pmid stringlengths 8 8 | pmcid stringlengths 8 11 ⌀ | source stringclasses 2
values | rank int64 1 9.78k | sections unknown | tokens int64 3 46.7k |
|---|---|---|---|---|---|
31816954 | PMC6955790 | pmc | 6,699 | {
"abstract": "This is a literature teaching resource review for biologically inspired microfluidics courses or exploring the diverse applications of microfluidics. The structure is around key papers and model organisms. While courses gradually change over time, a focus remains on understanding how microfluidics has developed as well as what it can and cannot do for researchers. As a primary starting point, we cover micro-fluid mechanics principles and microfabrication of devices. A variety of applications are discussed using model prokaryotic and eukaryotic organisms from the set of bacteria ( Escherichia coli ), trypanosomes ( Trypanosoma brucei ), yeast ( Saccharomyces cerevisiae ), slime molds ( Physarum polycephalum ), worms ( Caenorhabditis elegans ), flies ( Drosophila melangoster ), plants ( Arabidopsis thaliana ), and mouse immune cells ( Mus musculus ). Other engineering and biochemical methods discussed include biomimetics, organ on a chip, inkjet, droplet microfluidics, biotic games, and diagnostics. While we have not yet reached the end-all lab on a chip, microfluidics can still be used effectively for specific applications.",
"conclusion": "21. Conclusions This has been a wide overview of microfluidics with primarily biological applications. There are now tens of thousands of microfluidics research articles. With the references in this article, a large amount of ground can be explored in a microfluidics course even if only for a half-semester. By understanding the variety of applications of microfluidics, one can get a bigger picture of how microfluidics is influencing science. The all-encompassing lab on a chip may have not yet arrived, but there is still a lot that can be done. Microfluidics is here to stay and is at the forefront of interdisciplinary research.",
"introduction": "1. Introduction This review closely follows how microfluidics is taught in my course. The course usually consists of twelve lectures and six practical sessions aimed primarily for biology graduate students. My favorite textbook for biologically inspired microfluidics is Folch’s Introduction to bioMEMS. There is broad coverage of topics and an extensive collection of color figures from the literature [ 1 ]. There exist other good books on biophysics which can be used in conjunction like the Physical Biology of the Cell, which is also recommended for similar reasons as Folch’s book [ 2 ]. My way of organizing the course is to consider the physical principles and microfabrication techniques in the first two classes. The next classes address diverse topics in microfluidics applied to biology. It may be beneficial to arrange a course where students present their own project ideas at the end. Designing a project tests whether students have mastered the material and if they are ready for research. It is usually not a massive investment to try new microfluidics research projects once a lab is already setup. Hopefully, this review can help others prepare their courses or discover new avenues of research in microfluidics."
} | 750 |
36760273 | PMC9890976 | pmc | 6,702 | {
"abstract": "Harnessing solar energy for clean and sustainable fuel production by photoelectrochemical water oxidation over different timescales has been extensively investigated. However, the light-driven photoelectrochemical water oxidation reaction for artificial photosynthesis suffers from poor photon-to-current efficiency. Herein, we demonstrate an experimental analysis of electrolytic pH on photoelectrochemical syngas production by varying the pH of the KOH and NaOH electrolytes using the N–ZnO photoelectrode and analyzing all variables. A maximum photocurrent of 13.80 mA cm −2 at 1.23 V vs. RHE with a 43.51% photon-to-current conversion efficiency was obtained at pH 13 in the aqueous NaOH electrolyte.",
"conclusion": "Conclusions In summary, nanostructured N–ZnO was deposited on the ITO glass substrate, and its PEC performance was studied using KOH and NaOH electrolytes in the pH range of 9–14. The maximum photoresponse was observed in the case of the NaOH electrolyte because Na + is more electropositive and has a high charge density than K + , thus polarizing the water molecules more and lowering the energy requirement, as evident from the lower applied voltage required for water splitting in the NaOH electrolyte. To evidence the effect of electrolytic pH on water splitting, current–voltage, Mott–Schottky, EIS, V oc measurements were carried out, which established the fact that higher pH values facilitate improvement in the water-splitting rate. Nanostructured N–ZnO exhibited low V fb , high N d , and V oc value in the NaOH electrolytic conditions compared with the KOH electrolytes, and therefore, better efficiency was observed. The results also demonstrated that the band edge positions ( E cb , E vb and E f ) of the semiconductor at the semiconductor/electrolyte interface upshifted with rising pH in both electrolytes. However, the photoresponse decreased with rising pH beyond the optimum pH of the electrolytes. These results show that the overall photocatalytic performance is regulated by various factors, and the more dominating factor decides the efficiency, as evidenced by the slowed ionic mobility and boosted conductivity at high pH.",
"introduction": "Introduction The global demand for sustainable energy and related environmental crises have motivated scientists worldwide to find eco-friendly and renewable energy sources. 1,2 Photoelectrochemical water splitting, popularly known as artificial photosynthesis, produces green and sustainable fuel for environmental remediation. 3 The photoelectrochemical (PEC) system is an attractive way for solar energy conversion ( via water splitting) toward generating green hydrogen fuel. 4 Water splitting is a thermodynamically difficult and kinetically sluggish reaction, which requires 1.23 V to evolve O 2 and H 2 molecules. Efforts have been made to improve the PEC performance of semiconductor materials (ZnO, α-Fe 2 O 3 , BiVO 4 , and TiO 2 ) for hydrogen production like doping, heterojunctions, plasmons, etc. 5–11 However, as part of these modification strategies developed to improve PEC response for water splitting, the band edge position and charge carrier, solution conductivity, etc. are some of the issues that need more attention. The band edge position at the semiconductor/electrolyte interface and the charge density of the electrolyte are the most fundamental properties that affect PEC performance. Zinc oxide (ZnO) is a non-toxic n-type semiconductor with an appropriate band edge position and stability and has emerged as one of the most promising PEC candidates. 12–22 ZnO occurs in the form of three crystal systems: wurtzite, zinc blend, and rock salt. The wurtzite crystal structure shows photocatalytic activity and thermodynamic stability and is easy to synthesize. 23 However, the band edge position of ZnO and the search for suitable electrolytes remain as obstacles to producing scalable efficiency in solar syngas production. Previous studies on ZnO-based PEC systems have focused on nanostructuring, metal or non-metal doping, heterojunctions, surface modification with plasmon modification, quantum dot modification, etc. 24–28 Nitrogen incorporation in ZnO reduces the optical band gap and improves absorption in the solar spectrum for better PEC efficiency. 29–37 However, the effect of pH on electrolytes and their effect on PEC activity have not been reported. Acidic electrolytes increase corrosion, while KOH and NaOH are basic electrolytes that provide a stable medium for metal oxides in PEC water splitting towards solar hydrogen production. 29 The positions of the valence band energy ( E vb ) and conduction band energy ( E cb ) of N–ZnO straddle well with the water redox potential. In this study, we have attempted to explain the effect of pH of electrolytes on the band edge positions at the semiconductor/electrolyte interface using different electrolytes. In addition to this, the effect of NaOH and KOH in terms of the charge density of metal ions present in the electrolytes is also investigated. Here, we demonstrate the pH effect on PEC water-splitting performance based on results obtained at different pH, viz. 9, 10, 11, 12, 13, and 14 of the electrolytes (KOH and NaOH) using N–ZnO thin films. The study reveals that the band edge position is upshifted at the electrolyte/semiconductor interface with rising pH, reducing the photocurrent onset potential. In addition, the Na + ion in NaOH contains a higher charge density than the K + ion in KOH, which facilitates more polarization in water. This polarity profile in the PEC solvent induces binding energy, which improves charge transfer efficiency. PEC characterization in the present study confirms that the rise in electrolyte pH improves photoelectrochemical activity.",
"discussion": "Results and discussion The X-ray diffraction (XRD) pattern of N–ZnO showed three peaks for the (100), (002), and (101) planes (Fig. S1 † ), confirming that the as-grown thin film had the wurtzite phase (hexagonal system, JCPDS database file 36-1457). FESEM imaging of the photoelectrodes (Fig. S2 † ) confirmed successful photocatalyst deposition, with an average particle size of 47 nm (Fig. S3 † ). The chemical composition of the film based on EDAX confirmed the deposition of nanostructured N–ZnO on the ITO glass substrate (Fig. S4, and Table S1 † ). The photoluminescence (PL) lifetime was measured for nanostructured N–ZnO as 4.46 nm (Fig. S5 † ). The Brunauer–Emmett–Teller (BET) analysis of nanostructured N–ZnO was performed for the calculation of surface area (Fig. S6 † ), and a value of 1.853 × 10 −4 m 2 g −1 was observed. The photoelectrode made of nanostructured N–ZnO thin films with 327 nm thickness exhibited the maximum photocurrent due to its better light-harvesting performance and improved charge transfer rate, and was selected for the investigation of the effect of varying pH on PEC performance in different electrolytes. The schematic view of this study is shown in Fig. 1 . The PEC properties of the N–ZnO electrode were investigated at five different pH using two different electrolytes, viz. KOH and NaOH. Fig. 1 The schematic view of the study of effect of varying pH on PEC performance in various electrolytes. Solar-driven PEC water-splitting was studied at six different pH in two electrolytes viz. KOH and NaOH using nanostructured N–ZnO photoelectrode under 139 mW cm −2 illumination, as shown in Fig. 1 . The maximum photocurrent density of 13.80 mA cm −2 was achieved at pH 13 in the NaOH electrolyte, and 11.30 mA cm −2 was obtained in the KOH electrolyte (see Fig. 2(a and b) ). With a reduction in pH, i.e. , from pH 12 to 9, a decline in photocurrent density could be seen ( Table 1 ). The molar ratio of the electrolytes increased with the rise in pH, as shown in Table S2, † and the literature shows that the viscosity of the aqueous NaOH solution is more than that of the KOH electrolytes (as shown in Fig. S7 † ). 43 We measured the photocurrent density in the range of 0.02–0.08 mA cm −2 at the lower pH values 9–11 in both the electrolytes due to the low concentration of electrolytes ( i.e. 0.00001 M to 0.01 M). The values presented in Table 1 indicate that photocurrent density increased up to an optimum pH (pH 13) and then started decreasing, which may be attributed to the increasing photoexcited carrier recombination rate and slower ionic movement under illumination at higher pH. 44 The current density under dark conditions was approximately zero at different pH due to the insulating nature of N–ZnO in the dark (see Fig. 2(c and d) ). The higher charge density of the Na + ions facilitates the higher polarization of hydronium and hydroxyl ions, thus improving the photoresponse in NaOH in comparison with KOH. This is responsible for the reduction in the overpotential requirement for water splitting to take place. 44,45 Fig. 2 (a and b) The photocurrent density of nanostructured N–ZnO at pH 9–14 in aqueous NaOH and KOH electrolytes. (c and f) The open-circuit voltage. (d and e) The dark current density of N–ZnO at pH 9–14 in aqueous NaOH and KOH electrolytes. Photocurrent density of 327 nm thick N–ZnO thin film electrode in aqueous NaOH and KOH electrolytes at different pH pH Photocurrent density (mA cm −2 ) Values of open-circuit voltage ( V oc vs. RHE) KOH electrolyte NaOH electrolyte KOH electrolyte NaOH electrolyte 9 0.02 0.04 0.62 0.58 10 0.05 0.06 0.61 0.57 11 0.06 0.08 0.57 0.55 12 1.24 3.51 0.49 0.45 13 \n 11.30 \n \n 13.80 \n 0.46 0.42 14 7.60 7.50 \n 0.35 \n \n 0.34 \n The decrease in open-circuit voltage ( V oc vs. RHE) was observed with an increase in pH, as shown in Fig. 2(c and f) , which corroborate with the Mott–Schottky and EIS analyses. The least V oc value in N–ZnO at higher pH confirms the decreased rate of photogenerated charge recombination. The values of V oc vs. RHE measured for nanostructured N–ZnO at pH 9–14 in the KOH and NaOH electrolytes are given in Table 1 . \n Fig. 3(a–h) reveal noisy readings for nanostructured N–ZnO during the Mott–Schottky analysis on account of the low pH (9–12) of the KOH and NaOH electrolytes. The Mott–Schottky plot of the N–ZnO electrode exhibited a positive slope, characteristic of an n-type semiconductor. The flat band potential at pH 14 of NaOH was estimated as 0.05 V versus the reversible hydrogen electrode, which is lesser in comparison with the KOH electrolyte (as shown in Table 2 and Fig. 3(i–l) ), confirming better charge separation at the semiconductor/NaOH electrolyte interface. From these analyses, it can be concluded that flat band potential depends on the pH of the electrolyte. An increment in donor density was observed with rising pH in both KOH and NaOH electrolytes (as shown in Table 2 ). Fig. 3 (a–l) The Mott–Schottky plots of nanostructured N–ZnO at pH 9–14 in the NaOH and KOH electrolytes. The flat band potential ( V fb vs. RHE) and donor density (cm −3 ) of N–ZnO at different pH in aqueous NaOH and KOH electrolytes pH Flat band potential ( V fb vs. RHE) Donor density (cm −3 ) KOH electrolyte NaOH electrolyte KOH electrolyte NaOH electrolyte 9 — — 2.40 × 10 16 4.18 × 10 16 10 — — 8.66 × 10 16 2.14 × 10 17 11 — — 2.40 × 10 17 3.03 × 10 17 12 — — 3.26 × 10 17 7.08 × 10 18 13 0.24 0.16 9.82 × 10 18 7.28 × 10 18 14 \n 0.12 \n \n 0.05 \n \n 1.07 × 10 \n \n 19 \n \n \n 8.24 × 10 \n \n 18 \n \n \n Fig. 4(a and b) exhibit the charge transfer resistance properties of the N–ZnO photoelectrode at different pH values, as obtained by analyzing the Nyquist plots. At pH 14, a left shift in the semicircle was seen in both KOH and NaOH electrolytes, confirming the least solution resistance at the higher pH. Fig. 4(c and d) exhibit the band edge positions calculated using eqn (3)–(5) . Table 3 shows the values of the band edge positions, viz. conduction band energy ( E cb ), valency band energy ( E vb ), and Fermi level energy ( E f ). Fig. 4 (a and b) The Nyquist plots and (c and d) band edge positions of nanostructured N–ZnO pH at 9–14 in the NaOH and KOH electrolytes; (e) the external quantum efficiency (EQE) of nanostructured N–ZnO at pH 13 in aqueous (a) NaOH and (b) KOH electrolytes. The band edge positions of nanostructured N–ZnO at different pH in aqueous KOH electrolytes pH Band edge position of nanostructured N–ZnO KOH electrolyte KOH electrolyte \n E \n cb \n \n E \n f \n \n E \n vb \n \n E \n cb \n \n E \n f \n \n E \n vb \n 9 −4.424 eV −5.087 eV −7.524 eV −4.410 eV −5.073 eV −7.510 eV 10 −4.391 eV −5.054 eV −7.491 eV −4.368 eV −5.031 eV −7.468 eV 11 −4.365 eV −5.028 eV −7.465 eV −4.359 eV −5.022 eV −7.459 eV 12 −4.357 eV −5.020 eV −7.457 eV −4.278 eV −4.941 eV −7.378 eV 13 −4.270 eV −4.933 eV −7.370 eV −4.277 eV −4.940 eV −7.377 eV 14 −4.268 eV −4.931 eV −7.368 eV −4.274 eV −4.937 eV −7.374 eV It is visible from the findings that the downward shift of the conduction band is responsible for better charge separation at the semiconductor/electrolyte interface, which in turn corresponds with the better charge transfer rate. Fig. 4(e) presents the external quantum efficiency (EQE) values at pH 13 in the KOH and NaOH electrolytes. N–ZnO offers a 43.51% efficiency in the NaOH electrolyte and a 38.26% efficiency in KOH electrolyte."
} | 3,300 |
36557352 | PMC9784831 | pmc | 6,703 | {
"abstract": "In recent years, environmental problems caused by natural disasters due to global warming have seriously affected human production and life. Fortunately, with the rapid rise of the Internet of Things (IoT) technology and the decreasing power consumption of microelectronic devices, it is possible to set up a multi-node environmental monitoring system. However, regular replacement of conventional chemical batteries for the huge number of microelectronic devices still faces great challenges, especially in remote areas. In this study, we developed a rotating hybrid nanogenerator for wind energy harvesting. Using the output characteristics of triboelectric nanogenerator (TENG) with low frequency and high voltage and electromagnetic generator (EMG) with high frequency and high current, we are able to effectively broaden the output voltage range while shortening the capacitor voltage rising time, thus obtaining energy harvesting at wide frequency wind speed. The TENG adopts the flexible contact method of arch-shaped film to solve the problem of insufficient flexible contact and the short service life of the rotating triboelectric generator. After 80,000 cycles of TENG operation, the maximum output voltage drops by 7.9%, which can maintain a good and stable output. Through experimental tests, the maximum output power of this triboelectric nanogenerator is 0.55 mW at 400 rpm (wind speed of about 8.3 m/s) and TENG part at an external load of 5 MΩ. The maximum output power of the EMG part is 15.5 mW at an external load of 360 Ω. The hybrid nanogenerator can continuously supply power to the anemometer after running for 9 s and 35 s under the simulated wind speed of 8.3 m/s and natural wind speed of 5.6 m/s, respectively. It provides a reference value for solving the power supply problem of low-power environmental monitoring equipment.",
"conclusion": "5. Conclusions In this paper, a rotating triboelectric-electromagnetic hybrid power generation device for wind energy harvesting and self-powered environmental monitoring is proposed. In this hybrid nanogenerator, the design of the arch-shaped structure in the TENG part results in a good contact area between the triboelectric materials. Meanwhile, the flexible polymer film reduces the frictional resistance and solves the problem of severe triboelectric loss of TENG. Comparative experimental results show that the TENG output prepared from PTFE films of 0.1 mm thickness is higher than that of other thicknesses with other kinds of polymer films. The maximum output power that the TENG and EMG can produce is 0.55 mW and 15.5 mW at an external load of 5 MΩ and 360 Ω respectively. The maximum voltage output of the triboelectric nanogenerator decreases to 7.9% after 80,000 cycles of continuous operation. In experiments on charging capacitors, the hybrid nanogenerator has a wider voltage range and shorter capacitor voltage rise time compared to individual generator sets. To prove the feasibility of its practical application, we conducted an LED lighting test. At 230 rpm, both TENG and EMG can light up 20 LEDs. At 75 rpm, only the TENG was able to light 20 LEDs, proving that the TENG can collect energy more efficiently than the EMG in a low-frequency speed environment. In addition, the hybrid nanogenerator was successfully realized to power a commercial anemometer after operating at a simulated wind speed of 8.3 m/s for 9 s time and at a natural wind speed of 5.6 m/s for 35 s, respectively. In summary, this study provides a new solution to the power supply problem of self-powered environmental equipment monitoring based on wind energy harvesting.",
"introduction": "1. Introduction With the rapid development of IoT technology, the demand for various sensors for environmental monitoring has increased dramatically in number. As the lifeblood of a sensing system, a reliable energy supply is a key factor for interactive communication. However, most of the current sensing networks rely mainly on batteries for their energy supply. On the one hand, the limited lifetime of batteries causes subsequent replacement and charging problems, resulting in intermittent data transmission. On the other hand, chemical batteries are difficult to adapt to various high and low temperatures and other complex environments and cause certain environmental pollution problems [ 1 ]. Therefore, the problem of power supply for sensing devices has become a major problem that hinders the further development of IoT systems, and electronic devices are in urgent need of more suitable power supply solutions. The use of obtaining environmental energy as a self-powering method for sensing nodes and electronic devices provides a new idea to solve the drawbacks of traditional power supply methods [ 2 , 3 ]. Wind energy has significant advantages in terms of wide distribution, resourcefulness, sustainability, and non-pollution [ 4 ]. The traditional method of converting wind energy into electricity is based on the principle of electromagnetic induction and turbine structure. The turbines of this wind turbine are complex and built with bulky magnet components. They are expensive, require high installation heights, and work with high start-up wind speeds. Based on these constraints, these types of wind turbines have not been fully utilized in distributed micropower sources and low-speed wind resources [ 5 , 6 ]. There is an urgent need for new methods to achieve efficient acquisition and in situ utilization of wind energy. TENG is electrically generated by coupling triboeletrifiction [ 7 , 8 ] and electrostatic induction. It is a promising technology for converting ambient mechanical energy into electrical energy. It has been used to obtain energy from a variety of sources. It has the unique advantages of high power density, high efficiency, and low fabrication cost [ 9 , 10 , 11 ]. Due to its unique mechanism, TENG has higher performance than EMG in low-frequency environments, meaning that it may be the best application for low-frequency energy harvesting [ 12 ]. TENGs have high output voltages, but output currents are only in the μA level [ 13 , 14 ]. In contrast, EMGs have output currents up to the mA level [ 15 , 16 , 17 , 18 ]. However, electrical devices used for low power consumption use a DC power supply, the TENG and EMG generate AC power that needs to be converted to DC power through a rectifier bridge circuit. The existing rectifier bridge circuit will have a voltage drop of about 0.45 V, which will be very detrimental to the low voltage generated by EMG at low speed, and will have little effect on the high voltage output of TENG. At low-frequency movement, TENG is better than EMG for supplying energy to ultra-low power consumption appliances, but when the speed continues to rise, the advantages of the EMG high current will be revealed. The complementary outputs of TENG and EMG can be mixed and maximized to harvest green energy over a wide range of wind speeds. Of course, TENGs not only have the function of powering low-power electronic devices but also their applications in self-powered rotational speed detection, fluid rolling robots with voltage-driven oscillating liquids, and active sorting of droplets by using an electro-conjugate fluid micropump will become a hot topic of research. Hence, it remains a challenge to optimize the design and construction of hybrid structures for their different usage scenarios and movement pattern characteristics. The main hybrid nanogenerators currently available for wind energy harvesting are thin-film chattering [ 19 , 20 ], rotary-contact separation [ 21 , 22 , 23 ], and rotating structures [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Wang [ 19 ] et al. designed a thin-film chattering hybrid nanogenerator with the upper and lower electrode films as the basic mechanism, as shown in Figure 1 a. The wind energy is first converted into vibration energy of the upper and lower electrodes by vibrating the film, and then the electrical energy is generated by the relative motion of the film during vibration. However, the irregular chattering motion causes the output performance of the chattering type to be difficult to keep stable. And this open structure causes water droplets and dust to adhere to the electrode surface, requiring long-term maintenance work. Therefore, this lower output performance with the open structure leads to the thin-film chattering type hybrid nano-generation cannot meet the energy supply demand of monitoring devices in complex environments. Fan [ 21 ] et al. designed a rotating-contact separation hybrid nano-generator that converts the rotational motion into linear reciprocating motion, as shown in Figure 1 b. The magnet in the EMG maintains relative rotational motion with the coil, and the TENG with two electrodes of mutual contact separation generates an output voltage. This structure has good output performance at low wind speeds. However, due to the existence of motion mode switching, electrostatic adsorption, and collision effect during contact separation, which will consume a lot of mechanical energy to reduce the motion frequency. In the ultra-high wind speed condition of 16 m/s, the generator speed is 250 rpm, which is not conducive to the output of EMG. In conventional rotating structures, TENGs mostly use paddle-type flexible contact or disc-type rigid contact. Wang [ 27 ] et al. designed paddle-type TENG structure with flexible contact is prone to insufficient contact problems such as point contact and line contact between triboelectric electrodes in lower motion frequencies, as shown in Figure 1 c. The maximum output voltage of the TENG is 35 V at 200 rpm, which is not conducive to the collection and utilization of low-speed wind by the hybrid nanogenerator. Zhang [ 28 ] et al. used a disc-type flat contact structure in a rotating TENG, as shown in Figure 1 d. An open-circuit voltage of 80 V can be output at 300–900 rpm, but the durability of the TENG was not verified, and the poor durability of the TENG is a major hindrance to the application of this structure. Xu [ 29 ] et al. used an open windmill shape to combine the TENG with the EMG, which is poorly adaptable in outdoor environments. Zhang [ 30 ] et al. designed a wind energy generator applied to a high-speed train system, and the performance of the TENG was maintained at 89.04% of the original after 50,000 cycles. Wang [ 31 ] et al. combined the rotating type with the gravitational potential energy of a heavy object, and the complex gearing made the mechanical energy loss huge. Based on the above research results, the motion characteristics of the rotating mechanism are combined. We have studied a fully enclosed, wide operating frequency and durable rotating hybrid nanogenerator. It consists of an arch-shaped structure of a TENG part and a rotating EMG part. Compared with the thin-film chattering hybrid nanogenerator, it has a fully enclosed structure, which reduces the influence of environmental factors on the power generation performance. It solves the problem of large mechanical energy loss of the rotating-contact separated hybrid nanogenerator, which is unfavorable to wind energy harvesting by EMG under high-speed wind. For the conventional rotating hybrid nanogenerator, due to the structure of the arch-shaped film in the TENG, which solves the problem of insufficient contact in the paddle-type flexible contact. Compared with disc-type rigid contact, flexible contact reduces frictional resistance between triboelectric materials and has good durability. In addition, the disk-based EMG also does the rotational motion synchronously to improve the hybrid output performance of the wind energy generator. In this paper, the open-circuit voltage and output power of the optimized hybrid nanogenerator are tested. And the durability of this hybrid nanogenerator is analyzed by persistence test. On this basis, the applicability of the device is proved by charging the capacitor and lighting the light-emitting diode (LED). Finally, we conducted experiments under simulated and natural wind conditions and confirmed that the device reached the capability of powering a commercial anemometer after running for 9 s at a wind speed of 8.3 m/s and 35 s at a wind speed of 5.6 m/s, respectively. Therefore, this hybrid nanogenerator can provide some reference value for solving the power supply problem of environmental monitoring equipment.",
"discussion": "4. Results and Discussion 4.1. Output Characterization In order to obtain better output performance, this study first conducted a comparative experiment on the types of triboelectric materials. PTFE and FEP with 0.1 mm thickness were selected as negative triboelectric materials for power generation performance testing, and the results are shown in Figure 5 . The TENG was tested in the speed range of 100–700 rpm in steps of 200 rpm for different materials. It was demonstrated that the open-circuit voltage of PTFE is significantly higher than that of FEP at 100–300 rpm. At 500–700 rpm, the open-circuit voltage using PTFE as triboelectric material is about the same as that using FEP as triboelectric material. Considering that the advantage of TENG is its high-power generation capacity at low wind speed, PTFE was chosen as the negative triboelectric material. In the next experiments, we investigated the effect of different thicknesses of PTFE materials on the power generation efficiency of TENG. A constant rotational speed of 100 rpm was selected for the test, and three different thicknesses of PTFE, 0.05 mm, 0.1 mm, and 0.2 mm, were used to test the open circuit voltage of the TENG. As shown in Figure 6 , varying the thickness of the PTFE had a significant effect on the power generation performance of the TENG, with all other conditions remaining the same. The TENG using 0.1 mm thickness of PTFE with nylon 66 produced the best power generation capability with a peak voltage of 79.9 V. The peak voltage generated by the TENG using 0.05 mm thickness PTFE was 53.8 V and the peak voltage generated by the TENG using 0.2 mm thickness PTFE was 21.9 V. The reason is that PTFE produces more folds at 0.05 mm thickness, which causes the contact area between positive and negative friction materials to decrease, resulting in poor TENG power generation performance. As the thickness increases, the flatness of the material surface improves, the contact area between the positive and negative triboelectric materials increases, and the performance of TENG power generation is improved. When the thickness exceeds 0.1 mm, the material is insulating, resulting in a large amount of charge generated by triboelectric gathering on the triboelectric surface, which cannot cross the film into the copper electrode and form a charge flow with the external circuit, causing a decrease in the amount of transferred charge and further leading to a decrease in voltage. Through the above-mentioned comparative experiments, PTFE with 0.1 mm thickness was selected as the negative triboelectric material for subsequent experiments in this study. In order to characterize the hybrid nanogenerator for wind energy harvesting, its output performance was investigated. Figure 7 a illustrates the dependence of the open-circuit voltage of the TENG on the rotational speed in the speed range of 100–700 rpm. Referring to the literature [ 34 , 35 ], the open-circuit voltage amplitude for a rotating TENG is calculated as\n (1) V T E N G = σ S C \nwhere σ is the surface charge density of the polymer film, S is the contact area of the two triboelectric materials, and C is the capacitance of the two adjacent electrodes. The output voltage magnitude is highly dependent on the maximum value of the transmitted charge, which is proportional to the relative contact area S between the triboelectric materials and the surface charge density σ . In the rotational speed interval below 500 rpm. The surface charge of the frictional triboelectric materials increases with the speed, the surface charge density σ increases, and the maximum open-circuit voltage of the TENG slowly increases from 79.7 V to 126 V. In the speed interval above 500 rpm. As the rotational speed continues to increase, the maximum open-circuit voltage of TENG increases rapidly from 126 V to 159 V. The reason is that at low speed, the arch-shaped nylon film can produce good contact with the PTFE film on the inner wall, and the relative contact area S does not change much. In the case of high rotational speed, the nylon film is deformed due to centrifugal force. The relative contact area S between the triboelectric materials becomes larger, which leads to a rapid increase in voltage. Figure 7 c,e show the output voltage and current of the TENG at 400 rpm speed. The output power of the TENG is further investigated using a resistor as an external load for the conversion of mechanical motion to electrical energy. As the external load resistance increases from 290 kΩ to 20 MΩ, the current amplitude decreases from 20.7 μA to 4.4 μA. At a load resistance of 5 MΩ, the maximum output power of the TENG is 0.55 mW. In addition, we have also characterized the output performance of the EMG section systematically, as shown in Figure 7 b. With the help of Faraday’s law [ 36 ], the open-circuit voltage of the EMG can be calculated as\n (2) V E M G = − n N d ∅ B d t \nwhere n is the number of coils, N is the number of turns per coil and ∅ B is the total magnetic flux in each coil. Obviously, the rate of change of the magnetic flux is related to the speed. From Equation (2), the open-circuit voltage of the EMG is proportional to the rotational speed. In this experiment, n is 8 and N is 480. The maximum open-circuit voltage is from 1.47 V to 8.57 V in the speed interval of 100–700 rpm. The linear relationship between the open-circuit voltage and the rate of change of magnetic flux d ∅ B / d t in the above equation is satisfied. Similarly, we use external resistors to evaluate the output performance of the EMG, as shown in Figure 7 d,f. At 400 rpm, as the external load resistance increases from 100 Ω to 1800 Ω, the EMG output voltage increases from 1.02 V to 4.08 V and the output current amplitude decreases from 10.2 mA to 2.3 mA. at a load resistance of 360Ω, the EMG maximum instantaneous power is up to 15.5 mW. The decrease in power generation capacity after continuous operation is one of the main problems of the rotating TENG. We sampled and collected the output voltage of TENG after the new material, 40,000 cycles, and 80,000 cycles, and the results are shown in Figure 8 . Black represents the maximum output voltage of TENG with new material is 126 V. Red represents the output voltage of TENG after 40,000 cycles of testing. The maximum voltage that can be generated is 120 V. Blue represents that the maximum output voltage of TENG after 80,000 cycles of testing is 116 V. The results found that the triboelectric material in the proposed arch flexible contact method of TENG has a good service life, and its maximum output voltage decreases by 7.9% after 80,000 cycles. 4.2. Demonstration In general, the energy conversion device should have good stability and consistency. In the charging test, the hybrid nanogenerator was first used to charge capacitors of different capacities. To obtain the DC output from TENG and EMG, the conversion of AC signals has been achieved with two bridge rectification circuits. Figure 9 a shows the schematic diagram of the rectifier circuit. When the speed is 400 rpm, the hybrid nanogenerator charges the capacitors of 4.7 μF and 47 μF capacity, and the charging curves are shown in Figure 9 b,c respectively. The gray curve represents the TENG part, the red curve represents the EMG part, and the blue curve represents the combined charging of TENG and EMG. Since the EMG has a high output current and low output voltage. When the EMG charges the capacitor, the voltage of the capacitor quickly rises to about 4.3 V output voltage, and then the voltage basically remains the same. The output voltage of the EMG limits the maximum voltage of the capacitor, resulting in a large energy loss. In addition, the TENG has the electrical characteristics of high output voltage and low output current. After the output voltage of TENG in the hybrid nanogenerator reaches the limit of the EMG output voltage, it is still possible to continue to increase the voltage of the capacitors. In addition, due to the electrical characteristics of TENG with high output voltage and low output current, the voltage of the TENG part of the hybrid nanogenerator can continue to increase the voltage of the capacitor even after the voltage of the EMG part reaches the limit of the output voltage. Therefore, combining TENG with EMG can provide fast charging speed and charging voltage. In addition, when the hybrid nanogenerator charges capacitors of different capacities, the charging voltage is mainly provided by the TENG after the charging voltage reaches the limit voltage of the EMG, and the charging speed decreases with the increase of the capacitor capacity. It indicates that the main charging property changes from EMG to TENG at the later stage of charging. The results show that the combination of TENG and EMG has a significant improvement in energy harvesting and conversion compared to the energy harvesting of each single part. We found that the hybrid nanogenerator can easily output a much wider voltage range by charging capacitors of different capacities experimentally. The power supply needs of different devices in the external circuit are met without any additional circuit changes. In addition to charging capacitors, this hybrid nanogenerator can also supply power to small electronic devices. Taking lighting LEDs as an example, the TENG is suitable for the series powering of LEDs due to its high voltage characteristics. The EMG has high current characteristics and is suitable for the parallel powering of LEDs. The LEDs are all of the same type, with red and yellow colors at voltages of 1.8 V to 2.0 V operating voltage, as shown in Figure 9 d. At 75 rpm, TENG was the first to light up 20 red LEDs. Hence, the TENG is already capable of driving small electronic devices at very low operating frequencies. At 230 rpm, the EMG lights up 20 yellow LEDs. The yellow LEDs driven by the EMG have a stronger brightness when lit, indicating that the EMG has a stronger power generation capability at higher speeds or higher operating frequencies. Therefore, the TENG part and the EMG part have good complementarity in providing energy in a wider frequency range of rotational speed. We used this hybrid nanogenerator to power a commercial anemometer to detect the wind speed, as shown in Figure 9 e. A wind speed of 8.3 m/s, after operating for 9 s, successfully powers the anemometer. The relationship between wind speed and generator speed was measured as Figure 9 f, and the wind speed was basically linear with the speed. Finally, to show the application potential in a real environment, we conducted experiments on wind energy harvesting in a natural wind environment. As shown in Figure 10 a, the output of the hybrid nanogenerator is converted from AC voltage to DC voltage by two different rectification circuits, which together charge the capacitor. The positive and negative terminals of the capacitor are connected in parallel with the positive and negative terminals of the anemometer to realize the power supply to the anemometer. In addition, we recorded the voltage variation curves at the terminals of the capacitor as shown in Figure 10 b. Running continuously for 35 s at a natural wind speed of about 5.6 m/s, the voltage across the capacitor rises sharply and then slowly, finally reaching the minimum starting voltage of 2.5 V for the anemometer, which proves the high charging efficiency of the EMG at the same frequency and the continuous boosting feature of the TENG. The hybrid output shortens the voltage rise time and widens the voltage output range. The voltage across the capacitor drops to 2 V due to the higher power required for anemometer start-up. After 13 s, the voltage across the capacitor is smoothly maintained at about 2.2 V, achieving a dynamic balance between charging and discharging of the capacitor. In summary, the results show that the hybrid nanogenerator has good potential for application in solving the power supply of low-power environmental detection devices, especially in remote mountainous areas, deserts, and oceans."
} | 6,145 |
28782021 | PMC5529063 | pmc | 6,704 | {
"abstract": "Temporal fluctuations in species richness are frequently regulated, exhibiting a tendency to return toward a central level.",
"introduction": "INTRODUCTION Life is regulated at many levels: from elemental composition within cells, to physiological homeostasis within individuals, to constraints on population growth ( 1 ) and per-capita demographic rates ( 2 ). These lower-level regulatory processes may even contribute to emergent properties of stability, feedback loops, and resilience at the organizational scale of food webs and ecosystems ( 3 – 5 ). But are entire ecological communities also regulated? Community-level regulation ( 6 – 8 ) is important because it may dampen biodiversity fluctuations in the face of environmental change ( 9 ) and may cause species richness ( S ) or total abundance ( N ) to return toward a central level following a strong perturbation. Thus, understanding the prevalence of community regulation is critical for monitoring and interpreting biodiversity change in the Anthropocene. Here, we use a global survey of communities to show, for the first time, that community-level regulation is surprisingly common for both species richness and total abundance. We use a broad statistical definition of regulation, which is that a regulated community exhibits a constant mean and variance in N or S with an autocorrelation function that decays quickly to 0 ( 10 ). We test for this pattern with the Augmented Dickey-Fuller (ADF) test ( 11 ), in which the null hypothesis is an unconstrained random walk that leads to a nonconstant variance in a time series. This test is widely used for time series analysis of econometric data ( 12 ). Rejecting the ADF test means that the time series is centered on a long-term mean (or a long-term trend line) and will return toward it if displaced, rather than drifting freely. We analyzed with the ADF test 59 high-quality data sets from across the globe in which multispecies communities of plants and animals have been monitored for 10 or more years with standardized census methods (see Materials and Methods). Fig. 1 Regulated and unregulated time series of species richness and total abundance. Histograms of statistically significant (dark hue) and nonsignificant (light hue) ADF test results for species richness ( top ) and total abundance ( bottom ) of 59 monitored assemblages. Individual P values for each assemblage were converted to standardized deviates for plotting on a continuous scale. Standardized effect sizes (SES) of less than ~−2.0 are statistically significant at P < 0.05 and indicated a pattern of regulated temporal fluctuations. The vertical black zero line, which indicates a tail probability of 0.50, is highlighted for comparison. Box 1 Definitions. ADF test. A statistical test for detecting regulation in a time series of observations ( 11 ). The test fits an autoregressive (AR) time series model with a lag of one time step (AR1) to a data series. The coefficient φ in the AR1 model reflects the degree of regulation. The extreme cases are φ = 0, which represents a white noise (Gaussian) distribution that shows strong regulation following a perturbation, and φ = 1, which represents an unregulated random walk that does not recover or return to a central value following a perturbation. The ADF test estimates the probability that the fitted value of |φ| < 1, which corresponds to a regulated process. The null hypothesis is that the time series represents a random walk with φ = 1. AR time series model. A statistical model for a variable (such as abundance or species richness) that changes through time. In any AR model, the system has a “memory” so that current fluctuations are mathematically dependent on all previous values, although the strength of the effect diminishes as two observations are separated further in time. The behavior of an AR model depends on the parameter φ. If φ = 1, the system is completely dominated by the value it had at the last time step, corresponding to an unregulated random walk. If φ = 0, the system is unaffected by past values and will return sharply toward its equilibrium level in the next time step (white noise distribution). In between these extremes, an AR1 process will “remember” previous values and display a tendency to return to a central value whenever it is perturbed, but the degree of regulation is weaker as φ approaches 1. Beta diversity. A measure of the degree to which species composition differs among sites or within a site among times. Pairwise beta diversity is quantified by the number of shared and unique species in two communities. It can be partitioned into a component that is caused by changes in species turnover and changes in total species richness ( 29 ). Community-wide regulation. A variable quantified for an entire community, such as species richness, total abundance, or biomass, is measured repeatedly at a site through time. If the variable is regulated, it will have a long-term constant mean and a bounded variance ( 10 ). If the variable is pushed above the mean, it will be more likely to decrease than to increase, and if the variable is pushed below the mean, it will be more likely to increase than to decrease. Community-wide regulation does not imply a single equilibrium point but a distribution that is constrained so that the time series does not resemble a random walk. Community-wide regulation may occur when a universally shared resource, such as energy, is in limited supply ( 14 ). If environmental conditions are changing through time, community-wide regulation may be accompanied by changes in species composition and species traits ( 15 ). Compensatory fluctuations. If species pairs in an assemblage are not independent, but covary negatively (because of negative species interactions or changing environmental conditions), the variance of the sum of their abundances will be less than the sum of the variances of their abundances. These measures form the basis for variance ratio tests for compensatory fluctuations, compared to a null hypothesis of species independence ( 23 , 24 ). These tests are based on randomization of observed abundance or species richness data collected through time, so they assume that the source pool is constant and that population processes (colonization, extinction, and changes in abundance) do not change through time. Compensatory fluctuations represent one kind of community regulation, but statistical tests for compensatory fluctuations will not detect all cases of regulation in which the response variable is constrained and does not follow a random walk. Environmental tracking. If an assemblage of species show a similar response to an abiotic variable, such as temperature, their abundances may exhibit correlated fluctuations by tracking the variable through time. In this case, total abundance may still be regulated, but the time series will not show evidence of compensatory fluctuations because of positive covariation between the abundances of many of the species ( 25 ). MacArthur-Wilson equilibrium model. A model in which a mainland source pool of species can potentially colonize an island or discrete patches. Populations on the islands fluctuate independently and stochastically so that extinction and recolonization are common. The model links extinction rates to island area and immigration rates to island isolation or distance ( 19 ), but the concept of species-specific colonization and extinction rates can be generalized to other dynamic communities ( 50 ). Markov patch model. A discrete-time transition model in which the replacement of one species by another (or one community by another) is specified as a probability that reflects species-specific interactions and probabilities of colonization, extinction, or persistence in a patch ( 21 , 22 ). Niche. The set of abiotic conditions (temperature, moisture, pH, etc.) and biotic conditions (presence of predators, parasites, competitors, prey, etc.) that jointly determine whether a species can colonize a site and achieve positive population growth ( dN / dt > 0) ( 51 ). Portfolio effect. In financial investments, a diversified portfolio will usually fluctuate less in value than an investment in a single vehicle. In analyses of community regulation, metrics such as the coefficient of variation in total abundance will have smaller values for assemblages composed of more species. This artifact can be avoided by using statistical tests that are not sensitive to the species richness or total abundance of an assemblage ( 52 ). Random walk. A variable that increases or decreases with equal probability in each step of a time series. Random walks do not maintain a constant long-term average or bounded variance and serve as an appropriate null model for detecting community-wide regulation. If an assemblage that is unregulated and following a random walk is perturbed by a single shock (such as the removal of most species), it will subsequently fluctuate at a new, reduced level ( Fig. 3D ). In contrast, a regulated assemblage will begin to recover and show rapid or gradual increases in species richness and abundance following a single perturbation ( Fig. 3C ). Zero-sum assumption. Classic neutral models of species assemblages explicitly assume that total abundance is constant so that when an individual dies, it is immediately replaced by another randomly selected individual ( 18 ). In a constant environment, this assumption reflects a constraint on total energy and will generate a constrained time series of species richness and total abundance. A strict “zero-sum” assumption is not necessary to achieve community regulation, and a bounded distribution of abundance and total species richness will result if species colonization and extinction probabilities are constant through time. These conditions arise in the MacArthur-Wilson equilibrium model ( 19 ), in which species colonize and become extinct at random ( 20 ). These conditions also arise in Markov patch models, in which the probability of colonization and extinction is determined by the identity of the species currently occupying a patch ( 21 , 22 ). In the neutral model, the MacArthur-Wilson model, and Markov patch models, an empty landscape will be colonized and rise to a bounded distribution of species richness and total abundance. If abundance and species richness are pushed above this distribution, they will decline back toward it. In a closed system with a constant source pool, all three models will exhibit a pattern of community-wide regulation and may show evidence for compensatory fluctuations. In a changing environment, the distributions will still be bounded, but there may no longer be a simple pattern of compensatory fluctuations because colonization and extinction probabilities are changing through time.",
"discussion": "DISCUSSION Community-level regulation of species richness, total abundance, biomass, or energy flux ( Box 1 ) can arise from a variety of mechanisms in two broad categories: (i) regulation caused by a shared universal resource, such as energy ( 6 , 7 , 13 , 14 ), and (ii) regulation accompanied by shifting environmental conditions and open source pools ( 15 ), which may lead to species replacement and turnover ( 16 , 17 ). Regulation by a shared universal resource is embodied in the zero-sum assumption of the neutral model ( 18 ), but regulation is also implied by the constant colonization and extinction probabilities in the MacArthur-Wilson equilibrium model ( 19 , 20 ) and the assumption of constant species-specific replacement probabilities in Markov patch models ( 21 , 22 ). Previous “variance ratio” tests for regulation at the community level have focused on the idea that compensatory fluctuations in abundance or compensatory replacements of species ( Box 1 ) should generate a smaller variance in S or N than would be expected without compensation ( 23 , 24 ). For these communities, we applied the variance ratio test to total abundance and an analogous test for compensatory colonizations and extinction to species richness (see Supplementary Text). As in previous meta-analyses ( 24 , 25 ), there were very few communities in which there was evidence for local-scale compensatory regulation of S or N (tables S6 and S7 and Supplementary Text). How then do we account for the paradox that assemblage-level time series of species richness and total abundance appear to be stationary ( Fig. 1 ) despite little evidence for local-scale compensatory fluctuations? The time series in these analyses cover 10 years or longer, and the communities were monitored relatively recently, during periods of unprecedented environmental change ( 26 – 28 ). With changing environmental conditions, shifts in species composition and in the ecological niches represented by each species are expected to predominate. These shifts represent important species replacements but will not necessarily be reflected in statistical tests for compensatory dynamics, which assume a constant source pool. To further analyze the pattern of species change in these communities, we partitioned species composition into components of species turnover and species richness ( 29 ). For most of these communities, the dominant fraction of change came from species turnover, which could lead to stationary distributions of species richness and total abundance ( Fig. 2 , blue fraction). However, most communities also contained some component of beta diversity attributed to a change in species richness ( Fig. 2 , green fraction), which may have obscured the signature of regulation in statistical tests for compensatory fluctuations. Fig. 2 Beta diversity partition of assemblage time series. Each pie chart represents a different assemblage, plotted at its jittered location on the globe. Beta diversity was partitioned using the method of Baselga ( 29 ). Blue fraction, proportion of beta diversity attributable to changes in species composition; green fraction, proportion of beta diversity attributable to changes in species richness. We note that, even with a constant source pool, the trajectories of random walks versus regulated assemblages may be very hard to distinguish ( Fig. 3A versus Fig. 3B ). However, if these communities are perturbed by reducing abundance or species richness to a low level, the differences are clear ( Fig. 3C versus Fig. 3D ). Regulated communities begin to trend upward, with a slow or fast return toward a stationary distribution ( Fig. 3C ). In contrast, random walks remain on average at chronically low levels following a perturbation—and may even decline stochastically to 0—but do not consistently rebound ( Fig. 3D ). A myriad of empirical studies, including controlled removal experiments ( 30 ), unintended anthropogenic perturbations ( 31 ), and comparisons of terrestrial vegetation structure from chronosequences ( 32 ), show that communities frequently do trend upward initially in S and N following species loss. Fig. 3 Contrasting dynamics of regulated assemblages versus random walks. In the absence of a perturbation, it is difficult to visually distinguish the dynamics of a regulated assemblage ( A ) versus an unconstrained random walk ( B ), although they are discriminated by the ADF test [ P = 0.020 (A) and P = 0.545 (B)]. However, if the assemblage is reduced in a single time step from its equilibrium level of 100 species to 10 species, the regulated assemblage recovers ( C ), whereas the unregulated assemblage does not ( D ). Trajectories were simulated with an AR1 autoregressive model, Δ N = N t + 1 − N t = − ( N t − c )(1 − φ) + ε t , where c = 100 and ε t ~ N (0, σ = 2). For the regulated trajectories, φ = 0.900, and for the random walk trajectories, φ = 0.999. To simplify the appearance of (D), simulated values that were less than 1 were redrawn as 1. The ADF test is a one-tailed statistical test for whether |φ| < 1.0. With long-term environmental change, a variety of scenarios for short- and long-term changes in species richness, composition, and abundance are possible ( 33 ). A scenario of environmental change triggering changes in species composition and niches, accompanied by relative constancy in species number or abundance, is consistent with more detailed studies of long-term fluctuations in desert rodent assemblages of the southeastern United States ( 6 ) and groundfish assemblages of the Northeast Atlantic ( 34 ). There are some caveats and limitations to our analysis. The 59 studies used here represent the longest time series available from the compilation of Dornelas et al. ( 35 ). As in many other ecological meta-analyses, the surveys in this compilation are dominated by temperate-zone communities in North America and Europe, with relatively few examples of tropical communities in Asia, Africa, and Australia. Because the same community has been monitored with standardized census methods for 10 years or longer, the compilation does not include landscapes that have been radically transformed by human activity such as urbanization or crop planting ( 36 ). However, it would be a mistake to suggest that the communities were sampled from “habitats that are mostly intact and yet to be fully exploited by humans” ( 37 ). Some of these studies were conducted near nuclear power plants; in suburban landscapes of mixed forest, agriculture, and housing; and in coastal areas that are heavily affected by fisheries ( 38 ). Human domination of the biosphere implies indirect effects on biodiversity that extend well beyond areas of obvious anthropogenic transformation. For this reason, it is especially interesting to see a signature of community-level regulation in these high-quality long-term data sets. Although we found evidence for widespread community-level regulation, note that almost half of the trajectories were unregulated and could not be distinguished from a random walk. What rules or factors determine whether a community is regulated or not? We stratified the data set by latitudinal zones, taxonomic groups, and habitat and then tested for differences in the strength of community regulation among subsets of communities. The only pattern that emerged was that the total abundance (but not species richness) of marine communities was more strongly regulated than that of terrestrial communities. However, even this difference accounted for only 16% of the variation in effect size (fig. S14). Moreover, the strength of regulation was not related to the length of the time series or the number of species in the community (see Supplementary Text). The answer may lie at a lower level of analysis. If a community is subdivided into trait-based functional groups of species ( 39 ), strong interactions and species replacements within these groups might be driving temporal trajectories of species richness and total abundance. However, the functional status of most species in these surveys is currently unknown, although this knowledge gap could diminish with the continued development of public-domain databases of species ecological traits ( 40 ). Spatial and temporal heterogeneity is another source of variation that may affect community-level regulation ( 41 ). The temporal grain of most of these studies was roughly 1 year, and different patterns of regulation may be expected to appear at both shorter and longer time scales ( 42 ). In a similar way, the temporal trajectories may also change with the spatial grain and extent of sampling ( 33 ). Resilient recovery and bounded trajectories of species richness and abundance should not be confused with a stasis of biodiversity. On the contrary, these patterns of weak constancy in S and N and recovery from perturbation are often accompanied by very strong changes in species composition that cannot be explained by classic equilibrium community models ( 18 , 19 ). The evidence that substantial change can occur in communities while key properties such as total abundance and species richness show a stationary distribution with a constant mean is counterintuitive and likely an important signal of underlying processes and perhaps represents a previously unrecognized general pattern in community ecology ( 43 , 44 ). Current theory seems inadequate to explain the observed phenomenon of widespread community regulation. A better understanding of why communities are regulated is important to discern and predict whether communities can persist in the face of large anthropogenic impacts ( 33 , 45 ) or whether they are about to collapse or disassemble. Better understanding of which aspects of communities are regulated (abundance and richness in this paper) and not regulated [species composition in Dornelas et al. ( 35 )] is also important in predicting how the ecosystem functions that humans depend on will be altered. Finally, the existence of regulation at the community level highlights the need to study human impacts on whole communities, not just on selected species or populations. Long-term measurements of key shared resources and physiological tolerances of the species that appear and disappear through time should provide new insights into the details of community regulation and may guide strategies for managing assemblages in the face of strong environmental change."
} | 5,339 |
40180965 | PMC11968795 | pmc | 6,707 | {
"abstract": "Tearing tough soft solids such as rubbers, leather or meat is much harder than cutting them with a sharp blade. To understand why, we use samples labeled with mechanically sensitive fluorophores to investigate cutting and fracture behavior in PDMS elastomers and quantify the extent of bond scission resulting from cutting pre-stretched samples. Our findings reveal that stretch-induced cracks produce significant deformation, bond scission and blunting near the crack tip, requiring more energy to propagate. In contrast, using blades reduces the amount of stretching and blunting required for crack propagation, resulting in a lower fracture energy. The measured linear correlation between fracture energy and the areal density of broken chains clarifies the relationship between pre-stretching, blunting, and molecular damage. These multi-scale insights demonstrate the key differences between fracture and cutting mechanics of soft materials, allowing to optimize engineering applications, such as rubber and food processing, energy-efficient recycling, biomedical and surgical devices, protective equipment and sports gear.",
"introduction": "Introduction Soft and tough materials such as meat, leather, or filled rubbers are very difficult to tear apart, even when a small cut is made to weaken them. In contrast, cutting with a sharp blade is much easier, involving both lower forces and better control of the shape and surface finish of the parts. This sensitivity to blade cutting can also be a major limitation in the design of protective equipment, which has to resist penetration by needles or blades, or sports gear, where very tough materials can result in unexpected failure by accidental contact with blades. Understanding the origin of the different resistance of a material to cutting and tearing can open new promising strategies to design materials where these two properties can be triggered independently. Fracture mechanics teaches us that the critical force is not the right metric to compare tearing and cutting since it strongly depends on the size and shape of the objects as well as on the way the loads are applied. The proper physical quantity to assess the resistance to crack growth is the fracture energy, i.e., the energy required to extend the crack by a unit area. Whether fracture energy can be used as a proper material metric to characterize fracture by cutting has been a matter of extensive research and is still debated 1 – 7 . Both tearing and cutting involve new surface creation in a material by the extension of an existing localized crack. However, in the tearing process, the energy required to extend the crack is due to the application of a remote load and reaches the crack tip through the singular stress/strain concentration fields. On the contrary, in the cutting process, the energy required to extend the crack is dominated by the work provided by the local contact of the blade against the material, which also modifies the local stress/strain fields. Although both processes can be analyzed with the Griffith energy balance criterion: 1 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$G=-\\frac{d{U}_{el}}{dA}+\\frac{dW}{dA}=\\Gamma$$\\end{document} G = − d U e l d A + d W d A = Γ (where U el and W are, respectively, the elastic energy stored in the sample and the external work, while A is the area of the crack surface) they provide different values of the crack extension energy \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Gamma$$\\end{document} Γ of the same material, even when minimizing the energy losses due to friction of the blade by pre-stretching the target material 1 . It is thus convenient to use different words to address them, i.e., respectively, the fracture energy and the cutting energy, while using the term crack extension energy to refer to either of them. Research on blade-induced damage in soft materials remains relatively scarce, even if basic cutting experimental setups, like pure-shear 2 and Y-shape cutting 1 , 8 , 9 and their theoretical framework started decades ago. The influence of the blades’ geometry 10 , sharpness 11 , 12 , slicing 13 , 14 , and surface friction 15 on the cutting energy have been addressed. More recently, Mars et al. combined numerical simulations and strain field measurements to show that cutting allows to localize large strains on a smaller region compared to fracture 16 , 17 . Hutchens et al. have proposed a dimensionless fracture-cutting parameter to phenomenologically link the values of the fracture and cutting energy through a power law combination of other material parameters, pointing out the prime importance of the strain-hardening characteristics of the material 6 , 7 . Yet, most of these experiments and numerical simulations have been conducted within the framework of continuum mechanics. A molecular-level understanding of the coupling between the nonlinear large strain and the damage occurring in the polymer network during the cutting process remains unknown. Addressing this shortcoming requires the development of new methodologies in order to link the crack extension energy to molecular-level energy dissipation, and in particular, energy dissipated by bond scission, which is not captured by viscoelastic models of energy dissipation 18 . Recent advances in mechanochemistry applied to soft materials have made it possible to visualize and quantify molecular damage before 19 and during fracture propagation 20 in soft polymer networks under various loading situations and geometries 21 . This is possible by labeling the network with a force-sensitive molecule, or mechanophore incorporated as a crosslinker. As stress is applied, the molecule breaks irreversibly, and one of the fragments becomes fluorescent. Optical observation of the material with laser scanning confocal microscopy and proper calibration makes it possible to map and quantify the bond scission occurring near the fracture surface obtained by a macroscopic fracture process 21 – 23 . Quantitative mechanochemistry with properly labeled samples has, for example, revealed how viscoelastic energy dissipation and sacrificial bond scission couple during crack growth in multiple network elastomers 23 . The combination of damage and stress mapping in the process zone ahead of the crack tip has shed light on the mechanics of damage during the fracture of soft materials 24 , both in monotonic loading up to failure and in crack growth by cyclic fatigue 22 . Here, we present the first use of mechanochemistry to reveal how contact with a sharp blade influences molecular damage mechanisms in the macromolecular network during crack extension. The pure-shear cutting experiment used in this study (Fig. 1a ) was first reported by Lake and Yeoh 1 in order to minimize the frictional contributions during the cutting process and to provide a well-defined steady-state cutting process (provided the crack propagation is intrinsically stable for the selected material). Using the Griffith energy approach Eq. ( 1 ) as a crack propagation criterion in steady-state conditions, Lake and Yeoh propose to split the strain energy release rate G in two contributions, G 1 and G 2 , from cutting and stretching, respectively. The cutting term G 1 results from the work done per unit crack area by the constant force f acting on the blade: 2 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${G}_{1}=f/t$$\\end{document} G 1 = f / t where t is the thickness of the sample, while the stretching term G 2 (also called tearing, since it is the only active term in pure-shear tearing) results from the release of the constant elastic energy density stored in the pre-stretched material. For the present range of applied stretch ( λ < 1.5), G 2 can be conveniently approximated by the expression for a Neo-Hookean material as: 3 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${G}_{2}={h}_{0}\\frac{E}{6}\\left({\\lambda }^{2}+\\frac{1}{{\\lambda }^{2}}-2\\right)$$\\end{document} G 2 = h 0 E 6 λ 2 + 1 λ 2 − 2 where h 0 is the unstretched sample width, E is the Young modulus of the soft incompressible elastomer, and λ = h/h 0 is the uniform pre-stretch applied to the material. The total strain energy release rate becomes then: 4 \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$G={G}_{1}+{G}_{2}$$\\end{document} G = G 1 + G 2 Fig. 1 Schematic representation of the pure-shear cutting and damage mapping. a Sketch of the pure-shear cutting and stretching experiment. b Sketch of the PDMS network at the molecular scale with unbroken mechanophores represented as blue stars and activated mechanophores as yellow stars. This damage occurs in the blunted crack tip zone during the combined stretching/cutting process. c Sketch of the post-mortem PDMS cutting sample. In steady-state conditions, fracture mechanics implies that G should have a constant value: the crack extension energy \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Gamma$$\\end{document} Γ . Using a custom cutting and stretching setup (described in Methods and in the Supplementary information), we carried out a series of crack propagation experiments under combined stretching and cutting on mechanophore-labeled PDMS elastomer samples to quantify the changes in molecular damage occurring when the relative amount of applied stretching and cutting is systematically varied (Fig. 1b, c ).",
"discussion": "Discussion This investigation provides the first mapping of the extent of molecular damage occurring during the combined cutting and stretching of elastomers. The total cutting energy was shown to be proportional to the number of broken bonds per unit area (quantified by mechanophores), much like in fracture propagation. However, the use of a blade allows to reduce the amount of stretch required for fracture propagation. This reduces, in turn, the blunting radius of the crack tip and, thus, the volume where the material undergoes large strains that can induce the failure of overloaded chains in the network. In other words, using a blade localizes the bond-breaking region to a smaller scale than simple tearing, thus reducing the total energy required for crack extension. Although we used a relatively brittle elastomer model system (Γ ∼ 500 J m −2 ) to facilitate the incorporation of mechanophores, the reduction of the extent of bond scission achieved with a sharp blade should be quite general, and all the more effective for the case of tougher and more viscoelastic elastomers such as styrene-butadiene rubber (SBR) (Γ ∼ 10–50 kJ m − 2 ) 35 , multiple network elastomers (Γ ∼ 10 kJ m -2 ) 23 , or filled rubbers (Γ ∼ 10–100 kJ m -2 ) 36 or even thermoplastic elastomers (Γ ∼ 140 kJ m − 2 ) 37 , where the damage zone remains large even under threshold conditions. In these tough materials, the blunting associated with fracture propagation can be huge, and the spread of the damage over a large scale underpins their very high fracture toughness. By limiting blunting, the blade is expected to reduce both the extent of damage and the overall fracture energy, an area that warrants further investigation. However, a key challenge in this research approach lies in the need to develop specific chemical strategies for incorporating mechanophores into different materials 38 while ensuring minimal impact on their mechanical properties. In addition, the application of mechanophores in other soft materials, such as tough hydrogels, remains constrained by their low emission efficiency in aqueous environments. Understanding the relationship between network architecture and the varying degrees of molecular damage undercutting and tearing conditions can play a crucial role in optimizing material design. By tailoring network structures, it becomes possible to achieve specific and independent resistance to these different fracture modes, enabling the development of materials with enhanced durability and performance. This knowledge is particularly valuable in applications such as protective gears, biomedical devices, soft robotics components, and high-performance elastomers used in tires and industrial seals, where resistance to both sharp incisions and gradual tearing is essential."
} | 3,376 |
34618047 | PMC8644718 | pmc | 6,708 | {
"abstract": "Abstract Most land plants live in close contact with beneficial soil microbes: the majority of land plant species establish symbiosis with arbuscular mycorrhizal fungi, while most legumes, the third largest plant family, can form a symbiosis with nitrogen-fixing rhizobia. These microbes contribute to plant nutrition via endosymbiotic processes that require modulating the expression and function of plant transporter systems. The efficient contribution of these symbionts involves precisely controlled integration of transport, which is enabled by the adaptability and plasticity of their transporters. Advances in our understanding of these systems, driven by functional genomics research, are rapidly filling the gap in knowledge about plant membrane transport involved in these plant–microbe interactions. In this review, we synthesize recent findings associated with different stages of these symbioses, from the pre-symbiotic stage to nutrient exchange, and describe the role of host transport systems in both mycorrhizal and legume–rhizobia symbioses.",
"conclusion": "Conclusion Despite the obvious importance of transporters in symbioses and considerable recent progress, knowledge in this area is surprisingly sporadic (see Outstanding questions). Classical genetic approaches coupled with thorough biochemical characterization have enabled the discovery of the physiological functions of many such proteins. However, deciphering the functions of members of multigene families is not an easy task due to apparent functional redundancy. The source of such redundancy can be difficult to predict since small variations in protein sequence can greatly alter transport properties. However, symbiotic studies provide particular opportunities for transport research, including: (i) organ-driven specialization (e.g. legumes and nodules); (ii) process specialization (e.g. nodulation and hormone interplay); (iii) dedicated transport of species-specific compounds (isoflavonoids and legumes), and new functionality (e.g. ABCGs and specialized metabolite intermediates distribution/channeling). Equally important are identifying the molecules that are translocated, determining which are biologically relevant, and generating direct transport data. The latter is often technically challenging (e.g. lipid provision during AMS) but is crucial for characterizing the carriers as additional transport-driven processes are uncovered.",
"introduction": "Introduction Nitrogen (N) and phosphorus (P) are limiting nutrients in most natural soils ( Du et al., 2020 ). A high input of N-fertilizers is required for optimal crop yields in conventional agriculture, which leads to contamination of groundwater and contributes markedly to the release of greenhouse gases ( Fowler et al., 2013 ; Chai et al., 2019 ). Mutualistic fungal and bacterial symbionts are striking examples of soil microorganisms that have successfully coevolved with their hosts, allowing plants to better adapt to terrestrial ecosystems, and promoting their own success by gaining access to photosynthetic carbon ( Chen et al., 2018 ). The most widespread plant–fungal symbiosis is the arbuscular mycorrhizal symbiosis (AMS) and the majority of land–plant species engage in an interaction with fungi of the subphylum Glomeromycotina ( Wang and Qiu, 2006 ; Parniske, 2008 ; Spatafora et al., 2016 ), which has origins probably coinciding with the terrestrialization of plants ( Pirozynski and Malloch, 1975 ). Subsequently, around 100 million years ago (MYA), certain angiosperms, the so-called nitrogen-fixing root nodule clade, evolved nodulation, a symbiosis with nitrogen-fixing soil bacteria ( Griesmann et al., 2018 ; van Velzen et al., 2018) . Among all nodulating plants, legumes, which are able to establish the extensively studied legume–rhizobium symbiosis (LRS), are most prominent ( Huisman and Geurts, 2020 ). By forming endosymbiotic associations, plants obtain mineral nutrients and in turn supply the symbiont with organic and, in the case of LRS, also inorganic nutrients. LRS and AMS are intricate and finely tuned interactions that use host membrane transporters for the movement of a wide range of metabolites, including phytohormones, secondary metabolites, and nutrients, throughout the entire symbiotic process ( Bapaume and Reinhardt, 2012 ). Specialized plant membrane transporters represent a promising target to increase crop yields and quality, as well as to improve sustainable production of nutritious foods ( Schroeder et al., 2013 ). In this review, we synthesize current knowledge related to the host membrane transporters that participate in subsequent stages of AMS and LRS. We aim to: (1) demonstrate the importance of transporters in the establishment and maintenance of these symbioses; (2) highlight recent discoveries of long-sought-after transporters involved in translocation of crucial symbiotic molecules; and (3) suggest avenues worthy of future research."
} | 1,231 |
21798746 | null | s2 | 6,709 | {
"abstract": "Quorum sensing (QS) is a process by which bacteria use small molecules or peptidic signals to assess their local population densities. At sufficiently high density, bacteria can alter gene expression levels to regulate group behaviors involved in a range of important and diverse phenotypes, including virulence factor production, biofilm formation, root nodulation, and bioluminescence. Gram-negative bacteria most commonly use N-acylated l-homoserine lactones (AHLs) as their QS signals. The AHL lactone ring is hydrolyzed relatively rapidly at biological pH, and the ring-opened product is QS inactive. We seek to identify AHL analogues with heightened hydrolytic stability, and thereby potentially heightened activity, for use as non-native modulators of bacterial QS. As part of this effort, we probed the utility of thiolactone analogues in the current study as QS agonists and antagonists in Gram-negative bacteria. A focused library of thiolactone analogs was designed and rapidly synthesized in solution. We examined the activity of the library as agonists and antagonists of LuxR-type QS receptors in Pseudomonas aeruginosa (LasR), Vibrio fischeri (LuxR), and Agrobacterium tumefaciens (TraR) using bacterial reporter strains. The thiolactone library contained several highly active compounds, including some of the most active LuxR inhibitors and the most active synthetic TraR agonist reported to date. Analysis of a representative thiolactone analog revealed that its hydrolysis half-life was almost double that of its parent AHL in bacterial growth medium."
