate that RS-based film layers can be laminated onto a paraffin wax substrate for the realization of temperature-responsive bilayer system.",
"discussion": "3. Results and Discussion The production method adopted for the realization of thin films with the aid of living microorganisms is schematically reported in Figure 1 a. The yeast fermentation was implemented into a CaCl 2 –formic acid dissolution system, which can be used to produce large films ( Figure 1 b) with a nano-fibrillar structure ( Figure 1 c). Changes in the structure of the films deposited after the fermentation-assisted silk dissolution were detected by FTIR and XRD. The β-sheet (crystalline) content was determined by the deconvolution of the amide I region (1580–1720 cm −1 ) and estimating the ratio between the peak area in the wavenumber region of 1622~1637 cm −1 , which is the main absorbance region of β-sheet crystal in amide I [ 21 ], and the whole area of the amide I region comprising the peaks of the structural components, including turns (T) and random coils (R). The deconvolution of the amide I band provides an estimation of the β-sheet structure in the RS and RS/yeast of 37% and 44%, respectively ( Figure 1 d,e). The XRD data in Figure 1 f show that the RS/yeast film is characterized by diffraction peaks at 2Θ values of 20.4° and 25.4°, corresponding to a silk I structure [ 22 , 23 ]. The RS film also showed silk I and silk II crystal structures, having diffraction peaks at 20.4° and 29.0°. Compared with RS/yeast, the silk I peak at 25.0° disappeared, indicating more silk II formation. The cell division of yeast occurs by budding in which a daughter cell is initiated from the mother cell followed by nuclear division and finally cell separation. The yeast cell growth reported in Figure 2 shows three main phases: a lag phase where the individual cells are activated in preparation for division; an exponential phase once the cell starts actively metabolizing shortly after the cells divide; and finally a stationary phase when the metabolism slows and the cells stop rapid cell division [ 24 ]. The factors that cause cells to enter the stationary phase are related to change in the environment typically caused by high cell density. The data reported in Figure 2 a state the stability of the yeast cells to proliferate also with the presence of formic acid in the nutrient broth. The effects of RS addition during the growth curve are demonstrated in Figure 2 a by measuring the OD during the cell growth. The OD curve of the yeast cells is substantially altered by the RS addition, with the effect on the lag time and final cell yield being particularly pronounced: RS/yeast cells have a lag phase of ~2–3 h, after which they proliferate rapidly; in comparison, the neat yeast cells show an increased lag phase of ~10 h. The morphology of the stationary phase for the RS/yeast system observed by means of FESEM and optical microscopy ( Figure 2 b,c) indicates the cell proliferation for such culture. In order to demonstrate the potential application of such a living coating in consumer-exposed food, the mechanical robustness of the films is required to withstand, for example, handling procedures. Figure 3 a represents typical stress/strain curves obtained from testing of yeast, RS, and RS/yeast samples. The maximum average toughness (i.e., the area underlying the stress/strain curves) obtained from the RS/yeast sample was 0.14 MPa ( Figure 3 b), the highest average strength (i.e., the stress at the ultimate strain) obtained was 1.26 MPa, and the maximum elastic modulus recorded was 37.2 MPa. The improved mechanical properties with the fermentation-based dissolution of silkworm silk fibers agree with our previous studies reported for the fermentation-assisted synthesis of bionic composites [ 19 , 25 ]. In these studies, addition of carbon nanotubes (CNTs) and/or graphene in the fermentation broth resulted in composites with a higher toughness value. In our studies, tensile tests on dried composite films were rationalized in terms of a CNT cell-bridging mechanism where the strongly enhanced strength of the composite is governed by the adhesion energy between the bridging CNTs and the matrix. The presence of glucose can plasticize regenerated silks and increase the ultimate strain leading to toughness values (see Table 1 ) that are comparable to those measured for traditional biopolymers used for food packaging [ 26 , 27 ] being in our case as added value the edibility of the coating. In general, coatings for food packaging beyond the mechanical robustness should exert low permeability to water vapors; fruit dehydration is, in general, an indicator of the breakdown of the protective skin, which results in loss of turgor and water evaporation. We observe that the increase in beta-sheet content yields a lower water permeability through the RS/yeast membrane as indicated by the lower variation in the initial weight of soaked sponges coated with different types of coatings as reported in Figure 4 a (see Supplementary Material Figure S2 ). These results are in agreement with those obtained by Omenetto et al. [ 10 ], who showed that when the silk fibroin beta-sheet content is in the range of 36–58%, the water vapour permeability is 5 times smaller than that measured for the film with a lower beta-sheet content. Many fresh fruits have high metabolic activity, and due to microbial attack it results in a short conservation time, colour change ( Figure 4 b), and off-flavour. The change in colour of fresh fruit, in particular, is associated with ethylene production and cell respiration. To evaluate the exploitation of RS/yeast film as a barrier coating, the change in color of coated and non-coated fresh bananas was evaluated ( Figure 4 b). Time-lapse photography shows that an RS/yeast coating decreases the fruit degradation when compared to uncoated or RS-coated fruit at day 7. During the continuing metabolism of the fresh fruit, oxygen is transformed into carbon dioxide; thus, gas permeation through a coating film plays a crucial role in fruit storage. To prevent the spoilage of fresh fruits, it is necessary to reduce the breathing process. In our case, the higher beta-sheet content of the RS/yeast film decreases the production of CO 2 , which indicated a decrease in the respiration rate of the fruits ( Figure 4 c). Another very interesting property of such silk nanofibrils relies on their ability to self-assemble, giving rise to a sol–gel transition with rapid gelation time induced by the presence of salts [ 28 , 29 ]. Pregelation occurs when a fresh solution has a β-sheet content of about 20% with negligible intermolecular bindings; gelation is then induced by interchain interactions that become irreversible with the formation of the β-sheet intermolecular structures of the gel phase [ 29 ]. In our case, the gelation was observed when KNO 3 salt was added to a silk nanofibrils/yeast/formic acid solution ( Figure 5 ). Without salt, the RS/yeast retains with time a sol characteristic ( Figure 5 a). In comparison to the sol RS/yeast solution, the RS/yeast with salt solution transforms into a semi-solid gel within several hours together with the appearance of a strong infrared absorption peak at 1626 cm −1 due to the formation of strong β-sheet structures ( Figure 5 b). Considering the application of controlling the food cold chain, it is essential to design a bilayer packaging system where the mechanical properties of the top skin layer when laminated onto a soft substrate will be beneficial for the creation of temperature-driven surface patterning. Figure 6 a shows the RS and RS/yeast films laminated onto a paraffin wax substrate. Wrinkle formation is typically connected to the high thermal expansion coefficient of the substrate used for the transfer. Parafilm is used worldwide in research laboratories as a self-adhesive and sealant plastic foil. It is soft (tensile strength ≈2.0 MPa) with a high thermal expansion coefficient (i.e., 0.89 × 10 −3 K −1 ) and due to its low melting point (≈60 °C), it becomes adhesive when applying heat during lamination and sticks strongly to the receiving material. The formation of wrinkles occurs to minimize the total potential energy of the skin layer and the substrate induced by thermal expansion. The strategy for the realization of heat-driven wrinkle patterns is illustrated in Figure 6 b; once heated, the paraffin wax upon cooling to room temperature generates a compressive stress at the interface of the bilayer sample owing to the considerable mismatch between the modulus and thermal expansion ratio of the substrate and the stiff top layer made of RS or RS/yeast [ 30 ]. The wrinkling formation and height analysis of wrinkles can be determined with the use of the AFM. The AFM images reported in Figure 6 c show that the crumpled surface appears unfolded and smoothed once heated. Height histograms spanning a few wrinkles of the RS and RS/yeast cooled samples are given in Figure 6 d and indicate that the step heights are ≈1 μm high. The highest peak distributions indicate the periodicity in the surface morphology. Furthermore, it can be easily noticed that film layers form long line-shaped wrinkles (length in the order of 2 μm). Moreover, it is clearly evidenced by AFM height histograms that the film layer between two successive wrinkles remains at the same height level, suggesting that the film is now lying upon its substrate ( Figure 6 d). Further analysis of the AFM images reveals that the size of the peak (wrinkles’ wavelength) distribution decreased in the RS/yeast sample, indicating that, among the samples studied, the stiffer film layer produced the smallest periods and amplitudes. The periods of the wrinkle structures were measured by using Gwyddion 2.51 free software, and the amplitude (A) and wavelength (λ) of the wrinkled structures are reported in Table 2 . The amplitude and the wavelength of the wrinkles depend on the thickness and mechanical properties of the skin layer and the substrate according to linear bucking theory [ 31 , 32 , 33 , 34 , 35 ], which can be expressed as: A = h f ((σ 0 − σ c )/σ c ) 1/2 /a (1) \nand\n λ = 4.36h f (E f (1 − ν s 2 )/(E s (1 − ν f 2 )) 1/3 − 2A(1 − a)/a (2) \nwhere σ c refers to the critical stress to wrinkle formation and is given as 33 \n σ c = (1/4)(E f ′) 1/3 (E s ′) 2/3 (3) \nand σ 0 is the compressive stress of the film layer at a temperature below the heating temperature, which is given by the equation [ 36 ]: σ 0 = (E f (α s − α f )ΔT)/(1 − ν f ) (4) \nwhere E′ = E/(1 − ν 2 ) and the subscripts f and s refer to the film layer and the substrate of the bilayer system, respectively, and E′, E, and ν are the plane modulus, Young’s modulus, and Poisson’s ratio, respectively. h f is the film thickness and 0 ≤ a ≤ 1 is an adhesion parameter that we have introduced (ideally a = 1) for accounting for the non-ideal bonding between film and substrate (imposing the film’s inextensibility and simply assuming squared-shape wrinkles, i.e., 2A + λ cost). The Young’s modulus for RS, RS/yeast, and paraffin are 54 MPa, 37 MPa, and 1.4 MPa [ 18 ], respectively, and ν f = 0.5. The applied strain ε when the bilayer system is heated is calculated as ε = (α s − α f )xΔT, where α s and α f are the thermal expansion coefficients with α s >> α f . Finally, the theoretical wavelength and amplitude values obtained from Equations (1) and (2) are reported in Table 2 and compared with the experimental findings by fitting the single parameter a."
} | 4,720 |
27801294 | PMC5088516 | pmc | 1,660 | {
"abstract": "Background Magnetotactic bacteria (MTB) are a unique group of prokaryotes that have a potentially high impact on global geochemical cycling of significant primary elements because of their metabolic plasticity and the ability to biomineralize iron-rich magnetic particles called magnetosomes. Understanding the genetic composition of the few cultivated MTB along with the unique morphological features of this group of bacteria may provide an important framework for discerning their potential biogeochemical roles in natural environments. Results Genomic and ultrastructural analyses were combined to characterize the cultivated magnetotactic coccus Magnetofaba australis strain IT-1. Cells of this species synthesize a single chain of elongated, cuboctahedral magnetite (Fe 3 O 4 ) magnetosomes that cause them to align along magnetic field lines while they swim being propelled by two bundles of flagella at velocities up to 300 μm s −1 . High-speed microscopy imaging showed the cells move in a straight line rather than in the helical trajectory described for other magnetotactic cocci. Specific genes within the genome of Mf. australis strain IT-1 suggest the strain is capable of nitrogen fixation, sulfur reduction and oxidation, synthesis of intracellular polyphosphate granules and transporting iron with low and high affinity. Mf. australis strain IT-1 and Magnetococcus marinus strain MC-1 are closely related phylogenetically although similarity values between their homologous proteins are not very high. Conclusion \n Mf. australis strain IT-1 inhabits a constantly changing environment and its complete genome sequence reveals a great metabolic plasticity to deal with these changes. Aside from its chemoautotrophic and chemoheterotrophic metabolism, genomic data indicate the cells are capable of nitrogen fixation, possess high and low affinity iron transporters, and might be capable of reducing and oxidizing a number of sulfur compounds. The relatively large number of genes encoding transporters as well as chemotaxis receptors in the genome of Mf. australis strain IT-1 combined with its rapid swimming velocities, indicate that cells respond rapidly to environmental changes. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3064-9) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions The characterization of MTB, whether cultured or uncultured, is largely based on morphological, behavioral, metabolic and genomic aspects. Here we used phenotypic and genotypic data to describe features in Mf. australis strain IT-1, a cultured magnetotactic coccus, associated with its metabolism, its ability to produce magnetosomes, and its structure and behavior. The unique alignment of the linear magnetosome chain with flagella bundles may indicate the rotating cell body observed associated with a high speed swimming may help Mf. australis strain IT-1 to overcome physical barriers of objects encountered in sediment or help to quickly move away detrimental stimuli. Environmental conditions in Itaipu lagoon change constantly and the high number of genes encoding transport and transduction genes in strain IT-1 favors its survival. Genomic analysis indicates Mf. australis strain IT-1 and Mc. marinus strain MC-1 are closely related species but contain some different, distinctive features. Similarity values between the amino acid sequences of homologous proteins are not very high and genomic sequencing of other magnetotactic cocci such the marine bacterium strain MO-1 will improve our knowledge regarding the magnetotactic cocci in general based on genomic analyses.",
"discussion": "Results and discussion General genomic description The draft genome of Mf. australis strain IT-1 has a size of 4,986,701 bp, with a coding density of 82.64 % represented by 4,130 loci, with an average length of 1,010 bp. A total of 2,886 loci encode proteins with putative functions, 44 encode tRNAs and 1,194 encode hypothetical proteins (Additional file 2 ). Although there is no evidence for the presence of extrachromosomal elements such as plasmids, the occurrence of several lengthy repeat regions hindered genome closure. The genome assembly contains 21 contigs, with a coverage of 33X. Gaps between contigs are mostly regions with unresolved repetitions, caused by the high number of transposable elements found in the IT-1 genome. The G + C content is 57.95 %, slightly higher than G + C content of Mc. marinus strain MC-1 (54.17 %) and lower than G + C values found in the freshwater magnetotactic spirillar strains Ms. magneticum strain AMB-1 and Ms. gryphiswaldense strain MSR-1 (65.09 and 63.28 %, respectively). According to KEGG functional classification, most predicted ORFs are related to carbohydrate and amino acid metabolism (162 and 165 ORFs, respectively). A high number of ORFs are related to signal transduction, cell motility and membrane transport (156, 132 and 110 ORFs, respectively). As Mc. marinus strain MC-1 is the only magnetotactic coccus with an available complete genomic sequence, it is not surprising that 51.1 % of Mf. australis strain IT-1 predicted ORFs were more similar to ORFs described in Mc. marinus strain MC-1 than any other MTB. Nevertheless, similarity between many of their homologous gene sequences is relatively low. Other ORFs were more similar to Magnetospirillum magneticum (1.1 %) and to non-magnetotactic members of the Proteobacteria phylum (<1 % ORFs similar to various strains). Phenotypical characterization of Mf. australis strain IT-1 Ultrastructure and granular inclusions Whole-mount transmission electron microscopy (TEM) of cells of Mf. australis strain IT-1 shows that they have a unique morphology having both a concave and convex surface (Fig. 1a ) confirming the “faba” bean morphology of the cells. Each cell contains a single chain of elongated octahedral magnetosomes and intracellular granules described previously [ 13 ]. TEM of ultra-thin sections of cells (Fig. 1b ) show that the overall cell ultrastructure of Mf. australis strain IT-1 is consistent with a two-membrane structure typical of Gram-negative bacteria with a turgid periplasmic gel between them. On the surface of the outer membrane a layer of fibrillar material is present and may represent some type of capsule or S layer (Fig. 1b ; arrowheads); a similar structure was reported in uncultured magnetotactic cocci from the Itaipu lagoon, Brazil [ 24 ]. The cytoplasm contains a series of uncharacterized structured regions consisting of “pockets” of amorphous, globular electron-dense material interlaced with electron-lucent regions (Fig. 1b ; asterisks). We observed a tabular periodic structure parallel to the inner membrane of some cells in close association with the flagella bundles, most likely corresponding to chemoreceptor arrays (Fig. 1b ; white arrows), described also in Ms. gryphiswaldense strain MSR-1 [ 25 ], Magnetovibrio blakemorei strain MV-1 [ 26 ] and other motile non-magnetotactic strains from different bacterial phyla [ 27 ]. Fig. 1 Ultrastructure of Mf. australis strain IT-1. Ultrastructure of Mf. australis strain IT-1. a Whole mount TEM image showing a single magnetosome chain, P-rich (P) and sulfur (S) granules; ( b ) Ultrathin section TEM image of high pressure frozen and freeze-substituted cells showing P-rich (P) and sulfur (S) granules, two magnetosomes ( black arrows ), a flagella bundle (F) associated with chemoreceptor array ( white arrows ), and a fibrillar layer at the cell surface ( arrowheads ). Uncharacterized globular structures (G) embedded in an electron-lucent material (asterisks) can be observed \n Flagellar apparatus and motility Scanning electron microscopy (SEM) shows that cells possess two bundles of lophotrichous flagella, each at one extremity of the cell (Fig. 2a ). These bundles propel cells at swimming speeds up to 300 μm s −1 . Each flagella bundle consists up to seven separate flagella filaments, with a diameter of 13 ± 2 nm ( n = 50) and a length of 1.9 ± 0.5 μm ( n = 20). The flagella originate from within a pit located on the cell surface (Fig. 1b ). In hanging drop assays under oxic conditions, Mf. australis strain IT-1 exhibited South-seeking polar magnetotaxis swimming in the presence of the magnetic field of a bar magnet with a fast back and forth swimming pattern near the edge of the drop. A helical trajectory was observed when movement was recorded with a CCD camera using dark-field microscopy (Fig. 2b ). However, when analyzed using a high speed camera (500 to 1000 fps), we observed that over 90 % of Mf. australis strain IT-1 cells swim in a straight trajectory by rotating the cell body along an apparent axis parallel to the movement direction (Fig. 2c ). This axis is inclined relative to the position of the magnetosome chain which lies along long axis of the cell. The flagella bundles occur at the concave surface of the cell. Surprisingly, Mf. australis strain IT-1 seems to swim with the concave surface forward (Fig. 2c ), which strongly suggests that the flagella bundles rotate in front of the cell as the cell body moves. Fig. 2 Flagellar apparatus and motility in Mf. australis strain IT-1. Flagellar apparatus and motility in Mf. australis strain IT-1. a Scanning electron microscopy of a cell with two flagella bundles; ( b ) Dark-field image recorded trajectory of a cell showing an “helical” path recorded for 1s; ( c ) Sequential series of light microscopy DIC images of a swimming (left to right, top to bottom) cell imaged with a high-speed camera. Each frame represents 1/1000s. The morphology of the cell is similar to a “faba” bean with a convex ( left ) and a concave ( right ) side; several granules can be seen in the cell body. d Organization of genes involved in flagellar apparatus biosynthesis in Mf. australis IT-1. Fourteen copies of fliC genes occur elsewhere in the genome. HP ( light blue ) are hypothetical proteins \n In the genome of Mf. australis strain IT-1, genes encoding for every component of the flagellar apparatus ( flg , flh , fli ; Fig. 2d ) are present and those for chemotaxis ( cheA , cheB , cheB / cheR , cheW , motA , MCP to signal translation were also identified. There are approximately 64 genes involved in the synthesis of proteins related to flagellar apparatus and motility. FliPOZNMLWSQREFGI, FlgEKBCG, FlhAB, FlaG, FlgM and 14 copies of a protein containing a flagellar domain, FliC type (ORFs 1035, 1042 to 1044, 1047, 1049, 1059, 2652, 4643 and 5226 to 5230) were detected. These genes form separate groups along the genome (Fig. 2c ). Interestingly, the arrangement of the contig containing flgK , fliW , fliD , fliS \n 1 and fliS \n 2 was similar to the closely related strain MO-1 [ 28 ], which is also capable of very high swimming speeds. Flagellin genes share similarity with fliC genes from magnetococcal strains MC-1 and MO-1, with a slightly higher (1-5 %) similarity with genes from the fast swimming MO-1. Under aerobic conditions, as previously stated, over 90 % of the cells’ motility in Mf. australis strain IT-1 occur in linear rather than helical trajectories and at higher speeds (up to 300 μm.s −1 , with average of 186 ± 63 μm s −1 [ 13 ]) than most MTB (average speed ranging from 10 to 120 μm s −1 ) [ 29 , 30 ] and similar to that reported in strain MO-1 [ 31 ]. The linear trajectory and the magnetosome chain position in the cell raises important issues regarding magnetotaxis as an efficient mechanism for navigation. The magnetosome chain in Mf. australis strain IT-1 is positioned perpendicular to the axis of movement. Usually, the magnetosome chain is aligned with the axis of cell movement by flagellar propulsion which results in an efficient orientation along the magnetic field [ 23 ]. Although magnetosome chains are approximately perpendicular to the swimming axis in Mf. australis strain IT-1, cells orient along magnetic field lines and respond to changes in the magnetic field. Cells of the magnetotactic coccus strain MO-1 also swim in a straight trajectory, with the magnetosome chain not aligned to the axis of motility [ 32 ]. The lack of perfect alignment between the magnetosome chain and the magnetic field lines might be useful for cells to overcome obstacles in their trajectory. The mechanisms for magnetic field orientation and cell dislocation in vertical gradients by flagella propulsion differs from the traditional magnetotaxis model described for other MTB [ 5 , 30 ]. It is possible that Mf. autralis strain IT-1 cells always swim with their flagella in front of the cell body and reversal of swimming direction is achieved by reversing the sense of flagella rotation, as described in other MTB strains [ 30 ]. Possibly, MTB described as moving along helical paths is a misinterpretation of results generated by a low speed imaging systems. Interestingly, a flagellar sheath, observed in other magnetotactic cocci or ovoid cells [ 33 ], was not observed in Mf. australis strain IT-1. Genes similar to that encoding the Sap protein [ 34 ], related to the flagellar bundle sheath in both MO-1 and MC-1 [ 10 ], were not found in the IT-1 genome, confirming our microscopy observations and implying that at least for strain IT-1, the sheath is not required for “smooth” swimming as has been suggested [ 34 ]. Uncultured cocci from the Itaipu lagoon, when analyzed by freeze-fracture, also did not present a sheath around its bundle of flagella [ 35 ]. On the other hand, some cocci showed an intricate arrangement of fibrils that may help to coordinate flagellar movement. The chemoreceptor arrays, in close association with the flagellar bundles, might allow the cell to control and synchronize the direction and frequency of the rotation of the flagella in the bundle, obviating the need for the flagella sheath that works not only as a protection mechanism but also as a flagella rotation coordinator [ 34 ]. The proximity of the chemoreceptor array to the flagellar bundle might allow the cell to respond quickly to environmental changes with greater propulsive force necessary for fast swimming, ensuring cell survival. An MCP-like protein was shown to interact with MamK filaments in Ms. magneticum strain AMB-1 [ 36 ], but the similarity values between this protein and the ORFs annotated as MCP in Mf. australis strain IT-1 genome were not high enough to assign this function with certainty. However, due to the high plasticity of sensory domains, it is possible that a MCP whose ligand-binding domain is not described yet carries the coupling between chemotactic sensor and the magnetosome chain in Mf. australis strain IT-1. A very efficient locomotion system might have evolved in magnetotactic cocci that allowed them to move at high speeds to niches with suitable chemical gradients for their survival. This would make easier to respond to sudden changes in the vertical gradients that may occur in aquatic environments. With the exception of the marine Magnetospira sp. strain QH-2 [ 11 ], magnetotactic species with their genome sequenced have a large number of MCP-related genes, characteristic of motile bacteria, with metabolic versatility and that occupies dynamic environments [ 37 ]. Magnetosome crystalline habit and genes High resolution TEM images of magnetosome crystals (Fig. 3a-b ) were indexed with distances and angles between spots being consistent with cubic magnetite (Fe 3 O 4 ). Tomographic analysis (Fig. 3c ) was used to generate an idealized 3D model (Fig. 3d ) of octahedral crystal habit elongated along [111] crystallographic direction. Magnetite crystals from magnetosome were isolated from cells growing under heterotrophic conditions with acetate as the carbon source, and averaged 90.42 ± 19.62 nm in size with an average shape factor of 0.74. These values are similar to those reported in other alphaproteobacterial MTB [ 38 ] and, although the crystal size is similar to that reported in strain MC-1, the shape factor indicates strain IT-1 has more elongated crystals, similar to those described in strains MO-1 and QH-2 [ 38 ]. Fig. 3 Magnetosome crystalline habit in Mf. australis strain IT-1. Magnetosome crystalline habit in Mf. australis strain IT-1. a High resolution transmission electron microscopy image of a single magnetosome with elongated octahedral morphology. Inset shows the Fast Fourier Transform with indexed planes and zone axis, ( b ) Higher magnification image of the dashed boxed are shown in ( a ), The spacing of fringes shown between white arrows is 4.9 Å, consistent with (1 1 1) spacing for magnetite. c Tomography reconstruction using STEM/HAADF of the magnetosome shown in ( a ). d Idealized model of magnetosome crystal in same orientation shown in ( a ) \n \n Mf. australis strain IT-1 mam genes were detected in a 72,493 bp contig. Most genes from this region of the genome (a 40,399 bp fragment), particularly the mam genes, were previously described in Morillo et al. [ 13 ] and are more similar to homologous genes described in Mc. marinus strain MC-1, except mamC . Comparison of synteny between Mf. australis strain IT-1 and Mc. marinus strain MC-1 genomic regions containing mam genes is shown in Additional file 3 . Besides similarities in mam and mms gene organization described by Morillo et al. [ 13 ], ORFs encoding hypothetical proteins and a protein with a PilZ domain, known for their participation in chemotaxis [ 39 ], are in relatively close proximity to the mamAB gene clusters, a situation similar to that of Mc. marinus strain MC-1. BLASTP [ 40 ] homology search shows that the only protein similar to the predicted protein containing the PilZ domain belongs to Mc. marinus strain MC-1 (99 % coverage, 31 % ID and 49 % positives), suggesting its possible role in magnetotaxis, by regulating speed and direction of flagellar rotation [ 39 ]. The first predicted ORFs within this contig encode two transposases (ORFs 04933, 04812) and one resolvase (ORF 04811) with no homology to Mc. marinus strain MC-1. At the end of the contig genes encoding two transposases (ORF 04938; 04945) and integrases (ORF 05502; 05504) were predicted; these were similar to Mc. marinus strain MC-1 predicted ORFs (except ORF 04945), but were not found within the putative MAI in the genome [ 10 ]. The identity between the integrases at the end of the contig (ORF 05502 and 05504) is 98 %; however, the ORF 05504 represents less than 50 % of the ORF 05502 entire sequence. This contig has 18 predicted hypothetical proteins. Most hypothetical proteins coding ORFs flanking mam genes [ 13 ] are only similar to Mc. marinus strain MC-1 predicted hypothetical proteins (identity and positive values vary from 22 to 41 % and 39 to 55 %, respectively). Although homology value is not high, the only similar sequence in the database belonged to Mc. marinus strain MC-1 according to BLASP analysis using NCBI non-redundant protein sequence database. The low similarity values for these predicted hypothetical proteins when compared to Mc. marinus strain MC-1 sequences contrast with those found for a few hypothetical proteins within the mam gene clusters, which identity and positive values range from 22 to 75 % and 36 to 89 %, respectively. No results were found for ORFs 02810, 04936 and 02843 based on homology search. Magnetotaxis related genes ( mtx genes) are localized in a contig of approximately 616 kb in size. It is not possible to predict its distance from the mam gene cluster, but the contig appears to be part of another cluster, as described for Mc. marinus strain MC-1. This cluster includes three alphaproteobacterial mtx genes, a Sel1 domain-containing protein coding gene, the mtxA gene and an adenylate/guanylate cyclase coding gene. BLASTP best hits were homologous genes described for Mc. marinus strain MC-1 present in an mtx cluster [ 10 ]. Metabolism related genes in Mf. australis strain IT-1 Figure 4 is an overview of Mf. australis strain IT-1 genes showing main the main metabolic pathways and cell components potentially used by this bacterium. Below we describe selected aspects of the genetic information related to biomineralization and metabolism. Fig. 4 Schematic overview of Mf. australis strain IT-1 showing its main metabolic pathways and structural features. Schematic overview of Mf. australis strain IT-1 showing its main metabolic pathways and structural features. Cells are bilophotrichous with both flagella bundles in the concave face of the cell. Forty-two genes encode methyl-accepting chemoreceptors usually associated to the flagellar apparatus ( purple ). Strain IT-1 is chemolitoautotroph using the reverse tricarboxylic acids cycle (rTCA) or chemoorganoheterotroph, capable to grow using small organic molecules such as acetate and citrate. Cells are capable of nitrogen fixation, but probably do not to use nitrate as final electron acceptor ( green ). Genes for sulfate uptake and reduction were found as well as proteins responsible for sulfur compounds oxidation ( yellow ) and a gene for a sulfide:quinone reductase (S:Q R), responsible for the synthesis of sulfur globules (S). Genes encoding proteins for the synthesis of polyphosphate granules (P) are present as well as phosphate and phosphanate transporters ( orange ). High and low affinity iron transporters are encoded ( pink ) and there are copies of these genes located closely to magnetosome genes, responsible for the synthesis and organization of the chain of magnetite (Fe 3 O 4 ) cubo-octahedral magnetosomes. Other cell transporters are also depicted ( blue ) \n Chemoreceptors, transcription and transport Magneto-aerotaxis [ 5 ] depends on chemoreceptors capable of sensing the external or internal cell conditions. Methyl-accepting chemotaxis proteins (MCPs) are chemotactic sensors coupled to the flagellar apparatus through chemotactic enzymes CheA, CheZ, CheB and CheY, which catalyze (de-)methylation and (de-)phosphorylation reactions that result in the switching of flagella rotation either in the clockwise or anticlockwise direction [ 41 ]. According to the conserved topology of the domains and their interaction with internal membrane, MCPs have been classified into four main classes [ 42 ]. We performed analyses using SMART (Simple Modular Architecture Research Tool) [ 43 ] and the CDD (Conserved Domain Database) at NCBI [ 44 ] on locus automatically annotated as MCPs. Mf. australis strain IT-1 has 22 MCPs with topology of class I, three resembling a class II topology, 6 similar to class III and 7 type IV MCPs (Additional file 4 ). Genomes of MTB generally contain a large number of transcription factor and transport genes presumably enabling them to modulate their magnetotactic behavior in response to diverse environmental stimuli. The large number of transduction components might be used to regulate magnetosome synthesis and magnetotactic behavior [ 11 ] and is necessary to avoid unspecific cross talk between different regulatory pathways [ 45 ]. The genome of Mf. australis strain IT-1 contains 272 genes encoding signal transduction proteins (Additional file 5 ). This number is higher than the number of transduction genes described in the genome of other magnetotactic Alphaproteobacteria strains. Thirty-six transduction genes in Mf. australis strain IT-1 are related to chemotaxis and includes four genes encoding proteins with CheW domain, three with CheA, three with CheY, two with CheB and three with CheR, two with CheB/CheR, one with CheX and 13 genes encoding hemerythrin-like metal binding proteins. These hemerythrin-like proteins are involved in oxygen transport but might have domains related to chemotaxis and signal transduction [ 46 ]. Iron metabolism The first step in magnetite biomineralization is the transport of iron from the extracellular environment into the cell [ 47 ]. The genome of Mf. australis strain IT-1 appears to contain genes necessary for a complete and complex system for capture, transport and regulation of iron including: a large number of iron reductases; ABC-type transporters; ferritins; hemerythrins; and other proteins responsible for iron homeostasis within the cell. All of them are likely important not only for magnetosome biomineralization, but also for cell growth [ 48 , 49 ]. Magnetosome genes in the genome of IT-1 are organized in a contig that also contains genes encoding hypothetical proteins, transposases, integrases, resolvases and an ORF encoding a protein with PilZ domain (ORF 02273). This contig also has ORFs coding hemerythrin-like (ORFs 02806, 02811, 05565), ferritin/ribonucleotide reductase-like (ORF 02816 and 02834), cation diffusion facilitator family transporter (ORF 02835), chromosome partitioning protein (ORF 02842), replication initiator protein A (ORF 02844) and Fis family transcriptional regulator (ORF 04937). For three ORFs encoding hemerythrins, only ORF 02811 has similarity with a gene from Mc. marinus strain MC-1 (35 % identity) whereas two are more similar to predicted proteins from non-MTB (ORF 02806: Treponema brennaborense, 35 % identity; ORF 02812: Spirochaeta thermophile, 34 % identity). Because hemerythrin is a protein involved in oxygen transfer, chemotaxis and signal transduction [ 46 , 50 ], the hemerythrin that is common to Mf. australis strain IT-1 and Mc. marinus strain MC-1, encoded by ORF 02811, is located close to mam genes, and might be involved in magnetotaxis, as suggested [ 46 ]. Some ORFs encoding hemerythrin related proteins are also present in other contigs, all related to non-MTB sequences. Ferritin coding ORFs (ORFs 02816, 02834) are homologues to the predicted ferritin-like hypothetical protein in Mc. marinus strain MC-1 (34 % and 47 % identity, respectively). Ferritins are known for their function in iron detoxification, oxidation of Fe 2+ , Fe 3+ storage and for their controlled release of iron preventing cellular toxicity [ 51 ]. If transcribed, ferritins and hemerythrins closely associated with mam genes could be related to magneto-aerotaxis and magnetosome synthesis [ 46 , 51 ], controlling iron redox conditions on the magnetosome crystal or within the magnetosome vesicle, or the oxygen or iron flux. The presence of multiple copies of genes encoding ferritins and hemerythrins in Mf. australis strain IT-1 suggests their possible role in magnetosome synthesis, as previously described for feoA and feoB genes usually present in two copies in MTB genomes [ 52 ]. Two copies of the feoAB system were also found in Mf. australis strain IT-1 genome (ORFs 02827, 02828, 03818 and 03820). The best blast hits for both copies of feoB genes of strain IT-1 were sequences of this gene found in other magnetotactic Alphaproteobacteria (99 % coverage, at least 53 % identity), revealing the high similarity of this gene among MTB. One of the copies, feoB1 (ORF 02827) is located close to mam genes and might be directly involved in magnetosome synthesis whereas feoB2 (ORF 03818) might be involved in cell iron metabolism and detoxification of reactive oxygen species, as proposed in strain MSR-1 [ 49 ]. Two Fur (ferric uptake regulator) genes for Fe 3+ uptake were also present (ORFs 04154 and 01761), one common to other alphaproteobacterial MTB and other found only in Mc. marinus strain MC-1. When activated by iron or other metal ions, Fur protein binds to the operator of over 90 Fur-regulated genes in E. coli [ 53 ]. The Fur gene from Ms. gryphiswaldense strain MSR-1 has been shown to regulate iron and oxygen metabolism [ 54 ], besides having a role in magnetosome formation [ 48 ]. The Mf. australis strain IT-1 genome contains genes that encode iron receptors and transporters of high and low affinity. Genes encoding CDF (cation diffusion facilitator) proteins, also found in Mc. marinus strain MC-1 and other magnetotactic strains are present in the genome, which also contains a gene encoding an iron receptor in outer membrane, tonB , and four additional genes coding TonB family proteins. Besides mam genes involved in redox control and iron stoichiometry ( mamE, P, T, H, Z and X ) during magnetosome formation, a transmembrane protein containing ferric reductase domain (ORF 03272) was found in Mf. australis strain IT-1. This gene has a high similarity to a putative Fe 3+ reductase in other MTBs ( Ms. magneticum strain AMB- 1, Ms. gryphiswaldense strain MSR-1, Ms. magnetotacticum strain MS-1, Mc. marinus strain MC-1). Seven genes encoding Fe-S oxidoreductases are present in Mf. australis strain IT-1, a large number when compared to the alphaproteobacterial magnetotactic strains MC-1, AMB-1, MSR-1 and QH-2 , with a single gene encoding a Fe-S oxidoreductase. The genome of Ca. Magnetoglobus multicellularis contains 8 copies of Fe-S reductases, while strains Ca. Magnetoovum chiemensis and Ca. Magnetobacterium bavaricum have 10 and 13 copies, respectively. These cells synthesize a large number of magnetosomes and the presence of these reductases in greater number could be responsible for ensuring the availability of iron for cell growth and magnetosome synthesis. Carbon metabolism \n Mf. australis strain IT-1 grows chemoorganoheterotrophically on acetate and succinate. Chemolithoautotrophic growth occurs using sodium bicarbonate as the sole carbon source and thiosulfate as electron donor [ 13 ]. Genomic data suggest that autotrophic growth occurs via the reverse or reductive TCA cycle (rTCA) (Fig. 4 ), a trait shared with Mc. marinus strain MC-1 [ 10 ]. In other magnetotactic Alphaproteobacteria , including some Magnetospirillium species and Mv. blakemorei strain MV-1 [ 55 ], autotrophic growth occurs via the Calvin-Benson-Bassham cycle. In Mf. australis strain IT-1 genome, no predicted ORF encoding the ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCo) enzyme was detected. All enzymes required for oxidative TCA and reductive TCA cycles are encoded, but no genes encoding the enzymes necessary for the glyoxylate bypass were found. Nitrogen metabolism The genome of Mf. australis strain IT-1 contains all known genes necessary for nitrogen fixation, a common trait among other magnetotactic Alphaproteobacteria , with the exception of strain QH-2 [ 11 ]. Strain IT-1 grows in medium without addition of fixed nitrogen sources and therefore, it likely is able to fix N 2 under favorable environmental conditions. Genes necessary for this pathway occur in two clusters, nifZVXNEYTKDH (ORFs 03719 to 03740, with two transposases and hypothetical proteins between nifY and nifE ) and nifQBA (ORFs 02324, 02328 and 02330) with genes nifR (ORF 01176) and nifU (ORF 03853) occurring elsewhere in the genome. The ammonium produced by nitrogen fixation or absorbed from the environment might be assimilated through one of three pathways for which strain IT-1 carries all the necessary genes: alanine dehydrogenase (ORF 00976), glutamate dehydrogenase (ORF 03387) or glutamine synthase and glutamate synthase (ORF 03244) (GS-GOGAT cycle). These pathways are also present in the genome of Mc. marinus strain MC-1 [ 10 ] and in other alphaproteobacterial MTB. Several Magnetospirillum species are capable of reducing certain nitrogen oxides as terminal electron acceptors for growth, producing N 2 from nitrate through denitrification [ 56 ]. The first reaction of denitrification, the reduction of NO 3 \n − to NO 2 \n − , is catalyzed the enzyme dissimilatory nitrate reductase, of which there are two types: a periplasmic (Nap) and a membrane-bound (Nar) form. Nap has been shown to be involved in magnetite magnetosome biomineralization, probably through redox control, in some Magnetospirillum species [ 57 ] and, interestingly, nap has been found in the genomes of all magnetotactic Alphaproteobacteria studied so far [ 7 , 10 , 13 ] including strain IT-1 (ORF 01154) as well as in the genome of the uncultured MTB Candidatus Magnetobacterium bavaricum from the Nitrospirae phylum [ 20 ]. All these MTB biomineralize magnetite and it is possible that Nap plays a role in the biomineralization of magnetite in all MTB that produce this mineral. Surprisingly, however despite possessing nap genes, neither Mf. australis strain IT-1 or Mc. marinus MC-1 grows anaerobically with nitrate as a terminal electron acceptor. The genome of Mf. australis strain IT-1 does not contain genes for the subsequent reduction steps of denitrification ( nir , nor and nos genes), as do the genomes of some Magnetosprillum species and thus it appears that it is not capable of dissimilatory nitrite, nitric oxide and nitrous oxide reduction. The situation is similar with Mc. marinus strain MC-1 except that there are copies of nitric oxide reductase ( norCBQ and norD ) in the genome of this organism [ 10 ]. Genes for the assimilatory nitrate reduction ( nas ) pathway were not found in either of the genomes of Mf. australis strain IT-1 or Mc. marinus strain MC-1 [ 10 ]. Sulfur metabolism In aerobic environments, sulfur is mostly found in the oxidized form as sulfate (SO 4 \n −2 ) and must be reduced to be used by bacteria. The genome of Mf. australis strain IT-1 contains two groups of genes involved in assimilatory sulfate reduction. Two genes responsible for assimilative sulfate reduction were found, a sulfate adenylyltransferase that turns sulfate in adenylyl sulfate and other two adenylylsulfate kinases (ORFs 01705 and 03386) (Fig. 4 ). Mf. australis strain IT-1 does not have genes that encode the proteins CysH and CysI, capable of catalyzing 3′-phospho-5′-adenylyl sulfate to sulfite and reducing sulfite to H 2 S, respectively. ORFs encoding enzymes involved in dissimilatory sulfate reduction sulfate to sulfite (dissimilatory sulfate reductase – DsrAB and adenylyl-sulfate reductase – AprAB) are present in the genome of Mf. australis strain IT-1. We found six dsr genes in the Mf. australis strain IT-1 genome, dsrAB (ORFs 03242 and 03243) , dsrC (ORF 00945) and dsrHFE (ORFs 03513 to 03515) that are clustered close to two sulfite oxidase genes, yedY and yedZ (ORFs 03271 and 03272, respectively). This arrangement of the dsr and yed genes is also similar in the genomes of Ms. magneticum strain AMB-1 and Mc. marinus strain MC-1. In some bacteria, such as Allochromatium vinosum , the Dsr (dissimilatory sulfite reductase) proteins are essential for the oxidation of zero-valent sulfur in sulfur globules [ 58 ]. We have also found a gene encoding a sulfate adenylyltransferase (ORF 02663) with higher similarity to a marine Gammaproteobacteria (accession number: WP007226305, 99 % coverage, 78 % ID and 89 % positives) and genes encoding adenylyl-sulfate reductase subunits (AprAB, ORFs 2173 and 3566, respectively) with higher similarity to Thiocystis violascens (accession number: WP_014779579, 99 % coverage, 67 % ID and 77 % positives) and to Acromatium sp. (accession number: KOR32136, 98 % coverage, 85 % ID and 94 % positives), respectively. However, we could not unequivocally detect genes encoding the QmoABC membrane complex, responsible for electron transfer to the AprAB enzyme in sulfate reducing bacteria [ 59 ]. Under laboratory conditions, Mf. australis strain IT-1 is capable of oxidizing sulfide but does not grow anaerobically with sulfate as the sole terminal electron acceptor, however this feature would confer an important ecological advantage for Mf. australis strain IT-1, since the Itaipu lagoon receives high organic matter loads that decrease O 2 availability and as it is a marine habitat, the water is rich in sulfate. The genome of Mf. australis strain IT-1 contains two set of sox genes. Enzymes for this pathway are responsible for the oxidation of reduced sulfur compounds directly to sulfate, without a sulfite intermediate. The first set of genes is comprised of soxXYZAB (ORFs 03354 to 03358) and is organized similarly in the genome of Mc. marinus strain MC-1. The second set consists of soxEFXY (ORFs 02177 to 02182). The soxW (ORF 01228) gene is distant from the other sox genes. Two copies of soxY , that encodes an enzyme that binds the sulfur compound to be oxidized, are present (ORFs 02181 and 03355). However, soxC and soxD , both encoding for proteins involved in electron transfer chain, are not present in Mf. australis strain IT-1. Genes encoding a sulfide quinone reductase (ORFs 01515, 02178 and 02650), a key enzyme for sulfur globule synthesis, are present in Mf. australis strain IT-1 and confirms its ability to produce intracellular sulfur globules (Fig. 4 ), as observed by microscopy (see Fig. 1 ; [ 13 ]). Other genes encoding proteins that directly or indirectly act in sulfur metabolism are found dispersed in Mf. australis strain IT-1 genome and include three specific sulfate transporter (ORFs 00291, 02332 and 02706), one bifunctional sulfur transporter/tiasol synthase, four sulfotransferase and seven ABC transporters for nitrate/sulfonate/bicarbonate. Phosphorous metabolism Phosphorus, generally in the form of phosphate, plays a major role in diverse cellular processes and efficient control over the uptake and storage of phosphate and the regulation of phosphate metabolism is mandatory. The genome of Mf. australis strain IT-1 contains genes related to the Pho regulon (two copies of phoU and phoBR , ORFs 0718, 0148; 03223 and 03224), responsible for the uptake of phosphate, as well as genes (ORFs 01828 and 03685) regulating the absorption of phosphonates [ 60 , 61 ]. Genes encoding the high affinity transport of phosphorus ( pstBAC , ORF 04289, 04291 and 04292; pstS1 and pstS2, ORF 02354 and 03453), a permease (ORF 04293), three phosphonate transporters and a diguanylate cyclase/phosphodiesterase (ORF 04288) also occur in the genome of IT-1. A similar arrangement is present in the genome of Mc. marinus strain MC-1. This type of regulation has also been described for Magnetospirillum strains AMB-1, MS-1 and MSR-1 [ 10 ]. Both MC-1 and IT-1 have genes encoding polyphosphate kinase and exopolyphosphatase enzymes, both involved in the synthesis of polyphosphate granules [ 62 ] (Fig. 4 ). Polyphosphate granules function as an energy and phosphate reservoir as well as being involved in kinase reactions [ 63 ]. It has been shown that a lack of polyphosphate kinase hampers the cell response to environmental stress [ 64 – 66 ]. Phosphate metabolism may also be related to magnetosome biosynthesis. Phosphate granules are widespread in magnetotactic cocci whether sampled from the environment [ 67 ] or grown in culture even when the phosphate concentration is relatively low (Fig. 1 , [ 1 , 10 ]). It has been shown the involvement of a phosphate-rich ferric hydroxide phase for storage of iron inside magnetosome vesicle before magnetite crystals formation [ 68 ]. Detoxification of reactive oxygen species The genome of Mf. australis strain IT-1 contains 9 ORFs encoding proteins involved in detoxifying reactive oxygen species. There are four ORFs encoding cytochrome C peroxidase (ORFs 00488, 02063, 03177 and 03947), three encoding alkyl hydroperoxide reductase (ORFs 02341, 02949 and 03103), one catalase (ORF 01369) and one superoxide dismutase (02343). This number of ORFs is lower than that encoded in the genome of Ms. magneticum strain AMB-1 (15) but much higher than the 3 genes encountered in the Mc. marinus strain MC-1 genome, implying that Mf. australis might be more resistant to oxidative stress than the marine magnetotactic cocci."
} | 9,951 |
39757431 | PMC11817952 | pmc | 1,661 | {
"abstract": "Abstract Plasmonics and superhydrophobicity have garnered broad interest from academics and industry alike, spanning fundamental scientific inquiry and practical technological applications. Plasmonic activity and superhydrophobicity rely heavily on nanostructured surfaces, providing opportunities for their mutually beneficial integration. Engineering surfaces at microscopic and nanoscopic length scales is necessary to achieve superhydrophobicity and plasmonic activity. However, the dissimilar surface energies of materials commonly used in fabricating plasmonic and superhydrophobic surfaces and different length scales pose various challenges to harnessing their properties in synergy. In this review, an overview of various techniques and materials that researchers have developed over the years to overcome this challenge is provided. The underlying mechanisms of both plasmonics and superhydrophobicity are first overviewed. Next, a general classification scheme is introduced for strategies to achieve plasmonic and superhydrophobic properties. Following that, applications of multifunctional plasmonic and superhydrophobic surfaces are presented. Lastly, a future perspective is presented, highlighting shortcomings, and opportunities for new directions.",
"introduction": "1 Introduction We observe and interact with objects through their surfaces. While the bulk of an object remains consistent inside, surfaces exhibit phase and material differences, giving rise to new properties. Nobel laureate Wolfgang Pauli claimed to state, “God made the bulk, but surfaces were the work of the devil”, emphasizing the complexity of the surfaces. [ \n \n 1 \n \n ] Most surface properties, such as wetting, adhesion, and light scattering, are not a mere average of the corresponding bulk properties of the constituents. Besides, the interface topography can also significantly affect surface properties in a complex nonlinear manner. Structuring surfaces at small length scales and examining their effect on various properties have been facilitated by the advancement of nanotechnology and material science over the last few decades, leading to the illumination of working mechanisms and the discovery of new phenomena. Two fields that particularly benefited from these developments are plasmonics and superhydrophobicity. Nanostructured noble metals can generate highly localized electric fields and strong absorption upon exposure to light, stimulating their applications in signal enhancement in chemical sensors, light‐to‐heat conversion, and catalyzing chemical reactions. [ \n \n 2 \n \n ] In contrast, superhydrophobicity depends on the presence of microscale and nanoscale roughness, along with low surface energy materials, such as long‐chain hydrocarbons and their fluorinated counterparts. [ \n \n 3 \n \n ] Self‐cleaning, anti‐icing, anti‐fogging, and enriching aqueous analysis are some of the most widespread applications of superhydrophobic surfaces. [ \n \n 4 \n , \n 5 \n , \n 6 \n , \n 7 \n \n ] The combination of superhydrophobicity and plasmonic activity is advantageous for achieving superior properties through a cumulative fashion. [ \n \n 8 \n , \n 9 \n , \n 10 \n , \n 11 \n \n ] For example, self‐cleaning and anti‐icing properties of superhydrophobic surfaces can be coupled with the photothermal effect of plasmonic nanostructures to fabricate passive anti‐icing surfaces that require no energy input. [ \n \n 12 \n , \n 13 \n \n ] The synergy of superhydrophobicity and plasmonic activity can be exploited to enrich the local concentration of aqueous analytes onto a small area, followed by detecting the analytes using light‐based analytical techniques with enhanced sensitivity. [ \n \n 9 \n , \n 14 \n , \n 15 \n , \n 16 \n \n ] \n However, achieving both plasmonic activity and superhydrophobicity simultaneously on a surface is not straightforward for two main reasons. Firstly, metals like gold, silver, and copper, commonly used in plasmonic nanostructures, have some of the highest surface energy and are intrinsically hydrophilic. [ \n \n 17 \n , \n 18 \n \n ] Secondly, plasmonic activity generally requires nanoscale (<100 nm) topography, whereas superhydrophobicity can be achieved through a diverse range of roughness encompassing both micrometer and nanometer length scales. Notwithstanding, researchers have developed various techniques and materials to prepare both plasmonically active and superhydrophobic surfaces over the past decade. [ \n \n 9 \n , \n 19 \n \n ] \n In this article, we provide a comprehensive overview of such developments. It should be mentioned that a few reviews pertinent to this one exist in the literature, though with a focus on specific narrow topics. Z. F. Gao et al. reviewed recent developments in wettability‐based biosensing, focusing on the change of wetting properties as a sensing mechanism. [ \n \n 10 \n \n ] Y. Dong et al. focused on the application of wettability in molecular detection. [ \n \n 20 \n \n ] Here, we focus on fabrication methods and explore applications beyond molecular sensing, such as in photothermal, anti‐icing, and other niche areas. For researchers interested in fabricating either plasmonic materials [ \n \n 2 \n , \n 21 \n , \n 22 \n \n ] or superhydrophobic surfaces, [ \n \n 4 \n , \n 23 \n , \n 24 \n \n ] several excellent books and review articles are recommended. In the following, we first briefly introduce plasmonics and wetting properties. Then, we present a comprehensive overview of strategies available for fabricating both plasmonic and superhydrophobic surfaces, followed by their applications. Finally, we will conclude with a perspective on future research directions in this promising field."