} | 392 |
37679081 | PMC10602557 | pmc | 6,711 | {
"abstract": "Abstract Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano‐ and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi‐layer arrangement of the soft resistive composites is achieved. However, the magnitude of the signal response and the localization ability of the stimulus change with lighter presses of the bimodal skin. Hence, a learning‐based approach is employed that can help achieve predictions about the stimulus using 4500 probes. Similar to the cognitive functions in the human brain, the cross‐talk of sensory information between the two types of sensory information allows the learning architecture to make more accurate predictions of localization, depth, and temperature of the stimulus contiguously. Localization accuracies of 1.8 mm, depth errors of 0.22 mm, and temperature errors of 8.2 °C using 8 mechanoreceptive and 8 thermoreceptive sensing elements are achieved for the smaller inter‐element distances. Combining the bimodal sensing multilayer skins with the neural network learning approach brings the artificial tactile interface one step closer to imitating the sensory capabilities of biological skin.",
"conclusion": "3 Conclusion In this work, the performance of a multimodal biomimetic skin capable of detecting and quantify the locations, depths, and temperatures of tactile stimuli was demonstrated. The sensory structure of human skin was used as a design guide. Same as natural signals are transmitted to the somatosensory cortex, neural network processing provides adaptive sensing, capable of learning the non‐linearities of mechano‐ and thermoreceptor signals and localizing stimuli to sub interelement resolutions. The depth of the sensing elements significantly affected the localization of the stimulus. Additionally, the magnitude of the stimulus was dependent on the position and depth of the sensing elements on the skin and particularly for the top layers, the response was not selective to each stimulus. The sensor response was selective when the sensors were used as a single sensing element. When the sensors were integrated in a substrate and used over an area, there were significant limitations in the localization of the stimulus, particularly in the substrate area between neighboring elements. In this area, temperature recognition was not possible for large interelement distances. The neural network processing enabled the stimulus recognition and localization over the entire area of the skin, regardless of the proximity to the element. Despite the algorithm allowing stimulus recognition in areas that were not possible to detect before, the prediction accuracy showed a dependency on the interelement distance. The uppermost thermoresistive elements, which reflect the free nerve endings found in the skin's epidermis, provided not only thermal feedback from tactile stimuli, but also valuable information on the location of lighter touches that may minimally deform the lower mechanoreceptive elements. The lowermost mechanoreceptive elements represent the functionality provided by the Pacinian corpuscles and Ruffini endings for the detection of deep pressure and stretching. In combination with the upper elements, the light touch localizations of Merkel's discs and Meissner's corpuscles become reproducible with the help of neural network processing. It was also seen how multi‐modal sensor fusion through the network architecture led to higher accuracy when there was a cross‐talk between the thermoreceptive and mechanoreceptive sensing elements. In addition, this tunable architecture suggests the potential to adapt to dynamically changing environments in further implementations. Future work will aim to process the signals to similarly represent the full functionality that these provide in nature, including the recognition of high and low frequency vibrations and the separation of cutaneous stretching from associated signals. This combination of these behaviors into truly multi‐modal skin would significantly improve the potential and capabilities of both sensorized wearables and prosthetic devices.",
"introduction": "1 Introduction The skin in a natural organism is typically the largest sensory organ, as it involves multiple specialized neurons that convey important sensory information to the brain about the surroundings. [ \n \n 1 \n , \n 2 \n \n ] Electronic skin attempts to mimic the sensory capabilities of natural skin, primarily tactility, using artificial sensors. [ \n \n 3 \n , \n 4 \n \n ] While there has been significant progress in the development of stretchable electronics that can enable the detection of tactile stimuli, the applicability of such devices remains limited. [ \n \n 5 \n , \n 6 \n \n ] This is primarily because of the challenges associated with developing fully stretchable electronic skin without suffering from material non‐linearities like hysteresis and drift. [ \n \n 4 \n , \n 7 \n \n ] These challenges are amplified when multi‐modal sensing functionalities are added. Recent review articles agree that there is a great need for soft functional materials with multi‐modal sensing capabilities and more precise data processing algorithms to improve the applicability of e‐skin. [ \n \n 7 \n , \n 8 \n , \n 9 \n , \n 10 \n \n ] There are sensory features that can be adopted from nature and integrated into sensorized e‐skin. Having e‐skin that closer resembles the tactile capabilities of natural skin can improve the performance and control of robots, human/machine interfaces, and prosthetic devices. Natural skin possesses specialized sensory receptors for conveying dedicated information ( Figure \n 1 a ). In the epidermis, free nerve endings can detect changes in the temperature, attributing the capability of thermoreception. [ \n \n 11 \n , \n 12 \n \n ] Specialized mechanoreceptors can be found in the encapsulated form. Merkel's discs in the dermis and epidermis are responsible for the sensation of light touch. [ \n \n 13 \n \n ] Meissner's corpuscles are found beneath the epidermis and detect low frequency vibrations and light touch. [ \n \n 14 \n , \n 15 \n \n ] Both Merkel's discs and Meissner's corpuscles are finely calibrated and can precisely localize tactile stimuli. [ \n \n 16 \n \n ] Pacinian corpuscles and the Ruffini endings reside deeper in the dermis and subcutaneous tissue. [ \n \n 17 \n \n ] They are responsible for detecting pressure/high frequency variation and cutaneous stretching, respectively. [ \n \n 18 \n \n ] These two mechanoreceptors respond to deep pressure, but cannot detect the fine localization of the tactile stimulus. [ \n \n 19 \n , \n 20 \n \n ] The specialization of different receptors found in the skin and their arrangement in different layers can inspire the placement of the artificial receptors in multi‐layer formation, as will be seen in this study. Figure 1 a) Schematic of the modalities of mechano‐ and thermoreception found in the human skin. b) The biomimetic sensorized skin and processing methods presented in this work. The processing of sensory information in biological organisms can also inspire the development of e‐skin. While sensory receptors are responsible for detecting the presence of a tactile stimulus, the brain encodes the message and transforms it into relevant information for perception and action. An interplay of the signal of the different mechanoreceptor types is transmitted to the somatosensory cortex of the brain and can be processed to relevant information about the time, location, temperature, and intensity of the tactile stimulus (Figure 1a ). [ \n \n 21 \n , \n 22 \n , \n 23 \n \n ] \n The function of perception involves the interpretation of a sensation in the brain. [ \n \n 24 \n \n ] Millions of sensory neurons constantly transmit information that the human brain can identify, organize, and interpret sensory information with the perception process. [ \n \n 25 \n \n ] For the learning process, the brain must streamline its data processing and tune its sensitivity to relevant processes. [ \n \n 26 \n , \n 27 \n , \n 28 \n , \n 29 \n \n ] Although the exact neural circuitry of touch and thermal perception is not well understood, observations suggest that there is some level of cross‐talk among different somatosensory modalities. [ \n \n 30 \n , \n 31 \n \n ] This type of cross‐talk of bimodal sensory information can be used as a source of inspiration for the e‐skin and neural network processing (Figure 1b ). The development of e‐skin with the ability to detect multiple stimuli is an essential problem in the development of prostheses and robotic systems. [ \n \n 32 \n , \n 33 \n , \n 34 \n \n ] Multifunctional stretchable sensory skins for detecting pressure, proximity, temperature, etc. have recently been developed by stacking planar sensing layers with different functionalities [ \n \n 35 \n , \n 36 \n \n ] or by using 3D structures that are sensitive to physical stimuli. [ \n \n 37 \n , \n 38 \n \n ] Nonetheless, most of these technologies still incorporate rigid components in their design making them non‐stretchable and their sensing region is typically discrete. Another major challenge is the perception of multiple stimuli at the same time due to mutual interference. [ \n \n 3 \n , \n 39 \n \n ] Typically, this cross‐coupling effect is reduced to achieve multi‐modal sensitivity. [ \n \n 39 \n , \n 40 \n \n ] Tactile sensing is often used in wearable electronic devices. [ \n \n 41 \n , \n 42 \n \n ] Flexible sensors based on polymeric materials and composites can be used for detecting contact, while maintaining softness and stretchability, like biological skin. This type of sensing can be used for detecting when contact with an object has occurred [ \n \n 43 \n , \n 44 \n , \n 45 \n \n ] and typically the applied pressure can be quantified. [ \n \n 46 \n , \n 47 \n \n ] Recently, sensing that can relay simultaneous information about the applied force, strain, and temperature has become available. [ \n \n 48 \n , \n 49 \n \n ] However, achieving selectivity to one stimulus and at the same time obtaining information about the presence of multiple stimuli remains a challenge. [ \n \n 50 \n \n ] Looking from an information theory viewpoint, however, cross‐talk and mutual interference are not necessarily detrimental and in some cases can be advantages for state estimation. Such interelement interactions can be used for improving robustness to damage, [ \n \n 51 \n \n ] reducing modeling errors [ \n \n 52 \n \n ] or compensating for external environmental changes. [ \n \n 53 \n \n ] Typically, learning‐based approaches are used in such cases, similar to the methodology in this paper. [ \n \n 4 \n , \n 37 \n , \n 54 \n , \n 55 \n \n ] \n In this work, biomimetic tactility will be investigated in a sensorized e‐skin produced with material extrusion based additive manufacturing (MEX‐AM). Similar to the free nerve endings closer to the skin surface for temperature detection, two layers with integrated flexible thermistors were placed on top. Underneath, two layers with piezoresistive sensing elements, resembling the function of the Pacinian and Ruffini's corpuscles, were included. Their function is to detect the presence of pressure due to the deformation of the skin (piezoresistive response). For localizing the stimulus and measuring its magnitude, which is the function of Meissner's corpuscles and Merkel's discs, the piezoresistive sensing elements were used in combination with a learning‐based algorithm. Further details are presented in Section 4 . It was assumed that the entire skin is flexible and stretchable, making it easy to integrate into existing robots. Unlike related works on multi‐modal tactile sensing, [ \n \n 39 \n , \n 40 \n , \n 56 \n , \n 57 \n \n ] in the current study, the idea is to sense light touch, depth, and temperature contiguously. Inspired by the cross‐talk of multi‐modal sensory information in the brain, it is demonstrated how the fusion of information from the temperature and deformation receptors lead to better perception capabilities for both modalities. Such characteristics will pave the path for the foundation of artificial cognitive perception, a function that will enhance the applicability of stretchable electronics and affect several fields including soft robots, wearable, and prosthetic devices.",
"discussion": "2 Results and Discussion 2.1 Characterizing the Sensory Receptors: Single Point Measurements The response of the sensing elements was characterized during the application of mechanical or thermal stimuli with the probe of the robot at the defined point ( Figure \n 2 a,b ). The two sensor materials have been optimized for having good sensitivity to a specific stimulus. The thermoresistive sensor has a high thermal expansion coefficient base on semi‐crystalline structure. A low carbon filler has been selected below the percolation threshold to achieve high change in electrical resistance by small change in volume expansion. [ \n \n 58 \n \n ] In contrast, to achieve high sensitive mechanoresistive sensor properties, a high filler content is favored to achieve monotonic increase of resistivity in a large strain area. The increase in stain will result in an increase of the interparticle distance of the carbon black leading to the positive piezoresistive response. To avoid thermal resistive effect, an amorphous polymer matrix has to be selected. [ \n \n 58 \n \n ] With an increase in the strain as a result of the skin stretching, the distance of the particles increases. Figure 2 a) Schematic representation of the mechanoreceptive and thermoreceptive sensing elements arrangement in the sensorized skin and the definition of the three probed points during the testing. b) Photograph of the bimodal sensorized skin. Response at 50 and 100 °C for probing depth of 4 mm (deep pressure) for c) the mechanoreceptive and d) the thermoreceptive sensing elements, over four repetitions. Response at 50 and 100 °C for probing depth of 1 mm (light touch) for e) the mechanoreceptive and f) the thermoreceptive sensing elements, over four repetitions. First, the deep pressure test took place with a probing depth of 4 mm (Figure 2c,d , with the time series responses given in Figure S1 , Supporting Information). The response of the mechanoreceptive elements at point A was low, and the elements 2s, 3s, 6s, and 7s (surrounding point A) resulted in a low relative signal change of 1%, 1.4%, 4.5%, and 4%, respectively. This can be expected because point A is the crossing point of two thermoreceptive elements. The elements 6s and 7s on L4 (the fourth layer from the top), showed a two to three times higher relative signal change than the 2s and 3s elements on L3. By applying a probing depth of 4 mm, the soft e‐skin structure was deformed under a bending mode. Therefore, the lower the layer from the top, the higher the strain deformation. A higher deformation resulted in a higher resistance change of the mechanoreceptor element and therefore, it was expected that the sensors on the fourth layer would result in a higher relative signal change. It is worthwhile to mention that no significant resistance change could be observed for all other mechanoreceptive elements. According to this result, it was assumed that the e‐skin was only deformed locally. The response of the mechanoreceptive elements did not change more than 0.2% after the heating. This shows that the mechanoreceptive sensors are not significantly affected by the local heating of the elastic e‐skin and thus, they exhibit selective response to mechanical stimuli. Looking at the thermal response of the sensing elements when point A was probed, it can be seen that there was a change in the signal response for the 2t and 6t elements (Figure 2d ). Element 2t produced a response of magnitude of 15% and element 6t of 4%. Element 2t was on L1 (top layer) and element 6t in L2 (second layer). The differences in the magnitude of the response can be explained by the lower conductivity of the substrate L1 that is in‐between the 2t and 6t crossing point. Unexpectedly, there was also a change in the response of the elements 4t (3.5%) and 7t (4%), proximal to point A and this was attributed to the stretching of the skin. However, this was not seen for the other elements (1t and 3t on L1 and 5t on L2). A temperature increase to 100°C resulted in a higher relative signal change by the thermoreceptive element 2t (31%). For the 4t, 6t, and 7t elements, the response remained the same. Only the thermoreceptor 2t was in direct contact with the probe. As mentioned before, the low thermal conductivity of the support material significantly affected the performance of the thermoreceptors if they were not in direct contact with the heated probe. Longer term temperature responses over 10 min can be seen in Figure S2 (Supporting Information). Point B is the crossing point of mechanoreceptive elements 2 and 7s (Figure 2c ). Here, the 2s resulted in a relative signal response of 4.2%, whereas the relative signal response of the 7s was only 1%. A relative signal change of 2.3% and 1% for the elements 1 and 3s shows, that in this case, the deformation of the e‐skin is more lateral due to the softer support material closer to L1 and L2. This assumption correlates with the 6s sensor signal, which did not significantly change, and is placed in the fourth layer. The relative signal change was similar when the probe was heated to 100°C. This is in good agreement with the results discussed for point A and therefore the mechanoreceptive sensors are not sensitive to temperature changes. For the thermoreceptive elements near point B, a relative signal change of 8.5% (2t), 2% (6t), and 7.5% (7t) could be observed. All these three elements were proximal to point B. Unfortunately, the relative signal was similar when increasing the probe temperature up to 100°C. The low thermal conductivity of the support material did not transport the heat into the proximal thermoreceptive elements. Point C was the crossing point of elements 0t and 4s. For deep pressure (4 mm depth) and a temperature of 50°C, the thermoreceptor element 0t resulted in a relative signal change of 26% and the mechanoreceptor 4s in a change of 2.4% (Figure 2e ). This was in good agreement with the results achieved at point A, even though the relative resistance change for both receptor elements was different. It was assumed that the difference in relative signal change between points A and C was caused by the fact that the probing was proximal to the frame, where the elastic e‐skin was fixed. However, when the temperature of the probe increased to 100° the mechanoreceptor signal change did not vary significantly, whereas the signal response of the thermoreceptive elements increased, as expected. For the light touch test (probing 1 mm depth) the sensor response was examined at the same points (Figure 2e, f ) with time series responses given in Figure S3 , Supporting Information). For point A, all mechanoreceptive elements resulted in a very small signal change (<0.1%) that was not affected by the temperature change. The selectivity of the sensor response was in good agreement with the results of the deep pressure test. Selective sensors exhibit high sensitivity to one stimulus and minimal response to other interfering stimuli. [ \n \n 50 \n \n ] The thermoreceptive elements 2t and 6t produced relative signal changes of 4% and 1.8%, respectively. This response was significantly smaller than the one seen during the deep pressure test. When the temperature increased to 100 °C, only 2t element showed a significant increase of the relative signal (4.5%), as expected. It was evident that the thermoreceptive elements were not selective to the temperature stimulus. For point B, mechanoreceptive element 1s resulted in a signal change of 1.5% and this did not significantly change by increasing the temperature. As expected, this value was significantly smaller in comparison to the deep pressure tests (4 mm). For element 2t, a relative signal change of 4% and 4.3% for the two different temperatures 50 and 100°C was observed, respectively. Similar to point A, the temperature signal differed when compared to the deep pressure test. For the point C, all mechanoreceptive elements resulted in a very small signal change (< 0.1%), independent of the probe temperature. Only the thermoreceptive element 0t resulted in a relative signal change of 11%. Similar to points A and B, this relative change was lower in comparison to the deep pressure test. By increasing the temperature of the probe to 100%, the relative signal change increased to 17%. Based on the single point measurements, it was concluded that the mechanoreceptor elements were not significantly affected by the temperature window (50 and 100°C), whereas the signal of the thermoreceptor elements was affected by the probing depth (deep pressure and light touch tests). During the light touch tests (1 mm), the mechanoreceptive elements did not detect the location of the tactile stimulus. The neural network learning approach was necessary for being able to analyze experiments with low probing depth. It is worthwhile to mention that due to the short probing time (10 s), it was not possible to transfer the heat through the TPU support material, due to its low thermal conductivity. To investigate the effect of the stiffness of soft tissue, two Ecoflex materials and one silicone foam were placed underneath the e‐skin. Based on previous results, the deep pressure test was used to investigate the stiffness effect on the sensitivity of the receptor elements. Two mechanoreceptive and two thermoreceptive sensory elements were examined for the selected locations (A, B, and C) at 50 and 100 °C ( Figure \n 3 a,b ). It was seen that that the response was more sensitive when probing location B, exhibiting a localization ability (Figure 3c ). The differences between the three different substrates were not significant, but there is a slightly smaller sensitivity for the silicone foam material. Since point B is at the intersection of two mechanoreceptive elements (2s and 7s), there was not a significant dependency upon the temperature change. In the case of element 4s (Figure 3d ), localization ability was seen when point C was probed. However, in this case, the 00–10 substrate (lowest Shore hardness) gave the highest response magnitude. This difference was attributed to the deformation of the skin not transporting to the lowest layer (L4) evenly for all substrates, resulting in an asymmetric response. A similar asymmetry was observed for the mechanoreceptive elements 0t and 2t (Figure 3e, f ), but in this case, the low heat transfer was considered to be the cause. Figure 3 a) Photograph of the robot arm performing the probing. b) Probed locations with respect to the tested sensors. c–f) Differences in response magnitude of four selected sensors to a variety of 4 mm deep presses, when one of the four different substrates (Ecoflex 00–30, Ecoflex 00–10, and silicone foam) are placed underneath the large skin for sensing elements c) 2s d) 4s e) 0t f) 2t. five repetitions are performed for each. The percentage deviation was calculated from the baseline resistance during each response. The red line indicates the sensor element that was being investigated in each subfigure. 2.2 Tactile Stimulus Predictions Section 2.1 has demonstrated the initial feasibility of the e‐skin, mechanoreceptive elements responded selectively to strain, whereas the thermoreceptive elements responded to temperature and strain. In addition, it was demonstrated that the response of the sensors depended on the probing depth and the proximity of the sensing element to the stimulus. Inspired by the biomimetic skin structure, temperature sensors placed uppermost (Figure 1 ) were found to be the most sensitive, detecting changes in temperature as well as local strains. In real‐life applications, the usefulness of a sensor is not only given by its response signal but also by how well its response can be interpreted and aspects, like the tactile stimulus recognition (temperature or pressure) and localization are essential. Thus, the primary purpose of the featured sensors lies in their capability to recognize, locate and categorize tactile sensory inputs. To that end the raw data analysis from Figures 2 and 3 was used as input for the neural network, which was trained to output three parameters of the stimulus (lateral location, depth, temperature), as described in Section 4 . Figure \n 4 a illustrates the expected workflow once the networks have been trained: raw responses were directly mapped to the predictions, with separate networks for small and large skin sizes. This analysis is a useful process for handling large sets of data and different types of sensory information to achieve localization and recognition of the sensory stimulus with good accuracy. Figure 4 a) Using a trained network: raw responses are fed in to a network for the size under consideration, and four predictions are output. b) Mean test set ( n = 500) prediction errors of the trained networks, repeated five times with changes in the output targets. c) Dependence of prediction errors on the depth of pressing. Localization errors are scaled to have a mean of one for comparison purposes d) x‐y localization error distributions in the test sets ( n = 500), for networks trained separately on the large and small skins. The color bars are scaled proportionally with the skin size. e) Depth error distribution of the same networks. f) Actual location of probing (orange) versus predicted location of probing (blue) for a circle traced on the small skin. The mean prediction errors of neural networks trained using both the mechanoreceptive and thermoreceptive sensor responses are presented in Figure 4b . Each of the sensory outputs i.e., localization, depth, or temperature was trained separately by Partial Output. Conversely, Full Output predicted all values simultaneously. With no changes in architecture, switching between the two had a negligible effect on the prediction errors and since the Full Output model was more compact and data‐efficient, the networks were trained using a Full Output for all subsequent figures. Using this Full Output, the small skin's network was able to localize the test set's presses with a mean error of 1.8 mm. This value was considerably less than the resolutions achievable by looking at the highest sensor responses without the network (8.9 mm between adjacent strain sensors in the grid, or the 4.4 mm between sensors of any type). Similarly, the large skin's network localized with a sub‐grid resolution, with a mean error of 6.6 mm compared to the 17.8 mm between adjacent strain sensors. Though the size was doubled, the localization error increased by 270%, due to the fact that the probe diameter and the depth of pressing did not scale accordingly. Changing these parameters would be expected to result in a shift in mean error, and could quickly be accounted for by retraining just the network's final layer. [ \n \n 59 \n \n ] \n In Section 2.1 , it was hypothesized that a network's localization performance would improve with the probing depth. Thus, Figure 4c fulfilled these expectations for both the small and large skin. As seen in Figure 2 , the mechanoreceptive sensor layers localized light presses less accurately than deeper presses, especially within the first few mm of pressing. This observation agreed with the findings of analysing the raw sensor data response. Even with the neural network processing, for the small skin, 2 mm of depth was required for the error to reach a constant value, while this value is closer to 3 mm for the large skin. Designing a biomimetic skin to operate within this stable region is a sensible way of ensuring high localization performance, depending on a number of layers, layer thickness and substrate selection. There is a limitation in the depth of placing the sensory elements that significantly affects the localization precision. As for the depth prediction errors, the difference between small and large sizes was smaller than the localization predictions, as observed in Figure 4b . Since the range of probing depths did not change between experiments, the small skin's network gave a mean error of 0.22 mm, increasing to 0.27 mm in the large skin. Similarly, the error in temperature predictions was 8.2 °C (small skin) and 10.1 °C (large skin) of the ≈70 °C range. The networks significantly outperformed a naive prediction of the average value, which would give mean errors of 16.0 and 17.45 °C. Unlike the x/y localization, there was no dependency on the probing depth in the error of predicted temperature, as illustrated in Figure 4c . The large skin's average error begins to increase at the deepest stimuli, which was attributed to the increased likelihood of the deeper presses producing noise in the connections. In Figure 2 , it was seen that the response of the mechanoreceptive elements on deformation depended on the depth of the layer and was highly influenced by asymmetry effects. Using neural network processing with the cross‐talk between the two type of sensing elements, the x/y location and depth of the deformation by press could be localized more precisely, regardless of the location of the probing in the studied area. Figure 4d shows the distributions of the test set localization errors for the two skin sizes, with the color bars scaled for equivalence. As before, it was seen that the localization error of the large skin was larger than the skin's dimensions, but also that many of the larger errors were clustered on the left side of the grid. Therefore, for the large skin, the prediction failed to localize the stimulus, particularly for probings close to the left side of the grid. A possible justification for this effect could be a loose connection leading to the asymmetrical error distribution. The same effect was not apparent in the small skin, where the nearby strain sensor was at the same four‐layer depth. In that case, larger errors appeared disproportionately in the upper half of the characterization area. A similar asymmetry in the response was also observed for the raw data analysis and was associated with limitations due to stress shielding and low heat transfer effects. No such pattern appeared in the depth prediction errors from the same test set (Figure 4e see Figure S4 , Supporting Information for equivalent temperature error distributions). The error distribution was homogeneous, with very few localization errors (just 4.2%) exceeding 4.4 mm. The separation of adjacent grid lines suggested a better localization than it was possible to achieve by looking at the raw response magnitudes in Section 2.1 . This figure rises to 28.2% for the large sensor: though only 3.6% of errors exceed the strain sensor separation. To illustrate this, Figure 4f demonstrates the accuracy of the small skin sensor's network when a circle of random temperature/depth and 20 mm diameter is transcribed. The locations were accurately identified, and the reconstructed circle was clear, with more noise in the lighter depths. Such localization would be not possible without the biomimetic network. \n Figure \n 5 a investigates the success of temperature predictions by plotting the predicted versus actual temperatures for the small and large skin. Looking at the raw data response, it was expected that based on heat transfer limitations, the small skin should achieve more accurate predictions. However, the accuracy difference was not significant for the two sizes. The positive correlation coefficients of 0.79 and 0.78 for the small and large sizes, respectively. The similarity of the two temperature sensing performances was also observed in Figure 4b . In this case, it was seen that the large skin had slightly more outliers than the small skin, which marginally increased the mean error. Since the biomimetic design of the skin placed the temperature sensors on the surface, heat could transfer quickly to the temperature sensors during the pressing period for both skin sizes. To further improve the accuracy for the two skin sizes, longer presses, or a more thermally conductive substrate, could minimize this effect by improving the transmission of temperature information across the upper layers. Figure 5 a) Predicted versus actual temperatures for all presses in the test sets ( n = 500) of the small and large skins. The correlation coefficient, R , is calculated for each, which demonstrates a clear positive correlation. b) Training the neural networks using only responses from the strain sensors, only responses from the temperature sensors, or a combination of both. For all predictions and skin sizes, using both inputs produces the lowest errors. c) Training the large skin's neural networks using only the deepest presses (2.5–4 mm). The improvement in a network's predictions compared to Section 2.1 's naive estimations is due to its ability to simultaneously analyze and identify patterns in large quantities of data. It was seen in Figures 2 and 3 that the strain and temperature responses of the skin were not independent, and both sensor types were affected by changes in both strain and temperature. It was expected that the networks with a combination of both types of sensor responses would yield the most accurate predictions. This effect is demonstrated in Figure 5b , which shows the test set's mean error for localization, depth, and temperature predictions of the trained network. Each bar shows the average and range of errors for a different network, trained with the data from all sensors, only strain sensors or only temperature sensors. In all six cases, the redundancy provided by using both sensor types at the networks' input gave the lowest error in the bimodal system. For the small skin, the behavior of the individual sensory inputs was as expected. The strain input yields a lower mean error than the temperature input for the localization, and the temperature input yields a lower mean error for the temperature prediction than the strain input.The small skin clearly showed the benefits of coupling the two sensory inputs. Whilst pure strain responses can be used to train better localization/depth networks than temperature responses, the combination of the two gives the best response. Similarly, temperature errors were lower on a network trained with temperature responses over one trained with only strain responses, but the combination had higher accuracy. For the large skin, the results were not as expected. While using both inputs combined, yielded the expected outcome of having the smallest error, looking at the individual responses for each input the error values were not anticipated. The temperature inputs alone gave lower test set errors than the strain inputs for localization and depth predictions. At the same time, the strain input yielded a lower mean error for the temperature prediction than the temperature inputs. To understand better these contradicting results, an additional test was performed for the large skin. From the testing of the individual points, it was seen that the uppermost temperature layers proved useful in detecting and localizing the light touches. For that reason, the same investigation was repeated to include only the data points from the higher valued of depth (larger than 2.5 mm). This alteration resulted in a reduction of the mean error for the x/y localization, but for the other predictions the results didn't change (Figure 5c ). Thus, it was concluded that the large interelement distance resulted in areas in the skin with high values of error that led to a larger value of the mean error, especially for temperature sensing. Therefore, it is evident that the interelement distance is crucial for achieving accurate predictions. Even though this observation indicated a strong presence of sensor interdependence that was crucial for improving predictions in the small skin, the bimodal sensor cross‐talk yielded the best prediction accuracy for both sizes. Overall, combining the two different sensory types has the significant benefit of reducing the prediction error compared to using only one sensory input, regardless of the interelement distance."
} | 9,131 |
20161141 | null | s2 | 6,713 | {
"abstract": "Recent studies have indicated that plant growth-promoting bacteria (PGPB) can improve revegetation of arid mine tailings as measured by increased biomass production. The goals of the present study were first to evaluate how mode of application of known PGPB affects plant growth, and second to evaluate the effect of this inoculation on rhizosphere microbial community structure. PGPB application strategies investigated include preliminary surface sterilization of seeds (a common practice in phytoremediation trials) followed by a comparison of two application methods; immersion and alginate encapsulation. Results with two native desert plant species, Atriplex lentiformis and Buchloe dactyloides, suggest that seed surface sterilization prior to inoculation is not necessary to achieve beneficial effects of introduced PGPB. Both PGPB application techniques generally enhanced plant growth although results were both plant and PGPB specific. These results demonstrate that alginate encapsulation, which allows for long-term storage and easier application to seeds, is an effective way to inoculate PGPB. In addition, the influence of PGPB application on B. dactyloides rhizosphere community structure was evaluated using PCR-DGGE (denaturing gradient gel electrophoresis) analysis of bacterial DNA extracted from rhizosphere samples collected 75 d following planting. A comparative analysis of DGGE profiles was performed using canonical correspondence analysis (CCA). DGGE-CCA showed that rhizosphere community profiles from PGPB-inoculated treatments are significantly different from both uninoculated tailings rhizosphere profiles and profiles from the compost used to amend the tailings. Further, community profiles from B. dactyloides inoculated with the best performing PGPB (Arthro mix) were significantly different from two other PGPB tested. These results suggest that introduced PGPB have the potential to influence the development of the rhizosphere community structure found in plants grown in mine tailings."
} | 506 |
20161141 | null | s2 | 6,714 | {
"abstract": "Recent studies have indicated that plant growth-promoting bacteria (PGPB) can improve revegetation of arid mine tailings as measured by increased biomass production. The goals of the present study were first to evaluate how mode of application of known PGPB affects plant growth, and second to evaluate the effect of this inoculation on rhizosphere microbial community structure. PGPB application strategies investigated include preliminary surface sterilization of seeds (a common practice in phytoremediation trials) followed by a comparison of two application methods; immersion and alginate encapsulation. Results with two native desert plant species, Atriplex lentiformis and Buchloe dactyloides, suggest that seed surface sterilization prior to inoculation is not necessary to achieve beneficial effects of introduced PGPB. Both PGPB application techniques generally enhanced plant growth although results were both plant and PGPB specific. These results demonstrate that alginate encapsulation, which allows for long-term storage and easier application to seeds, is an effective way to inoculate PGPB. In addition, the influence of PGPB application on B. dactyloides rhizosphere community structure was evaluated using PCR-DGGE (denaturing gradient gel electrophoresis) analysis of bacterial DNA extracted from rhizosphere samples collected 75 d following planting. A comparative analysis of DGGE profiles was performed using canonical correspondence analysis (CCA). DGGE-CCA showed that rhizosphere community profiles from PGPB-inoculated treatments are significantly different from both uninoculated tailings rhizosphere profiles and profiles from the compost used to amend the tailings. Further, community profiles from B. dactyloides inoculated with the best performing PGPB (Arthro mix) were significantly different from two other PGPB tested. These results suggest that introduced PGPB have the potential to influence the development of the rhizosphere community structure found in plants grown in mine tailings."
} | 506 |
38564133 | PMC11058749 | pmc | 6,716 | {
"abstract": "This study investigates the impact of three key variables on the performance of nanoporous AM-3 and layered AM-4 titanosilicates in removing nine REEs (Y, La, Ce, Pr, Nd, Eu, Gd, Tb, and Dy) from natural mineral water and identifies optimal operational conditions using Response Surface Methodology (RSM). The experimental conditions were determined by a Box-Behnken Design of 3 factors-3 levels (pH 4, 6, and 8; sorbent dose 20, 100, and 180 mg/L; and element concentration 1, 3, and 5 μmol/L). Three-dimensional response surfaces were used to assess the linear, quadratic, and interaction influences of each factor on the REEs’ removal percentage. The pH was the most significant factor in the removal process using AM-3, while the sorbent dose was more important for AM-4. The results highlighted the sorbents’ strong capacity for REE removal. The optimal operating conditions obtained by RSM were applied to aqueous solutions with salinity 10 (common in coastal and transitional systems) and 30 (average seawater salinity). The results showed that AM-3 has a strong potential for removing REEs in solutions with salinity 10 and 30, while AM-4 was less efficient due to competition between REEs and other ions present in the solution. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-024-33063-w.",
"conclusion": "Conclusion The application of Design of Experiments enabled the identification of the most critical variables in these processes with minimal experimental efforts and optimized the removal of nine different REEs using AM-3 and AM-4. The structural differences between AM-3 (nanoporous titanosilicate) and AM-4 (layered titanosilicate) resulted in variations in the influence of variables on sorption. The pH had the most significant impact on AM-3’s removal efficiency, with higher performance at increased pH levels due to competition between H + and REE 3+ ions for the exchange of the extra-framework Na + cations. In contrast, AM-4’s removal efficiency was primarily affected by the dose of sorbent, with higher doses resulting in greater removals. Response Surface Methodology proved to be a valuable tool for gaining insights into the behavior of REE removal processes and the optimal responses expected within various operational conditions. The performance of the two titanosilicates under optimal conditions demonstrated that both materials have strong potential for this removal process. However, when considering process viability in more complex matrices, the two sorbents exhibit different behaviors. The influence of salinity on AM-3’s removal efficiency did not negatively impact its performance, unlike AM-4, which exhibited poor performance in the presence of salinity due to competition between REEs and other cations in the solution. Consequently, it is concluded that the AM-3 sorbent material is the superior choice for potential applications in real aqueous systems, where the presence of competitive ions is more common.",
"introduction": "Introduction Rare earth elements (REEs) are considered critical resources for the European Union due to their vital role in various applications. The International Union of Pure and Applied Chemistry (IUPAC) classifies REEs as a group of 15 lanthanides (La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu), along with Sc and Y. These elements are crucial for numerous applications, particularly in the automotive industry, where they contribute to alternative power and energy-saving technologies. La and Ce are found in hybrid batteries and catalytic converters, and Nd, Dy, Pr, and Tb are components of hybrid electric motors and generators, while Eu, Tb, and Y are utilized in color LCD screens (Malhotra et al. 2020 ). As a consequence of their increased use, the demand for REEs is anticipated to grow. Currently, mining serves as the primary production source for these elements. However, extraction processes, such as pyrometallurgical and hydrometallurgical methods, demand substantial energy and water resources and generate considerable amounts of chemical waste (Binnemans et al. 2013 ). This leads to adverse environmental and human health impacts. Moreover, rapid technological advancements have caused these elements to increasingly emerge as contaminants (World Economic Forum 2019 ). Hence, it is crucial to develop efficient techniques for REE removal and recovery from water sources to reduce mining reliance and preserve natural resources. Numerous methods have been proposed for removing chemical elements from aqueous solutions, with sorption emerging as the most promising and widely used technique. The sorption process offers several benefits, including versatility, simplicity, cost-effectiveness, and high efficiency (Anastopoulos et al. 2016 ). Numerous studies have highlighted the high sorption capacity of various sorbent materials for different REEs. Examples of these materials include live algae (Jacinto et al. 2018 ), bacteria (Liang and Shen 2022 ), activated carbon and silica compounds (Ramasamy et al. 2018 ), sericin/alginate particles (Da Costa et al. 2021 ), and by-pass cement dust (Ali et al. 2011 ). Nevertheless, certain limitations have been observed. For instance, in the case of biosorbents like algae, prolonged sorption contact times are needed to achieve high removal efficiencies. Additionally, some studies are conducted in a simple matrix, which prevents the evaluation of the impact of ion competition on sorption. Recently, zeolite-type materials have demonstrated significant potential for REE removal (Thakkar et al. 2019 ) due to their high selectivity, removal efficiency, rapid sorption kinetics and ease of removal and recovery. Titanosilicates are a class of inorganic materials which are part of the larger family of zeolites and zeolite-like materials, known for their unique framework structures with uniform pore sizes, high surface area, and remarkable ion-exchange properties (Sankar et al. 1996 ; Rocha and Anderson 2000 ). The negatively charged frameworks of microporous titanosilicates, built up of [TiO 6 ], rarely of [TiO 5 ], polyhedra and [SiO 4 ] tetrahedra, are balanced by extra-framework cations, such as Na + or K + . ETS-10 [(Na,K) 2 TiSi 5 O 13 ·4H 2 O] is the most representative member of this family of materials (Anderson et al. 1994 ). While the ion-exchange properties of titanosilicates have been much studied (Ferreira et al. 2009 ; Lopes et al. 2009 ), only a few publications have addressed the removal and recovery of REEs (Oleksiienko et al. 2017 ). Recently, ETS-10 was used in the selective recovery of Nd(III) from Ni − Nd acidic aqueous solutions and Nd − Dy acidic aqueous solutions, generated during the recycling of NiMH batteries and NdFeB permanent magnets, respectively (Thakkar et al. 2019 ). This study focuses on evaluating two titanosilicates as sorbent materials for REE removal from aqueous solutions and optimizing the sorption process. AM-3 (Aveiro-Manchester number 3) is a synthetic nanoporous titanosilicate, analogous to the mineral penkvilksite-2 O , with an ideal formula of Na 2 TiSi 4 O 11 ·2H 2 O. Its structure comprises SiO 4 tetrahedral chains interconnected by individual TiO 6 octahedral units, creating a three-dimensional framework that features 6-ring channels which are partially occupied by Na + cations and water molecules (Lin et al. 1997 ). On the other hand, AM-4 (Aveiro-Manchester number 4, Na 3 (Na,H)Ti 2 O 2 [Si 2 O 6 ] 2 ·2H 2 O) is composed of TiO 6 (M) octahedra and SiO 4 (T) tetrahedra, which form layers perpendicular to the [001] direction. Each layer consists of a five-tier sandwich structure of T-M-T-M-T. Na + cations and water molecules are located between the layers, with Na + cations also found within small cages inside the layers (Dadachov et al. 1997 ). Hence, the main difference between the two materials is that AM-3 exhibits a 3D framework while AM-4 has a 2D layered structure. Both titanosilicates have not been previously tested for REE removal. Design of Experiments (DoE) and Response Surface Methodology (RSM) are powerful statistical modeling techniques that have been widely used to optimize sorption processes (Ferreira et al. 2007 ; Witek-Krowiak et al. 2014 ; Fabre et al. 2021 ). DoE is used to study the effects of experimental variables, also known as factors, on the sorption process and assesses their significant impact on the response variable in an experiment. Employed in the initial stages of experimental research, DoE helps to identify the most crucial factors to consider in subsequent studies. This approach enables researchers to optimize experimental conditions, minimize errors, and efficiently allocate resources while obtaining meaningful results (Witek-Krowiak et al. 2014 ). RSM is then applied to the experimental data generated by DoE to construct a model that describes the relationship between the factors and the target response. The quality of the RSM model is contingent upon the quality of the experimental data generated by DoE. The RSM model can be utilized to optimize the response variable by identifying the optimal levels of the factors that either maximize or minimize the response (Witek-Krowiak et al. 2014 ). The Box-Behnken design is a widely used method in RSM due to its ability to determine optimal conditions with high precision while requiring a reduced number of experiments. This design is particularly beneficial when investigating the effects of multiple factors on a response variable within a specified experimental region (Ferreira et al. 2007 ). The primary objectives of this study are as follows: (i) to assess the influence of various experimental conditions (pH, sorbent dose, and initial REE concentration) on the removal of these elements from aqueous solutions using AM-3 and AM-4 titanosilicates, employing the Box-Behnken design; (ii) to develop a model that describes the process under the studied circumstances and determines the optimal conditions for REE removal by titanosilicates; and (iii) to compare the performance of the two titanosilicates in REEs sorption from high salinity matrices.",
"discussion": "Results and discussion Characterization of titanosilicates AM-3 and AM-4 The powder XRD patterns of pristine AM-3 and AM-4, shown in Fig. 1 , are in accord with the published ones (Lin et al. 1997 ). The bars in Fig. 1 depict the powder XRD reflections calculated from the crystalline structure of AM-3 and AM-4. No extra peak is observed, indicating the high purity of both materials. Fig. 1 XRD diffractograms of titanosilicates AM-3 and AM-4. The bars depict reflections calculated from the crystal structure SEM (Fig. 2 ) reveals that both materials contain plate like crystals, with sizes ranging from ca. 0.5 to 1.4 µm (AM-3) and 1.7 to 1.3 µm (AM-4) and widths of 0.05–0.22 µm (AM-3) and 0.07–0.11 µm (AM-4). We have previously shown that both materials do not adsorb significant amounts of nitrogen, but they do adsorb water in relatively large amounts: the water adsorption isotherms of AM-3 and AM-4 are of type I with maximum water uptakes of 0.117 and 0.070 g/g solid , respectively (Lin et al. 1997 ). Fig. 2 SEM images of titanosilicates AM-3 (left) and AM-4 (right) Development of regression model equations The DoE described in Table 2 was performed for 1 and 6 h, and the removal (%) of the different REEs is presented in Tables 3 and 4 .\n Table 3 Removal (%) of Y, La, Ce, Pr, Nd, Eu, Gd, Tb, and Dy, at 1 and 6 h, using the sorbent AM-3, under the experimental conditions presented in Table 2 Experiment Removal (%) Y La Ce Pr Nd Eu Gd Tb Dy 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1 67 69 80 82 80 82 78 81 79 81 78 80 77 80 77 80 76 78 2 3 5 2 4 3 7 3 7 4 8 8 12 7 8 8 11 7 11 3 1 2 0 1 1 0 1 0 1 1 3 3 2 3 3 2 2 2 4 0 1 0 2 0 4 0 5 0 5 0 7 2 6 3 8 2 8 5 81 83 86 92 86 93 86 92 85 92 86 92 84 91 88 93 85 90 6 55 55 63 64 68 68 65 66 66 66 65 65 66 66 65 66 65 65 7 0 3 0 4 2 8 2 9 2 10 5 13 3 10 6 13 6 12 8 2 4 2 5 4 9 4 9 4 10 6 12 4 9 7 11 9 12 9 8 8 6 7 7 9 8 9 8 9 13 11 11 10 14 12 14 12 10 12 12 9 12 15 21 16 22 17 22 29 33 25 29 32 36 32 35 11 0 3 0 0 1 2 0 2 0 2 1 4 1 3 1 2 1 2 12 0 0 0 0 0 1 0 2 0 2 0 4 0 2 0 2 0 0 13 0 0 1 0 1 1 1 0 2 1 3 3 3 2 4 5 5 6 14 2 3 2 1 2 1 2 1 2 2 1 4 1 3 2 5 2 5 15 76 72 84 82 84 82 83 80 83 81 83 80 82 80 83 80 82 79 Table 4 Removal (%) of Y, La, Ce, Pr, Nd, Eu, Gd, Tb, and Dy, at 1 and 6 h, using the sorbent AM-4, under the experimental conditions presented in Table 2 Experiment Removal (%) Y La Ce Pr Nd Eu Gd Tb Dy 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1h 6h 1 44 54 47 56 47 57 48 57 47 57 47 57 46 57 47 58 46 57 2 63 69 70 77 74 81 74 80 72 79 75 80 72 78 74 80 73 79 3 10 56 12 53 11 64 10 66 9 65 9 70 7 65 8 69 8 69 4 21 48 24 57 26 62 26 61 23 59 28 62 26 59 27 61 25 59 5 82 85 81 83 81 83 81 83 81 83 81 83 81 83 85 87 82 84 6 42 58 53 58 44 56 53 58 54 58 54 59 55 60 54 60 57 63 7 53 71 60 79 64 84 64 83 63 82 65 84 62 81 64 83 63 82 8 59 70 65 77 71 83 71 82 70 81 73 83 70 81 72 82 71 81 9 41 91 85 92 67 90 85 92 83 91 78 89 78 89 75 89 82 90 10 63 86 82 85 86 84 81 84 81 85 66 84 61 85 43 85 82 85 11 26 26 2 2 0 0 1 1 1 1 0 0 0 0 0 0 2 2 12 3 10 4 13 7 24 9 27 7 25 10 31 6 23 9 26 9 24 13 72 71 73 72 72 70 72 71 71 70 72 71 71 71 71 71 72 71 14 81 85 75 81 74 79 75 80 73 79 73 79 71 77 72 78 73 78 15 19 41 42 65 39 43 43 64 43 65 44 63 45 63 46 61 49 68 For AM-3, the highest values of the removal percentage were achieved in the Experiment 5 (initial REE concentration of 5 µmol/L, pH 8 and titanosilicate dosage of 100 mg/L), with 81–88% REE removal after 1 h and 83–93% REE removal after 6 h. The maximum removal achieved by titanosilicate AM-4 for most of the elements was observed in Experiment 9 (initial REE concentration of 5 µmol/L, pH 6 and titanosilicate dosage of 180 mg/L), with removal percentages of 41% for Y, 67% for Ce and 75–83% for the other REEs after 1 h, and 90–92% REE removal after 6 h. These results indicate that AM-4 supports higher concentrations of REEs and lower pH in water than AM-3, which in turn performs better at higher pH. Additionally, for AM-3, extending the experimental duration did not yield any benefits in terms of percentage removal. This finding is particularly significant for industrial applications, as longer contact times result in increased energy consumption and labor costs, making shorter sorption processes more appealing. The removal rates observed in various experiments exhibited considerable variability based on the tested parameters (pH, sorbent dosage, and initial REE concentration), necessitating the use of Response Surface Methodology for experimental parameter optimization. Consequently, a quadratic model was fitted to the experimental values after obtaining the response data for each trial. The effects of variables (linear, quadratic, and combined effects) on the REE removal percentage, calculated with a 95% confidence level, are displayed in Figs. 3 and S1 for AM-3 and Figs. 4 and S2 for AM-4. In the Pareto chart, the variables pH, sorbent dosage, and initial REE concentration are denoted by the letters A, B, and C, respectively. Factors surpassing the red line significantly affect the studied response ( p -value < 0.05); variables depicted with green bars positively impact the response (it increases as the variable value rises), while red bars signify a negative impact (the response decreases as the variable value increases), and grey bars represent non-significant variables. The p-values obtained for the fitted models are provided in the Supplementary data (Tables S1 and S2 ). Fig. 3 Pareto chart displaying the effects of variables on the studied response (removal percentage of Y, La, and Ce) at 1 and 6 h for AM-3. In the figure, A represents the solution pH, B denotes the sorbent dosage (mg/L), and C signifies the initial concentration of REEs (µmol/L). Variables with values below the dashed line are not significant Fig. 4 Pareto chart displaying the effects of variables on the studied response (remova l% of Y, La, and Ce) at 1 and 6 h for AM-4. In the figure: A represents the solution pH, B denotes the sorbent dosage (mg/L), and C signifies the initial concentration of REEs (µmol/L). Variables with values below the dashed line are not significant Regarding the removal percentage achieved by titanosilicate AM-3 (Figs. 3 and S1 ), it is evident that the impact of variables remains unchanged with time and element, with only the “pH” factor being significant. The linear and quadratic effects of pH contribute positively, indicating that a higher solution pH leads to an increased REE removal percentage. Examining the Pareto charts for experiments using titanosilicate AM-4 (Figs. 4 and S2 ), the pattern of significant factors is markedly different compared to AM-3. Additionally, the variables affecting Y removal differ from those influencing the removal of other REEs. For Y, only one factor impacted removal: the interaction between “pH” and “initial concentration” was significant at 1 h, while the “sorbent dosage” factor was significant at 6 h, both displaying positive effects. For the other eight REEs studied, “sorbent dosage” is the most influential variable at both contact times, exhibiting a positive effect, followed by the interaction between “pH” and “sorbent dosage,” which has a negative effect. Other variables are either non-significant at 1 h and become significant at 6 h, or vice versa. For instance, the quadratic term of “pH” negatively affects the removal of Ce, Pr, Nd, and Eu only after 6 h. The quadratic term of “sorbent dosage” impacts the removal of Ce, Eu, Gd, and Tb after 1 h, and after 6 h, it also becomes significant for La, Pr, Nd, and Dy removal. Conversely, the interaction between “pH” and “initial concentration” affects the eight REEs after 1 h, while after 6 h, it is only significant for the removal of La, Gd, Tb, and Dy. Finally, the removal of La, Gd, Tb, and Dy is positively influenced by the interaction between “sorbent dosage” and “initial concentration” after 6 h. In this study, the pH is an important factor in the removal efficiency by AM-3 due to competition between H + and REE 3+ ions for the exchange of the extra-framework Na + cations. At low pH values, protonation of active sites on the sorbent occurs, inhibiting its ability to bind with REEs (Ali et al. 2011 ; Da Costa et al. 2021 ). For AM-4, the most important factor is the dose of sorbent. This is mainly due to the increase of mass leading to a higher number of binding sites available for sorption of REEs, improving the sorption efficiency (Ali et al. 2011 ). Based on the insights provided by the Pareto charts, only significant variables were incorporated to generate the reduced models. Tables 5 and 6 display the reduced models for AM-3 and AM-4 in terms of the real values of the independent variables. The coefficient of determination ( R 2 ) and the adjusted coefficient of determination ( R 2 adj ) indicate the quality of the fits between the experimental and calculated data. AM-3 exhibited high R 2 values (0.9324–0.9793) that were very close to the R 2 adj values (0.9212–0.9758), demonstrating a strong fit and robustness of the models. Consequently, these models can accurately predict the response. In comparison, the R 2 values for AM-4 functions of Y (0.3452–0.4539) and the other eight REEs (0.7820–0.9149) were lower than those for AM-3, indicating a less accurate estimation of the response. Furthermore, the discrepancies between the R 2 and R 2 adj values obtained for Y (0.1666–0.4119) and for the other eight REEs (0.6949–0.8297) undermine the robustness of the models.\n Table 5 Reduced models of the response studied and the respective R 2 and R 2 adjusted as function of the significant variables ( p -value < 0.05) for AM-3 1h 6h Y Response reduced model Removal (%) = 185.18 − 77.79 pH + 7.92 pH*pH Removal (%) = 183.54 − 76.54 pH + 7.79 pH*pH R 2 0.9619 0.9618 R 2 adj 0.9556 0.9555 La Response reduced model Removal (%) = 217.54 − 90.98 pH + 9.20 pH*pH Removal (%) = 204.14 − 86.30 pH + 8.85 pH*pH R 2 0.9768 0.9721 R 2 adj 0.9730 0.9674 Ce Response reduced model Removal (%) = 210.57 − 88.28 pH + 8.99 pH*pH Removal (%) = 182.32 − 78.03 pH + 8.17 pH*pH R 2 0.9793 0.9683 R 2 adj 0.9758 0.9630 Pr Response reduced model Removal (%) = 202.29 − 85.11 pH + 8.70 pH*pH Removal (%) = 171.75 − 74.00 pH + 7.81 pH*pH R 2 0.9731 0.9652 R 2 adj 0.9686 0.9594 Nd Response reduced model Removal (%) = 202.25 − 85.00 pH + 8.69 pH*pH Removal (%) = 173.57 − 74.34 pH + 7.83 pH*pH R 2 0.9741 0.9651 R 2 adj 0.9698 0.9592 Eu Response reduced model Removal (%) = 176.29 − 74.86 pH + 7.82 pH*pH Removal (%) = 153.61 − 65.76 pH + 7.06 pH*pH R 2 0.9462 0.9420 R 2 adj 0.9372 0.9323 Gd Response reduced model Removal (%) = 182.82 − 77.34 pH + 8.02 pH*pH Removal (%) = 169.68 − 72.16 pH + 7.61 pH*pH R 2 0.9600 0.9523 R 2 adj 0.9533 0.9444 Tb Response reduced model Removal (%) = 169.75 − 72.19 pH + 7.59 pH*pH Removal (%) = 153.96 − 65.96 pH + 7.08 pH*pH R 2 0.9385 0.9326 R 2 adj 0.9283 0.9214 Dy Response reduced model Removal (%) = 166.00 − 70.63 pH + 7.44 pH*pH Removal (%) = 153.64 − 65.49 pH + 7.00 pH*pH R 2 0.9392 0.9324 R 2 adj 0.9290 0.9212 Table 6 Reduced models of the response studied and the respective R 2 and R 2 adjusted as function of the significant variables (p-value < 0.05) for AM-4 1h 6h Y Response reduced model Removal (%) = 172.39 − 19.25 pH − 42.13 concentration + 6.38 pH*concentration Removal (%) = 35.62 + 0.26 mass R 2 0.3452 0.4539 R 2 adj 0.1666 0.4119 La Response reduced model Removal (%) = 32.82 − 0.69 pH + 1.05 mass − 36.50 concentration − 0.12 pH*mass + 5.56 pH*concentration Removal (%) = 10.86 + 6.06 pH + 1.19 mass − 26.41 concentration − 0.0023 mass*mass − 0.11 pH*mass + 2.75 pH*concentration + 0.080 mass*concentration R 2 0.8257 0.9149 R 2 adj 0.7288 0.8297 Ce Response reduced model Removal (%) = 29.76 − 2.19 pH + 1.57 mass − 40.62 concentration − 0.0026 mass*mass − 0.13 pH*mass + 6.12 pH*concentration Removal (%) = − 226.91 + 68.83 pH + 1.76 mass − 4.39 pH*pH − 0.0033 mass*mass − 0.15 pH*mass R 2 0.8972 0.8604 R 2 adj 0.8201 0.7828 Pr Response reduced model Removal (%) = 32.49 − 0.34 pH + 1.05 mass − 36.69 concentration − 0.12 pH*mass + 5.62 pH*concentration Removal (%) = − 181.43 + 55.88 pH + 1.49 mass − 3.49 pH*pH − 0.0027 mass*mass − 0.11 pH*mass R 2 0.8077 0.8639 R 2 adj 0.7009 0.7882 Nd Response reduced model Removal (%) = 31.74 − 0.38 pH + 1.03 mass − 36.44 concentration − 0.12 pH*mass + 5.56 pH*concentration Removal (%) = − 175.49 + 53.51 pH + 1.47 mass − 3.30 pH*pH − 0.0027 mass*mass − 0.11 pH*mass R 2 0.8070 0.8660 R 2 adj 0.6998 0.7915 Eu Response reduced model Removal (%) = 16.48 − 0.50 pH + 1.55 mass − 36.38 concentration − 0.0028 mass*mass − 0.12 pH*mass + 5.63 pH*concentration Removal (%) = − 184.42 + 57.13 pH + 1.52 mass − 3.59 pH*pH − 0.0029 mass*mass − 0.11 pH*mass R 2 0.8826 0.8711 R 2 adj 0.7946 0.7994 Gd Response reduced model Removal (%) = 15.98 − 0.75 pH + 1.52 mass − 36.31 concentration − 0.0028 mass*mass − 0.11 pH*mass + 5.62 pH*concentration Removal (%) = − 65.89 + 14.22 pH + 1.48 mass − 0.0027 mass*mass − 0.11 pH*mass R 2 0.8810 0.7908 R 2 adj 0.7918 0.7071 Tb Response reduced model Removal (%) = 14.32 − 1.16 pH + 1.59 mass − 36.37 concentration − 0.0033 mass*mass − 0.11 pH*mass + 5.88 pH*concentration Removal (%) = − 67.86 + 14.72 pH + 1.55 mass − 0.0029 mass*mass − 0.12 pH*mass R 2 0.8511 0.7820 R 2 adj 0.7394 0.6949 Dy Response reduced model Removal (%) = 38.12 − 1.13 pH + 0.96 mass − 36.81 concentration − 0.11 pH*mass + 5.56 pH*concentration Removal (%) = − 60.49 + 13.41 pH + 1.44 mass − 0.0028 mass*mass − 0.10 pH*mass R 2 0.8233 0.8118 R 2 adj 0.7251 0.7365 REE removal from water and optimization of operational parameters by Response Surface Methodology Figures 5 and S3 show the 3D response surface plots for AM-3 at 1 and 6 h. The interactive effects of pH and dose of sorbent on the removal of the different REEs at the constant initial concentration of 3 μmol/L show that, independently of the dose of sorbent used, higher removal percentages are achieved at higher pH values. The interactive effects of pH and initial concentration were similar to the effects of pH and dose of sorbent and, therefore, are not shown. Fig. 5 3-D response surfaces obtained with the reduced models during 1 and 6 h of exposure of Y, La, and Ce to AM-3. The figures on left present the studied response after 1 h of contact, while the figures on right present the response after 6 h, as function of the dose of sorbent and pH Figures 6 , S4 , and S5 show the 3D response surface plots for AM-4, at 1 and 6 h. The interactive effects of pH and dose of sorbent on the removal at the constant initial concentration of 3 μmol/L show that the removal is more affected by the dose of sorbent than by the pH, and this effect increased with time. Concerning the interactive effects of pH and initial concentration (Figure S5 ), the sharp of curves demonstrate that the effect of initial concentration decreases with time. Fig. 6 3-D response surfaces obtained with the reduced models during 1 and 6 h of exposure of Y, La, and Ce to AM-4. The figures on left present the studied response after 1 h of contact, while the figures on right present the response after 6 h, as function of the dose of sorbent and pH One of the main goals of this study was to optimize the operational parameters to maximize the removal of REEs from water. The removals obtained in the experiments generated by Box-Behnken design revealed that with AM-3 there is no significant difference in the removal percentages after 1 and 6 h; likewise, the effects of independent variables do not change with time. Thus, since the reduction of sorption time is always preferable, the optimized removal time was 1 h. In contrast, the better performance of AM-4 at 6 h justifies a longer of the sorption time. The values of the optimized variables for the REE removal by AM-3 after 1 h and by AM-4 after 6 h are presented in Table 7 . The optimized values for the removal by AM-3 are: pH 8, dose of sorbent 180 mg/L and initial concentration 5 μmol/L, for a 1 h exposure, with expected removal varying between 70 and 80%. For AM-4, the optimal conditions differ only in the pH and time of exposure: pH 4.6, dose of sorbent 180 mg/L and initial concentration 5 μmol/L, for a 6 h exposure, with expected removal varying between 82 and 89%.\n Table 7 Optimum conditions determined for REE removal with AM-3 (at 1 h) and AM-4 (at 6 h) and their removal (%) in different matrices (in natural mineral water and water with salinity 10 and with salinity 30) Sorbent Time (h) REEs Optimum conditions % removal DoE % removal experimental pH Dose of sorbent (mg/L) Initial concentration (μmol/L) Salinity 0 Salinity 10 Salinity 30 AM-3 1 Y 8 180 5 70 84 75 71 La 78 93 91 89 Ce 80 93 93 90 Pr 78 93 92 90 Nd 78 93 92 89 Eu 78 93 92 89 Gd 77 92 90 88 Tb 78 91 89 88 Dy 77 90 86 84 AM-4 6 Y 4.6 180 5 82 92 24 16 La 89 93 14 11 Ce 88 93 27 16 Pr 89 93 30 15 Nd 88 93 29 15 Eu 88 93 41 23 Gd 87 93 33 18 Tb 87 93 41 23 Dy 87 93 43 24 Validation of the optimum conditions and the effect of salinity on REE removal The removal percentage predictions for REEs, as determined by the DOE, were experimentally validated. To achieve this, a test was conducted under the optimal conditions obtained through the DoE (Table 7 ) for AM-3 at 1 h and for AM-4 at 6 h. The removal percentage results for the different REEs, as determined by DoE and obtained in the experimental test, are presented in Table 7 . The experimentally obtained removal percentages using the AM-3 sorbent for 1 h (84–93%) were higher than those predicted by the DoE (70–80%). For AM-4, the removal percentages predicted by the DoE (82–89%) were still lower than those experimentally achieved after 6 h (92–93%), but closer than in the case of AM-3. The model for AM-3 underestimated the removal obtained, while the model for AM-4 predicted the removal with an error of less than 7% for all elements, except for Y (11%). The disparities observed between Y and the other REEs, in terms of removal percentage or the impact of the studied variables on its removal, could be attributed to the fact that Y is a transition metal and not part of the lanthanide group, which may imply distinct behaviors. According to Jacinto et al. ( 2018 ), the smaller ionic size of Y compared to other REEs appears to hinder Y’s approach to surface binding sites, due to the larger ionic size of the remaining REEs in solution. The viability of the sorption process in more complex matrices is a crucial parameter for industry, as industrial effluents are highly complex. The removal efficiencies of AM-3 and AM-4 were assessed in real aqueous solutions with salinity 10 (common salinity in coastal and transitional systems) and salinity 30 (average salinity of seawater). The removal percentage results for the different REEs are presented in Table 7 . In general, REE removal percentages using AM-3 were high in water with salinity 10 (75–93%) and salinity 30 (71–90%). For AM-4, the removal percentages in water with salinity 10 ranged between 14 and 43%, decreasing further in water with salinity 30 (11–24%). It is evident that titanosilicate AM-3 possesses strong potential for REE removal in intermediate salinity water (salinity 10) and in water with seawater salinity (salinity 30). Conversely, the REE removal efficiency of AM-4 significantly declined with increasing salinity, suggesting competition for binding sites with other ions present in the solution, such as Na + , Ca 2+ , and Mg 2+ (which are the dominant cations in this matrix). These differences between the two titanosilicates are likely related to their structure, as AM-3 is a nanoporous titanosilicate and AM-4 is a layered titanosilicate. According to Oleksiienko et al. ( 2017 ), AM-4 exhibits low affinity for alkali cations (Na + ) but high affinity for alkaline earth metals (Ca 2+ and Mg 2+ ) in neutral and alkaline media. This study demonstrates that, in terms of applicability in aquatic systems or natural waters, the use of AM-3 may be more advantageous compared to AM-4."
} | 7,593 |
32963345 | PMC7852563 | pmc | 6,718 | {
"abstract": "Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats.",
"introduction": "Introduction For billions of years, microbes and their metabolic activities have been shaping Earth’s physical, chemical, and mineralogical landscape. Although microbes comprise the majority of the planet’s biomass, most microbial species and their genomes remain uncharacterized [ 1 , 2 ]. These unknown aspects of microbial life, colloquially called “microbial dark matter” [ 1 , 3 ] represent a fundamental impediment to microbial ecology, as microbe-dominated ecosystems cannot be reliably characterized without a thorough understanding of the roles microbes and their gene products play in ecosystem processes. Currently, most of our knowledge of the microbial world is skewed by a few taxa that lend themselves to cultivation and genetic manipulation. Of the cultivated species, 88% are derived from just four phyla Proteobacteria, Firmicutes, Actinobacteria , and Bacteroidetes [ 1 ]. The uncultured and unsequenced microbial majority on Earth likely represents major evolutionary lines of descent within the tree of life and is expected to have played key roles in ecosystem formation, evolution, and function. Without a mechanistic approach to characterize the roles of these currently Unknown taxa in the ecosystem, we will not have a full understanding of how these organisms impact their neighbors, environment, or life as a whole. Recent efforts to provide insight into the uncharacterized and uncultured majority through next-generation sequencing technologies have significantly expanded the microbial tree of life [ 4 – 6 ]. Yet, despite the recent explosion of nucleic acid sequencing of microbial environments, much remains to be learned [ 7 , 8 ]. Truly understanding the ecological roles of Unknown taxa within communities requires a more comprehensive assessment of why Unknown taxa persist, or with whom they interact on a global scale. More importantly, it is unclear whether the presence of these unknown organisms confers a value that is not already provided by other, well-characterized microbes within the same ecosystem. To more fully understand microbial life, particularly the contributions of Unknown taxa, it is first critical to understand the connectivity and structure of microbial ecosystems. Networks have long been used as analytical tools to better understand species’ roles and interactions [ 9 – 14 ]. By mathematically modeling a microbial community as a network, where nodes are different species and edges represent their relationships [ 9 , 15 ], researchers can depict species interactions and study the structure of the environmental system. Network metrics, such as hub score, betweenness centrality, closeness centrality, and degree centrality [ 9 , 16 , 17 ], can be used to quantitatively describe these communities and pinpoint the most important taxa of a given environment, thereby providing essential clues about how specific taxa or gene products may contribute to ecosystem functioning [ 18 ]. Degree centrality is the number of edges in a network that connects one node (in our case operational taxonomic unit (OTU) or taxon) and is a measure of the level at which an OTU co-occurs, i.e., is present in the same samples and at similar levels, with other OTUs [ 19 ]. Betweenness centrality measures the extent to which a node lies on paths between other nodes and can be used to identify which OTUs communicate most with other members of the community network, thereby revealing which taxa are necessary for the co-occurrence with nearby taxa [ 19 , 20 ]. Closeness centrality measures how far a node is to all other nodes and can be used to find the most central taxa of a given community network [ 9 ]. Finally, members of a network that have both high degree and betweenness centrality are typically the most connected taxa within the community and are considered “hubs.” Hubs may have ecological relevance to the community as their removal affects the largest number of connections and paths, causing the highest impact on the connectivity of the network [ 19 , 21 ]. Although many advances have been made in microbial ecology using network-driven approaches [ 22 – 37 ] few, if any, revolve around Unknown taxa. Most network analyses focus on the role of known species, usually excluding any taxonomically unassigned or ambiguous taxa in early filtering steps. If included, any unassigned taxa are only briefly mentioned in connection to their interaction with more characterized, abundant phyla, leaving the role of unknown and uncultured taxa largely unexplored. Here, we present a network-based approach to assess the ecological relevance of Unknown taxa within a targeted community. To provide both a broad and accurate interpretation of results, a comprehensive dataset encompassing four different aquatic environments was compiled. Networks were constructed with and without the Unknown taxa and changes in the network metrics degree, betweenness, closeness, and hub score were evaluated. In this manner, the contribution Unknown taxa provide to the overall community structure was systematically evaluated and compared across taxonomic levels. To identify the most ecologically prominent components in each environment, the hub score of all nodes was calculated, and the frequency of Unknowns among the top hubs was noted. These hubs were then subsequently removed from the networks to assess their fragmentation. Thus, taxa with the highest hub scores were considered ecologically relevant and critical actors of their networks by their high connectedness and presence. These hub network analyses serve as signatures of the potential importance of the Unknown taxa and provide a means to prioritize Unknown hubs for future characterization efforts. We demonstrate one of several possible applications of this approach, using particularly relevant hub Unknowns as probes to detect novel adaptation-related genes within metagenome scaffolds. The application of network theory to identify and prioritize key unknown microbial members may thereby help shed light on potential adaptation mechanisms of successful Unknown taxa while enabling a more comprehensive understanding of ecosystem structure under a diverse range of environmental conditions.",
"discussion": "Discussion Microbial communities are complex and dynamic, however, with the vast majority of Earth’s microbes yet to be cultured or characterized, our understanding of these systems is likely limited or skewed by this large gap-in-knowledge. To more fully understand the impact of “microbial dark matter” on ecosystem structure and function, we have developed a network theory-based approach to assess the relevance of the uncultured and Unknown taxa within their microbial communities. Implementation of this bioinformatic pipeline demonstrated that: (1) specific patterns of the microbial network could be identified and compared for targeted ecosystems across taxonomic levels; (2) the comparison of centrality metrics between networks including and excluding the Unknown taxa is an effective strategy to reveal the relevance of these organisms within their communities, (3) Unknown taxa, just as Known taxa, can act as key hubs in ecosystem structure due to their high prevalence and strong central connections; and (4) network metrics can be used to prioritize Unknown taxa for downstream analysis by using the 16S rRNA gene sequences of top-ranked hubs as probes to screen publicly available metagenomic datasets for the identification of ecologically relevant gene functions. Harnessing the power of networks to elucidate “microbial dark matter” No matter the environment, previous research has shown that “microbial dark matter” represents a significant limitation to the exploration of the global microbiome [ 1 – 3 , 47 – 53 ]. To address and overcome this challenge, we developed a combined bioinformatics pipeline and network theory approach that was applied to a large, geographically diverse 16S rRNA dataset of four extreme aquatic environments to determine the ecological relevance of the unknown organisms in these communities. Although correlation-based microbial networks cannot infer the nature of ecological relationships, such as syntrophy or competition, they are indicative of social interactions within the community and can serve as important focal points for downstream analyses [ 54 ]. Our analysis clearly showed that the unclassified and uncultured taxa were prevalent and represented a significant proportion of the microbial diversity in all ecosystems examined, and therefore, should not be overlooked when examining community dynamics. Both Known and Unknown OTUs were found to be environment-specific, agreeing with previous reports of habitat-specific, niche-partitioning species of hypersaline lakes [ 55 ], deep sea vent communities [ 56 , 57 ], and polar lakes [ 58 , 59 ]. Furthermore, our work extended beyond simple composition analysis and demonstrated the consistent and significant contribution of Unknown OTUs to microbial community structure. By using network metrics to study these four extreme environment networks, additional insights into the ecological relevance of these unknown organisms could be gained. The Unknown OTUs positively contributed to betweenness and degree centralities (i.e., denoting microbial interactions). More importantly, the exclusion of Unknown taxa was more detrimental to the overall network than the exclusion of random Known components, as more central node connections (e.g., ecotype interactions) were lost, causing a greater network fragmentation. Moreover, Unknown taxa established co-occurrence relationships with themselves, suggesting that they might be phylogenetically related, as is the case for Known taxa [ 40 , 41 ]. The results presented here reveal that Unknown taxa are frequently key members of extremophilic microbial ecosystems and strongly advocate for the inclusion of Unknown taxa in any metagenomics or amplicon composition and interaction studies, as key biological interactions may remain undiscovered otherwise. Networks can prioritize the most ecologically relevant Unknown taxa in a community Unclassified microbial taxa often occurred as top hubs across all examined environments. Since hubs, by definition, significantly contribute to network structure and cohesiveness, these unknown microbes can be considered keystone taxa, most likely playing vital and meaningful roles within key ecosystem processes in these habitats. Moreover, hub positions indicated that these Unknown taxa were prevalent in their environments and co-varied with many other Known taxa, and hence can be considered successful components of their microbial communities. During the elaboration of the network analyses described in this work, a new version (138) of the SILVA database was published. Re-running our pipeline with SILVA v138, and latest DADA2 (version 1.14.1)/DECIPHER (version 2.14.0) software [ 60 , 61 ] returned similar results as presented here (not shown), confirming our conclusions held through software updates. These results support the value of our analysis and suggest that this approach could be used to identify the highly interacting OTU components of any microbial community. The frequent dominance of Unknown taxa as top-scoring hubs stresses the need for further exploration and functional characterization of these novel species and also offers new tools for prioritizing novel taxa for follow-up studies. Filling in gaps-in-knowledge of ecosystem functioning with hubs of Unknown taxa Understanding the emergent properties of an ecosystem, i.e., those taxa, genes, and functions that are important for a particular niche, can have a big impact on our understanding of that environment. Using amplicon-based approaches to address these questions, however, can be limited. For example, amplicon-focused tools, such as PICRUSt [ 62 ] and Tax4Fun [ 63 ], can retroactively predict gene function from 16S rRNA gene data [ 64 ] under an assumption that taxonomy and function are well conserved. However, this approach might only work for major pathways and more importantly, these tools require reference genomes to be present and well-annotated for each ecotype, and therefore, cannot be applied to novel, uncultured organisms. Here, we envisioned an alternative approach to probe metagenomics databases with 16S rRNA sequences prioritized from the top Unknown hubs of a given environment and used those sequences to investigate gene content of associated scaffold hits. While this is by no means a comprehensive characterization of the functional potential of unknown organisms in extreme environments, which would require a targeted experimental study, our approach leverages the wealth of metagenomics data currently available within public databases to gain novel insights on microbial function. These resources encompass hundreds of terabytes of data [ 7 , 65 ] and represents untapped sources of valuable information that can, and should be, exploited both for fundamental science and for potential biotechnological applications. The successful retrieval of unknown genes, potentially involved in environmental stress responses, from uncultured and unclassified organisms, indicated that this network and hub identification approach is an effective strategy to use prioritized OTUs for direct data-mining efforts. Notably, in this proof-of-concept effort, just a few top hub score OTUs within the hot spring networks were used to screen a fraction of the available metagenomics information, still recovering a substantial number of candidate genes. With one specific example, we illustrated the power of this methodology to unveil interesting gene functions. Supported by sufficient computational resources, up-scaling of this concept holds the potential for the large-scale discovery of novel gene functions and pathways, further unraveling roles that these Unknown taxa may play within their respective ecosystems. In summary, this approach has the potential to be extended to other aspects of the environmental microbiome, including, but not limited to, the archaeal and eukaryotic taxa, as well as other multi-omic platforms (e.g., metaproteomics, metabolomics, and metatranscriptomics). As reference databases continue to grow, taxa and gene co-occurrence network analyses and measurements can also be used to evaluate changes in ecosystem structure over different temporal and spatial scales. The application of this strategy to a variety of microbial ecosystems from all environments could be used to more fully understand those features of the hidden microbial world that are critical for environment-specific or global attributes of microbial ecosystems."
} | 4,048 |
25944863 | PMC4436056 | pmc | 6,719 | {
"abstract": "ABSTRACT Although plasmids and other episomes are recognized as key players in horizontal gene transfer among microbes, their diversity and dynamics among ecologically structured host populations in the wild remain poorly understood. Here, we show that natural populations of marine Vibrionaceae bacteria host large numbers of families of episomes, consisting of plasmids and a surprisingly high fraction of plasmid-like temperate phages. Episomes are unevenly distributed among host populations, and contrary to the notion that high-density communities in biofilms act as hot spots of gene transfer, we identified a strong bias for episomes to occur in free-living as opposed to particle-attached cells. Mapping of episomal families onto host phylogeny shows that, with the exception of all phage and a few plasmid families, most are of recent evolutionary origin and appear to have spread rapidly by horizontal transfer. Such high eco-evolutionary turnover is particularly surprising for plasmids that are, based on previously suggested categorization, putatively nontransmissible, indicating that this type of plasmid is indeed frequently transferred by currently unknown mechanisms. Finally, analysis of recent gene transfer among plasmids reveals a network of extensive exchange connecting nearly all episomes. Genes functioning in plasmid transfer and maintenance are frequently exchanged, suggesting that plasmids can be rapidly transformed from one category to another. The broad distribution of episomes among distantly related hosts and the observed promiscuous recombination patterns show how episomes can offer their hosts rapid assembly and dissemination of novel functions.",
"conclusion": "Conclusions. Overall, our data reveal surprising results about the diversity and distribution of episomes among Vibrionaceae populations. First, contrary to the expectation that, due to the requirement of cell-to-cell contact for transmission, plasmids should be preferentially associated with isolates recovered from surfaces, microcolonies, or biofilms ( 23 , 24 ), we show that they are significantly enriched in free-living, planktonic cells. Second, our data suggest that plasmids previously categorized as nontransmissible are subject to high evolutionary turnover and transfer frequently among populations. It is therefore likely that a currently unrecognized transfer mechanism is at work. Candidates are transformation, conjugation by cointegration into a conjugative element, and packaging into phage or membrane vesicles. Regardless, we propose that the name “nontransmissible” should be abandoned. Third, the high incidence of putative temperate phages that appear to propagate as plasmids is unexpected. Although phages have previously been described in plasmid metagenomes ( 8 ), there was no indication that these might be temperate phages, as suggested here by the stable propagation in our isolates. Such plasmid-like temperate phages have been previously described in only a very small number of studies. Accordingly, the phages detected here appear novel. Their prevalence suggests a previously unanticipated important role in the marine environment. Finally, analysis of gene transfer among plasmids indicates that genes involved in plasmid maintenance and transfer are a frequently exchanged, rapidly changing categorization of plasmids. This exchange also offers host strains a rich supply of external genetic materials that may allow the assembly of different functions on a backbone of plasmid functions perhaps adapted to specific host populations.",
"introduction": "INTRODUCTION Most studies on the diversity of plasmids and other episomes have focused on their role as major conduits for the spread of resistance and virulence genes in pathogenic bacteria ( 1 , 2 ). Only recently have whole genome sequencing of microbial hosts and direct extraction of episomes from environmental samples provided a more unbiased glimpse at their large diversity ( 3 – 8 ). This has shown that, although plasmids are best known for their ability to self-transfer among hosts ( 9 ), such conjugative plasmids have recently been suggested to be relatively rare in Proteobacteria ( 3 ). Most plasmids have been categorized as mobilizable or nontransmissible since they contain only genes that enable them to hitchhike with a conjugative plasmid or have no recognizable transfer function ( 3 ). Much remains, however, to be learned about the ecological and evolutionary dynamics of these different types of plasmids in the wild, such as their host range, nucleotide and gene content variation, and frequency and persistence within host populations. Even less well studied are extrachromosomal temperate phages that replicate as plasmid-like structures during the lysogenic phase of their life cycle. Examples of such phage episomes are some Tectiviridae , which have been found as linear plasmids in Bacillus species ( 10 ), and phage N15, which is a relative of phage lambda ( 11 ). Interestingly, the role of these phages in the spread of antibiotic resistance in Escherichia coli is currently being reevaluated, since a P1-like phage was found to carry an extended-spectrum β-lactamase ( 12 ) and a large-scale analysis of the episome content of antibiotic-resistant strains revealed several extrachromosomal prophages ( 13 ). Because plasmid and phage episomes play roles as molecular symbionts or parasites ( 14 ) and can mediate horizontal gene exchange ( 15 ), their biology must ultimately be studied in the context of the host populations they invade; however, this has remained difficult due to the dearth of suitable model systems of ecologically and genotypically well-constrained bacterial populations. Here, we take a population-genomic approach to determine carriage of different types of episomes in a recently established model for ecologically and genetically cohesive bacterial populations, asking whether different episomal types (i) are associated primarily to host phylogeny or ecology, (ii) show evidence for distinct transfer (and loss) patterns, and (iii) display different microevolutionary patterns. We use marine bacteria of the family Vibrionaceae as our model for environmentally differentiated host populations. These have previously been identified as genotypic clusters with characteristic distribution among environmental samples from the same geographic location, suggesting that they partition resources in the coastal ocean by differential occurrence among the free-living and associated (with suspended organic particles and zooplankton) fractions of bacterioplankton ( 16 – 18 ). Many of these populations do, however, also cooccur on the surfaces and in the guts of filter-feeding and other marine animals ( 19 ), providing opportunity for transfer of episomes via occasional contact. Finally, recent analysis of recombination has indicated that these ecological populations display cohesive behavior in terms of gene flow, making it possible for adaptive genes to spread in a population-specific manner ( 20 , 21 ). Because of these properties, these clusters are hypothesized to represent natural populations and provide a platform to study the diversity and dynamics of episomes. To explore the diversity of episomes within host populations, we screened a large collection of ecologically characterized Vibrionaceae isolates obtained from the coastal ocean in the spring and fall of 2006 ( 16 ). We aimed at comprehensively sampling and sequencing all detectable episomes of different sizes to obtain a picture of their diversity as unbiased as possible. Episomes were analyzed in a comparative genomic framework, integrating this analysis with both phylogenetic and habitat information of the bacterial populations in order to identify differential associations and dynamics.",
"discussion": "RESULTS AND DISCUSSION Detection and classification of episomes. We screened 660 Vibrionaceae isolates for the presence of episomes using multiple gel electrophoretic assays to resolve DNA of different sizes (see Materials and Methods). This identified 140 DNA bands distributed across 101 of the isolates and varying in size between 1 and 200 kbp. To further investigate these putative episomes, we excised all bands from gels and determined their sequence by the Illumina and 454 technologies. Although in many cases assembly produced single and frequently circular episomes (see Table S1 in the supplemental material), there were instances where several contigs resulted from a single band on electrophoresis gels. Because this may indicate incomplete assembly of a single episome or comigration of multiple, similarly sized episomes, we used additional information contained in the data to differentiate these possibilities. To test for multiple episomes per band, we developed a bioinformatics pipeline that considered whether (i) the combined length of the contigs considerably exceeded band size, (ii) some of the contigs had high similarity to episomes in other Vibrionaceae isolates, and (iii) coexisting contigs displayed substantially different coverage (see Materials and Methods). This method identified 187 putative episomes (here called “episomes” for simplicity). To classify these episomes into families, we (i) calculated pairwise similarity values by comparison of the nucleotide similarity among shared proteins (orthologs) normalized by the number of proteins of the larger of the two episomes and (ii) established clusters of episomes of high similarity using the orthoMCL ( 22 ) graph clustering algorithm (see Materials and Methods). This analysis enabled exploration of the episome eco-evolutionary dynamics among host populations. Association with host populations and lifestyle. Comparison of episome incidence across the phylogeny of the Vibrionaceae hosts shows that (i) they are abundant in a few populations but relatively sparse in most ( Fig. 1A ) and (ii) their presence is correlated to host lifestyle across populations ( Fig. 1B ). For example, episomes were not detected in population no. 5 ( Vibrio sp. F5), 7 ( Vibrio logei ), 9 ( Vibrio breoganii ), and 10 ( Vibrio sp. F10), whereas episomes were present in all isolates of population no. 6 ( Vibrio sp. F6) and 60% of the isolates in population no. 14 ( Vibrio kanaloae ). Whether these distribution differences are due to various degrees of selection for or against episomes within different populations, or due to greater transmission efficiency among some populations, is difficult to determine; however, a strong association of episomes with host lifestyle provides additional information. The Vibrionaceae populations display various degrees of free-living or associated existence (e.g., with organic particles, zooplankton). This is expressed in our data as the presence in one of four sequential size fractions, where the large-size fractions contain microbes attached to particles or organisms while the smallest-size fraction contains only unattached, free-living cells ( 16 ) ( Fig. 1A ). We tested whether episomes were enriched in one or more size fractions by calculating the phylogenetic correlation between episome carriage and the association to each of the size fractions (see Materials and Methods). Our approach controls for spurious correlations caused by phylogenetic clustering and calculates confidence intervals based on 100 bootstrap trees of the hsp60 gene marker used to demarcate Vibrio populations. Surprisingly, this analysis showed episome-positive strains to be significantly and strongly biased for the <1-µm size fraction, corresponding to occurrence as free-living cells ( Fig. 1B ). This association counters the previous suggestion that particle-associated bacteria, which live in diverse and dense communities, are more prone to acquire mobile elements ( 23 , 24 ). Acquisition of episomes could happen in animal guts within which most of the populations have the potential to encounter each other ( 19 ); however, it seems likely that the high incidence of episomes in free-living cells reflects stability within the host and/or environmental selection rather than high transmission. FIG 1 Distribution of episomes on the Vibrionaceae phylogeny and relation to environmental metadata. (A) Phylogeny based on the hsp60 protein-coding gene. Vibrio genotypes were isolated from size-fractionated seawater, and colored rings indicate the corresponding size fraction for each isolate (fraction labels in panel B). Dark bars indicate the presence of at least 1 episome. Populations boundaries are indicated by shaded areas, and the closest named species for each population are as follows: 1, Enterovibrio calviensis ; 2, Enterovibrio norvegicus ; 3, Vibrio ordalii ; 4, Vibrio rumoiensis -like; 5, Vibrio sp. F5; 6, Vibrio sp. F6; 7, Vibrio logei ; 8, Vibrio fischeri ; 9, Vibrio breoganii ; 10, Vibrio sp. F10; 11, Vibrio splendidus cluster 1; 12, Vibrio sp. F13; 13, Vibrio sp. nov.; 14, Vibrio kanaloae ; 15, Vibrio cyclitrophicus ; 16 and 17, Vibrio tasmaniensis ; and 18 to 25, Vibrio splendidus . Taxonomic assignments are as in reference 67 with the exception of population no. 12 and 13, which have been reassigned based on recent genomic comparisons. (B) Phylogenetic correlation between size fractions and presence of episomes. We calculate correlations on the phylogeny using a modified version of the phylogenetic contrast method ( 54 ), which allows us to estimate evolutionary linkage between traits (e.g., having an episome and association to one of the size fractions). The correlations are shown as frequency distributions because of the uncertainty in phylogenetic structure. Looking at the position of the distributions on the horizontal axis, we observe that episomes are strongly biased to occur in the free-living lifestyle (occurrence in the smallest-size fraction) and less in the large-size fractions. Episome categories. Sequence annotation identified putative phages and plasmids as the two main episomal categories within the Vibrionaceae populations (see Table S1 in the supplemental material). The first was present at a surprisingly high level (22 of 187 episomes) and consisted primarily of two families when a cutoff of 70% sequence similarity was used to define episomal families. These two have genome sizes ~20 and 40 kbp (see Table S1 ), with the smaller representing the most numerous episomal family ( Fig. 2 ). Other phage-like episomes were found only once, including one ~80 kbp and four <14 kbp in size. All of these putative phages appear novel, since none were closely related to known phages. Moreover, because of their propagation within the cells during culturing, they presumably represent temperate phages that replicate in a plasmid-like fashion and may not integrate into the host genome, since we found evidence of neither integrase genes nor related integrated phages in ~80 Vibrionaceae genomes available from the same collection ( 25 ). High proportions of phage-like elements have also been reported from recent plasmid metagenomic studies ( 8 ). Such high prevalence suggests an alternative interpretation to the observation of extrachromosomal phage sequences in metagenomic analysis of microbial communities in the ocean. These have been suggested to be lytic phages caught in the act of infecting cells ( 26 ) but might, in at least some cases, be plasmid-like lysogens. FIG 2 Episomal family age versus size. Average percentage identities are calculated as a proxy for episome family age and plotted against the size of the element in base pairs. The size of the points indicates the number of members in each episome family, which ranges from 2 to 13. Colors indicate episome classification. The analysis shows that most episome families, irrespective of size, are evolutionarily young (little or no DNA divergence). Plasmids, the other major category, comprised the majority of episomes and could be divided into conjugative, mobilizable, and nontransmissible according to a previously proposed scheme based on the presence/absence of signature genes ( 3 ) (see Fig. S1A and Table S1 in the supplemental material). Twenty-four episomes were judged to be conjugative plasmids since they contained at least 5 key genes of a type IV secretion system (T4SS), which, in combination with a relaxase, is necessary for self-transmission ( 27 ). These plasmids are the largest (average of ~60 kbp; see Fig. S1 ) and carry a high density of genes. Mobilizable plasmids, on the other hand, encode only a relaxase and hence presumably require a T4SS to act in trans for mobilization, most likely from a cooccurring conjugative plasmid ( 3 ). The 38 plasmids categorized as mobilizable encode, with few exceptions, only relatively few open reading frames (ORFs) and had similar, small average sizes (~11 kbp) (see Fig. S1A ). We also detected 103 plasmids lacking relaxases and T4SS and thus classified as putatively nontransmissible. The means of transmission of these elements are usually unclear even though they constitute the majority of the known plasmids in Proteobacteria ( 3 ). Nontransmissible plasmids displayed large size variation, from a few to over 100 kbp (with an average of ~20 kbp; see Fig. S1A ), and were, with 62%, the dominant plasmid category, while conjugative and mobilizable plasmids occurred at 15% and 23%, respectively. This frequency distribution is fairly similar to the proteobacterial average, which is ~20%, 30%, and 50% for conjugative, mobilizable, and nontransmissible plasmids, respectively ( 3 ). Genetic diversity. The episomes detected in this study carry a diversity of genes, albeit with 43% (2,043 ORFs), the largest portion are hypotheticals when annotated using both RAST ( 28 ) and the ACLAME database ( 29 ) (see Materials and Methods). This is consistent with the notion that mobile elements are enriched in genes with poorly understood function ( 30 , 31 ). The most important category of known functions is membrane transport, with 296, 70, and 10 genes annotated as members of T4SS, type 6 secretion systems (T6SS), and ABC transporters, respectively. As mentioned above, T4SS is most likely involved in conjugative transfer, and of the 27 T4SS detected, 19 were type F, 6 type T, and 2 type G. Conjugation systems of the F-type have thin, flexible pili that allow high frequency of conjugation in liquid media ( 32 ), while type T pili are rigid and are thought to perform better on surfaces ( 33 ), providing some indirect support for the biased occurrence (and potential transfer) of episomes among free-living hosts ( Fig. 1B ). On the other hand, T6SS can inject protein effectors into bacterial and eukaryotic cells and hence likely play a role in predation, pathogenesis, or predation defense ( 34 ). Finally, ABC transporters can catalyze translocation of a variety of molecules, including proteins, metabolites, and metals ( 35 , 36 ). A further 178 proteins are involved in functions ascribed to plasmid maintenance, including 50 resolvases, 29 replicases, 91 partitioning systems, 41 toxin-antitoxin systems, and 17 restriction-modification systems. The large number of partitioning systems detected may indicate that more than half of the plasmids might be low copy number, since partitioning mechanisms are often absent from high-copy-number plasmids ( 37 ). As in previous studies ( 15 , 38 , 39 ), general annotations indicate functions with possible host benefit and highlight the potential role of plasmids in horizontal transfer of a wide variety of genes. Among these are genes involved in the metabolism of amino acids (14 proteins) and carbohydrates (21 proteins) and stress response (21 genes). An example of a full pathway with potential host benefit is the detection of a siderophore gene cluster in a family of large, nontransmissible plasmids. Interestingly, we identified three 5S-rRNA and two tRNA genes. These are embedded in contigs that do not contain any additional ribosomal components and are only ~90% similar in sequence to the equivalents in their host strains, so that the detection of these informational genes is unlikely to be due to host chromosome contamination. Their presence therefore confirms previous findings that plasmids can occasionally contain rRNA genes ( 40 , 41 ) and can act as transfer vehicles for genes that are thought to be only infrequently involved in exchange among distantly related organisms ( 42 , 43 ). Episome cooccurrences within hosts. Most host cells containing episomes harbored either a single (~60%) or two (~30%) episomes; however, several isolates contained a large number of episomes, and some of these represented unusual combinations (see Fig. S1B in the supplemental material). For example, annotation suggested that in strain FF472, a conjugative, two mobilizable, and four nontransmissible plasmids were present along with a phage (see Table S2 in the supplemental material). Another strain (FF112) contained a phage and two different types of conjugative plasmids. Finally, systematic exploration of cooccurrence patterns across host isolates did not suggest any obvious codependencies of episomes on each other, since many were also detected as single episomes and never in the same combinations in multiple hosts. Inferred inheritance dynamics. Our data also allow estimation of the inheritance dynamics of episomes within and between populations. Considering the dominance of nontransmissible episomes among the Vibrionaceae populations and Proteobacteria in general ( 3 ), we were particularly interested in whether these plasmids are primarily vertically inherited and hence present in closely related isolates or whether there is evidence for their transfer among distantly related host populations. To differentiate these possibilities, we first classified episomes into families based on sequence similarity ( Fig. 2 ; see Table S3 in the supplemental material) and then constructed a network visualizing the occurrence of these families on the phylogeny of their hosts (see Materials and Methods) ( Fig. 3 ). FIG 3 Episome family network across the Vibrionaceae phylogeny calculated for different nucleotide similarities as cutoffs for family membership. The phylogeny is annotated (bubbles with population identifiers matching those in Fig. 1 ) to indicate the origin of the known ecological populations. Links connect strains that share episomes in the same family. Colors of links indicate whether an episome family is putatively classified as phage or conjugative, mobilizable, or nontransmissible plasmid. Episome families were defined with 70% (A) and 97% (B) nucleotide similarity cutoff (as a reference, the average gene content overlap between unrelated strains is only 40% [25]). The analysis shows that episomes are distributed among distantly related hosts, indicating spread by horizontal gene transfer. Restriction to families with only closely related members (97% sequence identity) preserves this pattern for most episomes except phage and some conjugative and nontransmissible plasmids. This analysis suggests that episomes have spread primarily by horizontal transmission rather than vertical inheritance ( Fig. 3A ). We identified 31 multimember families (with family size ranging from 2 to 13 members), while 94 episomes remained singletons, suggesting that there is a large pool of rare episomal types within these populations. In fact, low frequencies of genetic variants in a lineage are usually the result of recent introductions, suggesting that many of these elements have been recently transferred. Such transfers could originate from sporadic contact with other microbes on particles or guts of animals. Surprisingly, only a few families, most notably the two containing phage, have accumulated high nucleotide diversity, while most consist of highly similar elements with, on average, ≥98% nucleotide identity ( Fig. 2 ). Mapping the occurrence of these families onto the host phylogeny shows that the majority are distributed across distantly related hosts (overall gene content overlap of <40% [25]), implying that episomes have spread horizontally. This is the case for all phages, which may therefore possess broad host range, and, surprisingly, also for a large number of nontransmissible plasmids ( Fig. 3A ), whose horizontal transfer was previously suggested to depend on chance events and hence to be rare. Further consideration of inheritance patterns of episomes suggests that many, but especially nontransmissible, plasmids are subject to rapid evolutionary turnover, i.e., they arise, spread, and are lost frequently. This conclusion is based on restricting the network analysis to families with high sequence similarity (>97%) and reanalyzing their distribution. The resultant network shows that a very high percentage of episomes that have been transferred among Vibrionaceae populations are closely related ( Fig. 3B ). In fact, many of these have identical nucleotide sequences, suggesting that they have spread in a time frame that has not permitted the accumulation of nucleotide changes ( Fig. 2 ). This pattern is especially puzzling for “nontransmissible” plasmids and suggests that a currently unrecognized, direct transfer mechanism enables their rapid dissemination among bacterial populations. Several such mechanisms have been proposed. They include DNA vesicles ( 44 , 45 ), nanotubes ( 46 ), natural transformation ( 47 ), and transduction ( 48 ). Gene exchange among episomes. Because of the apparently rapid turnover of episomes, we investigated to what extent episomes themselves are evolutionarily stable entities by constructing a network of recently exchanged genes ( Fig. 4A ). To restrict the analysis to events of fairly recent transfer, we first clustered genes into closely related families (>97% in sequence identity) and then determined how many episomes share these families. This shows a network that connects most episomal families by recent gene exchange with a hub of strongly connected conjugative plasmids at the center ( Fig. 4A ). Although these share many types of genes, T4SS appear most frequently shared. Strongly connected to this hub are many nontransmissible plasmids, while the remainder of episomes are relatively sparsely connected. Closer inspection, however, reveals that, with the exception of phage, the limited number of connections has to be seen in the context of the small size of many mobilizable and nontransmissible plasmids. The fact that many of these small plasmids share only backbone genes at high nucleotide similarity, while having completely different functional gene content ( Fig. S2 ), provides evidence for the rapid evolution of these elements consistent with the known modular rapid evolution of mobile genetic elements ( 49 , 50 ). FIG 4 (A) Network of recent horizontal gene transfer among episomes. Episomes are connected by proteins (blue dots) shared by at least two episomes at ≥97% sequence similarity. The diameter of episome symbols indicates the size of the genome. The analysis shows that nearly all episomes have exchanged genes with a cluster of conjugative plasmids forming a hub at the center. (B) Family of nontransmissible plasmids containing siderophore biosynthesis genes. These elements are characterized by the absence of genes involved in self-transmission and have partitioning systems only in their backbones. (C) Gene content comparison of a mixed episome family (containing both conjugative and nontransmissible) reveals that the two episome categories can evolve from each other by either gain or loss of the conjugation machinery. Although episomes appear evolutionarily unstable and are subject to frequent reassortment of genes by recombination, the two phage, one conjugative, and two nontransmissible plasmid families are exceptions. We highlight the example of a family of large nontransmissible plasmids mentioned above ( Fig. 4B ) consisting of three modules. The first encodes a plasmid partition system (blue ORFs) that ensures reliable distribution into daughter cells. The function of the second module (orange) remains unidentified. The third module (green) encodes a protein complex that shares high sequence identity with siderophore biosynthesis genes from a previously characterized plasmid family from Vibrio vulnificus and Vibrio parahaemolyticus ( 51 ), suggesting that these genes are mobile among episomes. However, the nucleotide diversity of 92.5% across the plasmid family identified here suggests that the acquisition of the siderophore operon was a more ancient event and that these nontransmissible plasmids have persisted over longer evolutionary times than many of the other nontransmissible plasmids, which consist of clusters of highly identical genomes. Gene gain or loss may also change one plasmid category into another and may, in part, explain the rapid evolutionary turnover of most plasmids. For example, episome m161 and m096 are conjugative plasmids that share almost all of the backbone genes (blue ORFs), which are responsible for self-transmission ( Fig. 4C ). They differ, however, in two relatively large regions, which are present in m161 but absent in m096. These regions are also shared by the nontransmissible plasmid m031, which is overall more similar to m161 except for the lack of genes responsible for conjugative transfer. This confirms the view that plasmid gene repertoires change rapidly ( 52 , 53 ). It further suggests that nontransmissible plasmids may originate from loss of T4SS and relaxase genes. Conclusions. Overall, our data reveal surprising results about the diversity and distribution of episomes among Vibrionaceae populations. First, contrary to the expectation that, due to the requirement of cell-to-cell contact for transmission, plasmids should be preferentially associated with isolates recovered from surfaces, microcolonies, or biofilms ( 23 , 24 ), we show that they are significantly enriched in free-living, planktonic cells. Second, our data suggest that plasmids previously categorized as nontransmissible are subject to high evolutionary turnover and transfer frequently among populations. It is therefore likely that a currently unrecognized transfer mechanism is at work. Candidates are transformation, conjugation by cointegration into a conjugative element, and packaging into phage or membrane vesicles. Regardless, we propose that the name “nontransmissible” should be abandoned. Third, the high incidence of putative temperate phages that appear to propagate as plasmids is unexpected. Although phages have previously been described in plasmid metagenomes ( 8 ), there was no indication that these might be temperate phages, as suggested here by the stable propagation in our isolates. Such plasmid-like temperate phages have been previously described in only a very small number of studies. Accordingly, the phages detected here appear novel. Their prevalence suggests a previously unanticipated important role in the marine environment. Finally, analysis of gene transfer among plasmids indicates that genes involved in plasmid maintenance and transfer are a frequently exchanged, rapidly changing categorization of plasmids. This exchange also offers host strains a rich supply of external genetic materials that may allow the assembly of different functions on a backbone of plasmid functions perhaps adapted to specific host populations."
} | 7,879 |
40156030 | PMC11954327 | pmc | 6,721 | {
"abstract": "Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these applications. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a suite of algorithms that helps to identify and visualize errors at the level of connected pathways, rather than individual reactions. We show how MACAW highlights inaccuracies of varying severity in manually curated and automatically generated GSMMs for humans, yeast, and bacteria and helps to identify systematic issues to be addressed in future model construction efforts.\n Supplementary Information The online version contains supplementary material available at 10.1186/s13059-025-03533-6.",
"conclusion": "Conclusions We present MACAW, a tool for identifying and guiding the correction of errors in GSMMs. Our approach is particularly suited for identifying and correcting subtle errors in GSMMs that only become apparent when considering multiple reactions collectively. MACAW identifies numerous errors in both manually curated and automatically generated GSMMs, highlighting opportunities for improving current approaches to creating and refining GSMMs. Overall, MACAW represents a useful and innovative addition to the set of tools available for improving the quality of GSMMs, both in their capacity as repositories of biochemical knowledge and in their capacity as tools for predicting metabolic phenotypes.",
"discussion": "Discussion We have created MACAW, an ensemble of network-based algorithms for identifying and visualizing potential errors in GSMMs. MACAW incorporates variations of previously published approaches for highlighting errors with individual reactions and extends them to also identify errors which only become apparent at the level of whole pathways of reactions. By following up on errors highlighted by MACAW in Human-GEM, we have demonstrably improved its capacity to accurately predict the impacts of knockouts. More generally, we have also used MACAW to analyze how different approaches to reconstruction and curation of GSMMs affect the kinds of errors they contain. BioISO [ 42 ], MONGOOSE [ 52 ], MEMOTE [ 53 ], and ErrorTracer [ 54 ] resemble MACAW in that they are also collections of algorithms for highlighting potential errors in arbitrary GSMMs, but differ in a number of key respects (Table 1 ). Most notably, MACAW identifies more kinds of near-duplicate reactions than any of these other error-detecting tools. While all of the duplicates that MACAW flags are not necessarily erroneous, many of the errors we identified in Human-GEM as a result of the duplicate test in MACAW (Additional File 2: Table S1) would not have been flagged by the other tools. While some of the tests that comprise MACAW are similar to tests included in other tools, MACAW provides more information in a different, and we believe more helpful, format than the other tools. For example, while the different loop tests identify the same set of reactions in a given GSMM, only MACAW also groups the reactions into distinct loops, which significantly simplifies the process of understanding what changes to the component reactions would be necessary to resolve the loop. Similarly, while all dead-end tests mostly identify the same reactions, only the MACAW and MONGOOSE tests connect the identified reactions into pathways to clearly illustrate how the dead-ends relate to each other. BioISO and ErrorTracer connect all reactions in the given GSMM into a single network and leave it up to the user to manually separate each dead-end pathway from those large and generally densely interconnected networks. MEMOTE also contains many tests unrelated to those in MACAW or other comparable tools, many of which focus on very fundamental errors in GSMMs that would prevent them from being used for most potential applications of GSMMs. The two tools are more complementary than redundant: the tests in MEMOTE serve as tools for getting GSMMs to a basic level of functionality and usability, while the tests in MACAW facilitate further refinement of GSMMs beyond that point.\n Table 1 Comparison of MACAW to similar previously published tools MACAW MEMOTE ErrorTracer BioISO MONGOOSE Duplicate reactions Completely identical All Some All None None Same metabolites, different coefficients All Some Some None None Same metabolites, different directions All Some Some None None Same metabolites, different reversibility All Some Some None None Same genes, everything else potentially different Some All Some None None Same metabolites except for redox metabolites All None None None None Dead-ends Metabolites that can only be produced All All All All All Metabolites that can only be consumed All All All All All Reactions that involve those metabolites All All All All All Reactions upstream or downstream of those All All Some All All Accounts for effectively irreversible reactions? yes no yes no yes Reaction Loops Blocked by dilution constraints All Some Some None Some Internal loops (type III extreme pathways) All All All None All Energy-generating cycles Some All Some None All Effectively irreversible reactions are reversible reactions that have at least one product or reactant that can only be consumed by every other reaction that it participates in or only be produced by every other reaction that it participates in. See “Methods” for definition of dilution constraints. Internal loops (type III extreme pathways) are groups of reactions that can sustain non-zero steady-state fluxes while no exchange reactions (reactions that represent consumption of nutrients from or secretion of metabolites into the environment of a cell) have non-zero steady-state fluxes. Energy-generating cycles are sets of reactions that can sustain non-zero steady-state fluxes while the only exchange reactions with non-zero steady-state fluxes are those for energy-currency metabolites such as ATP and NAD(H) The large proportions of reactions found to be dead-ends in most of the GSMMs tested, especially the manually curated ones (Human-GEM, yeast-GEM, and iML1515), should not necessarily be interpreted as a metric of their quality. While GSMMs are commonly used to predict steady-state metabolic fluxes, for which dead-end reactions are useless, GSMMs are also used as repositories of biochemical knowledge about a particular organism. As our knowledge of the metabolic capacities of many organisms is largely incomplete [ 101 ], a GSMM that contains dead-end reactions may simply accurately reflect the current state of our understanding. A GSMM that contains no dead-end reactions may seem more useful or complete in the context of predicting fluxes, but might fail to represent dozens or hundreds of reactions known to occur in the modeled organism, and thus be less useful as a repository of knowledge. The proportion of dead-end reactions in a given GSMM may have more to say about how much remains to be discovered about the organism it represents than the accuracy of the GSMM. Since the process of determining whether or not each individual dead-end reaction represents an error in the GSMM or a frontier in our knowledge of the underlying biochemistry is highly context-specific and idiosyncratic, MACAW’s dead-end test does not attempt to automatically distinguish the two kinds of dead-ends, as previous attempts to do so have had mixed results [ 48 – 50 ]. The dead-end test can highlight opportunities for new experiments to fill gaps in the current understanding of a particular organism’s history and metabolic capabilities. For example, some reactions may have an important role even if they cannot sustain steady-state flux, and other reactions may be remnants of pathways that are on their way to evolutionary extinction. While we found that the proportion of reactions in GSMMs produced by automatic reconstruction methods depends strongly on the choice of method (Fig. 4 ), a potential future direction would be to investigate the extent to which this proportion also depends on the phylogenetic relationship between the target organism and the organism(s) represented by any GSMMs used as templates for reconstructing a new GSMM (e.g., in terms of biomass composition). The constraints used in the dilution test are inspired by the dilution constraints used in previous works to improve flux predictions as well as predictions of the effect of genetic knockouts on viability [ 62 , 63 ]. This is, to our knowledge, the first application of dilution constraints to the context of highlighting errors in GSMMs during the curation process. In line with previous works that interpreted the fluxes through dilution reactions as representing the dilution of intermediate metabolites as cells grow and divide [ 62 ], one interesting feature of our dilution algorithm is that it sets a single dilution rate for all metabolites, compatible with this biological interpretation. Along the same line, our dilution fluxes could also be thought of as representing loss of intermediate metabolites to side reactions, such as with reactive oxygen species. This phenomenon is well captured by our choice to constrain the fluxes through dilution reactions to be a proportion of the sum of the non-dilution fluxes involving the metabolite being diluted: all chemical reactions involve the formation of unstable transition states that are frequently capable of forming into more than just two sets of reactants or products, each with varying probabilities of occurring [ 102 , 103 ]. The more times a particular transition state forms, the more times it will resolve into less common sets of products, thus the dilution flux for a given metabolite scales with the total magnitude of the non-dilution fluxes involving that metabolite. While we identified several errors in lipoic acid metabolism in Human-GEM using the dilution test, the corrected version of Human-GEM was only able to accurately predict the impact of knockouts in that pathway when dilution constraints were imposed for all metabolites (Fig. 6 C). Dilution constraints can therefore play a multifaceted role in improving the quality of GSMMs. A variety of tools exist for mitigating the problem of thermodynamically infeasible loops when using GSMMs to predict steady-state fluxes [ 38 , 64 , 65 , 86 – 88 ]. While they all certainly produce more realistic predictions than the infinite fluxes such loops are capable of sustaining otherwise, some are computationally expensive and thus difficult to apply to larger GSMMs, some require thermodynamic and/or kinetic data that is not known for most reactions in most organisms and is difficult to estimate accurately, and the less computationally and data-intensive approaches rely on approximations and heuristics that render the predictions questionably relevant in many biological contexts. Furthermore, all but the most trivial GSMMs (e.g., those that only represent a few “core” pathways like central carbon metabolism) have multiple configurations of fluxes through the network that satisfy all constraints [ 104 ], and few of the existing methods for mitigating loop fluxes are compatible with algorithms for characterizing distributions of all possible fluxes through a GSMM [ 105 – 107 ]. While loops are not always caused by errors in the construction of a GSMM, many are. Identifying such loops using MACAW’s loop test can allow one to mitigate this problem before engaging with the question of how best to predict fluxes from a GSMM."
} | 2,908 |
35696548 | PMC10084317 | pmc | 6,725 | {
"abstract": "Abstract Animals often search for food more efficiently with experience. However, the contribution of experience to foraging success under direct competition has rarely been examined. Here we used colonies of an individually foraging desert ant to investigate the value of spatial experience. First, we trained worker groups of equal numbers to solve either a complex or a simple maze. We then tested pairs of both groups against one another in reaching a food reward. This task required solving the same complex maze that one of the groups had been trained in, to determine which group would exploit better the food reward. The worker groups previously trained in the complex mazes reached the food reward faster and more of these workers fed on the food than those trained in simple mazes, but only in the intermediate size group. To determine the relative importance of group size versus spatial experience in exploiting food patches, we then tested smaller trained worker groups against larger untrained ones. The larger groups outcompeted the smaller ones, despite the latter's advantage of spatial experience. The contribution of spatial experience, as found here, appears to be small, and depends on group size: an advantage of a few workers of the untrained group over the trained group negates its benefits.",
"introduction": "Introduction Competition, that is, the negative effect of one organism on the fitness of another by either depleting shared resources or preventing access, is a fundamental phenomenon in ecology and evolution that often affects diverse phenotypic traits of the interacting species, for example by character displacement (Mitchell et al ., 1990 ; Gurevitch et al ., 1992 ; Keddy, 2001 ; Bolnick, 2004 ; Grant & Grant, 2006 ). Intraspecific competition is usually stronger than interspecific competition because the niches of individuals of the same species overlap more than those of individuals of different species (Adler et al ., 2018 ). Competition indeed intensifies with the similarity in the ecological niche of the competitors and with increasing densities (Abrams, 1975 ; Meszéna et al ., 2006 ). There are however several mechanisms that facilitate coexistence either within or between species, such as niche broadening and segregation in time, space, or diet (Bolnick, 2001 ; Perrin & Kotler, 2005 ; Svanbäck & Bolnick, 2007 ). Some individuals or species are stronger competitors than others, dominating resources and preventing others from accessing them (Steinwascher, 1978 ; Charter et al ., 2013 ). Inferior competitors must therefore rely on other strategies, such as better dispersal capabilities (Zirkle et al ., 1988 ; Bolin et al ., 2018 ). In ants, a trade‐off between food discovery and dominance can be found in many communities, with some species discovering food more quickly, while others arrive later but are then able to dominate the resources (Fellers, 1987 ; Perfecto & Vandermeer, 2011 ; Cerdá et al ., 2013 ; Tiong & Morse, 2021 ). A similar trade‐off is that of exploration vs. exploitation, in which some species discover resources faster while others exploit them more thoroughly (Schmitt, 1996 ; Monk et al ., 2018 ). Such a trade‐off can occur among colonies of the same species but of different sizes or with different behavioral characteristics and can pertain also to targets other than food resources (Hills et al ., 2015 ; Katz & Naug, 2015 ; Kembro et al ., 2019 ). Foraging for food, which in many species improves with experience, is a vital behavior with direct consequences for reproduction and survival. In ants, a recent successful foraging event, for example, can increase the likelihood of the ants leaving their nests and foraging again (Robinson et al ., 2012 ; Gilad et al ., 2022a ). Foraging also improves with increasing spatial familiarity with the habitat, which assists in exploration and food discovery (Dukas & Real, 1993 ; Wolf, 2008 ). Central‐place foragers, such as social insects, nesting birds, and burrow‐dwelling rodents, leave the nest/burrow to forage and then return to it (Orians & Pearson, 1979 ; Ydenberg et al ., 1986 ). For these animals, experience should play a particularly important role in foraging, especially when it is profitable to revisit patches that have not been fully depleted. Such experience should be expressed in faster arrival at the food patch and return to the nest (Dyer, 1998 ; VanderSal, 2008 ). As central‐place foragers, ants benefit from foraging experience through the increased likelihood of discovering food and increased speed of return to the nest and handling the food (Johnson, 1991 ; Chameron et al ., 1998 ; Saar et al ., 2020 ). Competition is common and strong both within and between ant species (Heinze et al ., 1996 ; Parr & Gibb, 2010 ; Cerdá et al ., 2013 ; Erős et al ., 2020 ). The competition outcome can be influenced by colony size, with larger colonies dominating smaller ones (Palmer, 2004 ; Tanner, 2006 ; see also McGlynn, 2000 , for a more complex pattern, depending also on interaction type). The role of spatial experience in direct intraspecific competition among ant colonies has never been examined, and colony size may interact with experience to affect competition over food. Our aim here was to determine whether spatial experience can provide an advantage in direct intraspecific competition for food between groups of ant workers. Our model was the individually foraging desert ant, Cataglyphis niger , searching for food in a laboratory maze. Our goal was to employ a laboratory design that would be challenging for the ants, and in which spatial learning might play a role, rather than to imitate the natural conditions. We have demonstrated previously in a series of laboratory experiments that C. niger ants improve with experience in solving a maze and that the improvement is based on a combination of spatial learning and elevated motivation to search (Saar et al ., 2017 ; Bega et al ., 2020 ; Gilad et al ., 2022a ). The congeneric species C. cursor was also studied in the laboratory and was shown to associate landmarks with the correct routes to the food reward (Chameron et al ., 1998 ; Schatz et al ., 1999 ). Cataglyphis ants are diurnal individual foragers with no recruitment other than stimulating additional workers to leave the nest if food is found (Lenoir et al ., 2009 ; Amor et al ., 2010 ). This lack of recruitment leads to individual learning by each worker. If recruitment did take place, it would be sufficient for a single worker to learn the way to the food and then recruit the others. A system of individual foragers, however, is less affected by a single event of one worker discovering food, and the colony's learning performance here is the true sum of the behavior of all the individual foragers (rather than a single worker recruiting all the others). Cataglyphis spp. forage over long distances in arid habitats, are known for their navigational abilities, and have served as models for studies on central forager navigation (Cheng & Wehner, 2002 ; Wehner, 2003 ; Mangan & Webb, 2012 ). We examined whether prior experience in a complex maze would improve the food detection ability, food exploitation, and hence foraging of a group of workers competing against another similar sized group that had only been trained in a simple maze. We hypothesized that experience would provide the complex maze trained group with an advantage over the simple maze trained group when both competed in a complex maze. We expected experience to be more important in larger groups than in smaller ones, because in larger groups more workers leave the colony to forage and acquire experience. If spatial experience is indeed of major importance, we expected smaller but trained worker groups to arrive faster and with more workers at the food source than larger untrained worker groups. If it is not, then larger groups should arrive first at the food owing to the positive correlation between group size and the number of foragers.",
"discussion": "Discussion We examined here in the ant Cataglyphis niger whether familiarity with a complex maze enhances foraging success when searching in the same complex maze and competing against a group of workers untrained in the same maze. We found that foraging experience in a complex maze could lead to faster food discovery under competition conditions and to more workers feeding. This advantage however was limited only to the intermediate group size tested, was absent in smaller or larger groups, and was mitigated by a larger group size of untrained opponents. Our hypothesis that experience contributes to food discovery and exploitation under conditions of direct competition is therefore only partially supported. Why the advantage of training was only evident in the intermediate group size of 34 workers is intriguing. Training smaller groups might prevent enough workers from gaining sufficient experience in foraging. In the studied species, as well as in several other ant species, because only ∼10% of the workers contribute to foraging (Porter & Jorgensen, 1981 ; Retana & Cerdá, 1990 ; Bega et al ., 2019 ), it is probable that not enough workers in the small groups acquired sufficient exposure to the maze. On the other hand, colonies over a certain size may rely less on experience and instead “flood” the maze with foragers, which may ultimately eliminate the advantage of training. Our results suggest that experience may be important for young, small colonies during their growth period, with the effect of training, although size‐specific, relevant for all colonies during early ontogeny, as all start with a single queen and small colony size. It is probable that the contribution of each individual worker to small colonies is higher than that in larger colonies, and it is therefore more profitable for workers in small colonies to forage more efficiently. Interference appears to have been the main competition type in our experiment, as evident in the frequent antagonistic interactions between workers from different groups (e.g., invading the nest of the other group). That said, our aim was not to quantify the relative weight of interference and exploitation, but, rather, to uncover a mechanism by which experience might contribute to foraging success under competition conditions. We suggest that learning took place during the training phase, albeit which of the two—spatial learning or associative learning—was dominant here is uncertain. Trained workers could either learn the faster way to reach the food reward (spatial learning) or could associate the opening of the nest door with the existence of food nearby (associative learning). There is evidence for both from similar experiments with the same species and in the current set‐up (Bega et al ., 2020 ; Gilad et al ., 2022a ). Independent of the exact learning mechanism, we suggest that our experiment provides some support for a possible contribution of learning under direct competition conditions. Specifically, trained groups of 34 workers discovered the food faster under competition conditions than untrained groups. Becoming familiar with the habitat is necessary when conditions change or following nest relocation. We have no data on nest relocation in the studied species, but the congeneric Cataglyphis iberica occasionally relocates its nest following aggression with another ant species (Cerdá & Retana, 1998 ). Losing habitat familiarity could be an additional cost of relocation on top of moving and digging a new nest, especially when there are many other nests in the vicinity. Very few experiments have examined the contribution of learning under competition conditions. One exception is that by Mery & Kawecki ( 2003 ), who selected for improved learning ability in flies and demonstrated that those flies were nonetheless inferior when competing against a line that had not undergone artificial selection for improved learning (reviewed in Kawecki, 2010 ). However, in their experiment, the two lines did not compete against one another but against a reference line. Although testing competition against a reference line is common in the literature (e.g., Santos et al ., 1992 ), the two competitors may each interact differently with a reference line and direct competition, as performed here, constitutes a stronger experimental design. A common coexistence mechanism of competing species is that of the dominance‐discovery trade‐off, with some species detecting resources faster and other species either dominating resources or exploiting them more efficiently (Adler et al ., 2007 ; LeBrun & Feener, 2007 ; Perfecto & Vandermeer, 2011 ). Our studied species, C. niger , which forages individually, is probably inferior in direct competition against other ant species that can recruit many workers. A high ability for spatial learning, experience, or habitat familiarity, may compensate for C. niger ’s competitive inferiority and allow its coexistence with worker‐recruiting species, especially considering that C. niger forages for ephemeral resources. A similar mechanism of relying more on spatial learning than on group size may assist smaller colonies to coexist with larger ones of the same species, which can elevate their foraging performance simply by increasing the number of foraging workers. Furthermore, colonies usually dominate the area around the nest entrance. Spatial learning may contribute to expanding this area of domination, even for small colonies, and thus provide such colonies with a competitive advantage. All these are suggestions that remain to be tested. It is important to note that competing animals have often been assumed to perform similarly over time independent of experience (Kotler & Mitchell, 1995 ). The present findings suggest that, at least under some conditions (here, groups of 34 trained workers), experience may change the outcome of competition and should be considered if the experiment results differ from the theoretical predictions. Due to our low sample size per group (10 pairs of colonies), we believe that our results underestimate the contribution of training to foraging success. Future studies should increase the sample size and examine additional conditions under which training may contribute to competition success. Increasing the sample size can compensate for cases in which no workers left the nest to forage, perhaps because no foragers were selected when composing the competing groups. This is clearly more relevant for small groups. Future experiments should also match colonies with both familiar neighbors and more distant colonies. The type of response to neighbors may be either stronger or weaker, if such neighboring colonies represent a lower or higher threat, respectively, than that of other colonies. These two alternative hypotheses are termed the “dear enemy effect” and “nasty neighbor effect” (cf. Heinze et al ., 1996 ; Newey et al ., 2010 ). The interaction of spatial learning with the distance between competing colonies has never been previously studied. Furthermore, examining the same question in the field with competing colonies is an important future step to understand whether the results obtained in the laboratory hold also true at a larger scale in the ant's natural habitat. In conclusion, we have demonstrated here the moderate contribution of spatial experience to food discovery in ants under competition conditions and under a limited number of scenarios. We suggest that learning or experience should be considered as a possible mechanism enabling coexistence between competing individuals or species."
} | 3,936 |
34699568 | PMC8547673 | pmc | 6,727 | {
"abstract": "Plant associated microbiomes are known to confer fitness advantages to the host. Understanding how plant factors including biochemical traits influence host associated microbiome assembly could facilitate the development of microbiome-mediated solutions for sustainable plant production. Here, we examined microbial community structures of a set of well-characterized Arabidopsis thaliana mutants disrupted in metabolic pathways for the production of glucosinolates, flavonoids, or a number of defense signalling molecules. A . thaliana lines were grown in a natural soil and maintained under greenhouse conditions for 4 weeks before collection of roots for bacterial and fungal community profiling. We found distinct relative abundances and diversities of bacterial and fungal communities assembled in the individual A . thaliana mutants compared to their parental lines. Bacterial and fungal genera were mostly enriched than depleted in secondary metabolite and defense signaling mutants, except for flavonoid mutations on fungi communities. Bacterial genera Azospirillum and Flavobacterium were significantly enriched in most of the glucosinolate, flavonoid and signalling mutants while the fungal taxa Sporobolomyces and Emericellopsis were enriched in several glucosinolates and signalling mutants. Whilst the present study revealed marked differences in microbiomes of Arabidopsis mutants and their parental lines, it is suggestive that unknown enzymatic and pleiotropic activities of the mutated genes could contribute to the identified host-associated microbiomes. Notwithstanding, this study revealed interesting gene-microbiota links, and thus represents valuable resource data for selecting candidate A . thaliana mutants for analyzing the links between host genetics and the associated microbiome.",
"conclusion": "Conclusion Arabidopsis mutants carrying gene disruptions in pathways of the plant secondary metabolites GLS and FLV, or the signalling molecules SA, ABA, ET, or FAD, assembled distinct microbiomes compared to their parental lines. Most earlier studies on the effects of disruption of metabolic pathway have only considered bacterial communities. In this study, we demonstrated dramatic effects of such mutations also on fungal communities. We found distinct relative abundances and diversities of bacterial and fungal taxa in the mutants. Differential analysis at OTU level revealed significantly affected taxa between the mutants and parental lines. Also, the bacterial and fungal genera were mostly enriched than depleted in mutants, except for flavonoid mutations on fungi communities. These results strongly support the perception that many synthesized plant secondary metabolites and DSMs regulate the assembly of the plant root microbiome. However, the interconnectedness in metabolic and signalling pathways presents a high complexity, and thus, mutant lines with several mutations with the possible elimination of overlapping defense-signalling functions are suggested for further studies. The present screening study revealed significant gene-microbiota links, and thus serve as an important resource for in-depth plant-omics analysis in the future.",
"introduction": "Introduction Plants interact with a vast diversity of microorganisms both above- and belowground, and the outcomes of those interactions may be either beneficial or detrimental to the plant. Essentially, the plant employs a range of strategies such as the action of constitutive and/or induced chemical compounds in combination with the plant innate immune system to assemble its associated microbiota [ 1 ]. The plant secondary metabolites glucosinolates (GLS) and flavonoids (FLVs) have been widely studied for several microbiota-mediating and plant protective functions [ 2 ]. For instance, GLS from the roots of Brassica species were found to inhibit microbial pathogens including Pseudomonas syringae , Alternaria brassicicola , Gaeumannomyces graminis , Botrytis cinerea , Fusarium oxysporum and Hyaloperonospora parasitica [ 3 , 4 ]. The FLVs are well known for their chemoattractant and signalling function in legume-rhizobia interactions resulting in N-fixation and role in plant-mycorrhizal associations, but also as phytoanticipins [ 5 , 6 ]. Phytohormones serve as signalling molecules in regulating the innate immune network, and salicylic acid (SA), jasmonic acid (JA), ethylene (ET) and abscisic acid (ABA) act as molecular switches in stimulating inducible defense against biotic and abiotic stresses [ 7 , 8 ]. Owing to its robust and overarching activation of defense repertoires, the immune system is perceived to affect microbial community structures [ 1 , 9 , 10 ]. The biosynthetic pathways and genes involved in GLS [ 11 , 12 ], FLV [ 13 – 15 ] and defense signalling [ 16 – 18 ] are well described, and research is directed towards exploiting these pathways to study the links between plant gene functions and microbiome assemblage. Several well-characterized mutants of the model plant, Arabidopsis thaliana (hereafter Arabidopsis) have become quintessential for studying those relationships. For example, Badri et al . [ 19 ] reported effects on microbial communities of a mutation in a plant ATP transporter involved in exudation of plant secondary metabolites, and further concluded that individual plant genes are actively involved in the interaction with microbial communities. By using GLS [ 20 ], FLV [ 19 ] and benzoxazinoid (BX) mutants [ 21 ], the influence of plant defensive secondary metabolites on the plant-associated microbiota has been demonstrated. For example, distinct microbiomes were observed in maize parental lines and their isogenic mutants ( bx1 , bx2 and bx6 ) carrying disruptions in genes encoding enzymes in different steps of the BX pathway [ 21 ]. This study further demonstrated a gatekeeper role of BXs in modulating plant-associated microbiomes associated with plant roots. In other studies, the coumarin-impaired mutants, myb72-2 and f6’h1 were used to demonstrate the impact of coumarins on microbial community structures [ 22 , 23 ]. In addition, studies have used Arabidopsis mutants to examine the influence of phytohormones including fatty acid desaturases (FAD) on microbial community structures [ 24 ]. Mechanistic processes at the rhizoplane, including the gating role of plant secondary metabolites and defense signalling molecules (DSMs) could control the assembly of host specific microbiomes. We hypothesized that mutations in pathways for the synthesis of certain secondary metabolites and DSMs disrupt the ability of the plant to assemble an optimal microbiome. Findings from other studies using different experimental systems including either a single or a few mutants have reported contrasting effects of plant metabolites on the plant associated microbial community structures. Moreover, previous studies have relied on in-vitro systems where metabolites were exogenously applied and their effect on microorganisms examined. However, such studies do not always reveal the precise effects of these metabolites in a natural system. Our objective in the present study was to assess root microbiome assembly in natural soils in a range of plant mutants with gene disruption in different steps of defense-related biosynthetic or signalling pathways. For this, we selected a range of well-characterized Arabidopsis mutants disrupted in GLS, FLV and DSM synthesis and examined their effects on bacterial and fungal communities in a field soil. The analysis of these mutants using identical soils and growth conditions will provide comparable insights of the effects of these mutations on bacterial as well as fungal community structures, the latter has received little attention in previous studies.",
"discussion": "Discussion GLSs have notable effects on the host root-associated microbiome Our results revealed notable effects of glucosinolates (GLS) mutants on relative abundances, and alpha- and beta-diversities of microbial communities, suggesting that specific GLS metabolites affect the composition of the root microbiome. The separation of GLS mutants from the parental line in the PCoA plots suggests differential effects of the GLS mutations on the Arabidopsis microbiome, thus supporting previous studies [ 47 ]. Specifically, tu3 that carries a gene disruption upstream of the aliphatic GLS pathway, had the strongest effect on both bacterial and fungal communities. Aliphatic GLS and their hydrolysis products have been reported to have higher effects on microorganisms compared to indole glucosinolates (iGLS) [ 48 ]. The toxicity of aliphatic GLS towards microorganisms is attributed to the complex degradation products isothiocyanates, thiocyanates, oxazolidinethiones and nitriles that are produced from the enzymatic cleavage of glucosinolates by myrosinase [ 49 ]. The strong effect of tu3 also confirmed that gene disruptions at the initial steps of a biosynthetic pathway generally have more pronounced effects on the host-associated microbiota [ 21 ]. However, other mutants including cyp79B3 and the double mutant cyp79B2cyp79B3 in the same indole GLS pathways or the gsm1_1 in the aliphatic GLS pathways, with upstream gene disruptions only had minor, but significant, effects on the microbiome. These results suggest a differential regulatory role of metabolic genes and their effects on host associated microbiota. Comparatively, the variations in glucosinolates profiles could cause the differential effects of mutants tu3 and gsm1-1 on microbial communities. The tu3 produce glucosinolates that are deficient in gsm1-1 (that is, aliphatic glucosinolates with butyl, pentyl, or hexyl core groups) but lacks aliphatic glucosinolates with heptyl and octyl core groups [ 3 , 12 ]. Brader et al . [ 50 ] found differences in the induction of the CYP79B2 and CYP79B3 genes upon treatment with culture filtrates of the bacterium Erwinia carotovora . Hence, it is possible that unknown enzymatic and pleiotropic activities of the mutated genes could contribute to the observed differences of microbial communities. Furthermore, Ludwig-Müller et al . [ 51 ] reported that several TU mutants, having different contents of GLS intermediate products, developed varying degrees of clubroot disease symptoms caused by Plasmodiophora brassicae . Together, these suggest distinct gene functions in GLS pathways which possibly underline mechanistic processes in microbiome assembly. GLSs had distinct effects on specific microbial groups as confirmed by the identification of a range of bacterial and fungal taxa responding to the different GLS mutations. The increased abundance of individual bacterial genera such as Azospirillum and Fluviicola as well as the fungal genera Sporobolomyces and Emericellopsis in several of the GLS mutants further confirmed the selective effects of metabolites on individual microbial taxa and thus corroborates previous studies [ 21 , 23 ]. For instance, soils amended with isothiocyanates (allyl, butyl, phenyl, and benzyl ITC) were reported to affect fungal communities more dramatically than bacterial communities [ 52 ]. The authors observed changes in community composition including increased Humicola abundance in allyl ITC and Mortierella abundance in butyl ITC amended soils, while the bacterial phylum Firmicutes temporally increased in response to amendment with allyl ITC [ 52 ]. Plant metabolic compounds with antimicrobial properties including GLS are known to be part of the boundary layers of the root rhizoplane that modulate root microbiome assemblage [ 53 ]. Other genera such as Nocardioides and the plant beneficial taxa Streptomyces and Flavobacterium were also enriched in most of the GLS mutants. Azospirillum contains several beneficial species, widely known for their plant growth promoting traits including nitrogen fixation and synthesis of phytohormones and other compounds required for both biotic and abiotic stress tolerance [ 54 ]. The yeast Sporobolomyces , that was enriched in the GLS lines and the other mutants, is an abundant member of the plant mycobiome [ 55 , 56 ] and is antagonistic against pathogens [ 57 ]. Also, the strongly enriched genus Emericellopsis in both the indole GLS mutants cyp79B2cyp79B3 , myb51 and aliphatic GLS lines gsm1_1 and tu3 is known to possess biocontrol traits via the antimicrobial compound emericellipsin A, and have been shown to suppress the pathogen Aspergillus niger [ 58 ]. In addition, the strong enrichment of Fusarium in pen3_1_NahG could suggest that both physical and chemical barriers (GLS and SA) affect this genus [ 59 , 60 ]. Flavonoids have a higher effect on root-associated fungal communities Flavonoids (FLVs) are some of the most studied phytochemicals due to their profound role in plant-microbe interactions. Analysis of microbiota from FLV mutants impaired in different steps of the FLV pathway revealed differential effects on bacterial and fungal communities. While the FLV mutations did not affect bacterial alpha diversities significantly, weak but significant effects of the individual FLV mutations were observed on microbial community composition. The FLV mutant tt7_7 which lacks orthodihydroxy flavonoids (for example quercetin, naringenin, genistein, luteolin, daidzein, and morin), and accumulates pelargonidin rather than the cyanidin found in wild-type plants [ 61 ], had significant effects on both bacterial and fungal communities. Differential responses of pelargonidin and cyanidin to both fungi and bacteria have been reported earlier [ 62 ]. The orthodihydroxy flavonoids have been shown to mediate plant-microbe interactions, especially in nodule formation and in enhancing arbuscular mycorrhizal colonization, and by inhibiting bacterial and fungal pathogens [ 63 , 64 ]. Hence, the disruption of the synthesis of these FLVs would likely affect microbial communities and the strong enrichment of the bacterial genera Flavobacterium and Rhodanobacter in tt7_7 could suggest a modulating role of orthodihydroxy flavonoids on bacterial communities. Similarly, the pap1_D which accumulates anthocyanin pigments, (mainly cyanins) and other secondary metabolites [ 65 , 66 ] significantly affected only the fungal communities. The tt3 mutant which accumulates high concentrations of both kaempferol and quercetin [ 13 ] had a more profound effect on fungal communities compared to the bacterial communities. Both kaempferol and quercetin are highly secreted in Arabidopsis [ 67 ], and their accumulation were likely to affect microbial communities. Quercetin enhances mycorrhizal-plant symbiosis by stimulating host penetration and hyphal growth [ 5 ], while kaempferol inhibits germination of pathogenic fungal spores [ 68 ]. However, Vikram et al . [ 69 ] reported that kaempferol and quercetin disrupts quorum sensing and biofilm formation in bacterial communities. Schultz et al . [ 70 ] found higher relative abundances of Proteobacteria in a quercetin-treated soil compared with non-treated soil. Moreover, alpha diversity indices were observed to significant decline after quercetin treatment [ 70 ]. Yu et al ., [ 71 ] showed that flavones lead to enrichment of the plant beneficial Oxalobacteraceae in the rhizosphere of maize. Guenoune et al . [ 72 ] reported antifungal effects of the FLV maackiain against the fungal pathogen Rhizoctonia solani . Moreover, the enrichment of some members of the order Pleosporales (genus Paraphaeosphaeria ) in tt3 and tt5 , Westerdykella in tt5 and tt7_7 or depletion of Alternaria in tt7_7 , suggests differential effects of FLVs on members of this order. The species Holtermanniella takashimae , which was enriched in tt3 and tt5 , was reported to be negatively co-occurring with Fusarium species that were pathogenic in wheat [ 73 ]. Although tt5 (impaired in naringenin chalcone) did not affect microbial community composition significantly, naringenin chalcone inhibits spore germination of plant pathogens [ 68 ]. In addition, Vandeputte et al . [ 68 , 74 ] demonstrated that plant produced naringenin and catechin is important in reducing the production of quorum sensing-controlled virulence factors in Pseudomonas aeruginosa PAO1. The higher number of differentially abundant fungal taxa compared to bacterial taxa, also point to a higher effect of FLVs on fungi, thus supporting the profound role of FLVs on fungi [ 75 ]. Defense signalling mutations have complex effects on microbial taxa Defense signaling molecules (DSMs) including JA, ABA, SA, FAD and the gaseous molecule ET are well known for their role in mediating plant-microbe interactions. We found that DSM mutants distinctively affected microbial relative abundances and alpha- and beta diversity, thus confirming previous studies [ 24 , 76 , 77 ]. Both etr1_3 (ET insensitive) and 35S :: ERF (high ET synthesis) displayed noticeable differences in bacterial and fungal relative abundances and diversity, and thus supports previous studies [ 78 , 79 ]. The distinct effect of etr1_3 and 35S :: ERF could be caused by their differential activation of ethylene. The etr1_3 has reduced ethylene binding activity while 35S :: ERF encodes a transcription factor that regulates plant-microbe interactions, as well as integration of signaling pathways to activate ethylene and jasmonate-dependent responses to pathogens [ 80 , 81 ]. Using a sterile system with artificially constructed bacterial community, Bodenhausen et al . [ 78 ] showed that the ethylene-insensitive mutant ein2 assemble distinct bacterial community compared with the parental line, with a noticeable enrichment of the genus Variovorax . Comparably, our study revealed differential enrichment of the genus Variovorax , suggesting a selective effect of etr1_3 on this genus. ABA is an essential molecule in modulating abiotic stress (e.g drought stress and salinity stress), as well as overall plant associated microbial communities [ 82 ]. We found that the ABA deficient mutant aba3_2 affected both bacterial and fungal communities, but surprisingly, only a few bacterial taxa at the genus level, including Bradyrhizhobium and Pseudarthrobacter were slightly enriched, whereas several fungal genera were enriched. These results indicate a higher antagonistic effect of ABA on fungal communities. In another study, exogenous application of ABA was found to change community composition as well as enrich the genera Massilia , Cellvibrio , Limnobacter [ 83 ]. Also, the JA mutant dde2 (impaired in JA biosynthesis) significantly affected bacterial fungal community composition, with a strong enrichment of bacterial taxa corroborating previous studies [ 77 , 78 ]. In addition, FAD is pivotal in the phytohormone signalling network by modulating both the SA [ 84 ] and JA pathways [ 85 ], and its role in mediating plant-microbe interactions has been reported [ 18 , 24 ]. Likewise, our study revealed distinct effects of FAD mutants on microbial community structures, as both Azospirillum and Sporobolomyces were strongly enriched in fad3_2 and fad7_1fad8_1 , as was also observed in a previous study in which the Arabidopsis triple mutant fad3fad7fad8 was enriched in several species within Alpha- and Gammaproteobacteria [ 24 ]. Differential effects of FAD genes on microbial taxa have been reported, for instance, while the transcription of the FAD3 gene was shown to be unresponsive upon inoculation of the bacterial pathogen Xanthomonas campestris [ 84 ], the FAD7 gene was induced by fungal effectors [ 86 ]. The distinct enrichment of a number of bacterial genera in the different DSM mutants is indicative of the selective effects of DSMs in shaping the Arabidopsis root microbiome. However, the resident microbial community can interfere with the plant-hormonal pathways [ 87 ]. For example, Finkel et al . [ 88 ] demonstrated that the bacterial genus Variovorax utilizes an auxin-degradation operon to alter plant-hormone balances, enabling it to reverse the severe inhibition of root growth that was induced by a wide diversity of bacterial strains. Thus, the analysis of the effect of plant DSMs on microbiomes should be done with caution. The increasing interest in studying plant metabolites will enable us to better quantify plant host effects on associated microbial communities. However, it is currently challenging to quantify specific effects and mechanisms of important metabolites on plant microbiomes due to methodological limitations. For example, when using mutants, pleiotropic effects arising from gene disruptions in both metabolic and hormonal pathways, makes it impossible to account for individual effects of targeted compounds on microbial community structures. Moreover, because immune signaling activation is complex due to hormonal crosstalk mechanisms it is difficult to quantify the effects of individual hormones on microbial communities. We therefore suggest that, in future studies, detailed analyses should include mutants having complete abolishment of interactive pathways and be complemented with other omics analysis techniques. Furthermore, the host-associated microbiota can alter metabolite synthesis and are also capable of producing several phytohormones [ 89 ] and it is therefore important to adopt experimental approaches that will be able to strictly account for plant-derived compounds and their impact on the plant microbiome."
} | 5,385 |
38895271 | PMC11185756 | pmc | 6,730 | {
"abstract": "Photonic devices are cutting-edge optical materials that produce narrow, intense beams of light, but their synthesis typically requires toxic, complex methodology. Here we employ a synthetic biology approach to produce environmentally-friendly, living microlenses with tunable structural properties. We engineered Escherichia coli bacteria to display the silica biomineralization enzyme silicatein from aquatic sea sponges. Our silicatein-expressing bacteria can self-assemble a shell of polysilicate “bioglass” around themselves. Remarkably, the polysilicate-encapsulated bacteria can focus light into intense nanojets that are nearly an order of magnitude brighter than unmodified bacteria. Polysilicate-encapsulated bacteria are metabolically active for up to four months, potentially allowing them to sense and respond to stimuli over time. Our data demonstrate that engineered bacterial particles have the potential to revolutionize the development of multiple optical and photonic technologies.",
"introduction": "Introduction In nature, organisms have evolved innate abilities to produce multifunctional structures with complex compositions and advanced optical properties. These biologically-produced structures have vast potential to be used to design and produce new optical materials and devices using ecologically friendly manufacturing methods. In particular, certain biological structures show great promise as next-generation microlenses and optical microparticles. Microscopic organisms, cells, and materials produced by living organisms have already been shown to exhibit unique and beneficial properties for manipulating light. These “bio-microlenses” have been demonstrated to act as super-resolution magnifiers ( 1 ), to enhance upconversion in fluorescence ( 2 ), to sense light direction ( 3 ), to enable sub-diffractive focusing ( 4 ), and to act as waveguides ( 5 ). In addition, cell-based bio-microlenses are similar in size and shape to photonic structures that are used for subwavelength microscopy and the formation of photonic nanojets ( 6 – 11 ). However, in order to tailor biological particles to specific applications, we need the ability to tune their optical properties. Synthetic biology offers a path to engineer single cells by altering their size, length, shape, and refractive index, all of which can be used to optimize their ability to act as bio-photonic microlenses. Synthetic biology approaches can also be used to combine unique optical functions of different organisms. Many aquatic organisms, including sea stars and sponges, possess the ability to synthesize natural structures that perform both optical and structural functions. The arms of the brittlestar are coated in light-sensitive calcite plates, which provide protection and also act as highly efficient light-capturing microlens arrays ( 12 , 13 ). Similarly, hexactinellid sponges create silica spicules that are responsible for their structural stability and also display waveguide properties ( 14 , 15 ). Siliceous sponges deposit silica into needle-like spicules, which is accomplished by polymerizing silica into polysilicate, also known as “bioglass,” using a unique silicatein enzyme ( 16 – 20 ). Silicatein-catalyzed silica deposition requires only a single gene to be expressed and can be performed at physiological temperature, pressure, and pH without the use of harsh or toxic chemicals ( 21 , 22 ). Silicatein is thought to be the only natural biomineralizing enzyme ( 23 ), and its ability to fabricate polysilicate structures offers a powerful addition to the synthetic biology toolbox. In this article, we use multiphysics modeling to demonstrate that polysilicate-encapsulated bacterial cells are predicted to have enhanced abilities to scatter and focus light into photonic nanojets. We then demonstrate for the first time that engineered Escherichia coli bacteria that express surface-displayed silicatein enzymes from sea sponges Tethya aurantia and Suberites domuncula can coat themselves in a layer of polysilicate. We observe that these cells scatter and focus light in a manner that resembles the modeled data. Using a fluorescent probe, we show that the polysilicate-encapsulated bacterial cells, when exposed to planar illumination, create intense beams of focused light that form a peak of intensity outside the cell. In contrast, wild-type bacteria create much weaker beams that peak at the cell surface. Remarkably, polysilicate-encapsulated cells survive for up to four months post-encapsulation and are still able to scatter light with a comparable focal peak after they become metabolically inactive. These optically-tuned bacteria are the first example of biologically engineered microlenses, and they have the potential to create new biologically-active devices with controllable properties for optimizing optical performance across a variety of applications including advanced biosensing, super-resolution imaging, Raman scattering, nanolithography, and photovoltaics ( 3 , 4 ).",
"discussion": "Discussion In this work, we demonstrate for the first time that microbes can be rationally engineered to improve their ability to focus light into photonic nanojets. We have fused the sea-sponge enzyme silicatein to the outer membrane protein OmpA, directing silicatein to the surface of E. coli cells where it can mineralize a polysilicate shell. In agreement with multiphysics simulations, these polysilicate-encapsulated cells are able to scatter an intense beam of light that is focused a short distance downstream from the cell, creating photonic nanojets that are much brighter than wild-type cells. Our self-assembled polysilicate-encapsulated bacteria represent the first engineered biological microlenses and serve as a proof-of-concept that cells can be engineered to act as tunable photonic components. Our work builds on previous research using biological approaches for the production of polysilicate materials. Both sea-sponge silicatein enzymes and the silaffin peptide from diatoms have been used to create polymerized silica and silicone materials in vitro ( 8 , 32 , 33 ). E. coli bacteria have been modified to express silicatein, both cytoplasmically 7 and on the bacterial surface, and were able to mineralize polysilicate or polylactic acid ( 34 ). In a separate study, E. coli bacteria were also modified to express recombinant silaffin R5 peptide from diatoms ( 35 , 6 ), which was able to create silica nanostructures when co-expressed with post-translational modification enzymes that enhance the biosilification activity of the peptide. Our work is the first to demonstrate that engineered bacteria can become encapsulated in a layer of polysilicate, and that this coating enhances the optical properties of the bacteria. Furthermore, this polysilicate-mineralization activity can be implemented by introducing a single enzyme, without requiring additional, exogeneous post-translational modification enzymes. Microparticles capable of producing photonic nanojets can be manufactured using non-biological approaches, but current techniques have several limitations. While microspheres are commercially available using materials such as silica and polystyrene, the spherical geometry is known to produce short nanojets limited to distances close to the particle surface ( 37 ). Such short-range nanojets make it difficult to couple the nanojets to other devices or surfaces, limiting their usefulness. Other geometries, such as microcuboids ( 38 ), micropyramids ( 39 ), and microdisks ( 40 ), have been explored in hopes of producing longer nanojets. Additionally, microspheres with multiple layers have been predicted to produce longer nanojets ( 41 ), and microspheres etched with a concentric ring pattern were shown to produce long-working-distance nanojets ( 42 ) . However, producing these more complicated structures has proven challenging and typically requires either low-throughput techniques ( 42 ) like focused ion beam etching, toxic chemicals such as hydrofluoric acid ( 39 ), or both. Our approach overcomes several limitations of traditional microparticle manufacturing methods. Bacteria naturally adopt a rod-shaped geometry that is similar to a microcylinder. Although the length of these cylinders is disperse (~1–3 μm), their width is tightly regulated and is maintained to tolerances of approximately 10% ( 43 ). We have shown that we can produce a layered structure by polymerizing a polysilicate shell around our bacteria, and that this layered structure extends the intensity and working distance of the associated photonic nanojets compared to uncoated bacteria. Furthermore, our microparticles do not require expensive specialized equipment to fabricate and are made under ambient conditions without the use of harsh or toxic chemicals. Silicatein-displaying bacteria therefore offer an environmentally-friendly, high-throughput alternative to traditional manufacturing techniques. This study provides a proof-of-concept demonstration that synthetic biological manufacturing platforms such as ours have the potential to be employed for the fabrication of a variety of different types of optical devices. Our cells have a demonstrated ability to maintain metabolic activity for an extended timeframe of four to five months post-encapsulation, opening the door to a variety of applications wherein the live bio-microlenses could be used in sense-and-response applications to respond to environmental cues by activating a reporter pathway that would change their optical properties. Additionally, the well-documented ability of the silicatein enzyme to mineralize a range of chemical substrates ( 6 – 8 , 33 – 35 , 44 – 48 ) could allow for the creation of bacteria coated in a variety of materials that will convey unique optical or mechanical properties. Lastly, bacteria can be engineered to grow to different sizes and shapes, offering the potential to fabricate devices with bespoke optical properties, e.g. optimizing the size and length of photonic nanojets. The application of synthetic biology to create advanced photonic devices has the potential to greatly advance fields including microscopy, nanolithography, and biomedicine ( 3 )."
} | 2,555 |
36261484 | PMC9582017 | pmc | 6,731 | {
"abstract": "Rhodococcus opacus PD630 has considerable potential as a platform for valorizing lignin due to its innate “biological funneling” pathways. However, the transcriptional regulation of the aromatic catabolic pathways and the mechanisms controlling aromatic catabolic operons in response to different aromatic mixtures are still underexplored. Here, we identified and studied the transcription factors for aromatic degradation using GFP-based sensors and comprehensive deletion analyses. Our results demonstrate that the funneling pathways for phenol, guaiacol, 4-hydroxybenzoate, and vanillate are controlled by transcriptional activators. The two different branches of the β-ketoadipate pathway, however, are controlled by transcriptional repressors. Additionally, promoter activity assays revealed that the substrate hierarchy in R. opacus may be ascribed to the transcriptional cross-regulation of the individual aromatic funneling pathways. These results provide clues to clarify the molecule-level mechanisms underlying the complex regulation of aromatic catabolism, which facilitates the development of R. opacus as a promising chassis for valorizing lignin.",
"introduction": "Introduction The serious environmental problems caused by society’s dependence on fossil fuels, including climate crisis and ocean pollution, make it important to seek alternative methods to produce chemicals and fuels, particularly from renewable non-food biomass 1 . Lignocellulose, which consists of carbohydrate polymers (e.g., cellulose and hemicellulose) and aromatic polymers (e.g., lignin), represents the most abundant raw material for the potential production of next-generation renewable biofuels and chemicals 2 . To date, the major components of lignocellulose—cellulose, and hemicellulose—have been efficiently converted into various chemicals through biochemical routes 3 . Lignin, which is the second most abundant polymer on earth, holds promise as a renewable feedstock for the production of fuels and platform chemicals, due to its high carbon-to-oxygen ratio (above 2:1) and great energy density 4 – 7 . Moreover, recent research indicates that converting lignin to high-value fuels and chemicals would improve the overall competitiveness of biorefineries 8 – 12 . However, due to the structural heterogeneity of lignin, the depolymerization process typically results in diverse aromatic products, which are challenging to valorize 4 , 13 ; consequently, lignin is still under-utilized and treated primarily as waste 14 . To date, the most predominant strategies used for valorization—including depolymerization and fragmentation—require extensive separation and purification procedures, which are commercially non-profitable due to low yields and the low quality of their final products. Beyond chemical processing, biological treatment is a promising choice for lignin valorization; particularly, bacterial systems are increasingly attracting attention due to their inherent “biological funneling” processes 15 , 16 . Diverse aromatic streams can be funneled into uniform compounds (catechol (CAT) and protocatechuic acid (PCA)) and then be degraded through the β-ketoadipate pathway 17 , a process that can potentially overcome the challenges associated with the heterogeneity of lignin breakdown products 18 . Rhodococcus opacus PD630 (hereafter, R. opacus ), a Gram-positive soil bacterium that has the natural ability to tolerate and consume toxic aromatic compounds, has been considered a promising chassis for producing valuable products from aromatics 19 – 23 . Previous transcriptomic analysis of R. opacus cells grown on single aromatic compounds has identified the distinct funneling pathways for five lignin model compounds (benzoate, 4-hydroxybenzoate, phenol, vanillate, and guaiacol) 18 , 24 . Moreover, the roles of the two branches of the β-ketoadipate pathway in the degradation of those five compounds have been confirmed by gene knockout experiments 18 . Constitutively maintaining this catabolic flexibility could impose a metabolic burden on the host microbe, but this handicap is typically overcome by arranging the genes of each degradative pathway as operons whose summed expression is controlled by specific regulators and inducers. Thus, the success of a particular catabolic pathway depends not only on the efficacy of the catabolic enzymes but also on the specific regulatory elements governing their expression 25 . As genomic, genetic, and biochemical data have been accumulated, various regulatory proteins that control the expression of the aromatic degradation pathways have been identified and classified into different protein families 26 . Of additional interest are the sensory mechanisms of the regulatory elements, because regulatory proteins and their cognate promoters have the potential to be developed into biosensors that can be used in many applications, including drug discovery, biomedicine, food safety, defense, security, and environmental monitoring 27 . In R. opacus , comparative transcriptomics has revealed that the five aromatic funneling pathways previously identified were significantly upregulated in response to a subset of the aromatic compounds tested, suggesting that the expression of those pathway genes is likely to be tightly controlled by specific regulatory mechanisms. Furthermore, via the application of whole genome sequencing and comparative genomics, we have identified different families of transcription factors (TFs) that are located adjacent to R. opacus ’s proposed aromatic catabolic pathways 18 , 24 . However, the specific roles of these TFs in regulating the aromatic degradation pathways—such as the signals that trigger pathway expression and the exact mechanisms of activation and/or repression—are still unclear. In natural environments, carbon sources are commonly found as heterogeneous mixtures. To handle these mixtures, including the portion of aromatic compounds that are toxic, most bacteria have evolved a hierarchy of substrate utilization that enables them to quickly adapt their intracellular metabolic network toward a preferred substrate, which is vital for competition in these environments. This phenomenon, termed carbon catabolite repression (CCR), has been extensively reported in the utilization of sugar mixtures and non-sugar substrates 28 – 30 , but the number of studies conducted on substrate combinations containing only aromatic compounds is limited. To date, most studies have focused on benzoate and 4-hydroxybenzoate, which are commonly metabolized via the two parallel branches of the β-ketoadipate pathway. For example, in the γ-proteobacteria Pseudomonas putida PRS2000 and Acinetobacter sp. Strain ADP1, benzoate has been found to be the preferred substrate of the two 31 , 32 . Similarly, in the β-proteobacterium Cupriavidus necator JMP134, the same utilization hierarchy between benzoate and 4-hydroxybenzoate has been observed 33 . In Rhodococcus sp. strain DK17, a catabolite repression-like response has been reported when cells are simultaneously provided with benzoate and phthalate 34 . Moreover, benzoate catabolite repression of phenol degradation has been observed in Acinetobacter calcoaceticus PHEA-2 35 . Finally, in R. opacus , in a mixture of the five lignin model compounds previously described, benzoate was found to be consumed preferentially 18 , which suggests the existence of a substrate hierarchy. Although it is important for microbial lignin conversion strategies, this sequential consumption order is still underexplored. In this study, to identify those TFs involved in regulating the degradation of lignin model compounds, we selected potential TFs in the genomic neighborhood of aromatic operons and knocked them out via homologous recombination. By using metabolite sensors derived from native R. opacus promoters that can detect aromatic compounds and by comparing the cell growth and aromatic consumptions of these TF deletion mutants to those of the wild type (WT), we evaluated the roles of the candidate TFs in regulating the degradation pathway of each lignin model compound. To establish the substrate hierarchy of the tested lignin model compounds, we performed time-course analyses of the consumption of individual aromatics in the mixture, revealing that these compounds were consumed in a distinct order. Moreover, by testing the responses of the funneling pathways in WT cells grown on both individual lignin model compounds and a mixture, we confirmed that R. opacus can differentially and specifically regulate the funneling pathways in response to specific compounds, which is critical for the utilization of the preferred aromatic substrate. Taken together, these results advance our understanding of the regulatory patterns of the aromatic degradation pathways, which is critical to constructing a more efficient bacterial chassis for comprehensively utilizing lignin. Moreover, these insights into the mechanism of hierarchical utilization of aromatics in R. opacus are of great significance for achieving rapid consumption of complex aromatic mixtures, enabling more cost-effective conversion of lignin into fuels, chemicals, and materials.",
"discussion": "Discussion Unraveling the complex transcriptional regulation of the catabolism of aromatics in R. opacus is a prerequisite for engineering this promising chassis for many biotechnological applications. It was discovered that many related catabolic pathways did not carry the same regulatory system, suggesting that the regulatory systems and their target operons seem to become associated independently, which makes the regulatory system varied and complex 26 . In this study, the AraC-type regulators we found for controlling the expression of the phenol and the guaiacol funneling pathways are transcriptional activators (Figs. 1 and 2 ). Our results also demonstrated that phenol is the effector compound for inducing expression of the pheR2 - pheB2A2 cluster, but intriguingly, there is evidence to suggest that cis - cis muconate is also a ligand for TF pheR2 (Supplementary Fig. 1b ). While unexpected, this behavior is not unprecedented: our multi-omics data have revealed that the corresponding phenol degradation cluster ( pheB2A2 ) is also highly upregulated when treated with guaiacol (121-477-fold) 18 , another compound which is degraded through the CAT branch of the β-ketoadipate pathway. IclR-type TFs are generally recognized as transcriptional repressors 26 ; however, assays of promoter activity in both the WT and the mutant strains indicated that the IclR-type TF hbaR1 and vanR1 both work as transcriptional activators in regulating the expression of 4-hydroxybenzoate and vanillate funneling pathways, respectively (Figs. 3 and 4 ). This is not inconsistent with previous data, as IclR-type TFs have also been found to work as activators in regulating the catabolic pathways 42 . In addition, our findings also demonstrated that the transcriptional regulation of the two different branches of the β-ketoadipate pathway is controlled by separate IclR-type transcriptional repressors (Figs. 5 and 6 ). For the CAT branch, two regulatory modes have been reported: when CAT-degradation genes are controlled by LysR-type activators, the effector compound is usually the intermediate cis - cis muconate, whereas in operons under the regulation of IclR-type repressors, this role is mostly fulfilled by aromatic substrates 37 . Although the IclR-type transcriptional regulators have a similar structure as the LysR-type regulators 43 , the rather dissimilar amino acid sequences distinguish these two families. In this study, our results confirmed that the effector compound for induction of the CAT degradation pathway is cis - cis muconate, which is dramatically different from that of closely-related strain R. erythropolis CCM2595, where the expression of CAT-degradation genes is induced by phenol, rather than CAT or cis - cis muconate 37 . Although, the entire PCA branch of β-ketoadipate pathway consists of 8 individual genes, our analysis indicated a single, IclR-type repressor, pcaR (LPD05454), is responsible for regulating the entire PCA operon (Fig. 6 ). This is also a departure from precedent, as for example, the mycobacterium C. glutamicum places the PCA degradation pathway under the control of two different regulators: pcaIJ and pcaFDO are regulated by the IclR-type repressor pcaR , whereas the expression of pcaHG is controlled by an atypically large ATP-binding LuxR family (LAL)-type activator pcaO 38 , 44 . In general, the coding gene for an IclR-type TF lies upstream of its target gene cluster and is transcribed in the opposite direction 26 . In this study, however, we found that the location and transcription orientation of IclR-type regulators varied. For instance, the catR regulator is located upstream of the CAT degradation operon, and is transcribed divergently (Fig. 5a ); however, the upstream-located hbaR1 is transcribed in the same orientation as the critical gene in the 4-hydroxybenzoate funneling pathway (Fig. 3a ). Notably, the vanR1 and pcaR are located within their target operons (Figs. 4 a and 6a ), arranged in their genomic neighborhoods in a way which may provide extra regulatory functions. vanR1 is located immediately downstream of the vanillate monooxygenase reductase vanB (LPD00563) and shares a promoter with this operon (Fig. 4a ), an arrangement which suggests that the vanillate degradation process may be regulated through a positive feedback loop (PFL) 45 . While studies have demonstrated that the positive-feedback response to an environmental signal is slower than in those systems that produce the regulatory protein constitutively 46 , this moderate delay could be beneficial in R. opacus as a means of ordering the action of cellular response mechanisms in time, e.g., by upregulating the gene clusters involved in one-carbon compound metabolism to prepare for the harmful formaldehyde released during a demethylation step of vanillate catabolism. For pcaR , however, the regulator lies in the middle of the pcaH - pcaF (LPD05450-05455) coding region and thus shares the promoter, resulting in a negative feedback loop (NFL). Because some of the pca genes ( e.g ., pcaIJ and pcaFD ) are also involved in the degradation of CAT, this NFL allows the enhanced PcaR to downregulate the expression of those genes, which consequently serves as a metabolic node for controlling the carbon flux of the β-ketoadipate pathway towards the TCA cycle to reduce succinate overflow 47 . In nature, carbon and energy resources are often limited. Thus, specific bacteria that are more efficient or more selective in utilizing the carbon sources in their habitat will have significantly higher growth rates and therefore greater competitive success than other microorganisms 48 . In this work, we found that benzoate is the most-preferred substrate among the model compounds we tested. Additionally, we were able to firmly establish the substrate hierarchy of all tested compounds in R. opacus (Figs. 7 – 8 and Supplementary Figs. 5 – 9 ). The preference for benzoate can presumably be traced to the different energetic demands of the funneling pathways—the conversion of benzoate to CAT consumes no net reducing equivalents because the NADH oxidized in the first step is recovered during the next dehydrogenation reaction by an NAD + -dependent dehydrogenase. In contrast, the conversions of 4-hydroxybenzoate or vanillate to PCA and phenol or guaiacol to CAT require the oxidation of NAD(P)H 18 , 34 . Energetic considerations cannot, however, explain the preference for 4-hydroxybenzoate, since the initial steps in the metabolisms of 4-hydroxybenzoate, phenol, and guaiacol have similar requirements for reducing equivalents. To facilitate understanding the complex intracellular dynamics, a cybernetic model has been previously developed to describe the resource allocation and microbial kinetics that influence the hierarchical utilization of carbon sources 49 ; different variants of this model have been able to account for a variety of instances of preferential carbon uptake in E. coli 50 . From this resource-allocation point of view, it is reasonable to find that R. opacus prefers 4-hydroxybenzoate over phenol and guaiacol. Compared to phenol, which has redundant funneling pathways, and guaiacol, which requires an accessory demethylation pathway to detoxify formaldehyde released from the funneling process, the 4-hydroxybenzoate funneling pathway’s “single regulator-single enzyme” unit is relatively simple and thus less resource-intensive. Intriguingly, although the vanillate and guaiacol funneling pathways both require reducing equivalents and the accessory demethylation pathway, our results indicated preferential utilization of vanillate over guaiacol (Supplementary Fig. 9 ). This preference cannot be explained by the two theories discussed above, but the difference in the structures of the two funneling pathway operons may indicate that the preference is attributable to the mechanistic model 51 . Specifically, in a mixture of vanillate and guaiacol, the proposed PFL regulation pattern for vanillate keeps the TF vanR1 preferentially expressed, which in turn could further promote the induction of the vanillate funneling pathway, supporting fast cell growth. Fast growth on vanillate may result in dilution of guaR , thus preventing effective induction of the guaiacol funneling pathway. While these models developed in E. coli can be used to provide a coarse-grained description of the preferential utilization of aromatics, we believe that a new generation of models, specifically and precisely tuned for R. opacus , is still needed. Our results also provide some insights into the mechanism of the substrate hierarchy in R. opacus , or more specifically, the transcriptional cross-regulation of the funneling pathways. Induction prevention has been used to describe the preferential utilization of aromatics in other strains 31 – 33 . However, our results showed that in the mutants ∆ hbaR1 and ∆ pobA (for which degradation of 4-hydroxybenzoate was abolished), no transcriptional repression of the phenol or guaiacol funneling pathway was observed (Fig. 9 and Supplementary Fig. 10 ), suggesting that the preference for 4-hydroxybenzoate cannot be explained by induction prevention alone. Inspired by the resource allocation view, we speculate that this preference might be controlled by a novel global regulation mechanism. More specifically, due to the relative simplicity of the 4-hydroxybenzoate funneling pathway, the investment towards the utilization of 4-hydroxybenzoate is much smaller, which allows cells to maximize profit. In this scenario, once cells detect 4-hydroxybenzoate in the environment, more resources are allocated to the 4-hydroxybenzoate degradation pathway, with transcription and translation of the operons which enable the degradation of phenol or guaiacol consequently decreased, resulting in the sequential utilization of the three compounds. Transporter-mediated inducer exclusion has been used to describe the molecular mechanism of the substrate hierarchy in Bacillus species 29 . In this study, we also studied the transcriptional regulation of the annotated shikimate transporter LPD06699 and found that the expression of this transporter could be induced by all the model compounds we tested; furthermore, this upregulation was enhanced in the TF deletion mutant (∆LPD06698) (Supplementary Figs. 11 – 12 ). Interestingly, in the vanillate scenario alone, the upregulation of this transporter improved carbon utilization (Supplementary Fig. 12e ), supporting the hypothesis that inducer exclusion might be one of the mechanisms responsible for the substrate hierarchy in R. opacus that still needs to be evaluated. In conclusion, we identified and investigated the TFs involved in regulating several aromatic degradation pathways in R. opacus by combining gene knockouts with aromatic sensors. We also observed that individual lignin model compounds in an aromatic mixture are consumed by R. opacus in sequential order and that this preferential utilization pattern can be ascribed to the transcriptional cross-regulation of the funneling pathways. While we have been able to describe many mechanisms of individual pathway control, aspects of how these pathways interact have yet to be fully explained. Nonetheless, our results can inform the development of strain-specific models of R. opacus metabolism for industrial applications."