} | 1,411 |
36477295 | PMC9728908 | pmc | 1,665 | {
"abstract": "Inspired by self-assembled biological growth, the Circuit Tile Assembly Model (cTAM) was developed to provide insights into signal propagation, information processing, and computation in bioelectric networks. The cTAM is an abstract model that produces a family of circuits of different sizes that is amenable to exact analysis. Here, the cTAM is extended to the Boolean Circuit Tile Assembly Model (bcTAM) that implements a computationally complete set of Boolean gates through self-assembled and self-controlled growth. The proposed model approximates axonal growth in neural networks and thus, investigates the computational capability of dynamic biological networks, for example, in growing networks of axons. Thus, the bcTAM models the effect of electrical activity on growth and shows how that growth might implement Boolean computations. In this sense, given a set of input voltages, the bcTAM is a system that is able to monitor and make decisions about its own growth.",
"introduction": "Introduction Distributions of electric potentials in bioelectric networks influence gene expression, and thus, the development of complex biological patterns [ 1 – 4 ]. This feedback between bioelectric and biomolecular mechanisms is postulated to be an ancient mechanism and operates in many cellular processes, including embryonic growth and morphological differentiation [ 5 – 7 ]. Electric signals are also the basis for both communication and computation in neural networks. In this paper, a simple circuit model for growth processes that are influenced by electric potentials, the Circuit Tile Assembly Model (cTAM) [ 8 , 9 ], is extended to implement a computationally complete set of Boolean logic gates in the Boolean Circuit Tile Assembly Model (bcTAM) . Thus, the bcTAM informs not only the electrically-influenced growth process, but also how growth can result in computation. Self-assembly is a model inspired by biological growth in which a larger, more capable system is constructed from smaller components through localized interaction. In the cTAM, larger circuits are self-assembled from unit tiles consisting of basic electrical components. An electric potential drives growth by activating glues to which new tiles bind. As growth proceeds, the potential dissipates, eventually falling below a predefined threshold value, where growth ceases. Thus, the electric potential acts similarly to a finite nutrient supply in a bacterial colony, or for that matter, the electric potential in artificial growth processes, like electroplating [ 10 ]. Though a nonbiological system, the cTAM achieves life-like properties, such as self-assembled, self-controlled growth [ 8 , 9 , 11 – 13 ], and self-replication [ 14 ]. Also, the cTAM model is a dynamic model where glues activate when certain criteria are fulfilled. This property is similar to the signal-passing tile assembly model introduced by Padilla, et al . [ 15 ]. In 1952, Hodgkin and Huxley described how action potentials are initiated and propagated with an equivalent circuit model [ 16 ]. The ladder circuits in the cTAM closely resemble those for the propagation of action potentials down axons. This relationship has been more fully explored in [ 11 ]. Thus, the bioelectric network that the bcTAM most closely resembles are networks of axons whose growth is influenced by active electric signals, which can elongate axons and change the growth dynamics [ 17 ], are potentially important in neural development [ 17 – 19 ], and when coupled with gene expression, have a fundamental role in the growth and organization of neural networks [ 18 ]. Our previous works [ 8 , 9 , 11 – 14 ] investigated electric signal propagation and its impact on dynamic circuit configurations, and this work focuses on the capacity for logical decision-making in the Circuit Tile Assembly Model. The bcTAM model shows the capability of performing Boolean functions in simple, biological mechanisms, such as axon growth. Using different computational models, others have shown the computational power of axons [ 20 ], but without growth. In this paper, the bcTAM model and its working principles and growth mechanisms are defined. Growth is essential to the functionality of the Boolean gates defined in the bcTAM, with growth providing different connections that activate one state of a gate or another. In the abstract, this is similar to conformal changes that produce different functionality in molecular biology. The bcTAM system is able to use logic to reason about its own growth or lack thereof. This extends to Boolean satisfiability problems and a version of bcTAM growth that is NP-complete, demonstrating the complexity of which it is capable.\n\nIntroducing Boolean Circuit Tile Assembly Model (bcTAM) Biological organisms control molecular self-assembly using biochemical circuits and algorithms [ 21 ]. Motivated by these mechanisms, the Circuit Tile Assembly Model combines chemically-inspired glues and electric circuitry. The basic cTAM is a self-controlled self-assembly model [ 8 , 9 ], and achieves self-replication with modified electric circuit components in the Replicating Circuit Tile Assembly Model (rcTAM) [ 14 ]. Capacitors and time-varying signals are incorporated into the cTAM in [ 11 ], termed the Axonal cTAM (acTAM) , in which the exact response and signal propagation of the network is calculated. This work adds an additional capability, i.e . molecular computation, with a modified cTAM model, termed the Boolean Circuit Tile Assembly Model (bcTAM) . Definition 1 (bcTAM circuit). A bcTAM circuit is a tuple Ψ = ( N , E , C , g , ∂ N ) where N and E represent electrical nodes and edges of a circuit respectively. Thus, the circuit is analogous to a graph ( N , E ). C is a set of circuit components required to build the tile types, and g is the glue set necessary for attachment among input and output nodes. ∂ N = N in ∪ N out consists of input nodes and output nodes of the circuit at which glues bind tiles together. Multiple pairs of glues are possible in order to connect an output to multiple inputs, for example. Definition 2 (Boolean Circuit Tile Assembly Model). A Boolean cTAM assembler is a tuple C = ( Γ , S , G , τ , ν , ζ ) , where Γ is a finite set of circuit tile types built with basic electrical circuit components, S ⊆ Γ is a set of seed tile types that are the starting point for the growth of an assembly, G ⊆ Γ is a set of gate tile types that is capable of computing Boolean logic functions, τ ∈ R + is the threshold voltage, the parameter to determine the eligibility of further attachment, ν ∈ R + is the node potential, i.e . electric potential energy at the node relative to the ground node of the circuit, and ζ maps input nodes to output nodes according to the glue rules, i.e . ζ: Γ( N in ) × Γ( N out ) → {0, 1}. Description of the assembly process An assembly describes a complete electrical circuit. It starts growing from the seed tile, and growth continues by attaching tiles based on the glue rules and a predefined threshold voltage. If the differential voltage across a node pair is greater than or equal to the threshold τ , the glues of the nodes are activated. The potential difference between two nodes ( p , q ) will be denoted as V ( p , q ) γ ( k ) , where the first node p ∈ N in an edge refers to the more positive potential, γ ∈ Γ is the tile type, and the index k ∈ {1, …, n }, denotes a specific tile in the ladder assembly of size n , as well as timestep. Inspired by the DNA tile assembly, the attachment rules of the cTAM is based on DNA Watson-Crick complementary oligonucleotides, where a glue matches with its complement. Each tile of the bcTAM tileset has a particular set of glues, denoted as g k m where g indicates the glue type, m indicates the assembly number, and k denotes the timestep. For example, for the first step of the first ladder will have glues g 1 1 where g = a , b , …. A stable attachment may occur if the potential difference between the nodes (either input or output) is greater than the threshold voltage, and the tiles have complementary glues, i.e . g attaches to g ¯ . Fig 1 shows an example of two ladders with two tile types and four tiles. Tile A, the seed tile, has two glues at the output nodes and tile B has two glues at input nodes and two glues across the output nodes. For the tile A of ladder 1, m = 1, k = 1, for the tile B of ladder 1, m = 1, k = 2, for the tile A of ladder 2, m = 2, k = 1, and for the tile B of ladder 2, m = 2, k = 2. Therefore, tile A of ladder 1 has an output node pair with glues, a 1 1 and b 1 1 that matches with tile B having glues a ¯ 1 1 and b ¯ 1 1 on its input node pair. A tile can attach to a growing ladder if the voltage drop across the output nodes of the ladder is greater than or equal to the threshold voltage. Here, tile A and tile B of the corresponding ladder will attach if the potential across the input or output nodes ≥ τ . An attachment requires four complementary glues. Since each rung of the ladder is instrumented with a gate, the required number of glues is ( n − 1) * 4 + 2, where n is the length of the assembly. For this example case, the connection occurs between { a 1 1 - a ¯ 1 1 } , { b 1 1 - b ¯ 1 1 } , { a 1 2 - a ¯ 1 2 } , and { b 1 2 - b ¯ 1 2 } , shown in the example case of Fig 1 . Also, tile B has glues { c 1 2 , d 1 2 } for ladder 1, and { c 2 2 , d 2 2 } for ladder 2. As n = 2, (2 − 1) * 4 + 2 = 6 glues are required for each of the ladders. Both of the ladders are two tile assembly, making the total number of required glues 12, as shown in Fig 1 . 10.1371/journal.pone.0278033.g001 Fig 1 Matching glue rules. An example represents the matching glue rules. Here, the assembly system has four tiles of two tile types(tile A and tile B). The first tile A has glues { a 1 1 , b 1 1 } and the second tile A has glues { a 1 2 , b 1 2 } at the output nodes. For the tile B, one tile has glues { a ¯ 1 1 , b ¯ 1 1 } , and the other tile has glues { a ¯ 1 2 , b ¯ 1 2 } at the input nodes. According to the glue rules, a stable attachment will occur between the glue pairs { a 1 1 - a ¯ 1 1 } , { b 1 1 - b ¯ 1 1 } , { a 1 2 - a ¯ 1 2 } , and { b 1 2 - b ¯ 1 2 } if Δ ν ≥ τ , shown with dotted arrow in the figure. Definition 3 (Terminal Circuit). A terminal assembly is a stable configuration in which no further attachment is possible. A circuit tile assembly model represents a dynamic circuit configuration in which growth continues based on the threshold voltage and matching glues criteria. When growth has stopped, the final circuit configuration is termed as terminal circuit . The number of tiles in a terminal assembly is denoted by n . Logic gates and their truth tables In digital logic design, the most common logic gates are AND, OR, NOT, NAND, and NOR, and we can build any logic circuitry with these gates. Table 1 shows the truth table for the logic gates of AND, OR, NOR, and NAND with two inputs, and Table 2 shows the truth table of a single input NOT gate. This work aims to build a tile assembly model that has the functionalities of these five gates. The bcTAM system has one seed tile, one circuit tile, and a set of gate tiles consisting of five tile types. Each gate tile computes one particular Boolean function among the set AND, OR, NOT, NAND, NOR. Another common logic gate XOR is excluded here, as the Boolean expression of an XOR output is A ¯ B + A B ¯ , which is a combination of AND, OR, NOT gate. These five gates are enough to represent any Boolean expression, and hence, this work focuses on implementing them. 10.1371/journal.pone.0278033.t001 Table 1 Truth table of logic gates (OR, AND, NOR, NAND). Inputs Outputs Input 1 Input 2 OR Output AND Output NOR Output NAND Output 0 0 0 0 1 1 0 1 1 0 0 1 1 0 1 0 0 1 1 1 1 1 0 0 10.1371/journal.pone.0278033.t002 Table 2 Truth table of NOT gate. Input Output 0 1 1 0 Description of tiles Seed tile Seed tile (Tile A) of bcTAM consists of two loops, where the first loop is built with one voltage source ν 0 (at node {1, 0}), two resistors R (at node {1, 2}), and αR (at node {2, 0}) connected as a series circuit ( Fig 2 ). The second loop has one large value resistor βR (at node {1, 4}), one dependent voltage source V x = ν 0 2 τ ν 0 (at node {3, 2}), and one ideal diode D 1 (at node {3, 4}) with threshold voltage τ = 0. The dependent source V x equals to DC voltage source of 2 τ if connected with ν 0 ; otherwise it is not activated. When the dependent source is not connected to the DC voltage source, it doesnot provide any voltage supply to the second loop of the seed tile, and hence glues are not activated which is the desired condition. Four ideal diodes with zero threshold voltage are connected across the βR resistor: D 2 at node {5, 4}, D 3 at node {5, 6}, D 4 at node {7, 1}, and D 5 at node {7, 8}. This diode bridge prevents current flow from the next tile to the seed tile, i.e . they act as an isolator between two adjoining tiles. 10.1371/journal.pone.0278033.g002 Fig 2 Seed tile A. Tile A (Seed Tile) for the bcTAM consisting with one DC voltage source ν 0 , one dependent voltage source V x = 2 τ ν 0 ν 0 , five ideal diodes D 1 , D 2 , D 3 , D 4 , D 5 with τ = 0, and three resistors ( R , αR , and βR where βR is a large value resistor compared to R and αR ). It has two output terminals across node {1, 2} and node {6, 8}. Node 1 has glue { a 1 , c 1 }, node 2 has glue { b 1 , d 1 }, node 6 has glue c 1 , and node 8 has glue d 1 . The tile has two pairs of output nodes at {1, 2} and {6, 8} with glues g (1) = { a 1 , c 1 }, g (2) = { b 1 , d 1 }, g (6) = { c 1 }, and g (8) = { d 1 }. The first loop of seed tile A acts as a voltage divider circuit where ν 0 is divided between the resistors R and αR . The second loop has a dependent voltage source that provides a 2 τ DC voltage source if activated. According to Kirchhoff’s Voltage Law (KVL), the algebraic sum of the voltage around a closed loop of a circuit must be zero. Using KVL along nodes {1, 2, 3, 4, 1} at tile A:\n V x - V D 1 - V ( 4 , 1 ) A ( 1 ) - V ( 1 , 2 ) A ( 1 ) = 0 , 2 τ - 0 - V ( 4 , 1 ) A ( 1 ) - V ( 1 , 2 ) A ( 1 ) = 0 , V ( 4 , 1 ) A ( 1 ) = 2 τ - V ( 1 , 2 ) A ( 1 ) . \n (1) \nIf V ( 1 , 2 ) A ( 1 ) > τ , then V ( 4 , 1 ) A ( 1 ) < τ and if V ( 1 , 2 ) A ( 1 ) < τ , then V ( 4 , 1 ) A ( 1 ) > τ . From Eq 1 , V ( 1 , 2 ) A = τ , V ( 4 , 1 ) A = τ creates an unwanted condition of activation for both outputs V ( 1 , 2 ) A and V ( 6 , 8 ) A . This condition can be avoided by choosing appropriate values of the input parameters ( ν 0 , τ , α ) such that the tip voltage goes from V (1,2) ( n − 1) > 2 τ for a ladder of length ( n − 1) to V (1,2) ( n ) < τ for a ladder of length n , at which point the assembly terminates. To prove that there exists values of the input parameters that will reduce the tip voltage from > 2 τ to < τ with the addition of a single tile, the values of the voltage will be bound, and the existence of a gap between the bound voltages shows that the condition is possible to achieve. For a ladder of length ( n − 1), the desired condition is that V (1,2) ( n − 1) > 2 τ . Since R is greater than the equivalent resistance for a given length ladder, a voltage divider between R and αR is used to upper bound the voltage,\n 2 τ < V n - 1 < ( 1 1 + α ) n - 1 ν 0 . \n (2) \nSolving for n , produces\n 1 + log ( 2 τ / ν 0 ) log ( 1 1 + α ) < n . \n (3) \nFor a ladder of length n , the equivalent resistance for an infinite length ladder, R e q ∞ , is used in the bound since it is less than the actual equivalent resistance. Its value\n R e q ∞ = R [ - α + α 2 + 4 α 2 ] = R χ . \n (4) \nwas derived in [ 9 ]. Therefore, the bound on the tip voltage for a ladder of length n is\n [ R e q ∞ R e q ∞ + α R ] n ν 0 < V n < τ . \n (5) \nSolving for n results in\n n < log ( τ / ν 0 ) log ( χ χ + α ) . \n (6) \nCombining Eqs 3 and 6 , requiring that Eq 6 be at least one tile larger than Eq 3 , and setting α = 1, produces\n log ( 2 τ / ν 0 ) log ( 1 / 2 ) < log ( τ / ν 0 ) log ( 1 1 + ϕ ) , \n (7) \nwhere ϕ is the golden ratio [ 8 ]. Solving gives\n 0 . 0839 ν 0 < τ , \n (8) \nwhich can be satisfied for any ν 0 by an appropriate choice of τ , proving that the condition of the tip voltage identically equal to τ can be avoided. Therefore, in this work, we will consider Logic 1 = HIGH (> τ ), Logic 0 = LOW (< τ ) and exclude the condition of tip potential is exactly equal to τ . Now, when V ( 4 , 1 ) A ( 1 ) > τ , the output nodes V ( 6 , 8 ) A ( 1 ) > τ due to the open loop condition at node {6, 8}. So, before any attachment, the glues of the output nodes {6, 8} are activated and ready to attach with other tiles of matching glues. However, after the attachment, it will contribute LOW (< τ ) potential for the next tile as the diode bridge acts as an open circuit. To sum up, when the growth is continuing, output {1, 2} activates, and it provides HIGH (> τ ) potential to attach a tile to these nodes. In contrast, when V ( 1 , 2 ) A < τ , V ( 6 , 8 ) A > τ , output {6, 8} activates, and it provides LOW (< τ ) potential to the next tile attached to these nodes. Circuit tile Circuit tile (Tile B) has the same circuit configuration as the seed tile except for the supply voltage ( Fig 3 ). It has one pair of input nodes at {1, 9} and two pairs of output nodes {1, 2} and {6, 8} same as the seed tile. It has glues: g ( 1 ) = { a i , c i , a ¯ i - 1 } , g ( 2 ) = { b i , d i } , g ( 6 ) = { c i } , g ( 8 ) = { d i } , and g ( 9 ) = b ¯ i - 1 , where i = 2, 3, …. It provides HIGH input when V ( 1 , 2 ) B ( k ) > τ , and provides LOW input when V ( 1 , 2 ) B ( k ) < τ . 10.1371/journal.pone.0278033.g003 Fig 3 Circuit tile B. Tile B (Circuit Tile) consists with three resistors ( R , αR , and βR ), one dependent voltage source V x = 2 τ ν 0 ν 0 , five ideal diodes D 1 , D 2 , D 3 , D 4 , D 5 with τ = 0. It has one input node terminal at node {1, 9} and two output node terminals at node {1, 2} and node {6, 8}. Glues: g ( 1 ) = { a i , c i , a ¯ i - 1 } , g (2) = { b i , d i }, g (6) = c i , g (8) = d i , and g ( 9 ) = b ¯ i - 1 , where i = 2, 3, …. OR tile A bcTAM system has a set of gate tiles: OR tile, AND tile, NOT tile, NOR tile, and NAND tile. The gate tiles function as their name suggests, such as the OR tile works as an OR gate whose output is HIGH if any of its inputs are HIGH. The OR tile consists of two large value βR resistors (at node {1, 3} and {3, 2}) connected in series. It has two input node pairs across each βR resistors, i.e . at {1, 3} and {3, 2} and one output node pair at {1, 2}. It has glues: g ( 1 ) = { c ¯ i } , g ( 2 ) = { d ¯ j } , and g ( 3 ) = { c ¯ j , d ¯ i } , where i = 1, 2, … and j = 1, 2, … indicate the location of the assembly ( Fig 4 ). 10.1371/journal.pone.0278033.g004 Fig 4 OR tile. OR tile consists of two βR resistors connected in series. It has two input nodes across {1, 3} and {3, 2} and one output node at {1, 2}. Glues: g ( 1 ) = { c ¯ i } , g ( 2 ) = { d ¯ j } , and g ( 3 ) = { c ¯ j , d ¯ i } , where i = 1, 2, … and j = 1, 2, … indicate the location of the assembly. For all of the two input gate tiles, i and j will indicate the location for the attachment at different assemblies. Also, the glue set will be unique for each ladder at each step. The output of OR tile is the potential across node {1, 2}, which equals to:\n V ( 1 , 3 ) O R + V ( 3 , 2 ) O R = V ( 1 , 2 ) O R . \n (9) \nIf any or both input nodes are connected with the assembly location {1, 2}, the tip potential is HIGH (> τ ), then the output potential is also HIGH. If any input nodes (such as {1, 3}) of the OR gate connects with the location {6, 8} of the assembly at step k , according to the KVL,\n V ( 4 , 1 ) ( k ) + V ( 1 , 7 ) ( k ) + V ( 7 , 8 ) ( k ) + V ( 1 , 3 ) O R ( k ) + V ( 6 , 5 ) ( k ) + V ( 5 , 4 ) ( k ) = 0 . \n (10) \nAs the diode D 2 and D 4 are reverse biased, no current can flow in this loop, and V ( 1 , 3 ) O R ( k ) = 0 , indicating LOW potential. From the Eq 9 , if any of the inputs or both inputs ( V ( 1 , 3 ) O R or V ( 3 , 2 ) O R ) are HIGH, output V ( 1 , 2 ) O R is HIGH (> τ ). If both input is LOW ( i.e . Zero), then the output V ( 1 , 2 ) O R is LOW. Therefore, it matches with the truth table of an OR gate. The two-input OR gate can be modified for an m input OR gate by adding m number of βR resistors and unique glues. NOT tile The NOT tile implements logical negation of its input. It is a single loop circuit with two large value resistors ( βR at node {1, 2} and γR at node {4, 1} and γ > > β ), one ideal diode D 1 at node {3, 4}, and one dependent voltage source of V x = ν 0 2 τ ν 0 at {3, 2}. The threshold voltage of the diode, V thr = 0. It has one input node at {1, 2} and one output node at {4, 1}. It has glues: g ( 1 ) = c ¯ i and g ( 2 ) = d ¯ i ( Fig 5 ). 10.1371/journal.pone.0278033.g005 Fig 5 NOT tile. NOT tile consists of two large value resistors ( βR and γR , where γ > > β ), one ideal diode D 1 with V thr = 0, and one dependent voltage source V x = 2 τ ν 0 ν 0 in series connection. It has input node across node {1, 2} and output node at {4, 1}. Glues: g ( 1 ) = { c ¯ i } , g ( 2 ) = { d ¯ i } . The working mechanism of the NOT tile is similar to NOT gate. Using KVL along the tile:\n V x - V D 1 - V ( 4 , 1 ) N O T - V ( 1 , 2 ) N O T = 0 , 2 τ - 0 - V ( 4 , 1 ) N O T - V ( 1 , 2 ) N O T = 0 , V ( 4 , 1 ) N O T = 2 τ - V ( 1 , 2 ) N O T . \n (11) \nIf the NOT tile has logic HIGH as input i.e . V ( 1 , 2 ) N O T ( k ) > τ , using Eq 11 , V ( 4 , 1 ) N O T < τ . In contrast, if the tile is connected with a terminal circuit at node {6, 8}, it gets a LOW input across resistor βR . Then, V ( 1 , 2 ) N O T ( k ) < τ and from the Eq 11 , V ( 4 , 1 ) N O T > τ . Thus, the NOT tile’s output potential is inverted with respect to its input potential, acting like a NOT gate ( Table 2 ). AND tile The AND tile implements logical conjunction where a HIGH output results if all the inputs of the AND tile are HIGH. The AND tile consists of a series connection among two diodes D 1 , D 2 (Ideal diodes with threshold τ ), one DC voltage source V 1 = τ , and three resistors (Two βR resistors and one γR resistor where γ > > β > > R ) ( Fig 6 ). It has two input nodes at {1, 5} and {4, 2}. The output nodes are across γR resistor at node {1, 2}. It has glues: g ( 1 ) = c ¯ i , g ( 5 ) = d ¯ i , g ( 4 ) = c ¯ j , g ( 2 ) = d ¯ j . 10.1371/journal.pone.0278033.g006 Fig 6 AND tile. AND tile consists of two ideal diodes D 1 and D 2 with V thr = τ , two large value βR resistors, one large value γR resistor, and one DC voltage source V 1 = τ . It has input nodes across node pair {1, 5} and {4, 2}, and output node at {1, 2}. Glues: g ( 1 ) = { c ¯ i } , g ( 2 ) = { d ¯ j } , g ( 4 ) = { c ¯ j } , g ( 5 ) = { d ¯ i } . When an AND tile is floating (not connected with the seeded assembly), both diodes are reverse-biased, and no current flows through the γR resistor. If any input nodes ( V ( 1 , 5 ) A N D or V ( 4 , 2 ) A N D ) connects with the LOW potential output terminal, i.e . node {6, 8} of the assembly, the corresponding diode of AND tile is still in reverse bias condition, acts as an open circuit, no current flows through the tile, and hence V ( 1 , 2 ) A N D = 0 < τ . If both of the input nodes are connected with node pair {1, 2} of the growing assembly, they get HIGH potential (> τ ). So, both diodes become forward bias, current flows through the loop {5, 6, 1, 2, 3, 4, 5}. Using KVL at the loop:\n V 1 + V ( 5 , 1 ) A N D - V ( 1 , 2 ) A N D + V ( 2 , 4 ) A N D = 0 , V ( 1 , 2 ) A N D = τ + V ( 5 , 1 ) A N D + V ( 2 , 4 ) A N D . \n (12) \nIf both diodes D 1 and D 2 are forward biased, V ( 5 , 1 ) A N D and V ( 2 , 4 ) A N D are greater than τ . Using Eq 12 , V ( 1 , 2 ) A N D > τ = HIGH output. These properties match with a two-input AND gate. Same as the OR tile, it can be modified to make it an m input AND gate by adding m number of diode-resistor pairs on the input side with new glue pairs. NOR tile The NOR tile works as a NOR gate of a digital logic design, where a HIGH output results if both of the inputs are LOW. Fig 7 shows a NOR tile with bcTAM . It has one loop consisting of three resistors in series ( βR resistor at node {1, 3}, βR resistor at {3, 2}, and γR resistor at node {5, 1} where γ > > β ), an ideal diode D 1 at node {4, 5} with V thr = 0, and one dependent voltage source V x = ν 0 2 τ ν 0 at node {4, 2}. It has two input nodes across two βR resistors and output nodes across γR resistor. The glues are: g ( 1 ) = { c ¯ i } , g ( 2 ) = { d ¯ j } , and g ( 3 ) = { c ¯ j , d ¯ i } . 10.1371/journal.pone.0278033.g007 Fig 7 NOR tile. NOR tile has three resistors (two βR resistors, and γR resistors where γ > > β > > R ), one ideal diode D 1 with zero threshold voltage, and one dependent voltage source V x = 2 τ ν 0 ν 0 . It has two input node pairs across node {1, 3} and {3, 2}. This tile has an output node at {5, 1}. Glues: g ( 1 ) = { c ¯ i } , g ( 2 ) = { d ¯ j } , and g ( 3 ) = { c ¯ j , d ¯ i } . The NOR tile can attach to two assemblies with a complementary glues { c i − d i } or { c j − d j }. Applying KVL to the loop:\n V x - V D 1 - V ( 5 , 1 ) N O R - V ( 1 , 3 ) N O R - V ( 3 , 2 ) N O R = 0 , 2 τ - 0 - V ( 5 , 1 ) N O R - V ( 1 , 3 ) N O R - V ( 3 , 2 ) N O R = 0 , V ( 5 , 1 ) N O R = 2 τ - V ( 1 , 3 ) N O R - V ( 3 , 2 ) N O R . \n (13) \nIf both inputs ( V ( 1 , 3 ) N O R and V ( 3 , 2 ) N O R ) are LOW, the potential is approximately zero as per our previous discussion. From Eq 13 , V ( 5 , 1 ) N O R = 2 τ - 0 = 2 τ , which indicates HIGH output. But if both or either of the inputs are HIGH, V ( 5 , 1 ) N O R < τ , indicating LOW output. So, the output is HIGH iff both inputs are LOW, and the output is LOW otherwise, representing the NOR operation. NAND tile The last tile for the logic gate set of the bcTAM is the NAND tile. The functionality of this tile is the same as a NAND gate, where the output is HIGH if both inputs are LOW or any one of its inputs is LOW. This tile has two loops. The first loop has one voltage source V 1 = τ (at node {5, 4}), three resistors: Two βR resistors (at node {5, 6} and {2, 3}), one γR resistor (at node {1, 2}), two ideal diodes D 1 (at node {1, 6}) and D 2 (at node {4, 3}) with V thr = τ . The second loop is similar to the NOT tile with one dependent voltage source V x (at node {7, 2}), one ideal diode D 3 (at node {7, 8}) with τ = 0, and one δR resistor at {8, 1}. Among the resistor values, δ > > γ > > β > > R . It has input nodes across node pair {1, 5} and {4, 2}, and output node at {8, 1} ( Fig 8 ). The glues are: g ( 1 ) = { c ¯ i } , g ( 2 ) = { d ¯ j } , g ( 4 ) = { c ¯ j } , g ( 5 ) = { d ¯ i } . 10.1371/journal.pone.0278033.g008 Fig 8 NAND tile. NAND tile consists of three ideal diodes (Diode D 1 , D 2 have V thr = τ , and diode D 3 has V thr = 0), four resistors (Two βR resistors, one γR resistor, and one δR resistor, where δ > > γ > > β > > R ), and two voltage sources (One DC voltage source V 1 = τ , and one dependent voltage source of V x = 2 τ ν 0 ν 0 ). It has two input terminals across node pairs {1, 5} and {4, 2}, and the output terminal at {8, 1}. Glues: g ( 1 ) = { c ¯ i } , g ( 2 ) = { d ¯ j } , g ( 4 ) = { c ¯ j } , and g ( 5 ) = { d ¯ i } . The tile acts as a two-input NAND gate for the Boolean circuit tile assembly model. The first loop is the same as AND tile, and the second loop is the same as NOT Tile. Using KVL for the second loop:\n V ( 8 , 1 ) N A N D = 2 τ - V ( 1 , 2 ) N A N D . \n (14) \nFrom the working principle of AND tile, it is proven that if both of the diodes D 1 and D 2 are forward biased due to the HIGH input node potential, then V ( 1 , 2 ) N A N D > τ . From Eq 14 , V ( 8 , 1 ) N A N D < τ . In contrast, if any or both input diodes are reverse biased due to the LOW input node potential, V ( 1 , 2 ) N A N D < τ and V ( 8 , 1 ) N A N D > τ . Thus, all the input conditions for the NAND truth table are satisfied with the NAND tile. Sensing growth with bcTAM This section will discuss an example problem and its solution using bcTAM that shows how Boolean computations determine when a set of ladders have stopped growing. The set of ladders are supplied with variable input voltages. Each tile of each ladder must be connected to a logic gate tile. To demonstrate, the case of two growing ladders is highlighted. We can design it with two seed tiles (tile A), multiple circuit tiles (tile B), and multiple NOR tiles (tile C). We annotated the tileset based on the number of seed tiles, as the number of seed tiles decides the number of ladders. As the system has two seed tiles, two ladders will grow; hence, there will be two distinct glue sets: i and j . The glues are denoted as g k m where g indicates the glue types (such as a , b , c , d ), m indicates the assembly number, and k denotes the timestep. For example, the first assembly ( m = 1) will have a glues as a 1 1 , a 2 1 , a 3 1 and the second assembly will have glues a 1 2 , a 2 2 , a 3 2 for k = 1, 2, 3, respectively. The same glue notations will be used for other glues: b , c , d . Except for the seed tile, there is no independent voltage source in other tiles. Therefore, all output node potentials will be less than the threshold as the dependent source is not activated until it is attached to the ν 0 . The growth starts from the seed tiles and compares the tip potential to the threshold voltage. If tip potential is higher than the threshold τ , another tile will attach based on the glue rules. The assembly starts with the seed tile (tile A). Let us assume both tile A has source potential > τ as well as V ( 1 , 2 ) A > τ . Also, we assume, the source potentials for the first and second assemblies are ν 01 and ν 02 respectively where ν 0 1 < ν 0 2 . For both of the assemblies, V ( 1 , 2 ) A ( 1 ) > τ , it activates the attached glues i.e . { a 1 1 - b 1 1 } , { c 1 1 - d 1 1 } , { a 1 2 - b 1 2 } , and { c 1 2 - d 1 2 } . A circuit tile with complementary glues { a ¯ 1 1 - b ¯ 1 1 } attaches to the first assembly at node {1–2}. Similarly, the second assembly attaches with the circuit tile having matching glues. A NOR tile (tile C) with input glues { c ¯ 1 1 , d ¯ 1 1 } , { c ¯ 1 2 , d ¯ 1 2 } also attaches to node pair {1–2}. Since V ( 1 , 2 ) C ( 1 ) > τ in NOR tile 1, V ( 5 , 1 ) C ( 1 ) < τ that indicates a LOW state ( Fig 9 ). 10.1371/journal.pone.0278033.g009 Fig 9 Step 1 of the example assembly. Fig. shows the step 1 for an example case with two seed tiles. Both of the assemblies have V ( 1 , 2 ) A > τ . A NOR tile will attach to both of them and the NOR output is < τ . For the next step, in tile B, input potential V ( 1 , 9 ) B ( 2 ) is further distributed in circuit components. Let us assume, V ( 1 , 2 ) B ( 2 ) < τ in assembly 1, and V ( 1 , 2 ) B ( 2 ) > τ in assembly 2. For the first assembly, according to the Kirchoff’s Voltage Law across the loop {1, 2, 3, 4, 1}, V ( 4 , 1 ) B ( 2 ) = V ( 6 , 8 ) B ( 2 ) > τ . It activates glue { c 2 1 - d 2 1 } only and a NOR tile C with glue { c ¯ 2 1 , d ¯ 2 1 } will attach to the node {6, 8}, and no further circuit tiles can attach to the assembly. But in case of the second assembly, V ( 1 , 2 ) B ( 2 ) > τ and it activates both the glues { a 2 2 - b 2 2 } and { c 2 2 - d 2 2 } . A circuit tile and a NOR tile with complementary glues will attach to the assembly at node {1, 2}. As the tile C is still having input greater than τ , V ( 5 , 1 ) C ( 2 ) < τ , that means LOW output ( Fig 10 ). 10.1371/journal.pone.0278033.g010 Fig 10 Step 2 of the example assembly. Here, the circuit tiles are connected to the assemblies. The second assembly has V ( 1 , 2 ) B > τ , whereas the first assembly has V ( 1 , 2 ) B < τ . Still, one input of the NOR tile is greater than the threshold and thus, NOR output is still < τ . In the third timestep, we assume, second assembly also has less than τ tip potential i.e . V ( 1 , 2 ) B ( 3 ) < τ and V ( 6 , 8 ) B ( 3 ) > τ . It activates only { c 3 2 - d 3 2 } glues. Hence, only a NOR tile attaches with glues { c ¯ 3 2 , d ¯ 3 2 } . As per the mechanism described in the earlier section, the input potential of tile C is LOW. In tile C, V ( 1 , 2 ) C ( 3 ) < τ resulting in V ( 5 , 1 ) C ( 3 ) > τ , a HIGH state of output ( Fig 11 ). Thus, the output terminal of NOR tile, V ( 5 , 1 ) C ( k ) is HIGH(> τ ) iff both assemblies are in a terminal configuration and acts as an indicator of the moment when the system has no growing assembly. Figs 9 – 11 show the block diagram representaion of the step by step assembly process and Fig 12 shows the terminal configuration of the example with circuit configuration. 10.1371/journal.pone.0278033.g011 Fig 11 Step 3 of the example assembly. Fig. shows the third step for the example case. Both of the assemlies have V ( 1 , 2 ) B ( 3 ) < τ and the NOR output is also < τ . 10.1371/journal.pone.0278033.g012 Fig 12 Circuit configuration of the example assembly. Fig. shows the circuit configuration of the example assembly that represents the working mechanism of a NOR gate. Here, two assemblies are growing simultaneously. The dotted lines show attachment with glues. The first assembly has a length of two, and the second assembly has a length of three. As long as both of the assemblies, or any one of them is growing, the output potential of NOR tile (middle tier), V ( 5 , 1 ) C ( k ) < τ = L O W . When both assemblies are terminals, V ( 5 , 1 ) C ( t ) > τ = H I G H .",
"discussion": "Discussion and conclusions Biological systems have long inspired models of computation, from genetic algorithms to artificial neural networks. Logic gates are a widely accepted model of computation and decision-making [ 24 ]. In addition, self-assembly is a core mechanism for biological development and structure formation. In this work, by implementing a computationally complete set of Boolean gates through voltage-controlled self-assembled growth, the bcTAM connects these important ideas. The bcTAM explores how an organism responds in a dynamic environment, i.e . variable inputs and threshold. Being able to sense the environment, respond to it, and make a decision, whether conscious or not, is one characteristic of living systems. Moreover, the complexity of this capability is demonstrated by the NP-Completeness of a version of bcTAM assembly. Variable input voltages ( ν 0 ’s) could have biological relevance as well. They could represent variable sources of energy that produce growth. They could arise as output voltages from sensors, which is common whether the sensor is a neuron or some nonbiological sensor. As the input voltages vary, bcTAM produces different Boolean circuits and, thus, different electric potential distributions at the terminus, as well as throughout the circuit itself. This represents an abstraction of endogenous electric potential distributions, which are produced by membrane potentials. There is increasing evidence that these bioelectric networks influence gene expression and thus, have an important role in embryonic development, including morphogenesis, tissue regeneration, and general biological pattern formation [ 1 – 7 ]. Boolean networks have long been models for genetic regulatory networks [ 25 ], and the bcTAM provides an electric analog. In addition, the bcTAM is a system that can decide for itself when the target potential distribution have been achieved through growth by sensing the outputs of logic gates. Thus, with sensory inputs from seed voltages and the randomizing environment represented by the threshold, the bcTAM represents a system that through growth, can sense its environment and make logical inferences about it. This feature is similar to primitive biological mechanisms, such as conformation changes, or organisms, such as physarum. For example, the physarum can explore the paths in a maze, and find the shortest path to the nutrient supplies [ 26 ]. Moreover, the bcTAM is implemented with relatively simple circuit components that approximate the DC electric functionality of axons, showing the power inherent in axonal growth. Finally, the relationship between the length of the ladders and the electric potential is known [ 11 , 12 ], and thus, the input voltages can be determined to a given range based on the length of the ladder. Therefore, the bcTAM provides a new model for biological growth with powerful computational capabilities that might produce further understanding of the role of electric phenomena in biological form and function. The bcTAM is a resistive network model inspired by biological growth mechanisms, i.e . self-assembly, and with circuit components that approximate electrical conduction in axons. For a given set of input parameters, such as a finite number of seed tiles and gate tiles, the bcTAM is a directed system that produces one terminal assembly due to the matching rules criteria. However, if input parameters are variable or multiple gate tiles have the same input glues, it can show non-determinism for growth and result in more than one terminal configuration. The model performs logical computation with a tile assembly system that is driven by an electric potential. Thus, it provides a new perspective for computation in growing networks of axons, and by extension, the influence of distributions of electric potentials on the development of biological form and function. In the bcTAM, because of its abstraction of electric potential effects on biological growth mechanisms, the resulting networks are amenable to detailed analysis. For example, the range of input potentials to produce given lengths and how the potential changes for each step can be calculated. Thus, from a theoretical perspective, it might produce a better understanding of how Boolean decision-making arises in bioelectric phenomena."
} | 9,611 |
21691792 | PMC3136707 | pmc | 1,666 | {
"abstract": "The need to develop and improve sustainable energy resources is of eminent importance due to the finite nature of our fossil fuels. This review paper deals with a third generation renewable energy resource which does not compete with our food resources, cyanobacteria. We discuss the current state of the art in developing different types of bioenergy (ethanol, biodiesel, hydrogen, etc.) from cyanobacteria. The major important biochemical pathways in cyanobacteria are highlighted, and the possibility to influence these pathways to improve the production of specific types of energy forms the major part of this review.",
"conclusion": "Conclusions The renewed interest in alternative energies derived from biomass has been recently triggered by the prediction of a reduction in the crude oil production 10 years earlier than speculated by experts (Nashawi et al. 2010 ). In this context, cyanobacteria have received significant consideration stimulated by the fact that these microorganisms seem able to cope with some of the major difficulties encountered with preceding biofuel generations. Furthermore, cyanobacteria offer a promising biomass feedstock for various organic (ethanol, CH 4 and biodiesel) and inorganic (H 2 and electricity) biofuels. As the examples in “ Cyanobacteria as a source of renewable energy ” section illustrate, cyanobacteria are a potential source of a wide range of valuable biofuels using different substrates for their production. Many of the cited strategies are still under development and their energy yield may not be economically feasible yet at industrial production levels. Therefore, the metabolic network needs to be optimized to generate an efficient and economic biofuel production system extrapolatable to a commercial scale. A detailed study of the biosynthetic routes in cyanobacteria would assist us to evaluate the impact of genetic manipulations and its limitations in the entire metabolic network. Thus, this approach would further facilitate the design by genetic engineering of an optimized metabolic network for biofuel production in cyanobacteria. Previous work has shown that a minimum of the C derived from photosynthesis is directed to pathways involved in the biofuel production (Lindberg et al. 2010 ). Thus, in order to optimize the energy production by these microorganisms, new strategies of pathway engineering need to be proposed to redistribute the C flux among the biosynthetic pathways of other fuel feedstocks. Future directions in genetic engineering have been suggested along with this paper not only for an efficient energy production from sugars and lipids but also to expand the spectrum of the products targeted as bioenergy feedstocks (isoprene, propanol and butanol). Both considerations are crucial factors to properly implement cyanobacteria in a future large-scale system of biofuel production. As indicated previously, metabolic engineering of metabolic pathways could cause unplanned and unforeseen deleterious effects on cellular function. However, engineered organisms could still be a valuable tool for bioenergy production in case the manipulated genes are ligated to specific promoters that can be turned on after the organism reaches some pre-established desirable conditions. Previous studies revealed that temperature-modulated promoters are suitable for controlling ethanol production in Synechococcus (Wood et al. 2004 ). In conclusion, the success of future generation of biofuels will rely on the advances in metabolic engineering to optimize the existing energy-related biosynthetic pathways and to reduce the stress on the genetically modified organisms. An efficient and cost-effective fuel production from biomass should decrease our current dependence on conventional energies which are both scarce and polluting.",
"introduction": "Introduction Fossil fuels, including oil, coal and natural gas, are providing about 85% of our energy need worldwide. The effective use of this energy resource in a productive and economic way still remains to be a major challenge. The main drawback of fossil fuels is that it is a finite resource and will be depleted in the near future. The term “peak oil” is commonly used to describe when peak oil production will be reached. Peak oil will be followed by a rapid decline in our oil reserves. Nashawi et al. ( 2010 ) predicted that peak oil will be reached as early as 2014. This finite nature of our fossil fuels and the dangers associated with nuclear energy, as evident by the recent nuclear disaster in Japan, emphasizes the importance of finding economically viable alternative energies. Alternative energy refers to renewable energy sources not derived from fossil fuels or nuclear power. Nowadays, there is a renewed interest in the development of sustainable energies promoted by the global concern that fossil fuels are finite, the rapid increase of energy consumption by industrialized countries, and the environmental problems caused by the burning of fossil fuels and from the management and storage of nuclear waste. Unlike fossil and nuclear fuels, alternative energy comes from natural resources (wind, sunlight, geothermal power and biomass) which are constantly replaced. Using these resources to supply our energy needs further supports sustainable development by lowering greenhouse gas emissions. The development and use of renewable energies provide a considerable number of benefits to nations around the world including an increment of the energy production, environmental protection, reduction in pollution and job creation. Solar (thermal or photovoltaic), wind, hydroelectric, biomass and geothermal energy currently constitute the most common sustainable sources of energy. Each one of these sources has particular properties that determine their usefulness and application in our society. The different characteristics of a specific energy resource can be evaluated in terms of sustainability indicators (Afgan and Carvalho 2002 ). In 2006, sustainable energies represented about 18% of the global total energy consumption (REN21 2007 ) and are able to substitute traditional fuels at different levels in our society including power generation, heating, transport fuel and rural energy. Because of its common use in developing countries for local energy supply, biomass represents the major source of renewable energy (constituting up to a 75% of the renewable energy sources) (Hall and Moss 1983 ). Bioenergy is fuel derived from biological sources (biomass) and is also referred to as biofuel. Biomass is defined as any organic material coming from any form of life or its derived metabolic products. Biofuel (either biodiesel or bioethanol) is currently the only alternative energy source able to replace transport fuel in today’s vehicles without involving major modifications to vehicle engines (Kaygusuz 2009 ). Biofuel is, however, not yet economically competitive with conventional energies. Additional input in order to collect, harvest and store the material is involved, resulting in higher manufacturing costs. Furthermore, biofuel possesses lower energy content than fossil fuels. Table 1 compares the calorific values for the different types of fuels. Biomass possesses important advantages if compared to other sustainable sources, for instance, it is available throughout the world, its processing is relatively simple without involving expensive equipment and it can be stored over long periods of time. In addition, bioenergy can be generated from organic waste material which might otherwise be discarded thus contributing to the waste management. One of the main controversial issues related to the production of biofuel is the competition between energy crops and edible crops for arable land and water. There is a scarcity of productive land available and areas occupied for bioenergy production may therefore serve for other more elemental uses, such as food production or conservation. Intensive cultivation of energy crops may also cause negative effects in the ecosystem biodiversity due to the substitution of local species and utilization of areas with some ecological value (RFA 2008 ).\n Table 1 Comparison of calorific values between conventional and alternative fuels and the corresponding references Fuel type Cal value Reference Gasoline 47.00 kJ/g \n www.engineeringtoolbox.com \n Diesel 45.00 kJ/g (Hanumantha Rao 2009 ) Biodiesel 37.27 kJ/g \n http://www.berr.gov.uk \n Methane 35.60 kJ/L (Sialve et al. 2009 ) Biogas 43.00 kJ/g \n www.engineeringtoolbox.com \n Hydrogen 150.00 kJ/g \n www.engineeringtoolbox.com \n Coal 27.00 kJ/g (Matsunaga et al. 2009 ) Ethanol 30.00 kJ/g \n www.engineeringtoolbox.com \n Bioethanol 26.72 kJ/g \n http://bioenergy.ornl.gov \n Rapeseed 39.70 kJ/g \n www.biofuelsb2b.com . Sunflower 39.60 kJ/g \n www.biofuelsb2b.com . Switchgrass 16.70 kJ/g \n www.ecn.nl \n Wheat 15.00 kJ/g \n www.biofuelsb2b.com . Peanut 39.80 kJ/g \n www.biofuelsb2b.com . Sesame 39.30 kJ/g \n www.biofuelsb2b.com . Soybean 39.60 kJ/g \n www.biofuelsb2b.com . Jatropha 39.07 kJ/g (Hanumantha Rao 2009 ) Chlorella 21.00–28.00 kJ/g (Scragg et al. 2002 ) Microalgae 25.80 kJ/g (Matsunaga et al. 2009 ) \n Although biofuels are currently more expensive than fossil fuel, their production is exponentially increasing worldwide. Ethanol production experienced a twofold rise in the last 4 years reaching 67 billion litres in 2008. The increase in biodiesel production has even been more extraordinary, increasing sixfold up to 12 billion litres, in the same period of time (REN21 2009 ). Biodiesel and bioethanol derived from edible crops, using today’s technology, do not represent an effective alternative to substitute conventional fuel due to high costs of production and the land use competition with edible crops. Therefore, transition from the first (edible crops) and second generation (lignocellulosic biomass from dedicated non-edible crops like switchgrass and agricultural waste) to a third generation of biofuel, such as microalgae, is a promising option of sustainable biofuel production. For a description of all the different generations of biofuels, Gressel ( 2008 ) should be consulted. In addition to their higher yield per hectare, microalgae cultures do not compete with agriculture, requiring neither bio-productive lands nor freshwater (Chisti 2007 , 2008 ; Griffiths and Harrison 2009 ; Mata et al. 2010 , Rittmann 2008 ). In this review, we will discuss the potential of a third generation of feedstock (focusing on cyanobacteria) as a viable biofuel source for energy production and compare it to first generation biofuel crops. We will also discuss the current state of the art for the production of H 2 , ethanol, diesel, methane, electricity and photanol from these organisms. Additionally, we will focus on the carbohydrate, lipid and amino acid metabolism and discuss the possibilities of influencing these biochemical pathways in order to improve the production of a specific biofuel and to decrease the production costs."
} | 2,754 |
39838449 | PMC11753057 | pmc | 1,668 | {
"abstract": "This study explores the use of conductive material in scaling up anaerobic digestion for enhanced biogas production. Focusing on Direct Interspecies Electron Transfer (DIET), the research employs a syntrophic DIET-able consortium formed by Shewanella oneidensis and Methanosarcina barkerii in 3.8-L experiments utilizing reticulated vitreous carbon (RVC) as conductive material. In short-term tests with acetate the syntrophic co-culture with RVC resulted in 86% higher maximum velocity of methane production, while in long term with real feed 13% increased rate was observed: the addition of 1.77 (S/m)*m 2 RVC resulted in a faster methane production of 2.39 mL/gVS*h compared to 2.08 mL/gVS*h of the reference. The experimental conditions of syntrophic inoculum and RVC as conductive material gave a benefit in terms of process rate compared to the reference, considering the inoculum fate, Methanosarcina barkerii was among the dominant taxa at the end of the experiment, while Shewanella oneidensis was outcompeted. Among the methanogenesis production pathways, an increase of hydrogenotrophic methanogenesis has been observed in presence of conductive material. Further research is needed to understand the role of RVC in sulfur compounds production. Utilization of RVC to augment methane production yielded interesting results for real-scale application. As an added carrier, RVC remains unaltered and can be readily recuperated and reused multiple times. Supplementary Information The online version contains supplementary material available at 10.1186/s13068-025-02609-6.",
"conclusion": "Conclusions In short-term AD test CM addition (RVC) increased by 84% conversion rate of methane production. Also, Shewanella oneidensis as endoelectrogen and Methanosarcina barkerii as exoelectrogen in co-culture, confirmed to syntrophically convert organics to methane by electron transfer mechanism. In the 100 days experiment with real feed, the increased methane production velocity was confirmed, even if less than short-term test, at 13%. The high versatility pathway of Methanosarcinales is promoted by CM presence and results in a more stable microbial community able to cope with the variable feed composition present in a full-scale plant.",
"introduction": "Introduction Anaerobic digestion (AD) is a microbial-based process well known and applied for centuries, to convert organic material in the value-added byproduct biogas. The initial complex organic compounds, by the action of diverse microorganisms, are first hydrolyzed in smaller molecules and then are converted stepwise through acidogenesis and acetogenesis in the metabolic precursors of methane [ 2 ]. In the challenge of decarbonization policies, AD is still one of the most promising technological options to displace coal: comparing the same energy output of coal, methane emits in fact roughly three times less CO 2 , less than a tenth of sulfur oxides, a quarter of nitrogen oxides, and essentially no particulate matter or heavy metals compared to coal [ 29 ]. Therefore, there is high interest in optimizing the process performance to improve the overall energy balance by selecting industrial residues as feedstock with specific pre-treatment technologies [ 5 , 16 , 33 ], phase separation [ 14 , 18 , 36 , 37 , 39 ] and optimizing the process management [ 35 ]. In AD, syntrophic association between Bacteria and Archaea plays a major role in the metabolic conversion of organics to methane [ 4 ]. Different microbial taxa are involved in the methane production by many sequential reactions. The overall process performance is in fact, based on the equilibrium between fermenting bacteria (acid- and aceto-genic) and methanogen archaea. One mechanism supporting the syntrophic association is the exchange of electrons between fermenter taxa and methanogens known as Interspecies Electron Transfer [ 3 , 27 ]. DIET acronym for Direct Interspecies Electron Transfer, refers to this sharing of reducing power, without any molecular carrier, but through physical connections among different microbial taxa. The two functional groups involved are the electron donors and acceptors, defined, respectively, as exoelectrogens and endoelectrogens [ 18 ]. Among the many microbial taxa potentially involved in the first part of the process, i.e., acidogenesis and acetogenesis, many Bacteria have been reported as exoelectrogens: Geobacter sp., [ 26 , 31 , 45 ], Shewanella sp., [ 41 ], Clostridium sp. [ 23 ] and Thauera sp., [ 42 ]. For the methanogenesis phase, there are three different pathways possible to produce methane: the acetoclastic, the hydrogenotrophic and the methylotrophic. Out of the three methanogenic pathways, the methylotrophic is the less found in biogas plants and the acetoclastic is usually the most reported by AD operators [ 7 ]. High interest is placed on the hydrogenotrophic pathway as by reducing carbon dioxide, could contribute to atmospheric CO 2 reduction mitigating climate change. In the last decade, several scientific papers have reported the addition of conductive material (CM) to increase the performance of AD [ 28 , 30 , 48 ]. Among these studies, many focus on direct or mediated electron transfer detailing the process conditions, the microbial strains involved and the benefit in terms of process performance and methane production [ 18 , 19 ]. Although the mechanism of electron exchange is not yet fully clarified [ 40 ], many different CMs have been shown to have a positive impact on the process performance [ 8 ]. Among the benefits cited, increased cumulative methane production, shorten of lag phase, increased rate of conversion to methane were reported [ 43 , 46 ]. It must be noted that most of the studies published are however based on small scale lab tests, using alcohols as feedstock, and monitored for short-term periods (usually less than 15 days). Although recent published results are based on real feed (inter alia [ 38 ], DIET-based AD optimization experimental data from upscaled applications and combined with real and continuous feed are relevant for the scientific community. Among the questions still not fully addressed and of interest from an industrial application point of view, are the feasibility of using DIET-able microorganisms as bioaugmentation and the maintenance of the benefit of the CM presence over the limited experimental time period (Q. [ 47 ]). In this manuscript we tested a co-culture of Shewanella oneidensis and Methanosarcina barkerii for syntrophic methane production and evaluated the feasibility to use it as syntrophic DIET-able consortium in AD with real feed and reticulated vitreous carbon (RVC) as CM addition over 100 days process. Shewanella oneidensis has been selected as known and model electrogenic organism [ 6 ] while Methanosarcina barkerii for its high versatility in methanogenic pathways. Shewanella sp. involvement in DIET mechanisms has been chosen as already proved by [ 20 , 44 ] and Methanosarcina sp. is a taxon reported in DIET studies in AD [ 12 ]. In a real case application, the conductive carrier would have a biofilm of an electrogenic bacterium involved in the first steps of AD, and the methanogenesis would have been completed by the methanogens already present. The novelty of the present work lays in the confirmation of the advantages of DIET-based AD in real conditions, in particular real sludge feed and for 100 days monitoring and the fate of a DIET-able inoculum. These aspects are not yet fully addressed in literature and are of importance for the assessment of the Technology Readiness Level (TRL) of DIET-AD optimization.",
"discussion": "Results and discussion The use of conductive material to trigger a DIET effect to improve AD and methane production is widely reported in literature [ 1 , 24 , 25 ]. This is undoubtedly an interesting approach that need to be carefully analyzed before being applied at real scale. In fact, most of the papers published recently on this topic are limited to lab scale conditions [ 30 ]. Among the critical variables that could negatively impact on full-scale application of the DIET approach, are in fact volume reactors, retention time, feeding characteristics, use of electrogenic inoculum and indigenous microbial community in the digester. At first the two pure cultures’ ability to produce methane syntrophically with CM is assessed in short-term experiments; then with real feed in presence of CM, the fate of bioaugmentation inoculum, process performance and microbial community dynamic were evaluated. Inoculum ability to produce biogas and methane The capability of Shewanella oneidensis and Methanosarcina barkerii in presence of RVC as CM to convert the acetate in biogas was investigated in batch mode by studying the kinetic parameters of the cumulative biogas production curves. To start the experiment with a grown biofilm of Shewanella oneidensis , RVC was chosen as carrier (see material and methods section) with acetate as carbon source. The nonlinear fitting of the experimental data of the three tests is reported in Fig. 1 . Fig. 1 Non-linear fitting data for the three experimental setups with three replicates for each test, Gompertz mod. equation, and least square regression. R 2 > 0.81 The biogas cumulative production for the three tests showed different trends: the reactors with CM addition (SM RVCbio and SM RVC) showed a rapid increase in biogas production reaching the plateau around 100 h, while the reactor with just the syntrophic inoculum (SM) showed a less steep slope reaching the plateau at around 2 weeks (336 h). The exponential phase recorded for the presence of CM can be explained by a fast conversion of the carbonaceous compounds in the medium by the microorganisms in a syntrophic association. The slow increase in biogas production in the reactor with the microorganisms in suspension can be explained by the inoculum composition: the two taxa caused a change in the shape of the biogas production curve with an exponential phase, showing higher metabolic activity; the curve for the bioaugmented reactors with no CM shows a slower increase that however, reaches higher max cumulative production in the timeframe of the experiment (Fig. 2 ). Fig. 2 Comparison of a max rate of biogas production (rm) and b max cumulative biogas production. One-way ANOVA, Tukey’s post hoc, one asterisk (*) identifies adjusted P values between 0.01 and 0.05, four asterisks (****) identify adjusted P < 0.0001 Within the 14 days selected as short-term timeframe, we confirmed the positive effect of CM on biogas and methane production kinetic. As the inoculum was added in sterilized conditions, the fact that biogas and methane were produced proved that the syntrophic cooperation between Shewanella oneidensis and Methanosarcina barkerii in methanogenesis did occur. Considering the reactions involved in the conversion of acetate to methane, the last phase (methanogenic) resulted for all the tests the limiting factor as proven by the experimental data fitting with mod.Gompertz equation compared to both logistic and first order [ 9 ]. CM presence does not affect which process phase is limiting which remains the methanogenesis. Studying the biogas composition, the two biogas production curves with exponential slope (SM RVC and SM RVCbio) resulted in 45.8 and 54.4% in this phase. At plateau the best performing with 60.8% was SM RVC (Table 1 ). Table 1 Methane percentages measured in the three experiments Time [h] SM SM RVC SM RVCbio Beginning – – – 45–55 32.5 45.8 54.4 210–220 55.7 60.8 55.7 The comparison of the kinetic data for biogas production showed that the test SM RVCbio had the highest rate (r m ) compared to both the SM and the SM RVC, considering then the maximum cumulative production (Y m ), the highest value was for the two taxa in suspension (SM). Analyzing the biogas production rate (r m ) the three tests gave different results: test SM with the two microbial taxa in suspension gave the lowest value, higher was SM RVC with the conductive carrier and the highest value was recorded for SM RVCbio the test with microorganisms, conductive carrier and Shewanella sp. biofilm. The Y m kinetic parameter comparison resulted in statistically significant different values with the SM reactor with highest value and then the two tests with RVC addition. As the three tests were designed equal for the microorganisms’ content, considering also the Shewanella sp. cells attached to the RVC carrier, we can derive that the increased rate is due to the conductive carrier presence. In addition, the fact that the test with the added Shewanella sp. cells on RVC (SM RVCbio) gave an increased rate can support the occurrence of a DIET mechanism involved: syntrophic cells already attached on the surface, could exploit better the electric conductivity of the material. This finding is in agreement with literature as reported by [ 20 , 30 ]. Therefore, considering the material characteristics of RVC and the experimental settings, the data showed that the addition of 8.78 g RVC /L of RVC as CM with an available surface of 1.79 m 2 resulted in 86% higher maximum velocity comparing SMRVCbio (2.57 mL/g VS *h) with SM (0.37 mL/g VS *h). Fate of syntrophic DIET-able inoculum in real conditions The benefit of a bioaugmentation with DIET-able microorganisms in presence of indigenous microbial community and the long-term advantage of CM addition were addressed in the second set of experiments. Biogas and methane production measured at each feed were analyzed and the kinetic parameters calculated by nonlinear fitting (Gompertz mod) are reported in Figs. 3 , 4 Fig. 3 Rate of biogas production compared among feeds asterisks indicates significance (**** P < 0.0001 and ** P = 0.0032) one-way ANOVA Fig. 4 Biogas and methane production measured for each feed The maximum rate of biogas production (r m ) was significantly different for the two conditions at 1004 h and from the last two feeds, when the AD MRVCbio showed a faster biogas production as resulted by one-way ANOVA, Tukey’s post hoc, and adjusted P < 0.0001. Considering then the maximum cumulative biogas production (Y m ), at the beginning of the experiment, at the first feeding the AD MRVCbio had higher production and then the situation changed with the reference (reactor AD) producing statistically more cumulative biogas. Another important factor for the energetic value of biogas is the methane content measured as percentage and then converted to volumes: for AD the lowest value was 137.2 mL CH 4 /g VS, and for AD RVCbio was 164.5 mL CH 4 /g VS both measured at 1341 h. For both experiments, the highest values (401.7 for AD and 357.1 mL CH 4 /g VS for AD RVCbio) were recorded at the end of the monitoring (2354 h). In the tests were added 8.78 g RVC /L of CM corresponding to a surface 1.79 m 2 available for microbial attachment. In fact, by SEM observation, it was possible to visualize the RVC carrier fragment sampled from AD MRVCbio test, with microorganisms attached to the surface: putative Methanosarcina sp. cells are clearly recognizable due to the characteristic sarcinae morphology (see Fig. 5 ). Fig. 5 SEM observation of biofilm grown on carriers after 2354 h. Intensity 10 kV, secondary electrons mode and 9900 × magnification, arrow indicates a putative Methanosarcina cells Literature data on CM presence in 14 days tests in batch conditions reported the advantages for maximum methane production, increased velocity and reduced lag phase [ 15 ], in the present research, we show that with the addition of 1.77 (S/m)*m 2 RVC as carrier, after 100 days of process resulted in a faster methane production: 13% higher maximum velocity comparing AD MRVCbio (2.39 mL/gVS*h) with AD (2.08 mL/gVS*h). This advantage can be translated for process engineering applications in shorter retention time with possible smaller reactors volume. Microbial community dynamic with RVC and DIET-able inoculum addition In order to evaluate the fate of the syntrophic inoculum in presence of the complex community of the digestate, sequencing data were statistically evaluated. The compositional complexity of the whole microbial community in the two experimental settings evaluated as differences in the alpha diversity by Wilcoxon rank sum tests, showed that the two communities had the same complexity; the two settings however did evolve over time significantly diverse communities as evidenced by beta diversity indexes and compared by Wilcoxon Rank Sum and Signed Rank Tests as shown in Fig. 6 . Microbial communities after 669 h separated in two clusters showing that a change in composition became observable, maintaining however the same level of complexity (alpha diversity). Fig. 6 Cluster distance of the two communities over time. In red the OTUs sampled at different times for the AD MRVCbio and in green the OTUs sampled from AD reactors Most abundant OTUs The taxa abundance was evaluated at the different taxonomic ranks and the order was chosen as the most informative level. The abundance over time of the 20 taxa at order level is reported in Fig. 7 . In the heatmap it is possible to observe that the different experimental conditions resulted in similar community trends: most dominant taxa are represented by archaeal microorganisms belonging to the order of Methanotrichales , Methanomicrobiales and Methanobacteriales and the bacterial taxa of the orders Eubacteriales , Marinilabiliales , Anaerolineales , Syntrophales and Burkholderiales . Over time the bacterial Marinilabiliales , Anaerolineales , Syntrophales, Burkholderiales and the archaeal Methanobacteriales become less abundant, while the archaeal taxa of the Methanosarcinales become important and the bacterial Thermolithobacterales , Thiotrichales and the Synergistales increase. Fig. 7 Heatmap of the most abundant 20 taxa reported at order level Comparing then the two experimental settings, the difference in relative abundance for the reference microbial community (AD) respect to the reactors with the inoculum and the RVC addition (AD MRVCbio) results to be in the archaeal community composition dynamic: in the reference (AD) is reported an increase in the relative abundance of Methanotrichales, Methanomicrobiales and Methanosarcinales and a decrease in Methanobacteriales, while for the AD MRVCbio community Methanotrichales, Methanomicrobiales decrease their abundance and the Methanosarcinales increase up to become the most abundant of the 20 orders reported. Figure 8 reports the distance-based Redundancy Analysis (db-RDA) ordination plot that shows which process parameters as predictors can explain the taxonomical composition of the microbial community in the experimental settings. The contribution of the process parameters (process time and biogas volume), feeding characteristics (TS, VS, COD and total N) and biogas composition (CH 4 , CO 2 , H 2 S) in the shaping of the taxa composition resulted significant for time and H 2 S. Fig. 8 dbRDA describing the correlations between environmental variables and taxa. Pearson correlation method, 999 permutations and Mantel test For the AD MRVCbio test the microbial communities sampled at the end of the process (2014 and 2354 h) with Methanosarcinales playing an important role, are influenced by time and described by methane and CO 2 . Syntrophic inoculum fate Analyzing the taxa abundance expressed in terms of percentage, we focused on order level as it allowed to study the fate of the inoculum added in the AD MRVCbio experiment. Although Shewanella oneidensis was inoculated in the experiment AD MRVCbio, it was not found among the most relevant OTUs. Shewanella oneidensis belongs in fact to the order of Alteromonadales that was not among the most abundant identified taxa. The fact that it was not found abundant even in the sample after inoculation can be explained with over competition with the indigenous microorganisms or lack of expected cell diffusion from the initial biofilm into the digestate. Instead, Methanosarcina barkerii taxon was present both in the test with the added Methanosarcina barkerii in all the samples, but also in the non-inoculated reactors. Methanosarcina barkerii confirmed therefore to have an active role in the AD process and among the indigenous microorganisms of the digestate sampled from Chiasso plant. The changes in the microbial community potential function were evaluated by focusing on the capabilities of metabolic transformation of the compounds that showed to have an impact in shaping the microbial community structure as per db-RDA analysis: therefore, process time, H 2 S and biogas composition (CH 4 and CO 2 ) were considered. Methanotrix sp. (previously known as Methanosaeta sp.) is a known acetoclastic methanogen [ 21 ] and the Methanosarcinales taxa are known to have high versatility in the methane production pathways: Methanosarcina barkerii has proved to have acetoclastic, hydrogenotrophic, and methylotrophic pathway enzymes [ 13 ]. Methanosarcinales increased with the addition of RVC proving to give high redundancy in the methanogenesis production that results in a microbial community more resilient and a higher stability of the process [ 34 ]. Methanosarcinales also produce methane through an interspecific electron transfer process, contributing to its relative abundance [ 22 ], so it stands to reason that a CM addition is influencing the Methanosarcina taxon abundance. Also, the fact that we observed a decrease in the Methanotrichales order and a corresponding increase in the Methanosarcinales order at 100 days contact with RVC could indicate a shift toward the hydrogenotrophic methanogenesis. With regard to sulfur compounds metabolism, an increase over time in the abundance of taxa related to sulfur metabolism is observed for both reactors’ conditions, it seems that RVC presence could influence the H 2 S production, but data are not yet conclusive and further research will address this topic. The advantage in methane production rate quantified as 86% with RVC presence in short-term tests is reduced to 13% at 100 days process in semicontinuous mode. These results are in agreement with literature data considering batch mode of short-term test: [ 32 ] in an interesting review reported in fact that methane production rate increased between 79 and 300% in short-term laboratory-scale experiments with several material (Granulated Activated Carbon -GAC-, biochar, carbon cloths and graphene). The only value reported for continuous operation mode measured in 0.5L volume and acetate as substrate, resulted in 80% more methane production rate: the difference in the augmented rate is due to the different material used GAC instead of RVC, but mostly to the retention time considered 20 days compared to the 100 monitored in the present experiments. It is worth mentioning that the RVC used in this research experiments was acquired from an aerospace company as it could guarantee the quality and purity of the material, as a consequence the material cost makes its application in the AD industry less favorable than other materials such as biochar or activated carbon. The RVC was however added as solid carrier at the beginning of the tests and the chosen solid structure made it easy to recover it from the reactor to be reused. Biochar on the other hand could release chemicals with potential inhibiting effects, making long-term assessment in presence of indigenous microorganisms difficult."