} | 5,175 |
37485527 | PMC10359720 | pmc | 6,732 | {
"abstract": "There is virtually no environmental process that is not dependent on temperature. This includes the microbial processes that result in the production of CH 4 , an important greenhouse gas. Microbial CH 4 production is the result of a combination of many different microorganisms and microbial processes, which together achieve the mineralization of organic matter to CO 2 and CH 4 . Temperature dependence applies to each individual step and each individual microbe. This review will discuss the different aspects of temperature dependence including temperature affecting the kinetics and thermodynamics of the various microbial processes, affecting the pathways of organic matter degradation and CH 4 production, and affecting the composition of the microbial communities involved. For example, it was found that increasing temperature results in a change of the methanogenic pathway with increasing contribution from mainly acetate to mainly H 2 /CO 2 as immediate CH 4 precursor, and with replacement of aceticlastic methanogenic archaea by thermophilic syntrophic acetate-oxidizing bacteria plus thermophilic hydrogenotrophic methanogenic archaea. This shift is consistent with reaction energetics, but it is not obligatory, since high temperature environments exist in which acetate is consumed by thermophilic aceticlastic archaea. Many studies have shown that CH 4 production rates increase with temperature displaying a temperature optimum and a characteristic apparent activation energy ( E a ). Interestingly, CH 4 release from defined microbial cultures, from environmental samples and from wetland field sites all show similar E a values around 100 kJ mol −1 indicating that CH 4 production rates are limited by the methanogenic archaea rather than by hydrolysis of organic matter. Hence, the final rather than the initial step controls the methanogenic degradation of organic matter, which apparently is rarely in steady state.",
"conclusion": "Conclusion Methanogenic microbial communities catalyze the anaerobic degradation of organic matter to CO 2 and CH 4 . This process is basically the same in various environments, such as rice paddy fields, wetlands, lake sediments, peat bogs, anaerobic digestors, or the intestinal tract of animals. The pathway of the process is also basically the same. Irrespective of the particular environment, the degradation steps and the metabolic classes of the microrganisms involved are basically the same ( Figure 1 ). Nevertheless, the exact composition of the methanogenic microbial communities can be quite different in the different environments. Furthermore, the composition can change with temperature, thus resulting in a change of the pathway of the degradation process. Thus, it is frequently observed that the pathway and the responsible methanogenic microbial community changes from psychrophilic to mesophilic to thermophilic conditions, with dominance of aceticlastic methanogenesis at low and hydrogenotrophic methanogenesis at high temperatures ( Figure 5 ). Such behavior is consistent with thermodynamics of the critical steps in organic matter degradation, but is nevertheless not obligatory. Thus, environments exist, in which CH 4 production is dominated by hydrogenotrophic methanogenesis despite low temperatures and aceticlastic methanogenesis despite high temperatures, simply because these environments contain the respective psychrophilic and thermophilic species. Microorganisms proliferate within and tolerate a more or less wide range of temperatures. This is observed in environments that experience only small temperature fluctuations (lake sediments, technical digesters, hot springs) and also in others that experience dramatic temperature changes on a daily or seasonal range (littoral sediments, rice paddies, peatlands). The microorganisms generally display characteristic temperature optima and a characteristic increase of reaction kinetics with increasing temperature, which can be modelled by the Arrhenius equation using a characteristic apparent activation energy ( E a ). Interestingly, methanogenic archaea were found to exhibit a rather high range of E a values of >100 kJ mol −1 . In the environment, hydrolysis of complex organic matter is the first step of the methanogenic degradation processes, and this process can exhibit markedly lower E a values of about 60 kJ mol −1 . However, analysis of various methanogenic environments with complex microbial communities display E a values, which are not similar to those of hydrolysis but to those of the methanogenic archaea. This observation indicates that hydrolysis of organic matter is not the rate limiting step of CH 4 production in most environments, meaning that the microbial community is frequently supplied with pulses of easily degradable substrates.",
"introduction": "Introduction There is virtually no microbial activity that would not be regulated by temperature ( Wiegel, 1990 ). Biological activity is based on chemical reactions. Chemical reactions are controlled by temperature and so is biological activity. Therefore, temperature dependence of biological activity follows the same physical principles as chemical reactions do. In fact, it is predominantly enzyme-based biochemical reactions, which control biological activity. Therefore, biological activity always exhibits a temperature optimum, beyond which enzymes become inactivated ( Radmer and Kok, 1979 ), while purely chemical reactions may tolerate much higher temperatures. Microbial life is complex. Microorganisms contain many different enzymes, which may react differently upon temperature changes. Microbial populations consist of many different individual microorganisms, each possibly slightly different in its response to temperature. In nature, microbial communities consist of many different microbial populations with different physiologies and life styles and thus, with potentially different features of temperature dependence. Therefore, it is by principle very complex how environmental microbial communities will react to temperature changes and it is hard to make any predictions. Nevertheless, it is worthwhile to review the literature on temperature dependence of microbial activity to see whether there are any guiding principles. In the following I will primarily (but not exclusively) focus on methanogenic microbial communities living in anoxic environments such as flooded rice fields and aquatic sediments, whose temperature characteristics have frequently been studied over the last 40–50 years. A previous mini-review on this subject ( Conrad, 2008 ) will be updated and expanded. The temperature dependence of methanogenic communities is of particular interest, since methane is an important greenhouse gas, which is partially responsible for past and present climate change and will in turn be affected by the global temperature increase in many respects ( Kirschke et al., 2013 )."
} | 1,729 |
28470798 | null | s2 | 6,733 | {
"abstract": "Small regulatory RNAs have major roles in many regulatory circuits in Escherichia coli and other bacteria, including the transition from planktonic to biofilm growth. We tested Hfq-dependent sRNAs in E. coli for their ability, when overproduced, to inhibit or stimulate biofilm formation, in two different growth media. We identify two mutually exclusive pathways for biofilm formation. In LB, PgaA, encoding an adhesion export protein, played a critical role; biofilm was independent of the general stress factor RpoS or CsgD, regulator of curli and other biofilm genes. The PgaA-dependent pathway was stimulated upon overproduction of DsrA, via negative regulation of H-NS, or of GadY, likely by titration of CsrA. In yeast extract casamino acids (YESCA) media, biofilm was dependent on RpoS and CsgD, but independent of PgaA; RpoS appears to indirectly negatively regulate the PgaA-dependent pathway in YESCA medium. Deletions of most sRNAs had very little effect on biofilm, although deletion of hfq, encoding an RNA chaperone, was defective in both LB and YESCA. Deletion of ArcZ, a small RNA activator of RpoS, decreased biofilm in YESCA; only a portion of this defect could be bypassed by overproduction of RpoS. Overall, sRNAs highlight different pathways to biofilm formation."
} | 321 |
27221658 | PMC4879726 | pmc | 6,739 | {
"abstract": "Background We report on the functional screening and identification of an active quorum quenching (QQ) gene in the Komagataeibacter europaeus strain CECT 8546, which is a member of the acetic acid bacteria (AAB). Results Using a previously published screening protocol (Schipper et al., in Appl Environ Microbiol 75:224–233, 2009 . doi: 10.1128/AEM.01389-08) for QQ genes, we identified a single gene, designated gqqA , that interfered strongly with bacterial quorum sensing (QS) in various reporter strains. It encodes for a 281-amino acid protein with a molecular mass of 30 kDa. Although the GqqA protein is similar to predicted prephenate dehydratases, it does not complement Escherichia coli mutants of the pheA gene, thus indicating a potentially different function. Recombinant GqqA protein attenuated QS-dependent pyocyanin production and swarming motility in the Pseudomonas aeruginosa strain PAO1. Moreover, GqqA quenched the QS response of the Agrobacterium tumefaciens NTL4 and the Chromobacterium violaceum CV026 reporter strains. Interestingly, the addition of recombinant GqqA protein to growing cultures of the Komagataeibacter europaeus strain CECT 8546 altered the cellulose production phenotype of CECT 8546 and other AAB strains. In the presence of GqqA protein, cells were planktonic, and no visible cellulose biofilms formed. The addition of low levels of N -acylhomoserine lactones maintained the biofilm formation phenotype. Conclusions Our data provide evidence for an interconnection between QS and AAB cellulose biofilm formation as well as QQ activity of the GqqA protein. Electronic supplementary material The online version of this article (doi:10.1186/s12934-016-0482-y) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions The data provided within this work imply a noticeable effect of the GqqA protein on cellulose biofilm production for the strain CECT 8546 of Komagataeibacter europaeus and other AAB strains. This is a novel finding, and no report has been published in which a protein with light homology to other described AHL-degrading molecules and with high similarity to PDT enzymes presents QQ activity. Further work is necessary to elucidate the mechanisms and the regulatory circuits of this potential QQ protein.",
"discussion": "Discussion AAB are primarily known to be involved in vinegar production, in which they develop a biofilm at the air–liquid interface, generally when vinegar production is carried out with the traditional method [ 9 ]. In the last few years, an N -AHL-dependent QS mechanism designated as the GinI/GinR system in Komagataeibacter intermedius has been reported to be responsible for the repression of acetic acid and gluconic acid production, antifoam activity, and growth rate acceleration in the exponential growth phase [ 13 – 15 ]. This system is regulated by long chain N -AHL molecules such as N -C10- l -HSL and N-C12- l -HSL [ 13 ]. Despite current knowledge of the QS systems of AAB bacteria, no studies have yet been published examining QQ activities. Therefore, in this work, the first screening for QQ activity was carried out from the genome of Komagataeibacter europaeus CECT 8546, a cellulose-overproducing AAB strain [ 16 ]. A protein named GqqA was identified within a fosmid library of this strain, and its potential QQ role was characterized. Thereby, tests using the reporter strains A. tumefaciens NTL4 and C. violaceum Cv026 confirmed the QQ activity of the GqqA protein. Furthermore, we provided evidence that the GqqA protein affected QS-dependent processes in the P. aeruginosa strain PAO1, such as motility and pyocyanin production. Although the molecular mechanism by which GqqA acts on the AHL molecules is not yet known, these results suggest a modification of the QS mechanisms from the reporter strains. Additional assays were performed to test the possible effects of the GqqA protein on the growth and physiology of the strain CECT 8546 and other AAB cellulose-producing strains. Generally, the cells of the CECT 8546 strain tended to aggregate in a cellulose biofilm, but interestingly, in the presence of the GqqA protein, no cellulose aggregates were formed, and the turbidity of the medium increased. This observation was not only obtained for strain CECT 8546 of Komagataeibacter europaeus but also for other cellulose-producing strains belonging to the Acetobacter and Komagataeibacter genera. These results imply that the GqqA protein exerts an effect on the cellulose production of AAB strains. Because cellulose production during vinegar production is industrially undesirable [ 20 , 21 ], the finding that the GqqA protein interferes with cellulose production, at least in some AAB strains, is of biotechnological relevance. Moreover, these results could also contribute to further knowledge of the synthesis mechanism for this polymer in AAB. Conversely, as far as we know, there is no evidence of QS control for cellulose biofilm formation in AAB. However, it is well known that there is an inverse relationship between gluconic acid production and cellulose formed in this bacterial group and how these pathways are connected with sugar metabolism [ 22 ]. Moreover, it has been reported that gluconic acid biosynthesis is controlled by QS systems in Komagataeibacter intermedius [ 13 ]. Altogether, these data indicate a role for QS in cellulose formation in AAB. There are three main types of microbial enzymes whose activity has been demonstrated in N -AHL signaling interference: oxidoreductases, acylases, and lactonases [ 7 , 8 ]. The best characterized group of enzymes able to cleave the N -AHL molecules are lactonases, which can hydrolyze the lactone ring in a reversible way [ 7 ]. The phylogenetic analysis performed with the amino acid sequences of the GqqA protein and those of known QQ proteins grouped the GqqA protein in a separate cluster from the other QQ proteins. The predicted amino acid sequence of the GqqA protein as well as the DNA sequence of the ORF presented the highest homologies with predicted PDTs from AAB. Curiously, the GqqA protein was only faintly similar to PDT enzymes that had been functionally verified; a homology of 31 % with the PDT sequence of the strain E. coli H120 (EGB42307.1) was observed. Moreover, the complementation assays performed with two E. coli mutants of the pheA gene revealed that the gqq A gene could not restore these auxotrophic strains. These observations suggest a different function for the GqqA protein in the CECT 8454 strain and in other AAB."
} | 1,648 |
30038047 | PMC6148476 | pmc | 6,740 | {
"abstract": "Gram-negative metal-reducing bacteria utilize electron conduits, chains of redox proteins spanning the outer membrane, to transfer electrons to the extracellular surface. Only one pathway for electron transfer across the outer membrane of Geobacter sulfurreducens has been linked to Fe(III) reduction. However, G. sulfurreducens is able to respire a wide array of extracellular substrates. Here we present the first combinatorial genetic analysis of five different electron conduits via creation of new markerless deletion strains and complementation vectors. Multiple conduit gene clusters appear to have overlapping roles, including two that have never been linked to metal reduction. Another recently described cluster (ExtABCD) was the only electron conduit essential during electrode reduction, a substrate of special importance to biotechnological applications of this organism.",
"introduction": "INTRODUCTION Microorganisms capable of extracellular respiration can alter the redox state of particulate metal oxides in soils and sediments, controlling their solubility and bioavailability ( 1 – 6 ). To respire with extracellular metals, bacteria must first transfer electrons from the cell interior to outer surface redox proteins, requiring unique transmembrane pathways compared to those for growth with intracellularly reduced compounds. The use of surface-exposed electron transfer proteins and conductive appendages by these organisms presents opportunities for transformation of heavy metals, biological nanoparticle synthesis, and a new generation of microbially powered electrochemical devices using bacteria grown on electrodes ( 7 – 13 ). An extracellular electron transfer strategy must overcome several challenges. In Gram-negative cells, a conductive pathway capable of crossing the inner membrane, periplasm, and outer membrane must first be constructed ( 14 , 15 ). Such pathways are capable of delivering electrons to soluble metals or redox-active molecules, but insoluble metal oxides present additional barriers. Fe(III) and Mn(IV) oxides vary widely in chemistry, surface charge, redox state, and surface area; thus, an additional suite of proteins or appendages such as pili may be needed to link cell surfaces with different terminal minerals ( 16 – 18 ). Many metal-reducing bacteria can also transfer electrons to electrodes ( 8 , 19 – 21 ). Unlike metal oxide particles, electrodes represent unlimited electron acceptors allowing cells in contact with the inorganic surface to support growth of more distant cells, if they can create a conductive network of proteins that relay electrons to cells at the electrode. The physiological and chemical differences between soluble metals, metal particles, and electrodes raise the possibility that different electron transfer proteins are needed to access each kind of extracellular mineral, surface, or molecule. A model organism widely studied for its ability to reduce a diversity of metals and electrodes is the deltaproteobacterium Geobacter sulfurreducens , and recent work supports a model in which different electron transfer proteins are used depending on substrate conditions. At the inner membrane, where electrons first leave the quinone pool, a combination c - and b -type cytochrome, CbcL ( 22 ), is required only when extracellular metals and electrodes are below redox potentials of −0.1 V versus the standard hydrogen electrode (SHE), while the inner membrane c -type cytochrome ImcH ( 23 ) becomes essential if acceptors are at higher redox potentials ( 18 ). In another example, in the extracellular matrix beyond the cell surface, chemistry rather than redox potential appears to delineate which proteins are essential for electron transfer. The secreted cytochrome OmcZ and pilus-based appendages are primarily linked to electrode growth, while the secreted cytochrome PgcA enhances reduction of Fe(III) oxides without affecting electrode growth ( 24 – 31 ). Between the initial CbcL/ImcH-dependent event of inner membrane proton motive force generation and extracellular pilus/OmcZ/PgcA interactions lies the outer membrane, a less understood barrier that was recently found to contain electron transfer proteins of surprising complexity ( 32 – 34 ). The only known mechanism for nondiffusive electron transfer across the outer membrane is through a transmembrane “electron conduit” consisting of an integral outer membrane protein anchoring a periplasmic multiheme cytochrome to an outer surface lipoprotein cytochrome. By linking redox active cofactors within a membrane-spanning complex, electron flow is permitted ( 32 , 35 ). The first electron conduit described was the ∼210-kDa MtrCAB complex from Shewanella oneidensis , which catalyzes electron transfer across membranes when purified and placed in lipid vesicles ( 36 – 38 ). The mtrCAB gene cluster is essential for reduction of all tested soluble metals, electron shuttles, metal oxides, and electrodes by S. oneidensis ( 37 , 39 , 40 ). Related complexes capped with an extracellular dimethyl sulfoxide (DMSO) reductase allow Shewanella to reduce DMSO on the cell exterior, while similar outer membrane conduits support inward electron flow by Fe(II)-oxidizing Rhodopseudomonas TIE-1 cells ( 41 , 42 ). In G. sulfurreducens , a gene cluster encoding the periplasmic cytochrome OmbB, the outer membrane integral protein OmaB, and lipoprotein cytochrome OmcB forms a conduit complex functionally similar to MtrCAB, though the two complexes lack any sequence similarity ( 34 ). This ombB-omaB-omcB gene cluster is duplicated immediately downstream in the G. sulfurreducens genome as the nearly identical ombC-omaC-omcC , together forming the omcBC cluster. Antibiotic cassette insertions replacing omcB , as well as insertions replacing the entire ombB-omaB-omcB conduit, decrease growth with Fe(III) as an electron acceptor, but the impacts differ between reports and growth conditions ( 43 – 45 ). This variability and residual electron transfer activity suggested the presence of alternative pathways able to catalyze electron transfer across the outer membrane ( 33 ). New evidence for undiscovered outer membrane complexes was recently detected in genome-wide transposon data, which showed that insertions in omcB or omcC had no effect on G. sulfurreducens growth with electrodes poised at −0.1 V versus SHE, a low potential chosen to mimic the redox potential of Fe(III) oxides ( 46 ). Transposon insertions within an unstudied four-gene cluster containing c -type cytochrome conduit signatures caused significant defects during growth on the same −0.1-V electrodes ( 46 ). Deletion of this new cluster, named extABCD , severely affected growth on low-potential electrodes, while Δ extABCD mutants still grew similarly to the wild type with Fe(III) oxides. In contrast, deletion of the entire omcBC cluster had little impact on low-potential electrode growth ( 46 ). These data suggested that the outer membrane proteins essential for electron transfer across the membrane might vary depending on environmental conditions. However, these data involved only single deletions without complementation, and whether different gene clusters were necessary across the full range of environmentally relevant conditions, such as higher redox potentials, during growth with mineral forms such as Mn(VI), or when metals become soluble, was not tested. Using new markerless deletion methods, we constructed mutants containing all combinations of the four putative conduit clusters on the genome of G. sulfurreducens . Each of these 15 mutants plus 3 strains containing expression vectors were then directly compared with five electron acceptors: Fe(III) and Mn(IV) oxides, poised electrodes at two different redox potentials, and soluble Fe(III) citrate. We found that during metal reduction the largest defects were in Δ omcBC strains, but deletion of the newly identified cluster extEFG in the Δ omcBC background was necessary to most severely inhibit Fe(III) reduction, and deletion of all clusters was required to eliminate reduction of both soluble and insoluble metals. Strains containing only a single cluster showed preferences for reduction of different metals, such as the extEFG - and extHIJKL -only strains performing better with Mn(IV) oxides than Fe(III) oxides. When electrodes were the electron acceptor, only strains lacking extABCD showed a growth defect, and this effect was similar at all redox potentials. A strain still containing extABCD but lacking all other conduit clusters grew faster and to a higher final density on electrodes, and a complemented strain lacking all other conduit clusters expressing extABCD from a vector also grew faster than the wild type. These data provide evidence that different G. sulfurreducens conduit clusters are necessary during extracellular electron transfer depending on the extracellular substrate. (This article was submitted to an online preprint archive [ 47 ].)",
"discussion": "DISCUSSION Sequencing of the G. sulfurreducens genome revealed an unprecedented number of electron transfer proteins, with twice as many genes dedicated to respiratory and redox reactions as in organisms with similarly sized genomes ( 63 ). Out of 111 c -type cytochromes, 43 had no known homolog, and many were predicted to reside in the outer membrane. The large complement of outer membrane redox proteins in G. sulfurreducens became even more of an anomaly as the simpler electron transfer strategy of metal-reducing S. oneidensis emerged. If Shewanella requires only a single inner membrane cytochrome and a single outer membrane conduit to reduce a multitude of substrates ( 36 , 39 , 40 , 53 ), why does Geobacter have so many cytochromes? Evidence that more than one G. sulfurreducens outer membrane pathway exists for reduction of extracellular substrates accumulated in at least 11 separate studies since the discovery of OmcB ( 34 , 43 , 45 ). Deletion of omcB impacted Fe(III) reduction but had little effect on U(IV) or Mn(IV) oxide reduction ( 51 , 64 ). A Δ omcB suppressor strain that evolved for improved Fe(III) citrate growth still reduced Fe(III) oxides poorly ( 44 ). Strains lacking omcB grew similarly to the wild type on electrodes in four different studies ( 24 , 29 , 57 , 65 ), and OmcB abundance was shown to be lowest on cells near electrodes ( 66 ). An insertional mutant lacking six secreted or outer membrane-associated cytochromes in addition to OmcB still demonstrated Fe(III) oxide reduction ( 67 ). After replacing the entire omcBC region with an antibiotic resistance cassette and still finding residual Fe(III) reduction ability, Liu et al. ( 45 ) speculated that other c -type cytochrome conduit-like clusters in the genome might be active. Most recently, transposon sequencing (Tn-seq) analysis of electrode-grown cells revealed little effect of omcB cluster mutations yet identified significant defects from insertions in unstudied clusters with c -type cytochrome features ( 46 ). This combined evidence led us to seek alternative conduit gene clusters that could address both the long-standing mystery of growth by omcB mutants and the complexity of electron transfer proteins in the Geobacter genome. The genetic analysis presented here supports a role for these unstudied conduit gene clusters during extracellular respiration. All mutants still containing at least one cluster retained a partial ability to reduce metals, while deletion of all clusters—the entire omcBC region, plus all three ext clusters—finally was able to eliminate metal reduction. This overlapping function of some clusters helps explain the reported variability between laboratory strains and the rapid evolution of suppressors in Δ omcB mutants. In the case of electrodes at both high and low potentials, only deletion of extABCD altered phenotypes. Additionally, a strain with only extABCD remaining on the genome outperformed the wild type in terms of growth rate and final current density when grown on electrodes. Since expression of extABCD was also able to restore reduction of the soluble acceptor Fe(III) citrate, this cluster can confer the phenotype of extracellular respiration under a condition where pili and secreted cytochromes are not known to be important, supporting the conclusion that extABCD encodes proteins involved in electron transfer. Overall, these data show that for all tested metal acceptors, more than one conduit cluster is necessary for wild-type levels of reduction, any one cluster can support partial reduction of many metals, and only one cluster can be linked to electrode respiration. Genetic analyses are typically a first step, designed to reveal which genes are necessary for a phenotype and worthy of further study. Biochemical and biophysical analyses will be needed to (i) prove if products of ext gene clusters indeed function as conduits to transfer electrons across the outer membrane and (ii) identify the proteins or metals these complexes interact with to explain why these clusters seem so tightly linked to growth with certain substrates. Expression analyses failed to detect large differences in ext or omcBC family genes during transitions between acceptors, arguing against changes in expression as an explanation for specificity. Our ability to complement growth with electrodes in the Δ5 mutant by expressing extABCD from a vector, while the omcB conduit could not complement growth, further argues against expression differences causing these phenotypes. Unknown posttranscriptional events could be caused by the absence of different gene clusters, but the conclusion that these gene clusters are necessary remains the same. To reduce metal particles or surfaces likely requires each membrane-bound complex to interact with extracellular proteins, such as OmcZ, OmcS, PgcA, or pili, to aid transfer of electrons to the final destination. If these partner proteins are not expressed or made available under all conditions, an outer membrane complex may not be capable of contributing to respiration. In the case of soluble metals such as Fe(III) citrate, conduit complexes should be able to directly reduce the acceptor, making apparent specificity more likely due to the ability of the complex(es) to interact with Fe(III) directly. It is also important to consider lessons from insertional deletions in G. sulfurreducens , such as the diheme peroxidase MacA. MacA was initially hypothesized to be an inner membrane quinone oxioreductase, based on the defective phenotype of Δ macA mutants during Fe(III) citrate reduction ( 68 ); this phenotype was later explained by Δ macA mutants not expressing omcB , as the Δ macA phenotype could be rescued by expressing omcB from a vector ( 69 , 70 ). As MacA is now known to instead be a soluble peroxidase, oxidative stress in early Δ macA mutants studied could have resulted in global downregulation of cytochromes. In our work, the availability of every combination of gene cluster deletion and acceptor condition allows many general downregulation hypotheses to be eliminated. For example, if deletion of extABCD suppressed production of pili or cytochromes such as OmcS, all Δ extABCD mutants would be predicted to show both electrode and metal oxide defects, which we did not observe. Initial transcriptomic surveys also failed to find severe or off-target transcriptional effects on known electron transfer proteins from deletion of ombB-omaB-omcB-orfS-ombC-omaC -o mcC , extEFG , or extHIJKL that could explain the enhanced growth of the extABCD + mutant. The fact that only the ombB-omaB-omcB cluster was necessary to restore Fe(III) citrate reduction further indicated that orfS was not essential. However, all of these deletions removed many parts of the genome which were not tested for complementation by single genes, leaving open the possibility of regulatory interactions. Also, in a complex system such as this, posttranslational events such as polymerization of pilin monomers into filaments and extracellular cytochrome secretion could be affected by the absence of specific proteins under specific conditions. It is difficult to detect negative interactions via RNA-seq or proteomic analyses when mutants fail to grow, but such effects should be addressed in future suppressor and heterologous expression studies, now that these clusters have been identified. Insights from similar gene clusters in related organisms. It remains difficult to predict any function for multiheme cytochromes based on sequence alone, so their genetic context may reveal other clues to their role and aid identification of such clusters in other genomes. None of the ext regions fits the pattern of the mtr 3-gene cytochrome conduit operon of genes for one small (∼40-kDa) periplasmic cytochrome, an integral outer membrane protein, and one large (>90-kDa) lipoprotein cytochrome. For example, extABCD includes genes for two small lipoprotein cytochromes, extEFG is part of a hydrogenase family transcriptional unit, and extHIJKL contains the gene for a rhodanese-like lipoprotein instead of an extracellular cytochrome ( Fig. 1 ). Specifically, the transcriptional unit beginning with extEFG includes a homolog of YedY family periplasmic protein repair systems described for Escherichia coli ( 71 ), followed by the gene for an NiFe hydrogenase similar to bidirectional Hox hydrogenases used to recycle reducing equivalents in cyanobacteria ( 72 – 74 ). Rhodanase-like proteins related to ExtH typically are involved in sulfur metabolism ( 75 – 77 ), and an outer surface ExtH/rhodanese-like protein is linked to extracellular oxidation of metal sulfides by Acidithiobacillus ferrooxidans ( 78 ). Deletion of extI in G. sulfurreducens causes a severe defect in selenite and tellurite reduction ( 79 ). These links to metabolism of hydrogen, sulfur, and other oxyanions suggest roles outside metal reduction, and future genomic searches for electron conduit clusters should consider the possibility of noncytochrome components, such as FeS clusters, as the exposed lipoprotein. Now that genes from ext operons can be used in searches of other genomes, an interesting pattern emerges in putative conduit regions throughout Desulfuromonadales strains isolated from freshwater, saline, subsurface, and fuel cell environments ( Fig. 9 ). In about one-third of cases, an entire cluster is conserved intact, such as extABCD in Geobacter anodireducens , Geobacter soli , and Geobacter pickeringii ( Fig. 9B ). However, when differences exist, they are typically nonorthologous replacements of the outer surface lipoprotein, such as where extABC is followed by a new cytochrome in Geobacter metallireducens , Geoalkalibacter ferrihydriticus , and Desulfuromonas soudanensis . Conservation of the periplasmic cytochrome with replacement of the outer surface redox lipoprotein also occurs frequently in the omcB and extHIJKL clusters ( Fig. 9A and D ). For example, of 18 extHIJKL regions, 10 contain a different extracellular rhodanese-like protein with extIJKL , each with less than 40% identity to extH . This remarkable variability in extracellular components, compared to conservation of periplasmic redox proteins, suggests much higher rates of gene transfer and replacement of domains that are exposed to electron acceptors and the external environment. FIG 9 Cytochrome conduit conservation across the order Desulfuromonodales . Shown is a representation of cytochrome conduit clusters from the Desulfuromonodales with homologs to either OmcBC (A), ExtABCD (B), ExtEFG (C), or ExtHIJKL (D). Complete clusters with all components sharing >40% identity to the corresponding G. sulfurreducens cytochrome conduit are indicated in boxes to the left of each gene cluster. Clusters in which one or more proteins are replaced by a new element with <40% identity are listed on the right side of each gene cluster. Numbers with proteins indicate the percent identity to the G. sulfurreducens version. Red arrows, putative outer membrane products with a predicted lipid attachment site; yellow arrows, predicted periplasmic components; green arrows, predicted outer membrane anchor components. Superscript letters a to d indicate the following: a, OmcBC homologs in these gene clusters also encode Hox hydrogenase complexes; b, gene clusters have contiguous extBCD loci but extA is not in the vicinity, as extA was found in separate parts of the genome for some of those organisms (see Table S2 in the supplemental material); c, the gene cluster has additional lipoprotein decaheme c -type cytochrome upstream of extE ; d, lipid attachment sites corresponding to ExtJL could not be found, but there is an additional small lipoprotein encoded within the gene cluster. For ExtHIJKL clusters, homologs depicted above extH are found in gene clusters containing only extI , whereas homologs depicted below extH are found in gene clusters containing full extHIJKL loci. Upstream and on the opposite strand to all gene clusters homologous to extHIJKL there is a transcription regulator of the LysR family, except where marked by superscript letter e, where there is no transcriptional regulator in that region, and superscript letter f, where there are transcriptional regulators of the TetR family instead. Summary. The data presented here significantly expand the number of genes encoding outer membrane redox proteins necessary during electron transfer in G. sulfurreducens and highlight a key difference in the Geobacter electron transfer strategy from those of other model organisms. In general, the pattern of multiple genes encoding seemingly overlapping or redundant roles is less like solitary respiratory reductases and more reminiscent of systems such as those of cellulolytic bacteria that produce numerous similar glucosidases to attack a constantly changing polysaccharide substrate ( 36 , 40 , 59 ). A need for multiple outer membrane strategies could be a response to the complexity of metal oxides during reduction; minerals rapidly diversify to become multiphase assemblages of more crystalline phases, the cell-metal interface can become enriched in Fe(II), and organic materials can bind to alter the surface ( 80 – 82 ). Constitutively expressing an array of electron transfer pathways could make cells competitive at all stages with all electron acceptors, allowing Geobacter to outgrow more specialized organisms during rapid perturbations in the environment."
} | 5,664 |
31057862 | PMC6444989 | pmc | 6,741 | {
"abstract": "A complex and functional living cellular system requires the interaction of one or more cell types to perform specific tasks, such as sensing, processing, or force production. Modular and flexible platforms for fabrication of such multi-cellular modules and their characterization have been lacking. Here, we present a modular cellular system, made up of multi-layered tissue rings containing integrated skeletal muscle and motor neurons (MNs) embedded in an extracellular matrix. The MNs were differentiated from mouse embryonic stem cells through the formation of embryoid bodies (EBs), which are spherical aggregations of cells grown in a suspension culture. The EBs were integrated into a tissue ring with skeletal muscle, which was differentiated in parallel, to create a co-culture amenable to both cell types. The multi-layered rings were then sequentially placed on a stationary three-dimensional-printed hydrogel structure resembling an anatomical muscle–tendon–bone organization. We demonstrate that the site-specific innervation of a group of muscle fibers in the multi-layered tissue rings allows for muscle contraction via chemical stimulation of MNs with glutamate, a major excitatory neurotransmitter in the mammalian nervous system, with the frequency of contraction increasing with glutamate concentration. The addition of tubocurarine chloride (a nicotinic receptor antagonist) halted the contractions, indicating that muscle contraction was MN induced. With a bio-fabricated system permitting controllable mechanical and geometric attributes in a range of length scales, our novel engineered cellular system can be utilized for easier integration of other modular “building blocks” in living cellular and biological machines.",
"introduction": "Introduction Engineering living cellular machines requires the interaction of one or more cell types in an instructive environment 1 . These systems could be composed of micro- or macro-scale subunits engineered to cooperatively perform certain tasks. The modularity of these subunits (consisting of cells, tissues, and biomaterials, along with growth factors or other biochemical signals) allows for “forward-engineering” of the system by assembling the components in a diverse manner, like building blocks, thus expanding the functionality of the system. For example, we recently demonstrated a modular skeletal muscle tissue “ring” that could be coupled to a three-dimensional (3D)-printed skeleton to produce net motion 2 . This bio-integrated actuator (bio-bot) was a functional cellular system that exhibited dynamic and adaptive behavior based on both its inherent design and its surroundings. Our previously demonstrated muscle-powered biological machines 2 , 3 used externally applied electrical or optogenetic signals to stimulate an engineered skeletal muscle tissue to contract. Skeletal muscle is the principal actuator in many animals 4 , and its inherently modular and scalable nature renders it a natural component of many cellular systems. The ability to respond to stimuli by producing force (resulting in events such as fluid motion or net displacement, in a pump or motile bioactuator, for example) is an intuitive design principle of many systems. However, a more complex biological system with greater functionality would likely require the integration and coordination of multiple cell types, that is, moving from a homotypic cluster such as a cell sheet 5–7 or an engineered muscle strip 8 , 9 (with a singular cell type) towards a heterotypic co-culture such as a neuromuscular junction (NMJ), with multiple cell types 1 . In vivo , skeletal muscle fibers are innervated by the axons of somatic motor neurons (MNs) and do not inherently contract without stimulation from an excitatory neurotransmitter 10 . Early research on the formation of NMJs in two dimensions (2D) has primarily focused on either the co-culture of excised or isolated muscle and neural tissues in vitro 11 , 12 , or the differentiation of mouse 13–16 or human 17–19 embryonic stem cells into MNs (usually through the formation of embryoid bodies or EBs), which were then co-cultured with excised or engineered muscle tissues. Beyond applications in regenerative medicine and therapeutics 20 , however, only a few studies have produced 3D NMJ platforms or applied neuromuscular research to living cellular systems using embryonic or neural stem cells 21–23 . Furthermore, there is a lack of research demonstrating the possibility of translating such an arrangement into a platform that is potentially autonomous, scalable, and forward-engineered, which are necessary characteristics of a mobile and functional cellular system or machine. Here we present a modular cellular system made up of multi-layered tissue rings containing integrated skeletal muscle and MNs embedded in an extracellular matrix (ECM). The first layer contained differentiated skeletal muscle myotubes ( Figure 1a ) mixed with ECM to form an engineered muscle tissue ring ( Figure 1b ). Simultaneously, MNs were differentiated from mouse embryonic stem cells (mESCs) through the formation of EBs, spherical aggregations of cells grown in suspension culture ( Figures 1c and d ). The EBs were mixed with ECM proteins ( Figure 1e ) to form a second tissue layer that integrated with the differentiated muscle tissue ring to create a co-culture amenable to both cell types. After the multi-layered rings sequentially compacted and fused together ( Figure 1f ), they were then placed on a stationary hydrogel skeleton that had been 3D printed in parallel ( Figures 1g and h ). The use of stereolithographic 3D printing (an additive rapid prototyping technique) 24 , 25 to create a flexible yet integrated tissue arrangement allows for iterative design modifications on a range of length scales. This system demonstrates functional NMJ behavior and controllable outputs, including engineered muscle contraction upon applied chemical stimulation, and permits control over physical, mechanical, and biochemical cues.",
"discussion": "Discussion The engineered hydrogel-muscle ring platform, which we demonstrated previously for muscle-powered biological machines 2 , is ideal for introducing different cell types and biomaterials. Here we present a method for overcoming some of the challenges associated with innervating 3D muscles 15 . Prior work has confirmed that ESC-derived MNs attained electrophysiological properties that were characteristic of native spinal MNs 14 ; we demonstrate an ability to integrate MN-containing EBs into a cellular system and achieve outputs representative of a functional NMJ. A ring tissue design with directional force production allowed for a physiological neuron-muscle co-culture with greater potential for innervation in 3D, whereas an adaptable fabrication system provided physical cues and structural support for maturation and synergy of both neurons and muscle in a relevant engineered tissue system. By allowing the two major cell types to differentiate in parallel before combining them into one co-culture system, we were able to create a flexible platform in which cells and tissues can be combined with 3D-printed scaffolds in a modular and user-friendly manner. Compaction in the hydrogel ring mold or transfer to the skeleton did not hinder the further maturation of either major cell type. C2C12s differentiated into mature myotubes in the presence of IGF-1, whose use we previously reported to accelerate muscle differentiation in 3D engineered systems in a physiologically relevant manner 3 . We also hypothesized that forcing the tissue to compact and differentiate in this constrained environment would result in greater myotube alignment along the longitudinal axis 2 , as the imposition of this static mechanical cue during muscle development would contribute to improved functionality and force production 30 , 31 . The design and fabrication of an instructive environment for this cellular system were easily achieved with the use of stereolithographic 3D printing 24 , 25 . This manufacturing technology has been widely utilized for applications in tissue engineering, not only due to the user’s control over the specific design, geometric, and mechanical parameters but also for its ability to fabricate biomaterials (hydrogels whose properties can mimic cells’ natural micro-environments) and encapsulate various cell types in three dimensions 32 , 33 in a short time frame and over a range of length scales. The mammalian NMJ forms as a result of mutually stimulating signaling from both MNs and skeletal muscle fibers. Neurons can provoke the post-synaptic terminal site at the muscle, and likewise, skeletal fibers can induce pre-synaptic differentiation of neurons 27 . One outcome is the clustering of AChRs, which are uniformly distributed throughout myotubes but become greatly concentrated at the post-synaptic membrane, due to both AChR redistribution throughout the membrane and increased synthesis 34 . Another outcome is the extension and branching of the neuron’s axon into a motor nerve terminal that can release neurotransmitters (such as ACh) at the NMJ. We observed both outcomes, signifying functional NMJ formation. The extension of neurites across 2D surfaces ( Figure 3 ) was a promising observation, indicating the potential to extend neurites throughout engineered tissues and innervate the skeletal muscle. Indeed, we observed a similar phenomenon in 3D multi-layered tissue rings ( Figure 6b ). In a functioning NMJ, muscle contraction is induced by an excitatory neurotransmitter that is released from an MN at the synaptic cleft between cells, binds to a post-synaptic receptor, and depolarizes the cell on which it acts, thus increasing that cell’s excitability and probability of firing an action potential 35 . When the nicotinic neurotransmitter ACh binds to its specific membrane receptor (AChR) on the muscle cell, it initiates an intracellular signaling cascade resulting in the release of calcium ions from the sarcoplasmic reticulum in the muscle fiber, terminating in actin–myosin contraction 10 , 36 . Before ACh is released, however, the MN must be chemically stimulated by an excitatory neurotransmitter that induces a neuronal action potential 35 , 37 . Various studies have reported the use of glutamate in chemical activation of neuromuscular systems with high success, as it is a major excitatory neurotransmitter in the mammalian nervous system. We demonstrate that the site-specific innervation of a group of muscle fibers in the multi-layered tissue rings allowed for muscle contraction via chemical stimulation of MNs, with the frequency of contraction increasing with glutamate concentration. The decrease in displacement per contraction followed a physiological relationship between force output and frequency for functional skeletal muscle 3 , 38 ; the engineered tissue ring had less time to return to baseline tension between each successive stimulus as the frequency of neuronal firing increased. Because the addition of curare terminated the contractions, we confirmed both that the muscle contraction was MN-induced and the presence of a functional NMJ. Further enhancements to the multi-layered tissue ring system could allow for the development of an autonomous biological actuator. With a bio-fabricated system permitting controllable mechanical and geometric attributes on a range of length scales, our novel engineered cellular system can be utilized for easier integration of other modular “building blocks” in living cellular and biological machines. This modular NMJ platform is the foundation of a novel heterotypic cellular system and has the potential to address larger challenges in medicine and biology. Target applications could include microscale tissue fabrication for organ-on-a-chip mimics of neurodegenerative diseases or drug screening for neuromuscular diseases in an autonomous platform."
} | 2,981 |
30523402 | PMC6394664 | pmc | 6,742 | {
"abstract": "Aquatic habitats are often characterized by both high diversity and the threat of multiple anthropogenic stressors. Our research deals with temporal and spatial aspects of two of the main threats for biodiversity, namely eutrophication and fragmentation. It is known that pulsed nutrient addition creates temporal differences in environmental conditions, promoting higher diversity by preventing the best competitor from dominating. Furthermore, a metacommunity landscape with intermediate connectivity increases autotrophs’ diversity and stability. However, it is yet unclear if these two factors are additive in increasing diversity and if the effects extend to the consumer level. With the goal of understanding how eutrophication impacts biodiversity in a metacommunity landscape, we hypothesized that pulsed nutrient addition will increase diversity among both autotrophs and heterotrophs, and this effect will be even greater in a metacommunity landscape. We simulated eutrophication and fragmentation in a microcosm experiment using phytoplankton as primary producers and microzooplankton as grazers. Four treatment combinations were tested including two different landscapes (metacommunity and isolated community) and two forms of nutrient supply (pulsed and continuous): metacommunity/continuous nutrient addition (MC); metacommunity/pulsed nutrient addition (MP); isolated community/continuous nutrient addition (IC); isolated community/pulsed nutrient addition (IP). As expected, pulsed nutrient addition had a persistent positive effect on phytoplankton diversity, with a weaker influence of landscape type. In contrast, the grazer community strongly benefited from a metacommunity landscape, with less significance of pulsed or continuous nutrient addition. Overall, the metacommunity landscape with pulsed nutrient supply supported higher diversity of primary producers and grazers. Electronic supplementary material The online version of this article (10.1007/s00442-018-4319-8) contains supplementary material, which is available to authorized users.",
"introduction": "Introduction Community ecology has long focused on processes that regulate patterns of species distribution and abundance. Not only have these processes taken on new significance in an era with significant biodiversity loss, but it has also become clear that maintaining biodiversity is essential for maintaining ecosystem functions (Tilman et al. 2001 ). Local and regional biodiversity measurements are known to be important tools to understand ecosystem dynamics for further application on environmental issues (Resetarits et al. 2018 ). The dispersal of organisms at the regional scale can counter local diversity loss, taking on an important role in ecosystem productivity and stability (Holyoak et al. 2005 ). Model aquatic systems have long been used to test metacommunity theory, but they are applicable to real aquatic environments, even those that have constantly changing connectivity between local patches, such as interconnected lagoons, estuarine lakes and salt ponds (Smeti et al. 2016 ). It has been shown that higher diversity results in greater stability, higher productivity and better resource use efficiency by increasing either the likelihood that at least one high-productivity species is present, or the number of niches that can be occupied (Tilman and Downing 1994 ; Tilman et al. 2001 ; Cardinale 2011 ). When higher species richness leads to more efficient resource use and, therefore, higher community biomass, this can stabilize communities by reducing demographic stochasticity (de Mazancourt et al. 2013 ). Despite its many benefits for ecosystems, biodiversity is increasingly under threat. The present research deals with the temporal and spatial aspects of two of these threats: habitat fragmentation and eutrophication. It was already clear to MacArthur and Wilson ( 1967 ) that habitat fragmentation can lead to local extinction, and if there is no connection between newly isolated patches, the local extinction can occur remarkably quickly (Gibson et al. 2013 ). Higher trophic levels are known to be more sensitive to landscape changes, mainly because of their larger body size and lower richness, which can result in the alteration of ecosystem functions driven by top-down extinction (Duffy 2003 ; Chisholm et al. 2011 ). However, the connectivity between fragments plays an important role in mitigating the biodiversity loss, allowing the dispersal of organisms among the patches (Staddon et al. 2010 ; Chisholm et al. 2011 ). If patches are connected to an appropriate degree, building a metacommunity, the regional (across patch) community can persist longer than a single large patch (Holyoak and Lawler 1996 ; Holyoak 2000a ). Here, we tested this prediction, predicting longer survival of organisms in smaller interconnected patches (metacommunity) than in a larger isolated habitat. There is a consensus that local biodiversity is generally highest at an intermediate productivity (Kassen et al. 2000 ; Hillebrand and Lehmpfuhl 2011 ). This implies that eutrophication (anthropogenically caused increases in nutrient addition) can have positive effects on biodiversity in systems that were originally low-productive, or negative effects if the water body already had medium to high nutrient loadings (Heino 2013 ). Habitats with originally low nutrient concentrations tend to be highly sensitive towards small changes in nutrient enrichment (Taylor et al. 2014 ), which can result in primary producer shifts affecting subsequently the composition of higher trophic levels (Cook et al. 2018 ). At a regional scale, however, aquatic biodiversity has been found to increase continuously with increasing productivity as more productive patches (ponds in this case) were more dissimilar to one another than at low productivity (Chase and Leibold 2002 ). It is plausible, but still untested, that a metacommunity of patches with varying productivity would result in higher diversity as different nutrient concentrations among patches would allow different species to survive. Despite the existence of many studies on dispersal effects and nutrient loading (pulsed or continuous), these issues have been largely approached separately and commonly using only one trophic level. In this study, we addressed two less understood aspects: the combined effect of dispersal and nutrient addition in influencing biodiversity and productivity, and their effects on grazers and primary producers. Experimental work, using only phytoplankton assemblages, has shown that diversity is more influenced by species characteristics than by dispersal rate (connectivity) in a metacommunity structure (Smeti et al. 2016 ). Nutrient availability in aquatic systems influences diversity and biomass of primary producers and, moreover, it can act as a disturbance when it is loaded in a pulsed way, influencing the growth and losses of the populations (Roelke 2000 ; Roelke and Eldridge 2007 ). The pulsed inflows create environmental changes through the temporal fluctuations of resources, allowing different species to coexist according to their abilities to survive (Roelke and Spatharis 2015 ; Smeti et al. 2016 ; Papanikolopoulou et al. 2018 ). Pulsed nutrient addition has long been known to increase phytoplankton diversity by preventing competitive dominance under equilibrium conditions (Sommer 1985 ). Furthermore, the benefit of intermediate connectivity in a metacommunity landscape in increasing primary producers’ diversity is also known (Limberger and Wickham 2012a ; Steiner et al. 2013 ). However, it is yet unclear if those two factors are additive in increasing diversity and if the grazers’ responses are related to their prey. Here, we aimed to elucidate the interaction of nutrient addition and habitat fragmentation (landscape) in influencing biodiversity. To achieve this goal, we tested two different landscapes, isolated and interconnected communities, in a microcosm experiment. The landscape treatment was crossed with nutrients supplied either daily (continuously) or weekly (pulsed), resulting in four treatment combinations: metacommunity/continuous nutrient addition (MC); metacommunity/pulsed nutrient addition (MP); isolated community/continuous nutrient addition (IC); isolated community/pulsed nutrient addition (IP). The species used as model organisms consisted of three species of algae and cyanobacteria as primary producers, and six ciliates and rotifer species as grazers. Prior to the experiment, the grazers were established to be true competitors for the autotrophs used as their resource. The initial environmental conditions and species composition of all patches were identical. This research seeks to investigate the following hypotheses: By increasing the temporal variability in nutrient concentrations, pulsed nutrient addition will increase diversity not only among autotrophs (Sommer 1985 ) but also in their grazers (H 1 ); additionally, these effects are expected to be even greater in a metacommunity landscape (in the MP treatment). The fragmented patches, interconnected by dispersal (metacommunity) and receiving weekly nutrient supply, would have yet higher diversity, as temporal niche differentiation (due to differences in nutrient concentrations) would allow more species to coexist (H 2 ). However, we expected stronger effects in the grazer community, since only one species of phytoplankton was actively mobile. Therefore, landscape effects on autotrophs are expected to be primarily through the top-down grazing pressure (H 2 ′); we further predicted that the metacommunity patches under pulsed supply would have higher beta-diversity (here measured as Bray–Curtis dissimilarities) than the patches under continuous nutrient addition, as the time interval between the pulses would accentuate stochasticity in nutrient availability, resulting in higher dissimilarity among patches (H 3 ); finally, we predicted that higher community biomass and higher zooplankton:phytoplankton biomass ratio (resource use efficiency—RUE) would be promoted by metacommunities with pulsed nutrient addition (H 4 ). More specifically, we expected highest diversity measures (local, regional and beta-diversity) as well as highest biomass and abundance in MP treatments, followed by MC treatments. Consecutively, IP and IC treatments are expected to have lower local diversity and standing stock measures (biomass and abundance) than the metacommunities, with even lower values in the latter.",
"discussion": "Discussion The questions addressed in this study concerning metacommunity dynamics and how they interact with eutrophication in influencing biodiversity are subjects that cannot be easily approached at a macroscopic scale. The aquatic model system used could be experimentally manipulated on a reasonable timeframe, allowing the simulation of human impacts with a microcosm design. Additionally, the individuals used as model organisms co-occur in nature and showed sensitivity to the treatments tested, in agreement with previous work that successfully used protists to illuminate several aspects of metacommunity dynamics (Limberger and Wickham 2011a , b , 2012a , b ). The two factors manipulated here, nutrient addition and landscape, showed direct positive or negative effects depending on the group of organisms analyzed, as well as significant interactive effects. Nutrient addition was the treatment that affected phytoplankton diversity most, with a persistent positive effect of pulsed supply, consistent with previous studies (Sommer 1985 ; Flöder and Sommer 1999 ). Additionally, our results show that the same response in phytoplankton can be seen independently of landscape type and under grazing pressure, even at limited phytoplankton species richness. Partially rejecting H 1 , we could not confirm a parallel response of grazers and producers under pulsed nutrient addition, as we found very low grazer diversity and very high producer diversity in IP treatments. The very low grazer diversity in IP treatments is explained by the quality of prey that was dominating this habitat. We used three prey species with different sizes and nutritional qualities, which could have influenced the generalists’ consumption and growth. Despite the positive results for autotrophs in IP treatments, the abundance of the flagellate Cryptomonas sp . was close to zero over the entire experiment, becoming nearly unavailable as a food resource from the first week on in this treatment. It has been widely suggested that Cryptomonas sp . is a preferred food source of zooplankton (Stemberger 1981 ; Skogstad et al. 1987 ; Ahlgren et al. 1990 ; Demott and Müller-navarra 1997 ) and this could have been the case in our experiment as well. Moreover, it is known that changes in nutrient availability can drastically affect the consumers through their prey composition (Laspoumaderes et al. 2010 ; Glibert et al. 2013 ). In our microcosms, the phytoplankton biomass was mostly contributed by the green algae Desmodesmus abundans, together with Synechococcus sp . (in all four treatment combinations). Cyanobacteria are commonly classified as a poor food source for zooplankton because of their deficiency in essential omega-3 fatty acids, whereas green algae are of better food quality, except for their relatively low concentrations of highly unsaturated fatty acids (Taipale et al. 2016 ; Peltomaa et al. 2017 ). Furthermore, it has been shown that Desmodesmus forms colonies against the grazing pressure, which can hamper zooplankton growth (Vanormelingen et al. 2009 ). Cryptomonads, however, are rated as high food quality because of their rich lipid composition (Skogstad et al. 1987 ; Demott and Müller-navarra 1997 ; Marzetz et al. 2017 ; Taipale et al. 2018 ). In previous studies, grazer growth was suggested to increase with fatty acid supply encountered in primary producers (Demott and Müller-navarra 1997 ; Marzetz et al. 2017 ). This implies, however, that high phytoplankton diversity and biomass are of less importance than the abundance of high quality prey: in the isolated communities with pulsed nutrient addition, the low Cryptomonas sp. abundance also meant that the average quality of available food was relatively low, resulting in a very low diversity and biomass of zooplankton. Conversely, in the MP treatment, where the phytoplankton diversity was also higher, the grazers could find more favorable conditions, due to the higher abundance of Cryptomonas sp . in this treatment, in combination with an interconnected landscape. Supporting this idea, a previous study has demonstrated the importance of high food quality (cryptophytes), even in low abundances, for the nutritional support of zooplankton (Taipale et al. 2018 ). Therefore, our second hypothesis (H 2 ) was confirmed only for the zooplankton community, with higher diversity being observed in MP treatments. However, and partially contradicting H 2 , landscape had little effect on phytoplankton diversity. This result might not be surprising, since only one species was actively motile. However, we expected grazer responses to reflect on their prey, in a cascading top-down control (H 2 ′). Nonetheless, consistent with a recent study (Gusha et al. 2019 ), our results indicate that the bottom-up effects were more significant than the inverse relation in controlling the phytoplankton species. Furthermore, previous studies have demonstrated that with increasing trophic levels, the sensitivity to landscape conditions also increases (Sergio et al. 2008 ; Blüthgen et al. 2016 ). This can be related to the general assumption that larger organisms are more mobile than smaller organisms (Peters 1986 ) and, therefore, a wider spatial scale can be covered by the former, while the latter tend to remain more locally (McCann et al. 2005 ; McCann and Rooney 2009 ). Because of the larger body size, the larger area occupied and the lower abundance, these organisms are more affected by habitat fragmentation (Holyoak 2000b ; Duffy 2003 ; Staddon et al. 2010 ). The overall greater impact of metacommunities on grazers traits can be related broadly to results showing the relevance of dispersal for the maintenance of ecosystem functions (Loreau et al. 2003 ; Gonzalez and Loreau 2009 ; Verreydt et al. 2012 ). Moreover, the dispersal of species among local communities is long known to be a key of species persistence at regional scale, preventing biodiversity losses (Andrewartha and Birch 1986 ; Holyoak et al. 2005 ; Howeth and Leibold 2010 ). Our results suggest that not only an interconnected landscape is relevant for regional species survival, but also that good quality food in at least one patch is important in maintaining grazer diversity. No less important than local and regional diversity, beta-diversity and its components are valuable in understanding metacommunity organization and dynamics, and are important issues for conservation management (Soininen et al. 2007 ; Heino 2011 ; Suurkuukka et al. 2012 ). Habitat heterogeneity is suggested to be positively correlated with beta-diversity (Gabriel et al. 2006 ; Patzkowsky and Holland 2007 ; Suurkuukka et al. 2012 ). Interestingly, even under equal treatment conditions, the four patches of a metacommunity showed increased dissimilarities in species abundance over time, but to a greater degree under pulsed nutrient supply. Thus, we could confirm our assumption that pulsed supply promotes higher stochasticity and, therefore, higher dissimilarity among patches (H 3 ). Various nutritional niches can be formed as a consequence of the different degrees of nutrient availability, which enable a higher number of species to coexist according to their survival abilities (Chesson 2000 ; Behmer and Joern 2008 ; Roelke and Spatharis 2015 ). While the increase in beta-diversity was primarily due to nestedness in both metacommunities, it decreased in importance over time in MP treatments, being superseded by turnover. Nestedness was the dominating component of beta-diversity in MC treatments, reflecting that with continuous nutrient addition one patch was species-rich while other patches were species-poor, which in turn indicates differences in species abundance gradients among sites. The existence of only few patches supporting higher number of species (Patterson 1987 ) could be the reason why beta-diversity has been negatively related to nestedness (Wright and Reeves 1992 ). In a different way, the MP treatments had the turnover component scaling up from the second week on, while nestedness decreased. It has been suggested that beta-diversity shaped by the replacement of species (turnover) can indicate the predominance of the species-sorting process (Legendre and Cáceres 2013 ; Yang et al. 2018 ), which implies that environmental heterogeneity was an important factor in structuring this metacommunity type (MP). A previous study has also suggested a positive relationship between species replacement and habitat heterogeneity when dispersal is possible (i.e., in a metacommunity) (Gianuca et al. 2017 ). Once the weekly nutrient supply created temporal differences in nutrient concentrations among the metacommunity patches, it also fostered higher dissimilarities in community compositions. The prevalence of nestedness in MC treatments contradicts the results from a recent meta-analysis of beta-diversity components, which concluded that most of the studies have found turnover as the main driver of beta-diversity (Soininen et al. 2018 ). Therefore, our study provides interesting results of nestedness and turnover, which were found to be strongly influenced by the temporal differences in nutrient addition. We assumed that higher RUE would be a consequence of the MP treatment (H4), since the resource use efficiency of an ecosystem is expected to be positively correlated with the number of species coexisting in the environment (Tilman et al. 1997 ; Abonyi et al. 2018 ). In agreement with our assumption, the RUE in both metacommunities showed an increase after the first week, but declining subsequently. Nonetheless, the decrease in MP treatments was slower than in MC treatments, stabilizing by the fourth week. In the isolated communities, we observed a persistent decrease of the biomass ratio between zooplankton and phytoplankton, which can be related to a declining number of grazers found in this landscape, rather than an increase in phytoplankton. In summary, landscape structure and nutrient addition influenced plankton diversity, with different patterns for primary producers and grazers. The benefit of a metacommunity structure, with an appropriate interconnection among patches, was confirmed in our study, but mainly for grazers’ diversity. Nutrient supply was also important for species organization, directly affecting the primary producers. The interaction between the two treatments was extremely important for grazer survival. The Shannon index can potentially lose information by combining aspects of richness and evenness. In our study, however, Shannon diversity, when combined with abundance data, well described the species dynamics in the experimental treatments (Spatharis et al. 2011 ). Furthermore, the highest beta-diversity was promoted by pulsed nutrient addition, indicating that MP treatment resulted in greater patch dissimilarity and in greater balance in species abundance among the sites (higher turnover). Our results suggest that if the time between nutrient pulses is sufficient, species coexistence can be facilitated in a metacommunity landscape even when nutrient addition is simultaneous in all patches. Finally, all statistical analyses showed strong time dependency, as has been seen in previous work using different aquatic model communities (Limberger and Wickham 2012b ), indicating how important it is to take into account the temporal aspects of the experiments. The results achieved with this work and their interpretation can only aim at a very small slice of the interaction between metacommunities and nutrient addition. Clearly, there is still plenty of interesting work to be done. There are many more possibilities of testing anthropogenic factors on a microcosm scale, and if the results are carefully interpreted, they can contribute to a better understanding of biodiversity loss in real scenarios."
} | 5,618 |
28127401 | PMC5251296 | pmc | 6,743 | {
"abstract": "Background With increasing concerns over the environment, biological production of cadaverine has been suggested as an alternative route to replace polyamides generated from the petroleum-based process. For an ideal bioprocess, cadaverine should be produced with high yield and productivity from various sugars abundant in biomass. However, most microorganisms are not able to efficiently metabolize other biomass-derived sugars as fast as glucose. This results in reduced growth rate and low carbon flux toward the production of desired bio-chemicals. Thus, redesign of microorganisms is necessary for utilizing those carbon sources with enhanced carbon flux and product formation. Results In this study, we engineered Escherichia coli to produce cadaverine with rapid assimilation of galactose, a promising future feedstock. To achieve this, genes related to the metabolic pathway were maximally expressed to amplify the flux toward cadaverine production via synthetic expression cassettes consisting of predictive and quantitative genetic parts (promoters, 5′-untranslated regions, and terminators). Furthermore, the feedback inhibition of metabolic enzymes and degradation/re-uptake pathways was inactivated to robustly produce cadaverine. Finally, the resultant strain, DHK4, produced 8.80 g/L cadaverine with high yield (0.170 g/g) and productivity (0.293 g/L/h) during fed-batch fermentation, which was similar to or better than the previous glucose fermentation. Conclusions Taken together, synthetic redesign of a microorganism with predictive and quantitative genetic parts is a prerequisite for converting sugars from abundant biomass into desired platform chemicals. This is the first report to produce cadaverine from galactose. Moreover, the yield (0.170 g/g) was the highest among engineered E. coli systems. Electronic supplementary material The online version of this article (doi:10.1186/s13068-017-0707-2) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusions In summary, we re-constructed the metabolic pathway of E. coli , using synthetic expression designs to efficiently produce cadaverine from galactose. Additional improvement on cadaverine production was achieved by removing cadaverine degradation and re-uptake pathways. From fed-batch fermentation, our engineered strain showed 8.80 g/L of cadaverine production with 0.170 g/g of yield and 0.293 g/L/h of productivity. To our best knowledge, this is the first report to produce cadaverine from galactose with the yield for cadaverine production being the highest compared to those of previous studies in engineered E. coli .",
"discussion": "Discussion By nature, microorganisms have evolved for rapid growth by fast utilization of the preferred carbon source. In addition, tight regulations on metabolic pathways by chemical-responsive transcription factors [ 29 – 31 ], riboswitches [ 32 ], and feedback inhibition [ 33 ] allow efficient carbon allocation and a reduction in wasted resources. This robustness of the metabolic network often incurs difficulties in the redesign of microorganisms as chemical-producing cell factories. Therefore, native metabolic networks should be altered through modification of fluxes toward both desired and unwanted pathways [ 20 , 24 , 34 ]. With the recent advances in synthetic biology, various predictive and quantitative genetic elements to control the expression of specific gene(s) are now available, and these tools can be efficiently utilized to redesign microorganisms. Depending on the required strength, the transcription machinery can be chosen from a synthetic promoter library [ 35 – 37 ]. Similarly, the 5′-UTR sequence, which is critical for translation initiation, can be precisely designed with consideration of the upstream coding sequence [ 26 , 38 ]. As demonstrated in this study, we are now able to easily design and build synthetic pathways with maximum metabolic activity in a rational manner (galactose utilization and cadaverine production pathways). Moreover, the initial design can be further refined by modifying the design criteria as shown in the case of cadA . Accordingly, we successfully re-designed the metabolic pathways to produce cadaverine from galactose, resulting in the development of the DHK4 strain, which demonstrated high titer (8.80 g/L), yield (0.170 g/g), and productivity (0.293 g/L/h). These results are similar to those of glucose-based fermentation, demonstrating the highest yield ever studied in E. coli . Although a fair amount of acetate (4.99 g/L) was still observed in the flask culture, it was significantly lower than that of the previous study [ 1 ]. When the fed-batch culture was conducted, 3.29-fold of cadaverine was produced compared to the batch culture while only 1.26-fold of acetate was accumulated. It is plausible that controlled aeration to maintain the saturated dissolved oxygen level in a bioreactor might help to enhance the activity of the electron transport chain for synthesizing ATP [ 39 ]. In addition, using synthetic and controllable genetic parts, carbon flux might be increasingly accelerated toward cadaverine formation even with reduced acetate formation. These results support the engineered DHK4 strain, which led to improved cellular performance with an increased yield. Alternatively, chromosomal deletion of ackA - pta , a main pathway for acetate production, is a possible trial as previously described [ 40 , 41 ]. However, this deletion should be carefully applied as it sometimes causes reduced target chemical production with changes in growth pattern, although acetate production is significantly decreased [ 16 ]. Alternatively, replenishment of a key intermediate in the TCA cycle such as oxaloacetate can be an option to directly supply a precursor for cadaverine production and energy generation [ 42 ]. Therefore, optimal carbon flux distribution around the phosphoenolpyruvate–oxaloacetate node can presumably enhance the production of cadaverine from galactose. Optimization might be achieved by controlling the activity of anaplerotic enzymes such as PEP carboxylase [ 38 ]."
} | 1,530 |
34593633 | PMC8501876 | pmc | 6,744 | {
"abstract": "Significance The Fe-catalyzed oxidation of sulfide by dioxygen in hydrothermal vent plumes is shown to be a source of reactive oxygen species (ROS) to the deep ocean. ROS are a class of powerful oxidants, the most reactive of which can react with recalcitrant organic molecules at near diffusion limited rates. ROS production in hydrothermal systems may be comparable to the known photochemical yields of ROS in surface waters. The discovery of this abundant hydrothermal source of ROS demonstrates a mechanism for the alteration of refractory organic matter in the deep ocean.",
"discussion": "Discussion The strong correlation between initial oxygen concentration and H 2 O 2 ( Fig. 4 ) suggested that O 2 was the limiting reactant ( Eqs. 1 and 2 ) during initial mixing in the plumes. The experimentally derived rate constant for the oxidation of Fe(II) by dioxygen is on the order of 10 2 M −1 ⋅ s −1 under the solution conditions interrogated in this study ( 4 , 7 ). The absence of detectable Fe(III) species was consistent with the forward reaction ( Eq. 4 ) and rapid production of superoxide ( Eq. 1 ) following reduction of superoxide by Mn(II) ( 25 ) or sulfide ( 26 ). Within 0.5 m of the Q-vent orifice (∼25 °C), the maximum O 2 concentration (and thus H 2 O 2 that could be produced) was predicted to be 39 μM based on Eqs. 1 and 2 and the mixing of seawater with plume water ( Eq. 7 ). However, the H 2 O 2 was about 2 μM, consistent with the rapid reaction with ambient Fe(II) ( 1 ) and other reductants in the plume [e.g., H 2 S and S(0)]. As temperature decreased to 2 °C, the measured H 2 O 2 concentration increased as available reductants decreased in concentration (H 2 O 2 production became greater than consumption), and reactions were slowed. The instantaneous concentration of H 2 O 2 measured could easily have been maintained by the cyclic oxidation of measured Fe(II)- or Mn(II)-containing species ( Eqs. 2 and 6 ) ( 25 ) in conjunction with ambient sulfur species ( Eq. 5 ) ( 6 , 7 , 26 ). Eq. 5 predicted a similar trend for H 2 O 2 and S 0 production in the plume. The concentration of S 0 was measured at several sites during the cruise ( 23 ) but only overlapped with ROS sampling during dive 4890 and only at biovent. The range of S 0 concentrations in the Biovent plume was 1 to 19 μM ( 23 ), very close to the range of the measured H 2 O 2 for that dive (2.2 to 5.8 μM, Table 1 ). While Fe(III) was the likely oxidant for H 2 S in the plume, the absence of detectable Fe(III) in the recovered plume water was expected where an excess of H 2 S would sustain the Fe(II) cycles ( Eq. 4 ) ( 6 , 7 , 12 , 23 ). These results were consistent with coupling of the Fe and S cycles in support of ROS production as depicted in Fig. 2 . An estimate of the efficiency of H 2 O 2 production during metal-mediated sulfide oxidation in plumes was derived from the slope of the correlation for the initial O 2 input and measured H 2 O 2 from Fig. 4 . The slope of the correlation yielded a predicted production efficiency of ∼6.5% on a mole/mole basis, indicating the intensity of consumption reactions for O 2 and/or H 2 O 2 during initial plume mixing. The negative intercept for H 2 O 2 was consistent with the short lifetime of H 2 O 2 under plume conditions that resulted in an underprediction of the efficiency of the ROS chain propagation. However, even at 6.5% production efficiency, the reduction of O 2 by reduced vent species [e.g., sulfide and Fe(II)] accounted for a major source of H 2 O 2 to the deep ocean. Subsequent reduction of the resulting Fe(III) by excess sulfide as the plume rose would have continued to produce H 2 O 2 and, at the measured pH range of the plume ( Table 1 ), the more reactive hydroxyl radical ( Eq. 3 ) ( 1 , 5 – 7 , 25 – 28 ). Hydroxyl radical is an unselective oxidant that can be reduced by organic and inorganic species often at nearly diffusion-controlled rates. Two factors may be critical to the impact of hydroxyl radical production in plume waters on DOC lifetimes: the intensity of ROS production and the fraction of that production available for reactions with DOC. The intensity of ROS production was demonstrated by the measured inventory of H 2 O 2 that was maintained, despite the high concentrations of potential reactive sinks in the plume [i.e., Fe(II) and HS − ]. The relative rates of reaction with Fe(II) (k = 1,660 M −1 ⋅ s −1 ) ( 5 ) versus HS − (k = 82 M −1 ⋅ s −1 ) ( 29 ) at the median pH of our samples (pH 6) in the oxygen-depleted plume confirmed that the production of hydroxyl radical via the Fenton reaction was the dominant sink for H 2 O 2 . A parallel first order kinetic model developed for batch reactors ( 30 ) was applied to estimate the fraction of hydroxyl radical that remained available for reaction with ambient DOC. The model developed for the current study was based on the measured and estimated concentrations of reducing materials in the vent region that were known to react with hydroxyl radicals (reference SI Appendix for model details). The model predicted that as much as 1.6% of the hydroxyl radical would have been available to react with ambient DOC under the high-temperature conditions observed during initial mixing. The fraction fell to 0.67% under conditions observed further up the plume and 0.36% for ambient seawater. The model was also run for a surface freshwater model DOC fraction for comparison. The fraction fell from 0.36 to 0.15% under plume conditions for the surface model DOC. However, the efficiency of the process for either model DOC is within an order of magnitude of that typically observed in technological systems engineered for hydroxyl radical production: for example, O 3 /H 2 O 2 systems built for wastewater treatment ( 30 ). The model indicated that the concentration of bromide was a critical factor for determining the availability of hydroxyl radical for reacting with DOC. The low concentration of bromide in hydrothermal plumes relative to seawater indicated that hydroxyl radical would be more important for DOC oxidation in the plume environment than would be expected in the open water column. Furthermore, the locally high-ROS production intensity in this continuous flow-through system is likely to make this a significant mechanism of hydroxyl radical reaction with DOC in the ocean. Global Estimate of Hydrothermal ROS Production. The estimated reaction efficiency from Fig. 4 provided an approximation of the impact of the reoxidation of the global hydrothermal sulfide flux as ROS production. The reaction efficiency from Fig. 4 suggests that at least 6.5% of the hydrothermal H 2 S flux would yield H 2 O 2 . This is a minimum estimate of the ROS production and neglects consumption in the high-temperature plume. The further oxidation of S 0 to sulfate could result in even higher-ROS yields, but the more conservative reaction efficiency was assumed here. Murphy et al. ( 6 ) predicted that from 5 to 50% of the reoxidation of microbially produced sulfide could lead to ROS production based on laboratory studies of model systems. The estimated production efficiency in plumes is in agreement with the low end of the range for the laboratory results. Given that the global vent flux of reduced S species is on the same order as that generated through microbially mediated sulfate reduction ( 9 , 10 , 31 , 32 ), plume ROS production (as hydrogen peroxide) is important in maintaining the global ROS inventory. The production of ROS in plumes can speculatively be scaled based on the input of reduced sulfur from ridges and ridge flanks that is on the order of 10 13 moles S/y ( 9 , 10 ), similar to global microbial sulfate reduction ( 31 ). Based on the estimated 6.5% efficiency of the chain reaction leading to H 2 O 2 production, the observed process is expected to lead to global production of ROS on the order of 0.7 × 10 12 moles ROS/y (as H 2 O 2 ). This is consistent with a separate estimate of 0.2 to 1.2 × 10 12 moles ROS/y that results from simply multiplying the plume water flux of ∼2 × 10 17 L/y ( 9 , 10 ) by the range of measured H 2 O 2 concentration from this study. The potential impact of plume-derived ROS on the global ROS budget can be scaled against the estimated 1 to 10 × 10 12 moles H 2 O 2 /y generated by photochemical processes ( 3 , 7 ). This preliminary study documents hydrothermally derived ROS and the initial comparison it affords suggests these nonphotochemical sources of ROS may be comparable in magnitude to surface waters. Given the relative spatial heterogeneity of plume sources, the local intensity of ROS cycling adjacent to hydrothermal vents may be among the highest yet measured in a natural system. Implications with Respect to the Global C Cycle. The oceanic carbon reservoir, as refractory DOC, is on the same order of the carbon reservoir (as CO 2 ) in the atmosphere, and processes that mediate the mineralization of DOC to CO 2 are critical to the global carbon cycle. While it has been proposed that the oceanic inventory of refractory DOC is controlled by photochemical degradation in surface waters ( 33 ), the deep refractory pool of DOC (>1,000 m) is less photochemically reactive than shallower material ( 34 ). Photochemically mediated production of ROS leads to near-surface ocean enrichments of hydrogen peroxide in the 20 to 80 nanomolar range ( 35 ) and may contribute to the mineralization of DOC fractions via photo-Fenton reactions ( 1 , 3 , 34 ). However, the abundance of these species is low compared to that measured in hydrothermal plumes, and ambient surface ocean pH (∼8) is less favorable to production of the more reactive ROS, hydroxyl radical. In contrast, high–hydrogen peroxide concentrations and the lower-pH values observed in plumes are more consistent with much more reactive natural systems like cloudwaters ( 36 ) and engineered systems ( 30 ). The depleted DOC inventory of about 14 μM measured previously in the high-temperature plume at the EPR ( 18 ) is consistent with the hydrothermal alteration/decomposition of nonaromatic compounds ( 37 ) during hydrothermal circulation. The observed increase in hydroxylated aromatic compounds in plumes from this and other vent locations ( 18 , 38 , 39 ) suggest that this abiotic oxidation process is not unique to this hydrothermal system and is related to the high intensity of hydroxyl radical production and reactivity reported here. Overall, the measured chemical inventories of ROS in plumes indicate an efficient flow-through reactor for refractory DOC in the oceans. In summary, hydrothermal plumes represent a source of ROS to the deep ocean. The measured H 2 O 2 production, pH values, and abundant reduced S and Fe species in plumes indicated conditions likely to result in significant ROS production, as H 2 O 2 , under conditions likely to support hydroxyl radical production as well ( 25 , 26 ). Hydroxyl radical reacts with organic molecules at essentially diffusion-controlled rates ( 26 ) making plumes a possible sink for even the most refractory DOC and POC in the deep ocean. Oxidized derivatives of benzoic acid (2,3-dihydroxybenzoic acid, a catechol, and 4-hydroxybenzoic acid) and the organic sulfur compound 2,3-dihydroxypropane-1-sulfonate have been reported in plumes ( 18 ) as have increases in O/C ratios and polyphenolic compounds ( 38 ), all circumstantial evidence for a role for ROS in the fate of refractory carbon. The hydrothermal ROS production estimated here would have a major impact on the oceans DOC inventory. In this context, it is not surprising that the measured ages of DOC in the deep ocean ( 40 , 41 ) are on the same order as the estimates for the circulation time of the ocean through vent plumes ( 14 )."