} | 5,909 |
22028591 | PMC3197265 | pmc | 1,669 | {
"abstract": "Insecurity in the supply of fossil fuels, volatile fuel prices, and major concerns regarding climate change have sparked renewed interest in the production of fuels from renewable resources. Because of this, the use of biodiesel has grown dramatically during the last few years and is expected to increase even further in the future. Biodiesel production through the use of microbial systems has marked a turning point in the field of biofuels since it is emerging as an attractive alternative to conventional technology. Recent progress in synthetic biology has accelerated the ability to analyze, construct, and/or redesign microbial metabolic pathways with unprecedented precision, in order to permit biofuel production that is amenable to industrial applications. The review presented here focuses specifically on the role of synthetic biology in the design of microbial cell factories for efficient production of biodiesel.",
"conclusion": "4. Conclusions and Future Perspectives As noted throughout this review, increasing interest in the development of microbial processes for the efficient production of biodiesel has emerged in recent years. Metabolic engineering and synthetic biology, as the latest approaches, have been essential for allowing new technologies to be developed, as evident in the design and refinement of microbial cell factories amenable to industrial applications. The research cited in this review clearly demonstrates the feasibility of direct production of biodiesel by microbes. The possibility of developing a synthetic host for efficient target-molecule production presents great opportunities for further biofuel research. However, investment of significant amounts of time and effort is still required in order to produce a better host, carrying novel metabolic pathways to lead to satisfactory biofuel production. This is because the complexity of the intertwined metabolic pathways creates substantial limitations. Development of a recombinant host that possesses a well-defined metabolic pathway, but which is also devoid of any competing pathways to hinder the production of a specific metabolite, can be seen as the most desirable goal. However, mere engineering of all of the genes needed to produce the desired pathway is not sufficient to confer a novel characteristic to a recombinant cell, since there are always many other unexplored pathways as well. Even a simple biocatalyst like E. coli is a complex system involving an estimated 4,603 genes, 2,077 reactions, and 1,039 unique metabolites [ 69 , 84 ]. Fortunately, it appears likely that the formal integration of functional genomics and systems biology with synthetic biology and metabolic engineering will lead to enhancement of lipid accumulation and improved engineered pathways for in vivo biodiesel synthesis. Microorganisms may therefore become an ideal platform for the production of biodiesel in the future.",
"introduction": "1. Introduction Global warming and the continued depletion of nonrenewable fuel resources are two major problems that entangle our planet today and demand immediate solutions [ 1 ]. The extensive use of fossil fuels has caused greenhouse gas emissions and damage to the environment, and has also led to the current instability of oil supplies and continuous fluctuations in prices. These factors, which revolve around economic, environmental and geopolitical issues, are central to the continued interest seen in renewable energy sources [ 2 ]. An entire branch of biotechnology, referred to as “white biotechnology” [ 3 ], centers on the bioproduction of fuels and chemicals from renewable sources. For biofuels, delicate optimization, and fine tuning of these processes to maximize productivity and yield is of particular concern, as the viability of any biofuel process is extremely sensitive to factors related to both raw material supply and production costs [ 4 ]. About 90% of the current biofuel market is represented by biodiesel and bioethanol. However, bioethanol is not seen as an ideal biofuel for the future because of its low energy density and incompatibility with the existing fuel infrastructure [ 5 , 6 ]. On the contrary, biodiesel is already better established [ 7 ] and is preferable to petrodiesel in terms of several characteristics, such as environmental friendliness, renewability, reduced emissions, higher combustion efficiency, improved lubricity, and higher levels of safety [ 8 ]. Chemically, biodiesel comprises a mixture of fatty acid alkyl esters (FAAEs). The most commonly used method to produce biodiesel is the in vitro transesterification process, where triacylglycerides (TAGs) of vegetable oils are combined with methanol to form fatty acid methyl esters (FAMEs) and the byproduct glycerol ( Figure 1 ). Alkalies (e.g., sodium hydroxide, potassium hydroxide, sodium metoxide, and potassium metoxide) [ 9 – 12 ], acids (e.g., sulfuric acid) [ 13 ], or enzymes can be used to catalyze this reaction [ 14 ]. However, issues related to high cost and limited availability of vegetable oils have become growing concerns for large-scale commercial viability of biodiesel production [ 15 ]. Also, the in vitro transesterification reaction presents some unresolved issues, such as the need to use large amounts of toxic compounds (sodium hydroxide, sulfuric acid, or methanol) and the high cost of isolation and immobilization of enzyme catalysts [ 16 , 17 ]. Various approaches to addressing these problems have been explored. First, increasing interest in developing microbial processes for the production of biodiesel from a wide range of other raw materials may represent a promising alternative to the vegetable oils. Second, technologies now exist that use living cells to synthesize products that are more easily biodegradable, require less energy, and create less waste during production than those obtained by chemical synthesis. In order for a fermentation process to compete with existing petroleum-based processes, the target molecule must be produced at high levels of yield, titer, and productivity. These goals can be difficult to attain with naturally occurring microbes. While metabolic engineering has enabled extraordinary advances in the redesign of pathways for efficient target molecule production, including biofuels [ 5 , 18 – 20 ], tools from synthetic biology make it possible to create new biological functions that do not exist in nature. Essentially, this is achieved either by heterologous expression of natural pathways or design of de novo pathways. This paper reviews approaches to microbial synthesis of biodiesel, focusing on the role of synthetic biology as an enabling technology in the design of optimal cell factories."
} | 1,675 |
32444627 | PMC7244743 | pmc | 1,670 | {
"abstract": "The mutualistic association between leguminous plants and endosymbiotic rhizobial bacteria is a paradigmatic example of a symbiosis driven by metabolic exchanges. Here, we report the reconstruction and modelling of a genome-scale metabolic network of Medicago truncatula (plant) nodulated by Sinorhizobium meliloti (bacterium). The reconstructed nodule tissue contains five spatially distinct developmental zones and encompasses the metabolism of both the plant and the bacterium. Flux balance analysis (FBA) suggests that the metabolic costs associated with symbiotic nitrogen fixation are primarily related to supporting nitrogenase activity, and increasing N 2 -fixation efficiency is associated with diminishing returns in terms of plant growth. Our analyses support that differentiating bacteroids have access to sugars as major carbon sources, ammonium is the main nitrogen export product of N 2 -fixing bacteria, and N 2 fixation depends on proton transfer from the plant cytoplasm to the bacteria through acidification of the peribacteroid space. We expect that our model, called ‘Virtual Nodule Environment’ (ViNE), will contribute to a better understanding of the functioning of legume nodules, and may guide experimental studies and engineering of symbiotic nitrogen fixation.",
"introduction": "Introduction Macroorganisms are colonized by a staggering diversity of microorganisms, collectively referred to as a ‘holobiont’ 1 , 2 . The intimate association between organisms is often driven by metabolic exchanges: many insects obtain essential nutrients from obligate bacterial symbionts 3 , most plants can obtain phosphorus from arbuscular mycorrhiza in exchange for carbon 4 , and the gut microbiota is thought to contribute to animal nutrition 5 , 6 . Complex global patterns often emerge during these intimate biological associations 7 , especially when nutritional inter-dependencies are involved 8 – 10 . The communication between the two metabolic networks of the interacting organisms may give rise to unpredicted phenotypic traits and unexpected emergent properties. Metabolic relationships can span over a large taxonomic range and have profound biological relevance 11 – 14 . For example, the interactions between bacteria and multicellular organisms have been suggested to be key drivers of evolutionary transitions, leading to eukaryotic diversification and to the occupancy of novel niches 9 , 15 , 16 . The study of the association of two biological entities is mainly challenged by the size of the system and by the unpredictability of their metabolic interactions. Theoretical, systems-level models are required to unravel the intimate functioning of metabolic associations and to eventually exploit their potential in biotechnological applications. Symbiotic nitrogen fixation (SNF) is a paradigmatic example of the importance and the complexity of natural biological associations. SNF is a mutualistic relationship between a group of plant families, including the Fabaceae, and a polyphyletic group of alpha- and beta-proteobacteria known as rhizobia, or a taxa of Actinobacteria ( Frankia spp.), in which the plants provide a niche and carbon to the bacteria in exchange for fixed nitrogen 17 . SNF involves constant metabolic cross-talk between the plant and the bacteria 18 , and it is a paradigmatic example of bacterial cellular differentiation 19 and sociomicrobiological interactions 20 . The rhizobia intra-cellularly colonize plant cells of a specialized organ known as a root (or stem) nodule. The intra-cellular rhizobia (referred to as bacteroids) are surrounded by a plant-derived membrane, and the term symbiosome is used in reference to the structure consisting of the bacteroid, the plant-derived membrane (i.e., the peribacteroid membrane), and the intervening space (i.e., the peribacteroid space). Nodules with an indeterminate structure, such as those formed by the plant Medicago truncatula , are divided into spatially distinct developmental zones 21 with a distal apical meristem and a proximal nitrogen fixation zone. SNF plays a key role in the global nitrogen cycle and is central to sustainable agricultural practices by reducing the usage of synthetic nitrogen fertilizers whose application results in a multitude of adverse environmental consequences 22 – 24 . Unfortunately, our ability to maximize the benefit of SNF is limited since rhizobial inoculants are often poorly effective due to low competitiveness 25 , 26 and because rhizobium symbioses are specific to leguminous plants. Manipulating the rhizobium – legume interaction for biotechnological purposes will require an in-depth understanding of the symbiotic interaction, as well as an ability to predict the consequences of genetic changes and environmental perturbations. From a metabolic perspective, genome-scale metabolic reconstruction (GENREs) and constraint-based modeling has great potential to fulfill these roles. A GENRE also serves as a comprehensive knowledgebase of an organism’s metabolism, containing hundreds to thousands of metabolic and transport reactions, most of which are linked to the corresponding gene(s) whose gene product(s) catalyzes the reaction 27 , 28 . With the aid of mathematical approaches such as flux balance analysis (FBA), GENREs can be used to identify emergent system-level properties, to predict active reactions, and to identify essential genes 29 . Compared to simple enrichment analyses that are typical in-omics studies, GENRE-based methods allow for the interpretation of data in a connected manner based on network topology and to infer the effects of changes in remote pathways on the overall cell physiology. When considering interacting entities, for example, this approach can predict the consequence of mutations in one organism on the metabolism of the other. However, multi-organism metabolic reconstructions are still in their infancy, and very few examples of combined models exist compared to single strain GENREs 8 , 14 , 30 – 32 . Despite the importance of metabolism to SNF 18 , there has been limited use of metabolic modeling in the study of rhizobia and SNF. To date, GENREs of varying quality have been reported for only three rhizobia: Sinorhizobium meliloti 33 – 35 , Rhizobium etli 36 – 38 , and Bradyrhizobium diazoefficiens 39 . Currently, M. truncatula 40 and Glycine max 41 are the only legumes with published GENREs. With the exception of the G. max GENRE, these GENREs have been used in preliminary analyses of SNF, providing results generally consistent with expectations. However, all analyses to date suffer from two major limitations. Simulations with the rhizobium models ignore plant metabolism, while simulations with the M. truncatula GENRE (based on the genome sequence published in 2011 42 , which has since been updated in 2014 43 and again 2018 44 ) involved a very limited draft S. meliloti metabolic reconstruction (whose genome sequence was published in 2001 45 ). Furthermore, all simulations have focused on the final stage of SNF and have not considered the different steps of the preceding developmental progression where metabolism remains poorly understood 18 . Here, we report a holistic in silico representation of the integrated metabolism of the holobiont consisting of a M. truncatula plant nodulated by S. meliloti , which we refer to as a Vi rtual N odule E nvironment (ViNE). Our combined, multi-compartment reconstruction accounts for the metabolic activity of shoot and root tissues together with a nodule consisting of five developmental zones. We report initial characterizations of ViNE using FBA, including zone-specific metabolic properties, trade-offs between nitrogen-fixation and plant growth, and the usage of dicarboxylates as a carbon source by bacteroids. Going forward, we expect ViNE will provide a powerful platform for hypothesis generation aimed at understanding and quantitatively evaluating SNF, as well as guiding attempts at engineering SNF for increased symbiotic efficiency.",
"discussion": "Discussion Models of the integrated metabolism of various holobionts (consisting of a host and its symbiotic microorganisms) would be valuable tools to understand the emergent properties of these systems 92 , 93 . However, to date there are few examples of constraint-based metabolic modeling being used to study metabolic interactions [e.g. 8 , 31 ], with this approach most commonly used to study the human gut microbiome 94 . Here, we developed a broadly adaptable pipeline for modeling the metabolism of interacting organisms across physiologically distinct tissue (sub)sections. Using metabolic network reconstruction and constraint-based modeling, we studied the metabolism of a legume root nodule and SNF, a well-established model of inter-organismal metabolic exchange and cellular differentiation. Our model (ViNE) accounts for plant shoot, root, and nodule tissues, with the nodule encompassing the metabolism of both the plant and bacterial partners and subdivided into five developmental zones. This is an advance over previous attempts at modeling SNF 33 , 35 – 40 , most of which focused solely on bacterial metabolism while treating the plant as a black box. The increased complexity of ViNE allows for: i) more accurate simulations of the nutrient exchange, ii) analysis of the metabolic differentiation associated with nodule development, iii) examination of unexpected emergent properties of the symbiosis resulting from inter-organism interactions, and iv) the possibility to perturb the network at the single reaction level. Initial simulations with ViNE supported that this model does a good job at capturing the metabolism of a legume nodule. Nevertheless, as with all models, ViNE predictions were imperfect; of the 38 genes both present in ViNE and listed as being involved in symbiosis according to Additional File 5 of Galardini et al. 95 (excluding genes involved in early nodulation, a process absent in ViNE), deletion of 84% were correctly predicted to have a symbiotic phenotype in ViNE. However, as we often compared simulated phenotypes for M. truncatula with experimental data for M. sativa , and given that rhizobium mutant phenotypes are often plant specific (e.g. 96 – 98 ), we cannot rule out that some of the inconsistencies are the result of plant-specific phenotypes. Going forward, we intend to continue to manually refine and update ViNE to maximize consistency with experimental observations. FBA simulations with ViNE revealed a pattern of diminishing returns in terms of plant growth (as a proxy for fitness) as the N 2 -fixation efficiency increased, assuming that the rate of nodulation could also vary (Fig. 5c ). This observation has potential implications for engineering SNF for biotechnological applications. It suggests that when developing rhizobial inoculants, maximizing competition for nodule occupancy may have a greater impact than maximizing the rate of N 2 -fixation. This result also supports efforts aimed at engineering N 2 -fixing symbiosis with cereals 99 by highlighting how even a low-efficiency symbiosis has the potential to have a noticeable benefit on crop yield. At the same time, the pattern of diminishing returns is interesting from an evolutionary perspective 100 . In particular, the evolution of N 2 -fixation efficiency may be influenced by the rhizobium community diversity, assuming that nodule infection increases the fitness of rhizobia 101 . In an environment dominated by a single rhizobium, kin selection may favor the evolution of a poorly efficient symbiosis as it would increase nodule number and thus the size of the niche for colonization by the rhizobia. On the other hand, in a highly diverse environment, evolution of strains capable of entering into a highly efficient symbiosis may be favored, as this would lead to fewer nodules and thus less plant resources being allocated to competing rhizobium strains, thereby limiting the spread of less mutualist (viz. cheater) strains 20 , 102 . Of particular interest to us were the metabolic exchanges between the plant and rhizobia, both during N 2 -fixation and during differentiation. The carbon source(s) of differentiating rhizobia remain poorly understood. Results with ViNE suggested that sucrose may be a major carbon source for the differentiating bacteroids. However, S. meliloti mutants unable to transport sucrose are not impaired in nodule formation 103 , suggesting that differentiating bacteroids have access to at least one other carbon source. Interestingly, a S. meliloti pyc mutant unable to grow with glycolytic carbon sources was not impaired in differentiation 60 . Similarly, S. meliloti pckA 58 and tpi 104 mutants unable to grow with gluconeogenic carbon sources remained capable of differentiating. Thus, it seems likely that differentiating bacteroids have access to a variety of glycolytic and gluconeogenic carbon substrate, with sugars possibly serving as the main carbon source in wild type nodules. If so, the restriction of carbon flow to N 2 -fixing bacteroids to just C 4 -dicarboxylates may be the result of active remodeling of the peribacteroid membrane during differentiation. In attempting to identify conditions favoring the use of C 4 -dicarboxylates as a carbon source by N 2 -fixing bacteroids, ViNE also provided insights into the metabolic exchange in the N 2 -fixation zone. The peribacteroid space of N 2 -fixing bacteroids is known to be acidic due to the activity of H + -ATPases on the peribacteroid membrane 81 – 83 . This acidification contributes to the import of C 4 -dicarboxylates and the export of ammonium from/to the plant cytosol and the peribacteroid space 105 , and it may contribute to the lysis of non-functional symbiosomes 106 . Our FBA simulations suggest that the plant-derived protons of the peribacteroid space may also be actively used by the bacteroid to support its metabolism. Although it is generally accepted that nodules are low oxygen environments 84 , the site of O 2 -limitation has been debated. Based on the average concentration of free oxygen in the nodule, enzyme kinetics data are consistent with the mitochondria being O 2 -limited and the bacteroids being O 2 -sufficient 84 – 87 . Measurements of the adenylate pools of the plant and bacterial nodule fractions support this conclusion 90 . However, others have argued that nodule adenylate measurements suggest that bacteroids, not the plant, are O 2 -limited 107 . Similarly, it was suggested that mitochondria cluster near the periphery of the cell near air pockets, resulting in elevated local O 2 concentrations 108 , 109 . The FBA results presented here predicted that C 4 -dicarboxylates are the optimal carbon source for N 2 -fixing bacteroids only when the plant mitochondria are O 2 -limited while the bacteroids are O 2 -sufficient (Fig. 6 ). This result supports the hypothesis that mitochondria, and not bacteroids, are O 2 -limited in wild type nodules. Although potentially powerful, the use of metabolic modeling to study SNF is not without limitations. In particular, the accuracy of predictions is restricted by the quality of the imposed flux constraints, and unfortunately, experimental kinetic data for key enzymes and nutrient exchange reactions in the nodule is generally lacking. FBA also fails to actively incorporate regulatory feedback control during simulations that could influence the metabolic properties of the nodule. Furthermore, the lack of finished legume genomes, an incomplete ability to ensure correct subcellular compartmentalization in eukaryotic cells, and difficulty in experimentally validating the functions of plant genes can limit the quality of models of SNF. In sum, this work presented a complex metabolic model representing the full metabolism of a rhizobium-nodulated legume, as well as a series of simulations demonstrating the potential for this model to help address genetic, evolutionary, metabolic, and sociobiological questions. Future work will be aimed at continuing to refine and improve the quality of the model, and to using it to generate hypotheses to guide experimental studies and to assist in the interpretation of experimental datasets."
} | 4,059 |
36874510 | PMC9979088 | pmc | 1,671 | {
"abstract": "Environmental sustainability is an increasingly important issue in industry. As an environmentally friendly and sustainable way, constructing microbial cell factories to produce all kinds of valuable products has attracted more and more attention. In the process of constructing microbial cell factories, systems biology plays a crucial role. This review summarizes the recent applications of systems biology in the design and construction of microbial cell factories from four perspectives, including functional genes/enzymes discovery, bottleneck pathways identification, strains tolerance improvement and design and construction of synthetic microbial consortia. Systems biology tools can be employed to identify functional genes/enzymes involved in the biosynthetic pathways of products. These discovered genes are introduced into appropriate chassis strains to build engineering microorganisms capable of producing products. Subsequently, systems biology tools are used to identify bottleneck pathways, improve strains tolerance and guide design and construction of synthetic microbial consortia, resulting in increasing the yield of engineered strains and constructing microbial cell factories successfully.",
"conclusion": "4 Conclusions Constructing microbial cell factories to produce various products is an environmentally friendly and sustainable way, which has attracted more and more attention. This review summarizes the recent applications of systems biology in the design and construction of microbial cell factories. And it is summarized from four perspectives, including functional genes/enzymes discovery, bottleneck pathways identification, strains tolerance improvement and design and construction of synthetic microbial consortia. In conclusion, systems biology plays a crucial role in the design and construction of microbial cell factories. And this review is expected to provide reference for the construction of microbial cell factories for more valuable products in the future from the perspective of systems biology.",
"introduction": "1 Introduction Environmental sustainability is an increasingly important issue in industry. So, it is critical to find a sustainable, green and clean way to produce enzymes, fuels, bulk chemicals and natural products for maintaining a sustainable social economy. As an environmentally friendly and sustainable way, biotechnological industry has attracted more and more attention. With the increased size of the bio-based production market, microbial cell factories offer a promising approach to manufacturing valuable products from renewable resources [ 1 ]. More and more high-value chemicals have been successfully industrialized through microbial cell factories, such as raspberry ketonel [ 2 ], salidroside [ 3 ], gastrodin [ 4 ], salvianic acid A [ 5 ] and artemisinin [ 6 ]. One noteworthy example is the development of a process for the production of 1,4-butanediol directly in Escherichia coli by the company Genomatica (San Diego, CA), which reached the production scale of 30,000 tons/year [ 7 ]. Thus, we can see the great potential of using microbial cell factories to produce all kinds of valuable products. However, from the design to the construction of microbial cell factories, there are some crucial problems to resolve. Firstly, the biosynthetic pathways of most desired products are not fully elucidated and enzymes that catalyze proposed pathways remain unknown. Therefore, finding crucially functional genes/enzymes involved in the biosynthetic pathways is the first key step to construct microbial cell factories. After the biosynthetic pathways are constructed in suitable hosts, it's key to find bottleneck pathways that limit the improvement in the yield of desired products, and then alleviate these bottleneck pathways to facilitate the construction of microbial cell factories. In the process of constructing microbial cell factories, substrates, toxic intermediates, products or other environmental conditions may limit the growth ability of strains and the yield of products. Therefore, it's also crucial to improve the tolerance of strains to various stresses. In addition, during the process of constructing microbial cell factories, using single engineered strain to produce chemicals often faces a large metabolic burden due to the long and complex biosynthetic pathways, resulting in low production efficiency. Design and construction of synthetic microbial consortia is an effective strategy to relive metabolic burden and improve production efficiency. Systems biology, as an important tool, plays an important role in solving these mentioned problems to facilitate the design and construction of microbial cell factories. The scope of systems biology is to investigate biological systems in a holistic manner to elucidate the mechanisms underlying the cellular behavior [ 8 ]. With the continuous development of systems biology, it has been possible to comprehensively understand the metabolic network of strains from the genomic scale, including the structural genes that constitute metabolic pathways, the complex regulatory mechanisms of cell metabolism, and the effects of genetic and environmental disturbances on cell global metabolism, so as to establish metabolic models to evaluate and predict the possible effects of genetic engineering operations [ 9 , 10 ]. And omics technologies (e.g. genomics, transcriptomics, proteomics, metabolomics, and fluxomics) are the major analytical tools of systems biology. By analyzing the metabolic network of strains obtained by genetic engineering, we can better guide the metabolic engineering and improve the physiological function and production efficiency of strains. Genomics, transcriptomics and proteomics are the most common tools to discover functional genes/enzymes involved in the biosynthetic pathways of desired products. To identify bottlenecks in metabolic pathways, metabolomics is one of the most effective tools. Through the detection of relevant metabolic perturbations, metabolomics can identify potential bottleneck pathways for strains improvement [ 11 ]. For improving the tolerance of strains, transcriptomics, proteomics and metabolomics can be used to identify potential tolerance-related genes, proteins and metabolites to guide strains tolerance engineering. For designing and constructing microbial consortia, systems biology analysis can be employed to systematically analyze the genetic and metabolic pathways in microbial consortia, contributing to elucidating comprehensive molecular mechanisms of interactions in microbial consortia. In conclusion, from design to construction of microbial cell factories, systems biology plays an important role [ 12 ]. The general process of design and construction of microbial cell factories based on systems biology is shown in Fig. 1 . First, systems biology tools are employed to identify functional genes/enzymes involved in the biosynthetic pathways of products. These discovered genes will be introduced into appropriate chassis strains to build engineering strains capable of producing products. Then, in order to improve the yield of engineered strains for facilitating the construction of microbial cell factories, systems biology tools are used to identify bottleneck pathways, improve strains tolerance and guide design and construction of synthetic microbial consortia. After these optimization steps, microbial cell factories capable of high-yielding products are successfully constructed. Fig. 1 Design and construction of microbial cell factories based on systems biology analysis. Fig. 1 This review summarizes the recent applications of systems biology in the design and construction of microbial cell factories. And it is summarized from four perspectives, including functional genes/enzymes discovery, bottleneck pathways identification, strains tolerance improvement and design and construction of synthetic microbial consortia."
} | 1,983 |
30693008 | PMC6339933 | pmc | 1,672 | {
"abstract": "The implementation of sustainable agriculture encompasses practices enhancing the activity of beneficial soil microorganisms, able to modulate biogeochemical soil cycles and to affect soil fertility. Among them, arbuscular mycorrhizal fungi (AMF) establish symbioses with the roots of most food crops and play a key role in nutrient uptake and plant protection from biotic and abiotic stresses. Such beneficial services, encompassing improved crop performances, and soil resources availability, are the outcome of the synergistic action of AMF and the vast communities of mycorrhizospheric bacteria living strictly associated with their mycelium and spores, most of which showing plant growth promoting (PGP) activities, such as the ability to solubilize phosphate and produce siderophores and indole acetic acid (IAA). One of the strategies devised to exploit AMF benefits is represented by the inoculation of selected isolates, either as single species or in a mixture. Here, for the first time, the microbiota associated with a commercial AMF inoculum was identified and characterized, using a polyphasic approach, i.e., a combination of culture-dependent analyses and metagenomic sequencing. Overall, 276 bacterial genera were identified by Illumina high-throughput sequencing, belonging to 165 families, 107 orders, and 23 phyla, mostly represented by Proteobacteria and Bacteroidetes. The commercial inoculum harbored a rich culturable heterotrophic bacterial community, whose populations ranged from 2.5 to 6.1 × 10 6 CFU/mL. The isolation of functional groups allowed the selection of 36 bacterial strains showing PGP activities. Among them, 14 strains showed strong IAA and/or siderophores production and were affiliated with Actinomycetales ( Microbacterium trichotecenolyticum, Streptomyces deccanensis/scabiei ), Bacillales ( Bacillus litoralis, Bacillus megaterium ), Enterobacteriales ( Enterobacter ), Rhizobiales ( Rhizobium radiobacter ). This work demonstrates for the first time that an AMF inoculum, obtained following industrial production processes, is home of a large and diverse community of bacteria with important functional PGP traits, possibly acting in synergy with AMF and providing additional services and benefits. Such bacteria, available in pure culture, could be utilized, individually and/or in multispecies consortia with AMF, as biofertilizers and bioenhancers in sustainable agroecosystems, aimed at minimizing the use of chemical fertilizers and pesticides, promoting primary production, and maintaining soil health and fertility.",
"introduction": "Introduction Worldwide, a major shift is taking place in agriculture, in order to meet the growing global demand for a safe production of high-quality food, able to maintain or enhance environmental quality and to conserve natural resources for future generations. The implementation of sustainable agriculture encompasses practices enhancing the activity of soil biogeochemical cycles, at the basis of long-term soil productivity and health. The most important players of soil biological fertility are represented by beneficial soil microorganisms, able to modulate biochemical and physiological soil processes, and to affect its biological and nutritional characteristics (Barea et al., 2005 ). Among them, arbuscular mycorrhizal fungi (AMF, Glomeromycota) are recognized as ecologically and economically important elements of sustainable food production systems, given the key role played in plant nutrition and health, by reducing the input of chemical fertilizers and pesticides (Smith and Read, 2008 ). AMF are obligate mutualistic biotrophs, establishing symbioses with the roots of most land plants, including the major food and feed crops, from cereals and legumes to fruits and vegetables, including also important industrial plants, such as sunflower, tobacco, cotton, and medicinal plants (Smith and Read, 2008 ). AMF symbionts facilitate plant nutrient uptake, mainly phosphorus (P), nitrogen (N), sulfur (S) potassium (K), calcium (Ca), copper (Cu), and zinc (Zn), by means of a large network of extraradical hyphae spreading from colonized roots to the surrounding soil and functioning as a supplementary absorbing system (Giovannetti et al., 2001 ; Avio et al., 2006 ). Moreover, they protect plants from biotic and abiotic stresses (Augé, 2001 ; Evelin et al., 2009 ; Sikes et al., 2009 ), provide essential ecosystem services (Gianinazzi et al., 2010 ), and affect the biosynthesis of beneficial plant secondary metabolites, contributing to the production of safe and high quality food (Sbrana et al., 2014 ; Avio et al., 2018 ). However, such beneficial services, encompassing improved crop performances and soil resources availability, are the outcome of the synergistic action of AMF and the vast communities of mycorrhizospheric bacteria living strictly associated with their mycelium and spores (Hildebrandt et al., 2006 ; Agnolucci et al., 2015 ). AMF-associated microbiota has been reported to promote mycorrhizal activity (Mayo et al., 1986 ; Xavier and Germida, 2003 ; Horii and Ishii, 2006 ; Giovannetti et al., 2010 ), to protect plants from soilborne pathogens (Citernesi et al., 1996 ; Budi et al., 1999 ; Li et al., 2007 ; Bharadwaj et al., 2008a , b ) and to provide nutrients and growth factors (Barea et al., 2002 ; Xavier and Germida, 2003 ), thus being considered as plant growth promoting (PGP) bacteria (PGPB) (Philippot et al., 2013 ). Molecular investigations allowed the description of the complexity and diversity of bacterial communities associated to AMF spores belonging to different species and isolates, suggesting that their differential occurrence may affect the performance of the relevant taxa in terms of infectivity and efficiency, given their important functional roles as PGPB (Roesti et al., 2005 ; Long et al., 2008 ; Agnolucci et al., 2015 ). Other studies, aimed at isolating and functionally characterizing spore associated bacteria, reported the occurrence of bacteria showing antagonistic activity against plant pathogens (Budi et al., 1999 ; Bharadwaj et al., 2008a ), phosphate-solubilizing and nitrogenase activity (Cruz et al., 2008 ; Cruz and Ishii, 2011 ), and indole acetic acid (IAA) production (Bharadwaj et al., 2008a ). A recent work, using a culture-dependent approach, showed that bacterial strains isolated in pure culture from Rhizophagus intraradices spores were able to solubilize P from phytate and inorganic sources (69.7 and 49.2%, respectively), produce siderophores (65.6%), and IAA (42.6%) (Battini et al., 2016 ). The last two molecules are very important for plant growth and nutrition. Actually, IAA, a phytohormone of the auxin class, affects the morphology and physiology of roots, enhancing cell division and elongation, and the formation of lateral roots, thus improving water and nutrient uptake and playing a key role in the regulation of plant development (Khalid et al., 2004 ; Aloni et al., 2006 ; Duca et al., 2014 ). Siderophores are low molecular weight, high-affinity iron-chelating compounds able to bind soluble Fe 3 , even at high pH when Fe solubility decreases (Mimmo et al., 2014 ), thus making it available to bacteria and plants (Colombo et al., 2014 ). Given the essential role played by iron in plant biochemical processes, such as photosynthesis and respiration (Kobayashi and Nishizawa, 2012 ), bacterial siderophores, facilitating plant Fe acquisition, represent important factors of plant growth and development (Crowley et al., 1988 ; Duijff et al., 1994a , b ; Walter et al., 1994 ; Yehuda et al., 1996 ; Siebner-Freibach et al., 2003 ; Jin et al., 2006 ; Vansuyt et al., 2007 ; Robin et al., 2008 ). Moreover, siderophores have been reported to possess biocontrol activity against soilborne diseases, by means of iron competition (Thomashow et al., 1990 ; Glick, 1995 ; Whipps, 2001 ), inhibiting the development of deleterious plant pathogens (Davison, 1988 ; Arora et al., 2001 ). Although the individual roles of AMF and their associated bacteria in optimizing plant performance are still to be completely dissected, AMF are progressively more considered among the main factors of sustainable food (primary) production (Philippot et al., 2013 ; Rouphael et al., 2015 ). Two main strategies have been devised to exploit the benefits deriving from the mycorrhizal symbionts: the adoption of specific management practices and the use of AMF inoculation. The first one focuses on the improvement of the activity of native AMF, pursued by using crop rotation and mycotrophic cover crops, able to raise soil mycorrhizal potential and to shape native AMF communities (Kabir and Koide, 2002 ; Karasawa and Takebe, 2012 ; Lehman et al., 2012 ; Njeru et al., 2014 , 2015 ; Turrini et al., 2016 , 2017 ), and by reducing tillage intensity or chemical fertilizations, which affect AMF species composition, spore abundance and mycorrhizal colonization (Douds et al., 1995 ; Jansa et al., 2003 ; Oehl et al., 2004 ; Castillo et al., 2006 ; Brito et al., 2012 ; Avio et al., 2013 ). The second strategy focuses on the inoculation of selected AMF, either as single species or in a mixture, reported as efficient root colonizers and plant nutrition enhancers (Jeffries et al., 2003 ; Gianinazzi and Vosatka, 2004 ; Lekberg and Koide, 2005 ; Rouphael et al., 2015 ). Many types of commercial AMF inoculum are available on the market, including sterile products obtained in vitro using genetically modified Ri T-DNA roots and the species Rhizoglomus irregulare (synonym Rhizophagus irregularis , basionym Glomus irregulare ). However, most of the commercial products are obtained from greenhouse multiplication on mycotrophic trap plants and represent a multipartite symbiosis, where a rich community of bacteria may thrive, associated with AMF propagules, and exert important functional activities, as PGPB. Here, for the first time, we explored the bacterial metagenome of a commercially available AMF inoculum by Illumina high-throughput sequencing, a method able to provide information about culturable and unculturable members of the inoculum microbiota. Moreover, we isolated and functionally selected culturable bacteria showing important PGP traits, as the ability to produce IAA and siderophores, to be utilized, individually and/or in multispecies consortia with AMF as beneficial biofertilizers/bioenhancers in sustainable agroecosystems.",
"discussion": "Discussion In this work, for the first time, the microbiota associated with a commercial AMF inoculum was identified and characterized, using a polyphasic approach, i.e., a combination of traditional microbiological culture-dependent analyses and metagenomic sequencing. A complex and highly diverse bacterial community was identified by Illumina high-throughput sequencing and several bacteria showing important PGP traits, as the ability to produce IAA and siderophores, were isolated and identified. The assessment of mycorrhizal colonization of the roots contained in the inoculum and of the MIP was the necessary prerequisite for the feasibility of our study, given the recent data on the poor colonization of plant roots by a commercial AMF inoculum (Berruti et al., 2013 ). In our material, both roots contained in commercial inoculum and those of the plants used for the MIP bioassay were well colonized, showing that the commercial inoculum was highly infective and able to rapidly establish the mycorrhizal symbiosis. The crude inoculum analyzed, consisting of the substrate where trap plants were grown (mycorrhizal root fragments, AMF spores and mycelium) harbored a rich culturable heterotrophic bacterial community, whose populations ranged from 2.5 to 6.1 × 10 6 CFU/mL. Such values are high, when considering the origin of the sampled material, which did not derive from living roots, but from a dry inoculum, and show that the rich bacterial community thriving in the particular ecological niche, rich in nutrients and exudates, represented by trap plants during AMF inoculum production, is able to maintain its vitality and activity through the different phases leading to the production of the commercial AMF inoculum, from plant harvest to substrate drying. Moreover, present data confirm previous molecular findings which detected large and complex bacterial communities associated with AMF spores (Roesti et al., 2005 ; Long et al., 2008 ; Agnolucci et al., 2015 ). The culture-independent approach revealed the occurrence of 7 most represented bacterial genera known to include species isolated from a variety of environments that can be subjected to different environmental stresses. For example, bacteria belonging to Sphingobacterium , the most represented genus in the commercial inoculum, can survive at temperatures lower than 5°C (Shivaji et al., 1992 ) and higher than 65°C (Yoo et al., 2007 ), or can survive in soil contaminated with herbicides (Lü et al., 2006 ) or solvents (Mohammad et al., 2006 ). Some species of this genus have been reported to have PGP activities, such as inorganic phosphate solubilization, surfactant and IAA production (Marques et al., 2010 ; Ahmad et al., 2014 ; Ali et al., 2017 ), that can improve the efficacy of AMF inocula. Plant growth-promoting traits were also reported in bacteria belonging to other genera associated with the inoculum, including Flavobacterium (phosphate solubilization, production of phytohormones and antimicrobial compounds, Nishioka et al., 2016 ), Brevundimonas (production of IAA and ammonia, Kumar and Gera, 2014 ), Stenotrophomonas (production of antibiotics and plant growth regulators, Messiha et al., 2007 ) and Devosia (development of a nitrogen-fixing root-nodule symbiosis, Rivas et al., 2002 ). The potential contribution of these bacteria to the efficacy of AMF inocula is supported by recent findings reporting that inoculation with PGPB Flavobacterium and Stenotrophomonas can be effective in promoting plant growth under draft (Gontia-Mishra et al., 2016 ) or salinity stress (Singh and Jha, 2017 ). Interestingly, several sequences (2.9%) were assigned to Cellvibrio , a genus known for its cellulose and complex carbohydrate degradation potential, which was previously retrieved from AMF spores, where it was supposed to feed on components of the spore walls, thus facilitating AMF spore germination (Roesti et al., 2005 ). Many other genera were represented in the bacterial community associated with the commercial inoculum (Supplementary Material 1 ). Among them, several sequences occurring at low frequencies were ascribed to Streptomyces (0.22%), Enterobacter (0.24%), Bacillus (0.66%), Microbacterium (0.83%), genera to which our selected strains belonged. Here, the inoculation and successive purification on selective media allowed the initial isolation of 36 bacterial strains, and their subsequent screening allowed the selection of the 14 best performing strains showing important PGP traits. Six and five strains were strong producers of IAA and siderophores, respectively, while two of them (N-67 and N-92), displayed at high levels the two PGP traits. The occurrence of such bacterial functional groups in the commercial inoculum further supports our previous evidence that the beneficial microbiota associated with AMF maintains not only its vitality and activity, but also its functional properties during the different phases of the life cycle (Battini et al., 2016 ). The ability of such strains to produce IAA, a hormone enhancing cell division and boosting the development of plant root systems (Patten and Glick, 2002 ) and siderophores, able to facilitate plant acquisition of Fe, thus acting as potential biocontrol agents against soilborne plant pathogens (Glick, 1995 ; Arora et al., 2001 ; Whipps, 2001 ; Battini et al., 2016 ), confirms the need and utility of adopting culture-dependent methods in order to gain knowledge on functional traits of AMF-associated bacteria. The availability of such beneficial bacteria in pure culture allows their use in ecological studies aimed at investigating their mycorrhizospheric competence and role in plant growth promotion. Fourteen bacterial strains showing the best combination of PGP traits were identified by 16S rDNA sequencing. Interestingly, 5 out of 14 strains (36%) belonged to Actinomycetales: among them, the Microbacterium trichotecenolyticum strains N-21, N-75, N-78, N-87, and the Streptomyces sp. strain P-57 were strong siderophores and IAA producers, respectively. Actinobacteria are ubiquitous in the soil and able to produce many biologically active secondary metabolites, including antibacterial, antifungal, antiparasitic, anticancer and immunosuppressant drugs (Wolf and Zähner, 1972 ; Weitnauer et al., 2001 ; Ritacco and Eveleigh, 2008 ; Qin et al., 2014 ) and/or to utilize a wide range of complex compounds (Vandera et al., 2015 ). They were previously reported to live in strict association with spores and hyphae of different AMF, including F. coronatum, F. mosseae , and R. intraradices (Walley and Germida, 1996 ; Andrade et al., 1997 ; Bharadwaj et al., 2008b ; Agnolucci et al., 2015 ; Battini et al., 2016 ). Many Actinobacteria showed PGP traits, acting as antagonists against plant pathogens, and mycorrhizal helper traits, enhancing mycorrhizal colonization and AMF functionality (Bharadwaj et al., 2008a ; Hamdali et al., 2008 ; Giovannetti et al., 2010 ). Members of the genus Microbacterium are ubiquitous in many environments and considered important players of biogeochemical cycles, due to their diazotrophic properties and endophytic behavior (Miliute et al., 2015 ). Consistent with our findings a M. trichotecenolyticum strain isolated from roots of wild Dodonaea viscosa L. was reported to possess multiple plant growth promoting activities, such as siderophore and IAA production (Afzal et al., 2017 ). The genus Streptomyces is one of the main component of soil bacterial communities and is considered within the promising taxa to be investigated for PGP activity, given its ability to solubilize phosphates and produce growth regulators (Mohandas et al., 2013 ; Hamedi and Mohammadipanah, 2015 ), two activities shown also by our strain P-57. Actually, two Streptomyces strains, W94 and W77, isolated from the spores of the AM fungus R. irregularis IMA6, significantly increased the uptake and translocation of 33 P in maize plants, and hyphal length specific 33 P uptake, respectively, compared with control plants (Battini et al., 2017 ). On the other hand, other IAA-producing bacteria isolated from AMF propagules were able to increase AMF development (Bidondo et al., 2011 ), in agreement with previous data reporting that Streptomyces spp. boosted AMF spore germination and hyphal growth (Mugnier and Mosse, 1987 ; Tylka et al., 1991 ; Carpenter-Boggs et al., 1995 ), thus showing mycorrhizal helper traits. Four out of 14 strains (28%) were affiliated with Bacillales, and belonged to the species Bacillus megaterium and Bacillus litoralis . All of them produced siderophores, activity previously reported in other members of the order (Battini et al., 2016 ), known for their ability to control soilborne pathogens (Jeong et al., 2014 ) and to act as PGP and mycorrhizal helper bacteria, facilitating mycorrhizal establishment and improving plant growth (Budi et al., 2013 ; Pérez-Montaño et al., 2014 ; Zhao et al., 2014 ). The isolation of Bacillus species from our commercial inoculum represents a further confirmation of previous data obtained by culture-independent methods (Agnolucci et al., 2015 ). One strain, Rhizobium radiobacter (syn. Agrobacterium radiobacter/tumefaciens ) N-67, was affiliated to the Rhizobiales, an order thoroughly investigated for the ability of its members to fix nitrogen. This isolate was one of the two only strains able to produce both IAA and siderophores, confirming previous data on PGP ability of some rhizobia to boost plant nutritional status by producing phytohormones (Zahir et al., 2003 ; Chandra et al., 2007 ; Dodd et al., 2010 ). Its persistence in the AMF inoculum may be ascribed to the formation of biofilms containing exopolysaccharides which allow an efficient colonization of roots and mycorrhizal hyphae (Bianciotto et al., 1996 ; Toljander et al., 2006 ). A very interesting finding is represented by the isolation of 4 strains, P-30, P-36, P-42, N-92, affiliated with Enterobacteriales ( Enterobacter cloacae/ludwigii ), which were strong producers of IAA, confirming previous data on the capacity of a strain of E. cloacae to produce as much IAA as a Pseudomonas strain (Imen et al., 2013 ). Recent works reported that a few strains of the genus Enterobacter , isolated from legume plants, possessed multiple plant-growth promoting characteristics, such as phosphate solubilisation activity and IAA production, thus affecting plant growth and development (Ghosh et al., 2015 ; Khalifa et al., 2016 ). On the other hand, one of our isolates, N-92, produced also siderophores, activity already reported for members of the genus Enterobacter (Tian et al., 2009 ). In conclusion, this work demonstrates for the first time that an AMF inoculum, produced following industrial production processes, is home of a large and diverse community of bacteria with important functional PGP properties, possibly acting in synergy with AMF and providing new services and benefits. The commercial AMF product could be enriched with the selected beneficial bacterial isolates utilized as an additional inoculum, further boosting plant growth, nutrition and health, in order to optimize plant performance in sustainable food production systems. Indeed, our findings imply a new perspective of AM symbiosis, that of a multipartite association - host plants, AMF and bacteria - where different microbial functional groups are active: for example, specific mycorrhizospheric bacteria, by solubilizing P and fixing N, may improve the availability of key mineral nutrients, then absorbed and translocated to the host plant by AMF extraradical hyphae, while other bacteria, by producing siderophores and IAA, may control plant pathogens and promote plant growth. Notwithstanding, so far only few works have been carried out either on the isolation and functional characterization of mycorrhizospheric microbiota, or on their occurrence and significance in AMF inocula. Yet, these studies are necessary and urgent, in the perspective of developing new strategies for sustainable intensification in agriculture, aimed at minimizing the use of chemical fertilizers and pesticides, promoting primary production and maintaining soil health and fertility. To this aim, the most diverse combinations of AMF and bacteria should be studied, in model experimental systems and in the field, to discover possible synergistic effects on different host plants, in order to select the best performing ones for their targeted use in sustainable food production systems in the years to come."
} | 5,790 |
40087381 | PMC11909208 | pmc | 1,675 | {
"abstract": "The capacity of photosynthetic microorganisms to fix carbon dioxide into biomass positions them as promising cell factories for sustainable biomanufacturing. However, limitations in screening throughput hinder the identification of enzymes, strains, and growth conditions needed to realize this potential. Here we present a microplate-based high-throughput cultivation system that can be integrated into existing automation infrastructure and supports growth of both prokaryotic and eukaryotic photosynthetic microorganisms. We validate this system by optimizing BG-11 medium compositions for Synechococcus elongatus UTEX 2973, Chlamydomonas reinhardtii UTEX 90 and Nostoc hatei CUBC1040, resulting in growth rates increases of 38.4% to 61.6%. We also identify small molecules that influence growth rates in Synechococcus elongatus UTEX 2973, including candidate compounds for growth rate increase and dozens that prevent growth. The sensitivity, throughput, and extensibility of this system support screening, strain isolation, and growth optimization needed for the development of photosynthetic microbial cell factories.",
"conclusion": "Conclusions We designed, built, and tested a consistent lighting system for microplate-based photosynthetic microorganism cultivation that can be integrated into existing high-throughput automation infrastructure expanding the sensitivity, throughput, and extensibility of screening applications. The system enables users to leverage existing infrastructure and expands the range of assays amenable to the parallel screening of 100 s to 10,000 s of photosynthetic or photo-responsive microorganisms in microplates. Using response surface modelling of optimal growth conditions in 384-well formats, we were able to demonstrate the applicability of this system across three diverse microalgal strains. Optimized cultivation conditions led to scalable growth increases over several orders of magnitude, indicating potential utility in bioprocess optimization. Future efforts should further expand both the diversity of strains tested, including marine and biofilm-forming photosynthetic microorganisms, as well as the diversity of assays and experimental designs. As the system was designed for integration into standardized automation and laboratory infrastructure, it is amenable to a large range of chemical and biological assays, including photopigment and chemical assay fluorescence, and streamlined incorporation into downstream workflows. Moreover, the system can serve as a primary screening mode for growth optimization and cultivation of distinct genetic clones or strain variants, or a secondary process used to validate hits identified in pooled or droplet-based screening paradigms including the high-throughput study of optogenetics and light-driven metabolic activity or genetic circuits across the tree of life 34 , 45 .",
"introduction": "Introduction Microalgae represented by prokaryotic and eukaryotic photosynthetic microorganisms are primary producers that play integral roles in food web structures and global biogeochemical cycling 1 , 2 . In addition to these roles, the capacity of microalgae to sequester carbon dioxide (CO 2 ) into a range on organic compounds, including biopolymers, lipids, and bioactive molecules, makes them interesting targets for industrial carbon fixation and sustainable manufacturing 3 – 9 . Microalgae are also important model organisms in the study of photosynthesis, optogenetics and evolution, representing some of the oldest and most diverse prokaryotic and eukaryotic phyla 10 . Despite their recognized potential for sustainable bioproduction, current applications of photosynthetic microorganisms as microbial cell factories remain in early stages of development 8 , 11 , 12 . This is partly due to a limited number of tractable genetic systems 13 – 18 , compounded by a lack of platforms supporting high-throughput cultivation at laboratory scale 19 . This latter issue makes it difficult to conduct functional screens, select strains, or optimize growth conditions for energy and materials production using multifactorial experimental design. Standard flask and photobioreactor-based cultivation systems currently limit throughput to 10 s of individual conditions or strains 19 . One of the reasons for this constraint is that microalgal cultures are highly sensitive to changes in growth conditions, with consistent light availability and sufficient gas exchange being a prerequisite to controlled experimentation 20 – 23 . This presents additional engineering challenges associated with the design of high-throughput systems in comparison to heterotrophs 19 , such as E. coli and yeast, where energy sources like sugars can be provided in growth medium at even abundances. Several strategies have been employed to address these limitations and increase throughput of microalgal screening, with varying trade-offs associated with each method. Pooled selection allows for targeted enrichment of strains that perform best under specific growth conditions from pools of 10,000 s to 1,000,000 s of distinct genotypes 15 , 24 , 25 . Although this strategy can be very useful for large library screening, the desired phenotype must provide a fitness benefit and all strains are subjected to the same set of growth conditions. This can be very effective in recovering strains that grow under extremes of pH, temperature, or salinity, etc., but may not be transferrable to screening paradigms investigating specific biosynthetic processes or traits not directly associated with growth rate. Individual strains from the resulting enrichment still require isolation and validation at scale, potentially limiting the scalability of these strategies. Droplet- or flow-cytometry-based screening is an alternative solution involving the encapsulation of individual cells in droplets that can be incubated before sorting or direct sorting of cells based of fluorescence or absorbance 15 , 20 , 26 , 27 . This strategy enables the screening of 10,000 s to 100,000 s of distinct genotypes, the application of alternative assays, and generation of concentration gradients within droplets 20 . Although multiple rounds of either selection of droplet-based screening can lead to hit enrichment, both methods are likely to result in false positives that require extensive rounds of downstream isolate validation. Microplate or micro-photobioreactor (micro-PBR)-based screening systems have been explored to enable intermediate-throughput screening of 10 s to 1000 s of individual cultures 28 – 34 . Several devices have been constructed for photosynthetic microorganism cultivation and optogenetics, enabling well-level control of light intensity in 24 to 96-well formats 21 , 28 , 31 , 33 , 34 . Although this throughput is limited in comparison to pooled and droplet-based screening, the format of these systems enables the testing of distinct medium compositions on clonal populations or synthetic consortia 30 , 31 , 35 under a wide range of growth conditions 36 . The optimization of medium compositions, through the parallel testing of distinct growth conditions, enables the identification of limiting resources and shifting of selective pressures to drive processes of interest 31 , 35 , 37 – 39 , a prerequisite for metabolic engineering applications, which are often selected against under standard cultivation conditions 40 – 43 . However, current lighting systems developed for microplate screening or for optogenetics involve either specialized stand-alone devices (one 24 to 96-well plate) 22 , 28 , 33 , 34 , 44 , 45 , or the manual placement of microplates across an illuminated surface, also limiting throughput 23 , 30 , 46 . Here we design, build, and test a consistent lighting system that can be integrated into standard laboratory automation infrastructure for microplate screening. This enables users to leverage existing infrastructure for the parallel screening of 100 s to 10,000 s of photosynthetic or photo-responsive microorganisms while expanding the range of assays amenable to microplate formats. We demonstrate the utility and extensibility of this system using both prokaryotic and eukaryotic microalgae including Synechococcus elongatus UTEX 2973, Nostoc hatei CUBC1040, and Chlamydomonas reinhardtii UTEX 90.",