} | 2,957 |
38611957 | PMC11013808 | pmc | 6,746 | {
"abstract": "This study evaluated the feasibility of contextually producing hydrogen, microbial proteins, and polyhydroxybutyrate (PHB) using a mixed culture of purple phototrophic bacteria biomass under photo fermentative conditions. To this end, three consecutive batch tests were conducted to analyze the biomass growth curve and to explore the potential for optimizing the production process. Experimental findings indicated that inoculating reactors with microorganisms from the exponential growth phase reduced the duration of the process. Furthermore, the most effective approach for simultaneous hydrogen production and the valorization of microbial biomass was found when conducting the process during the exponential growth phase of the biomass. At this stage, achieved after 3 days of fermentation, the productivities of hydrogen, PHB, and microbial proteins were measured at 63.63 L/m 3 d, 0.049 kg/m 3 d, and 0.045 kg/m 3 d, respectively. The biomass composition comprised a total intracellular compound percentage of 56%, with 27% representing PHB and 29% representing proteins. Under these conditions, the estimated daily revenue was maximized, amounting to 0.6 $/m 3 d.",
"conclusion": "4. Conclusions PPB represent one of the most interesting microorganism groups due to their versatility in metabolism, allowing them to convert organic substrates into a variety of valuable products. The results obtained in this study showed that, under photo fermentative conditions aimed at hydrogen production, it is possible to contextually obtain a valuable biomass with a high content of PHB and proteins. The productivity and preliminary economic analysis showed that a striking strategy to concomitantly optimize these three products is to conduct the process during the exponential growth phase of the biomass. Therefore, the recommendations of this study include to conduct the process under repeated-batch or semi-continuous/continuous mode, using the day of the maximum biomass production rate as the hydraulic retention time. Further studies will also explore the overall product portfolio, likely including additional value-added compounds like bacteriochlorophylls and carotenoids in order to better discuss process feasibility at a larger scale.",
"introduction": "1. Introduction Sustainable development is driving a rapid transition in the wastewater and organic waste treatment sectors, shifting from a “removal and disposal” strategy to the recovery and reuse of energy and materials. In this context, Purple Phototrophic Bacteria (PPB) are gaining increasing attention due to their metabolic adaptability, allowing for the valorization of waste/by-products for the recovery of energy and natural biobased products [ 1 ]. PPB are photosynthetic microorganisms capable of converting light into chemical energy. They thrive under anaerobic conditions, utilizing both organic and inorganic carbon sources and light as an energy source. Moreover, they are able to grow both anaerobically and aerobically (as chemotrophs) [ 2 ]. During photoautotrophic growth, PPB can fix inorganic carbon from dissolved carbon dioxide (CO 2 ) and bicarbonate (HCO 3 − ), bypassing the use of oxygen as an electron acceptor to avoid inhibiting photosynthetic complex formation. Alternatively, in the photoorganoheterotrophic metabolism, PPB preferentially utilize organic compounds as carbon and electron sources [ 1 ]. Unlike chemoorganoheterotrophic organisms, which require significant organic catabolism to produce ATP, photoorganoheterotrophs derive energy directly from light, leading to high biomass yields over organic substrates. Under this mode, PPB use organics as electron donors and biomass as electron acceptors in balanced conditions [ 3 ]. In addition to these metabolic pathways, PPB can activate photo fermentation (PF) metabolism, particularly under nitrogen scarcity and carbon excess, promoting hydrogen production [ 4 ]. This pathway involves substrate degradation primarily as a dissipative mechanism with redirected electrons toward hydrogen formation instead of biomass, via enzymes like nitrogenase and hydrogenase [ 2 ]. The metabolic versatility of PPB makes them suitable to produce various high-value products such as hydrogen and biomass-linked products, like polyhydroxyalkanoates (PHAs) and microbial proteins. These products have promising applications, including as renewable energy sources, biodegradable plastics, and alternative protein sources, contributing to sustainable resource management [ 1 ]. The majority of literature studies on PPB have utilized pure cultures focusing on specific metabolic pathways (photo fermentation aimed at hydrogen production or photoorganoheterotrophy for biomass production). For instance, Rhodobacter and Rhodopseudomonas species, Rhodospirillum rubrum and Rhodovulum sulfidophilum , have been extensively studied for their ability to produce high rates (from 0.4 to 3.2 mM/h) of molecular hydrogen [ 5 ]. Rhodopseudomonas palustris and Rhodobacter capsulatus have been reported to produce PHAs from 30 to 90% of their CDW under photoorganoheterotrophic growth [ 6 ]. Concerning the production of proteins, to date, most of the highest protein yields (74–90%) among PPB have been associated with Rhodopseudomonas palustris [ 7 ]. Although most studies on PPB have utilized pure cultures focusing on specific metabolic pathways and, therefore, specific products, recent research suggests that mixed and non-aseptic cultures can simultaneously produce hydrogen and PHAs, thanks to diverse microbial species within consortia [ 8 , 9 ]. This approach not only enhances product diversity but also facilitates scalability, as mixed cultures are more robust and adaptable to varying conditions. However, the simultaneous production of proteins alongside hydrogen and PHAs has not been explored in the existing literature, representing a significant research gap. Proteins, being one of the most valuable products from biomass, present promising opportunities for further investigation and exploitation. This study evaluated the feasibility of producing hydrogen, proteins, and polyhydroxybutyrate (PHB) under photo fermentative conditions. After assessing biomass growth through sequential batch experiments, the final photo fermentation test utilized exponentially growing biomass enriched from previous batches to optimize the simultaneous production of hydrogen, PHB, and microbial proteins in terms of biomass quality, output productivity, and economic viability.",
"discussion": "2. Results and Discussion In this study, the valorization of PPB biomass was studied under a photo fermentation condition. Figure 1 reports the biomass growth curves obtained from the biomass growth, inoculum production, and hydrogen production experiments. Figure 1 shows that the maximum TSS value was similar for all experiments and ranged between 0.98 and 0.99 g TSS /L. This result was due to the experimental conditions, which were identical in all experimental tests (except the inoculum). During the first experiment ( Figure 1 a), a lag phase of about 2 days occurred, followed by an increase in the TSS values until day 9. After day 9, it is possible to observe the stationary phase. The maximum biomass growth rate was observed between day 2 and day 3 with a value of 0.0124 g TSS /L h. The second experiment ( Figure 1 b) showed very similar results, with a lag phase of two days and a maximum growth rate of 0.0145 g TSS /L h between day 3 and day 4. Conversely, a significant improvement in the biomass growth was obtained in the third experiment ( Figure 1 c), which was conducted by using the inoculum of the previous reactor, sampled during the exponential phase. Indeed, in this experiment, no lag phase was observed, and the maximum growth rate was reached already between day 1 and day 2. Moreover, the growth rate value was 0.0158 g TSS /L h. On day 3, the biomass concentration reached the 80% of the maximum achieved concentration, with a 98% increase compared to test 1 and 92% increase compared to test 2. Such observations confirm that by inoculating reactors with microorganisms from the exponential growth phase, the process duration can be considerably reduced. This evidence is in agreement with a previous study reporting the effectiveness of using a pure culture of Rhodobacter capsulatus inoculum during its exponential growth phase instead of prolonged cultivation conditions [ 10 ]. Figure 2 reports the results of all products generated during the final hydrogen production test, in terms of total biogas, hydrogen, and PHB, as well as the results of the nitrogen compounds produced during the experiment (i.e., ammoniacal nitrogen, nitric nitrogen, and proteins). Figure 2 a shows that the biogas production started immediately at the beginning of the fermentation period, with an increased rate between days 2 and 3. From day 0 to day 3, a contextual increase in microbial products (i.e., PHB and proteins) was detected, confirming that, as observed previously [ 8 ], PPB are involved in both anabolic and catabolic reactions during the first days of fermentation. From day 3 to day 7, no hydrogen was detected, whilst a slight increase in PHB and proteins was observed. This period corresponded to a decrease in the biomass production rate, which has been already observed in previous PF experiments [ 8 ] and attributed to a biomass adaptation to a substrate shift in mixed substrate environments. In the present study, only ethanol has been used as carbon source. However, it has to be taken into account that PPB produces and successively consumes organic acids. Therefore, even if a single substrate medium is used, they are always subjected to the presence of mixed carbon sources [ 9 ]. Another explanation may be due to the multiple nitrogen sources. In fact, in Figure 2 c, it is possible to notice that part of the nitrogen (i.e., 17%), which was initially present in organic form in the medium, was converted into ammoniacal nitrogen during the initial days of fermentation. The ammoniacal nitrogen was subsequently consumed; so, even from the nitrogen substrate point of view, the bacteria were subjected to the presence of multiple forms of nitrogen and a change in their metabolism. In contrast, this phenomenon regarding the cessation of hydrogen production for a period of the fermentation test has not been observed in previous studies using ethanol and glutamate as carbon and nitrogen sources [ 11 ]. The discrepancy, however, can certainly be attributed to the nature of mixed culture processes, in which different species coexist and dominant microorganisms may not be the same in different studies. During the final phase of the process (after day 7), hydrogen was produced again. This result is in accordance with previous studies reporting hydrogen production even at the beginning of the stationary phase [ 12 ]. After day 9, hydrogen was not produced anymore. In this final process phase, a decrease in the PHB accumulation was observed, as it was consumed for biomass maintenance. Indeed, in accordance with the feast–famine theory [ 13 ], PPB use PHB as a carbon reserve for their survival when nutrients become scarce. Consistently with this observation, the decrease in PHB corresponded to an increase in protein production, which reached the maximum concentration during this final phase of the process. As already mentioned, ammoniacal nitrogen was produced in the initial days of fermentation. Conversely, the presence of nitric nitrogen has never been detected. The production of ammoniacal nitrogen from glutamate stems from the oxidative deamination process of glutamate, catalyzed by glutamate dehydrogenase, wherein the amino group is removed from glutamic acid and converted into ammonia [ 14 ]. This phenomenon aligns with findings from previous studies on photofermentation. For example, Kim et al. (2012) noticed a decline in nitrogenase activity coinciding with an increase in ammonium ion concentration, as ammonia ions resulting from glutamate deamination may inhibit nitrogenase activity [ 15 ]. A study by Hillmer and Gest (1977) similarly reported such outcomes, where excess glutamate underwent partial deamination and ammonium ions were detected in the medium using the photosynthetic bacterium Rhodopseudomonas capsulata [ 16 ]. In contrast to the mentioned studies, which did not observe the depletion of ammoniacal nitrogen after its production, the results of the present study show the complete degradation of ammonium after day 3. Most likely, the presence of mixed cultures facilitated the assimilation of diverse nutrient sources. Figure 3 reports the results of the COD and nitrogen balances, as well as the biomass composition over time. The COD balance indicates that, after day 7, all the initial substrate was converted to the detected products. Such results agree with previous works reporting a stable PF effluent at the end of the process [ 8 ]. Indeed, if no inhibition occurs, all the organics contained in the medium (i.e., organic substrates and intermediate organic acids) can be converted to fermentation products. The COD balance also indicates that, despite the process being capable of producing hydrogen, most of the input substrate was converted to biomass and its associated products (i.e., PHB and proteins). Therefore, the contextual recovery of both energy and biomass products, which is paramount in the framework of a biorefinery concept, can be pursued. Considering the biomass composition, it is possible to observe that both the protein and the PHB contents varied during the fermentation period, although they usually represented approximately 50% of the biomass composition. The remaining part, which still represents a significant fraction, has been identified as active biomass (other than proteins and PHB). According to the literature, this part of the biomass, besides containing microbial cell constituents, may also contain bacteriochlorophylls and carotenoids [ 4 ]. These represent other important products worth investigating as a perspective of this study. Indeed, according to the literature, these compounds have significant relevance in various industrial applications (e.g., food, pharmaceutical, and cosmetic sectors). For instance, carotenoids are commonly used as coloring agents in food and as additives in cosmetics, whereas bacteriochlorophylls show promise as chemical compounds for photodynamic therapy [ 17 ]. Regarding the nitrogen balance, it was possible to observe that, during the fermentation process, nitrogen was progressively transformed into proteins. Only on day 2, the presence of ammoniacal nitrogen was noticed. However, it was immediately consumed. Beyond day 7, residual nitrogen was no longer present. However, the amount of nitrogen calculated from protein measurements appears to be overestimated, as the balance does not close with an excess of nitrogen of 17% and 9% on days 8 and 9, respectively. This could be due to the fact that the estimate was made indirectly, using albumin as reference. Table 1 reports the data regarding the production of hydrogen, energy, PHB, and proteins relative to the days of their maximum production. The day of maximum biomass growth rate was also considered. Additionally, the average estimated economical gains potentially obtainable from the recovery of the products have been reported. The cumulative production of hydrogen was optimized towards the end of the fermentation process (day 9). This day corresponded to the lowest value of PHB production and a high production of proteins, although it was not the highest value observed. Considering the maximum PHB production, it was reached on day 7, which, however, corresponded to low values of hydrogen and protein production. The maximum protein production was instead achieved on day 8, which corresponded to intermediate production levels of both PHB and hydrogen. Considering the potentially obtainable revenue, the latter was maximized on day 8 at USD 3423 per cubic meter of working volume, mainly due to the high market value of proteins. On this day, proteins and PHB represented 34% and 14% of the dry weight, respectively, totaling 48% of the biomass weight. Comparing these results with the literature reveals that PHB values are lower than in previous studies conducted with pure cultures and in line with studies conducted with mixed cultures [ 9 , 18 ]. This result is due to the nature of mixed cultures, which contain a multitude of species, of which only some may be specialized in PHB accumulation. Regarding protein accumulation, the limited presence of studies in the literature reporting protein production in the mixed culture photo fermentation process prevents a comparison. The maximum protein percentage of 34% is lower compared to studies conducted under photoorganoheterotrophic conditions. Indeed, previous studies reported that the protein content of PPB can vary between 45% and 75% [ 19 ]. This result was predictable, since, in this study, the process was conducted under photo fermentative conditions instead of photoorganoheterotrophy. Nevertheless, the observed percentages of proteins and PHB suggest that the biomass obtained, even under these conditions, possesses a good quality to be utilized for the recovery of these valuable products. Based on the findings of the biomass growth study ( Figure 1 ), it became evident that employing an exponentially growing inoculum is advantageous for the PF process. Moreover, since all outputs were generated simultaneously during the initial fermentation phase ( Figure 2 ), the analysis also included the day corresponding to the maximum biomass production rate, which was observed on day 3. From the table, it is possible to observe that, in terms of rates, all outputs are maximized during the exponential growth phase of biomass (on day 3). Thus, this observation allows us to state that the best strategy to contextually produce hydrogen and valorize the PPB biomass would be to interrupt the batch process on day 3 and then repeat it using the exponentially growing inoculum. An advantageous solution may be to conduct the process in semi-continuous or continuous mode, using an HRT value of about 3 days (when the exponential phase is reached). Furthermore, even considering the composition of the biomass ( Figure 3 ), it was found to be better on day 3, being composed of a maximized total intracellular compound percentage of 56%, of which 27% is PHB and 29% is proteins. In these conditions, daily revenue is maximized and amounts to USD 0.6/m 3 d. Currently, semi-continuous mode has been identified in previous studies conducted on pure cultures for hydrogen production as the best feeding mode [ 18 ]. However, semi-continuous studies in which proteins and PHB are also taken into account have not yet been conducted, neither with pure cultures nor with mixed cultures, and represent the perspectives of this work."
} | 4,745 |
33637751 | PMC7910463 | pmc | 6,748 | {
"abstract": "Nature fascinates with living organisms showing mechanically adaptive behavior. In contrast to gels or elastomers, it is profoundly challenging to switch mechanical properties in stiff bioinspired nanocomposites as they contain high fractions of immobile reinforcements. Here, we introduce facile electrical switching to the field of bioinspired nanocomposites, and show how the mechanical properties adapt to low direct current (DC). This is realized for renewable cellulose nanofibrils/polymer nanopapers with tailor-made interactions by deposition of thin single-walled carbon nanotube electrode layers for Joule heating. Application of DC at specific voltages translates into significant electrothermal softening via dynamization and breakage of the thermo-reversible supramolecular bonds. The altered mechanical properties are reversibly switchable in power on/power off cycles. Furthermore, we showcase electricity-adaptive patterns and reconfiguration of deformation patterns using electrode patterning techniques. The simple and generic approach opens avenues for bioinspired nanocomposites for facile application in adaptive damping and structural materials, and soft robotics.",
"introduction": "Introduction The development of mechanically adaptive nanocomposites has been inspired by species of echinoderms, which share the fascinating ability to rapidly and reversibly alter the stiffness of their inner dermis when threatened 1 – 5 . Sea cucumbers can morph their inner dermis within seconds to endow essential survival traits. It has been proposed that the adaptive mechanical behavior is achieved by a proper control of the stress transfer through transient interactions within their hierarchical architecture composed of a soft and viscoelastic matrix which is reinforced with rigid, high-aspect ratio collagen fibrils 3 , 6 , 7 . Mimicking such capabilities to change mechanical properties on demand constitutes an important milestone to enable potential applications in adaptive materials systems that range among active dampening systems, soft robotics and tissue growth 8 – 10 . Prominent classes of synthetic mechanically switchable materials include thermo-, photo-, and chemo-responsive soft materials, such as hydrogels, elastomers, or semicrystalline resins 2 , 3 , 11 – 18 . While these materials show mechanical changes upon triggers (some exhibit viscosity/modulus changes of several orders of magnitude), the vast majority exhibit a very low stiffness (modulus in kPa–MPa range). Currently, mechanically adaptive materials with high and changeable stiffness in the GPa regime are extremely limited. For instance, bioinspired high-performance nanocomposite materials, inspired by biological load-bearing structures, are a particular material class that would strongly benefit from encoding mechanical adaptivity. These bioinspired nanocomposites aim for highly ordered hard/soft structures at high fractions of reinforcements (typically above 50 wt%) and with precisely engineered energy-dissipation mechanisms 19 – 23 . However, installing a programmable trigger and realizing an adaptation to external signals in such bioinspired nanocomposites is highly challenging as the adaptivity has to ultimately be provided through the soft component. This latter is only present at minor fractions (<50 wt%) and nanoconfinement conditions complicate the behavior 4 , 24 – 27 . In the aspect of triggers, electricity excels and is highly desirable, as it is easily accessible and controllable, highly penetrating, eco-friendly and thus of high relevance to real-life structural material applications. Recently, electricity-triggered changes in polymer materials present some progress, with the most notable examples dealing with dielectric soft actuators employing ultrahigh electric fields or electro-strictive elastomers 28 – 32 . However, electricity-induced changes in material properties, even simple on/off softening effects, are unprecedented in highly-reinforced high-performance bioinspired nanocomposites. The large body of work in bioinspired high-performance materials focuses on improvements of the static material behavior and new processing approaches 33 – 37 . To reach the adaptation of mechanical properties, we hypothesized that electrothermal heating (i.e., Joule or resistive heating) might be particularly appealing, as it is an electrolyte-free and low voltage driven process, and more critically, allows a control over the material properties (temperature and mechanical behavior) as a function of the energy input 38 – 40 . Here, we design electricity-adaptive, highly-reinforced bioinspired nanocomposites by incorporation of a rapid electrothermal energy transfer cascade allowing a reversible modulation of mechanical properties using low voltage direct current (DC). The bioinspired nanocomposites are formed by combining bio-sourced and sustainable wood-based cellulose nanofibrils (CNFs) with water-soluble, low- T g (=glass transition temperature) copolymers equipped with thermo-reversible supramolecular motifs. CNFs offer extremely high stiffness ( E = 135–145 GPa), and are highly promising for renewable, biodegradable, and versatile functional applications 25 , 41 – 45 . The thermo-reversible supramolecularly linked polymers undergo efficient de-linking during Joule heating so as to be able to achieve large property changes. The electrothermal conversion is realized by simple deposition and spray coating of single-walled carbon nanotubes (SWNTs) on the bioinspired CNF/polymer nanocomposites. The thin SWNT layer serves as resistive heater, allowing a rapid, repeatable, and homogeneous heat generation in the bioinspired nanocomposites that is able to break the supramolecular bonds and enhance the molecular motion. This leads to significant softening, control over stress-relaxation properties, and the possibility to program the mechanical properties from stiff-to-soft with voltage supply. More importantly, we show that spatially selective application of voltage by differently connecting electrode patterns on the films leads to electro-programmable mechanical deformation patterns.",
"discussion": "Discussion We demonstrated a simple energy transfer approach for tailored, programmable and reconfigurable electricity-adaptation within highly-reinforced bioinspired CNF/polymer nanocomposites by showcasing fast, reversible electricity-adaptive modulation of mechanical properties and spatially controlled mechanical patterns upon stretching. The fabrication of those electricity-adaptive nanocomposites combines water-borne, highly-reinforced CNF nanocomposites with rationally designed copolymers bearing supramolecular motifs, and surface coated SWNT thin layers as a resistance heater. The use of thermo-reversible supramolecular motifs is crucial for voltage-regulated adaptive reprogramming of such bioinspired nanocomposites containing large reinforcement fractions with large property changes, as they allow defined electrothermal dynamization and dissociation of crosslinks. Moreover, we showed that such electricity-adaptive mechanical properties allow lateral mechanical patterns via spatially selective softening of the nanocomposites. This can be achieved by either placing electrodes onto fully conductive films or more complex and arbitrary patterns can be obtained by spray-coating conductive flexible electrodes on the surface that can be wired up on demand to reconfigure mechanical patterns. Compared to other spatiotemporally active molecular triggers, in particular light, we see significant advantages of electrothermal heating. For instance, direct light switching of light-responsive crosslinkers is hampered in the depth of the effect simply due to the high optical density at high fractions of active groups and the concurrent absorption of the nanocomposite material in the typical UV-blue light regime of photo-switchable motifs 45 , 61 . Moreover, the response dynamics are slow when such motifs are in bulk. This might be addressed partly using, e.g., more deeply penetrating NIR light, but still the challenge remains to really integrate this into an on-demand switchable materials system, as light may not easily be available in an application setting and red-shifted photo-switchable reversibly crosslinkers are only emerging 62 . In comparison to integrating conductive material into the bulk of a bioinspired nanocomposite (e.g., graphene-based nacre-mimetics 63 , 64 ), the approach of using surface layers is more versatile as it can also be used for nonconductive bioinspired nanocomposites as shown here. Additionally, deformation patterns and reconfiguration of deformation patterns would be hard to achieve in such approaches as they strictly require defined electrode patterns. Certainly, for increased thicknesses—which is a challenge by itself for the preparation of bioinspired nanocomposites—a bulk conductivity may need to be achieved, but clever lamination principles using electrode layers may still be able to address this challenge using the concepts developed herein, and 3D printing using conductive materials to make volume patterns may also open high opportunities 37 , 65 – 67 . Looking out to the future, the developed approach pushes sustainable mechanical high-performance materials based on cellulose nanofibers to a higher functionality level and opens ample possibilities to design mechanically adaptive high-performance materials that can be triggered by readily available low DC power supplies. The strategy should be widely applicable to other bioinspired nanocomposites. It gives rise to possibly engineer future highly-reinforced bioinspired systems with dynamic adaptation capabilities. The promotion of mechanical patterns might increase in the near future as they allow to mimic closer the structural complexity of natural materials using other precision molecular design and processing approaches, such as 3D printing. We foresee applications for adaptive damping materials, adaptive structural materials, adaptive tissue scaffolds and tissue replacements, and for soft robotics applications."
} | 2,524 |
39906407 | PMC11791322 | pmc | 6,749 | {
"abstract": "Highlights • Methane emissions from ruminant livestock are a major greenhouse gas source. • Bromoform feed additives can reduce livestock methane by >90 %. • Curvularia soil fungi produce bromoform and prevent rumen methane emission. • These natural, culturable fungi offer a unique climate change strategy.",
"conclusion": "5 Conclusion The robust expression of VHPO in a culturable fungus creates an exciting new potential alternative for enteric methane mitigation from ruminant livestock. Being a culturable fungus amenable for industrial scale fermentation using very basic media ingredients has significant advantages over existing naturally occurring alternatives for producing bromoform. In addition, this fungal enzyme is highly stable in H 2 O 2, solvents and at higher temperatures [ 18 , 50 , 51 ] making further processing and product development opportunities possible.",
"introduction": "1 Introduction The world's livestock industry produces 14 % of global anthropogenic greenhouse gas emissions, mostly as enteric methane [ 1 ]. Methane is produced as a by-product of anaerobic fermentation by methanogenic archaea present in the rumen of livestock [ 2 ]. Enteric methane production is also reported to cause a 2–15 % loss of dietary gross energy intake [ 3 , 4 ]. Mitigation of enteric methane emissions would be highly advantageous, both in helping to reduce greenhouse gas emissions from the agricultural sector and improving productivity efficiencies, particularly as the global meat and milk industry is predicted to increase from 2010 levels by >50 % in 2050 [ 1 ]. Trihalogenated compounds like chloroform and bromoform significantly reduce methanogenesis by inhibition of cobamide-dependent methyl-transfer reactions [ 5 , 6 ]. These compounds suppress bacterial methanogenesis by reacting with the B12 cofactor of the methyltransferase complex responsible for the formation of methyl co-enzyme M in the penultimate step of methanogenesis [ 2 , 7 ]. Halogenated compounds ( i.e. , containing Br, Cl or I) are produced biologically by vanadate-dependent haloperoxidase enzymes (VHPOs) which oxidise halogens in the presence of H 2 O 2 to produce hypohalous acids. These acids react with nucleophilic receptors to form halogenated compounds [ 8 ]. The VHPO enzymes include chloro-, bromo- and iodoperoxidases. Chloroperoxidases utilise all three halogens, bromoperoxidases generate brominated and iodinated compounds and iodoperoxidases are limited to iodination reactions [ 9 ]. Many algae use bromoperoxidases to produce bromoform (CHBr 3 ) which diffuses or is transported out of cells to act as a biofumigant that prevents surface fouling by other organisms [ 10 ]. Asparagopsis taxiformis and A. armata , marine macroalgae, accumulate high levels of bromoform by storing it in specialised gland cells [ 10 ]. When included in ruminant diets in vivo or in vitro as a feed additive these algae significantly suppress enteric methanogenesis [ [11] , [12] , [13] , [14] ]. Consequently, Asparagopsis derived feed additives for ruminants are being developed, however, it is improbable that sufficient algal biomass can be produced to meet abatement targets in global livestock production systems. Currently the global seaweed industry produces 3–6 million metric tonnes (MMT) dry weight of product for human and animal consumption and industrial use [ 15 ]. It is calculated that 3.5 MMT of dried Asparagopsis seaweed, over half the world's current total seaweed production, would be required annually to feed the America herd of 93 million cattle. Supplying the entire global herd of 1.4 billion cattle is therefore unfeasible [ 15 ]. Other concerns for a ruminant seaweed industry include product variability, the cost of production ( Asparagopsis takes 10 −11 months to grow and is estimated to be 3–4 times more expensive than synthetic alternatives) and the algae's invasive potential in non-native habitats. New sources of bromoform containing feed ingredients are required to support international mitigation targets. We have therefore investigated microbial fermentation strategies as alternative natural sources of bromoform. Microbial fermentation has significant advantages over aquaculture in that it is an economical, highly scalable and well-established food additive production process. It is environmentally contained and utilises rapidly growing micro-organisms, with cheap substrate inputs and can be readily located close to market sites thereby minimising transport and supply chain emissions. Bacteria, fungi and lichens also use VHPO enzymes to synthesize halogenated compounds [ 16 ]. Screening of dematiaceous hyphomycete fungal species previously identified chloroperoxidase activity ranging from high to low in 83 of 112 species/isolates tested [ 17 ]. The highest VHPO expressing fungi included Alternaria, Curvularia, Drechslera, Ulocladium and Botrytis species [ 17 ]. Partially purified chloroperoxidase extracts from Curvularia inaequalis produced a range of halogenated compounds including, 3-chloro-2-bromo-1-propanol; 3-bromo-2-chloro-1-propanol; 2,3-dichloro-1-propanol; 2,3-dibromo-1-propanediol; and chloro and bromo propanediols, depending upon the substrates available [ 17 ]. The VHPO enzyme of the saprophytic fungus C. inaequalis [ [18] , [19] , [20] , [21] ] has both chloroperoxidase and bromoperoxidase activity [ 19 ]. This secreted peroxidase is a 609 amino acid protein encoded by a single VHPO gene in a C. inaequalis isolate examined [ 8 ]. In saprophytic fungi like C. inaequalis , these chloroperoxidases are believed to facilitate the oxidative degradation of plant cell walls [ 8 , [21] , [22] , [23] ]. The ability of VHPO expressing fungal species to provide alternative, natural sources of bromoform at commercially viable concentrations for enteric methane mitigation has not been shown. Here we demonstrate the potential of a Curvularia isolate to bio-manufacture a novel bromoform source for the mitigation of enteric methane.",
"discussion": "4 Discussion Curvularia inaequalis and C. clavata have been found in diverse habitats including soil, wood and on plants. The endogenous role of VHPO in C. inaequalis is suggested to be degradation of lignocellulose and penetration of plant cell walls [ 22 , 34 ]. This fungus has been shown to produce chlorinated lignin fragments when inoculated onto gymnosperm wood [ 23 ]. The probable carbon catabolite repression exerted upon this gene, observed here and in other studies [ 8 , 34 ], is consistent with the proposed secondary metabolic role of this enzyme. The optimisation of Curvularia isolate 4388 cultures enabled bromoform production at concentrations up to 10 mM (= 2.53 g/L). These bromoform concentrations are theoretically sufficient to suppress methanogenesis when included as a minor (by weight) ingredient in ruminant diets. Feeding an alternative source of bromoform, namely Asparagopsis taxiformis, to cattle has been shown to significantly reduce enteric methane emissions [ 13 ]. To achieve almost complete mitigation Asparagopsis containing 6.55 mg of bromoform/g DM was fed at a rate of 0.2 % on an organic matter intake basis. This inclusion was equivalent to 120 mg of bromoform per animal per day or 250 µg of bromoform per kg live weight for the animals used in that trial [ 13 ]. To achieve similar dietary concentrations of bromoform, 47 mL of a Curvularia culture producing 10 mM bromoform would be required per animal per day. The in vitro data observed here for total VFA, acetate, propionate and butyrate concentration are similar to those reported after 72 h incubations using Asparagopsis and a Rhodes grass hay substrate [ 38 ]. In the current work in vitro fermentation of the substrate was not negatively affected by extracts of fungal isolate 4388. This was demonstrated by only minor effects on total gas production, no effects on total VFA and the reduction in ratio of acetate to propionate, all of which confirms the potential of this fungus as an alternative source of bromoform for mitigating enteric methanogenesis. An alternative to biological sources of methanogenesis inhibitors like Asparagopsis and now Curvularia , are synthetic compounds like 3-nitrooxypropanol (3-NOP) which gives a reported mean reduction in enteric methane of 30 % [ 39 ]. The 3-NOP compound, now marketed as Bovaer®, structurally mimics methyl-coenzyme M reductase and competitively inhibits the final catalytic step of methanogenesis by ruminal archaea [ 7 ]. Whilst food products from naturally derived sources resonate positively with consumers, widespread adoption of methane abatement technologies across international markets will depend on efficacy, cost and regulatory approval. Compared with a 3-NOP dose of 100 mg/kg dry matter intake to achieve a considered optimal level of mitigation [ 39 ], a dietary bromoform inclusion of ∼13 mg/kg dry matter intake results in a similar outcome for reducing enteric methane from cattle [ 13 ]. Obvious questions associated with fungal derived supplements are potential product toxicity and acceptance by livestock when incorporated into the diet. Some Curvularia species are known to produce mammalian toxins including squalestatin S1 [ 40 ], methyl 5-(hydroxymethyl) furan-2-carboxylate [ 41 ], curvularol [ 42 ], curvularin [ 43 ] and cytochalasin A and B [ 44 ]. However, there are no reported mammalian toxins produced by C. inaequalis , or C. clavata to our knowledge; consistent with the absence of known toxin gene clusters in the genome sequence of fungal isolate 4388 and the absence of known toxins in its metabolomic analyses. In addition, 4388 samples grown in the absence of KBr and incapable of producing bromoform showed little inhibition of M. smithii growth and had no effect on methanogenesis or in vitro fermentation characteristics. Although these analyses did not include apparent digestibility, pH or hydrogen yield they can be assessed in future assays. Curvularia species have been reported as possible human allergens and rarely as opportunistic pathogens [ 45 ]. However, supernatant samples of fungal isolate 4388 contained minimal fungal tissue and therefore have reduced allergenicity potential but have similar in vitro methanogenesis suppression efficacy to homogenised cultures. The use of bromoform as a feed supplement in ruminant production systems, regardless of the source, also raises environmental and consumer health concerns. The potential environmental impact of introducing bromoform into livestock systems based on the cultivation of Asparagopsis has been described [ 46 ]. In brief, the bromoform lost into the atmosphere from the production of a naturally derived halogenated methane analogue (HMA) to supply 50 % of feedlot and dairies in Australia would amount to <0.02 % of the global ozone depletion potential weighted emissions. Microbial fermentation offers greater bromoform containment possibilities during production due to the closed nature of fermentation platforms. In addition, bromoform is not formally listed as an ozone depleting substance in a Montreal Protocol context, because it is predominantly generated by natural sources [ 47 ] and is also regarded as a very short-lived substance having an atmospheric lifetime of less than six months. Nevertheless, for bromoform to be accepted under regulatory guidelines as a feed additive for methane mitigation, it is essential to demonstrate that meat and edible offal from livestock fed this HMA is safe for human consumption and that elevated bromoform levels are not detected in tissues or meat products. As a reference point the World Health Organisation standard for bromoform in drinking water for human consumption is 100 μg/L. The LD50 of bromoform in rats is around 1200 mg of bromoform/kg/day and no effect on these animals was observed at concentrations of 500 mg/kg/day [ 48 ]. No bromoform residues have been found in samples of kidney, liver, fat, muscle tissue or milk taken from sheep [ 11 ] and beef cattle [ 13 , 14 ]. These results independently support recent in vitro studies demonstrating dehalogenation of CHBr 3 to CH 2 Br 2 by rumen microbes and then likely CH 3 Br, and finally methane and bromide [ 49 ]. The need for monitoring actual bromoform release in livestock environments appears unwarranted given the extent of rumen degradation and targeted mode of action, but in vivo investigations will be required to validate the suggested pathway for degradation of this HMA in the rumen."
} | 3,135 |
35893135 | PMC9330720 | pmc | 6,750 | {
"abstract": "Concerns over climate change have led to increased interest in renewable fuels in recent years. Microbial production of advanced fuels from renewable and readily available carbon sources has emerged as an attractive alternative to the traditional production of transportation fuels. Here, we engineered the yeast Pichia pastoris , an industrial powerhouse in heterologous enzyme production, to produce the advanced biofuel isobutanol from sugarcane trash hydrolysates. Our strategy involved overexpressing a heterologous xylose isomerase and the endogenous xylulokinase to enable the yeast to consume both C5 and C6 sugars in biomass. To enable the yeast to produce isobutanol, we then overexpressed the endogenous amino acid biosynthetic pathway and the 2-keto acid degradation pathway. The engineered strains produced isobutanol at a titer of up to 48.2 ± 1.7 mg/L directly from a minimal medium containing sugarcane trash hydrolysates as the sole carbon source. To our knowledge, this is the first demonstration of advanced biofuel production using agricultural waste-derived hydrolysates in the yeast P. pastoris . We envision that our work will pave the way for a scalable route to this advanced biofuel and further establish P. pastoris as a versatile production platform for fuels and high-value chemicals.",
"conclusion": "4. Conclusions In this study, we engineered the yeast P. pastoris to produce isobutanol from sugarcane trash hydrolysates. Heterologous expression of xylose isomerase and overexpression of the endogenous xylulokinase enabled the yeast to grow in a medium containing xylose as the sole carbon source. Further introduction of the isobutanol pathway genes comprising the valine biosynthetic pathway genes as well as the 2-keto acid degradation pathway genes resulted in yeast strains PPY0311 and PPY0312 that can produce isobutanol from a mixed-carbon source medium. Moreover, PPY0311 and PPY0312 produced isobutanol at a titer of up to 48.2 ± 1.7 mg/L directly from a minimal medium containing sugarcane trash hydrolysates as the sole carbon source. To our knowledge, this is the first demonstration of advanced biofuel production using agricultural waste-derived hydrolysates in the yeast P. pastoris . We envision that our work will pave the way for a scalable route to this advanced biofuel and further establish P. pastoris as a versatile production platform for fuels and other chemicals.",
"introduction": "1. Introduction Rising energy demands and growing concerns over climate change have led to significant interest in renewable fuels and chemicals [ 1 , 2 , 3 ]. In recent years, microbial production of advanced fuels via economically efficient bioprocesses has emerged as an attractive alternative to the traditional production of transportation fuels [ 4 ]. While microbial fermentation of ethanol has been pivotal in the transition to bio-based fuels, ethanol is not ideal as a gasoline replacement due to its poor physicochemical properties; ethanol has relatively low energy density, high hygroscopicity, and high vapor pressure [ 3 ]. On the other hand, branched-chain and higher alcohols, such as isobutanol and isoamyl alcohol, have higher energy density (at 90% and 110% of gasoline’s energy content, respectively) and are compatible with the existing storage and distribution infrastructures [ 5 , 6 ]. These superior properties, as well as their higher octane numbers compared with their straight-chain counterparts, make branched-chain and higher alcohols ideal gasoline substitutes for high-performance petrol engines. Despite significant efforts in optimizing the natural producers of these alcohols, commercial production of the vast majority of these alcohols in native organisms such as several Clostridium species is not economically feasible at present [ 7 ]. Other disadvantages of using Clostridium species as a production host include their intolerance to oxygen, their slow growth, and their production of the byproducts acetone, butyrate, and ethanol. Therefore, the development of an efficient production platform in a non-native host for higher branched-chain alcohols is needed. In addition to product yield, the economic feasibility of a biofuel production process also depends on the choice of feedstocks [ 8 ]. Sugars derived from food crops are relatively expensive and can divert water and other scarce resources when demand for food and water is expected to surge [ 9 ]. Using agricultural waste-derived lignocellulosic feedstocks that do not compete for water and land with food would decrease costs and provide the most significant CO 2 emission offsets [ 10 , 11 ]. Sugarcane trash is an abundant and underutilized biomass in sugar-producing countries worldwide [ 12 ]. It consists of approximately 15% of the total above-ground biomass at harvest, equivalent to 10–15 tons per hectare of dry matter. At present, only a fraction of sugarcane trash is converted to fuel at the sugar mill, while the rest is burned on site, creating dire pollution problems in haze and fine particles [ 13 ]. Using sugarcane trash as a lignocellulosic feedstock for biofuel production is therefore an attractive solution to both improve the economic feasibility of biofuels and alleviate the environmental problems caused by burning. Here, we engineered the yeast P. pastoris (recently renamed Komagataella phaffii ) to produce isobutanol from sugarcane trash hydrolysates. Our strategy involved overexpressing a heterologous xylose isomerase and the endogenous xylulokinase to enable the yeast to consume C5 and C6 sugars in the biomass hydrolysates. To enable the yeast to produce isobutanol, we overexpressed the endogenous L-valine biosynthetic pathway and the 2-keto acid degradation pathway. The engineered strains produced isobutanol at a titer of up to 48.2 ± 1.7 mg/L directly from a minimal medium containing sugarcane trash hydrolysates as the sole carbon source. We envision that our work will set the stage for a scalable route to this advanced biofuel. Moreover, our work further establishes P. pastoris as a versatile production platform for fuels and other chemicals.",
"discussion": "3. Results and Discussion 3.1. Overexpression of Xylose Isomerase and Xylulokinase in P. pastoris Enables Cells to Grow in a Medium Containing Xylose as the Sole Carbon Source Since xylose is the second most abundant component of lignocellulosic biomass, having a bioprocess whereby the microbial host can efficiently consume xylose and produce advanced biofuels is of great interest. Therefore, we set out to engineer P. pastoris to efficiently utilize xylose as a carbon source and produce the advanced biofuel isobutanol ( Figure 1 ). Two distinct pathways exist in xylose-fermenting organisms for the conversion of xylose to xylulose: the xylose reductase–xylitol dehydrogenase (XR–XDH) pathway in yeast and aerobic fungal species and the xylose–isomerase (XI) pathway in bacteria and anaerobic fungal species. We chose to overexpress the XI pathway in P. pastoris as this pathway neither requires any redox cofactor nor leads to the formation of the side product xylitol. Moreover, previous works have shown that xylose isomerases can be functionally expressed in P. pastoris [ 17 ]. For example, Li and coworkers co-expressed the Orpinomyces spp. XI and a gene encoding β-mannanase in P. pastoris GS115 leading to a strain that can produce the enzyme β-mannanase from a xylose-containing medium [ 18 ]. In addition to the XI pathway enzyme, we also overexpressed the endogenous xylulokinase (PpXKS) enzyme to facilitate the conversion of D-xylulose to D-xylulose 5-phosphate. P. pastoris expressing PpXKS and either the bacterial or fungal XI grew significantly better than the control strain (the laboratory strain KM71) in a medium containing xylose as the sole carbon source ( Table 1 and Figure 2 ). Strain PPY0011, which overexpresses the endogenous PpXKS and XI from the fungus Piromyces sp. E2 (PspXI), has a specific growth rate of 0.00267 ± 0.00005 h −1 ; a 3.8-fold improvement over KM71’s specific growth rate of 0.00070 ± 0.00008. Similarly, strain PPY0012, which overexpresses PpXKS and XI from the bacterium Lachnoclostridium phytofermentans (LpXI), has a specific growth rate of 0.00317 ± 0.00005 h −1 ; a 4.5-fold improvement over KM71′s value. Interestingly, strains PPY0001 and PPY0002, which overexpress the XI enzymes, but not PpXKS, exhibit only 10–19% improvement in specific growth rates over the control strain. These results suggest that the conversion of D-xylulose to D-xylulose 5-phosphate might be a potential bottleneck in the pathway and underscore the importance of XKS overexpression. Accumulatively, these results indicate that the heterologous expression of XI and overexpression of the endogenous XKS enabled the yeast to utilize xylose as a carbon source. 3.2. Engineered Strains with Overexpression of Xylose Isomerase and Xylulokinase Produce Higher Titer of Isobutanol in a Medium Containing Xylose and Glucose as a Mixed Carbon Source Having demonstrated that P. pastoris can be engineered to utilize xylose as the sole carbon source by overexpressing a heterologous XI and the endogenous XKS, we next set out to introduce the isobutanol biosynthetic pathway into P. pastoris . In our earlier work, we engineered P . pastoris to produce isobutanol from glucose with a titer of up to 2.2 g/L [ 14 ]. This was achieved by exploiting the yeast’s endogenous amino acid biosynthetic pathway and diverting the amino acid intermediates to the 2-keto acid degradation pathway for higher alcohol production ( Figure 1 ). Specifically, we overexpressed the endogenous L-valine biosynthetic pathway enzymes, PpIlv2, PpIlv6, PpIlv5, and PpIlv3 as well as the 2-keto acid degradation pathway enzymes ScADH7 from Saccharomyces cerevisiae and LlkivD from Lactococcus lactis . Expression of the latter two enzymes was targeted to the mitochondria to compartmentalize the isobutanol pathway enzymes into a single organelle, a technique that was used successfully to boost isobutanol production in the yeast S. cerevisiae by Avalos and coworkers and later in P. pastoris by our team [ 14 , 19 ]. Using these previous studies as a model for our strain design, we integrated the isobutanol pathway genes into both engineered strains PPY0011 and PPY0012, resulting in strains PPY0311 and PPY0312, respectively ( Table 1 ). We next tested the ability of the engineered strains to produce isobutanol in a minimal medium containing a mixture of glucose and xylose at a ratio of 84.5:15.2 (2% total sugar; 1.70% glucose and 0.30% xylose). We set the ratio of the two carbon sources as such to emulate the ratio obtained in sugarcane bagasse hydrolysates obtained from our previous work [ 20 ]. The engineered strains PPY0311 and PPY0312 produced isobutanol at titers of 76.0 ± 4.0 and 70.6 ± 4.4 mg/L, respectively, a two-fold improvement over strain PPY0300 (33.8 ± 2.0 mg/L) that overexpresses only the isobutanol pathway genes but not the xylose utilization pathway genes ( Figure 3 and Figure S2 ). Strains overexpressing only the xylose utilization pathway genes (PPY0011 and PPY0012) also produced isobutanol, though at lower titers compared with strains PPY0311 and PPY0312. It is important to note that, consistent with our previous findings [ 14 ], we also observed a small amount of isobutanol (9.4 ± 0.2 mg/L) being produced by the unengineered laboratory strain KM71. 3.3. Preparation of Sugarcane Trash Hydrolysates Having demonstrated that strains PPY0311 and PPY0312 can produce isobutanol from a minimal medium containing a mixture of xylose and glucose, we turned to evaluate production from lignocellulosic agricultural wastes, specifically sugarcane trash. Sugarcane trash typically contains 40–60% cellulose content, 20–30% hemicellulose content, and the remaining 15–30% as lignin and ash [ 21 ]. We previously demonstrated that liquid hot water (LHW) pretreatment is an effective pretreatment method for sugarcane bagasse, resulting in high glucose and xylose yields [ 20 , 22 , 23 , 24 ]. LHW is a promising green pretreatment method using water as the sole reaction medium. Under high-pressure conditions, water exists as hydronium (H 3 O + ) species and cleaves the side chain acetic acid from hemicellulose. This results in the generation of acetic acid, which then autocatalyzes hydrolysis of the hemicellulose fraction and also partial removal of the associated lignin. For this work, we used liquid hot water pretreatment at 180 °C, followed by enzymatic hydrolysis of the pretreated biomass using the commercial Cellic ® CTec2 enzyme. The resulting hydrolysates contained 30.22 ± 0.18 g/L total sugar content (22.66 ± 0.14 g/L glucose and 7.55 ± 0.11 g/L xylose), which is equivalent to 92.39% glucose recovery and 69.92% xylose recovery based on cellulose and hemicellulose available in pretreated sugarcane trash. In addition to sugars, the hydrolysates also contained organic acids (0.59 ± 0.00 g/L lactic acid, 4.84 ± 0.01 g/L acetic acid, and 0.02 ± 0.00 g/L levulinic acid) as pretreatment byproducts. 3.4. Isobutanol Production from Sugarcane Trash Hydrolysates Encouraged by the results from the experiments using the mixed carbon source medium, we turned to test the engineered strains’ ability to produce isobutanol directly from sugarcane trash hydrolysates. While production of advanced biofuels and chemicals from lignocellulosic hydrolysates using model organisms such as the budding yeast S. cerevisiae and the bacterium E. coli is well established, similar work using the yeast P. pastoris remains scarce [ 11 , 25 ]. Indeed, we found only two previous reports of engineering P. pastoris to produce bulk enzymes from biomass hydrolysates and just one report using the yeast as a whole-cell biocatalyst for converting xylose to xylitol [ 18 , 26 , 27 ]. Li and coworkers set the stage by engineering P. pastoris to be able to consume xylose—the first study to do so—and produce β-mannanase, a bulk enzyme used in animal feed, textile, and other industries [ 18 ]. More recently, Bankefa and coworkers engineered P. pastoris to produce the enzymes β-galactosidase and β-mannanase [ 26 ]. For chemical production, Louie and coworkers expressed a xylitol dehydrogenase enzyme (along with a glucose dehydrogenase to regenerate the cofactor NAD(P)H) in P. pastoris . The recombinant strain was then used as a biocatalyst to convert xylose into xylitol [ 27 ]. The sugarcane trash hydrolysates obtained in this study from the liquid hot water pretreatment followed by enzymatic hydrolysis contained several organic acids (0.59 ± 0.00 g/L lactic acid, 4.84 ± 0.01 g/L acetic acid, and 0.02 ± 0.00 g/L levulinic acid) as pretreatment byproducts. A large body of work indicates that organic acids (along with other pretreatment inhibitors) can adversely affect yeast’s growth and even inhibit it completely at high concentrations [ 28 , 29 ]. As such, an optimal concentration of sugarcane trash hydrolysate should be empirically determined; not too low as to result in low isobutanol titer but also not too high to result in growth inhibition. While many strategies exist to detoxify the inhibitory compounds in lignocellulosic hydrolysates, such as the application of microbial or enzymatic detoxification, they tend to add cost and time to the bioprocess [ 30 ]. Therefore, in this work, we opted to use the sugarcane trash hydrolysates without an additional detoxification step. We screened two concentrations (1.5% and 2.0% total sugar concentration) of sugarcane trash hydrolysates to identify the optimal concentration for isobutanol production and yeast growth ( Figure 4 ). Overall, the engineered strains exhibit robust growth in a medium containing sugarcane trash hydrolysates at 1.5% total sugar concentration. However, at 2.0% total sugar concentration, all tested strains displayed a prolonged lag phase, commonly observed when cells undergo a genomic adaptation process in response to the inhibitors [ 31 ]. After the lag phase, all tested strains achieved similar growth at the 72-h time point compared with the strains grown in the lower concentration of sugarcane trash hydrolysates. We tested the ability of the engineered strains to produce isobutanol in a minimal medium containing sugarcane trash hydrolysates as the sole carbon source ( Figure 5 ). We used the two screened sugarcane trash hydrolysate concentrations (total sugar concentrations of 1.5% and 2.0%). Gratifyingly, we observed isobutanol production in strains overexpressing the xylose utilization pathway genes and isobutanol pathway genes at both sugarcane trash hydrolysate concentrations. Despite the prolonged lag phase observed in the higher concentration of sugarcane trash hydrolysates resulting in an undetectable amount of isobutanol on Day 1, all tested strains produced higher amounts of isobutanol on subsequent days. In particular, strain PPY0312 produced isobutanol at 48.2 ± 1.7 mg/L in sugarcane trash hydrolysate medium with a total sugar content of 2%. The observed titer is a 34% improvement over the level produced by strain PPY0300 (36.1 ± 1.8 mg/L), which lacks the xylose utilization pathway, and a 230% improvement over the laboratory strain KM71 (14.6 ± 2.4 mg/L). Similarly, strain PPY0311 produced isobutanol at 42.6 ± 0.4 mg/L, an 18% and 191% improvement over strain PPY0300 and KM71, respectively, in the same medium. To verify that the increase in isobutanol production in sugarcane trash hydrolysate medium with a total sugar content of 2% correlated with increased expression levels of the pathway genes, we performed RT-PCR in the three engineered strains, PPY0311, PPY0312, PPY0300, as well as the laboratory strain KM71 ( Figure 6 ). The expression levels of the isobutanol pathway genes ( Pp Ilv2, Pp Ilv5, Pp Ilv3, Pp Ilv6, LlkivD , and ScADH7 ) and the xylose utilization genes ( PpXKS and PspXI (for PPY0311) or LpXI ( for PPY0312)) were higher in sugarcane trash hydrolysate medium with a total sugar content of 2% compared with the levels observed at lower sugarcane trash hydrolysate concentration ( Figure 6 a vs. Figure 6 c and Figure 6 b vs. Figure 6 d). Our results are consistent with previous findings and indicate that the total sugar concentration in the medium can impact the strengths of P GAP and P GCW14 promoters, which were used to drive the expression of the isobutanol pathway genes and the xylose utilization pathway genes [ 14 ]. Notably, the isobutanol titers produced by our engineered strains PPY0311 and PPY0312 using sugarcane trash hydrolysates remain well below the grams/liter level obtained when using pure glucose as the carbon source described in our earlier work [ 14 , 15 ]. While product titers can typically be improved by increasing the amount of carbon source in the fermentation medium (though only to a certain point and product yield might be adversely affected) [ 32 ], in our case, however, increasing the concentration of sugarcane trash hydrolysates also means letting in higher concentrations of the toxic pretreatment inhibitors which would be detrimental to yeast growth. As such, we posit that our engineered strains would most benefit from increased tolerance to pretreatment inhibitors. Several strategies exist to achieve this. For example, strains PPY0311 and PPY0312 can be subjected to adaptive laboratory evolution in a medium with increasing concentrations of sugarcane trash hydrolysates to obtain strains that better tolerate pretreatment inhibitors. This strategy has been successfully applied to improve the fermentation performance of other microbial hosts and should apply to P. pastoris as well [ 33 ]. In addition to increasing the strain’s tolerance to pretreatment inhibitors, other strategies to boost the consumption of the sugarcane trash hydrolysates and isobutanol production include fine tuning the expression levels of the xylose utilization pathway and isobutanol pathway genes. This can be done by varying the copy number of the introduced genes either by using iterative integration of the gene expression construct or by using an episomal plasmid-based expression system [ 34 , 35 ]. Alternatively, one can vary the promoter strength driving the expression of these genes. Recent works have identified several constitutive promoters of various strengths that can drive gene expression in P. pastoris [ 36 , 37 , 38 ]. Finally, one can also use metabolic flux analysis to identify bottlenecks in the pathway and engineer the strain further to relieve these bottlenecks [ 39 , 40 ]."
} | 5,158 |
26562022 | PMC4642986 | pmc | 6,751 | {
"abstract": "Trichodesmium is a biogeochemically important marine cyanobacterium, responsible for a significant proportion of the annual ‘new’ nitrogen introduced into the global ocean. These non-heterocystous filamentous diazotrophs employ a potentially unique strategy of near-concurrent nitrogen fixation and oxygenic photosynthesis, potentially burdening Trichodesmium with a particularly high iron requirement due to the iron-binding proteins involved in these processes. Iron availability may therefore have a significant influence on the biogeography of Trichodesmium . Previous investigations of molecular responses to iron stress in this keystone marine microbe have largely been targeted. Here a holistic approach was taken using a label-free quantitative proteomics technique (MS E ) to reveal a sophisticated multi-faceted proteomic response of Trichodesmium erythraeum IMS101 to iron stress. Increased abundances of proteins known to be involved in acclimation to iron stress and proteins known or predicted to be involved in iron uptake were observed, alongside decreases in the abundances of iron-binding proteins involved in photosynthesis and nitrogen fixation. Preferential loss of proteins with a high iron content contributed to overall reductions of 55–60% in estimated proteomic iron requirements. Changes in the abundances of iron-binding proteins also suggested the potential importance of alternate photosynthetic pathways as Trichodesmium reallocates the limiting resource under iron stress. Trichodesmium therefore displays a significant and integrated proteomic response to iron availability that likely contributes to the ecological success of this species in the ocean.",
"conclusion": "Conclusion The results presented describe holistic, quantified proteomic changes in Trichodesmium in response to reduced Fe availability. The observed proteomic plasticity indicates how co-occurring processes simultaneously act to alleviate Fe deficiency through extracellular uptake and intracellular recycling/repurposing processes. Further, a general decrease in abundance, and therefore the maximum enzymatic capacities, of the photosynthetic and N 2 -fixing apparatus suggested a general Fe retrenchment response under conditions where Trichodesmium IMS101 had exhausted acquisitional and compensatory adaptations to Fe deficiency. In addition, the radical remodelling of the photosynthetic electron transfer chain suggests the potential for alternative Fe-efficient electron-flow pathways under Fe stress conditions. Remodelling of core physiological processes resulted in a proteome predicted to require ~50% less metabolic Fe, driven in most part by reduction of Fe-rich proteins, with subsequent impact on the elemental and macromolecular composition of Trichodesmium which would have significant consequences for biogeochemical cycles [ 7 , 92 , 98 ]. When considered in the context of intermittent and spatially variable Fe inputs to the surface ocean [ 99 – 101 ], the observed multi-faceted proteomic response to Fe stress, involving storage, acquisition and compensation strategies, may afford Trichodesmium the ability to readily reduce their proteomic Fe burden whilst maintaining core physiological processes while awaiting more favourable growth conditions.",
"introduction": "Introduction The low (bio-)availability of iron (Fe) in oxic seawater [ 1 ] appears to play a key role in controlling the distribution and activity of oceanic diazotrophic cyanobacteria [ 2 – 6 ]. The metabolic pathways of N 2 -fixation and oxygenic photosynthesis both have a high absolute requirement for Fe, together accounting for the majority of Fe in diazotrophic cyanobacteria [ 7 , 8 ]. Trichodesmium spp . likely represent the most abundant marine diazotrophic cyanobacteria, with the widespread distribution of this genus throughout the surface tropical oceans estimated to supply 60–80 of the 100–200 Tg N yr -1 of annual global oceanic N 2 fixation [ 9 , 10 ]. However, the availability of Fe appears to be a key control on the broad-scale biogeography and activity of Trichodesmium spp . [ 11 ] and, potentially, the overall contribution of this genus to global N 2 -fixation. N 2 -fixation and oxygenic photosynthesis can be considered antagonistic due to the potential for irreversible inactivation of the enzyme catalyzing N 2 -fixation, nitrogenase, by molecular oxygen (O 2 ) produced as a photosynthetic by-product [ 12 ]. Diazotrophic cyanobacteria typically mitigate against this antagonism through the spatial segregation of N 2 -fixation into anoxygenic, non-photosynthetic cells termed heterocysts [ 13 ], or through undertaking N 2 -fixation at night [ 14 ]. However Trichodesmium spp . simultaneously perform N 2 -fixation and photosynthesis during the day without complete spatial or temporal segregation [ 15 – 18 ]. Partial and reversible differentiation of a subset of cells along the filament into ‘diazocytes’ with elevated localized N 2 -fixation capacity [ 16 , 19 ], coupled with finely regulated control of photosynthesis and N 2 -fixation within the photoperiod [ 16 ] appears to facilitate this daytime N 2 fixation, but likely places an increased Fe requirement on Trichodesmium spp . [ 16 , 20 ]. \n Trichodesmium spp . can readily adapt their proteome [ 7 , 21 , 22 ], transcriptome [ 23 ] and elemental composition [ 24 , 25 ] in response to environmental forcings. To determine the molecular-level response of Trichodesmium to Fe stress, targeted studies have identified several key molecular adaptations. These include: expression and accumulation of the chlorophyll-binding Fe-stress-induced protein IsiA [ 7 , 26 ]; the Fe-deficiency-induced protein IdiA [ 27 ]; the soluble electron carrier IsiB (flavodoxin) [ 28 ], which functionally replaces Fe-binding ferredoxin [ 29 ]; a reworking of the photosynthetic and nitrogenase apparatus [ 7 , 26 ]; and changes in Fe uptake and storage processes [ 29 ]. Whilst these previous studies have identified specific genes/proteins that change in abundance in relation to Fe stress, no holistic analysis of the coordinated proteomic response to Fe stress has been performed to date. Here we present label-free quantitative proteomic profiles from a controlled Fe stress study on T . erythraeum IMS101 cultures. Using ultra-performance liquid chromatography-mass spectrometry (1D-UPLC-MS E ) [ 30 , 31 ] we describe and quantify whole-proteome-scale coordinated responses to Fe stress in Trichodesmium , with a specific focus on the Fe-binding proteins.",
"discussion": "Results and Discussion Physiological and proteomic sampling Late exponential phase cultures with 120 nM added Fe (~350 pM Fe’) used as innocula for the experimental time series displayed evidence of mild Fe stress (supressed F v /F m ), consistent with previous studies using similar experimental conditions [ 34 ]. Subsequently, within the experimental time series F v /F m increased rapidly in Fe-replete (Fe+, 400 nM added Fe cultures), which displayed significantly higher F v /F m (~0.41–0.54) compared with no added Fe (Fe-, 0 nM added Fe) cultures (0.31–0.41) over the whole duration of the experiment (Student’s t-test, n = 3 P<0.05). Fe+ Trichodesmium cultures displayed elevated growth rates (0.43 ± 0.02 d -1 ) compared to Fe stressed cultures (Fe-) (0.36 ± 0.03 d -1 ) (Student's t-test, n = 3 P<0.05), with statistically significant increases in cell density in Fe+ cultures apparent after ~7 days growth ( Fig 1 ). Recovery of growth rates and F v /F m following the addition of 400 nM Fe to Fe- cultures after day 7 further confirmed the role of Fe availability in dictating these physiological changes. Observed physiological responses to Fe stress were similar to previous reports [ 7 , 26 , 46 ]. 10.1371/journal.pone.0142626.g001 Fig 1 Culture physiology data. Physiological growth parameters observed throughout the culture experiment showing Fe-replete (red), Fe-deplete (blue) and Fe re-fed (green) samples. A) Photosynthetic efficiency (F v /F m ). B) Cell counts (cells ml -1 ). Sampling for proteomic profiling was conducted on day 7, upon which assessment of re-fed cultures began. Time points where data show a statistically significant difference (Student's t-test, n = 3, P<0.05) between Fe-replete and Fe-deplete cultures are indicated by black outer circles for the Fe-replete data points. Of the 4,451 predicted protein-encoding genes in the T . erythraeum IMS101 genome ( www.jgi.doe.gov ), 1104 discrete proteins were observed across all samples using the MS E method. Quantified proteins accounted for 40–80% (average 56%) of the 500 ng of protein loaded on the column with the vast majority of identified spectra confidently identified as T . erythraeum IMS101 peptides. Remaining unassigned protein may be comprised of peptides incompatible with the proteomic method utilized and/or proteins where post-translational modifications may have prevented annotation to the genome. Individual protein concentrations ranged over 2 orders of magnitude (~2–600 fmol μg -1 total protein) with amino acid sequence coverage for all observed proteins ranging from 0.5–95.7% (mean ± σ = 24.7 ± 15.7%), with a minimal sequence coverage for subsequently quantified proteins of 2.9%. The absolute number of observed proteins compares favourably with that of Sandh et al. [ 22 ], who detected 1106 potential protein spots using non-quantitative comparative 2-DE/MALDI-TOF-MS in samples of Trichodesmium grown with a source of reduced nitrogen. Comparison with Pfreundt et al. [ 23 ], who observed 1810 protein-coding transcripts in the Trichodesmium primary transcriptome, suggests that our proteome is potentially representative of up to ~60% of expressed genes. Of the 1104 quantified proteins observed here, 573 were observed across all treatments, with 276 only in cultures sampled 2 hours after the onset of the photoperiod (T1+ and T1-) and 37 proteins only in cultures sampled 6 hours after the onset of the photoperiod (T2+ and T2-) (Figs 2 and 3 ). Forty-seven proteins were exclusively present in Fe-deplete cultures (Fe-) and 202 proteins were exclusively observed in Fe-replete cultures (Fe+) ( Fig 2A ). 10.1371/journal.pone.0142626.g002 Fig 2 Summary of acquired proteomic data. A) Venn diagram depicting all observed proteins and their distribution amongst our 4 sample treatments (T1+, T1-, T2+ and T2-). B) Pie chart depicting gene ontology terms for all observed proteins as annotated using Blast2GO; external and internal charts show level 2 and 3 cellular component terms, respectively. 10.1371/journal.pone.0142626.g003 Fig 3 Overview of between-treatment changes in protein abundance. Overview depicting between-treatment changes in protein abundance. Plots A and B show the diel proteomic changes for T1-/T2- and T1+/T2+ respectively. Plots C and D show Fe stress induced proteomic changes for T1-/T1+ and T2-/T2+, respectively. Significant and non-significant changes in protein abundance are colored in red and blue, respectively. The 1104 observed proteins are ordered sequentially along a linear plot of the chromosome and annotated with T . erythraeum IMS101 locus tags or protein abbreviation/description. Proteins of particular interest are labelled, with some proteins appearing as abbreviations found throughout the text. Proteins for which we observed a statistically significant presence/absence response are plotted as a nominal fold change coloured in purple. Further detail on proteins showing a statistically significant change in abundance can be found in Tables A-D in S1 Data . Gene ontology (GO) terms assigned ~92% of the function of identified proteins to catalytic activity (including photosynthesis and N 2 -fixation) or binding ( Fig 2B ), with oxidoreductase activity and ion binding being the most abundant terms in each parent category. The most abundant proteins were conserved across all 4 treatments and associated with light harvesting, photosynthesis, ATP synthesis and N 2 -fixation (observed at high concentrations ≳100 fmol ug -1 ). Of these, the most abundant proteins were subunits of the phycobilisome light-harvesting complex (concentrations of ~200–600 fmol μg -1 total protein, collectively accounting for 9.4–11.3% of the total measured proteome), in agreement with photosynthetic light-harvesting complexes dominating cyanobacterial C and N pools [ 47 ]. Differentially abundant proteins Statistical analysis identified a total of 210 differentially abundant proteins across the 4 treatments (T1+, T1-, T2+, T2-) ( Fig 3 ). Greatest changes were observed between sampling time points (T1 to T2), indicating significant short-term temporal regulation of metabolic processes ( Fig 3 ). A total of 137 and 109 proteins had significantly different abundances between T1+ and T2+, and T1- and T2-, respectively (Tables A and B in S1 Data ). Such a diel change in proteomic composition is consistent with a significant diel cycle in metabolic processes, with T1 and T2 likely representing peak photosynthetic and peak nitrogen fixation rate, respectively [ 48 ]. Comparisons between the Fe+ and Fe- proteomes identified fewer differentially abundant proteins, 50 between T1- and T1+, and 111 between T2- and T2+ (Tables C and D in S1 Data ). Within the current study of Fe stress physiology we focus on the subset of proteins showing regulation by Fe availability over both sampling time points to identify proteins showing a true Fe stress response rather than changes driven by diel variability. Fe-induced changes to the proteome 1. Fe uptake and storage Deficiency of Fe induced a number of proteomic changes potentially relating to Fe acquisition and storage. Trichodesmium is known to utilize inorganic Fe(II) and Fe(III) compounds, along with certain Fe-siderophore complexes [ 49 – 52 ]; however, detailed mechanisms of Fe uptake remain unclear [ 50 , 51 , 53 ]. FeoAB is an Fe(II) uptake system first characterized in Escherichia coli [ 54 ]. Although transcripts of FeoB (Tery_2878) have previously been observed to be upregulated in iron-stressed Trichodesmium [ 29 ], neither protein (FeoA or FeoB) was detected by Sandh et al. [ 22 ] nor in any of our samples, potentially suggesting limited importance under the specific Fe conditions analyzed, or that these proteins are not readily detectable with the employed proteomic methods. \n Trichodesmium is thought to utilise the FutABC system for cytoplasmic Fe(III) membrane transport [ 29 ]. Observed elevated concentrations of FutA/IdiA [Tery_3377] (Figs 3 and 4 ), a protein previously suggested as an environmental Fe stress biomarker [ 27 , 55 ], suggests an increased ability to transport Fe(III) from the periplasm to cytoplasm under Fe-deplete conditions, potentially signifying an increased effort to acquire extracellular Fe. 10.1371/journal.pone.0142626.g004 Fig 4 Observed abundance and fold change of selected proteins. All the specifically mentioned proteins alongside their abundances (fmol μg -1 ). Fold changes are shown as sparklines (blue = more abundant during Fe-deplete conditions, red = more abundant during Fe-replete conditions) and statistical significance of change is denoted with an asterisk. The reporting in previous studies is listed in the final column. Fe uptake may also be facilitated through reduction of Fe(III) to the more available Fe(II) by an extracellular or outer-membrane reductive process [ 51 , 52 , 56 ]. Superoxide-mediated reductive uptake of Fe (III) has recently been proposed for Trichodesmium [ 50 ]. Increased oxidative stress may also be expected under Fe stress conditions [ 57 ]. Within our Fe stressed cultures we observed elevated concentrations of a number of proteins linked to the reduction of oxidative stress through removal of superoxides and other reactive oxygen species. These included nickel superoxide dismutase (NiSOD) [Tery_0971] [ 58 , 59 ], a putative peroxiredoxin (Tery_0162), and two thioredoxins homologous to TrxA and TrxB (Tery_3311 and Tery_0945, respectively). Such observations suggest an increased production of superoxide, which we speculate may be be involved in reductive Fe(III) uptake [ 50 ] under Fe stress conditions. \n Trichodesmium spp . appear to be capable of directly accessing some forms of particulate Fe(III), for example in the form of atmospheric dust [ 52 ]. Physical interaction between the cell surface and particulates, alongside extracellular reductive pathway(s) involving the active donation of electrons to insoluble extracellular electron acceptors (such as Fe(III) oxides), may involve structures such as pilins or ‘bacterial nanowires’ [ 60 – 62 ]. For example, Lamb et al . [ 63 ] recently demonstrated a reduced ability for a pilA1 mutant (major pilin protein) of the cyanobacteria Synechocystis sp . PCC6803 to grow on Fe oxides, suggestive of a role for PilA1 in cyanobacterial reductive Fe uptake. The PilA homolog in T . erythraeum IMS101 [Tery_2388] was highly abundant in low Fe (T1- and T2-) proteomes and significantly less abundant in high Fe (T1+ and T2+) proteomes (Figs 3 and 4 ). The observed strong Fe dependence thus suggests a significant role for PilA in the Fe stress response of Trichodesmium , potentially linked to reductive Fe uptake [ 63 , 64 ] or motility of particles at the cell surface [ 52 ]. A number of the proteins elevated under Fe-deplete conditions were uncharacterized (Tables C and D in S1 Data ). Of these, Tery_3823, Tery_3825 and Tery_3826 are of particular interest given their sequential position in the T . erythraeum IMS101 genome and large increases in abundance under Fe stressed conditions (Figs 3 and 4 and S2 Fig ). Tery_3823 is a 162-amino-acid protein, previously observed at the transcript level [ 23 ], containing the lipocalin 5-conserved domain (pfam13924). Siderocalins are a sub-group of lipocalins specifically involved in siderophore scavenging, including uptake of the siderophore enterobactin [ 65 ], Fe from which is released by ferric enterobactin esterase [ 66 ]. No homolog of ferric enterobactin esterase is present in the T . erythraeum IMS101 genome but Tery_3825 contains a putative lipase/esterase conserved domain (pfam01764) ( S2 Fig ). Tery_3824 (observed but not Fe regulated) contains a TrkA conserved domain predicted to be involved in inorganic ion transport (COG2072), whilst Tery_3826 has a conserved flavin-binding monooxygenase (pfam13738) known to be involved in siderophore production [ 67 ] ( S2 Fig ). Although not thought to produce siderophores, Trichodesmium colonies have been shown to be capable of Fe acquisition from externally derived siderophores [ 49 , 51 ]. Considering that T . erythraeum IMS101 has no known TonB-dependent outer-membrane receptors [ 29 , 68 ], we suggest that the Fe-regulated cluster of proteins Tery_3823–3826 may be related to an unconventional Fe stress-induced siderophore uptake system. In contrast, the periplasmic ferric siderophore-binding protein FhuD (Tery_3943), thought to be involved in the uptake of ferrichrome and other hydroxamate siderophores [ 69 , 70 ], was observed at low abundances across all conditions. Heme oxygenase (HO, Tery_0335), responsible for catalyzing the degradation of heme and the subsequent release of Fe, was observed at elevated Fe availability, particularly within sample T2+ ( Fig 3 ) when N 2 -fixation was likely higher [ 16 ]. The presence of HO may indicate a degree of intracellular Fe re-purposing such as proposed for the elevated HO observed during the active N 2 -fixing period of Crocosphaera watsonii [ 37 ]. Alternatively, HO localization in the outer membrane as a mechanism for exogenous heme uptake has been well studied in pathogenic bacteria [ 71 ] and a few marine bacterial isolates [ 72 – 75 ]. Lastly, the Fe-storage protein ferritin (Tery_4282) was abundant under Fe-replete conditions, as previously observed [ 22 ], but entirely absent under Fe-deficient conditions (Figs 3 & 4 ). Ferritin facilitates luxury uptake and storage under Fe-replete conditions, generating reserves that are presumably available to the cell under Fe stress. The absence of ferritin under Fe stress thus suggests that proteomic Fe was dominated by functional pools under these Fe-depleted conditions. 2. Metabolism The data-independent method, MS E , provides high confidence in the quantification of individual proteins and allows for the interrogation of the internal stoichiometry of multi-subunit complexes such as nitrogenase and the photosynthetic catalysts ( S3 Fig ). In the majority of cases, subunit complexes demonstrated consistency with predictions based on known structures and associated subunit stoichiometry ( Table 1 and S3 Fig ). Occasionally, this approach also revealed potentially interesting deviations in subunit:complex ratios which may warrant further investigation. One such observation was the consistent 2:1 ratio of PsbO:PSII. PsbO is involved in stabilization of the catalytic site in photosystem II (PSII) and the crystal structure of cyanobacterial PSII suggests a PsbO:PSII stoichiometry of 1:1 [ 76 , 77 ]. However, experimental evidence suggests a 2:1 stoichiometry in higher plants [ 78 , 79 ], with the different PsbO isoforms enabling increased stabilization of the PSII catalytic site. Our data may suggest an as-yet-unconsidered role of PsbO in vivo in cyanobacterial species. 10.1371/journal.pone.0142626.t001 Table 1 Average concentration of select multi-subunit protein complexes. Complex T1+ T1- T2+ T2- \n PSII \n 85.0 ± 13.0 112.4 ± 17.2 131.6 ± 19.8 148.3 ± 21.8 \n Cytochrome b \n 6 \n f \n 69.7 ± 14.9 (0 . 8 : 1) \n 56.3 ± 14.2 (0 . 5 : 1) \n 84.0 ± 24.2 (0 . 6 : 1) \n 71.5 ± 17.5 (0 . 5 : 1) \n \n PSI \n 134.0 ± 16.0 (1 . 6 : 1) \n 56.3 ± 12.8 (0 . 5 : 1) \n 156.1 ± 22.5 (1 . 2 : 1) \n 74.4 ± 24.9 (0 . 5 : 1) \n \n RuBisCO \n 224.1 ± 30.1 (2 . 6 : 1) \n 255.4 ± 113.44 (2 . 3 : 1) \n 224.0 ± 46.8 (1 . 7 : 1) \n 250.9 ± 84.8 (1 . 7 : 1) \n \n ATP Synthase \n 97.4 ± 22.0 (1 . 1 : 1) \n 113.9 ± 33.1 (1 . 0 : 1) \n 144.7 ± 35.2 (1 . 1 : 1) \n 134.3 ± 41.9 (0 . 9 : 1) \n \n Nitrogenase \n 81.5 ± 15.8 (1 . 0 : 1) \n 39.4 ± 6.8 (0 . 4 : 1) \n 96 ± 15.6 (0 . 7 : 1) \n 25.2 ± 7.4 (0 . 2 : 1) \n \n IsiA \n 20.5 (0 . 2 : 1) \n 258.2 (2 . 3 : 1) \n n.d. (NA ) 295.7 (2 . 0 : 1) \n Average concentration ± standard error (fmol.μg -1 total protein) for select multi-subunit protein complexes observed across each of the 4 samples. Bracketed and in italic font are the complex:PSII ratios. Generally, the largest decreases observed under reduced Fe availability were associated with those proteins known to contain Fe cofactors, the most significant of which were the Fe-containing components of nitrogenase NifH, NifD and NifK (Tery_4136, 4137 and 4138, respectively) (Figs 3 & 4 ). Concentrations of the whole nitrogenase complex were therefore significantly reduced under Fe stress (Student’s t-test, P<0.05), with complex abundances for Fe- samples ~2-3-fold lower than for Fe+ samples ( Table 1 ). Such a reduction in Fe-rich functional nitrogenase proteins NifD/K and NifH is a well-documented Fe compensation response, having been observed at both the transcriptional [ 26 ] and protein levels [ 7 ], as well as being consistent with observed nitrogen fixation rates [ 20 ]. Corresponding decreases in ancillary nitrogenase proteins NifX [Tery_4140], ORF2 [Tery_4141] and NifW [Tery_4142] were also observed ( Fig 3 and S1 File ). The abundances of numerous proteins involved in photosynthetic electron transport also decreased under Fe stress. Largest changes were observed for subunits of the Fe-rich photosystem I (PSI) complex, including PsaA (4 Fe, Tery_4669), PsaB (Tery_4668), PsaC (8 Fe, Tery_0454), PsaD (Tery_3791) and PsaE (Tery_1014) ( Fig 3 ) indicating a significant decrease in overall PSI concentrations ( Table 1 ). Reduction of the Fe-rich PSI complex in the absence of similar changes in photosystem II (PSII) concentrations resulted in a marked shift in PSI:PSII stoichiometry, decreasing from ~1.2–1.6:1 under Fe-replete growth conditions to ~0.5:1 under Fe-deplete growth conditions ( Table 1 ). Such changes are in agreement with previous transcriptional studies [ 26 ] and antibody-based quantifications of Trichodesmium PSI:PSII ratios under Fe stress [ 7 , 21 ] as well as being broadly consistent with observations from non-diazatrophic cyanobacteria [ 80 , 81 ]. Reductions in the number of complete PSI complexes were also accompanied by increases in the abundance of the chlorophyll-binding Fe stress-induced protein IsiA [Tery_1667], which was ~13-fold more abundant in low Fe samples ( Fig 3 and Table 1 ) resulting in an IsiA:PSI ratio of 4–4.5:1 under Fe-stress. IsiA has been proposed to increase the absorption cross-section of PSI as a compensation strategy to mitigate reductions in PSI reaction centre concentration [ 81 – 84 ] through the formation of a IsiA-PSI supercomplex having an ~6:1 IsiA:PSI ratio. IsiA may also fulfil additional roles in photoprotection and/or as a chlorophyll store [ 81 – 84 ]. Such multiple functions may explain why measured IsiA:PSI ratios can exceed 6:1 under Fe stress in cyanobacteria [ 80 , 81 ]. Within the current study, the observed <6:1 ratio of IsiA:PSI is consistent with previous reports from culture and field-studies [ 7 ] potentially suggesting that Trichodesmium does not accumulate significant surplus amounts of IsiA under iron-stress [ 80 , 85 ]. Irrespective of the specific role of IsiA, elevated concentrations under Fe stress confirm its role in a Fe-efficient photosynthetic strategy. Although the abundance of PSI was significantly reduced under Fe stress, no equivalent reductions in the abundance of the Fe-rich Cytochrome b \n 6 \n f (Cyt- b \n 6 \n f ) complex were observed, as reflected in the minimal Fe-stress induced changes in Cyt-b 6 f:PSII ratios ( Table 1 ) which were in line with previous observations of Fe-stressed cyanobacteria [ 80 ]. Hence PSI:Cyt- b \n 6 \n f reduced from ~2:1 under Fe-replete conditions to 1:1 under Fe-deplete conditions ( Table 1 and Fig 5 ), potentially linked to the dual role of the Cyt- b \n 6 \n f complex in respiratory and photosynthetic electron transfer in cyanobacterial cells [ 86 ]. 10.1371/journal.pone.0142626.g005 Fig 5 Linear electron flow schematic during high and low Fe conditions. Simplified schematic demonstrating the linear electron transport pathway during both high Fe (A, T1+) and low Fe (B, T1-) conditions. Protein complexes are shown as circles with their diameter indicative of observed complex concentration. Circles are coloured so as to show the predicted Fe concentration of that protein complex. Abbreviations include–PSII–photosystem II, Cyt b 6 f –Cytochrome b 6 f, PSI–photosystem I, Pc–plastocyanin, IsiA–iron stress induced protein A, Fv–Flavodoxin, FNR–Ferredoxin-NADP reductase, Rbc–RuBisCO, Nif–nitrogenase, ATP synthase–adenosine triphosphate synthase. Flavodoxin [Tery_1666], the Fe-free functional replacement of the soluble electron carrier ferredoxin, was present in all proteomes and demonstrated a significant increase in abundance under Fe stress conditions ( Fig 5 ), a well-documented Fe compensation strategy [ 27 , 28 ]. Ferredoxin was not observed in any treatment and is potentially not detectible using the employed MS E method. However we note that the concentration of ferredoxin-NADP reductase (FNR, [Tery_3658]), which transports electrons from ferredoxin/flavodoxin to nicotinamide adenine dinucleotide phosphate (NADPH), was similar between treatments ( Fig 5 ). Fructose bisphosphate aldolase (FBA) enzymes also displayed highly significant Fe stress responses in T . erythraeum IMS101, with pairwise substitution of the class II FBA [Tery_4099], which was abundant under Fe-replete conditions, for a class I FBA [Tery_1687] under Fe stress. Similar substitutions have been observed in diatoms under Fe stress conditions [ 87 , 88 ]. Class II FBAs are dependent on a divalent cation such as Zn 2+ or Fe 2+ [ 89 , 90 ]. Although Tery_4099 is most similar to the Co 2+ -containing FBA of Thermus aquaticus [ 91 ], it appears that replacement of class II with class I FBAs may be a common response to Fe stress [ 88 ]. Irrespectively, ratios of Tery_1687/Tery_4099 may be a good candidate marker of Fe stress within field populations of Trichodesmium . Indeed, changes in both FBA I or II concentrations were amongst the largest observed under Fe stress, suggesting that FBAI/II ratios may be a more sensitive and reliable biomarker than, for example, flavodoxin. Proteomic Fe Allocation The applied proteomic technique enables estimation of absolute protein concentrations [ 30 , 31 ], facilitating estimation of relative metabolic cellular Fe quotas for each sample (see Methods ). Of the 1104 total observed proteins, 43 were predicted to be Fe-binding. Concentrations of Fe associated with these proteins (fmol Fe μg -1 total protein) were derived from the product of protein abundance and known or predicted Fe binding stoichiometries. A full sampling of the complete Fe-binding proteome cannot be guaranteed using the MS E proteomic technique. However, consideration of the proportional contributions of observed Fe-binding proteins as a function of protein concentration suggests that any under-sampled low abundance proteins likely contributed little to the overall Fe-binding proteome ( S4 Fig ). A significant decrease in total protein-associated Fe was observed within both the Fe-deplete proteomes, with T1- and T2- predicted to contain 55% and 60% less protein-bound Fe than their Fe-replete counterparts ( Fig 6 ). Consistent with previous reports [ 7 , 92 ], the largest pool of metabolic intracellular Fe in T . erythraeum IMS101 was estimated to be associated with the multi-subunit nitrogenase complex, with 46–56% and 49–50% of the predicted protein-bound Fe associated with this complex under Fe-deplete and Fe-replete conditions, respectively ( Fig 6 ). The various Fe-binding components of the photosynthetic electron transport chain (PSII, Cyt- b \n 6 \n f and PSI cumulatively) accounted for a further 33–41% of total proteomic Fe, with the largest share of this attributed to PSI (11–13% Fe-deplete, 22% Fe-replete) ( Fig 6 ). In addition to Fe bound within metabolic complexes, Fe storage within ferritin was likely significant under high Fe conditions. Quantification of the Fe associated with ferritin is complicated by the potential variable loading of ferrihydrite-phosphate within the core of the multimeric complex [ 93 ]. However, if, for example, we assume a stoichiometry of ~260 Fe:protein [ 93 ], we would estimate that up to 84% of the total cellular Fe might be stored within ferritin under Fe-replete conditions. 10.1371/journal.pone.0142626.g006 Fig 6 Total predicted protein associated Fe. Total protein derived Fe concentration for each of the four experimental treatments (T1+, T2+, T1- and T2-) expressed as fmol Fe ug -1 total protein. Fe concentration is subdivided into the major Fe containing complexes: Nitrogenase, cytochrome b \n 6 \n f , PSII, PSI and other. Further statistical analysis demonstrated that the overall reduction in non-ferritin proteomic Fe following development of Fe stress was predominantly associated with proteins containing >2 Fe atoms within iron-sulphur (FeS) complexes ( Fig 7 ). Conversely, heme-containing proteins such as those of Cyt- b \n 6 \n f or the PsbE/ Cyt b \n 559 of PSII, were less impacted by Fe stress. Proteins with the largest individual Fe requirements hence appear to be preferentially sacrificed in response to Fe stress [ 26 ]. Such a strategy would be expected to result in the minimum potential alteration of the overall proteome for a given decrease in metabolic Fe. Thus, although significant impacts on metabolic processes associated with preferentially lost proteins are likely [ 26 ], Trichodesmium might reasonably be expected to achieve a degree of minimization of overall metabolic impacts for a given Fe saving through preferential decreases in the most Fe-rich proteins. 10.1371/journal.pone.0142626.g007 Fig 7 Predicted protein Fe concentrations. Scatter plots showing predicted Fe concentration per total protein for those proteins thought to contain one or more Fe cofactors as derived from bioinformatical method described in the text and the quantified protein concentrations observed. Comparisons are shown for T1+/T1- and T2+/T2-. Ferritin has been omitted due to the potential variable Fe:protein ratio discussed in the text. Shade denotes the predicted number of Fe atoms per protein. Proteins showing a statistically significant difference between treatments are shown as circles whilst proteins having non-significant differences are shown as triangles. Select proteins are labelled: NifH (1), NifD (2), NifK (3), PsaA (4) PsaC (5). Note logarithmic scale. Also note that statistical significance was determined from observed protein concentrations and not predicted Fe concentrations. Consequently in some cases non-statistically significant changes in observed protein concentrations could still represent substantial differences in overall protein-bound Fe where proteins have high predicted Fe-binding stoichiometries. Potential implications for alternative photosynthetic strategy under Fe stress Estimates of resource allocation to components of the photosynthetic electron transport chain enables consideration of potential changes in overall photosynthetic strategy [ 94 ]. Oxygenic photosynthesis produces both reductant, as the electron carrier NADPH, and energy, as adenosine triphosphate (ATP) ( Fig 5 ). Linear photosynthetic pathways (Linear electron flow, LEF), where electrons flow from PSII to Cyt- b \n 6 \n f to PSI, result in a relatively high NADPH to ATP ratio [ 94 , 95 ]. However, the overall ATP demand in cells is typically greater than NADPH demand so a variety of alternative photosynthetic pathways (Alternative electron flow, AEF) cycle electrons round PSI and transfer electrons from PSII to water via midstream or terminal oxidases [ 94 , 96 ], resulting in enhanced ATP to NADPH generation. AEF pathways are likely to be particularly important in diazotrophs, which require additional ATP for N 2 -fixation (>16 ATP per N 2 fixed)[ 97 ]. Predicted Fe demands and PSII:PSI ratios associated with maintenance of different LEF and AEF pathways [ 94 , 96 ] suggest that AEF involving PSII and increased PSII>PSI can result in a more efficient use of Fe to generate ATP. Under Fe-replete conditions our proteomic data suggest a potential excess of PSI and hence maximum PSI capacities may exceed maximum PSII capacities ( Table 1 and Fig 5 ), suggestive of a reliance on (cyclic or pseudocyclic) electron cycling around PSI to generate additional ATP [ 16 , 18 ]. Use of AEF around PSI potentially reduces oxygen evolution and formation of reactive oxygen species from PSII, which can be detrimental to nitrogenase and may even contribute to lowering cellular O 2 through a pseudocyclic pathway [ 18 ]. However, substantial Fe may be required [ 94 , 96 ]. Conversely, under Fe stress, maximum PSII capacities may exceed maximum PSI capacities, significantly reducing Fe demands for additional ATP production [ 94 ]. Increases in PSII:PSI under Fe stress ( Table 1 ) might necessitate increased scavenging of molecular oxygen or superoxide generated by PSII to prevent nitrogenase inhibition [ 18 , 22 ] for example using aforementioned NiSOD, peroxiredoxin, TrxA and TrxB. While cyclic PSI electron flow is likely still important, the observed reduction of PSI:PSII, from ~1.2–1.6 during Fe-replete conditions to ~0.5 during Fe-deplete conditions, may suggest increased reliance on AEF pathways associated with PSII-catalyzed water-water cycles [ 94 ]. Midstream oxidase (PSII-MOX) or PSII respiratory terminal oxidase (PSII-RTO) are thought to be required to facilitate these AEF pathways. Although the T . erythraeum IMS101 genome codes for two RTOs, cytochrome c oxidase (COX, Tery_1777–9) and an alternative respiratory terminal oxidase (ARTO, Tery_0276–8), neither could be observed, suggesting they were either below detectible abundances or were not readily detectable using the adopted proteomic method. Speculatively, transfer of electrons to the cell surface to facilitate reductive Fe uptake [ 53 , 56 , 63 ] could form an alternate sink for a small proportion of the surplus electrons derived from PSII under Fe stress."