"discussion": "Results & discussion Existing HTP lighting systems Several strategies and engineering solutions have been developed for the high-throughput screening of photosynthetic microorganisms or other photoresponsive systems 31 , 45 . Table 1 gives an overview of existing screening systems, including throughput capacity, tested strains, scalability, controllability, cost, limitations, and other technical specifications. Table 1 Existing high-throughput screening systems for photosynthetic microorganisms Capacity Tested strains Light intensity Lighting system Cost Scalability Controllability Limitations Reference Approximately 1000 microdroplets Synechocystis sp. PCC 6803; Synechococcus sp. UTEX 2973; Synechococcus sp. UTEX 3154 Up to 60 μmol m −2 s −1 White fluorescent lamps N/A Scalable Flow rate with syringes - Low light intensity - Limited volume - Limited assay types - Biomass recovery challenges 20 96-well plate 12 microalgal strains isolated from 15 different fresh water sites in Republic of Korea Up to 650 μmol m −2 s −1 6 × 12 LED array (6000 K) Assembled from low-cost, available materials Standalone device Light intensity (by row) - Limited throughput 21 , 31 Parachlorella sp. JD-076; Scenedesmus sp. YC001; Chlorella sp. HS-2 Custom 96-deepwell plate Synechocystis sp. PCC 6803 1.5 to 73 μmol m −2 s −1 Fluorescent illumination lengthwise along cultures N/A Standalone device - Light intensity - Programmable shakers - Temperature control - Limited throughput - Non-standard consumables 23 96-well plate Prochlorococcus strains (MIT9211, SS120, MIT9313, NATL1A, MIT9515, MIT9312) Up to approximately 250 μmol m −2 s −1 8 × 12 LED array (from above) N/A Standalone device Light intensity (by column) - Limited throughput 33 48-well plate Chorella vulgaris Up to 620 μmol m −2 s −1 LED-based illumination from bottom (120 LEDs of 3 types) N/A Standalone device Individual LED control - Limited throughput 22 24-well plate E. coli Up to 245 μmol m −2 s −1 48 LED array (two wavelength of light) Low, cost, widely available materials and use of 3D printed parts Standalone device Individual LED control - Limited throughput 34 Custom 64-well plate 8 species of green algae Up to 1948 μmol m −2 s −1 8 × 8 LED array (natural white LEDS (4500 K)) Total cost of system below 150 € Standalone device Individual LED control - Limited throughput - Non-standard consumables 44 96-well plate Dunaliella tertiolecta Up to 100 μE cm −2 8 × 12 LED array N/A Standalone device Individual LED control - Limited throughput 103 96-well plate or 384-well plate HEK-293 cells N/A 2 × 96 LED arrays for single-colour LED or bi-colour LED illumination ~$600 USD to assemble using custom manufacturing (3D printing) of necessary parts Standalone device Individual LED control - Limited throughput 45 24-well or 96-well N/A up to 20 μW mm −2 LEDs under multiplate N/A Standalone device Individual LED control - Limited throughput 51 Many of these devices enable precise control of well-level light intensities, allowing researchers to study how light influences the activity of photosynthetic strains and optogenetic circuits. However, most of these systems are standalone devices that were not designed with increased scalability in mind, with the majority supporting 24–96 cultures in parallel 31 , 45 . Only some of these leverage standardized laboratory consumables, such as microplates, limiting available assays and ease of downstream processing. More high-throughput screening of microalgae has relied on manual placement of microplates on a flat illuminated surface, limiting automation potential and precise control of light intensity and humidity 30 . There is therefore a need for lighting systems supporting high-throughput screening of photosynthetic or photo-responsive microorganisms and optogenetic circuits based on integration into existing laboratory automation infrastructure. This need helped define engineering requirements for the lighting system described here. Defining functional requirements To design and build a lighting system for high-throughput microplate screening in standardized automation infrastructure (e.g., Cytomat 5 C450 incubator, Thermo Fisher Scientific), a set of functional requirements was defined. First, the system needed to provide even and consistent light intensity and spectrum across a 384-well microplate. Although light availability has a significant impact on metabolic activity and growth rate in cyanobacteria, most studies investigate the impacts of 2–10-fold variations in light intensity 47 , 48 . It has been observed that the most significant variations in growth rate based on light availability are under light-limiting conditions 47 , 49 . To attempt to set light intensity variability thresholds for the system, we leveraged a protein economy model of Synochocystis sp. PCC 6803 to investigate the impact of variable light intensity on cyanobacterial growth rate and protein production 47 . Protein economy models are theoretical frameworks used to understand the allocation of resources and energy toward protein production and maintenance within cells 47 , 50 . These models enable the study of trade-offs in cellular functions, such as growth and stress response, and are useful in predicting the impact of light intensity on metabolic activity and resource allocation 50 . A model of Synochocystis sp. PCC 6803 was used to approximate steady-state growth rates and cellular component concentrations at increments of 10 μmol m −2 s −1 from 20–1000 μmol m −2 s −1 (Supplementary Fig. 6A, B ). Relative concentrations of cellular components and the rate of change of concentrations were calculated across light intensities and growth rates (Supplementary Fig. 6C, D ). Results confirmed that the most significant variability in protein and metabolite concentrations were in light limiting conditions. Above 100 μmol m −2 s −1 , which resulted in a growth rate of 46.9% the maximum observed growth rate, the variability in protein and metabolite concentrations decreases substantially as growth rate nears its maximum (Supplementary Fig. 6E, F ). Although modelling indicated that growth rate is not directly proportional to metabolic activity or gene expression, minimizing variability in growth rate will result in decreased variability of metabolic activity when light availability is not limiting. Based on these results, we decided to target <5% variability in growth in non-light-limited conditions. Second, under these conditions, light intensity needed to be adjustable between 50 μmol m −2 s −1 and 500 μmol m −2 s −1 for up to 50 384-well plates simultaneously, corresponding to 19,200 individual wells. Third, the system needed to conform to standard form-factors, as defined by the Society for Laboratory Automation and Screening (SLAS), for integration into the Cytomat 5 C450 incubator or other automation infrastructure. Finally, we considered five non-functional requirements to improve extensibility and accessibility, including that the system should be 1) modular and customizable, 2) constructed with parts that are easily accessible or can be printed or ordered to the defined specifications, 3) cost < $10,000 CAD ( < 10% of cost of Cytomat 5 C450 incubator and < 1% cost of automation infrastructure), 4) tolerate up to 85% humidity, and 5) cleanable with distilled water or 70% ethanol or isopropanol. The combined requirement set directed choices related to component selection, and the design of electrical circuits and software (Fig. 1A ). Fig. 1 Overview of lighting system form factor and integration into the Cytomat 5 C450 incubator . A Each of 5 racks can hold up to 10 LED arrays and microplates, enabling the screening of up to 3840 distinct cultures per rack, for a total incubator capacity of 19,200 cultures. LED array and support frame are placed directly beneath microplates and provide illumination upwards. Terminal blocks are mounted to each rack to connect to slipring and split power between LED arrays. An additional intermediate set of terminal blocks is required to split power between multiple racks. The slipring and slipring connector enable rotation of the carousel within the Cytomat 5 C450 incubator. A single set of positive and negative wires connects slipring to the LED driver and dimming circuit. B LED array and support structure conform to the form factor of standard 384-well microplates, as defined by the Society for Laboratory Automation and Screening (SLAS). 384 individual LEDs sit directly beneath wells. Grooves in the support structure secure the LED array and ridges on the outside of the structure ensure proper placement within the Cytomat 5 C450 rack. This figure was generated by Arman Aituar. Electrical circuit design Several lighting systems have been developed enabling well-level control of light intensity across a 96-well plate, for application in cultivation of photosynthetic microorganisms and optogenetics 21 , 28 , 34 , 44 , 45 , 51 . Although these systems are useful when investigating the impact of light intensity on a given biological process, this level of control requires complicated circuitry connecting individual LEDs to a controller, resulting in standalone devices that could not easily be scaled to multiple plates or integrated into automation systems. The system described here was designed to maintain consistent lighting across microplates and is strictly limited in form factor for integration into standard automation infrastructure. To satisfy these requirements, a metal plate-based LED array with single positive and negative connections provided a suitable solution. We custom-manufactured 384-LED arrays, with LED positioning matching the position of wells in a 384-well microplate, which would result in 4 LEDs per well in a standard 96-well microplate and 32 LEDs per well in a standard 12-well plate (Supplementary Fig. 14 ). Initially these were manufactured on copper and superconducting aluminium alloy plates for heat distribution, with either 4044 K, 5091 K or 6083 K broad-spectrum LEDs (400 nm to 750 nm) (Supplementary Fig. 18 ). Each 384-LED array includes 24 parallel series of 16 LEDs and requires approximately 48 volts (V) of power and supports a current of up to 150 mA. The manufacturers specified 40°C to 60°C as the optimal working temperature range of LEDs, with a maximum range of 80°C to 90°C. As these LED arrays are the primary functional component of the lighting system, additional structures were designed to accommodate these arrays including a supporting structure to maintain the position of the LED array in relationship to the microplate allowing airflow across the LED array facilitating heat dispersion (Fig. 1B ). The design conforms to SLAS standard microplate dimension 52 , with ridges for simplified placement of the LED array in a Cytomat 5 C450 incubator (Supplementary File 1 ). These ridges may need to be adjusted for alternative incubation systems. The electrical circuit was designed to enable powering and controlling of multiple LED arrays simultaneously while accommodating LED power requirements and the lighting requirements defined above. The core structure of the designed circuit includes an LED driver, a dimming circuit, an Arduino UNO microcontroller, and the LED arrays in parallel (Supplementary Fig. 15 ). The LED driver provides 48 V of power to the LED arrays and enables the adjusting of current and resulting light-intensity. Current output of the LED driver is controlled by the dimming circuit, which relays the pulse-width-modulation (PWM) signal from the Arduino UNO to the LED driver. This signal can be defined by modifying Arduino code, enabling the defining of light intensity emitted by LED arrays. Electrical components were selected to conform to design specifications and enable assembly of a circuit able to power LED arrays based on user-defined specifications. Downstream testing of individual components and optimization of cultivation conditions was required to validate the resulting lighting system. Component validation High-throughput screening requires consideration of single-well microenvironments and maintenance of consistent growth conditions between wells and microplates. Although LEDs are more energy efficient than incandescent and fluorescent lights, they also generate heat, and proximity of LEDs on the array to cells in wells could produce localized regions of increased or variable temperature 53 . We used mounting media consisting of copper, which is the industry-standard material for heat distribution and conductivity, and a superconducting aluminium alloy in the design of the LED arrays. Both metals should distribute heat generated by the LEDs across the plate, enabling more efficient conduction in a temperature-controlled incubator. This was tested in a series of experiments using photodiodes and thermistors, to monitor light intensity and temperature when operating over periods of 16 h at currents of 10 mA. Significant differences in temperature variance (70 mA: F(3029,2923) = 0.0052, p < 2.2 × 10^−16, Cohen’s d = 7.72; 10 mA: F(5746,5726) = 0.51, p < 2.2 × 10^−16, Cohen’s d = 0.87) and light variance (70 mA: F(3065,2967) = 0.094, p < 2.2 × 10^−16, Cohen’s d = 6.78; 10 mA: F(5837,5807) = 0.26, p < 2.2 × 10^−16, Cohen’s d = 1.39) were observed between copper- and aluminium-mounted LED arrays at both currents (Supplementary Fig. 16 ). Here, copper-mounted LED arrays exhibited significantly more variability (Supplementary Fig. 17A ), with a mean standard deviation in light intensity of 1.47 100 μmol m −2 s −1 and 5.16 μmol m −2 s −1 at 10 mA and 70 mA, respectively, in comparison to 0.56 μmol m −2 s −1 and 0.52 μmol m −2 s −1 for superconducting aluminium-mounted arrays. This variability was mirrored by temperature fluctuations, with mean standard deviations of 0.88 °C and 3.53 °C exhibited by copper-mounted arrays and 0.31 °C and 0.29 °C exhibited by aluminium-mounted arrays at 10 mA and 70 mA. Results also indicated a positive correlation between temperature and light intensity (Supplementary Fig. 17A ). LEDs typically manifest the inverse relationship, where an increase in temperature leads to a decrease in light emission intensity 54 . This may indicate that the observed temperature variability was a function of current fluctuations resulting in temporal changes in light intensity and heat production. As the copper-mounted LED arrays exhibited significantly more variability in temperature and light intensity, we selected the superconducting aluminium arrays for downstream system design. Configuration testing A series of growth experiments using fast-growing Synechococcus elongatus UTEX 2973 was conducted to identify suitable cultivation conditions for microplate growth using the lighting system. 384-well plates were used as they provide a higher level of granularity in potential inter-well variability on a single microplate. These experiments explored various LED spectra, use of different seals or lids for microplates during cultivation, positioning of LED arrays in relation to microplates, and selection of microplate types for cultivation and screening. These experiments were designed to identify cultivation conditions minimizing variability in growth rate and photopigment production across a microplate. Photopigment production in cyanobacteria, including light-harvesting pigments and photoprotective carotenoids, is highly dependent on light intensity and associated with metabolic state 55 . Observing pigment production through absorption measurements at photopigment absorbance peaks, in addition to growth rate, provided insight into metabolism and light exposure across the microplate. Selecting 5091 K light spectrum LED Light spectrum has a significant impact on photosynthetic efficiency, photopigment biosynthesis, and growth rates in photosynthetic microorganisms 49 , 56 , 57 . This is a result of the varying absorption peaks of individual photopigments, including chlorophylls (430–475 nm and 630–700 nm), phycobilins (500–675 nm), and carotenoids (400–500 nm) that comprise photosynthetic light harvesting complexes 49 , 57 , 58 . The photosynthetic complexes of different microorganisms can be structured to more efficiently harvest certain wavelengths of light and genetic regulatory mechanisms can drive photopigment production in response to specific light spectra 49 , 57 , 58 . Cool white phosphor-converted-LEDs support growth by emitting broad spectrum photosynthetically active radiation (PAR), with emission peaks mirroring photopigment absorption peaks 56 , 59 . Based in this, broad-spectrum phosphor-converted-LED arrays were initially manufactured with colour temperatures of 4044 K, 5091 K and 6083 K (Supplementary Fig. 18 ). Emission spectra indicated 5091 K LEDs provided the most even light distribution across key photosynthetically active wavelengths (Supplementary Fig. 18 ). A cultivation experiment was also performed to investigate the impact of LED spectra on S. elongatus UTEX 2973 growth. S. elongatus UTEX 2973 pre-cultures in Erlenmeyer flasks were incubated for one week under each of the three LED spectra in isolation. Cultures were then transferred to a 384-well microplate for cultivation under the cognate LED spectra. 5091 K LEDs led to the highest median growth over a 3-day cultivation experiment (one-way ANOVA, F(2,1149) = 90.39, p < 2 × 10^−16, η 2 = 0.14) (Supplementary Fig. 17B ). An even emission spectrum and increased growth of S. elongatus UTEX 2973 led to the selection of the 5091 K array for downstream experiments. Specific strains and screening paradigms may benefit from alternative LED spectra. Breathable seals increase growth rate and evenness Although we observed a faster median growth rate under the 5091 K spectrum, we also noted substantial variability in optical density across all microplates, with the outside wells evaporating quickly, resulting in significantly decreased growth (Supplementary Fig. 19 ). These edge effects are common in high-throughput screening, often resulting from variability in gas exchange, humidity, or temperature across the plate. To mitigate the impact of these effects, we compared growth of S. elongatus UTEX 2973 in microplates with one of two breathable seals (AeraSeal [Excel Scientific, USA] and Breathe-Easy [Diversified Biotech, USA]) or transparent microplate lids. LEDs arrays were positioned beneath the microplate when sealed with translucent or opaque seals and positioned above the microplate with a transparent lid, as we observed that this led to decreased condensation on the lid. Results showed significant differences in growth depending on microplate seal (one-way ANOVA, F(2,1149) = 5108, p < 2 × 10^−16, η 2 = 0.90 at Day 3) (Supplementary Fig. 17C / Supplementary Fig. 20 ). Specifically, AeraSeal, an opaque woven-rayon seal with high-breathability, showed significantly higher growth compared to both Breathe-Easy (mean difference = −0.76, p < 2 × 10^−16) and the microplate lid (mean difference = −0.56, p < 2 × 10^−16) conditions, while the lid showed moderately higher growth than Breathe-Easy (mean difference = 0.20, p < 2 × 10^−16). As the AeraSeal seal is not amenable to automated microplate sealing and pealing infrastructure, downstream experimentation used PermASeal (ITS Scientific, UK), a woven paper-based seal that showed a comparable increase in growth. This seal works with the Agilent PlateLoc Thermal Microplate Sealer (Agilent, USA) incorporated into the automation infrastructure used in system testing. Alternative woven breathable seals should be considered depending on available instrumentation. The significant variability in growth based on seal selection is likely the result of variation in gas exchange rates and indicates the importance of considering gas exchange and circulation in relation to photosynthetic growth and the design of high-throughput lighting systems. These experiments also suggested that evaporation rates may be an important consideration when selecting microplate seals. To explore this, we conducted experiments to measure the rate of evaporation from 384-well microplates. In these experiments, 384-well plates were loaded with S. elongatus UTEX 2973 culture or BG-11 medium and sealed with either the AeraSeal, Breathe-Easy, or PermASeal seals or with a transparent microplate lid. Plates were then loaded into the 5 C450 incubator and well volumes were measured every 24 h for 4 days using the Echo 525 acoustic liquid handling system. Results indicated that wells lose approximately 56.7 μL their volume after 96 hours, using breathable PermASeal seals, which is the highest evaporation rate of any seal type, but with less across-plate variability than microplate lids or Breathe-Easy rayon seals (Supplementary Fig. 21 ). Significant differences in evaporation were observed across plates sections for most seal types after 96 hours (PermASeal: F(7,376) = 2.78, p = 0.00788, Cohen’s f = 0.23; AeraSeal: F(7,376) = 7.61, p = 1.35 × 10^−8, Cohen’s f = 0.38; Nunc Lid: F(7,376) = 15.97, p < 2 × 10^−16, Cohen’s f = 0.55), with Breathe-Easy seals showing marginally significant differences (F(7,376) = 1.97, p = 0.0584, Cohen’s f = 0.19). Although differences were observed, the effect sizes suggest PermASeal and Breathe-Easy seals demonstrate a relatively low level of variations between plate sections. These differences highlight the importance of seal type in controlling evaporation rates, with experimental durations potentially limited to 72–96 h depending on incubator temperature and humidity control. Close proximity of LEDs to microplate leads to even lighting The selection of opaque woven seals for microplate sealing during cultivation experiments necessitates the placement of LED arrays below microplates, illuminating cultures through clear well bottoms. Each LED array and supporting structure was then positioned in place of a microplate directly below the cultivation microplate in the Cytomat 5 C450 incubator rack (Fig. 1A ). This configuration permits a maximum height of 20 mm of the LED array and supporting structure, while still accommodating automated plate retrieval. This maximum height means that the LED array can be positioned between 3 mm and 20 mm from the bottom of the microplate. As a result of the conical shape of LED light emission, we hypothesized that placement of the LED array as close to the base of the microplate as possible would result in the most even light intensity across the microplate. To test this hypothesis, we assembled an array of 24 photodiode light sensors, with the outside row of sensors in line with the outside 2 rows of wells in a 384-well microplate (Supplementary Fig. 7 ). We then varied the distance between the LED and photodiode arrays, while maintaining a consistent intensity at the photodiode array (100 μmol m −2 s −1 ). By observing the ratio between the intensity measured by the 16 outside photodiodes and the 8 inside photodiodes (Supplementary Figs. 22A and 22C ) and the standard deviation of light intensity across the photodiode array (Supplementary Figs. 22B and 22D ), we confirmed that minimizing this distance reduced variability in light intensity across a microplate. In the 20 mm to 3 mm range permitting integration into the Cytomat 5 C450 incubator, a 3 mm distance led to the most even light distribution across the photodiode array (Supplementary Fig. 22 ). Despite a decreased variability in light intensity at 3 mm in comparison to 20 mm, the ratio between the median intensity measured by the outside photodiodes and the median intensity measured by the inside photodiodes was still 0.82, indicating an 18% variability between the inside and outside (down from 30% at 20 mm). A 3 mm distance also resulted in a standard deviation of 11.3% of the mean across photodiodes, down from 22.2% at 20 mm. However, because of the physical parameters of photodiode light sensors, including a half intensity angle of 55°, we believe this is likely an overestimate of the well-well variability in a microplate (Supplementary Fig. 23 ). Based on these results, the supporting structure for the LED arrays was redesigned to minimize the distance between the LED array and microplate (Fig. 1B ). This proximity of the light source to cultures provides the most even distribution of light across wells but could make cultures more susceptible to temperature increases as a result of heat generated by the LED arrays, therefore relying on temperature regulation within the incubator. Although we did not observe an impact of this in experiments using Chlamydomonas reinhardtii , which is thought to be sensitive to temperatures over 30°C, potential thermal effects of the lighting system should be considered for temperature-sensitive strains. Incubator temperatures can be adjusted downward or additional ventilation or air circulation systems can be incorporated to mitigate potential thermal impacts of the lighting system. The LED array placement described above positioned us to evaluate the impact of variable light intensity on growth rate and metabolic activity across plate types with different optical properties. Comparing transparent and opaque microplates To investigate the impact of transparent versus opaque plates on growth rate and photopigment production using the lighting system, S. elongatus UTEX 2973 was cultivated in three transparent (clear) and three opaque (black) microplates in parallel, in standard BG-11 medium without CO 2 supplementation. The results showed that S. elongatus UTEX 2973 growth (OD 750 ) and growth rates (μ[h −1 ]) in clear and black plates varied significantly in early timepoints (24 h: t = −130.46, p < 2.2 × 10^−16, Cohen’s d = −5.436; 48 h: t = −65.893, p < 2.2 × 10^−16, Cohen’s d = −2.746), before converging after 96 h (96 h: t = 1.160, p = 0.246, Cohen’s d = 0.048). Although final biomass accumulation (OD 750 ) was comparable, with the median OD 750 values in black plates 1.14 times that of clear plates, growth rates lagged in black plates over the first 24 h with a median growth rate across 1152 wells of 0.005 μ[h −1 ] (sd = 0.003 μ[h −1 ]) at 24 h, indicating almost no growth. Median growth rates in black plates then increased rapidly between 24 and 72 h, reaching a maximum of 0.047 μ[h −1 ] (sd = 0.011 μ[h −1 ]) at 72 h before quickly decreasing to 0.0167 μ[h −1 ] (sd = 0.012 μ[h −1 ]) at 96 h (Fig. 2A ). In comparison, median growth rates in clear plates showed less temporal variability, with a gradual decrease in growth rate over the 8-day cultivation experiment, from a maximum median growth rate of 0.018 μ[h −1 ] (sd = 0.005 μ[h −1 ]) at 24 h to a minimum of 0.008 μ[h −1 ] (sd = 0.005 μ[h −1 ]) at 196 h (final timepoint) (Fig. 2A ). The maximum growth rate 0.047 μ[h −1 ] observed in black plates at 72 h is near the model predicted rate in Synechocystis sp. PCC 6803 of 0.053 μ[h −1 ] at 100 μmol m −2 s −1 , but well under the reported S. elongatus UTEX 2973 growth rate of 0.151 μ[h −1 ] at 30 °C, albeit with CO 2 supplementation (3%) and an increased light intensity (300 μmol m −2 s −1 ). Although growth rate in black plates increased rapidly afterward, this delay in growth suggests the need for S. elongatus UTEX 2973 to adapt to conditions in black plates, possibly indicating decreased light intensity. Fig. 2 Plate type comparison. Growth and photopigment production in Synechococcus elongatus UTEX 2973 were compared between and across 3 black-well and 3 clear-well 384-well microplates. A Growth in black-well plates is slightly delayed in comparison to clear-well plates but reaches comparable OD 750 values after approximately 72 h. Growth rates in clear plates were relatively constant over the 8-day cultivation experiment, whereas growth rates peaked in black plates after 3 days of cultivation. Each point represents a median of 384 values across a single microplate. B To compare growth rates across a plate, plates were divided into 8 sections, based on location on the plate. These sections will be used for downstream evaluation of growth rate and photopigment production across the microplate. C Variability in OD 750 values by plate section at T3 and T5. D Density plots at T5 show the lower distribution of OD 750 values in section 1 in black-well plates and the positive skew of OD 750 values in clear-well plates. E Relative Chlorophyll a (444 nm) and carotenoid (495 nm) absorbance are significantly lower in black-well plates. Phycocyanin (634 nm) absorbance follows a similar trend in black- and clear-well plates. Each point represents a median of 384 values across a single microplate. F Relative photopigment fluorescence at T5 stays consistent across individual black- and clear-well microplates but with significantly more variability in the clear-well plate. This increase in the number of low outliers mirrors the high outliers observed in OD 750 values in clear-well plates. To visualize variability in growth (OD 750 ) across individual microplates, wells were divided into plate sections based on position (Fig. 2B ). Results indicated some temporal variability in growth across both plate types, with growth lagging slightly in early timepoints in sections 1–3, representing the three outside rows of a 384-well plate (72 h black plate: one-way ANOVA, F(7,1144) = 192.8, p < 2 × 10^−16, Cohen’s f = 1.09; 72 h clear plate: one-way ANOVA, F(7,1144) = 7.5, p = 7.52 × 10^−9, Cohen’s f = 0.21). By Timepoint 4 (T4/96 h), only black plate section 1, representing the outside row of wells, showed a significantly decreased growth (96 hours black plate: one-way ANOVA, F(7,1144) = 46.21, p < 2 × 10^−16, Cohen’s f = 0.53) (Fig. 2C / Supplementary Fig. 24 ). This decrease was less pronounced in clear plates and was not statistically significant by 96 h (96 h clear plate: one-way ANOVA, F(7,1144) = 0.863, p = 0.535, Cohen’s f = 0.07). However, more high outliers were observed in clear plates, indicating some stochasticity in well-to-well growth rates (Fig. 2C / Supplementary Fig. 24 ). Relative chlorophyll a (444 nm), carotenoid (495 nm) and phycocyanin (634 nm) absorbance was used as an approximation of photopigment abundance for comparison between cultivation conditions. These absorbances were also significantly lower in black-well plates, suggesting a lower light intensity across the plate (T5 - 444 nm: t = −11.663, p < 2.2 × 10^−16, Cohen’s d = −0.44; 495 nm: t = −11.603, p < 2.2 × 10^−16, Cohen’s d = −0.44; 634 nm: t = 28.711, p < 2.2 × 10^−16, Cohen’s d = 1.04) (Fig. 2E ). Photopigment absorbance trends were, however, consistent between triplicates of each plate type (Fig. 2E ). Black plates also showed significant differences between relative photopigment absorbance in section 1 and that in sections 2–8 throughout the 8-day experiment (444 nm: F(7,1144) = 33.88, p < 2 × 10^−16, Cohen’s f = 0.46; 495 nm: F(7,1144) = 31.46, p < 2 × 10^−16, Cohen’s f = 0.44; 634 nm: F(7,1144) = 2.048, p = 0.0464, Cohen’s f = 0.1) (Fig. 2F / Supplementary Fig. 25 ). By 96 hours, clear plates showed no or marginally significant differences in relative photopigment production across plate sections (444 nm: F(7,1144) = 2.013, p = 0.0505, Cohen’s f = 0.11; 495 nm: F(7,1144) = 2.311, p = 0.0242, Cohen’s f = 0.12; 634 nm: F(7,1144) = 0.776, p = 0.607, Cohen’s f = 0.07) (Fig. 2F / Supplementary Fig. 25 ). The difference in relative photopigment abundance between plate types can likely be explained by refraction and internal reflection of light throughout clear-well microplates and into LEDs, potentially resulting in localized variability in light intensity. These phenomena could also explain the increased growth rate in the outside row of clear plates (section 1) and increased variability in growth rate across clear plates. Because no obvious differences in evaporation were observed across wells of a 384-well plate (Supplementary Fig. 21 ) and the outside wells of clear plates did not grow significantly slower, we believe this variability is likely due to light availability, rather than possible temperature differences at the outside wells or culture evaporation. Combined, these results indicate that either clear-well or black-well microplates can be used in the lighting system. Users should consider that edge-effects will be more pronounced in black-well plates and growth rate stochasticity may be more prominent when using a clear-well plate. Plate type selection should therefore be dependent on experimental design parameters and screening paradigm. System adaptation and extensibility Although we believe that design specifications, including plate seal selection, light source positioning in relation to plates, and electrical component selection, will be extensible across many incubation systems, some of these may need to be customized or adapted for specific applications. Electrical components need to be selected based on capacity requirements and available power sources, which may vary by country, and LED arrays can be custom manufactured based on described specifications. Additional modifications of 3D-printed LED array support structures will likely be required based on intended incubation environment. The model of this structure includes 4 tabs that extend horizontally on the outside of the support structure. These tabs ensure a proper fit within the incubator rack and should be modified based on incubator design. Demonstration use cases High-throughput manipulation of growth conditions To demonstrate the utility of the lighting system, high-throughput screening experiments were initially designed to evaluate medium composition and antibiotic treatment on S. elongatus UTEX 2973 growth. A black plate was selected for both experiments, as the 308 wells in sections 2–8 (excluding outside row of wells) enable the testing of 4 conditions at 11 concentrations and with 7 replicates per concentration. With conditions randomly distributed across two microplates, the impact of individually varying the concentration of each of the 8 components of BG-11 medium was evaluated. Cells were washed twice with distilled water prior to dispensing into microplates. The results indicated that the concentrations of K 2 HPO 4 , NaHCO 3 , NaNO 3 , MgSO 4 and trace elements have the most significant impact on S. elongatus UTEX 2973 growth rates, with phosphorus (K 2 HPO 4 ), inorganic carbon (NaHCO 3 ) and nitrogen (NaNO 3 ) potentially being limiting at a standard BG-11 composition (Fig. 3A and B ). Antibiotic experiments showed S. elongatus UTEX 2973 sensitivity across all tested antibiotics, with streptomycin and carbenicillin exhibiting antibacterial activity at the lowest concentrations (Fig. 3C and D / Supplementary Fig. 26 ). The results conform to expected values, indicating that the lighting system enables both reproducibility and the identification of phenotypes consistent with commonly used cultivation formats 20 , 60 , 61 . Based on this consistency, we extended medium composition experiments using response surface modelling (RSM) to design optimized medium compositions and screened a library of bioactive small molecules for increased biomass production. These validation steps are directly related to bioproduction applications and highlight the potential of this lighting system to identify factors that increase biomass production using chemical genetic approaches. Fig. 3 BG-11 medium component concentration and antibiotic sensitivity testing in Synechococcus elongatus UTEX 2973 . To demonstrate applicability of the lighting system, the impact of individually varying the concentration of each of the 8 components of BG-11 medium was tested. 8 components at 11 concentrations and with 7 replicates per concentration were distributed randomly across sections 2–8 of two black-well microplates, resulting in 716 individual cultures. A Growth curves show impact of nutrient limitation over the course of a 120 h experiment. B Response curves at T3 (72 h) show limiting nutrients and tight condition-dependent clustering of OD 750 values. Sensitivity to 4 antibiotics was tested at 11 concentrations and with 7 replicates per concentration. C Growth curves show impact of on growth rate over the course of a 120 h experiment. D Response curves at T3 (72 h) show antibiotic sensitivity and tight condition-dependant clustering of OD 750 values. Strain-specific BG-11 medium optimization Response surface modelling (RSM) supports determination of optimal settings of inputs to maximize, minimize or target a specific output 62 . In the context of medium optimization experiments performed here, RSM enabled the simultaneous identification of factors with the most significant impact on growth rate, including limiting and excess nutrients, and optimization of inputs to maximize biomass production, including evaluation of the relationship between concentrations of multiple inputs 62 – 65 . To demonstrate cross-strain compatibility, RSM-based optimization experiments were performed with the objective of strain-specific BG-11 medium optimization. Synechococcus elongatus UTEX 2973, Chlamydomonas reinhardtii UTEX 90 and Nostoc hatei CUBC1040 were selected, as they span cyanobacterial and eukaryotic photosynthetic microorganisms, including both unicellular and filamentous strains. These optimization experiments enabled identification of limiting nutrients for each strain, as well as optimized medium compositions for increased growth under defined conditions. Parallel cross-strain medium optimization also served as a demonstration of the lighting system in a high-throughput screening paradigm relevant to development of industrial bioprocesses 39 , 41 , 66 . A central composite design, or Box-Wilson design, is a standard experimental design method in RSM and was used here for strain-specific BG-11 medium optimization 63 , 67 . This design strategy defined three levels of each factor, including a centre point (0), a low value (−1) and a high value (1). As atmospheric carbon capture is a desired objective of industrial application of photosynthetic microorganisms, optimization was performed at fixed NaHCO 3 concentrations. These were set at either 0 g/L NaHCO 3 , making strains entirely dependent on atmospheric CO 2 , or the standard BG-11 concentration of 0.42 g/L NaHCO 3 . This resulted in an eight-factor BG-11 optimization, which requires a minimum of 51 conditions. A single 384-well plate supports an experiment with 61 conditions, including 24 replicates of the centre condition (0 value for all factors) and 6 replicates of the 60 additional conditions (Supplementary Table 1 ). Factor levels were defined using the standard BG-11 composition as a centre point (Table 2 ). Table 2 BG-11 composition optimization factor levels for central composite design and response surface modelling 0x BG-11 (g/L) 1x BG-11 (g/L) 2x BG-11 (g/L) Component −1 0 1 NaNO 3 0 3.00 6.00 K 2 HPO 4 0 0.063 0.125 MgSO 4 x 7 H 2 O 0 0.148 0.296 CaCl 2 x 2 H 2 O 0 0.074 0.147 EDTA 0 0.002 0.004 Na 2 CO 3 0 0.081 0.161 Citrate solution 0 0.026 0.052 Trace elements 0 mL/L 1 mL/L 2 mL/L NaHCO 3 0 g/L or 0.42 g/L A single round of lighting system growth optimization was performed prior to flask-based testing (Fig. 4 ). Results indicate that nitrogen content (NaNO 3 ) was the factor with the most significant impact on growth rate of both S. elongatus UTEX 2973 and C. reinhardtii UTEX 90, regardless of NaHCO 3 concentration (Fig. 4B ). MgSO 4 , K 2 HPO 4 , and CaCl 2 were also important factors across both strains (Fig. 4C / Supplementary Figss. 8 – 11 ). MgSO 4 was the most important factor in Nostoc hatei CUBC1040 growth rate, with NaNO 3 content being the fourth or fifth most important factor (Fig. 4B / Supplementary Figs. 12 – 13 ). This result is consistent with expectations as Nostoc hatei CUBC1040 is heterocystous and capable of fixing atmospheric N 2 68 . These trends can be visualized by observing the OD 750 values of individual cultures in relationship to the depletion status of a given nutrient (Supplementary Fig. 27 ). Results support model predictions, suggesting that S. elongatus UTEX 2973 and C. reinhardtii UTEX 90 are very sensitive to depletion of NaNO 3 , MgSO 4 , and K 2 HPO 4 , whereas Nostoc hatei CUBC1040 only shows this degree of sensitivity to MgSO 4 depletion. The decreased sensitivity of Nostoc hatei CUBC1040 to K 2 HPO 4 depletion may result from the ability of Nostoc strains to store phosphorus as polyphosphate bodies 69 , 70 . This stored phosphorus can be utilized during periods of nutrient scarcity, enabling initial growth even when external phosphorus levels are low. This strategy is crucial for survival in phosphorus-limited environments, as it allows Nostoc to maintain metabolic functions and initiate growth before external phosphorus becomes available again 69 , 70 . Fig. 4 BG-11 medium optimization and response surface modelling . BG-11 optimization and RSM were performed through a 61-condition, 8-factor central composite design experiment. A Response surface model showing the relationships between components NaNO 3 and MgSO 4 and components CaCl 2 and K 2 HPO 4 , as well as their predicted impact of growth. B Predicted effects of individual medium components on growth of each strain with or without NaHCO 3 . Values are plotted as LogWorth = −log 10 ( p- value). C Flask-based cultivation in standard and optimized BG-11 media showed reproducible increases in biomass accumulation over 7-day cultivation experiments. Points represent biomass from individual 50 mL flask cultures. Green bars represent mean biomass of three flasks in standard and optimized medium. Response surface models for each strain indicated that increasing NaNO 3 concentrations to the highest tested concentration would maximize growth rates, which is consistent with previous observations (Fig. 4A ) 20 , 71 . Rather than increasing nutrient provision to all cultures, RSM was leveraged to design optimal medium composition with a defined NaNO 3 concentration of 3.0 g/L, which is that of standard BG-11 medium (Table 2 ). Results indicated that variations in the concentrations of K 2 HPO 4 , MgSO 4 , CaCl 2 citrate and trace elements all contributed to optimized growth in microplates. EDTA, a chelating agent, supported growth in the absence of NaHCO 3 but decreased growth in the presence of NaHCO 3 . EDTA is a known inhibitor of carbonic anhydrases (CAs) through chelation of the Zn 2+ cofactor in the active site of many microalgal CAs 72 , 73 . This could interfere with the conversion of HCO 3 − to CO 2 in the carboxysome for fixation by ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCo) 73 . Na 2 CO 3 concentrations should be decreased to maximize growth in all cases except S. elongatus UTEX 2973, in the absence of alternative inorganic carbon supplementation. Validation experiments in flasks were performed for optimized medium compositions containing NaHCO 3 at standard BG-11 concentrations across all three strains, as well as in S. elongatus UTEX 2973 in NaHCO 3 -deficient medium (Table 3 ). Cultures were inoculated in Erlenmeyer flasks in triplicate in standard and optimized BG-11 medium. OD 750 values were collected at days 1, 3, and 7 and biomass dry weight was collected at day 7 to evaluate final impact of optimized medium on biomass accumulation. Under NaHCO 3 supplemented conditions, optimized medium significantly increased biomass accumulation in UTEX 2973 ( t (3.74) = 11.93, p = 0.0004, Cohen’s d = 9.74). Although increases were observed in the other two strains, these were marginally significant in UTEX 90 ( t (2.52) = 3.46, p = 0.053, Cohen’s d = 2.82) and non-significant in Nostoc sp. ( t (2.38) = 1.71, p = 0.209, Cohen’s d = 1.40), likely as a result of increased variability. In NaHCO 3 -deficient medium, tested only in UTEX 2973, optimized medium also significantly increased biomass ( t (3.20) = 4.30, p = 0.020, Cohen’s d = 3.51). Increases across all strains ranged from 38.4% to 61.6% in total biomass accumulation (dry weight) over 7 days of cultivation (Fig. 4C ). These results are consistent with findings in microplates and further demonstrate the potential of high-throughput screening systems to optimize growth conditions prior to scale-up. Indeed, if scalable, the biomass increases observed using optimized media could lead to 10,000 s tonnes of additional CO 2 fixation and promote economic feasibility of algae-based carbon capture strategies without the need for metabolic engineering. Table 3 Optimized BG-11 medium compositions for each strain and inorganic carbon supplementation condition Nostoc hatei (-Ci) Nostoc hatei (+Ci) UTEX 90 (-Ci) UTEX 90 (+Ci) UTEX 2973 (-Ci) UTEX 2973 (+Ci) NaNO 3 0 0 0 0 0 0 K 2 HPO 4 1 0.8 0.8 0.6 0.5 0.8 MgSO 4 0.4 0.5 0.45 0.4 0.5 0.5 CaCl 2 1 1 1 1 1 1 EDTA 1 −0.3 0.3 −0.1 0.5 −0.1 Na 2 CO 3 −0.5 −0.2 −1 −1 1 −1 Citrate 0.7 1 1 0.9 0.6 1 Trace elements 0.5 1 1 0.1 0.4 0.4 Predicted OD 750 1.435 1.569 2.196 1.913 1.392 1.609 NaNO 3 compositions were set to standard BG-11 concentrations. Values range from −1 to 1. Predicted OD 750 values (bolded) are based on response surface modelling through JMP interface. Screening for bioactive molecules affecting algal growth Bioactive molecule screening in S. elongatus UTEX 2973 was performed in three rounds, prior to the selection of candidate compounds for downstream characterization. The primary screen (round 1) involved a single replicate of all 4240 bioactive molecules in the composite library at 5 μM, in 14 clear 384-well plates. These plates were screened in 2 sets of 9 and 5, respectively (Fig. 5 ). Although the median OD 750 value at the final timepoint varied between Set 1 and Set 2 (Fig. 5A ), values were normalized by calculating Z-scores on a plate-by-plate basis (Fig. 5B ). The use of Z-scores for outlier detection assumes a normal distribution of data. Skewness and kurtosis tests gave values of −0.01 and 8.61, respectively, indicating very little skew across the entire dataset and a sharper central peak than that of a standard normal distribution (Fig. 5B / Supplementary Fig. 28 ). This results from most cultures growing at near the mean rate, with a relatively small number of significant outliers. The negative skew of the OD 750 and Z-score values in Set 2 likely resulted from increased separation between median growth rates of cultures that continued to grow and those treated with compounds that prevented growth. Hits were selected based on a Z-score > 2.5 or < −2.5, for “High” and “Low” OD 750 hits, respectively (Fig. 5B / Table 4 ). This resulted in the selection of 59 high-OD 750 hits (top 1.4% of values) and 87 low-OD 750 hits (bottom 2.1% of values). A full list of selected hits can be found in Supplementary Table 2 . Fig. 5 High-throughput bioactive molecule screening and gradient testing of select candidate compounds. A Round 1 of bioactive molecule screen was run in two sets, monitoring OD 750 values in each well. Shades of grey indicate OD 750 from 14 384-well plates. Solid lines represent median OD 750 values for each set. B Plate-by-plate Z-scores were calculated to normalize data and identify outliers. Colour represents selected outliers (dark green). Solid line represents Z-score of 0. Dashed lines represent Z-score of ± 2.5. Data showed near normal distribution. C All OD 750 data points from round 2 screening. 146 hits (each in triplicate) and 396 negative control wells, across three 384-well plates. D Selected hits from round 2 with negative control wells. Negative control data showed a positively skewed distribution. E Round 3 gradient testing for selected hits inducing growth and F inhibiting growth. Line colours indicate selected compounds for downstream testing. G Lines show a median of 8–12 replicate wells at each concentration of selected candidate compounds. H Fold-change in OD 750 compared to negative control wells (no gossypetin treatment), showing 12 replicates at each concentration [non-linear x-axis]. I Median (solid), 25th percentile (dashed), and 75th percentile (dashed) OD 750 measurements across 15 gossypetin concentrations [linear x-axis]. J Flask-based cultivation at 10 μM and 20 μM gossypetin showed reproducible increases in biomass accumulation over a 7-day cultivation experiment. Table 4 Tally of hits from round 1 of bioactive screen in Synechococcus elongatus UTEX 2973 Hit OD 750 Hit Type Count No - 4094 Yes High 59 Yes Low 87 The 146 hits selected in the round 1 screen were rescreened in triplicate in round 2 across three identical 384-well plates (Fig. 5C ). Each replicate plate included 132 negative control wells (0.1% DMSO). Interestingly, control data in round 2 did not show the same slight negative skew as round 1 data. Skewness and kurtosis tests gave values of 1.29 and 6.09, respectively, indicating a positive skew and a sharper central peak than that of a standard normal distribution (Fig. 5D / Supplementary Fig. 29 ). This positive skew can also be explained by observed growth dynamics in clear 384-well plates, where low values are bound by the minimum value of no growth and high-values can vary significantly (Fig. 5D ). Based on these observations, outliers for future screening were selected by calculating p -values for each experimental OD 750 measurement. Compounds were then filtered to those that had p -values < 0.1, calculated using a one-tailed normal distribution test, in at least 2 of 3 replicates. This selection criterion accounted for observed stochasticity in high OD 750 values in clear-well plates, as the probability that the same compound is the subject of a randomly occurring high OD 750 value multiple times is low. A total of 64 low-OD 750 outliers and 6 high-OD 750 outliers matched this criterion (Supplementary Table 3 ). All 6 high-OD 750 outliers were selected for downstream characterization. Eight of the 64 low-OD 750 outliers were also selected by maximizing the difference between the mean of the negative control data and the mean of the 3 experimental replicates (Table 5 ). Low-OD 750 hits were clearly distinguished from negative controls, whereas high-OD 750 hits appeared at the upper end of the distribution of negative controls (Fig. 5D ). Table 5 Selected hits from round 2 of bioactive screen Chemical name Significant Measurements OD 750 GOSSYPETIN 2 High BEPHENIUM HYDROXYNAPTHOATE 2 High METHYL 7-DESHYDROXYPYROGALLIN-4-CARBOXYLATE 2 High IRIDIN 2 High COUMOPHOS 2 High DL-alpha-Methyl-p-tyrosine 2 High Remerine HCl 3 Low RIBOFLAVIN 3 Low THIMEROSAL 3 Low GENTIAN VIOLET 3 Low PHENYLMERCURIC ACETATE 3 Low Riboflavin 3 Low Disulfiram 3 Low Gliotoxin 3 Low 'Significant Measurements' column indicates numbers or replicates with p -value < 0.1 The 6 high-OD 750 and 7 low-OD 750 hits selected from round 2 were subsequently tested over a narrow concentration gradient (1.667 μM, 3.333 μM, 5 μM and 6.667 μM). This gradient provided additional replication and preliminary insight into dose-dependence of observed growth effects (Fig. 5E, F ). High-OD 750 candidate compounds were selected if at least 3 treatments showed higher OD 750 values than the negative control. Based on the results, all high-OD 750 candidate compounds were selected for further characterization with exception of methyl 7-deshydroxypyrogallin-4-carboxylate. Cyanocidal compounds, such as the low-OD 750 candidate compounds identified here, have applications in the mitigation of harmful algal blooms in the aquaculture industry and environmental settings 74 . Although the identification of cyanocidal compounds was not the primary objective of this screen, gentian violet and disulfiram were selected for further characterization. Gentian violet and disulfiram showed the most significant response at the lowest tested concentration of 1.667 μM, outside of organomercury compounds thimerosal and phenylmercuric acid. Both thimerosal and phenylmercuric acid contain a covalently bound atom of mercury (Hg). These compounds were not selected because, at tested concentrations of 1.667 μM, this results in a Hg concentration of 0.334 mg/L, which is 334 times the maximum acceptable concentration in drinking water according to the Government of Canada 75 . Although gentian violet has been shown to persist in certain environments, it is amenable to bioremediation by some species of bacteria, fungi and algae 76 . Less is known about the environmental stability of disulfiram, which is a drug used to treat alcohol dependence and has been proposed as a narrow-spectrum antibiotic 77 , 78 . However, other compounds containing disulphide bonds are cleaved in reductive environments, such as freshwater and marine sediments, indicating a possible bioremediation route 79 . Although riboflavin was not selected for testing at scale, it is interesting that this essential vitamin was identified as cyanocidal. We did not find any reports of toxicity in cyanobacteria and riboflavin has been successfully overproduced in model strain Synechococcus sp. PCC 7002 under non-degrading red light conditions 80 , though riboflavin is known to have antimicrobial properties in a range of Gram-positive and Gram-negative bacterial strains 81 , 82 . However, it has been observed that upon exposure to UV or white light, riboflavin will photolyse into lumichrome or lumiflavin in neutral or basic conditions, respectively. Lumichrome has been shown to be toxic to cyanobacteria Microcystis, providing a possible explanation for the observed toxicity 83 . The resulting set of 7 candidate compounds was purchased in larger quantities from various suppliers for additional testing (Supplementary Table 3 ). Each compound was tested at 12 concentrations, from 0.1 μM to 15 μM, with 8 to 12 replicates at each concentration. Both gentian violet and disulfiram significantly decreased growth at concentrations of 2 μM and above (Fig. 5G ). However, only gossypetin among the high-OD 750 candidates resulted in a significant growth phenotype (Fig. 5G ). The impact of gossypetin on growth was further explored up to 100 μM in microplate format, with 12 replicates at each concentration. Results indicated a significant increase in OD 750 above 6 μM gossypetin (one-way ANOVA, F(15,279) = 11.7, p < 2 × 10^−16, Cohen’s f = 0.79), with a maximum increase in OD 750 between 20 μM to 50 μM. A 1.4-fold median increase was observed at 20 μM gossypetin (Fig. 5H, I ). A distinctive phenotype observed when treating S. elongatus UTEX 2973 with gossypetin was a visible colour change of the culture from green to near-black, despite gossypetin having a yellow colour when dissolved in DMSO (Supplementary Figs. 30 , 31 ). This colour change was observed in BG-11 medium, as well as the S. elongatus UTEX 2973 culture, but not in distilled water, and is likely the result of iron-mediated oxidation and complexation resulting in colour changes in other flavonoids 84 . An absorbance scan indicated that gossypetin treatment leads to an absorbance peak at 370 nm in BG-11 medium, S. elongatus UTEX 2973 culture, and dH 2 O, with an increase in absorption across the visible spectrum observed only in BG-11 medium and S. elongatus UTEX 2973 culture (Supplementary Fig. 32A ). A slight increase in absorbance was also observed at OD 750 , but this is insufficient to explain the increase in measured OD 750 during the time course of the growth experiment (Supplementary Fig. 32B ). Gossypetin is a flavonol, a class of flavonoids, that was initially isolated from Hibiscus sabdariffa . Flavonoids are widespread in plants and plant products 85 and are often considered antioxidants with the potential to scavenge reactive oxygen species (ROS) and protect from ROS-induced DNA damage and cellular stress 86 . Gossypetin has been shown to reduce λ-radiation induced DNA damage 87 , as well as display anti-atherosclerotic effects and anti-cancer effects, as well as lead to the induction of Aβ plaque phagocytosis, in the context of Alzheimer’s disease 88 – 90 . Interestingly, gossypetin and related flavonols have also been observed to have antimicrobial effects 91 , 92 . However, gram-negative prokaryotes, such as cyanobacteria, may be less susceptible to this class of compounds 93 . In cyanobacteria, another flavonoid, naringenin, has also been reported to increase growth rate and chlorophyll production 94 . The authors proposed that this is the result of membrane disruption and increased permeability, resulting in polysaccharide excretion. This may function as a carbon sink, increasing metabolic efficiency. The observed absorbance peak of gossypetin-treated cultures and medium in the UV range also suggests a photoprotective mechanism is possible at higher light intensities, in addition to antioxidant effects, and impacts on membrane permeability. Iridin, one of the other compounds selected for round 3 screening, is a related flavonoid, indicating that this effect may be shared by other compounds of this class. To determine whether the impact of gossypetin on Synechococcus elongatus UTEX 2973 growth could scale to larger culture volumes, we performed a 50 mL flask-based cultivation experiment. Cultures were inoculated in Erlenmeyer flasks in triplicate, in standard BG-11 medium, with 10 μM and 20 μM gossypetin treatments and untreated. OD 750 values and absorbance scans were collected at days 1, 3, and 7, and biomass dry weight was collected at day 7 to evaluate final impact of gossypetin on biomass accumulation. These results indicated significant increases in total biomass accumulation over 7 days of cultivation with both 10 µM (Welch’s t-test, t (3.74) = −3.67, p = 0.024, Cohen’s d = −3.00) and 20 µM gossypetin (t(3.48) = −10.57, p = 0.0009, Cohen’s d = −8.63), showing 16.5% and 68.0% increases respectively, consistent with microplate results."
} | 16,681 |
29027713 | null | s2 | 1,677 | {
"abstract": "A specific and reversible method is reported to engineer cell-membrane function by embedding DNA-origami nanodevices onto the cell surface. Robust membrane functionalization across epithelial, mesenchymal, and nonadherent immune cells is achieved with DNA nanoplatforms that enable functions including the construction of higher-order DNA assemblies at the cell surface and programed cell-cell adhesion between homotypic and heterotypic cells via sequence-specific DNA hybridization. It is anticipated that integration of DNA-origami nanodevices can transform the cell membrane into an engineered material that can mimic, manipulate, and measure biophysical and biochemical function within the plasma membrane of living cells."
} | 181 |
30307942 | PMC6198993 | pmc | 1,678 | {
"abstract": "Ants, termites and humans often form well-organized and highly efficient trails between different locations. Yet the microscopic traffic rules responsible for this organization and efficiency are not fully understood. In previous experimental studies with leaf-cutting ants ( Atta colombica ), a set of local priority rules were isolated and it was proposed that these rules govern the temporal and spatial organization of the traffic on the trails. Here we introduce a model based on these priority rules to investigate whether they are sufficient to produce traffic similar to that observed in the experiments on both a narrow and a wider trail. We establish that the model is able to reproduce key characteristics of the traffic on the trails. In particular, we show that the proposed priority rules induce de-synchronization into clusters of inbound and outbound ants on a narrow trail, and that priority-type dependent segregated traffic emerges on a wider trail. Due to the generic nature of the proposed priority rules we speculate that they may be used to model traffic organization in a variety of other ant species.",
"introduction": "Introduction Animal collective movement is a widespread phenomenon that occurs at various spatial and temporal scales in a variety of living organisms from cells to pedestrians [ 1 – 4 ]. Often there are no identifiable leaders or coordinators present and group coordination relies on a completely decentralized process. The global pattern is not explicitly encoded but emerges from numerous interactions between individuals that only have access to local and limited information [ 5 – 7 ]. In many situations the motion within the collective is unidirectional because it is related to migratory phenomena and involve individuals moving in the same direction. Social insects and humans are some of the rare organisms in which the movements within the collective are predominantly bidirectional [ 8 – 12 ]. In particular, ants are central-place foragers and must return to their nest with the food collected after each foraging event which often lead to the formation of trails with a steady stream of traffic between the nest and the food source. In some species the traffic flow on these trails can be extremely high, reaching more than a hundred ants per minute, e.g. red wood ants [ 13 ], leaf cutting ants [ 14 ] and army ants [ 15 ]. When the local concentration of individuals is very high on the trail the high rate of head-on collisions may slow down the individuals [ 16 – 18 ]. These effects can provoke group dysfunctions and reduce the colony’s overall foraging efficiency. Such negative effects can be avoided if ants make use of dispersal mechanisms allowing a better organization of the traffic [ 8 ]. Traffic in ants can be organized either on a spatial or on a temporal scale [ 8 ]. Spatial organization of traffic is characterized by lane segregation, i.e. the flows of inbound and outbound ants are not completely intermingled [ 19 – 21 ] and the temporal organization of the flow is characterized by a sequence of alternating clusters of inbound and outbound ants [ 16 , 22 ]. To date, the emergence of both the spatial and the temporal organizations observed in these systems remains largely unexplained. In particular, the microscopic traffic rules that individual ants follow when navigating on trails are largely unknown. In an attempt to isolate the microscopic traffic rules experimental studies on traffic organization in leaf-cutting ants Atta colombica on a narrow trail [ 22 ] and on a wide trail [ 20 ] were performed. Leaf-cutting ant trails guide workers to and from the foraging site where they cut vegetation into small fragments and transport them back to the nest. These fragments are then incorporated into a fungus on which the colony feed. In the experiments, to reach the leaf source, ants were forced to move on either a narrow trail allowing the passage of only one moving individual at a time [ 22 ] or on a wide trail ten times larger [ 20 ]. On the narrow trail de-synchronization of inbound and outbound traffic involving the formation of alternating clusters of inbound and outbound ants was observed and on the wide trail a degree of lane segregation with leaf carrying ants travelling almost exclusively on the central section of the trail was described. Summaries of the results obtained in the experiments may be found in the supporting information S1 Table (narrow trail) and S2 Table (wide trail). The authors suggested that both organizations may result from a set of local priority rules observed at the individual level when ants encountered other ants on the trail [ 20 , 22 ]. However, whether these proposed individual priority rules are sufficient to produce the observed temporal and spatial traffic organization is unknown and to investigate this modelling is required. There are many well known models for ant traffic, most of which are mean-field models studying the macroscopic properties of the traffic on trails [ 23 – 31 ]. However, to investigate the group level traffic that emerges from repeated local interactions between moving individuals so-called self-propelled particle models are more appropriate. Self-propelled particle models are spatially-explicit individual based models where particles interact locally with each other according to a set of rules. These models range from minimal models used to investigate fundamental properties of collective motion [ 32 – 35 ] to more involved species specific models of collective motion in everything from cells to insects, fish, birds, sheepdogs and pedestrians [ 9 , 10 , 36 – 44 ]. The self-propelled particle model approach has been successfully applied to model ant traffic in army ants [ 19 ] and black garden ants [ 45 ]. However, in these models the characteristic features of outbound and nest-bound ants were not distinguished despite the fact that variation in their maneuverability and speeds exist due to food transport [ 22 , 46 ]. Here we introduce a self-propelled particle model to reproduce the experiments with leaf-cutting ants Atta colombica on a narrow trail [ 22 ] and on a wide trail [ 20 ], and to investigate whether the local priority rules proposed in these studies are sufficient to reproduce the traffic organization observed.",
"discussion": "Discussion Our self-propelled particle model based on the local priority rules presented in [ 22 ] generates traffic organization that share several characteristics with the traffic organization observed in the narrow and wide trail experiments. In particular, the narrow trail model reproduces de-synchronization of inbound and outbound traffic and the groups that emerge share several features with the experimentally observed groups ( Fig 2 ). The wide trail model generated segregated traffic that share certain properties with the traffic observed in the experiments ( Fig 3 ). In particular, that the proportion of ants of a given type (L, O or U) traveling in the central zone increased with the priority of each type (L>O>U) in both model and experiment. This suggests that it is plausible that the priority rules proposed in [ 22 ] are key drivers of organizing the traffic on these trails because the main features of the observed traffic emerge from them even when the rest of the system is heavily idealized. For example, we use simplified model ants with constant speed and lengths that always follow the rules and the only stochastic components in the models are related to leaving times and entry positions. In particular, we believe that the strict rule following produces the main discrepancy between simulation results and data, i.e. the over representation of groups of size 1 on the narrow trail ( Fig 2A ). We also know that introducing various types of stochastic rule violations in the model does not solve this problem, and we are confident that the rule violations are far from randomly occurring and new experiments would be required to investigate this. However, while it would be interesting and potentially useful to conduct new experiments to obtain data that allows us to make certain aspects of the model more realistic this would inevitably make the model more complex and thus make it harder to isolate the effects of generic mechanisms underlying traffic organization. Understanding the basic principles that govern traffic organization on trails and identifying the factors that influence the movements of ants on trails are of fundamental importance in the biology of social insect colonies. When ants were forced to move on a narrow trail the formation of alternating groups of inbound and outbound ants was observed. Groups of inbound ants were frequently headed by laden ants, which are slower, followed by unladen ants. The model replicates this behavior, most likely due to the rule that dictates that inbound unladen ants do not attempt to overtake laden ants in front of them (Rule 1). This behavior may appear detrimental because unladen ants move slower by staying behind a laden ant instead of progressing more rapidly by moving at its desired speed. However, the model also includes the so-called cooperative rule that allows for the possibility of unladen ants following a laden ant to benefit from the passage of the laden (See rules 2 and 3). These unladen ants avoid head-on collisions with outbound ants and thus spare time they would otherwise waste by stopping as they normally do when they meet an outbound ant. Moreover, this organization promotes information transfer about the level of leaf availability by increasing the number of contacts between outbound and inbound laden ants which stimulate the former to cut and retrieve leaf fragments when reaching the end of the trail [ 47 – 53 ]. Following the same idea, on the wide trail, an intermingled flow of outbound and unladen ants instead of a strict lane segregation might appear sub-optimal, but it actually promotes information transfer between ants and stimulate outbound workers to cut and collect leaf material at the end of the trail, thus contributing to increased foraging efficiency [ 8 , 20 , 48 , 49 ]. Our findings suggest that the ants may be using the same generic priority rules on both trails and the observed differences results from constraints imposed by the environment. In particular, on a wider trail there is room to turn during an encounter whereas on a narrow trail this option is not available so the ants have to stop when giving way. In fact, due to the simplicity of these rules we believe that they could be valid, with appropriate modifications, for other species of ant under similar conditions. For example, the same priority rule between laden and unladen ants has been observed in another leaf-cutting ant Atta cephalotes [ 47 ] and in the red wood ant Formica rufa [ 13 ]. In addition, similar types of priority rules are likely to be operating in army ants because returning laden ants are known to be less mobile and have less maneuverability than unladen outbound ants [ 46 ]. We also note that there is a correspondence between the priority rules and the potential utility of each type of ant with respect to leaf collection. Laden ants have the highest priority and they are collecting leaves, outbound ants have the second highest priority and they are potentially going to collect leaves, and inbound unladen ants have the lowest priority and they are not collecting leaves. We speculate that priority rules in other species are likely to correspond to the potential utility of each type of ant with respect to the colony’s foraging activity. Our model distinguishes itself from earlier spp-models of ant traffic in several ways. In particular, we model three types of ants (outbound, unladen and laden) whereas [ 19 , 45 ] only include two; inbound and outbound, and the two types are essentially identical except for different avoidance turning rates in [ 19 ]. Furthermore, the avoidance turning rate is the same for all individuals of a certain type, i.e. outbound or inbound, despite the fact that variation in maneuverability and speeds exist in real ants due to food transport [ 22 , 46 ]. Our model includes this variability and our priority rules are flexible enough to model traffic on both narrow and wide trails. In [ 19 , 45 ] only traffic on wider trails are modeled and while the avoidance turning rate approach may be modified to work on narrow trails, which presumably both army ants [ 19 ] and black garden ants [ 45 ] occasionally travel on in the wild, we predict that unless the inbound flow is separated into unladen and laden ants with different behaviors the model will not be able to generate traffic consistent with the real ant traffic [ 16 ]. One often thinks about the similarities between ant traffic, pedestrian traffic and vehicular traffic. These analogies have inspired multiple investigations [ 54 – 58 ]. However, even if at first sight traffic on ant trails may appear similar to human traffic there are important differences to consider when comparing their traffic organization. First, ant traffic is of a cooperative nature because all ants share a common objective, namely harvesting food for the colony. Second, ants do not have the same mechanical constraints as pedestrians or vehicles. Because of their small mass they have a low inertia and are not damaged by collisions, allowing a certain degree of mixing of opposite flows on foraging trails. Despite this, ant traffic remains an important source of inspiration for various researchers working with large groups of interacting particles in disciplines as diverse as molecular biology [ 59 ], statistical physics [ 60 ] and telecommunication sciences [ 61 ]."
} | 3,434 |
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