} | 9,142 |
26912334 | PMC4765114 | pmc | 6,752 | {
"abstract": "Background Polysaccharides comprising plant biomass are potential resources for conversion to fuels and chemicals. These polysaccharides include xylans derived from the hemicellulose of hardwoods and grasses, soluble β-glucans from cereals and starch as the primary form of energy storage in plants. Paenibacillus sp. JDR-2 (Pjdr2) has evolved a system for bioprocessing xylans. The central component of this xylan utilization system is a multimodular glycoside hydrolase family 10 (GH10) endoxylanase with carbohydrate binding modules (CBM) for binding xylans and surface layer homology (SLH) domains for cell surface anchoring. These attributes allow efficient utilization of xylans by generating oligosaccharides proximal to the cell surface for rapid assimilation. Coordinate expression of genes in response to growth on xylans has identified regulons contributing to depolymerization, importation of oligosaccharides and intracellular processing to generate xylose as well as arabinose and methylglucuronate. The genome of Pjdr2 encodes several other putative surface anchored multimodular enzymes including those for utilization of β-1,3/1,4 mixed linkage soluble glucan and starch. Results To further define polysaccharide utilization systems in Pjdr2, its transcriptome has been determined by RNA sequencing following growth on barley-derived soluble β-glucan, starch, cellobiose, maltose, glucose, xylose and arabinose. The putative function of genes encoding transcriptional regulators, ABC transporters, and glycoside hydrolases belonging to the corresponding substrate responsive regulon were deduced by their coordinate expression and locations in the genome. These results are compared to observations from the previously defined xylan utilization systems in Pjdr2. The findings from this study show that Pjdr2 efficiently utilizes these glucans in a manner similar to xylans. From transcriptomic and genomic analyses we infer a common strategy evolved by Pjdr2 for efficient bioprocessing of polysaccharides. Conclusions The barley β-glucan and starch utilization systems in Pjdr2 include extracellular glycoside hydrolases bearing CBM and SLH domains for depolymerization of these polysaccharides. Overlapping regulation observed during growth on these polysaccharides suggests they are preferentially utilized in the order of starch before xylan before barley β-glucan. These systems defined in Pjdr2 may serve as a paradigm for developing biocatalysts for efficient bioprocessing of plant biomass to targeted biofuels and chemicals. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2436-5) contains supplementary material, which is available to authorized users.",
"conclusion": "Conclusion The genome of Pjdr2 comprises genes encoding extracellular cell-associated depolymerizing enzymes to bioprocess various plant polysaccharides and these include xylans, soluble β-glucans, starch, and also arabinans and galactomannans (Fig. 4 ). The polysaccharide utilization systems in Pjdr2 serve as potential candidates for further evaluation or for introduction into other related fermentative bacteria to serve as biocatalysts to achieve direct conversion of non-cellulosic biomass to desired products. Preliminary studies have shown the ability of Pjdr2 to produce fermentative products including lactate, acetate, and ethanol from xylans, β-1,3(4)-glucans, and starch, under oxygen limiting conditions (unpublished). The potential of Pjdr2 to produce individual cell-associated glycoside hydrolases for processing non-cellulosic polysaccharides is an alternative strategy to the cell-associated cellulosome complexes evolved by cellulolytic Clostridium [ 14 ]. Pjdr2 may be considered for direct bioprocessing of hemicelluloses or may be co-cultured with cellulolytic organisms tolerant of microaerophilic conditions for conversion of biomass to targeted products. Pjdr2 is a candidate for further development as a biocatalyst for consolidated bioprocessing of biomass derived from energy crops and agricultural residues to targeted biofuels and chemicals.",
"discussion": "Results and discussion Experimental design For this transcriptome study, we sought a greater understanding of how Pjdr2 utilizes polymeric sugars. Genome analysis and polysaccharide growth studies supported efficient utilization of the polysaccharides soluble β-glucan and starch. Through bioconversion these abundant biomass-derived sugar polymers may contribute to the production of value-added chemical or fuels. To obtain a broad understanding of how these polysaccharides are utilized by Pjdr2, total RNA was prepared from early-mid exponentially growing cultures growing on these polysaccharides as well as their limit enzymatic hydrolysis products and their constituent simple sugars. The sample preparation and RNA-seq data acquisition portions of this work overlap with a recently published xylan utilization transcriptome and the results presented here are compared with this earlier work to provide perspective, draw conclusions and identify themes which define the efficient manner in which Pjdr2 utilizes polysaccharides [ 1 ]. The saccharides used in the study that provided the final comparative data set include barley β-glucan (B) and cellobiose (C) representing β-configured glucans, starch (S) and maltose (M) representing α-configured glucans, sweetgum glucuronoxylan (SG) and sorghum glucuronoarabinoxylan (SO) representing different xylan types and the constituent monosaccharides of these polysaccharides, including glucose (G), xylose (X) and arabinose (A). Further, each condition was routinely compared to a yeast extract (YE) control condition which consisted of 0.5 % YE without added carbohydrate and a sweetgum xylan with no YE (SGnoYE) control. Throughout the manuscript where specific genes are considered, their respective transcript levels or their encoded proteins are routinely identified with an accompanying abbreviated locus tag accession number consisting of the four digit number, e. g. the locus tag Pjdr2_0001 would be described as 0001. The total data set normalized RPKM ( R eads P er K ilobase per M illion reads sequenced) values were compared by fold changes taken from the ratio of the condition in consideration over the YE control condition unless otherwise stated. Data was judged to be significant given a 4-fold change and a p -value, < 0.05. Genes involved in barley β-glucan utilization Recent studies have shown that Pjdr2 may utilize soluble β-glucans [ 18 ]. Barley β-glucan consists of a linear polysaccharide chain of β-1,4 linked glucose frequently and regularly interrupted with β-1,3-linked glucose [ 24 ]. This polysaccharide lacks side chain substitutions such as those found in xylan, hence the extracellular degradation of barley-β-glucan is correspondingly less complex, presumably requiring fewer enzymes. The genome of Pjdr2 encodes three GH16 enzymes (genes 0951, 0952, and 0824) annotated as licheninases or laminarinases through domain analysis. All three enzymes are predicted to be secreted and while one consists of a singular GH16 catalytic module, the other two have an extensive multimodular architecture. Both of these modular enzymes (genes 0951 and 0824) contain triplicate N-terminal SLH domains, presumably for cell surface localization, and multiple CBM’s similar to those observed for the Xyn10A 1 enzyme involved in xylan utilization (Fig. 4 ). During growth on barley-β-glucan, the genes encoding the multimodular Bgl16A 1 (gene 0951) and the non-modular Bgl16A 2 (gene 0952) increased 80-fold and 25-fold respectively compared to growth on the yeast extract control (YE) not supplemented with carbohydrate (Table 1 ). The bgl16A 3 gene (0824) encoding the second large multimodular GH16 enzyme is expressed only at low levels on all substrates tested. Bgl16A 1 shares 34 % amino acid similarity with the catalytic domain of the GH16 laminarinase from Thermotoga maritima (UniProt accession: Q9WXN1) [ 25 ] and Bgl16A 2 shares 71 % similarity to a probable licheninase from Bacillus subtilis (UniProt accession: P04957) (Table 2 ) [ 26 ]. In support of these annotations, recombinant Bgl16A 1 has been shown to have activity against barley β-glucan and laminarin as both substrates contain the requisite β-1,3-glucan linkage while recombinant Bgl16A 2 shows its highest activity on barley β-glucan [ 18 ]. Table 1 Expression analysis of polysaccharide processing genes during growth on barley β-glucan, starch and maltose Family a \n LT b \n Protein product Name c \n SP d \n Fold change e \n Linear RPKM Values f \n B/YE C/YE G/YE S/YE M/YE B C G S M YE Barley β-glucan utilization GH16 0951 laminarinase \n Bgl16A \n 1 \n Yes \n 80.3 \n 0.2 0.2 0.4 0.1 170.2 0.4 0.5 0.8 0.3 2.1 GH16 0952 endo-β-1,3-1,4 glucanase \n Bgl16A \n 2 \n Yes \n 24.5 \n 0.2 0.2 0.3 0.1 124.1 1.1 0.8 1.5 0.6 5.1 GH16 0824 laminarinase \n Bgl16A \n 3 \n Yes 0.4 0.5 0.6 0.6 0.6 1.7 2.2 2.7 2.4 2.4 4.1 GH3 g \n 0317 glycoside hydrolase No \n 2.7 \n \n 2.7 \n 0.5 NS \n 2.4 \n 379.0 376.6 70.7 156.1 338.1 141.5 Barley β-glucan induced xylanases GH67 1323 α-glucuronidase \n Agu67A \n No \n 20.6 \n 0.8 0.4 0.6 0.4 120.2 4.4 2.1 3.5 2.5 5.8 GH8 1182 exooligoxylanase Xyn8 No \n 39.3 \n 0.4 0.4 0.7 0.5 297.2 3.3 3.1 5.1 3.5 7.6 GH11 4664 endoxylanase Xyn11 Yes \n 3.7 \n 0.4* 0.2 0.3* 0.2 7.5 0.8 0.4 0.5 0.4 2.1 GH10 0221 endoxylanase \n Xyn10A \n 1 \n Yes \n 8.8 \n 0.2 0.1 0.3 0.1 52.0 1.2 0.8 1.6 0.6 5.9 GH10 1324 endoxylanase \n Xyn10A \n 2 \n No \n 24.0 \n 0.8* 0.5 0.6* 0.6 185.3 6.2 3.5 4.7 4.4 7.7 GH43 1325 xylosidase Xyn43B 1 \n No \n 19.6 \n 0.9* 0.5 0.7* 0.8* 222.1 9.9 5.9 7.6 8.6 11.3 GH43 0750 xylosidase Xyn43B 2 \n No \n 3.2 \n \n 176.6 \n NS NS NS 5.0 276.7 2.7 2.6 1.8 1.6 GH43 1907 xylosidase Xyn43B 3 \n No \n 111.5 \n \n 2.3 \n NS NS 0.6 1410.9 29.6 12.2 13.5 7.6 12.7 Starch utilization GH13 0774 α-amylase Amy13A 1 \n Yes 0.2 0.2 0.1 \n 114.1 \n \n 69.7 \n 2.0 1.8 1.3 1127.3 688.4 9.9 GH13 5200 α-amylase Amy13A 2 \n Yes 0.2 0.2 0.2 \n 56.8 \n \n 4.2 \n 1.5 1.5 1.2 395.7 29.3 7.0 GH13 0783 α-amylase Amy13A 3 \n No 1.5 NS NS \n 112.2 \n \n 95.6 \n 7.8 5.7 6.5 584.3 497.9 5.2 GH13 1045 α-amylase Amy13A 4 \n No NS 2.7 3.8 NS NS 2.6 4.7 6.6 1.5 1.4 1.7 GT g \n 1149 α-glucan phosphorylase MalP No 0.3 0.1 0.5 NS 0.2 2.4 0.9 0.7 6.6 1.5 8.9 Maltose utilization ND 5587 oxidoreductase ThuB No NS NS NS NS \n 8.8 \n 7.4 7.0 7.3 9.0 73.8 8.4 GATase1 5588 hypothetical protein ThuA No 0.7 0.7 NS NS \n 8.1 \n 7.1 2.6 2.5 2.5 30.3 3.8 \n a GH, glycoside hydrolase; GT, glycosyltransferase; ND, not determined; GATase1, type 1 glutamine amidotransferase (GATase1)-like domain \n b LT, locus tag annotated as Pjdr2_#### abbreviated to only consist of the numeric portion, #### \n c The name assigned to gene candidates with enzymes characterized in our laboratory in bold \n d SP, sequence encodes a predicted signal peptide for secretion \n e Transcript levels of candidate genes that were expressed 2-fold greater (underlined) and those that were expressed 4-fold greater (bold) than the yeast extract without carbohydrate growth are indicated. The growth substrates are shown as follows: B, barley β-glucan; C, cellobiose; G, glucose; S, starch; M, maltose; YE, yeast extract. Significance of fold change data is judged by having a p -value no more than 0.01. Data with p -values between 0.01 and 0.05 are denoted with an asterisk, and those with p -values greater than 0.05 are designated as not significant (NS) \n f RPKM values are defined as R eads P er K ilobase per M illion reads sequenced \n g Gene 0317 and gene 1149 are included in this table as genes of interest in barley β-glucan and starch utilization pathways, respectively. Gene 0317 is increased 5.4-fold on barley β-glucan ( p -value < 0.0002) and gene 1149 is increased 9.4-fold on starch ( p -value < 0.037) relative to growth on glucose Table 2 Orthologs of translated sequences encoded by candidate genes from Pjdr2 LT a \n Protein Orthologue (UniProt accession) Identity (%) b \n 0951 multimodular Bgl16A 1 \n laminarinase from Thermotoga maritima (Q9WXN1) 34 0952 Bgl16A 2 \n probable lichinase from Bacillus subtilis (P04957) 71 0774 Amy13A 1 \n extracellular amylase from Bacillus megaterium (P20845) 46 5200 multimodular Amy13A 2 \n amylopullanase from Thermoanaerobacter pseudethanolicus (P38939) 33 0783 Amy13A 3 \n intracellular maltogenic amylase from B. subtilis (O06988) 47 0771 SBP c \n maltodextrin binding protein from Bacillus subtilis 168 (O06989) 33 1340 SYM d \n AraE xylose and arabinose symporter in B. subtilis (X98354) 49 \n a LT, locus tag annotated as Pjdr2_#### abbreviated to only consist of the numeric portion, #### \n b amino acid sequence identity \n c SBP, solute binding protein \n d SYM, symporter Hydrolysis of barley β-glucan with GH16 laminarinase and licheninase enzymes is expected to liberate β-1,3/1,4 mixed linkage glucooligosaccharides. Increased transcript levels for two predicted ABC transporter gene cassettes were observed during growth on barley β-glucan compared to the YE control. The first cassette consisting of genes 0949, 0950 and 0953, flanks the enzyme encoding genes bgl16A 1 and bgl16A 2 described above and showed greater than a 1200-fold increase in transcript levels during growth on barley β-glucan (Table 3 ). These barley β-glucan utilization genes constitute an apparent operon specifically responsive to growth on soluble β-1,3 (4)-glucans and no other tested substrate. This operon is directly linked to a β-glucan-responsive set of putative transcriptional regulators (genes 0947 and 0948) located immediately upstream but transcribed in the opposite direction. Together, these seven genes, 0947 through 0953, constitute the glucan utilization gene cluster (Fig. 1b ). The second ABC transporter gene cassette consisting of genes 5314, 5315, and 5316 has increased expression on barley β-glucan and was also increased on xylan [ 1 ]. This overlapping regulation will be discussed below. Table 3 Expression analysis of genes encoding ABC transporters during growth on barley β-glucan, starch, cellobiose and maltose LT a \n Protein product b \n Fold change c \n Linear RPKM Values d \n B/YE C/YE G/YE S/YE M/YE B C G S M YE 0472 BPD transport system IMP NS \n 23.1 \n \n 18.3 \n NS \n 2.7* \n 1.7 27.7 22 1.1 3.3 1.2 0473 BPD transport system IMP NS \n 20.8 \n \n 18.9 \n NS \n 3 \n 1.8 31 28.2 1.9 4.4 1.5 0474 extracellular SBP 0.7 \n 7.4 \n \n 9.6 \n NS NS 6.5 71.7 92.8 8.6 13.4 9.7 0728 extracellular SBP 0.3 \n 7.2 \n 0.2 NS 0.2 7.1 173.8 4 19 6 24 0729 BPD transport system IMP 0.4 \n 8.6 \n 0.5 NS 0.4 6.2 119.1 6.5 12.8 5.1 13.9 0730 BPD transport system IMP 0.5 \n 8.8 \n 0.5 NS 0.5 7.3 134.5 8.2 16.4 7.7 15.2 0771 extracellular SBP 0.1 0 0 \n 86.4 \n \n 24.1 \n 3.9 2.4 2.6 5524 1543.1 64 0772 BPD transport system IMP 0.1 0.1 0.1 \n 94.3 \n \n 34.7 \n 1.1 0.8 0.9 1174 432.6 12.5 0773 BPD transport system IMP 0.1 0.1 0.1 \n 134.8 \n \n 58.3 \n 1.3 0.8 0.8 1390 600.5 10.3 0949 BPD transport system IMP \n 1451.2 \n NS NS NS NS 3602 2.1 2 2.2 3.1 2.5 0950 BPD transport system IMP \n 1498.5 \n 1.7 NS NS NS 5522 6.4 3.5 4.8 4.9 3.7 0953 extracellular SBP \n 1220.8 \n \n 2 \n NS NS 0.6 5381 8.8 3.5 5 2.6 4.4 1320 extracellular SBP 0.2* 0.6 0.1 0.4* 0.1 4.2 11.5 1.4 6.9 1.8 18.6 1321 BPD transport system IMP 0.1 NS 0.1 0.6* 0.1 0.7 4.8 0.5 3 0.5 5 1322 BPD transport system IMP \n 15.1 \n NS 0.3 0.6 0.4 111.3 8.1 2.6 4.6 3.2 7.4 3245 periplasmic binding protein \n 3.5 \n \n 2.6 \n \n 2.3 \n \n 2.3 \n \n 10.9 \n 158.4 119.1 103.9 107 500.4 45.7 3597 extracellular SBP \n 15 \n 0.5 0.6* \n 2.4* \n NS 19.9 0.7 0.8 3.2 1.1 1.3 5314 BPD transport system IMP \n 39.9 \n 0.4 0.2 0.5 0.3 426.6 4.4 2.1 5.8 2.7 10.7 5315 BPD transport system IMP \n 42.1 \n 0.4 0.2 0.4* 0.2 379.7 3.2 1.4 3.7 1.4 9 5316 extracellular SBP \n 31.1 \n 0.2 0.1 0.3 0.1 797.5 5.4 1.9 7.5 1.6 25.7 5589 extracellular SBP 0.5 0.5 0.5* NS \n 9.8 \n 2.2 2.1 2.3 3.3 42.9 4.4 5590 BPD transport system IMP NS NS NS NS \n 10.9 \n 1.1 1.2 1.8 1.4 15.3 1.4 5591 BPD transport system IMP 0.5 NS NS NS \n 14.9 \n 0.4 0.6 0.9 1.1 13.7 0.9 5596 BPD transport system IMP \n 3.1 \n \n 44.8 \n 0.6 0.6 0.4 19.7 281.7 3.5 3.5 2.4 6.3 5597 BPD transport system IMP \n 3.3 \n \n 52.2 \n 0.5* NS 0.2 14.1 219.5 2.1 2.2 1 4.2 5598 extracellular SBP \n 4.4 \n \n 54.5 \n 0.1 0.2* 0.1 40.3 494.9 1.1 2.3 0.9 9.1 5960 BPD transport system IMP \n 15.3 \n \n 154.6 \n \n 15.2 \n NS 0.5 269.7 2721 267.6 22.6 8.8 17.6 5961 BPD transport system IMP \n 16 \n \n 145.5 \n \n 12.7 \n NS 0.5 227.1 2062 179.8 22.6 7.8 14.2 5962 extracellular SBP \n 18.1 \n \n 118.5 \n \n 12.2 \n NS 0.3 620.6 4070 419.6 33.1 9.4 34.3 \n a LT, locus tag annotated as Pjdr2_#### abbreviated to consist only of the numeric portion, #### \n b SBP, solute binding protein; IMP, inner membrane protein; BPD, binding protein dependent \n c Transcript levels of candidate genes that were expressed 2-fold greater (underlined) and those that were expressed 4-fold greater (bold) than the yeast extract without carbohydrate are indicated. The growth substrates are shown as follows: B, barley β-glucan; C, cellobiose; G, glucose; S, starch; M, maltose; YE, yeast extract. Significance of fold change data is judged by having a p -value no more than 0.01. Data with p -values between 0.01 and 0.05 are denoted with an asterisk, and those with p -values greater than 0.05 are designated as not significant (NS) \n d RPKM values are defined as R eads P er K ilobase per M illion reads sequenced Comparison of the barley β-glucan utilization system described here to the xylan utilization system described previously [ 1 ] implies a missing enzymatic component for barley β-glucan utilization. Pjdr2 appears to transport the mixed linkage oligosaccharide products of the two secreted GH16 endoglucanases in a manner similar to xylan utilization. However, unlike the defined intracellular oligosaccharide processing in the xylan utilization systems, there is no clear evidence for increased expression of genes encoding enzymes for intracellular hydrolysis of glucooligosaccharides contributing to the barley β-glucan utilization system. Genome analysis has identified several genes encoding enzymes which could be involved in the further processing of intracellular glucooligosaccharides (e.g. the genome encodes fifteen GH3 enzymes), but none of these genes are confidently assigned to this role based on the transcriptomic data. One candidate, gene 0317, encoding an intracellular GH3 β-glucosidase attains elevated transcript levels with growth on barley-β-glucan and cellobiose. The comparatively high expression level on YE resulted in a limited relative increase of just 2.7-fold with barley β-glucan, although compared to growth on arabinose or glucose this gene had a 10 and 5.4-fold increase in expression, respectively (Table 1 ). Genes with increased expression during growth on cellobiose Cellobiose is thought to represent a primary limit product of mixed linkage β-glucan utilization and was chosen for study to discriminate between utilization of the barley β-glucan polymer and its hydrolysis products. Cellobiose as a growth substrate results in increased expression of several genes encoding putative ABC transporters in Pjdr2. The genes 5960, 5961 and 5962 show greater than 118-fold increase in expression on cellobiose relative to YE. Both glucose and barley β-glucan also induce the genes encoding this transporter although to a lower extent (Table 3 ). From the transcriptome data it is not known precisely how cellobiose is converted to glucose for entry into glycolysis. As detailed above for the intracellular processing of glucooligosaccharides resulting from barley β-glucan utilization the putative intracellular GH3 β-glucosidase (gene 0317) may serve this role. This gene is expressed on cellobiose at nearly the same increased level as found with growth on barley β-glucan relative to other sugars, but not YE (Table 1 ). In addition, gene 0750 encoding a putative intracellular β-xylosidase (Xyn43B 2 ) that was earlier predicted to be involved in xylan utilization due to its 100-fold increase on xylan relative to YE (Additional file 1 ) is found in this work to be increased 177-fold during growth on cellobiose [ 1 ] (Table 1 ). This gene may encode the enzyme primarily responsible for hydrolysis of cellobiose. If so, this putative xylosidase either has dual substrate specificity or it actually encodes a GH43 β-glucosidase the expression of which is induced by cellobiose and to a lesser extent xylobiose. The GH43 family does not as yet contain an enzyme with a reported β-glucosidase activity. The expression of xyn43B 2 is also increased on barley β-glucan by 3-fold (Table 1 ) relative to YE and may contribute as well to the hydrolysis of the glucooligosaccharides derived from this polymer. Genes involved in starch utilization Pjdr2 grows very efficiently on starch [ 17 ]. Utilization of this α-1,4-linked glucose storage polysaccharide appears similar to barley β-glucan as this polysaccharide is also chemically simple relative to xylans with fewer enzymes required for degradation to glucose. The genome of Pjdr2 encodes four GH13 amylases. Three of these, Amy13A 1 , Amy13A 2 , and Amy13A 3 have significantly increased transcript levels ranging from 55-fold to over 100-fold increased expression during growth on starch (Table 1 ). Both Amy13A 1 (gene 0774) and Amy13A 2 (gene 5200) are predicted to be secreted and primarily responsible for endo-hydrolysis of native starch. The amy13A 2 gene encodes a large multimodular enzyme including SLH domains and CBM’s for cell surface proximal substrate localization (Fig. 4 ) while amy13A 1 encodes only a catalytic domain. The starch utilization system in Pjdr2 also has a predicted intracellular amylase, Amy13A 3 (gene 0783) presumably to complete the degradation of the transported, intracellular maltodextrins. Amy13A 1 shares 46 % amino acid sequence identity with the extracellular amylase from Bacillus megaterium (UniProt accession: P20845) (Table 2 ) [ 27 , 28 ] and Amy13A 2 shares 33 % identity over a large portion of its modular sequence with an amylopullanase from Thermoanaerobacter pseudethanolicus (UniProt accession: P38939) [ 29 ]. Amy13A 3 shares 47 % amino acid identity with an intracellular maltogenic amylase from B. subtilis (UniProt accession: O06988) [ 30 ] where it is thought to function in the conversion of maltotriose and larger maltodextrins to maltose and glucose (Table 2 ). A single ABC transporter gene cassette showed increased transcript levels during growth on starch relative to YE (Table 3 ). The genes for this transporter (genes 0771, 0772 and 0773) are just upstream of amy13A 1 (0774) and form a predicted operon (Fig. 1c ). The solute binding protein (gene 0771) of this transporter shares 33 % amino acid identity with a maltodextrin binding protein from Bacillus subtilis 168 (Table 2 ) [ 31 ]. This putative maltose/maltodextrin ABC transporter gene cassette was shown to be markedly up-regulated during growth on xylans (Fig. 3b , Additional file 2 ) [ 1 ]. However, the genomic localization of this ABC gene cluster within a predicted operon containing the gene encoding extracellular amylase suggests its primary function is that of a maltodextrin transporter (Fig. 1c ). This overlap in regulation will be further discussed below. The high amino acid sequence identity between Amy13A 3 and the maltogenic amylase from B. subtilis suggest that this enzyme might process transported maltodextrins to glucose and maltose [ 30 ]. As a component of a complete starch utilization system, gene 1149 encodes a putative α-glucan phosphorylase (MalP) allowing for phosphorolytic cleavage of intracellular maltose [ 32 , 33 ]. Expression levels of this gene on starch compared to YE yielded insignificant results ( p -value, 0.403), but a 9.1-fold transcript increase is observed relative to growth on glucose ( p -value, 0.004) (Table 1 ). Transcript data for the fourth predicted amylase encoding gene, 1045, was not considered statistically significant ( p -value, 0.635) and did not appear to exhibit dynamic regulation on starch, barley β-glucan or xylan and linear RPKM values were comparatively low (1.2-1.6). Genes with increased expression during growth on maltose As the primary hydrolysis limit product of starch, maltose was included in this study to distinguish physiological features for efficient starch utilization. The transporter genes described above as part of the putative starch utilization operon are also upregulated. In addition, genes 5589, 5590 and 5591 encoding a second ABC transporter are up-regulated approximately 10-fold on maltose over YE (Table 3 ). Once internalized, maltose would be expected to follow the pathway similar to that predicted in starch utilization; however, for this growth condition expression of the gene encoding the MalP protein is not increased relative to any other growth condition. This finding reveals a difference between the intracellular processing of maltodextrins derived from starch hydrolysis by the surface localized multimodular Amy13A 2 and maltose directly assimilated. A focused search failed to identify homologs of genes known for the conversion of maltose or maltose-6-phosphase (e. g. glucose phosphorylase or 6-phospho-alpha-glucosidase). Two other genes upstream of those encoding the maltose specific transporter identified above code for proteins annotated as an oxidoreductase (gene 5587) and a hypothetical protein (gene 5588) and the predicted operon appears related to the thuAB encoding operon involved in trehalose utilization in Agrobacterium tumefaciens [ 34 ] (Table 1 ). This suggests that Pjdr2 converts maltose to 3-keto-maltose. Monosaccharide assimilation and metabolism From genome analysis [ 5 ], intracellular metabolism of the hexose, glucose, and the pentoses, xylose and arabinose, are expected to follow through the Embden-Meyerhof-Parnas (EMP) pathway and pentose phosphate pathway (PPP), respectively, for entrance into the tricarboxylic acid (TCA) cycle. Following transport of arabinose through the previously identified arabinose responsive ABC transporter [ 1 ], this sugar may be converted to ribulose-5-phosphate by the arabinose isomerase and ribulose kinase enzymes. In Pjdr2, the gene 2502 (Table 4 ) attains a 24-fold increase in transcript level with growth on arabinose and 4.9-fold on sorghum MeGAX n (Additional file 2 ). Based on transcript levels the candidate ribulose kinase enzyme is encoded by gene 4209. This enzyme is a distant homolog (~21 % ID) to the AraB protein which is a component of the L-arabinan utilization system of Geobacillus stearothermophilus [ 35 ] (Table 2 ). Transcript levels of gene 4209 are increased 17-fold on arabinose (Table 4 ) and 3-fold on sorghum MeGAX n relative to YE (Additional file 2 ). This gene does not show an increased transcript level on other carbohydrate growth conditions used in this study. The genes 0977, 0978 and 0979 encoding an ABC transporter are primarily responsive to xylose resulting in an average of 135-fold increase in transcript level relative to YE (Table 4 ). These genes also showed significant but much lower fold increases on glucose and arabinose. Additionally, a predicted symporter encoded by gene 1340 shares 49 % identity with the AraE xylose and arabinose symporter in B. subtilis (Table 2 ) [ 36 ]. Expression of this gene is responsive to xylose resulting in a 162-fold increase. This gene is also expressed on cellobiose (Additional file 2 ), glucose and arabinose although to a much lower extent than observed on xylose (Table 4 ). Conversion of xylose to xylulose-5-phosphate follows a similar path as arabinose since genes encoding xylose isomerase (gene 5159) and xylulose kinase (gene 5158) result in nearly a 100-fold and 63-fold increase in expression, respectively, on xylose compared to YE controls (Table 4 ). Growth on xylans also resulted in transcript increases of 35-fold and greater for these two genes (Additional file 2 ). Table 4 Regulation of genes involved in monosaccharide transport and introduction into metabolic pathways LT a \n Protein product b \n Fold change c \n Linear RPKM Values d \n G/YE X/YE A/YE G X A YE Monosaccharide metabolism 0170 glucokinase NS NS NS 119.2 140.7 131.2 128.5 2502 arabinose isomerase 0.5 0.6 \n 23.8 \n 81.6 96.3 4117.9 173.0 4209 ribulose kinase 0.6 NS \n 17.1 \n 13.0 20.9 369.6 21.7 5159 xylose isomerase 0.4 \n 99.9 \n 0.5 21.3 6046.1 30.9 60.5 5158 xylulose kinase 0.4 \n 62.9 \n \n 19.0 \n 16.9 2951.2 19.0 46.9 Monosaccharide transporters 0472 BPD transport system IMP \n 18.3 \n \n 6.4 \n \n 16.8 \n 22 7.7 20.2 1.2 0473 BPD transport system IMP \n 18.9 \n \n 6.0 \n \n 13.9 \n 28.2 9.0 20.8 1.5 0474 extracellular SBP \n 9.6 \n \n 3.1 \n \n 5.6 \n 92.8 30.4 54.2 9.7 0661 extracellular SBP 0.4* NS \n 1917.4 \n 1.4 4.8 7460.5 3.9 0662 NBD NS NS \n 1267.6 \n 1.0 2.1 3374.4 2.7 0663 BPD transport system IMP 0.2 0.6 \n 1301.2 \n 0.8 2.0 4343.5 3.3 0977 extracellular SBP \n 16.8 \n \n 190.6 \n \n 8.3 \n 154.2 1747.9 76.2 9.2 0978 NBD \n 15.2 \n \n 102.1 \n \n 5.9 \n 80.2 538.0 30.9 5.3 0979 BPD transport system IMP \n 17.6 \n \n 111.9 \n \n 6.4 \n 105.9 671.5 38.7 6.0 1340 symporter \n 39.1 \n \n 162.0 \n \n 14.0 \n 120.5 499.9 43.4 3.1 2400 extracellular SBP \n 4.8 \n \n 3.7 \n \n 3.8 \n 244.9 188.0 193.4 51.1 2401 NBD \n 4.3 \n 1.9 \n 2.3 \n 93.8 41.5 49.4 21.9 2402 BPD transport system IMP \n 4.8 \n \n 3.3 \n \n 3.5 \n 116.1 79.2 84.0 24.0 \n a LT, locus tag annotated as Pjdr2_#### abbreviated to consist only of the numeric portion, #### \n b SBP, solute binding protein; IMP, inner membrane protein; BPD, binding protein dependent; NBD, nucleotide binding domain \n c Transcript levels of candidate genes that were expressed 2-fold greater (underlined) and those that were expressed 4-fold greater (bold) than the yeast extract without carbohydrate are indicated. The growth substrates are shown as follows: G, glucose; X, xylose; A, arabinose; YE, yeast extract. Significance of fold change data is judged by having a p -value no more than 0.01. Data with p -values between 0.01 and 0.05 are denoted with an asterisk, and those with p -values greater than 0.05 are designated as not significant (NS) \n d RPKM values are defined as R eads P er K ilobase per M illion reads sequenced While the genes that encode the transporters that import xylose and arabinose can be identified based on homology and increased transcript levels, a system for efficient glucose assimilation is less apparent. Genes encoding three putative ABC transporters showed increased transcript levels with growth on glucose, but for only two of these ABC transporters (genes 0472, 0473 and 0474 and genes 2400, 2401 and 2402) is it possible that glucose may be the target sugar for transport. For both of these transporter gene sets transcript is increased not only on glucose, but also similarly increased on arabinose, xylose and cellobiose (Tables 3 and 4 ). The 0472–0474 gene set is increased more significantly at approximately 20-fold relative to the YE control, while the 2400–2402 gene set is just greater than the significance cutoff of 4-fold (Table 4 and Additional file 2 ). The third ABC transporter gene set whose transcript is significantly increased with growth on glucose (genes 0977, 0978 and 0979) is assigned as a xylose transporter. While its expression is significant with growth on glucose, it is very low relative to growth on xylose. Analysis for phosphotransferase systems (PTS) reveals two operons (gene sets 2007–2010 and 6221–6226) encoding all protein components of a complete PTS system. Based on homology, the 6221–6226 gene set appears very likely to be a mannitol transport system, while the 2007–2010 set encodes a EIIA component (gene 2010) which is annotated as a glucose superfamily transporter, and a separate protein product (gene 2009) encoding the EIIBC components annotated as an N-acetylglucosamine specific transporter (Additional file 2 ). None of the genes encoding the complete PTS system components have increased transcript responsive to growth on glucose relative to YE. Interestingly, two unlinked PTS system components (gene 3804 annotated as an Enzyme I complex and gene 0174 annotated as HPr phosphocarrier protein) have relatively high constitutive expression levels, but their roles are unclear (Additional file 2 ). The analysis for potential glucose specific transporters is not conclusive from this data. Once transported into the cell, conversion of glucose to glucose-6-phosphate for entry into glycolysis appears to be mediated by only a single enzyme: a glucokinase (gene 0170) which yields an average RPKM value of 128 ± 15 over all the tested growth conditions (Table 4 ). This physiological data underscores original research which showed that Pjdr2 does not efficiently utilize simple sugars in minimal salt media [ 2 ]. Overlapping regulation: starch > xylan > soluble β-glucan Unexpectedly, the combined data for barley β-glucan, starch and xylan reveals a regulatory connection for utilization of these polymers. This can be seen in quantitative comparisons of the expression of genes encoding the secreted multimodular GH16, GH13, and GH10 endolytic enzymes and those encoding their associated substrate binding proteins that serve as a representative of the specific ABC transporter for the saccharides generated by these enzymes on the cell surface (Fig. 2 ). Growth on xylans, both GX n and GAX n , supports the enhanced expression of genes associated with utilization of xylans and starch but not those associated with the utilization of soluble β-glucan. While barley β-glucan induces genes related to its extracellular degradation and assimilation, these results show that it also induces 8 of the 13 glycoside hydrolase genes involved in xylan utilization (Table 1 ) [ 1 ]. Furthermore, while growth on xylan does not induce any soluble β-glucan utilization genes it does induce genes encoding all of the GH13 α-amylases and the ABC transporter considered to be involved in starch depolymerization and transport for utilization (Fig. 3b and Additional file 2 ) [ 1 ] with the exception of the putative α-glucan phosphorylase gene, malP . Following growth on starch, Pjdr2 does not induce genes for either xylan or barley β-glucan utilization. These relationships are represented in the heat map shown in Fig. 3b in which expression of genes encoding ABC transporter proteins as well as accessory enzymes for intracellular metabolism of assimilated oligosaccharides are shown. These findings may be due to a metabolic substrate preference in a manner similar to glucose mediated catabolite repression, or result from evolved enzyme systems for utilization of polysaccharides that are typically associated. In cereal grains, these three carbohydrates can be found together, with xylan and β-glucan localized more to the cell wall and outer layers, and the starch consolidated in the endoplasm [ 37 ]. The model that currently describes this relationship (Fig. 3a ) can be described as starch first, xylan second and barley β-glucan third. From the observed coordinate gene expression, Pjdr2 appears prepared to utilize multiple polysaccharides (Fig. 3b ). Fig. 2 Comparison of gene expression of surface localized substrate specific glycoside hydrolases and their regulon associated solute binding protein. RPKM ( R eads Per K ilobase p er M illion reads sequenced) values from transcriptomic studies following Pjdr2 growth on sweetgum GXn (SG), sorghum GAXn (SO), barley β-glucan (B) or starch (S) for the genes encoding the large multimodular surface anchored glycoside hydrolase and ABC transporter solute binding protein (SBP) which represents regulation for each of the three polysaccharides. A culture containing only 0.5 % yeast extract without carbohydrate (YE) served as control for comparison. Locus tag annotated as Pjdr2_#### abbreviated to only consist of the numeric portion, #### Fig. 3 Overlapping regulation of polysaccharide utilization genes in Pjdr2. Schematic representation ( a ) of the regulatory connections between the studied polysaccharide substrates. Growth condition responsive genes ( b ) for barley β-glucan, starch and xylans were compared by hierarchical clustering relative to expression on the yeast extract control. High expression, red; low expression, blue. LT, Locus tag annotated as Pjdr2_#### abbreviated to only consist of the numeric portion, #### In consideration of the expression of the genes encoding intracellular xylanases, e.g. Xyn43B 1 and Xyn8 [ 1 ] on barley β-glucan, it is possible that these enzymes may have a bifunctional role in the intracellular hydrolysis of β-glucooligosaccharides, thereby, providing an additional route for the intracellular processing of the barley β-glucan derived glucooligosaccharides and cellobiose. Other genes involved in transport also show overlapping regulation. The predicted ABC transporter previously annotated as a “multiple sugar transport system” consisting of the genes 5314, 5315 and 5316 is shown to have increased expression on barley β-glucan (Table 3 ) similar to that observed for xylan [ 1 ]. This is the only ABC transporter gene set that follows the pattern of expression during growth on barley β-glucan as that observed for the xylan specific glycoside hydrolase genes (Fig. 3b ), supporting the possibility that it might be bifunctional in substrate recognition. One other gene encoding an ABC transporter component also follows this pattern. Gene 1322 (Table 3 ) encoding an inner membrane component, UgpE (BPD transport system IMP, Fig. 1a ), of the aldouronate utilization gene cluster [ 1 , 4 ] has a markedly increased transcript level (15-fold) on barley β-glucan. Studies are underway to elucidate these overlapping regulatory connections. Overlapping regulation: cellobiose and xylobiose Some genes with increased transcript levels during growth on cellobiose were found to also have increased expression levels with growth on xylans. The gene cluster 5596, 5597 and 5598 (Table 3 ) encodes an ABC transporter annotated as an “unknown carbohydrate transporter” and has been assigned a potential role in xylan utilization based on increased transcript levels (Additional file 1 ) [ 1 ]. Analysis of growth on cellobiose indicates these genes are expressed at a level comparable to that on xylan. A corollary to this finding is the observation that growth on cellobiose also resulted in increased transcript levels for genes 0728, 0729 and 0730 encoding an ABC transporter previously assigned a putative function in xylooligosaccharide ( X 2 and X 3 ) transport [ 1 ]. From the similar level of expression on both xylan and cellobiose, it is proposed that these transporters may be specific for disaccharides such as cellobiose and the primary neutral product of enzymatic xylan hydrolysis, xylobiose. These findings indicate that the transporters of β-configured oligosaccharides may be promiscuous in their substrate recognition. Proteins with SLH domains Enzyme systems utilized by Pjdr2 for the extracellular processing of xylan, barley β-glucan and starch share a common theme. These systems include extracellular cell-associated multimodular glycoside hydrolases to generate oligosaccharides that are released in close proximity to the bacterial cell wall. This functionality is mediated by surface layer homology (SLH) domains that anchor the enzymes to the cell surface [ 2 , 38 , 39 ] and carbohydrate binding modules (CBM) that presumably associate the enzyme with the target polysaccharide [ 40 ]. These cell-surface proximal oligosaccharides are then efficiently transported with substrate specific ABC transporters and further hydrolyzed within the cell to monosaccharides for introduction into catabolism. This surface localization represents a strategy for competitive utilization of these polysaccharides. As part of this work we sought to define the roles of SLH domains in Pjdr2 in the processing of plant polysaccharides. In total, there are 77 genes encoding proteins with regions homologous with SLH domains. Of these, 73 have two or more consecutive SLH domains which is the minimum set thought to be required for tight binding to the cell wall [ 41 ]. Of the 77 SLH domain containing proteins, 29 are predicted to be involved in carbohydrate processing (Table 5 ) as indicated through domain analysis. In this smaller set, the average calculated protein size is nearly 193 kDa and the average predicted pI is 4.74 with a standard deviation of just 0.10. Domain and BLASTp analysis (Table 5 ) shows the diversity of functions of associated carbohydrate active enzymes among these SLH proteins (Table 5 ). In the current transcriptomic data set, only a single SLH-bearing gene has been identified being involved in the catalysis of either xylan (gene 0221), barley β-glucan (gene 0951) or starch (gene 5200) utilization from the 29 identified SLH-encoding carbohydrate processing genes (Table 5 ). Of the other SLH domain containing proteins in this list it can be seen that Pjdr2 may utilize numerous other polysaccharides with the same strategy. Some of these include arabinan, galactomannan, chitin, pectin and hyaluronan (Fig. 4 ). Table 5 List of surface layer homology domain containing proteins of Pjdr2 proposed to be involved in extracellular polysaccharide processing LT a \n Domain architecture b,c,d \n Secretion f \n MW (kDa) g \n pI g \n Putative function h \n \n 0221/ Xyn10A \n 1 \n 3CBM4,9/ GH10 /CBM9/3SLH Yes 157 4.90 β-xylanase 0680 \n GH5 /FN3/3CBM11/3SLH Yes 204 4.72 β-mannanase 0824 3SLH/ GH16 /3CBM4,9/DUF1533/ GH16 /3CBM4,9/DUF1533/CBM4,9/DUF1533/CF/DUF1533/CF Yes 313 4.65 β-glucanase \n 0951/ Bgl16A \n 1 \n 3SLH/ GH16 /2CBM4,9/CBM6/2CBM4,9 Yes 154 4.83 β-glucanase 0964 RBT/2CBM6/ RHB /3SLH Yes 228 4.81 mycodextranase 1124 \n GH43 /CBM6/2BIG2/2SLH Yes 159 4.71 α-L-arabinofuranosidase 1125 \n GH20 /CBM6/BIG2/2SLH e \n Yes 213 4.75 α-glucuronidase 1167 DUF481/ RHB /FN3/CBM9/FN3/3SLH Yes 185 4.79 polysaccharide lyase 1173 2 PL3 /CBM9/3SLH Yes 210 4.81 pectate lyase 1611 \n CCT /PHP/BIG3/3SLH Yes 230 4.90 chitobiase (CCT/ESD) association 1796 2FN3/ GH18 /FN3/3SLH Yes 128 4.79 chitinase 1997 CBM4,9/ AL3 / HP /3SLH Yes 229 4.90 alginate lyase/heparinase 2544 2 NVS /2FN3/3SLH Yes 170 4.99 sialidase 3195 \n GAG /3SLH Yes 153 4.85 hyaluronate lyase 3554 \n GH30 /CBM6/CBM4,9/2FN3/BIG2/3SLH Yes 173 4.89 endo-1,6-beta-glucosidase 3565 \n 3KCH /2 CCT /5BIG3/CLD/3SLH Yes 227 4.65 CCT/ESD association 4054 2CBM6/ PL3 /RHB/3SLH Yes 237 4.75 polysaccharide lyase 4093 3FN3/ GH43 /CBM6/3SLH Yes 200 4.70 β-xylosidase 4104 \n GH27 /3CBMX2/3SLH Yes 164 4.73 α-galactosidase 4665 \n GH59 /CBM6/BIG4/BIG3/3SLH Yes 217 4.52 β-galactosidase 4730 \n GH53 /BIG4/2CBM4,9/3SLH Yes 122 4.73 arabinogalactan endo-β-1,4-galactanase 5040 \n GH66 /5CBM6/AMY/3SLH Yes 182 4.83 dextranase 5076 \n RHB /2CF/FN3/GH65/2CF/3SLH Yes 273 4.77 alpha-L-fucosidase \n 5200/ Amy13A \n 1 \n 4 ESD / AMY /2FN3/3SLH Yes 235 4.75 amylopullulanase 5272 2 CBM11 /BIG2/3SLH Yes 231 4.65 carbohydrate binding 5379 3SLH/ GH18 \n Yes 59 5.80 peptidoglycan hydrolase 5534 \n RHB /2CRD/2CLD/BIG2/3SLH Yes 176 4.58 polysaccharide lyase 5572 \n GH42 /3SLH Yes 162 4.72 β-galactosidase 6195 3 PBX /3SLH Yes 77 4.53 xylanase \n a LT, locus tag annotated as Pjdr2_#### abbreviated to consist only of the numeric portion, ####. Surface anchored proteins directly involved in the utilization of xylan, barley β-glucan and starch are denoted in bold \n b Domain predictions result from analysis of the proteins in the CCD (Conserved Domain Database) with an Expect Value threshold set to the default of 0.010 and increased to 0.10 to detect more divergent domains in unaccounted for regions or, as in some cases directly through the pfam database. Domain abbreviations are defined in order of appearance. CBM , Carbohydrate binding module; GH , Glycoside hydrolase; SLH , Surface layer homology domain; FN3 , Fibronectin type 3 domain; DUF , Domain of unknown function; CF , Coagulation factor 5/8 C-terminal domain; RBT , Ricin-type beta-trefoil; RHB , Right handed beta helix; BIG , Bacterial Ig-like domain; PL , Pectate lyase; CCT , Chitobiase/beta-hexosaminidase C-terminal domain in the early set domain superfamily; PHP , Polymerase and histidinol phosphatase domain; AL , Alginate lyase; HP , Heparinase; NVS , Non-viral sialidases; GAG , Glycosaminoglycan polysaccharide lyase family; KCH , Galactose oxidase central domain; CLD , Cadherin-like beta sandwich domain; AMY , Alpha amylase catalytic domain family; ESD , Early set domain; CPRD , Carboxypeptidase regulatory-like domain; PBX Putative bacterial xylanases \n c For any given domain an abbreviation is provided as defined under superscript (b), with numbers preceding the abbreviation indicating the number of consecutive occurrences greater than one (> 1) of the domain and numbers following the abbreviation indicating the specific family of the detected domain, if any \n d In each modular protein the domain used to establish the “putative function” (sixth column) is underlined \n e Annotated in the CAZy database as a GH115 \n f Secretion was deduced by detection of a signal peptide using the Signal-P server \n g Molecular weight (MW) and isoelectric point (pI) predictions were obtained through the ProtParam tool available through the ExPASy web server \n h Putative function is based either on the predicted function from domain assignment or a justification for assignment as a protein that is involved with sugar manipulations Fig. 4 Multimodular cell-associated enzymes from Pjdr2. A diagram representing the domain architecture of the three surface anchored enzymes central to the utilization of xylan, barley β-glucan and starch as reported in this work along with two others, further representing the broad use of SLH mediated surface localization of enzymes for polysaccharide assimilation in Pjdr2 as demonstrated in Table 5 . Coding sequence locus tag accession numbers are provided as Pjdr2_#### Polysaccharide utilization in Pjdr2 From the studies presented here for the utilization of barley-derived β-glucan and starch, we observe a similar strategy evolved by Pjdr2 as illustrated in the earlier xylan transcriptome report [ 1 ]. In each case coordinately expressed gene sets have been identified (Fig. 1 ) and central to each encoded enzyme system is a multimodular glycoside hydrolase containing carbohydrate binding modules which afford interaction with polysaccharide substrates and a triplicate set of SLH domains for cell surface localized formation of oligosaccharides (Fig. 4 ). For processing of β-1,3(4)-glucans, contiguous genes encoding transcriptional regulators, ABC transporters, the multimodular cell-associated Bgl16A 1 and the secreted non-modular Bgl16A 2 catalytic domain along with an associated ABC transporter comprise a β-glucan utilization regulon. In this case both secreted enzymes digest barley β-glucan to tri-, tetra-, penta- and hexasaccharides, and laminarin to mono-, di-, tri- and tetrasaccharides indicating similar functions for both enzymes [ 18 ]. These oligosaccharides resulting from extracellular barley β-glucan hydrolysis and cellobiose (from either barley β-glucan or growth on cellobiose) are presumably transported into the cell where they are subsequently degraded to monosaccharides by the action of a GH3 endoglucanase and/or a novel GH43 enzyme (Xyn43B 2 ) with β-glucosidase functionality. For starch processing, a regulon encoding a putative maltodextrin ABC transporter together with the non-modular Amy13A 1 managed starch utilization. Encoded distally, the multimodular cell-associated amylase Amy13A 2 likely produces small maltodextrins proximal to the cell surface. These may then be taken up and processed by intracellular maltogenic Amy13A 3 to yield maltose and glucose. Final conversion of maltose is thought to occur through the action of an α-glucan phosphorylase yielding glucose and glucose-1 phosphate. For the soluble β-glucan, starch and xylan utilization systems, two endo-acting hydrolases may work synergistically with each other for efficient depolymerization of the specific polymeric substrate to oligosaccharides. The modular property of the larger enzyme allows generation of oligosaccharides close to the cell surface without diffusion into the medium and hence couples the depolymerization process with assimilation by ABC transporters for intracellular processing and metabolism. These systems for polysaccharide utilization with minimized secretion of extracellular glycoside hydrolases coupled to transport of oligosaccharides in lieu of simple monomeric sugars potentially affords a significant conservation of cellular energy in the form of ATP as described for the processing of cellulose by C. thermocellum . Based on increased expression levels of genes during growth on multiple polysaccharides a regulatory connection is observed between utilization of barley β-glucan, starch and xylans. Barley β-glucan induces genes involved in extracellular depolymerization and assimilation specific to soluble β-1,3(4)-glucan. However, it also induces many of the genes shown to play a prominent role in the xylan utilization systems [ 1 ]. Although xylans do not induce genes specific to barley β-glucan utilization, they do induce genes belonging to the starch utilization system. When Pjdr2 was grown on starch, no genes specific to xylan or barley β-glucan utilization were found to be induced. These studies show the transcriptional induction and repression strategies evolved in Pjdr2 for utilizing a variety of polysaccharides. Interestingly, induction of the starch utilization genes with growth on xylan results in increased expression of amy13A 1 and amy13A 3 while the amy13A 2 gene encoding the large surface anchored amylase is expressed just enough to meet the significance selection cutoff (4-fold). This same pattern is also observed with growth on maltose. It appears the elevated expression of the amy13A 2 gene is specific for starch and the non-starch substrates which activate the expression of the starch utilization regulon (including amy13A 3 ) may poise Pjdr2 for rapid response to starch availability. SLH domains appear to play a vital role in interaction of Pjdr2 with its native environment. The 77 SLH domain-containing proteins encoded in the genome of Pjdr2 highlight the expanded use of this domain for cell wall associations and also hints to a modus operandi , at least regarding an approach to polymeric substrate utilization."
} | 12,681 |
27378842 | PMC4908299 | pmc | 6,753 | {
"abstract": "The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance.",
"introduction": "1. Introduction The nervous system allows individual animals and their populations to survive in severe environments by analyzing a huge amount of information from sensory organs and promptly generating adequate control signals for motor organs. This complex and intelligent information processing ability is autonomously obtained and adaptively maintained on its genetically developed physical basis, the network of neuronal cells. The nervous system consumes a sufficiently low power to allow for operation within the power supply limit of an animals' body; for example, the human brain consumes about 20 W (Clarke and Sokoloff, 1999 ), which is a lower power than mainstream CPUs. Because it is a network of neuronal cells with a wide variety of complex activities, the mechanisms of its information processing function are still poorly understood. It is attracting increased attention from biological, medical, and engineering fields. A silicon neuronal network is a network of silicon neurons (SNs) connected via silicon synapses (SSs), which are electronic circuits that reproduce the electrophysiological activity of neuronal cells and synapses, respectively. Unlike neuro-inspired artificial neural networks, it is an approach to neuromimetic systems that realize intelligent, autonomous, robust, and power-efficient information processing via an architecture comparable to the nervous system (Arthur and Boahen, 2011 ; Brink et al., 2013b ; Cassidy et al., 2013 ; Kohno et al., 2014b ; Qiao et al., 2015 ; Giulioni et al., 2016 ). Because it is a bottom-up approach with cell-level granularity and reproduces neuronal spiking activities, it is also applicable to biohybrid systems including neuroprosthetic devices that replace damaged nerve tissues (Ambroise et al., 2013 ). Generally, SN circuits are required to have the capability of reproducing complex neuronal activities, have a low power consumption, and be compact and highly integratable. In fields where the reproducibility is important, such as the biohybrid systems and high-speed simulators, SN circuits have been designed to solve ionic conductance neuronal models (Simoni and DeWeerth, 2006 ; Schemmel et al., 2010 ; Grassia et al., 2011 ; Saïghi et al., 2011 ). These models describe the dynamics of ionic currents that generate the dynamical behavior of the membrane potential by charging and discharging the membrane capacitance. They can precisely reproduce neuronal activities, but their equations are described by high-dimensional non-linear differential equations (DEs). It was demonstrated that their circuit implementations (conductance-based SNs) can well reproduce the neuronal activities of their target cells but require a relatively large amount of hardware resources and consume a relatively high power in the range of micro- to milliwatts. The ionic conductance models share a common structure, namely the Hodgkin–Huxley formalism, which allows their circuit implementation to mimic a variety of neuronal cells after fabrication by applying appropriate parameter voltages (Grassia et al., 2011 ; Saïghi et al., 2011 ). In fields where low power consumption and integratability are important, SN circuits that solve integrate-and-fire (I&F) models are widely used. These models describe the neuronal activities with simple DEs by treating a spike as an event and focusing on the timing of spike generation. Their analog and digital circuit implementations (I&F-based SNs) have been developed (Thomas and Luk, 2009 ; Arthur and Boahen, 2011 ; Cassidy et al., 2013 ; Merolla et al., 2014 ; Mayr et al., 2015 ; Qiao et al., 2015 ; Giulioni et al., 2016 ). Analog I&F-based SNs achieve ultralow power consumption down to several nanowatts and several hundreds of them were integrated on a chip with thousands of SS circuits. Although their digital implementations consume more power, they are more portable, easy-to-operate, and highly integratable. A milestone work is the TrueNorth chip (Merolla et al., 2014 ) that integrates 1 million SNs and 256 million SSs on an application-specific integrated circuit chip and consumes less than 70 mW. Silicon neuronal networks implemented on field-programmable gate array (FPGA) chips achieve far less integration (about 1000 SNs) and consume higher power, but their low cost and reconfigurability have attracted many researchers. Sophisticated I&F-based models such as the Izhikevich (IZH) (Izhikevich, 2004 ) and adaptive exponential I&F (Brette and Gerstner, 2005 ) models incorporate the viewpoint of qualitative neuronal modeling described below, which allows them to reproduce a variety of neuronal activities. In principle, however, they cannot reproduce some neuronal properties related to the variability of spikes, which are reported experimentally and indicated theoretically. For example, it was reported that the amplitude of spikes at an axon terminal in the hippocampus is gradedly dependent on the stimulus intensity (Alle and Geiger, 2006 ), and a mathematical analysis of neuronal models indicated that a class of neurons, Class II in Hodgkin's classification (Hodgkin, 1948 ), can generate spikes in the same manner (Rinzel and Ermentrout, 1998 ). In addition, the parameter setting of the I&F-based models requires careful tuning; for example, we pointed out that the phase response curve (PRC) of the IZH model in the Class II setting is discontinuous at θ = 0, which causes a severe reduction in the retrieval ability of an auto-associative memory in all-to-all connected networks (Osawa and Kohno, 2015 ). This problem can be solved by increasing the parameter d of the model, which distorts the waveforms of the membrane potential by producing a huge hyperpolarization after each spike. Another example is that the spiking patterns of the IZH model in the intrinsic bursting (IB) setting have different characteristics from IB cells when a strong stimulus is applied (Nanami and Kohno, 2016 ). These facts suggest the possibility that a network of I&F-based silicon neurons may have limited ability to reproduce particular information processing in the nervous system. In the field of qualitative neuronal modeling, the mathematical techniques of non-linear dynamics have been effectively applied to ionic-conductance models to produce low-dimensional and simple polynomial equations that qualitatively capture their dynamical properties (Rinzel and Ermentrout, 1998 ; Izhikevich, 2007 ). In contrast to the I&F approach, they do not ignore specific phenomena including the spike generation mechanism. The most well-known qualitative model is the FitzHugh–Nagumo (F-N) model (FitzHugh, 1961 ) that reproduces the dynamical structure in the Hodgkin–Huxley (H-H) model (Hodgkin and Huxley, 1952 ). The H-H model is described by four-variable non-linear DEs, whereas the F-N model is two-variable and its only non-linear term is cubic. The F-N model is Class II and can produce the graded spike response to a pulse stimulus. The first silicon nerve membrane circuit, the Nagumo circuit (Nagumo et al., 1962 ), implemented this model using the tunnel diode. Later, several SNs have implemented the F-N and other qualitative models using recent analog and digital circuit technologies (Linares-Barranco et al., 1991 ; Cosp et al., 2004 ; Weinstein and Lee, 2006 ). In our previous works (Kohno and Aihara, 2005 , 2007 , 2008a ; Sekikawa et al., 2008 ; Kohno and Aihara, 2010 ; Li et al., 2012 ; Kohno and Aihara, 2014a ; Kohno et al., 2014b ), we proposed a qualitative-modeling-based design approach for SNs. In this approach, a qualitative neuronal model that reproduces the dynamical structure in a target neuronal model is constructed by combining the formulae for the characteristic curves of favorable elemental circuit blocks instead of polynomials. The elemental circuit blocks are selected according to the required features of the SN; for example, subthreshold metal–oxide–semiconductor field-effect transistor (MOSFET) circuit blocks may be used for low-power SNs. Such a model is expected to be implemented efficiently in comparison to the direct implementation of polynomial-based qualitative models. In addition, a model that supports the mathematical structures in different classes of neurons can be designed, and one of them is invoked by appropriately selecting the model parameters. We developed a configurable low-power analog SN circuit (Kohno and Aihara, 2008a , b ; Sekikawa et al., 2008 ; Kohno and Aihara, 2010 ) and a configurable simple digital SN circuit (Kohno and Aihara, 2007 ; Li et al., 2012 , 2013 ). Our analog SN supports five classes of neuronal activities, Class I and II in the Hodgkin's classification, regular spiking (RS), square-wave bursting, and elliptic bursting (Wang and Rinzel, 2003 ) by appropriately setting the parameter voltages, and our digital SN supports Class I and II and Class I * (Tadokoro et al., 2011 ) neuronal activities. Basu and Hasler ( 2010 ) developed two ultralow-power analog SNs that consume several nanowatts (Brink et al., 2013b ) on the basis of a similar approach; one of them is dedicated to Class I and another to Class II. We are developing an analog SN that supports both classes and consumes a low amount of power that is comparable to their work (Kohno and Aihara, 2014a ). Most silicon neuronal networks incorporate the SS circuits that mimic the signal transmission in chemical synapses. Their three important features are the synaptic efficacy, plasticity, and summation (Destexhe et al., 1998 ; Song et al., 2000 ; Dan and Poo, 2004 ). A large (small) amount of synaptic current is injected into the postsynaptic neuronal cell if the synaptic efficacy is high (low). The synaptic efficacy is modulated on the basis of some factors including the neuronal spikes generated by the pre- and postsynaptic neuronal cells (the synaptic plasticity). It is called the spike-timing-dependent plasticity (STDP) if its rule (a learning rule) is based on the timing of neuronal spikes in the pre- and postsynaptic neuronal cells (Song et al., 2000 ; Dan and Poo, 2004 ). The synaptic summation allows a bursting spike input to enhance the effect of synaptic transmission. It was shown that this feature can play a critical role in spike timing recognition (Gütig and Sompolinsky, 2006 ). Note that the information of an input spike's magnitude can be transmitted via the time period of neurotransmitter release. The compactness and low-power consumption of SS circuits are also an important issue because the number of SSs in a silicon neuronal network is generally larger than that of SNs. In Merolla et al. ( 2014 ), the integration of a huge number of digital SSs was realized by limiting the functionality of the SS to the synaptic efficacy. Their synaptic weights have to be calculated by a off-chip system, but this is not a limitation in engineering applications in which “ready-trained” discriminators are required. They reported that this circuit could realize a multiobject detection and classification system. Only the synaptic efficacy was supported also in early FPGA-based silicon neuronal networks (Rice et al., 2009 ; Thomas and Luk, 2009 ), but in recent works, the synaptic summation is supported in Ambroise et al. ( 2013 ) and all of the three features are supported in Li et al. ( 2013 ) and Cassidy et al. ( 2013 ). The analog SS circuit in Giulioni et al. ( 2016 , 2015 ) implements the synaptic efficacy and the plasticity. Their silicon neuronal network chip integrates 128 leaky I&F SNs and 16384 SSs whose synaptic efficacy is stored in a bistable memory and controlled by a Hebbian-type STDP rule (Fusi et al., 2000 ). They realized an autoassociative visual memory (Giulioni et al., 2015 ) and motion detectors (Giulioni et al., 2016 ). The analog SS circuit in Qiao et al. ( 2015 ) implements all of the three features of synapses. The synaptic summation is realized by a low-power current-mode integrator circuit called a differential-pair integrator (DPI). To reduce the circuit size, a DPI circuit is shared by multiple synapses by exploiting its linearity. The synaptic efficacy is stored in a bistable memory and controlled by an STDP-based algorithm (Brader et al., 2007 ). This chip integrates 256 adaptive exponential I&F SNs with more than 128,000 SSs and was applied to image classification tasks. Another full-featured analog SS in Brink et al. ( 2013b ) stores the synaptic efficacy in an analog non-volatile memory based on a floating-gate device and supports an asymmetrical STDP learning rule. This chip integrates 100 Class II SNs with 30000 SSs and realized a winner-take-all network and a rhythm generator (Brink et al., 2013a ). In this article, we briefly review our SN circuits designed by a qualitative-modeling-based approach. The next section summarizes the mathematical methods of qualitative neuronal modeling that are applied to SN design. Section 3 explains our analog and digital SNs and Section 4 concludes this review.",
"discussion": "4. Discussion As reviewed above, our silicon neuron circuits can realize different classes of neuronal activities by selecting appropriate parameter values and their characteristics can be modified by finely tuning the parameters as shown in Figure 5D . This high configurability is advantageous not only for bio-silico hybrid systems but also for constructing “ field-programmable” silicon neuronal networks in which each SN can be reconfigured after fabrication or each SN autonomously obtains appropriate dynamical properties on the basis of the history of stimulus inputs as in the brain. This high configurability arises from the fact that the activity of many neuronal classes can be explained using common dynamical structures that are reproduced in our models by a combination of implementation-efficient formulae. In contrast, the circuitry is simplified by supporting only one neuronal class in the non-I&F-based SN circuits developed by a similar approach (Basu and Hasler, 2010 ; Brink et al., 2013b ). These circuits realize ultralow power consumption down to several nanowatts at the expense of configurability. In their SN network systems, the configurability is supplemented by accommodating a sufficiently large SN circuit pool, in which the appropriate SNs for a desired network are activated. Our circuit in Section 3.2 supports both Class I and II neuronal activities and consumes a similar power; however, it has the drawback of high configurability. The circuit has to be configured appropriately by tuning a number of parameter values, and additional circuits are required for storing parameter values. The complexity of the configuration process is solved by parameter tuning procedures that utilize the nullcline drawing circuits as explained in detail in Section 3.1. This procedure is still not straightforward, but all of the students who worked on our circuit learned to be able to finish the tuning procedure within several tens of minutes. For a large-scale silicon neuronal network, this procedure has to be automated. It may be done by metaheuristic approaches similar to those utilized in Grassia et al. ( 2011 ). The power consumption and area occupied by additional circuits for storing parameter values may be reduced by evolving non-volatile memory technologies such as memristors. In digital silicon neuronal networks, the accumulation of synaptic inputs consumes a considerably larger amount of hardware resources than SN circuits. Thus, the compactness of the SN circuit is not a major issue. The advantage of our circuit is that its model is non-I&F-based and thus can mimic the spike-generation-related properties in neuronal activities more finely than I&F-based circuits. One of these properties is the graded response in Class II neurons. Because the graded response is found in the brain, as mentioned in the introduction, there is possibility that it plays some roles in information processing in the brain. Our silicon neuronal network model intends to provide a platform in which a wide variety of neuronal activities including the dynamics of spike generation is qualitatively reproduced without a major increase in hardware resource consumption. For this goal, our SN model is being expanded so that it can realize more classes of neurons including RS, LTS, and IB as well as autonomous bursting supported by our analog SN. It has four variables (two original and two additional slow variables) but still can be solved by one multiplication per a numerical integration step. The details of this model is explained in Nanami and Kohno ( 2016 ). A goal of our analog silicon neuronal circuits is to establish an ultralow-power general-purpose silicon neuronal network platform that will be applicable to neuromimetic computing when the mechanism of information processing in the nervous system is elucidated. We expect that it has an advantage also in the application to large-scale neuronal network simulators (Schemmel et al., 2010 ; Stromatias et al., 2013 ) and brain-prosthetic devices such as an artificial hippocampus (Berger et al., 2012 ; Hampson et al., 2013 ; Song et al., 2015 ), an artificial cerebellum (Hogri et al., 2015 ), and an artificial prefrontal cortex (Hampson et al., 2012 ) because our circuits meet their requirements of a low power consumption and the ability to mimic various complex neuronal activities finely. Construction of such systems may contribute to the elucidation of the brain's mechanisms by the “analysis by synthesis” approach. Our digital silicon neuronal network platform is also applicable to neuromimetic computing and large-scale neural network simulation. It consumes more power than analog circuits but has advantage in scalability."
} | 4,831 |
35548138 | PMC9086479 | pmc | 6,754 | {
"abstract": "Plant growth-promoting rhizobacteria (PGPR) have been extensively investigated in combination remediation with plants in heavy metal contaminated soil. However, being biosorbent, few studies of live and dead cells of PGPR have been undertaken. Meanwhile, the application of live or dead biomass for the removal of heavy metals continues to be debated. Therefore, this study uses living and non-living biosorbents of Cupriavidus necator GX_5, Sphingomonas sp. GX_15, and Curtobacterium sp. GX_31 to compare their Cd( ii ) adsorption capacities by SEM-EDX, FTIR, and adsorption experiments. In the present study, whether the cells were living or dead and whatever the initial Cd( ii ) concentration was, removal efficiency and adsorption capacity can be arranged as GX_31 > GX_15 > GX_5 ( p < 0.05). However, removal efficiency in live and dead biosorbents was quite different and it greatly affected by the initial Cd( ii ) concentrations. The dead cells exhibited a higher adsorption capacity than the live cells of GX_31. Nevertheless, for GX_5 and GX_15, the loading capacity of the non-living biomass was stronger than that of the living biomass at 20 mg L −1 of Cd( ii ), but the capacity was similar at 100 mg L −1 of Cd( ii ). Minor changes of spectra were found after autoclaving and it seemed that more functional groups of the dead biosorbent were involved in Cd( ii ) binding by FTIR analysis, which also illustrated that the hydroxyl, amino, amide, and carboxyl groups played an important role in complexation with Cd( ii ). Based on these findings, we concluded that the dead cells were more potent for Cd( ii ) remediation, especially for GX_31.",
"conclusion": "4. Conclusions The removal efficiencies and adsorption capacities of live and dead biosorbents of Cupriavidus necator GX_5, Sphingomonas sp. GX_15, and Curtobacterium sp. GX_31 are quite different and strongly affected by the initial Cd( ii ) concentrations. Whether the cells are living or dead, the adsorption capacity among strains can be listed as GX_31 > GX_15 > GX_5. SEM-EDX, FTIR analysis and adsorption studies indicate that both live and dead cells have the ability to adsorb Cd( ii ), but the dead biomass is superior to the live biomass within the experimental conditions tested, especially when the concentration of Cd( ii ) is low. However, to obtain more accurate results and apply them in practice, more strains should be employed to compare the remediation capacity between live and dead biosorbents for different metals, under a series of metal concentrations.",
"introduction": "1. Introduction Heavy metal contamination is a serious environmental issue, which primarily results from human activities, such as mining, the application of fertilizer, waste disposal, and sewage irrigation. 1 Among these metals, cadmium is a ubiquitous element that is hazardous in the environment. Cd( ii ) accumulation in soil is of great concern because of its adverse effects on plants, animals, and microorganisms and its characteristics of high toxicity, bioaccumulation, and non-biodegradation. 2 More importantly, Cd( ii ) accumulation may also cause health risks in humans through the food chain. 3 For example, an excessive intake of Cd( ii ) can lead to hepatic and renal damage as well as skeletal dysfunctions, such as osteoporosis, cartilage and bone deformities. Therefore, research on the remediation of heavy metal contaminated soil is an urgent and challenging task. 4–6 Physicochemical methods have been widely used for the removal of heavy metals from contaminated sites. However, these applications are mostly ineffective and very expensive and nonspecific, especially when the concentrations of heavy metals are low. 7 In contrast, biosorption has appeared to be an alternative to overcome these shortcomings of conventional methods for heavy metal removal, 7–9 which utilizes bacteria, fungi, yeast, algae, industrial wastes, agricultural wastes, other polysaccharide materials, 10 and exogenous carbonaceous materials. 11 The advantages of biosorption consist of a flexible operation, free availability, high adsorption capacity, low cost, and possible reuse. 12 Combination remediation refers to both plants and plant growth-promoting rhizobacteria (PGPR) or fungi involved in the process of remediation, which has been accepted as a promising approach for Cd( ii ) removal. 13 PGPR are beneficial bacteria that colonize the rhizosphere of plants and facilitate plant growth and development by direct or indirect mechanisms. 14 Developing a fundamental knowledge of the interaction between PGPR and metal ions allows for a better understanding of the bioavailability of heavy metals in the soil, which is also imperative for the development of combination remediation technologies. PGPR not only promote growth and heavy metal uptake by plants, but they are also effective biosorbents for the adsorption of heavy metals, like other bacteria. The adsorption of heavy metals using the biomass of live or dead bacteria, algae, and yeast has emerged as a potential strategy. 15–17 For example, Tangaromsuk et al. investigated Cd( ii ) uptake by Sphingomonas paucimobilis biomass and reported that the removal capacity of live cells was markedly higher than that of dead cells. 18 Xu et al. reported that both dead and live cells of Enterobacter cloacae TU had the ability to remove Cd( ii ), and that live cells were superior to dead cells. 19 Conversely, a study conducted by Huang et al. demonstrated that dead cells could adsorb cadmium to a greater degree than living cells. 20 Guo et al. also observed that the live and dead biomass of Pseudomonas plecoglossicida demonstrated almost the same uptake capacities for Cd( ii ). 21 Despite previous research, however, there continues to be a lack of information about the comparative study of live and dead cells for Cd( ii ) removal. There are also debates about removal capacities for heavy metals between live and dead cells based on the studies mentioned above. Moreover, little work has been done to compare the adsorption of Cd( ii ) using the live and dead biomass of various PGPR under different concentrations of Cd( ii ). As a result, the debated question about the use of live or dead biomass for the removal of heavy metals remains. The objectives of this study are: (1) to investigate the capacities of PGPR ( i.e. Cupriavidus necator GX_5 (CP002878), Sphingomonas sp. GX_15 (MF959440), and Curtobacterium sp. (MF959445) GX_31) for Cd( ii ) adsorption under the same experimental conditions; (2) to analyse morphology and functional group changes of live and dead cells after interaction with Cd( ii ) using SEM-EDX and FTIR; (3) to compare the removal capacities of the live and dead biomass of these strains; and (4) to evaluate the advantages of live and dead biosorbents for the remediation of contaminated sites.",
"discussion": "3. Results and discussion 3.1. Removal efficiency and adsorption capacity of live and dead biosorbents Under optimal conditions (pH, 6.0; reaction time, 6 h; biomass dosage, 1.0 g L −1 ) based on the preliminary data, 25 the adsorption experiments for Cd( ii ) of live and dead biosorbents of the three PGPR ( Cupriavidus necator GX_5, Sphingomonas sp. GX_15, and Curtobacterium sp. GX_31) were conducted with initial Cd( ii ) concentrations of 20, 50, and 100 mg L −1 . There were significant differences in the removal efficiency and adsorption capacity of live and dead biosorbents between the three strains, and the ranking can be ordered as GX_31 > GX_15 > GX_5 ( p < 0.05), whatever the initial Cd( ii ) concentration is (20, 50 or 100 mg L −1 ) ( Fig. 1 and 2 ). Other biosorbents have also been used for Cd( ii ) adsorption. For example, the maximum sorption capacity of NTAA-LCM for Cd( ii ) reached 143.4 mg g −1 with an initial Cd( ii ) concentration of 200 mg L −1 , 26 higher than that in this study (31.28 and 35.09 mg g −1 for the live and dead biomass of GX_31 with 100 mg L −1 of Cd( ii )) ( Fig. 2 ). On the contrary, the Cd( ii ) adsorption capacity of Penicillium simplicissimum was 21.5 mg g −1 , 27 lower than that of GX_31. In another investigation, the maximum adsorption capacity of the dry waste biofilms for Cd( ii ) (42 mg g −1 ) was also higher. However, the removal efficiency of Cd( ii ) was 89.3%, 28 which was lower than that of the dead biosorbent of GX_31 (98.27%) ( Fig. 1B ). It seems that the results from different studies may not be directly comparable on account of differences in experimental conditions, 29 but it is reasonable to make a comparison in the present study under the same conditions. The adsorption capacity and removal efficiency varied among the live and dead biomass of these strains due to their own adsorption mechanisms. 30 Fig. 1 The removal efficiency for Cd( ii ) of live (A) and dead (B) biosorbents of Cupriavidus necator Gx_5, Sphingomonas sp. Gx_15, and Curtobacterium sp. Gx_31 under 20, 50, and 100 mg L −1 of initial Cd( ii ) concentrations. Fig. 2 The adsorption capacity for Cd( ii ) of live (A) and dead (B) biosorbents of Cupriavidus necator Gx_5, Sphingomonas sp. Gx_15, and Curtobacterium sp. Gx_31 under 20, 50, and 100 mg L −1 of initial Cd( ii ) concentrations. Regarding the live biosorbent of the same strain, whatever the strain was, the removal efficiency and adsorption capacity also had significant variations under different Cd( ii ) concentrations ( Fig. 1A and 2A ). Fig. 1A shows that the removal efficiency was clearly higher under a lower Cd( ii ) concentration than under a higher Cd( ii ) concentration, which adheres to findings in other investigations. 31,32 At a lower Cd( ii ) concentration, there were easily enough free binding sites of live biosorbent for Cd( ii ) to interact, which resulted in a high removal efficiency. Therefore, the maximum Cd( ii ) removal efficiency of the live biomass of GX_5, GX_15 and GX_31 displayed with an initial Cd( ii ) concentration of 20 mg L −1 was 25.21%, 55.79% and 87.03%, respectively, and the minimum removal efficiency was 11.48%, 29.16% and 31.45%, respectively (100 mg L −1 of Cd( ii )) ( Fig. 1A ). On the other hand, the adsorption capacity of live biosorbent increased significantly with an increasing initial Cd( ii ) concentration for an identical strain ( Fig. 2A ), which was consistent with the study by Cheng et al. 33 The strongest and weakest adsorption capacities were 12.31, 21.83, and 31.28 mg g −1 and 6.98, 15.44, and 24.08 mg g −1 for GX_5, GX_15, and GX_31 at 100 mg L −1 and 20 mg L −1 of Cd( ii ), respectively ( Fig. 2A ). Considering the same amount of biomass dosage, a high initial concentration could supply a driving force to interact with limited Cd( ii ) binding sites and facilitate adsorption by the live biomass. 34 There was also a significant difference in the removal efficiency for Cd( ii ) of the dead biosorbent of the same strain under different Cd( ii ) concentrations ( Fig. 1B ). This displayed the same tendency as that of the live biosorbent, namely, that the removal efficiency decreased along with the increasing initial Cd( ii ) concentration, because at a high Cd( ii ) concentration, a lack of adequate binding sites restricted the removal efficiency. 35 Fig. 1B shows that the highest and lowest removal efficiencies of the dead biosorbents of GX_5, GX_15, and GX_31 were 32.95%, 63.77%, and 98.27% and 12.09%, 18.58%, and 32.51% under 20 and 100 mg L −1 of Cd( ii ), respectively. However, the adsorption capacity of the dead biosorbent did not show the same tendency as the live biosorbent did, except GX_5, which had the largest and smallest adsorption capacities of 12.97 and 9.12 mg g −1 at 100 and 20 mg L −1 of Cd( ii ), respectively ( Fig. 2B ). For the dead biomass of GX_15 and GX_31, the adsorption capacity increased with a higher metal concentration and it reached a saturation value due to finite binding sites. When the concentration of Cd( ii ) changed from 20 to 50 mg L −1 , the adsorption capacities varied from 17.65 to 18.87 mg L −1 for GX_15 and 27.19 to 35.09 mg L −1 for GX_31 ( Fig. 2B ). A higher Cd( ii ) concentration did not lead to a higher adsorption capacity. This phenomenon agrees with the study by Khadivinia et al. , who point out that at a higher Cd( ii ) concentration, the binding sites become fewer and the biosorbent sites adsorb metal ions more quickly at lower concentrations, which leads to the decrease of adsorption yield. 36 3.2. SEM-EDX analysis The surface structure and cell morphology changes of the live and dead biosorbent were determined by SEM coupled with EDX before and after interaction with 100 mg L −1 of Cd( ii ), as depicted in Fig. 3 and 4 . Before adsorption, all the live biosorbents were observed to be rod shapes with clear boundaries ( Fig. 3A-a–C-a ) and the dead biosorbents evidently seemed to join together, especially for GX_5 and GX_15 ( Fig. 3B-a and D-a ). However, after binding, the surfaces of both live and dead cells became rough, irregular, and noticeably covered with silvery white sediments ( Fig. 3 ). The SEM micrographs also indicated that Cd( ii ) exposure caused anomalous aggregation of the L. plantarum CCFM8610. 37 Similarly, the surface of the live and dead biomass of Spirulina sp. became rough after Cd( ii ) uptake, 38 which was in accordance with the SEM observations. For GX_15, some floccus precipitation was found on their surfaces ( Fig. 3C and D ), which was also observed by Lin et al. 39 As pointed out in another study, after Cd( ii ) adsorption, more flocculated sediments appeared near the cell surface of E. cloacae TU. 19 Likewise, under heavy metal stress, many flocculated particles could be discovered, which suggested the presence of heavy metals on the cell wall. 40 EDX spectra recorded the signals of carbon, nitrogen, oxygen, sodium, magnesium, and calcium, which were likely in the polysaccharides and proteins of the biomass ( Fig. 4 ). No Cd( ii ) signals were detected in unloaded biosorbent, but clear peaks for Cd( ii ) were observable after Cd( ii ) exposure, which indicates the presence of Cd( ii ) in the biosorbents after adsorption. SEM-EDX confirmed that both the live and dead biomass of these strains had potential for Cd( ii ) remediation in cadmium polluted environments. SEM-EDX is a useful tool for visual confirmation of surface morphology changes of cells after absorbing metal ions and it has been extensively applied in research. 24,41,42 Fig. 3 SEM images of live and dead biosorbents of Cupriavidus necator Gx_5 (live: A, and dead: B), Sphingomonas sp. Gx_15 (live: C, and dead: D), and Curtobacterium sp. Gx_31 (live: E, and dead: F) before (A) and after (B) interaction with 100 mg L −1 of Cd( ii ). Fig. 4 EDX images of live and dead biosorbents of Cupriavidus necator Gx_5 (live: A, and dead: B), Sphingomonas sp. Gx_15 (live: C, and dead: D), and Curtobacterium sp. Gx_31 (live: E, and dead: F) before (A) and after (B) interaction with 100 mg L −1 of Cd( ii ). 3.3. FTIR analysis The surface functional group of the biosorbent is an important factor for metal adsorption. FTIR spectra of the live and dead biosorbents of GX_5, GX_15, and GX_31 were recorded before and after Cd( ii ) uptake. Fig. 5 shows various functional groups present on the cell surface, and reveals the complex nature of these strains. The spectra of GX_15 and GX_31 are similar to each other but they differ from GX_5, especially in the region of 1400–700 cm −1 . Fig. 5 FTIR images of live and dead biosorbents of Cupriavidus necator Gx_5 (live: A, and dead: B), Sphingomonas sp. Gx_15 (live: C, and dead: D), and Curtobacterium sp. Gx_31 (live: E, and dead: F) before (A) and after (B) interaction with 100 mg L −1 of Cd( ii ). In this study, live and dead cells displayed similar spectra, and all the characteristic peaks were present on both biosorbents, which was in consistent with other studies ( Fig. 5 ). 16,43,44 Broad bands in the range of 3200–3500 cm −1 correspond to the hydroxyl group as well as the –NH bond of the amino group. 45 Two peaks at approximately 2930 and 2850 cm −1 were ascribed to symmetric and asymmetric –CH vibration in lipids. 36 The band at 1741 cm −1 represented the carbonyl group in the polyhydroxyalkanoic acid, identified by nuclear magnetic resonance analysis of a chloroform extract. 46 The peak positions around 1655 and 1543 reflected the presence of amide I (–CO– stretching vibration) and amide II (–NH bending and –CN stretching) in proteins, respectively. 47 Peaks at 1240 and 1071 cm −1 were assigned to the alcoholic –CN and –CO– stretching vibration, revealing the presence of amino and hydroxyl groups, 48 and a band at 1400 cm −1 also represented a hydroxyl group. 27 The –CO–, –CN, P \n \n\n<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.0\" width=\"13.200000pt\" height=\"16.000000pt\" viewBox=\"0 0 13.200000 16.000000\" preserveAspectRatio=\"xMidYMid meet\"><metadata>\nCreated by potrace 1.16, written by Peter Selinger 2001-2019\n</metadata><g transform=\"translate(1.000000,15.000000) scale(0.017500,-0.017500)\" fill=\"currentColor\" stroke=\"none\"><path d=\"M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z\"/></g></svg>\n\n O, and S O stretching vibrations all existed at the band at about 1291.09 cm −1 . 49 An adsorption band was positioned at 1080 cm −1 , corresponding to the –CO– group vibration in the cyclic structure of carbohydrates. 36 Although they had similar spectra, there were minor changes when we compared the spectra between live and dead biosorbents before adsorption. The bands were shifted from 3307.31, 1655.52, 1543.01, and 1292.01 cm −1 to 3415.13, 1649.69, 1544.36, and 1306.15 cm −1 for GX_5 ( Fig. 5A-a and B-a ); from 1653.55, and 1397.42 cm −1 to 1656.04, and 1396.81 cm −1 for GX_15 ( Fig. 5C-a and D-a ); and from 3308.16, 1545.35, and 1070.29 cm −1 to 3402.35, 1537.05, and 1067.42 cm −1 for GX_31 ( Fig. 5E-a and F-a ), which indicated that autoclave had some effect on the functional groups, though not significant. After adsorption, for GX_5, shifts were observed for the live biomass from 3307.31, and 1741.17 cm −1 to 3367.98, and 1728.50 cm −1 , and from 3415.13, 1649.69, and 1544.36 cm −1 to 3305.5, 1656.34, and 1536.76 cm −1 for dead biomass, revealing that hydroxyl, amino, carbonyl, or amide groups were involved for binding Cd( ii ) ( Fig. 5A and B ). Fig. 5C and D show that peaks at 1653.55, 1397.42, and 1082.43 cm −1 of live cells of GX_15 shifted to 1656.25, 1398.12, and 1065.38 cm −1 , while bands down shifted from 3303.36, 1656.04, 1543.38, 1396.81, 1241.26, and 1082.56 cm −1 to 3294.93, 1649.44, 1535.39, 1390.04, 1234.50, and 1058.79 cm −1 , indicating that hydroxyl, amino, amide, or carboxyl groups were responsible for the Cd( ii ) removal. However, it seemed that more bands were involved in the adsorption of Cd( ii ) in the dead biosorbent, which corresponds to the findings of a previous study by Huang et al. 20 In the case of GX_15, the peaks of live cells at 3308.16, 1399.15 and 1070.29 cm −1 were shifted to 3401.02, 1402.46, and 1067.89 cm −1 and those of dead cells, 3402.35, 1655.78, 1401.23, 1238.19, and 1067.42 cm −1 to 3409.65, 1651.22, 1403.42, 1233.52, and 1065.79 cm −1 ( Fig. 5E and F ), demonstrating that hydroxyl, amino, carboxyl, or amide groups played an important role for Cd( ii ) adsorption. It also appeared that more groups participated in Cd( ii ) binding. The present FTIR spectra analysis provides evidence that functional groups like hydroxyl, amino, amide, carbonyl, and carboxyl groups are involved in the binding of Cd( ii ) on surface of both live and dead biosorbents. Such findings have also been reported in other research. 50–53 3.4. Live and dead biosorbent comparison The removal efficiencies for Cd( ii ) of live and dead biosorbents of GX_5, GX_15, and GX_31 were compared, with different Cd( ii ) concentrations. It was clear that the performance of the removal efficiency was different between the live and dead biomass of these three strains ( Fig. 6 ). The Cd( ii ) removal capacity of dead cells was markedly higher than that of live cells with an initial Cd( ii ) concentration of 20 mg L −1 for all the strains ( Fig. 6 ). The increase of the metal adsorption capacity of the dead biomass can be explained in the following ways. A loss of cell membrane integrity during autoclaving allows the exposure of binding sites inside the cell. 16 According to the hypothesis, an increase of Cu( ii ) adsorption by Penicillium spinulosum and of U uptake by S. cerevisiae cells permeabilized by the action of detergents 53 or by the action of HCHO or HgCl 2 , 54 respectively, was also described. Machado assessed the membrane integrity of heat-treated S. cerevisiae cells with propidium iodide by fluorescence microscopy and observed membrane integrity was lost during the thermal treatment. 44 In addition, more functional groups are involved in Cd( ii ) uptake as depicted in the former section. Li et al. also pointed out that in their study more functional groups participated in the adsorption processes of the dead biomass, compared with those linked to the live biomass. 55 The third reason is the efflux mechanism of live cells, which could reduce Cd( ii ) adsorption. The most frequent mechanism of toxic divalent cation resistance is energy-dependent pumping out, that is membrane efflux pumps. 56 Many studies have focused on the Cd( ii ) efflux mechanism of live cells for coping with high Cd( ii ) stress. 57,58 When the Cd( ii ) concentrations were 50 and 100 mg L −1 , the dead cells of GX_31 still outperformed the live cells ( Fig. 6C ). However, for GX_15, the adsorption efficiency of the live biomass was observed to be slightly higher, but there were no differences in the dead biomass ( Fig. 6B ). Similarly, the adsorption capacity of GX_5 was significantly higher in the dead biosorbent than in the live biosorbent with a Cd( ii ) concentration of 50 mg L −1 . On the contrary, under a Cd( ii ) concentration of 100 mg L −1 , the metal loading capacity of the dead cells of GX_5 was equivalent to that of the living cells ( Fig. 6A ). Fig. 6 The removal efficiency for Cd( ii ) of live and dead biosorbents of Cupriavidus necator Gx_5 (A), Sphingomonas sp. Gx_15 (B), and Curtobacterium sp. Gx_31 (C) under 20, 50, and 100 mg L −1 initial Cd( ii ) concentrations. With an increase of the Cd( ii ) concentration, the adsorption efficiency of the live biomass got close to that of the dead biomass, which may be because of the additional intercellular accumulation of living cells. 2 The initial Cd( ii ) concentration had a significant effect on the adsorption. However, in this study, the live biosorbents did not exhibit a significantly higher adsorption capacity than the dead biosorbents, which is reported by other researchers. 18,38 The adsorption capacity of the live cells may be greater to, equal to, or less than that of the dead cells and it may depend on the bacteria under consideration, experimental methods and type of metal ions being used. 59 As shown by Machado et al. , the inactivated biomass of Saccharomyces cerevisiae displayed a greater Zn 2+ and Ni 2+ accumulation, but a similar accumulation of Cu 2+ than in the live biomass. 16 Cd( ii ) adsorption results of dead and live cells may suggest that the dead biomass is a preferred alternative for remediating Cd( ii ) in a contaminated environment. Furthermore, the dead biosorbent possessed some advantages: no requirements for nutrients, no toxicity limitations, easy recovery of metals, easy regeneration and reuse of biomass, and less affected by pH and temperature. 60–62 Despite the obvious advantages of using dead biomass over live biomass, many attributes of living microbes should be emphasized as well. Live strains could degrade organic pollutants and can adsorb, transport, complex and transform metals, metalloids and radionuclides, 63 especially the use of live PGPR for remediating heavy metal contaminated soils. 64"
} | 6,050 |
35338600 | PMC9131612 | pmc | 6,756 | {
"abstract": "Abstract Flexible electronic skin with features that include sensing, processing, and responding to stimuli have transformed human–robot interactions. However, more advanced capabilities, such as human‐like self‐protection modalities with a sense of pain, sign of injury, and healing, are more challenging. Herein, a novel, flexible, and robust diffusive memristor based on a copolymer of chlorotrifluoroethylene and vinylidene fluoride (FK‐800) as an artificial nociceptor (pain sensor) is reported. Devices composed of Ag/FK‐800/Pt have outstanding switching endurance >10 6 cycles, orders of magnitude higher than any other two‐terminal polymer/organic memristors in literature (typically 10 2 –10 3 cycles). In situ conductive atomic force microscopy is employed to dynamically switch individual filaments, which demonstrates that conductive filaments correlate with polymer grain boundaries and FK‐800 has superior morphological stability under repeated switching cycles. It is hypothesized that the high thermal stability and high elasticity of FK‐800 contribute to the stability under local Joule heating associated with electrical switching. To mimic biological nociceptors, four signature nociceptive characteristics are demonstrated: threshold triggering, no adaptation, relaxation, and sensitization. Lastly, by integrating a triboelectric generator (artificial mechanoreceptor), memristor (artificial nociceptor), and light emitting diode (artificial bruise), the first bioinspired injury response system capable of sensing pain, showing signs of injury, and healing, is demonstrated.",
"conclusion": "6 Conclusion In summary, we develop a new flexible, robust diffusive memristor based on a copolymer of chlorotrifluoroethylene and vinylidene fluoride (FK‐800) that shows outstanding switching endurance of >10 6 cycles, which is orders of magnitude higher than any other two‐terminal polymer/organic memristors reported in the literature (typically 10 2 –10 3 cycles) and comparable to that of the state‐of‐art inorganic memristors. The high thermal stability, low thermal expansion coefficient, and high elasticity of FK‐800 is likely contributing to the structural stability under local Joule heating associated with electrical switching, as is verified by in situ conductive AFM studies. With the realization of a flexible robust nociceptive memristor of Ag/FK‐800/Pt, we demonstrate the first bioinspired injury response system with advanced capabilities: a sense of pain, sign of injury, and healing. This artificial self‐protection modality for electronic skin is realized by the effective integration of a triboelectric generator (artificial mechanoreceptor), memristor (artificial nociceptor), and light emitting diode (artificial bruise). Although we demonstrated the nociceptor using just one isolated memristor in this work, we anticipate that arrays of diffusive memristor could be used to identify complex, time‐evolving stimuli. If the stimuli are temporally separated with a time constant that exceeds the relaxation of the memristors, the highly nonlinear IV characteristics of our devices will ensure that only one memristor, corresponding to a unique row‐column combination, responds (assuming opposite polarity applied to rows and columns). However, if distinct stimuli arrive with shorter times, there will be increased probability of sneak‐path formation and generation of a more complicated, time‐dependent response, in addition to the sensitization of specific memristors. While the sneak‐paths could be eliminated by addition of a transistor access devices (the 1T1R) configuration at the cost of increased complexity and power dissipation, [ \n \n 66 \n \n ] we anticipate that the emergence of time dependent sneak paths could be used to help identify stimuli in ways that will be presented elsewhere. On one hand, the material design strategy proposed here may help with future development of flexible robust organic memristors, and the diffusive memristor developed in this work can also be paired with nonvolatile memristor synapse to emulate more advanced neural activities, such as the integrate‐and‐firing properties of neurons. On the other hand, the injury response system demonstrated in this work may inspire the design of new bio‐related applications, e.g., light‐triggered bioresponse systems, [ \n \n 67 \n \n ] exemplified by UV‐sensing or UV‐damaging nociceptive systems, or visible light photonic nociceptors with light‐tunable threshold.",
"introduction": "1 Introduction In the past two decades, humanoid robotics has rapidly advanced with the aim of revolutionizing the way machines interact with humans and the environment. [ \n \n 1 \n , \n 2 \n , \n 3 \n , \n 4 \n \n ] Humanoid robot design seeks to use electronic systems to reproduce key human characteristics, including sensing, processing, and responding to environmental stimuli. [ \n \n 5 \n , \n 6 \n \n ] Inspired by the human skin—one of the principle organs receiving sensory input from the physical world—electronic skin has been developed for robots with various bioinspired features, such as a sense of touch enabled by arrays of piezoelectric/triboelectric generators, [ \n \n 7 \n , \n 8 \n , \n 9 \n \n ] a sense of heat by temperature sensors, [ \n \n 10 \n , \n 11 \n , \n 12 \n \n ] and a sense of environmental pollutants by chemical and gas sensors. [ \n \n 13 \n , \n 14 \n , \n 15 \n \n ] However, more advanced physiological functions, such as human‐like injury response remain less developed. For example, when our bodies experience noxious stimuli from the external environment, a series of complex physiological responses are triggered to protect us from harm or further damage, including a sense of pain, visible signs of injury, and healing. To the best of our knowledge, this advanced human‐like physiological protection modality has not yet been demonstrated in electronic skins. Pain, defined as a “complex constellation of unpleasant sensory, emotional and cognitive experiences provoked by real or perceived tissue damage and manifested by certain autonomic, psychological, and behavioral reactions,” is a protective somatic modality. [ \n \n 16 \n \n ] The detection of physical pain is through nociceptors, a type of sensory receptor that encodes the noxious stimuli into neural signals, and sends “possible threat” signals to the spinal cord and the brain, triggering a variety of physiological and behavioral responses. [ \n \n 17 \n \n ] Lacking the ability to feel physical pain, known as congenital insensitivity to pain, is an extremely dangerous condition as it often leads to unrealized infections, self‐mutilation, and shortened life spans. [ \n \n 18 \n , \n 19 \n \n ] \n A biological nociceptor has four characteristic features: i) a threshold pain level to initiate triggering, ii) continuous triggering in response to repeating pain signals, known as “no adaptation,” iii) relaxation after the noxious stimuli has stopped, and iv) sensitization, or lower thresholds while injured. [ \n \n 16 \n \n ] Mimicking this complex set of responses typically requires complicated multicomponent circuits when using conventional complementary metal oxide semiconductor devices. [ \n \n 20 \n , \n 21 \n \n ] However, it has been demonstrated that a two terminal memristor can simulate nociceptive behaviors. [ \n \n 22 \n , \n 23 \n , \n 24 \n , \n 25 \n , \n 26 \n \n ] A memristor is a memory resistor that can switch and retain internal resistance states according to their history of applied voltage. [ \n \n 27 \n , \n 28 \n \n ] Although simple in principle, a memristor can have rich switching dynamics that lead to multistate characteristics, history‐dependent resistance and electrical stimuli‐dependent thresholds. [ \n \n 27 \n , \n 29 \n , \n 30 \n , \n 31 \n \n ] These functions share interesting similarities with those of biosensory systems (synaptic and neuronal signals), making the memristor a promising candidate as an artificial synapse and neuron. [ \n \n 27 \n , \n 32 \n \n ] The vast majority of memristors investigated to date are nonvolatile, and are being actively investigated as synaptic elements for in‐memory computing applications. [ \n \n 32 \n , \n 33 \n , \n 34 \n \n ] By contrast, diffusive memristors only retain state when potential is applied. The volatile threshold switching with unique temporal evolution dynamics is particularly interesting to emulate synaptic plasticity with relaxation process and have been successfully demonstrated as artificial neuronal elements as well as access devices. [ \n \n 30 \n \n ] \n The memristive switching behavior is mainly attributed to the formation and rupture of local conductive filaments associated with ion and/or vacancy motion in electric fields. [ \n \n 35 \n , \n 36 \n \n ] Inorganic metal oxides and organic/polymer thin materials (with a thickness of tens to hundreds of nanometers) have been extensively investigated as insulating layers. [ \n \n 37 \n , \n 38 \n \n ] The mobile ion species can be oxygen vacancies in the oxides, [ \n \n 39 \n \n ] or metal ions originating from the active electrodes (such as Ag or Cu). [ \n \n 40 \n \n ] Once the conductive filaments bridge the two electrodes, the devices switch from insulating to conductive, with “on” current densities that can exceed 10 6 A cm −2 . [ \n \n 37 \n \n ] Due to the specific nature of organic materials, including mechanical flexibility, conformability, facile, and low‐cost preparation methods and rich switching mechanisms, considerable effort and progress has been made in the past two decades in developments of organic memristors. [ \n \n 26 \n , \n 41 \n , \n 42 \n \n ] A variety of organic materials and neuromorphic device configurations have thus been proposed in organic memristors for bio‐related applications. [ \n \n 25 \n , \n 43 \n , \n 44 \n \n ] However, the high “on” current density will potentially pose a key challenge for organic/polymer memristors: namely poor switching endurance (typically 10–10 3 cycles) attributed to local Joule heating and insufficient heat dissipation. [ \n \n 37 \n , \n 38 \n \n ] Although, inorganic memristors are more robust with cycle numbers >10 6 , they are less desirable for wearable applications due to their inherent brittleness and fabrication methods that are not easily adapted to complex shapes, such as wires. [ \n \n 37 \n , \n 45 \n \n ] \n To overcome one of the key challenges for organic memristors (poor endurance) and reliably emulate nociceptive behaviors, we design a new, flexible, and robust diffusive memristor based on a copolymer composed of chlorotrifluoroethylene (CTFE) and vinylidene fluoride (VF 2 ), known as FK‐800. FK‐800 is selected due to its high degradation temperature, low thermal expansion coefficient, and low elastic modulus, [ \n \n 46 \n , \n 47 \n \n ] all of which is expected to help maintain structural stability under local heat loads associated with electric switching. Silver (Ag) electrode is used as the active diffusion source, and platinum (Pt) as the counter electrode. Our flexible memristors made of Ag/FK‐800/Pt display outstanding switching endurance of >10 6 cycles, which is orders of magnitude higher than almost all other two terminal polymer/organic memristors reported in literature [ \n \n 48 \n , \n 49 \n , \n 50 \n , \n 51 \n , \n 52 \n , \n 53 \n , \n 54 \n , \n 55 \n \n ] (see Table S1 , Supporting Information) and comparable to that of the state‐of‐art inorganic memristors. [ \n \n 37 \n , \n 38 \n \n ] In situ conductive atomic force microscopy is employed to study the superior endurance of FK‐800, and it shows an outstanding morphological stability under electric switching compared with conventional polymers, such as polyvinylidene fluoride–polyvinylidene fluoride (PVDF), which is likely attributed to its high thermal stability and mechanical elasticity.Moreover, with the successful realization of a flexible robust diffusive memristor that exhibits nociceptive behaviors, we demonstrate the first bioinspired injury response system for artificial skin which includes a sense of pain, signs of injury, and the healing process in response to noxious stimuli. This artificial self‐protection modality is realized by the effective integration of an artificial mechanoreceptor (triboelectric generator), an artificial nociceptor (memristor), and an artificial bruise (light emitting diode)."
} | 3,069 |
39104224 | PMC11344169 | pmc | 6,760 | {
"abstract": "Abstract Plastics have become an indispensable material in many fields of human activities, with production increasing every year; however, most of the plastic waste is still incinerated or landfilled, and only 10% of the new plastic is recycled even once. Among all plastics, polyethylene terephthalate (PET) is the most produced polyester worldwide; ethylene glycol (EG) is one of the two monomers released by the biorecycling of PET. While most research focuses on bacterial EG metabolism, this work reports the ability of Saccharomyces cerevisiae and nine other common laboratory yeast species not only to consume EG, but also to produce glycolic acid (GA) as the main by-product. A two-step bioconversion of EG to GA by S. cerevisiae was optimized by a design of experiment approach, obtaining 4.51 ± 0.12 g l −1 of GA with a conversion of 94.25 ± 1.74% from 6.21 ± 0.04 g l −1 EG. To improve the titer, screening of yeast biodiversity identified Scheffersomyces stipitis as the best GA producer, obtaining 23.79 ± 1.19 g l −1 of GA (yield 76.68%) in bioreactor fermentation, with a single-step bioprocess. Our findings contribute in laying the ground for EG upcycling strategies with yeasts.",
"conclusion": "Conclusions The scope of this work was to lay the foundations to study the physiology of EG metabolism in different yeasts, as their specific metabolic traits can offer advantages in different process conditions and with different media compositions. Firstly, we demonstrated that EG consumption in S. cerevisiae growing in the presence of glucose only starts after the depletion of other carbon sources (glucose, acetate, and ethanol), unlike what happens in Y. lipolytica ; a DoE approach allowed to pinpoint the most important parameters to optimize a two-step bioconversion and to develop a predictive model for S. cerevisiae ; moreover, a putative pathway for the metabolism of EG by S. cerevisiae was proposed. Secondly, screening of ascomycete and basidiomycete yeasts revealed that the ability to oxidize EG to GA is a pretty common trait among yeasts. Since EG is not generally found in their natural niches, yeasts most likely did not evolve specific enzymes active on this molecule. From a structural point of view, EG can be associated to 1,2-propanediol (as often reported for bacteria), or most likely ethanol or glycerol; therefore, we can speculate that oxidation of EG is catalysed by promiscuous endogenous alcohol dehydrogenases evolved for other (poly)alcohols. Further studies are required to better understand the genes and metabolic pathways involved in EG oxidation, including deletion and/or overexpression of the identified genes; precision editing in S. cerevisiae can confirm or deny our initial hypotheses. In parallel, further efforts might be directed to the identification of more performing yeast species, or strains with different EG consumption strategies. One last remark should be made about the economic feasibility of the process. Indeed, the added value generated by conversion of EG to GA might not be sufficient to cover the cost of a process encompassing the enzymatic hydrolysis of PET and the subsequent production of GA. Most probably, yield and productivity will need to be improved, together with the identification of a more economic substrate for the growth of S. stipitis and a viable GA purification system. However, it must be taken into consideration that the TPA released by the PET hydrolysis can also be converted by bacteria to the much more valuable protocatechuic acid, as many examples are present in literature. Taken together, a techno-economic analysis will be necessary to understand the feasibility of such a complex process.",
"introduction": "Introduction Since the first synthetic polymer development in 1907, plastics have become an everyday necessity in almost every aspect of our life. Indeed, plastics have unique properties such as strength, water resistance, and durability (Amalia et al. 2024 ) that make them exceptional for a wide variety of applications. Almost 400 Mt of new plastics were produced in 2020, and it is estimated that the production will reach 1000 Mt by 2050 (Hundertmark et al. 2018 ). However, the properties that make plastic so widespread are the very same ones that make plastic recycling incredibly difficult. As an example, in 2020, less than 10% of the newly produced plastics was recycled even once, and less than 1% was recycled twice (Tiso et al. 2022 ). Incineration (42%), landfilling (19%), and export (10%) are the most common fates of postconsumer plastic waste; about 9% is dispersed or enters the environment due to waste mismanagement; overall, only 20% of postconsumer plastic waste was recycled in 2016 (Orlando et al. 2023 ). Recycling remains a complex process, requiring sorting, processing, and physical–chemical treatments that ultimately results in a lower quality product (Lee et al. 2023 ). Among all plastics, polyethylene terephthalate (PET) is the most abundant polyester produced worldwide (Soong et al. 2022 ) and it is one of the most used commodity plastics (Amalia et al. 2024 ). PET is a thermoplastic polymer obtained from the polycondensation of terephthalic acid (TPA) and ethylene glycol (EG), both mostly obtained from nonrenewable resources (Ren et al. 2024 ). It is widely used in the manufacturing of water bottles, food packaging, and textiles thanks to its ability to withstand high temperatures, and its resistance to chemical and physical degradation (Muringayil Joseph et al. 2024 ). The majority of waste PET is landfilled, incinerated, or dispersed in the environment; only about 30% is recycled, mostly by mechanical methods which create PET flakes to be melted in new products, generally with worse characteristics with respect to virgin PET (Muringayil Joseph et al. 2024 , Ren et al. 2024 ). Enzymatic recycling (or biorecycling) is an emerging strategy for PET depolymerization, employing enzymes to break down products into their monomers; the most studied enzymatic activities include PETases, cutinases, lipases, and carboxylesterases (Weiland et al. 2024 ). This approach is still in its early stages, however the French company Carbios is proving the feasibility of this approach and it is currently building a biorecycling plant with a 50k tons PET feedstock capacity to produce monomers of virgin-like quality (Tournier et al. 2020 , Carbios 2024 ). Several studies have been focusing on the upcycling of the monomers into more valuable commodity chemicals to enhance the economic viability of enzymatic recycling (Lee et al. 2023 , Amalia et al. 2024 ); such an example is the upcycling of EG to glycolic acid (GA). GA is a small two-carbon α-hydroxy acid with a widespread application in various industries, such as personal care, pharmaceuticals, medical, and textiles (Salusjärvi et al. 2019 ). The global GA market size is growing and it is expected to reach USD 565.3 million by 2030, registering a CAGR of 9.1% from 2023 to 2030, with the personal care segment holding the largest revenue share (61.1%) in 2022 ( https://www.grandviewresearch.com/press-release/global-glycolic-acid-market ; accessed 25 April 2024). GA is currently produced chemically from petrochemical resources, due to the relatively high price of EG (Salusjärvi et al. 2019 ). However, with EG from waste PET becoming more available, different strategies for the microbial production of GA from EG have been proposed (Kataoka et al. 2001 , Deng et al. 2018 , Hua et al. 2018 , Cabulong et al. 2019 , Carniel et al. 2023 , Yu et al. 2023 ). Two main EG assimilation pathways have been identified. In aerobic bacteria ( Pseudomonas spp., Escherichia coli ), EG is oxidized to glycolaldehyde (GAH) and then to GA; GA is incorporated in the central carbon metabolism by further oxidation to glyoxylate (GOX) and condensation to tartronate semialdehyde (Gao et al. 2022 ). In the anaerobic acetogenic bacterium Acetobacterium woodii , EG is dehydrated to acetaldehyde and then disproportionated to ethanol and acetate; unfortunately, the dehydratase is very oxygen-sensitive (Trifunović et al. 2016 , Levin and Balskus 2018 ). Some bacterial species, on the other hand, are able to metabolize EG only up to GA. This is the case for the most studied GA producer Gluconobacter oxydans , which is able to catalyse the oxidation of EG to GAH with Gox0313, a NAD + -dependent alcohol dehydrogenase (Zhang et al. 2015 ). To the best of our knowledge, EG oxidation to GA has been reported in yeast only by two works, still the pathway remains putative, most likely involving nonspecific dehydrogenases (Kataoka et al. 2001 , Carniel et al. 2023 ). No hypotheses on assimilation are present. This work aims to lay the ground for the physiology of EG metabolism in yeast, focusing on the well-known Saccharomyces cerevisiae and nine other different yeasts. S. cerevisiae ’s ability to metabolize EG was first investigated during growth in the presence of glucose; secondly, a two-step bioconversion of EG to GA was optimized by a design of experiment (DoE) approach, to understand the determining process parameters. Despite the optimization, EG consumption remains limited in S. cerevisiae , thus to improve the process performance nine non- Saccharomyces yeasts were screened for high EG consumption with high GA production. This ultimately allowed the design of a single-step bioprocess in 2 l bioreactors with the best producer. By design, the developed process does not rely on offline data for monitoring, thus avoiding the need of specialized instruments and personnel to assess the quality of the fermentation. The physiological data obtained in this work will pave the way for further genetic and metabolic studies to improve EG conversion to GA and to discover the genes involved in the pathway.",
"discussion": "Results and discussion EG toxicity test and native metabolism in S. cerevisiae With the aim to investigate EG metabolism with S. cerevisiae , an assessment on EG toxicity and its metabolism was required, as to the best of our knowledge, such information is not available in the scientific literature regarding this yeast. First, EG toxicity was evaluated by growing S. cerevisiae in 96-wells microplates and by measuring differences in the growth rate ( Fig. S1 ). To sample the toxicity around low concentrations of EG, the interval 0–150 mM was divided into six intervals using a logarithmic scale; at the same time, the effect of pH was evaluated, too. The range of EG concentrations was selected by taking into account that PET might be the source of EG: if this were the case, a release of 150 mM EG (9.3 g l −1 ) would correspond to a similar TPA concentration (150 mM, 25 g l −1 ). In terms of weak organic stress, 150 mM is a high amount, thus we decided to limit our study to this concentration, despite some yeast species are reported to tolerate much higher EG concentrations (Kataoka et al. 2001 , Carniel et al. 2023 ). Figure S1 shows the growth rates in the 24 conditions tested and OD raw data for each condition. Statistical results from the ANOVA analysis on the measured growth rates showed no significant differences in the class “[EG]”, while it did show a P -value < .05 for the class pH (which is expected, but independently from EG presence). Multiple comparisons between each condition couple showed no significant difference. From these results, we concluded that EG is not toxic for S. cerevisiae in the tested conditions and concentrations, and therefore 150 mM EG was used in the following experiments. Nonetheless, a recent investigation showed that Yarrowia lipolytic a can tolerate up to 2 M EG (Carniel et al. 2023 ), suggesting that S. cerevisiae tolerance might be actually higher than 150 mM. As HPLC analysis of the supernatants at 48 h showed a decrease in the concentration of EG (data not shown), we decided to better characterize the EG metabolism of S. cerevisiae in the presence of glucose. Thus, we grew S. cerevisiae in YPD + EG to understand when EG uptake happens and if by-products (GA, specifically) are produced (Fig. 1 ). Indeed, S. cerevisiae is able to consume EG and to produce GA as a by-product. Most importantly, EG consumption only starts after the depletion of all the other carbon sources (glucose, acetate, and ethanol), in a very late stage of the fermentation process. No differences of fermentation profiles were observed with the control condition, where EG was not provided in the growth medium (data not shown). A very similar behavior was observed in buffered defined minimal medium (Delft; Verduyn et al. 1992 ) with 150 mM EG (data not shown). Figure 1. Growth of S. cerevisiae on YPD + EG. The figure shows the fermentation profile of S. cerevisiae on YPD + EG in 250 ml shake flasks. The left y -axis shows OD (blue, circles), and glucose (red, squares), ethanol (green, triangles), and EG (dark blue, hollow squares) concentration in g l −1 ; the right y -axis shows glycerol (pink, diamonds), acetate (orange, triangles), and GA (yellow, hollow triangles) concentration in g l −1 . Values are the mean ± standard deviation of three independent experiments. To the best of our knowledge, EG metabolism in yeasts has not been described in detail. Few works are available in literature regarding EG transformation by yeasts and the majority of the studies focuses on Y. lipolytica (Kataoka et al. 2001 , Da Costa et al. 2020 , Kosiorowska et al. 2022a , 2022b , Sales et al. 2022 , 2023 , Carniel et al. 2023 ). Both Da Costa et al. ( 2020 ) and Kosiorowska et al. ( 2022b ) reported that Y. lipolytica (strain IMUFRJ 50682 and a strain derived from the A101 strain, respectively) is able to coconsume EG with glucose in rich medium (YPD); however, no mention of by-product(s) is present. Saccharomyces cerevisiae , however, seems to behave differently. Moreover, in the latest work of Kosiorowska et al. ( 2022b ), EG seems to strongly reduce glucose uptake rate; however, no explanation for the behavior is hypothesized. This is in contrast with the results obtained with S. cerevisiae , where the presence of EG does not affect growth rate and glucose consumption, as described by the abovementioned results (Fig. 1 ). It is worth mentioning that in the most recent study listed above, Carniel et al. ( 2023 ) reported GA production from EG in Y. lipolytica IMUFRJ 50682, hypothesizing the contribution of endogenous alcohol and aldehyde dehydrogenases. Taken together, our results suggest that the conversion of EG by S. cerevisiae is also very likely due to the activity of promiscuous enzymes, most probably dehydrogenases, possibly upregulated in the stationary phase. Based on the available literature, we propose the following EG oxidation pathway, outlined in Fig. 2 . The first reaction might be catalysed by YLL056C , a NADH-dependent aldehyde reductase, which catalyses the oxidation of EG to GAH; the reaction; however, is favored toward the formation of EG (Wang et al. 2017 ). Other proteins were reported to be active on GAH, such as the products of genes ADH1 (Jayakody et al. 2013 ), GRE2 (Jayakody et al. 2018 , Jayakody and Jin 2021 ), and others ( ADH7, SFA1, YML131W, YNL134C , and YKL107W ) (Jayakody et al. 2013 , Jayakody and Jin 2021 , Wang et al. 2019 ); however, no information on the directionality of the reaction toward the formation of the aldehyde is generally mentioned, as these works focus on GAH detoxification. Indeed, these aldehyde reductases are involved in aldehyde stress response, while YLL056C role still has not been elucidated. The subsequent oxidation of GAH to GA is reported to be catalysed by ALD2, ALD3, ALD4 , and ALD5 ( metacyc.org ). ALD2 and ALD3 encode two cytoplasmic stress-inducible isoforms, and are induced by a variety of stresses, among which oxidative stress and glucose exhaustion; ALD4 and ALD5 encode the mitochondrial isoforms. In the case of this oxidation reaction, the equilibrium is favored toward the formation of GA, rather than towards the reduction of GA to GAH. Figure 2. Proposed pathway for the oxidation of EG to GA in S. cerevisiae . EG is first oxidized to GAH by YLL056C , using NAD + as cofactor; the reaction equilibrium; however, is shifted toward EG. No information about the reversibility of the reaction catalysed by ADH1 and GRE2 is available. GAH is further oxidized to GA by the action of nonspecific aldehyde dehydrogenases ( ALD2 – 6, ARI1 ) using NAD(P) + as cofactor. All the reported genes are expressed in stress/limiting conditions, which is consistent with our observations: EG starts being consumed only after the exhaustion of the carbon sources in the growth medium (glucose, acetate, and ethanol). Further studies, however, are needed to confirm the suggested pathway. Process conditions optimization: DoE-aided biotransformation approach Since S. cerevisiae showed the natural ability to oxidize EG to GA, we decided to develop a process for the production of GA. Carniel et al. ( 2023 ) developed a single-step process with Y. lipolytica for the production of GA from EG in YP medium. Y. lipolytica , indeed, is able to grow on YP efficiently and thus accumulate enough biomass for a viable conversion process. On the other hand, S. cerevisiae is not able to grow efficiently on YP alone, and addition of glucose in the medium to obtain the desired amount of biomass would cause a long delay in the oxidation of EG. Because of these reasons, we opted for a two-step bioconversion approach: first, biomass is produced on YPD in a 2-l bioreactor, then it is resuspended in the reaction mixture in lower volume (therefore at a higher concentration) in shake flasks. This approach also has the advantage of conducting the bioconversion in the desired buffer, as growth and production are separated; this aspect is very interesting when considering downstream purification, as less complex solutions can facilitate GA recovery. Due to the lack of systematic information in literature about GA production from EG by yeast, a DoE approach was used to find the best bioconversion parameters for the second step of the process, to optimize production (final concentration of GA) and EG conversion; indeed, the use of an experimental design allows to evaluate the interaction among the variables, minimizing the experimental error and the number of experiments (Pagliari et al. 2022 ). The statistical model was developed in two stages: the first round had the objective to screen numerous parameters, to set up the base conditions and identify the factors that significantly influence EG conversion to GA; the second round was developed to refine the model using a lower number of factors, but with a better exploration of the response surface. Results from Round 1 Nine different parameters were considered in the first round (see the section “Materials and methods”); shaking, m/f ratio and flask type were considered to study the effect of oxygenation and mass transfer; two EG concentrations and two reaction times (1 or 7 days) were tested. As previous experiments suggested that the EG oxidation machinery is expressed in late stationary phase (Fig. 1 ), we decided to test whether this aspect was crucial for this approach as well: thus, cells harvested during the exponential phase and cells in late stationary phase were compared for bioconversion efficiency; the initial amount of biomass for the bioconversion step was also taken into consideration. Finally, two different reaction media were tested: the rich medium YP and 100 mM potassium phosphate buffer. The effect of the initial pH was considered too, as the production of GA is expected to cause a decrease of the pH over time. Table S2 reports the experimental matrix design of Round 1, with the experimental levels of the selected parameters and the results obtained for the analysed response variables, GA concentration (g l −1 ) and molar conversion of consumed EG to GA (% mol mol −1 ). The statistical significance of the response variables is reported in Table S3 . Medium composition resulted to be the most important factor, as it makes a positive contribution to both response variables. Shaking, growth phase and pH also showed a positive influence ( P < .05) on conversion. Overall, the chemometric analysis suggested the following parameters in the optimized conditions: shaking (280 rpm), growth phase (stationary), medium (YP), pH (8), EG concentration (100 mM), m/f ratio (15), flask type (baffled), biomass (50 OD ml −1 ), and time (7 days). The results suggest that YP is a better reaction medium, probably because the nutrients present can sustain basal cell metabolism; moreover, YP also has a buffering capacity suggesting that higher and more stable pH can favor GA accumulation. Finally, the phase of growth in which cells are collected had a significant effect, suggesting that cells in late stationary phase might be preadapted and expressing the key genes for EG conversion. While the other variables did not show any significant effect, a trend can still be observed in the main effects plots ( Fig. S2 ). Baffled shake flasks and higher m/f ratios increase mass transfer and oxygenation; a higher concentration of biomass improves the process, as well as a higher concentration of the reagent (EG); finally, the process proved to be relatively slow, with 7 days being the optimal bioconversion time. Results from Round 2 For the next round of optimization, we decided to focus on shaking, medium composition and pH, while keeping the other parameters to the optimum conditions identified in the previous round. For the shaking, we decided to sample lower agitations to evaluate if a lower oxygenation would still allow for efficient EG conversion. While this aspect might be trivial at lab scale, it might cause issues when scaling up. In Round 1 the “medium” factor was designed as a categorical variable: with the aim of reducing the amount of YP in the final product to facilitate GA purification, we transformed “medium” into a numerical variable, by considering different dilutions of the medium (1:1, 1:3, and 1:5). As pH cannot be increased above a certain threshold for toxicity reasons, we decided to dilute the YP medium with potassium phosphate buffer, and provide higher buffering capacity by modulating the concentration of the buffer (100, 200, or 300 mM); indeed, keeping a pH compatible with yeast growth and metabolism is key for an efficient process. Table 1 reports the experimental matrix design of Round 2, with the experimental levels of the independent variables (factors) and the results obtained for the analysed response variables, GA concentration (g l −1 ) and molar conversion of consumed EG to GA (% mol mol −1 ). The statistical significance of the response variables studied can be observed from the standardized pareto chart for each experimental factor (Fig. 3 ). The significance of the effects at 95% confidence level is highlighted by the vertical line in the chart whereas positive (green) and negative (red) effects in the response variables were indicated by different bar colors. Shaking resulted to be the only significant factor for both response variables, suggesting once again the importance of an efficient oxygenation and mass transfer. This aspect was also hypothesized by Kataoka et al. ( 2001 ) and Carniel et al. ( 2023 ), who reported that oxygenation might be an issue in the bioconversion process; moreover, from a metabolic point of view, sufficient oxygenation might be necessary to regenerate the NAD(P) + required for EG oxidation in the electron transport chain according to the proposed pathway (Fig. 2 ). Figure 3. Standardized Pareto Charts and Estimated Response Surfaces for GA production (top) and Conversion (bottom) from Round 2. The standardized Pareto Charts show the estimated positive (green) and negative (red) effects of each term in the Box–Behnken design model in decreasing order of significance. Bars beyond the red vertical line are statistically significant with a confidence level of 95%. The Estimated Response Surface plots represent the predicted value of GA production (g l −1 ) and Conversion (% mol mol −1 ) over the space of shaking (220–280 rpm) and medium (1.1–1.5); buffer concentration was held constant at 100 mM, which is the value in the optimized condition. Interestingly, dilution of the reaction medium YP and buffer concentration do not affect the response variables significantly, meaning that it is feasible to use the lowest concentration of buffer and a diluted medium to ease the purification process. Overall, the chemometric analysis suggested the following parameters in the optimized conditions: shaking (280 rpm), YP dilution (1:1), and buffer concentration (100 mM), predicting 5.19 g l −1 of GA (3.48–6.94 g l −1 , 95% limits) and a conversion of 85.42% (68.10%–100.00%, 95% limits). It is worth mentioning that these optimized conditions refer specifically to S. cerevisiae , as other yeast species may respond differently. The result was experimentally validated by performing the bioconversion in the optimized conditions, obtaining a production of 4.51 ± 0.12 g l −1 and a conversion of 94.25 ± 1.74%, reaching the optimum after only 4 days ( Fig. S3 ). While we were not able to significantly increase the production with respect to other conditions (e.g. conditions 13, 14, and 15; see Table 1 ), we were able to increase the conversion to almost 95%, which is the highest conversion obtained in this set of experiments, and the productivity, reducing process time from 7 days to only 4 days. Most importantly, these results are in line with the ones predicted by the model, falling in the range of the predicted lower and upper bounds. Of note, we observed GA consumption after the peak of production ( Fig. S3 ); to the best of our knowledge, this aspect has not been reported in literature: we speculate that GA might be consumed by further oxidation to GOX by GOR1 (Rintala et al. 2007 ), with a NAD + -dependent glyoxylate reductase activity usually reported to catalyse the reduction of GOX to GA (Koivistoinen et al. 2013 , Salusjärvi et al. 2017 ). Indeed, no peaks relative to GAH or GOX were observed in the HPLC chromatograms, suggesting that GOX is metabolized through the glyoxilate cycle. Further studies are needed to confirm the role of GOR1 and the destiny of GOX in the pathway. Screening of yeast biodiversity for better GA-producing species Screening of different yeast species The results obtained with the bioconversion experiments were promising; however, S. cerevisiae showed different downsides, such as the inability to coconsume EG with glucose and the necessity of a two-step process, combined with the generally low biomass yield as it is a Crabtree-positive yeast, and the poor uptake of EG. For these reasons we decided to explore yeast biodiversity for better GA-producing strains, as well as to elucidate whether the ability to oxidize EG is a shared trait among yeasts. Indeed, only a few yeasts are reported to be able to metabolize EG, namely Y. lipolytica, Pichia naganishii, Rhodotorula sp., and Hansenula sp . (Carniel et al. 2024 ). With this in mind, we screened nine yeasts in our library. In particular, among the phylum Ascomycota we considered K. lactis , the thermotolerant K. marxianus , the methylotrophic yeast K. phaffii , the robust S. stipitis , and the acid-tolerant yeasts Z. bailii and Z. parabailii . As many reports with the oleaginous yeast Y. lipolytica (Ascomycota) are available in literature, we decided to focus on oleaginous yeasts belonging to the phylum Basidiomycota and we included C. oleaginosus ATCC 20509, C. oleaginosus DSM 70022, and R. toruloides . The ability to metabolize EG was evaluated in rich medium YP + EG 150 mM (9.3 g l −1 ); as a control, S. cerevisiae was included in the experiment. The results are shown in Fig. 4 . Interestingly, all the assayed yeast species not only were able to metabolize EG, but they were also all able to accumulate GA as a by-product. These results suggest that the ability to metabolize EG might be a common trait among yeasts, even from different phyla. Three yeast species stood out with particularly elevated GA productions: K. phaffii produced 76.7 ± 0.4 mM (5.83 ± 0.03 g l −1 ), C. oleaginosus ATCC 20509 produced 83.0 ± 1.1 mM (6.31 ± 0.08 g l −1 ), and S. stipitis produced 112.0 ± 0.4 mM (8.53 ± 0.03 g l −1 ); in comparison, S. cerevisiae was only able to produce 34.2 ± 0.8 mM (2.60 ± 0.06 g l −1 ) of GA in these conditions. From these preliminary results, we decided to better characterize GA production with the top two performers. It is worth mentioning that K. phaffii remains a very interesting yeast for the oxidation of EG: indeed, an in vitro study (Isobe and Nishise 1994 ) demonstrated that the alcohol oxidase AOX1 is able to oxidize EG to GAH (and GAH to the undesired product glyoxal) in a nonreversible way. Thus, addition of methanol to strongly induce AOX1 might improve EG conversion to GA. Figure 4. GA production from EG by non- Saccharomyces yeasts. Bars are the mean of two independent experiments; the value of each replicate is represented by a circle; the value of the mean is expressed on top of the bars. Sc, S. cerevisiae; Kl, K. lactis; Km, K. marxianus; Kp, K. phaffii; Ss, S. stipitis; Zb, Z. bailii; Zp, Z. parabailii; Co 1 , C. oleaginosus (ATCC 20509); Co 2 , C. oleaginosus (DSM 70022); and Rt, R. toruloides . Characterization of GA production by S. stipitis and C. oleaginosus ATCC 20509 To better understand EG metabolism by S. stipitis and C. oleaginosus ATCC 20509, the two yeasts were grown in YP + EG in 6-deepwell microplates; as a control, the yeasts were also grown in YP without the addition of EG. To account for the drop in pH due to the production of GA, the medium was buffered to pH 7. Enzyscreen microplates were selected as they guarantee high oxygenation of the cultures (30–40 mmol O 2 l −1 h −1 ) while also limiting the evaporation ( enzyscreen.com ), as oxygenation and mass transfer revealed to be the key parameter in the bioconversion experiments with S. cerevisiae . The presence of EG did not cause any differences in the growth of the two yeasts, which showed the same maximum specific growth rate with the control condition (0.28 ± 0.01 h −1 and 0.26 ± 0.02 h −1 for S. stipitis , and 0.37 ± 0.05 h −1 and 0.36 ± 0.05 h −1 for C. oleaginosus ) (Fig. 5C ). Figure 5. Fermentation profiles, growth rate, and GA production by S. stipitis and C. oleaginosus (ATCC 20509) in 6-deepwell microplates. Panels (A) and (B) show the fermentation profiles of S. stipitis and C. oleaginosus , respectively; lighter shades of the color refer to the control conditions. Panel (C) shows the maximum specific growth rate in the exponential phase for S. stipitis and C. oleaginosus ; lighter shades of the color refer to the control conditions. Panel (D) shows EG consumption, GA production and conversion at 72 h and 144 h for S. stipitis ( Ss ) and C. oleaginosus ( Co ). The left y -axis shows EG at the beginning of the fermentation (dark blue bars), EG at 72 h (blue bars) and GA at 72 h (dark yellow bars), and EG at 144 h (light blue bars) and GA at 144 h (light yellow bars), in g l −1 ; the right y -axis shows EG conversion into GA at 72 h (dark red bars) and 144 h (light red bars); the percentage is expressed on the consumed EG; the value of the means is expressed inside each bar. Ss, S. stipitis; Co, C. oleaginosus (ATCC 20509). Values are the mean ± standard deviation of three independent experiments. In the case of S. stipitis , EG consumption and GA production most probably start at the beginning of the fermentation; however, quantification of GA was possible only from 6 h. The production of GA shows a linear trend, slowing down towards the end of the fermentation (56 h). The slower rate might be due to the decrease of the pH to 5 (Fig. 5A ). In the case of C. oleaginosus , two distinct phases of EG consumption and GA production can be observed. Initially (0–24 h), EG consumption is slow; between 24 and 72 h, a faster rate is observed, resulting in the maximum GA production at 72 h. Interestingly, a third phase is observed between 72 and 144 h, where EG and GA are coconsumed; no by-products could be observed in the chromatograms (Fig. 5B ). Scheffersomyces stipitis and C. oleaginosus seem to behave differently with respect to EG conversion. Moreover, their behavior is also different from what Carniel et al. ( 2023 ) reported for Y. lipolytica grown in similar conditions. Y. lipolytica shows an initial fast consumption of EG related to a low GA production; a second phase is characterized by a lower consumption rate of EG, however, related to a higher GA production rate. No GA consumption was observed. The highest GA production was obtained with S. stipitis , reaching 122.0 ± 1.7 mM (9.29 ± 0.13 g l −1 ) after 144 h, with a conversion yield of the consumed EG of 99%. This suggests that S. stipitis does not readily consume GA after the production. C. oleaginosus produced 81.5 ± 6.7 mM (6.20 ± 0.51 g l −1 ) after 72 h, with a conversion around 60%, suggesting that 40% of the consumed EG is metabolized either via another pathway, or further oxidized to GOX, as suggested for S. cerevisiae . The results are summarized in Fig. 5(D) . Compared with the results obtained in the bioconversion experiment with S. cerevisiae , both yeasts were able to produce a higher amount of GA, in a much simpler process with a relatively low cell density, with S. stipitis producing twice as much as S. cerevisiae , with a higher conversion rate. Because of this, S. stipitis was selected for the process scale-up to 2 l bioreactors. Production of GA with S. stipitis in 2 l bioreactors Given the promising results from the previous experiments, GA production was evaluated with S. stipitis in stirred tank reactors. The process starts with biomass accumulation; after the exhaustion of the carbon source, EG is added for the conversion. As sometimes working with defined media with S. stipitis is tricky (Mastella et al. 2022 ) and to maximize biomass yield, we decided to grow the yeast on YPD, with 40 g l −1 of glucose. This approach also ensured a similar medium composition (YP) as in the previous experiments, at the time of addition of EG at the end of growth phase (24 h). \n Scheffersomyces stipitis was able to completely convert 11 ml of EG (197 mmol, 250 mM, and 12.2 g) into 151 mmol (11.5 g) of GA; all the EG was consumed (100% consumption), and the yield of GA (% mol mol −1 ) accounting for volume loss due to evaporation during fermentation was 76.68%. The concentration of GA in the supernatant was 313 ± 16 mM (23.79 ± 1.19 g l −1 ), with a productivity of 190.41 mg GA l −1 h −1 . Figure 6 shows the fermentation profile and process statistics. Figure 6. Fermentation profile and process statistics of GA production from EG in 2 l bioreactors by S. stipitis . The left panel shows the fermentation profile of S. stipitis ; the left y -axis shows CDW (brown, circles), and glucose (red, squares), concentration in g l −1 ; the right y -axis shows EG (dark blue, hollow squares) and GA (yellow, hollow triangles) concentration in g l −1 . The right panel shows EG conversion, yield of GA (left y -axis), and productivity (right y -axis). Values are the mean ± standard error of two independent experiments. It is interesting to note that over the time of EG consumption, about 80 mmol of CO 2 were produced; considering that about 46 mmol of EG were not converted to GA, we can estimate the amount of CO 2 that would be produced if EG was completely oxidized, e.g. by conversion to GOX and further oxidation to CO 2 via the TCA cycle; this process would produce 2 moles of CO 2 for every mole of EG (Franden et al. 2018 ), for a total of 92 mmol of CO 2 , which is particularly close to the measured experimental value. These results suggest that EG is oxidized completely to GA; however, a small fraction of it might be further metabolized to GOX and ultimately respired to CO 2 . More in depth studies are needed to confirm this aspect. For the economics of the process, it must be noted that glucose is required to obtain the initial biomass. As a proof of concept, we utilized pure glucose, however, S. stipitis is a robust microbial cell factory capable of utilizing both C6 and C5 sugars (Mastella et al. 2022 ). Thus, a lignocellulose-derived growth medium could be used to produce the biomass in a more sustainable way; moreover, the presence of other sugars and nutrients might improve EG conversion to GA, sustaining growth during the production phase. More studies are needed to improve the proposed process: as an example, one of the main points would be to understand if C5 sugars inhibit EG oxidation as glucose does. Finally, the goal was also to develop a process, which did not rely on offline data. Growth and glucose consumption phase can be estimated by the online profile of the pO 2 , clearly indicating the time of addition of EG. Moreover, as GA is the only by-product, by separating growth and production phase it is possible to monitor the conversion of EG to GA by the addition of base, without requiring any special equipment (e.g. HPLC). To confirm that this was possible, we estimated the final amount of GA based on the volume of added KOH and compared it to the actual value. At the end of EG consumption, 86 ml of base had been added after the addition of EG, and using Equation ( 1 ), an estimate of 13 g of GA was obtained; this value is close to the value measured by HPLC at the end of the fermentation (11.5 g). We conclude that the base profile can be used for online monitoring of GA production from EG. Figure S4 shows a visual representation of the process monitoring described above. Taken together, our findings reveal yeasts as an interesting biorefinery for the upcycling of EG to GA. To the best of our knowledge, only two other works studied EG conversion to GA by yeast. Carniel et al. ( 2023 ) obtained similar results to ours; they were able to obtain 429 mM (32.6 g l −1 ) of GA with a yield on consumed EG of 74% (35% when considering total EG) by growing Y. lipolytica in YP + EG 1 M in a bioreactor setting; Kataoka et al. ( 2001 ) were able to obtain up to 1.45 M (110 g l −1 ) with a yield of 92% by growing Rhodotorula sp. in high density bioconversions (10.7 g of wet cell weight for a 100 ml reaction)."
} | 9,695 |
35351909 | PMC8964759 | pmc | 6,761 | {
"abstract": "Oleaginous microalgae can produce triacylglycerol (TAG) under stress, yet the underlying mechanism remains largely unknown. Here, we show that, in Nannochloropsis oceanica , a bZIP-family regulator NobZIP77 represses the transcription of a type-2 diacylgycerol acyltransferase encoding gene NoDGAT2B under nitrogen-repletion (N+), while nitrogen-depletion (N−) relieves such inhibition and activates NoDGAT2B expression and synthesis of TAG preferably from C16:1. Intriguingly, NobZIP77 is a sensor of blue light (BL), which reduces binding of NobZIP77 to the NoDGAT2B- promoter, unleashes NoDGAT2B and elevates TAG under N−. Under N+ and white light, NobZIP77 knockout fully preserves cell growth rate and nearly triples TAG productivity. Moreover, exposing the NobZIP77 -knockout line to BL under N− can double the peak productivity of TAG. These results underscore the potential of coupling light quality to oil synthesis in feedstock or bioprocess development.",
"introduction": "Introduction Due to their high energy density, triacylglycerols (TAGs, or oils) are a universal energy storage form in the cell. They are the main constituent of plant vegetable oil and human body fat 1 , and also precursors for biodiesels. Microalgae are promising feedstock for TAG production, due to their rapid autotrophic growth and high (up to 60%) oil content in the total biomass 2 – 4 . In oleaginous microalgae, oil accumulation is typically induced by environmental stresses, such as nutrient deprivation, high light, or heat 3 , 5 , 6 . The stress responses are foundation of a sustainable microalgal production scheme, yet exploitation of them for superior oil productivity has been hindered by lack of mechanistic insights. For example, nitrogen depletion (N-) has been intensively studied and practically used for triggering microalgal TAG accumulation 7 , 8 ; however, despite the many studies in various microalgae profiling transcriptomics, proteomics, and metabolomics of N− induced TAG synthesis 9 , as well as the plethora of transcriptional factors (TFs) predicted to be involved (e.g., Tisochrysis lutea 10 and N. oceanica 11 ), the molecular mechanisms of signaling that links TAG synthesis to stress exposure have largely remained elusive. Although effective in inducing microalgal TAG accumulation, depletion of N in culture as a way of process control (usually via natural consumption or transfer to N− medium) is inherently limited by difficulties in flexible, fast, and spatiotemporally precise control 3 , 12 . Moreover, nitrogen starvation usually leads to compromised photosynthesis and growth arrest 13 , as N itself is a key nutrient for cells. Consistently, growth inhibition has frequently (although not always) been a side effect of metabolic engineering that boosts cellular TAG content 14 (e.g., resulting in ~25% biomass decrease 15 , 16 ). Despite the progresses made in uncoupling TAG synthesis and cellular growth 17 – 22 , the oil productivity of industrial microalgae remains too low to support a cost-competitive industry. Therefore, environmental stimuli that allow efficient and precise control of cellular TAG assembly yet without compromising biomass productivity are highly desirable. Here, in the industrial oleaginous microalga Nannochloropsis oceanica , we discover that a TF NobZIP77 couples BL sensing to TAG production during N− induced oleaginousness. Exploiting this N− and BL co-regulated oleaginous mechanism, we show that a BL-induction strategy that exposes NobZIP77 -knockout N. oceanica to white light and then to BL doubles the peak productivity of TAG. These findings demonstrate a strategy where cellular sensing of light quality is explored for microalgal feedstock development and photobioreactor design.",
"discussion": "Discussion The BL-NobZIP77-DGAT2B working model is unexpected, since among the eleven putative type-2 DGATs in N. oceanica , extensive ex vivo and in vitro assays revealed only NoDGAT2A, 2C, 2D, 2K, and 2J as TAG-synthetic 16 , 21 , 22 , but not 2B. However, clearly, NoDGAT2B is a potent gene of oil traits (as its overproduction elevated TAG content by 163.0% while knockdown reduced it by 70.2%; Fig. 2D ). Moreover, NoDGAT2B’s fatty acid (FA) preference is unusual, featuring strong carbon chain−length specificity: for C16:1 yet against C18:0, C18:2, and C20:5 (Supplementary Fig. 12D ); in contrast, 2A, 2C, 2D, 2K, and 2J distinguish FA substrates based on degree of unsaturation 21 , 22 . Therefore, while raising the question of why it (and its TAG products) is specifically induced by BL, NoDGAT2B adds one dimension to the generation of “designer oils” 33 . However, this BL-induced oleaginousness mechanism can potentially inform how microalgae adapt to aquatic ecosystems. Stramenopiles have no flagella yet can exhibit BL-induced movement (e.g., in the xanthophyte Vaucheria frigida 32 , 34 ), and the mechanism is unknown. Considering that generally, only BL (among the bio-exploitable wavelength) can penetrate substantial depths in water 35 , aureochromes are only found in stramenopiles so far 36 and TAG content and composition can influence cellular buoyancy 37 , it is possible that the BL-NobZIP77-NoDGAT2B working model might underpin such phototropic behaviors by controlling microalgal floating depth (via modulating TAG content), to maximize solar-energy harvest or minimize photodestructive effects (e.g., UV-induced DNA damage). A TF such as NobZIP77 that responds to both light and N− while directly regulates TAG assembly is extraordinary. In microalgae, a large number of TFs potentially responsive to N− have been identified via transcript-level change under N− 11 , 15 , 18 , however, none of them were known to directly control LRGs 38 . For example, in N. gaditana , a TF of the Zn (II)2 Cys 6 family was found implicated in N− induced lipid production, and its knockdown by RNAi resulted in double lipid production yet decreased microalgal growth rate 15 ; however, neither its downstream target nor the signaling mechanism for its response to N depletion is clear. On the other hand, light-quality-sensing TFs have been reported in Neurospora 39 yet none were known to directly regulate TAG synthesis. Thus, NobZIP77 can directly convey light-related signals (i.e., not just light quality) to the transcription of metabolite-synthetic enzymes. As importantly, NobZIP77 appears to be a major master regulator that is lipid-specific, as (1) the promoters of at least seven LRGs carry its binding motifs, (2) its knockout nearly triple TAG under N+, and (3) its loss fully preserves microalgal growth rate. Therefore, illustration of the NobZIP77 regulatory network may present ample opportunities for feedstock development. Notably, BL greatly promotes microalgal TAG production: In fact, NobZIP77 -knockout N. oceanica exposed to WL first and then to BL more than doubles the peak productivity of TAG (Fig. 6B ). Such light-quality-based oil harvesting strategies are perhaps widely applicable, as homologs of the full-length NobZIP77 are widely found in stramenopiles 36 . Light-quality control, with its fast excitation, high temporal and spatial precision, reversibility, expandability, and energy efficiency 40 , presents an under-explored yet highly attractive opportunity for bioreactor and bioprocess design. In animal, plant, and microbes, known pathways that regulate metabolite synthesis in response to light quality usually consist of multiple layers of signal transduction 41 . On the other hand, aureochromes, although known to regulate photomorphogenesis, sexual cycle, and biological rhythm 28 , have rarely been implicated in the synthesis of carbon-storage compounds (although strong metabolic shift takes place upon red/blue-light transitions in diatoms 42 , 43 ). Therefore, unveiling of the BL-NobZIP77-NoDGAT2B working model, which directly links BL to metabolite synthesis, advocates Nannochloropsis spp. as an advantageous model system for optogenetics and should enable the rational design of opto-controlled algal and plant cell factories."
} | 2,017 |
29696070 | null | s2 | 6,762 | {
"abstract": "Humans are producing complex and often undesirable social and ecological outcomes in many landscapes around the world. To sustain biodiversity and ecosystem services in fragmented landscapes conservation planning has turned to the identification and protection of large-scale spatial ecological networks (SEN). Now widely adopted, this approach typically focuses on static connectivity, and ignores the feedbacks between changes to the network's topology and the eco-evolutionary dynamics on the network. We review theory showing that diversity, stability, ecosystem functioning and evolutionary adaptation all vary nonlinearly with connectivity. Measuring and modelling an SEN's long-term dynamics is immensely challenging but necessary if our goal is sustainability. We show an example where the robustness of an SEN's ecological properties to node and link loss depends on the centrality of the nodes targeted. The design and protection of sustainable SENs requires scenarios of how landscape change affects network structure and the feedback this will have on dynamics. Once established, SEN must be monitored if their design is to be adapted to keep their dynamics within a safe and socially just operating space. When SEN are co-designed with a broad array of stakeholders and actors they can be a powerful means of creating a more positive relationship between people and nature."
} | 346 |
29167704 | PMC5688640 | pmc | 6,763 | {
"abstract": "Cyanobacterial mats are laminated microbial ecosystems which occur in highly diverse environments and which may provide a possible model for early life on Earth. Their ability to produce hydrogen also makes them of interest from a biotechnological and bioenergy perspective. Samples of an intertidal microbial mat from the Elkhorn Slough estuary in Monterey Bay, California, were transplanted to a greenhouse at NASA Ames Research Center to study a 24-h diel cycle, in the presence or absence of molybdate (which inhibits biohydrogen consumption by sulfate reducers). Here, we present metagenomic analyses of four samples that will be used as references for future metatranscriptomic analyses of this diel time series.",
"conclusion": "Conclusions We sequenced and assembled metagenomes for four samples of microbial mat from the Elkhorn Slough estuary in Monterey Bay , California, to be used as reference data for a diel metatranscriptomic study in the presence or absence of molybdate. All four metagenomes were dominated by cyanobacterial sequences, primarily 10.1601/nm.701. Despite some differences in community composition between the four metagenomes (which may be partly due to spatial heterogeneity in the mat), their functional composition in terms of COG functional categories is quite similar.",
"introduction": "Introduction Microbial mats are amongst the most diverse microbial ecosystems on Earth, inhabiting some of the most inhospitable environments known, including hypersaline, dry, hot, cold, nutrient poor, and high UV environments. Photosynthetic microbial mats found in intertidal environments are stratified microbial communities. Microbial metabolism under anoxic conditions at night results in the generation of significant amounts of H 2 and organic acids. The high microbial diversity of microbial mats makes possible a highly complex series of metabolic interactions between the microbes, the nature and extent of which are currently under investigation. To address this challenge, we are using a combination of metagenomics, metatranscriptomics, metaproteomics, iTags and naturally collected, as well as culture-based simplified microbial mats to study biogeochemical cycling (H 2 production, N 2 fixation, and fermentation) in mats collected from Elkhorn Slough, Monterey Bay , California. We present here the metagenome data, which will be used as a reference for metatranscriptomic analysis in a later paper. Site information Cyanobacterial mats are compact, laminated, and highly structured microbial communities (Fig. 1 ) that comprise great diversity at both the metabolic and phylogenetic level [ 1 ] and typically exist in highly saline environments such as lagoons and salterns. These mats notably have a suite of phototrophic organisms and photosynthetic lifestyles, from the dominant cyanobacterial phototroph Coleofasciculus chthonoplastes (basionym 10.1601/nm.700\n chthonoplastes ) to purple sulfur and non-sulfur bacteria, and potentially other anoxygenic phototrophs. During the nighttime portion of the diel cycle, phototrophic organisms release fermentation byproducts which in turn help drive a shift from oxic to anoxic metabolism dominated by hydrogen consumption and sulfate reduction by sulfate reducing bacteria such as Desulfobacteriales [ 2 ]. Naturally occurring mats have a documented capacity to produce and liberate fermentation by-products (H2 and acetate primarily) [ 3 , 4 ] and to consume them [ 5 , 6 ] depending on the point in the diel cycle. Lastly, nitrogen assimilation is dominated by nitrogen fixation in these mats, typically by several members of the phylum 10.1601/nm.624 such as ESFC-1 and 10.1601/nm.698 sp. and by sulfate reducing bacteria [ 7 – 11 ]. The mats of Elkhorn Slough are situated in an estuary emptying into Monterey Bay , California and are located in a former salt production pond. The MIMS coding is shown in Table 1 . Fig. 1 \n a . Photograph of location of cores collected in the field from microbial mats at the Moss Landing Wildlife Area in Elkhorn Slough, Moss Landing, California on 07/11/11. Individual samples collected in core tubes were numbered and could be tracked throughout the diel experiment. b . Experimental apparatus used to incubate microbial mats throughout the diel period from 08/11/11 to 09/11/11. Incubation containers containing cores used for control and molybdate treatments are labeled \n Table 1 Study information Label CD2A CD6A MD2A MD6A IMG/M ID 3,300,000,347 3,300,000,354 3,300,000,919 3,300,000,353 SRA ID SRX2021703 SRX2021697 SRX2879537 SRX2021699 Study Gs0067861 Gs0067861 Gs0067861 Gs0067861 GOLD ID (sequencing project) Gp0053859 Gp0054619 Gp0054089 Gp0054045 GOLD ID (analysis project) Ga0026496 Ga0026141 Ga0011764 Ga0026171 NCBI BIOPROJECT PRJNA337838 PRJNA336658 PRJNA366469 PRJNA336698 Relevance Biotechnological; hydrogen production Biotechnological; hydrogen production Biotechnological; hydrogen production Biotechnological; hydrogen production \n Microbial mats like the ones at Elkhorn Slough have long been studied as a model for early life and gained prominence with the discovery that hypersaline mats in Guerrero Negro, Baja California, represented one of the most highly species-diverse microbiomes ever studied [ 1 ]. Though not as diverse as the 10.1601/nm.698 mats of the Guerrero Negro system, the Elkhorn Slough mat system captures a similar distribution of organisms observed in laminated seasonal microbial ecosystems [ 6 , 12 ]. Several areas of microbial mat physiology research are on-going at the Elkhorn Slough site. The site has been used to isolate a novel nitrogen fixer [ 9 ] and to show that the majority of fixation is attributable to a 10.1601/nm.698 sp. [ 10 ], and to identify the dominant SRB (10.1601/nm.3538) in the ecosystem [ 2 ]. Additionally, the site has been investigated for hydrogen cycling. Burow and colleagues [ 5 ], showed that hydrogen flux likely originates from the fermentation of photosynthate. This system has also been subjected to metatranscriptomics and metaproteomics analyses [ 12 , 13 ]."
} | 1,513 |
25081499 | PMC4118322 | pmc | 6,764 | {
"abstract": "Although the hypothesis that nestedness determines mutualistic ecosystem dynamics is accepted in general, results of some recent data analyses and theoretical studies have begun to cast doubt on the impact of nestedness on ecosystem stability. However, definite conclusions have not yet been reached because previous studies are mainly based on numerical simulations. Therefore, we reveal a mathematical architecture in the relationship between ecological mutualistic networks and local stability based on spectral graph analysis. In particular, we propose a theoretical method for estimating the dominant eigenvalue (i.e., spectral radius) of quantitative (or weighted) bipartite networks by extending spectral graph theory, and provide a theoretical prediction that the heterogeneity of node degrees and link weights primarily determines the local stability; on the other hand, nestedness additionally affects it. Numerical simulations demonstrate the validity of our theory and prediction. This study emphasizes the importance of ecological network heterogeneity in ecosystem dynamics, and it enhances our understanding of structure–stability relationships.",
"discussion": "Discussion We proposed theoretical methods for estimating the dominant eigenvalue of both binary and quantitative bipartite networks. Although the methods are obtained as natural extensions of the previous methods 22 , we obtained an interesting result. In particular, we revealed a mathematical architecture in the relationship between ecological mutualistic networks and local stability. Our methods and numerical simulations clearly showed the local stability is determined by the heterogeneities of node degrees and link weights rather than topological nestedness such as NODF and WNODF. This study is consistent with the conclusion obtained from the previous studies 6 14 15 ; in particular, it provides more conceptual (theoretical) evidence for the limited impact of nestedness in mutualistic ecosystems. However, this conclusion is limited to the context of topological nestedness such as NODF and WNODF. A previous study 6 has cast doubt on the importance of NODF and WNODF for measuring nestedness; in particular, it has argued that nestedness is strongly related to the dominant eigenvalue (i.e., local stability) according to the mathematical fact that the perfectly nested graph shows the dominant eigenvalue. That is, it remains possible that topological nestedness such as NODF and WNODF cannot well capture this mathematical feature. These facts highlight the need for a more suitable definition of nestedness although Staniczenko et al. 6 have proposed to directly use the dominant eigenvalue when evaluating nestedness. In addition, more careful examinations may be required because a number of factors influence ecosystem stability. One remarkable example of this is modularity. Modularity is observed in mutualistic networks 32 , and it is believed to decrease their persistence 11 . However, we believe there is a limited effect of modularity on local stability in empirical networks because the previous study 11 reported a weak significance of modularity in mutualistic networks, and our numerical simulations using null models demonstrated that the local stability does not change, even if the modularity-related parameter (i.e., C 4 ) is not preserved. In this manner, we carefully evaluated the effect of structural properties and dominant eigenvalue as much as possible, using several types of null models; however, it remains possible that other hidden structural properties primarily affect the dominant eigenvalue. Our analysis has such limitations, as do many other works on network analyses. The definition of ecosystem stability is controvertible. Our finding seems to be contradict a conclusion 11 that higher diversity (i.e., s ) and connectance (i.e., L /[ s ( s − 1)]) promote the persistence and resilience of mutualistic communities. In particular, Equations (8) and (9) suggest that higher network complexity (i.e., diversity × connectance ≈ L / s ) leads to lower local stability, similarly to May's stability criterion 1 . However, this inconsistency between our conclusion and the previous conclusion is because the ecosystem stability definitions of persistence and resilience are different to the definition of local stability. In particular, the persistence indicates the proportion of persisting species once equilibrium has been reached, and the resilience represents the speed at which the community returns to the equilibrium after a perturbation 11 . For example, locally stable ecosystems can be persistent; however, locally unstable ecosystems may still display persistence because of the existence of alternative stable states 33 . In this case, trivial local stability may be observed because the intra-species interactions of the community matrix are large enough to compensate for the potential destabilizing effect of heterogeneity. A future challenge is to combine local stability with persistence and resilience in order to analyze the overall robustness of mutualistic ecological communities. Equations (8) and (9) also imply that the heterogeneity of network structure and interaction strength decreases the local stability. This finding may answer why a weak significance of heterogeneous degree distributions is observed in empirical mutualistic networks 34 . That is, mutualistic ecosystems may avoid such a heterogeneous community structure in order to maintain or increase local stability. In addition to this, the avoidance of interaction strength heterogeneity may be related to a biologically feasible assumption that interaction strength decreases with increasing number of interacting species (i.e. node degree) 2 . In such a case, interaction strength homogeneity may remain despite the increase of interspecific interactions. This may be also a strategy for increasing the local stability of ecosystems. These findings emphasize the importance of heterogeneity of mutualistic networks in ecosystem stability, and they enhance our understanding of structure–stability relationships. The spectral radius is linked to local stability and other dynamical functions in wide-ranging networked systems 35 . The proposed framework may be useful for the theoretical analysis of a wide variety of systems."
} | 1,585 |
36383522 | PMC9668122 | pmc | 6,765 | {
"abstract": "Soil microbiome disruption methods are regularly used to reduce populations of microbial pathogens, often resulting in increased crop growth. However, little is known about the effect of soil microbiome disruption on non-pathogenic members of the soil microbiome. Here, we applied soil microbiome disruption in the form of moist-heat sterilization (autoclaving) to reduce populations of naturally occurring soil microbiota. The disruption was applied to analyze bacterial community rearrangement mediated by four crops (corn, beet, lettuce, and tomato) grown in three historically distinct agroecosystem soils (conventional, organic, and diseased). Applying the soil disruption enhanced plant influence on rhizosphere bacterial colonization, and significantly different bacterial communities were detected between the tested crops. Furthermore, bacterial genera showed significant abundance increases in ways both unique-to and shared-by each tested crop. As an example, corn uniquely promoted abundances of Pseudomonas and Sporocytophaga , regardless of the disrupted soil in which it was grown. Whereas the promotion of Bosea , Dyadobacter and Luteoliobacter was shared by all four crops when grown in disrupted soils. In summary, soil disruption followed by crop introduction amplified the plant colonization of potential beneficial bacterial genera in the rhizosphere.",
"conclusion": "Conclusions Our results indicate that bacterial responses to crop growth are amplified after soil disruption, and that crop-specific bacterial community changes were much weaker without the disruption treatment. Additionally, after disruption of the organic or conventional soils, taxa recruited in these soils significantly differed from taxa recruited by the same plants grown in the undisrupted organic or conventional soil, but this effect did not occur in the diseased soil. Additionally, results show that the combination of disruption and the introduction of a new crop to an agroecosystem is a promising means to achieve plant benefiting bacterial community rearrangement. Accordingly, the implementation of managerial practices to promote soil microbiome re-arrangement should be considered as we begin to re-generate agricultural soils. After disruption in all agroecosystems crops were shown to develop both crop-specific and crop-shared relationships alongside several bacterial genera. Crop-specific associations between these genera and their hosts may aid in future determination of core or symbiotic rhizobacteria for the plant species tested, whereas crop-shared taxa (e.g., Luteolibacter ) may be of interest in future determination of novel and generalist PGPRs.",
"introduction": "Introduction Relationships between crop plants and their native bacterial communities have become increasingly important factors of crop production in agriculture. Recent discoveries have revealed positive correlations between soil microbial diversity and plant health, yield, disease suppression, and soil ecosystem cycling [ 1 , 2 ]. Plants actively recruit their root microbiota by changing exudation patterns throughout growth to accommodate their developmental needs [ 3 , 4 ]. Thus, cultivation of plants in microbially diverse soils likely provides an assorted selection pool of symbionts that could assist plant growth [ 5 ]. In contrast, several monocultured agroecosystems are express signs of soil microbial dysbiosis, or imbalances in the rhizosphere microbiota [ 6 – 11 ]. Such symptoms include reduced bacterial diversity [ 12 ] and/or higher plant pathogen abundance [ 6 , 7 , 13 ]. Further, studies suggest that annual cultivation of the same plant species could allow for a sense of “microbial habituation” to the presence of plants repeatedly grown in the same sites; thus, paving the way for imbalances to occur in soil microbial communities because of the unchanged host [ 14 ]. However, rhizospheric microbial rearrangements are not solely attributed to plant rhizodeposition, as the structure and function of soil microbiota has also been observed to shift in response to management practices, resulting in variable effects on plant health [ 9 , 15 , 16 ]. Recent attempts to reshape imbalances in agricultural microbial communities have shown that applying soil sterilization methods (via moist- or dry-heat, chemical fumigation, microwave, or gamma-irradiation, etc.) can eventually provide a more balanced rhizosphere microbiome and promote plant growth [ 17 – 19 ]. Other attempts to reduce the plant-health impacts of imbalanced soil microbial communities have used inoculations of plant-growth promoting rhizobacteria (PGPRs) [ 20 ]. Such studies often result in the inability of the inoculated microbes to establish, as native microbiota are habituated and more fit to maintain colonization of the soil/rhizosphere ecosystem [ 21 , 22 ]. As an example, it was shown that when carbendazim (a fungicide) was used to disrupt soils prior to PGPR inoculation, the inoculated PGPR-microbes were able to show increases in colonization compared to the same microbes applied to undisrupted soils [ 21 ]. Additionally, a recent study shows that applying moist heat sterilization to soils reduced microbial load, subsequently allowing plants to recruit distinct microbiota from the native community, along with several plant growth-promoting microbial functions [ 17 ]. In the current study we explore plant rhizosphere communities that develop after soil disruption, and results show the presence of several plant-beneficial bacteria across different crops and soil types. Additional aims were to observe how plants recruit rhizobacteria from soils with differing managerial history and diversity in response to microbiome disruption and crop growth. To achieve these aims, we disrupted soils from three distinct agroecosystems (organic, conventional, and diseased) via autoclaving. Following disruption, crops from four differing families (Poaceae, Amaranthaceae, Asteraceae, and Solanaceae) were planted in disrupted and undisrupted counterparts of the same soil and grown in a greenhouse experiment. Rhizosphere samples were collected from plants grown in each soil after the experiment, and bacterial communities were analyzed by Illumina MiSeq sequencing of the bacterial 16S gene (V3-V4 region).",
"discussion": "Discussion Effects of autoclave disruption on agroecosystem soils Distinct field management techniques (organic, conventional, or disease management-focused) indirectly condition native soil microbes to their hosts and management practices, and these changes in microbiota may positively or negatively impact plant growth [ 6 , 7 , 16 , 18 ]. Here, we investigated how soil factors (both biological and chemical) in distinct cropping systems responded to autoclave disruption, and how these responses impact plant growth and rhizosphere bacteria. Soil autoclaving has been reported to increase extractable P and Mn content [ 33 , 34 ] and this effect was observed in all soils in the present study. Interestingly and unique to the diseased soil, both Zn and Cu content were reduced after disruption. Similar to [ 17 ], plant growth promotions were observed for each crop when grown in disputed soils, compared the same crops grown in undisrupted soil. The increased plant growth effect is probably not a result of increased P and Mn content, but more likely due to soil disruption and the resulting rhizobacteria as influenced by crop growth. Our 16S qPCR analyses revealed variable changes on 16s rRNA copies resulting from disruption and crop growth, with most 16s rRNA copies being reduced compared to undisrupted samples. As such, resulting increases in plant growth may be due to i) reducing plant-microbe competition for soil resources, or ii) the reduced population of soil bacteria lessened bacteria-bacteria competition for plant deposited resources, favoring recruitment of plant growth promoting related taxa. Effects of autoclave soil disruption on rhizobacterial community rearrangements Upon collection, it was observed that a higher bacterial diversity level (Shannon Index) persisted in the conventional and diseased soils compared to the organic site. However, disruption and plant growth resulted in the organic site to showing the highest bacterial diversity value out of all three soils. It was also observed that following growth in the disrupted soils, plants were able to significantly increase the abundances of a greater number of bacterial genera (12 genera) compared to their growth in undisrupted soils (4 genera). Combining the observations shared by disrupting three distinct soils (increases in plant growth, Shannon’s diversity index, and number of recruited genera following disruption); soil disruption may likely serve as a promising managerial technique in restoration of a balanced soil microbial community. Community analysis of rhizobacterial community rearrangements following soil disruption Soil disruption also caused significantly different bacterial community composition from the undisrupted soils, across all sites [Bray Curtis distance, disruption p-values: 0.001 (for all soils)]. These findings demonstrate that each crop was able to recruit significantly different rhizobacteria, based on the soil the crop was grown in, and whether disruption was administered or not. The strongest effect of crop-specific recruitment is exemplified in the organic soil. Each crop was shown to promote distinct rhizobacteria, both when comparing the crop to its undisrupted counterpart, and when comparing rhizobacteria from differing crop types to one another when all were grown in disrupted or undisrupted soils (disruption effect p-value: 0.001, Axis.1: 57.14%; crop effect p-value: 0.001, Axis.2: 6.06%; R 2 : 0.1372). Like the organic site, crops grown in conventional soil were also shown to recruit significantly different communities between different crops and between the same crops in disrupted or undisrupted soil (disruption effect p-value: 0.001, Axis.1: 69.51%; crop effect p-value: 0.001, Axis.2: 3.95%; R 2 : 0.060). Conventional management practice often employs synthetic fertilizers and broad-spectrum pest- or herbicides, which can result in detrimental effects on bacterial evenness [ 35 – 37 ]. Albeit these negative effects on microbiota from conventional management, bacteria in the conventional agroecosystem site were still able to be reshaped by plants like those in the organic agroecosystem. Furthermore, a beta diversity analysis confirmed that crops grown in disrupted conventional soils showed significantly higher community dispersion compared the same crops growing in undisrupted conventional soils (p-value 0.015) suggesting that bacterial community alterations were mediated by plants following disruption in both conventional and organic soil. The crops grown in the diseased soils were also able to recruit different rhizobacterial communities from one another (crop-effect p-value 0.001, R 2 : 0.148, Axis.2: 15.56%). However, disrupting the diseased soil did not allow any of the crops to recruit different bacteria from their same crop counterparts grown in the undisrupted soil (p-value: 0.080). A potential explanation of this could be based on the high presence of Bacillus and Colstridium genera, as these can survive heat treatment by the formation of thermotolerant endospores [ 38 ]. Thus, if these genera had survived autoclaving, they would likely dominate crop rhizo-communities when grown in the diseased soil. In our study, the relative abundance (RA) values of both Bacillus and Clostridium increased in abundance after autoclaving and crop growth, with the highest increase in RA being observed by Bacillus in the diseased agroecosystem. Crop-shared bacterial genera recruited following agroecosystem disruption Differential abundance analyses revealed several bacterial abundance increases overlapping between different crops after growth across all disrupted soils ( S10 Table ). Eleven genera and two families ( Bosea , Caenimonas , Brevundimonas , Lacibacter , Luteolibacter , Pedobacter , Sphingoaurantiacus , Sphingopyxis , Dyadobacter , Larkinella , Rhabdobacter and families Saccharimonadaceae and Sphingobacteriaceae; S10 Table ) were observed to increase in abundance following disruption and growth of all four crops. We hypothesize that crops formed associations with some of these genera due to previous literature describing their plant growth promotional (or plant-symbiotic) nature. For example, members of Bosea and Sphingopyxis genera have been described to be PGPRs; and members of Bosea spp. can produce the auxin IAA [ 39 , 40 ]. When Dyadobacter spp. were inoculated into soil, previous results showed the genus was positively correlated with nitrogen fixation and increased nitrate reductase activity in plant leaves [ 41 ]. Additionally, Brevundimonas , Pedobacter , Luteoliobacter , Lacibacter , and Caenimonas have all been isolated from the root communities of different plants [ 42 – 46 ]. Lastly, Larkinella , Rhabdobacter and Sphingoaurantiacus are genera that have been previously isolated from organic amendments ( Larkinella ) or soil systems ( Rhabdobacter and Sphingoaurantiacus ) [ 47 – 49 ]. Abundances of other bacterial genera were increased because of specific recruitment by three out of the four crops (but not all crops like those previously mentioned) following disruption, and these trends were also observed across pooled disrupted soils. The crop combinations that significantly increased the abundance of five bacterial genera ( Algoriphagus , Articibacter , Devosia , Oligoflexus , and Opitutus ) and additionally, these shared genera are reported to possess some potential PGP ability. Both Opitutus and Devosia genera were seen to be associated with rice rhizosphere [ 50 , 51 ], and studies show that members of the Devosia genus possess a myriad of plant-benefiting functions (IAA synthesis, production of ammonia, and production of siderophores) [ 52 , 53 ]. Additionally, the Devosia genus has been identified to comprise true plant-endophytes that colonize the interior tissues of tomato plants [ 54 ]. Algoriphagus has been observed to increase in relative abundance as a response to plant defense-inducing biochemicals (salicylic acid, methyl jasmonate, and abscisic acid) [ 55 ]. The notion that Algoriphagus shows increased abundance in response to plant-defense compounds may suggest an upregulation of plant-defenses carried out by members within the genus. Literature on Articibacter spp. are scant, but the genus is prevalent in soybean rhizosphere during the vegetative stage [ 56 ]. In addition, Oligoflexus tunisiensis was isolated from the rhizosphere of both buckwheat and barley [ 57 ]. Crop-specific bacterial genera resulting from agroecosystem disruption There were also observations of several potential PGPR genera specifically increased by individual crops across the pooled disrupted soils. The bacterial genera increased by beet were Roseococus , Peredibacter , Flavisolibacter , Parasegetibacter , UTBCD1 and Solimonas . The genus Roseococcus falls within the family Acetobacteraceae which has been associated with nitrogen fixation and PGP-ability [ 58 ]. Peredibacter spp. are soil bacteria that are bacterivorous toward gram-negative bacteria, suggesting a biocontrol ability of pathogenic bacteria by members of the Peredibacter genus [ 59 ]. Other studies show increased relative abundances of the genus when tomato plants were inoculated with the PGPR Pseudomonas sp. RU47 [ 60 ]. Flavisolibacter is a genus positively correlated with disease suppression of Rhizoctonia solani [ 61 ]. The understudied Parasegetibacter and UTBCD1 genera falls within the Chitinophagaceae family, and this family is comprised of genera recorded to possess plant-growth promotional ability [ 62 , 63 ]. Lastly, members of Solimonas have been isolated from agricultural soils growing ginseng [ 64 ]. Corn growth significantly increased abundances of the genera Sporocytophaga and Pseudomonas in addition to the families Fibrobacteraceae and Rhodothermaceae. Of the promoted bacterial genera by corn, only Pseudomonas members have been extensively documented due to their plant growth promotional abilities [ 65 – 67 ]. However, the genus Sporocytophaga is widespread in soils and members such as S . myxococcoides can hydrolyze cellulose [ 68 ]. Additionally, the Rhodothermaceae family bacteria also possess cellulolytic and xylanolytic activity [ 69 ]. Likely both Rhodothermaceae and Sporocytophaga members aid soil ecosystem in cycling of plant detritus, exuding carbon sources for neighboring, potentially plant-benefiting microbes. Lastly, the family Fibrobacteraceae has been recorded to closely associate with wheat, which is in the same plant family as corn (Poaceae) [ 70 ]. Lettuce growth promoted the abundance of 17 bacterial reads, and four of the promoted genera have been associated with plant symbiotic or protective abilities ( Rhodoferax , Fluviicola , Cytophaga , and Knoellia) . Rhodoferax members can degrade chemical herbicides [ 71 ], and Fluviicola is a common member of the rice rhizosphere [ 56 ]. Bacteria within the genus Cytophaga are present in the barley rhizosphere, and may contribute to the turnover of carbon, phosphorus, and nitrogen in soil ecosystems [ 72 ]. Lastly, a species within the Knoellia genus is a known endophyte of Costus speciosus (a type of ginger) [ 73 ]. Tomato growth in disrupted soils promoted nine bacterial genera ( Bacillus , Chelatococcus , Asticcacaulis , Yonghaparkia , Aminobacter , Cereibacter , Mucilaginibacter , Shimazuella , and Thermomonas ). Bacillus members are known genera documented on their plant growth promotional abilities [ 74 – 76 ], and Bacillus and Asticcacaulis were both considered members of the tomato endospheric bacterial community [ 77 ]. Two Muciliginibacter spp. were recently discovered to be PGPRs by increasing root length of tomato [ 78 ]. Shimazuella falls within the actinomycetes phylum and was isolated from Pueraria candollei (Kudzu) rhizosphere soil [ 79 ]. Another genus promoted by tomato, Yonghaparkia , can utilize the precursor to ethylene, ACC (1-Aminocyclopropane-1-carboxylic acid), as a nitrogen source [ 80 ]. Aminobacter members can produce the plant growth hormone cytokinin [ 81 ]. Lastly, some Thermomonas members are thermotolerant [ 82 ] explaining how these taxa were able to withstand disruption, although the literature on the relationship between genus and plants is lacking. Notable crop-shared bacterial genus-level abundance increases following soil disruption Our data showed that several PGPR-related genera were significantly increased in abundance and associated with all crops tested when grown in soils following disruption. Accordingly, we speculate that plant rhizodeposition during early growth and development plays a strong contribution to fill “empty” niches (caused by disruption) with plant symbiotic and/or beneficial bacterial taxa. Since our experiment occurred during the first seven weeks of crop-growth, it is possible that some of these crop-shared genera may represent generalist-PGPRs, plant-symbiotic taxa, or bacterial keystone species (which are described as low abundance early colonizers that aid in the establishment of the plant’s core microbiome) [ 83 – 85 ]. While all crop-shared bacteria may not represent keystone species, instead these may indicate understudied or novel PGPRs/crop-symbiotic bacterial taxa. As an example, observations of Luteolibacter were of particular interest as this genus was increased by all four crop families (Poaceae, Amaranthaceae, Asteraceae and Solanaceae) after growth in disrupted agroecosystems. Notably, however, this genus was not increased in either NPCK (disrupted/undisrupted) which likely indicates a plant-symbiotic nature of Luteolibacter . Further the genus falls within the Verrucomicrobia phylum, a bacterial phylum known to be present in varying plant-soil ecosystem interactions [ 44 , 84 – 86 ] in addition to being reported in the rhizospheres differing-family crops (Poaceae, Amaryllildaceae, and Solanaceae) [ 44 ]. Therefore, the bacterial taxa that showed an increased abundance and shared by all four crops may be worth investigating as novel and generalist PGPRs in further experimentation. We also suggest that they are responsive to plant presence (e.g. root exudation) as these bacterial taxa did not increase in abundance in the no plant controls. Conclusions Our results indicate that bacterial responses to crop growth are amplified after soil disruption, and that crop-specific bacterial community changes were much weaker without the disruption treatment. Additionally, after disruption of the organic or conventional soils, taxa recruited in these soils significantly differed from taxa recruited by the same plants grown in the undisrupted organic or conventional soil, but this effect did not occur in the diseased soil. Additionally, results show that the combination of disruption and the introduction of a new crop to an agroecosystem is a promising means to achieve plant benefiting bacterial community rearrangement. Accordingly, the implementation of managerial practices to promote soil microbiome re-arrangement should be considered as we begin to re-generate agricultural soils. After disruption in all agroecosystems crops were shown to develop both crop-specific and crop-shared relationships alongside several bacterial genera. Crop-specific associations between these genera and their hosts may aid in future determination of core or symbiotic rhizobacteria for the plant species tested, whereas crop-shared taxa (e.g., Luteolibacter ) may be of interest in future determination of novel and generalist PGPRs."
} | 5,485 |
34917063 | PMC8670094 | pmc | 6,767 | {
"abstract": "Rocks that react with liquid water are widespread but spatiotemporally limited throughout the solar system, except for Earth. Rock-forming minerals with high iron content and accessory minerals with high amounts of radioactive elements are essential to support rock-hosted microbial life by supplying organics, molecular hydrogen, and/or oxidants. Recent technological advances have broadened our understanding of the rocky biosphere, where microbial inhabitation appears to be difficult without nutrient and energy inputs from minerals. In particular, microbial proliferation in igneous rock basements has been revealed using innovative geomicrobiological techniques. These recent findings have dramatically changed our perspective on the nature and the extent of microbial life in the rocky biosphere, microbial interactions with minerals, and the influence of external factors on habitability. This study aimed to gather information from scientific and/or technological innovations, such as omics-based and single-cell level characterizations, targeting deep rocky habitats of organisms with minimal dependence on photosynthesis. By synthesizing pieces of rock-hosted life, we can explore the evo-phylogeny and ecophysiology of microbial life on Earth and the life’s potential on other planetary bodies.",
"conclusion": "Conclusion In this study, our current understanding of the rocky biosphere was documented to emphasize that microorganisms can harvest inorganic and organic energy sources independently from photosynthesis. Omics-based approaches and nanosolid characterizations have begun to unveil metabolic pathways suitable for thriving with mineral assemblages prevalent on early Earth and other planetary bodies potentially harboring extant life.",
"introduction": "Introduction The discovery of deep-sea hydrothermal vents has dramatically changed our perspective on life ( Corliss et al., 1979 ). The deep-sea hydrothermal vent, which is densely colonized by peculiar organisms, such as tubeworms and giant clams around black smoker chimneys, is known to be devoid of nutrients from photosynthesis; instead, nutritional dependence is mainly on chemicals emitted from the black smokers ( Felbeck, 1981 ). Chemosynthesis, a term commonly used to contrast photosynthesis, is vital for organisms to flourish on the dark seafloor where various reducing chemicals, such as H 2 , CH 4 , HS − , and Fe(II), are emitted from vent fluid and oxidized by O 2 and NO 3 − from seawater for microbial energy generation ( Amend and Teske, 2005 ). In this case, the reductants are produced by rock-water interactions and magma degassing, whereas the oxidants are produced by photosynthesis-based biogeochemical processes. Microbial life dependent on oxidants that are produced independent of photosynthesis might be analogous to the microbial life of the primitive ocean before the emergence of photosynthetic life. Thus, deep-sea hydrothermal vents are considered a window for the subvent biosphere where photosynthetic products are negligible ( Deming and Baross, 1993 ). The life search was extended from deep-sea hydrothermal vents at mid-oceanic ridges to ridge flanks associated with thermally and/or hydrologically driven fluid circulations ( Edwards et al., 2011 ). As the thermal limit of life extends deeper in the oceanic crust as ridges cool down with time during spreading, microbial ecosystems are expected to be found below the seafloor where the maximum optimal growth temperature reaches the 120°C isotherm at a depth of 6km ( Heberling et al., 2010 ; Heuer et al., 2020 ). Similar to oceanic settings, intensive investigations on the microbial life of hot springs and deep groundwater sources on land have been conducted. The subsurface lithoautotrophic microbial ecosystem (SLiME), which does not depend on phototrophic organisms, was first discovered in a deep aquifer sustained by cretaceous flood basalt ( Stevens and McKinley, 1995 ). The microbial ecosystem harvests energy by oxidizing H 2 produced by in situ reactions between groundwater and olivine and pyroxene group minerals. Given that low-temperature dissolution rates of olivine and pyroxene group minerals are slow under neutral to slightly alkaline pH conditions that prevail in the deep subsurface, elevated temperatures resulting from hydrothermal activities appear to be favorable for SLiMEs owing to the accelerated rates of mineral-water reactions, as exemplified by the dominance of methanogenic archaea in hot springs ( Chapelle et al., 2002 ). SLiMEs are important not only to understand primitive microbial life before the emergence of phototrophic organisms but also to search for extraterrestrial life on Mars, the surface of which has been harsh for phototrophic organisms for 3billion years ( Onstott et al., 2019 ). Without photosynthesis, energy sources and fluxes derived from magma degassing, water-rock interactions, and radiolysis are essential."
} | 1,227 |
39025801 | PMC11579526 | pmc | 6,768 | {
"abstract": "Synopsis Melanin is an essential product that plays an important role in innate immunity in a variety of organisms across the animal kingdom. Melanin synthesis is performed by many organisms using the tyrosine metabolism pathway, a general pathway that utilizes a type-three copper oxidase protein, called PO-candidates (phenoloxidase candidates). While melanin synthesis is well-characterized in organisms like arthropods and humans, it is not as well-understood in non-model organisms such as cnidarians. With the rising anthropomorphic climate change influence on marine ecosystems, cnidarians, specifically corals, are under an increased threat of bleaching and disease. Understanding innate immune pathways, such as melanin synthesis, is vital for gaining insights into how corals may be able to fight these threats. In this study, we use comparative bioinformatic approaches to provide a comprehensive analysis of genes involved in tyrosine-mediated melanin synthesis in cnidarians. Eighteen PO-candidates representing five phyla were studied to identify their evolutionary relationship. Cnidarian species were most similar to chordates due to domain presents in the amino acid sequences. From there, functionally conserved domains in coral proteins were identified in a coral disease dataset. Five stony corals exposed to stony coral tissue loss disease were leveraged to identify 18 putative tyrosine metabolism genes, genes with functionally conserved domains to their Homo sapiens counterpart. To put this pathway in the context of coral health, putative genes were correlated to melanin concentration from tissues of stony coral species in the disease exposure dataset. In this study, tyrosinase was identified in stony corals as correlated to melanin concentrations and likely plays a key role in immunity as a resistance trait. In addition, stony coral genes were assigned to all modules within the tyrosine metabolism pathway, indicating an evolutionary conservation of this pathway across phyla. Overall, this study provides a comprehensive analysis of the genes involved in tyrosine-mediated melanin synthesis in cnidarians.",
"conclusion": "Conclusions Understanding cnidarian tyrosine metabolism pathway can help cognize the evolutionary events that influenced melanin synthesis pathway and the mechanisms they use to maintain cellular integrity. In this study, cnidarian and coral melanin synthesis are associated with PO-candidate TYR, grouping with chordates such as humans in phylogenetic studies due to protein domain assignment. TYR was also found to have multiple copies in stony corals. The pattern of human similarity to human elements of immunity hold true for PO-candidates, as seen in other immune functions in cnidarians. Pattern recognition receptors such as nod-like receptors and toll-like receptors are more similar to human genes than other invertebrates and even have expansion in these receptors such as domain combinations not found within their human counterparts ( Dimos et al. 2019 ; Emery et al. 2021 ). The combination of these studies with our study, we demonstrate that downstream immune cascades follow this pattern of evolutionary similarity and increased repertoire of functionality as in cnidarian receptors. It is now more important than ever to identify key pathways in cnidarian immune systems as coral disease is a rising threat to reef ecosystems. This study provides 18 putative orthogroups from five stony corals that define tyrosine metabolism, a pathway involved in melanin synthesis, oxidative stress response, and tyrosine degradation as evidence by the correlation of orthologous gene expression to melanin concentration. Melanin is identified in this study as a potential immune resistance train in tissue loss diseases that helps to understand how stony corals may fight pathogens. The methods of this paper provide a blueprint for future comparative studies to obtain other biologically important cnidarian immune pathways.",
"introduction": "Introduction Melanin is a highly conserved multifunctional pigment best known for roles in mammalian skin and hair pigmentation ( D'Mello et al. 2016 ). In invertebrates, melanin has many other functions, including those related to innate immunity ( True 2003 ; Christensen et al. 2005 ; Liu et al. 2016 ; Qiao et al. 2016 ; Whitten and Coates 2017 ; Ehrlich and Zuk 2019 ). For example, insects utilize melanin in embryonic development, coloration, and pathogen encapsulation ( Dimopoulos 2003 ; Okada et al. 2006 ; Zou et al. 2008 ; Córdoba-Aguilar et al. 2009 ; Lee et al. 2018 ). In ecologically important cnidarians, such a stony corals, melanin production is associated with wound healing and disease, but the mechanisms by which these organisms produce these phenotypes are unresolved ( Zhuang et al. 2009 ; Fuess et al. 2018 ; Bailey et al. 2019 ; Ricci et al. 2019 ; Aziz et al. 2021 ). An understanding of the melanin cascade in basal metazoans, such as cnidarians, is important as it will provide better insight into the evolution of these pathways across phyla and will help us better understand cnidarian's stress and disease responses. The pathways of melanin synthesis such as melanogenesis and tyrosine metabolism are well characterized in organisms like Homo sapiens and Drosophila melanogaster . The core components of these melanin synthesis pathways include upstream cell signaling pathways such as Wnt or MAPK, the transcription factor MITF (Melanocyte Inducing Transcription Factor or Microphtalmia-Associated Transcription Factor), and rate limiting phenoloxidase (PO) enzymes. Melanin synthesis is initiated by the presence of tyrosine, indicating a conserved tyrosine-mediated melanin synthesis process ( Nappi and Christensen 2005 ; D'Mello et al. 2016 ; Bailey et al. 2019 ; Koike and Yamasaki 2020 ; Yu et al. 2021 ). A variety of studies in several species indicate that in melanin synthesis, the rate limiting enzymatic reactions and non-enzymatic reactions are equally important in creating the melanin product. The rate limiting enzymes perform the catalyzation and hydroxylation of monophenols into diphenols and quinone intermediates through an enzymatic process ( Hoeger and Harris 2020 ). Accordingly, non-enzymatic reactions in quinone-radical intermediates create the melanin product. There are a variety of enzymes that can catalyze the rate-limiting step of melanin synthesis. While different protein families make up these rate limiting enzymes, all are capable of producing melanin. Due to the diversity in protein families that can perform the rate-limiting step across phyla, we broadly categorize these enzymes as PO-candidates, proteins that are either PO or PO-like ( Hoeger and Harris 2020 ; Pavan et al. 2020 ). PO-candidates are all multicopper oxidase proteins that can have two to six copper atoms at their active sites. Hemocyanins, tyrosinases (TYRs), and catecholoxidases are type-three copper proteins enzymes that perform this rate limiting step and only have two copper atoms at their active site. Laccases are a general multicopper oxidase that perform the melanin rate-limiting step and have more than two copper atoms at the active site ( Janusz et al. 2020 ; Pavan et al. 2020 ). Laccase PO-candidates are annotated in several pathways that produce a melanin product, including melanogenesis and generic annotations of melanin synthesis pathways in literature ( D'Mello et al. 2016 ; Liu et al. 2016 ; Whitten and Coates 2017 ). Studies in H. sapiens and arthropods have driven the progress in melanin research as they have played important roles in life stages, coloration, and immunity. In these organisms, the variety of PO-candidates are due to the roles they play in that organism. For examples, humans have PO-candidates TYR and laccase that are used for radiation defense and inflammation ( Gasque and Jaffar-Bandjee 2015 ; Koike and Yamasaki 2020 ). However, insects have a variety of prophenoloxidases PO-candidates characterized by their hemocyanin domains, which encapsulate different types of pathogens, including bacteria, fungi, and viruses ( Christensen et al. 2005 ; Wang et al. 2017 , 2018 ). Arthropods have the capability of expressing all PO-candidates, including hemocyanin, TYR-like and laccase-like, and have been reported the expression of a variety of these PO-candidates during pathogen or stress challenges ( Aladaileh et al. 2007 ; Yu et al. 2014 ; Tassanakajon et al. 2018 ). The H. sapiens and the arthropod pathway differ in the types and uses of PO-candidates at the rate limiting steps. Building of the information of PO-candidates in model organisms, the understanding of melanin synthesis has expanded in non-model organisms through comparative immunology approaches on model to non-model organisms ( Esposito et al. 2012 ; Bailey et al. 2019 ). Cnidarians synthesize and use melanin as a part of their innate immune system ( Anctil 2009 ; Zaragoza et al. 2014 ; Parisi et al. 2020 ). Melanin production occurs as a reaction to stress, wound healing, pathogen or pathogen associated molecular patterns (PAMPs) exposure, and coral bleaching ( Palmer et al. 2010 ; Mydlarz and Palmer 2011 ; Palmer et al. 2011 ; Palmer et al. 2011 ; van de Water et al. 2015 ; Fuess et al. 2018 ; Palmer and Baird 2018 ; Bailey et al. 2019 ; Ricci et al. 2019 ; Parisi et al. 2020 ). Melanin can appear as a visible phenotype, which has been seen in the purpling of sea fans and black spot development on Eunicea during disease events ( Mydlarz and Harvell 2007 ; Fuess et al. 2018 ; Ricci et al. 2019 ). Gene expression studies provide further support of PO-candidates in the coral pathogen response, with differential expression of some PO-candidates in disease exposure, high expression of PO-candidates being linked to disease resistance, and TYR being characteristic of immune cell types ( Mydlarz and Palmer 2011 ; Vidal-Dupiol et al. 2014 ; van de Water et al. 2015 ; Connelly et al. 2020 ; Levy et al. 2021 ; MacKnight et al. 2022 ). Understanding an immune pathway in cnidarians is vital now, as coral disease is a rising threat to reefs. Coral disease poses an existential threat to Caribbean coral reefs, as seen in the stony corals afflicted by stony coral tissue loss disease (SCTLD), which has resulted in high mortality rates and loss over overall coral coverage on reefs ( Alvarez-Filip et al. 2022 ). In this study, PO-candidates and melanin synthesis cascades in cnidarians were surveyed using phylogenetic protein family comparisons and annotation of specific genes in the pathways. This study had two main goals: (i) to put the coral melanin pathway in comparative evolutionary context using protein sequence data and bioinformatic tools, and (ii) to use an existing dataset with active disease (SCTLD) to identify melanin synthesis and PO-candidates that may be important to coral's ability to survive disease. To accomplish the first goal, protein sequences of PO-candidates from a variety of species were compared using sequence alignment software to elucidate the evolutionary relationship in this protein family. In the second goal, the orthologous transcriptomic dataset from the active disease SCTLD study was leveraged to understand the connection between melanin synthesis and PO-candidate genes. This study quantified melanin concentration in samples from this active disease dataset to correlate to the putative orthogroups that represent a melanin synthesis pathway. Since there is not one universal gene responsible for melanin synthesis and the pathway is not understood in cnidarians, utilizing this study that encompasses five stony coral species provides insight into evolution of innate immunity and processes important to coral's ability to survive disease.",
"discussion": "Discussion This study provides a comprehensive analysis of the genes involved in tyrosine-mediated melanin synthesis in cnidarians. The study met its first goal by generating a phylogenetic tree that explained the phylogenetic relationship of protein family type-three copper oxidase proteins. The second goal resulted in putative stony coral genes being correlated to melanin concentration and the identification of an adaptive tyrosine metabolism pathway in stony corals. We have several key findings: cnidarians have TYR protein domains and group with humans while PO-candidates with hemocyanin domains exist mainly in insect groups, stony corals have correlated expression of melanin product to TYR in a disease susceptibility context, and stony coral have an evolutionary conserved tyrosine metabolism pathway ( Fig. 5 ). Fig. 5 Adapted tyrosine metabolism pathway in stony corals. The tyrosine metabolism was adapted from KEGG from H. sapiens , the species with the closest evolutionary relationship to the PO-candidates found in cnidarians. Overlayed are the orthogroups correlation to melanin concentration (bolded as eumelanin/melanin on the tyrosine metabolism adapted pathway). Orthogroups are colored based on their correlation to melanin concentration; positive (red), negative (blue), no correlation but present in the orthologs genes (yellow), not present in stony coral (gray). TH, DDC, and FAH were present as orthologous genes in the stony coral expression but were not correlated to melanin concentration. TYRP1 and DCT were considered not found in the orthogroups as they have uniquely evolved in humans. Evolutionary relationships of TYR The structural and evolutionary analysis of PO-candidates across 18 species indicates a divide of type-three copper oxidase proteins based on protein domains. Interestingly, cnidarians PO-candidates consistently group with evolutionary distant H. sapiens compared to more closely related arthropod species. Many coral immune genes are also similar to the human genes that are important for human system development, cell signaling, or immune response ( Miller et al. 2005 ; Watanabe et al. 2009 ; Steele et al. 2011 ; Mansfield et al. 2017 ; Williams and Gilmore 2020 ). The PO-candidates involved in melanin synthesis may reflect another conserved history between humans and cnidarians. While this study found all PO-candidates contain a TYR Pfam domain, the exclusivity of TYR domains in cnidarians and chordates and the lack of hemocyanin domains may be driving this similarity. The principles of phylogenetic studies and evolutionary processes that drive biological diversification are relevant at both gene and species levels ( Herrada et al. 2011 ). From a protein family perspective, the type-three copper oxidase evolutionary studies may support TYR being a conserved functional domain that evolved from basal metazoans. Evolutionary studies of the diversity of type-three copper proteins have indicated two ancient gene duplication events, as well as differential loss and expansions within specific phyla that could support the evolutionary relationships identified in this study ( Aguilera et al. 2013 ). After these duplication events and differential loss and expansions, arthropods may have developed derived traits associated with their specificity in PO proteins that drive the evolutionary distance between cnidarians and arthropods ( Wang et al. 2017 , 2018 ). From a species perspective, the unique evolutionary history of insects may continue to separate them from humans and cnidarians. Insects seem to have an extensive investment in melanin as a primary immune response as evident from their highly specialized PO-candidates that can be phenotypically unique to specific pathogens or specific immune responses ( Binggeli et al. 2014 ; Dudzic et al. 2015 ). This not only makes comparison to cnidarians and humans difficult but also identifies a key evolutionary event that could be driving these relationships identified in this study. Overall the type-three copper oxidase protein family is heavily influenced by the protein domains assigned to each species. The characterization of coral PO-candidates has remained elusive due to the implications of multiple PO-candidates in previous coral melanin precursor studies ( Mydlarz and Palmer 2011 ). There is the possibility of a laccase being a contender for the PO-candidate that can characterize coral melanization, as laccase has been found in coral gene expression and proteomic datasets ( Palmer et al. 2012 ; Vidal-Dupiol et al. 2014 ; Ricci et al. 2019 ; Connelly et al. 2020 ), and is a conserved multicopper oxidase in all animals ( Janusz et al. 2020 ). This gene, however, is not annotated to KEGG's tyrosine metabolism pathway and most likely will remain that way until clarity on laccase's role in melanin synthesis is found ( Hansakon et al. 2020 ; Bajpai et al. 2023 ). Increasing the number of identified and annotated genes for laccases in the KEGG annotation of tyrosine metabolism, as well as increasing the limited number of annotated PO-candidates, will provide resolution for laccases role in melanin production. However, this does not negate the very important role that TYR plays in cnidarians. TYR is supported as the primary PO-candidate in coral immunity as it's associated with immune cells identified in stony corals. Single-cell sequencing found a putative immune cell type that was enriched with TYR ( Levy et al. 2021 ). As such, this cell was assigned as one of only two immune cells in corals, with further functional studies confirming the presence of two distinct types of immune cells in cnidarian species ( Snyder et al. 2021 ). These cells bare a similarity to H. sapiens melanocytes, which produce melanin ( Gasque and Jaffar-Bandjee 2015 ) due to their unique and specialized proteins, functionality, and their location in the epithelial layer ( Palmer et al. 2011 ; Fuess et al. 2018 ; Ricci et al. 2019 ). In addition, histological studies and visualization have implicated melanin barriers in the mesoglea that is where the putative immune cells primarily reside ( Vargas-Ángel et al. 2007 ; Tracy et al. 2021 ). The presence of TYR in relation to SCTLD susceptibility also offers a unique perspective on how immune cell types may be utilized for preventive immune responses in cnidarians ( Netea et al. 2019 ; Prigot-Maurice et al. 2022 ). It could be hypothesized that TYR association with an immune cell type may employ melanin product as a preventive measure at the beginning of a disease event to mitigate the infection. In conclusion, the evolutionary conserved presence of TYR as a PO-candidate leads to the understanding of how immune response occurs within cnidarian species based on the cell types it is originating from. Tyrosine metabolism genes and melanin concentration reveals immune responses and homeostasis mechanisms in stony corals The tyrosine metabolism pathway is divided into four modules: (i) thyroid hormone biosynthesis, (ii) catecholamine biosynthesis, (iii) melanin synthesis, and (iv) tyrosine degradation. Modules are identified based on the ending products, such as tyrosine to triiodothyronine/thyroxine in the thyroid hormone biosynthesis module, tyrosine to adrenaline in the catecholamine biosynthesis module, tyrosine to melanin product in melanin synthesis, and tyrosine to a homogentisate in tyrosine degradation. Using these pathways, gene expression from five stony corals in an experimental SCTLD exposure study was leveraged to look for associations between the genes within these modules and melanin concentration. Melanin concentration did not vary between disease states, with no apparent upregulation of melanin concentration in response to SCTLD, suggesting that melanin is not the primary immune response in this disease. Melanin has been documented as a primary immune response in other coral diseases, such as Sea fan—aspergillosis system and Eunicea Black Band Disease ( Mydlarz and Harvell 2007 ; Fuess et al. 2018 ; Ricci et al. 2019 ). While the causative agent of SCTLD is unknown, it appears that the algal symbiont is afflicted during infection and other host immune responses are initiated ( Landsberg et al. 2020 ; Beavers et al. 2023 ). However, the importance of melanin in SCTLD immune response may be in preventing disease signs in certain coral species. Melanin concentration varied significantly between species within the study, suggesting a constitutive role of melanin in immunity to SCTLD. The melanin concentration inversely related to relative risk or susceptibility to SCTLD, with more resistant species having higher overall concentrations of melanin while more susceptible species had overall lower melanin concentrations, indicating the potential role melanin plays as resistance immune trait. The PO-candidate TYR has previously been found as a resistance train in another tissue loss disease, white plague, with the gene being identified as lineage specific, a gene that varied by species but not by disease state ( MacKnight et al. 2022 ). This supports the important role melanin may play in the prevention of lesions in stony corals by acting in a resistance role. Even though melanin concentration was not significant in disease states, we can still use the phenotypic data of melanin concentration to help contextualize the gene expression data. By correlating coral gene expression data to melanin concentration, we can identify key genes and pathways relevant to melanin production in stony corals. Most importantly, this study identified orthologous genes of the five cnidarian species that were found to have Pfam domain matching the H. sapiens tyrosine metabolism pathway genes, grounding the method of comparative immunology of stony corals to humans. The unassigned tyrosine metabolism and positive thyroid hormone biosynthesis putative orthogroups may play a role in oxidative stress in stony corals. Melanin synthesis increases the risk oxidative stress in tissues for two major reasons: (i) the pro-oxidant state in melanin synthesis, and (ii) the antioxidant state that normally occurs with pathological conditions when melanin synthesis is activated ( Denat et al. 2014 ). The majority of the unassigned putative orthogroups are alcohol dehydrogenases and other amine neurotransmitters. Alcohol dehydrogenases have been associated with reducing injury during a disease, such as mitigating liver damage ( Hou et al. 2019 ), and have a positive correlation in melanin product in corals, indicating a similar mitigation role. In addition to this, amine neurotransmitter MAOA in this study had a negative correlation to melanin concentration. Increased MAOA expression has been correlated to the development of pigmentation disorders ( Enkhtaivan and Lee 2021 ). The negative correlation of MAOA to melanin in corals could be mitigating the adverse effects of melanin production in cnidarians. The thyroid hormone biosynthesis module only has one enzyme; TPO, which is an enzyme involved in peroxidase activity and response to oxidative stress in a variety of vertebrate and invertebrates ( Taurog 1999 ). While traditionally associated with thyroid hormone synthesis in humans ( Ruf and Carayon 2006 ), the protein itself has peroxidase enzymatic capability that is important to oxidative stress. Peroxidase activity has been shown to be an important immune response in corals during a variety of stressors such as infections, injury, or heat stress ( Mydlarz and Harvell 2007 ; van de Water et al. 2015 ; Fuess et al. 2016 ). The relationship of TPO being positive to melanin concentration in this study indicates an ability to response to oxidative stress using this specific pathway. In addition, TPO's correlation to melanin product indicates a potential dual function, the role of tyrosine metabolism and the ability to perform antioxidant activity. The stony coral catecholamine biosynthesis module has a putative orthogroups positively and negatively correlated to melanin concentration. Catecholamine is associated with neurotransmission, as it is classified as a slow neurotransmitter. Slow transmitters and other neurologically relevant products such as adrenaline have been identified in cnidarian species in some capacity, but mechanisms for their production remain elusive ( Kass-Simon and Pierobon 2007 ). Currently, it is indicated that cnidarian genomes do not contain the specific rate-limiting enzymes involved in catecholamine biosynthesis based on comparative approaches, and instead many have unique cnidaria specific enzymes present to perform this pathway ( Moroz et al. 2021 ). In this study, we have found orthologous genes of five stony corals that have Pfam domains that may function in catecholamine biosynthesis. This can be used in future comparative studies to identify neurotransmitter production in cnidarians. In the search for the PO-candidates responsible for melanin synthesis in stony corals, this study found one of 14 TYR domain containing orthogroups to be correlated to melanin synthesis. This positive correlation indicates biological significance of this particular orthogroup as the amount of melanin product follows orthogroup expression. There were a total of 13 orthogroups with TYR Pfam domains that were not significantly correlated to melanin product, indicating a diversity of TYR-like genes in corals. It has been proposed that a diversity of TYR-like genes in cnidarian species may indicate an organism's ability to launch either specific responses or stronger responses to pathogen challenges and promoting disease resistance ( Bailey et al. 2019 ), and multiple copies have been found in Exaiptasia pallida and N. vectensis and Porites australiensis ( Anctil 2009 ; Bailey et al. 2019 ; Shinzato et al. 2021 ). The lack of correlations to melanin in the SCTLD dataset may point to the utility and specificity of these orthogroups in other immune capacities. Similar to the expansion of other immune genes in cnidarians ( Emery et al. 2021 ), the expansion of TYRs could contribute to greater immune specificity, especially to overcome the lack of an adaptive immune system. The number of cnidarians orthogroups may also reflect the expansion of TYR-like proteins similar to human TYR-like protein expansions. In humans, there have been multiple evolutionary events in TYRs that resulted in DCT and other TYR-related-proteins (TYRPs1) within melanin synthesis pathways ( Budd and Jackson 1995 ; Sturm et al. 1995 ; Camacho-Hübner et al. 2002 ) and have specific functions and implications in disease pathogenesis. While it is possible that stony corals could use TYR-like proteins, it is not expected that the orthogroups found is a TYR related protein or DCT gene due to this evolutionary event. However, the identification of multiple orthogroups that have TYR domains with only one orthogroup having significant to melanin concentration in this exposure study provides support to the theory of the expansion of immune genes in cnidarians for greater immune specificity. The identification and expression of all putative orthogroups in the tyrosine degradation module in the stony coral SCTLD dataset, indicates an evolutionary-conserved investment in this pathway. Basal metazoan, like the stony corals in this study, have an open-body plan, which is susceptible to disruptions in cellular homeostasis. While melanin synthesis can be an important and necessary component of the immune response, tyrosine isomer accumulation during tyrosine metabolism has a pathological association with free radicals that lead to oxidative stress and inflammation ( Molnár et al. 2016 ; Ipson et al. 2019 ), disrupting cellular homeostasis. By degrading tyrosine from the cell, an organism can effectively defend against oxidative stress associated with melanin synthesis ( Nguyen et al. 2020 ). Oxidative stress leads to tissue damage ( Palmer et al. 2011 ), and the dysbiosis of algal symbionts ( Weis 2019 ), and is critical to homeostasis of a cnidarian cell. The positive correlation of genes HPD and GSTZ1: This module indicates the homeostatic maintenance for the stony coral's open body plan. Humans, with melanocytes, have multiple pathways and regulations set up to avoid ROS stress within melanocytes and can keep the stress localized ( Denat et al. 2014 ). Cnidarians may rely on several pathways within their immune response to actively decrease tyrosine buildup to avoid tissue damage due to unregulated ROS stress. The cnidarian tyrosine degradation module is likely highly conserved due to the same reasons. Overall, the identification of putative orthogroups in stony corals provides targets for further understanding the tyrosine metabolism pathway in the context of disease and provides the first look at genes involved in melanin production and its role in disease response."
} | 7,183 |
38053678 | PMC10695191 | pmc | 6,772 | {
"abstract": "The anaerobic digestion (AD) of food waste (FW) was easy to acidify and accumulate ammonia nitrogen. Adding exogenous materials to the AD system can enhance its conversion efficiency by alleviating acidification and ammonia nitrogen inhibition. This work investigated the effects of the addition frequency and additive amount on the AD of FW with increasing organic loading rate (OLR). When the OLR was 3.0 g VS per L per day and the concentration of the additives was 0.5 g per L per day, the stable methane yield reached 263 ± 22 mL per g VS, which was higher than that of the group without the additives (189 mL per g VS). Methanosaetaceae was the dominant archaea, with a maximum abundance of 93.25%. Through machine learning analysis, it was found that the optimal daily methane yield could be achieved. When the OLR was within the range of 0–3.0 g VS per L per day, the pH was within the range of 7.6–8.0, and the additive concentration was more than 0.5 g per L per day. This study proposed a novel additive and determined its usage strategy for regulating the AD of FW through experimental and simulation approaches.",
"conclusion": "4. Conclusion In this study, we synthesized the additives for promoting the AD of FW. When the OLR was 3.0 g VS per L per day and the additive concentration was 0.5 g VS per L per day, the AD system was able to operate stably, with a methane yield of 263 ± 22 mL per g VS. In addition, the additives regulated the ammonia nitrogen concentration in high OLR systems to not exceed 2500 mg L −1 . This concentration alleviated the inhibition of methanogenesis. The microbial analysis showed that acetoclastic methanogens, Methanosaetaceae , was the dominant archaea. Through ML analysis, when the OLR was within the range of 0–3.0 g VS per L per day, the pH was within the range of 7.6–8.0, and the additive concentration was more than 0.5 g per L per day, the optimal daily methane yield would be achieved.",
"introduction": "1. Introduction In 2020, the total amount of municipal solid waste (MSW) will be 254.79 million tons in China, with a continuous growth rate of 5–6%. 1 Among them, food waste (FW) is the main source of MSW. 2 At present, the production of FW is steadily increasing. 3 In China, the output of FW accounted for 30–50% of MSW in 2020. 4 To date, common methods that are used to deal with FW in China include landfill and incineration; however, these methods face increasing economic and environmental pressure. 5 Therefore, it is necessary to study how to deal with FW in a harmless, reduced, and resourceful manner to achieve sustainable development. Anaerobic digestion (AD) is a technique that can effectively achieve the volume reduction and resourceful treatment of FW. 1 However, the continuous AD of FW will easily lead to the accumulation of volatile fatty acids (VFA) with an increase in organic loading rate (OLR). The accumulation of propionic/butyric acid is one of the crucial reasons for the acidification of the AD system. Because the oxidation of propionic acid/butyric acid is usually not spontaneous in thermodynamics, it requires hydrogenotrophic methanogens to continuously consume H 2 to maintain a low partial pressure (<10 −4 –10 −5 atm) to make propionic acid/butyric acid oxidation thermodynamically feasible. Therefore, how to promote the metabolism of methanogens is the key to solve the acidification. In 2014, Lovley et al. discovered that Geobacter could directly oxidize ethanol and transfer the electrons through conductive mycelium 6 or c-type cytochromes 7 to Metanothrix 8 or Metanosarcina . 9 These methanogens receive electrons and then reduce CO 2 to CH 4 . Compared to traditional methanogenesis, the direct interspecies electron transfer (DIET) pathway for methanogenesis has the following potential advantages: (1) complex organic matter can be directly metabolized into methane by DIET without hydrolysis and acidification. (2) Electron transfer does not require the diffusion of H 2 , thus overcoming the thermodynamic limitations of converting organic compounds into acids. According to reports, exogenous materials can enhance AD by promoting electron transfer. 10 For instance, Cruz Viggi et al. found that conductive magnetite accelerated the conversion of propionic acid to methane in AD. In particular, previous studies have added conductive carbon cloth to high OLR AD systems (ethanol as substrate) to prevent acid accumulation. 11 The results showed that when the influent water was pH 5.0, the control group almost did not produce methane. In contrast, the methane production rate of the AD with carbon cloth was maintained at a high level (200 mL per day). When the pH is readjusted to 7.0, the performance of the AD with carbon cloth can be recovered to 85% of that before the pH adjustment, while the control group is only 23–33%. These results indicated that conductive materials can enhance the acid shock load of the AD system and accelerate the recovery to steady state by promoting DIET methanogenesis. In addition, FW has a high proportion of protein. High nitrogen-containing substances will release a large amount of ammonia nitrogen or free ammonia during AD, which can lead to damage to the microbial cell, pH imbalance, and enzyme inactivation. Some research studies have found that conductive materials, such as biochar 12 and magnetite, 13 can strengthen the AD to resist the impact of high-concentration ammonia nitrogen or free ammonia. Currently, most of these conductive materials were either derived from waste conversion or extracted to control costs. Consequently, it was often challenging to control the properties of the resulting products. Further modifications increased the complexity of the process and production costs. Based on the above considerations, this study developed novel low-cost and orientation-control additives. A semicontinuous AD experiment was conducted to test the influence of the additives on the methane yield, VFA formation, and microbial structure in the AD of FW. In addition, machine learning (ML) has become an effective analytical tool for analyzing AD processes. 14 The ML model constructed by artificial neural network (ANNs) and random forest (RF) is able to predict the methane output without understanding the process mechanism. 15–17 Therefore, we conducted an in-depth exploration of the data through the ML model to determine the key parameters that have a significant impact on AD. Finally, the range values of these parameters were predicted using the model. These studies will provide novel additives with practical application prospects for the AD of FW, and provide reference for parameter optimization methods.",
"discussion": "3. Results and discussion 3.1 Characterization of the additives To investigate the effect of the material structure on the anaerobic microorganisms, the morphology and structure of the additives were analyzed using a scanning electron microscope (SEM). Fig. S1 † shows that the additives exhibit an irregular block-like structure with many edges and pore structures on the surface. Many flocculent agglomerated particles are attached to the surface, which may be due to the metal material adhering to the porous material. These particle structures have increased the surface area of the original material. The agglomerated particles and pore structures provide a suitable environment for the attachment and growth of microorganisms. 26 The nitrogen adsorption–desorption isotherm curves of the metal material and the additives are shown in Fig. S2. † The nitrogen adsorption–desorption isotherms of the metal material and the additives are type IV, indicating that the additives appear as vacancy condensation, and it can be inferred that they possess many mesopores. 27 Table S1 † provides the pore structure parameters of different samples. Compared with the metal material, the additives' specific surface area, pore capacity, and average pore size have increased. The specific surface area of the additives was 5.34 times greater than that of the metal material. This phenomenon is mainly due to mixing the metal material with the porous material and adhesive. In addition, the porous material, as a ESI, † increases the specific surface area, pore capacity, and pore size of the original material. In brief, the larger specific surface area can attach more methanogens, which is conducive for enhancing the DIET process of AD. 28 Furthermore, XPS was used to analyze the elemental valence and chemical groups on the surfaces of the metal materials and the additives. 29 The results showed that Fe, Al, Si, and O are the main elements for composition. Fig. S3(a) † shows that the full scan spectrum of the metal material exhibits the characteristic peaks of Fe 2p and O 1s, indicating the formation of iron oxides on the surface. Fig. S3(b) † shows the Fe 2p spectrum of the metal material. There are two peaks at the binding energy of 710.7 eV and 724.2 eV, representing Fe 2p 3/2 and Fe 2p 1/2 , respectively. 30 This indicates that the surface of the metal material is mainly composed of Fe 2 O 3 . The full scan spectrum (Fig. S3(c) † ) of the additives shows the characteristic peaks of Fe 2p, Al 2p, Si 2p, and O 1s, indicating the formation of iron oxide, aluminum oxide, and silicon oxide on the surface. Fig. S3(d) † is the Fe 2p spectrum of the additives. There are two peaks at the binding energy of 710.6 eV (Fe 2p 3/2 ) and 723.8 eV (Fe 2p 1/2 ), indicating that FeO exists on the surface of the additives. The peaks present at 712.7 eV and 725.9 eV were confirmed as Fe 2p 3/2 and Fe 2p 1/2 , respectively, indicating the presence of Fe 2 O 3 on the surface of the additives. These results indicate that the additive surface mainly exists in the form of FeO and Fe 2 O 3 . In addition, the crystal structure and phase composition of the additives were detected via XRD (Fig. S4 † ). XRD diffraction shows that the representative peak of the additives is Fe, and no iron oxide has been detected. 31 The above analysis indicated that the metal material reacted with oxygen in the air or with porous material and adhesive during the synthesis process of the additives, resulting in the formation of iron oxides on the surface. However, not all reactions occur internally, thus retaining the elemental iron. Ultimately, there are iron oxides on the surface of the additives, while the main component inside is elemental iron. 3.2 The effect of the additives on the biogas yield When the OLR is 1.0, 2.0, 3.0, 4.0, and 5.0 g VS per L per day, the biogas and methane yield of AD in each group is shown in Fig. 1(a) and (c) , and the average biogas and methane yield at the stable stage is shown in Fig. 1(b)–(d) and Table 2 . When the OLR was 1.0–2.0 g VS per L per day, the biogas and methane yield of each group decreased, indicating that the activity of the microorganism varied with changes in the OLR. However, as the OLR increases to 2.0–3.0 g VS per L per day, the biogas and methane yield increases, which means that the microorganisms adapt to higher OLR. 32 Subsequently, when the OLR continued to increase to 4.0 g VS per L per day, the biogas and methane yield of A2 and A3 decreased, but remained higher than “No additives” and “A1”. At this time, the additives still had a promoting effect. The promoting effect may be attributed to the porous structure and surface functional groups within, which facilitated DIET. Related research found that Fe 2 O 3 plays a positive role in promoting this process. 33 When the OLR increased from 4.0 to 5.0 g VS per L per day, and the biogas and methane yield of all groups rapidly decreased. With the increased OLR, the methanogens were inhibited, which may be attributed to the excessive accumulation of VFA. 34 Statistical analysis showed ( Fig. 2 ) that there was significant difference in the methane yield between A2 and A3 ( p < 0.05) when the OLR was 3.0 g VS per L per day and 4.0 g VS per L per day ( Fig. 2(c) and (d) ). When the OLR was 3.0 g VS per L per day, the methane yield of A2 and A3 is higher than 4.0 g VS per L per day ( Fig. 1(d) ). At 3.0 g VS per L per day, there was a significant difference between the methane yield of each group ( p < 0.05) ( Fig. 2(g) ). A3 has the highest methane yield at 289 ± 26 mL per g VS ( Table 2 ), which is 9.9% higher than that for A2. However, the additive concentration of A2 was only 50% of that of A3. We considered that 3.0 g VS per L per day OLR and 0.5 g VS per L per day additives were the optimal conditions for economy and maintaining high methane yield. Fig. 1 (a) Daily biogas and (c) methane yields; (b) average biogas and (d) methane yield of AD in steady-state under different OLR. Average biogas and methane yield of AD in steady-state under different OLR OLR (g VS per L per day) 1 2 3 4 5 Biogas yield (mL per g VS) No additives 217 ± 26 121 ± 21 175 ± 26 253 ± 31 21 ± 21 A1 227 ± 36 136 ± 20 308 ± 28 351 ± 22 59 ± 11 A2 323 ± 68 207 ± 9 498 ± 46 425 ± 30 196 ± 28 A3 394 ± 55 234 ± 8 551 ± 51 462 ± 17 209 ± 39 Methane yield (mL per g VS) No additives 118 ± 15 62 ± 11 74 ± 11 97 ± 14 1 ± 1 A1 129 ± 20 71 ± 10 151 ± 12 176 ± 13 4 ± 1 A2 181 ± 39 111 ± 6 263 ± 22 229 ± 19 98 ± 25 A3 223 ± 31 126 ± 6 289 ± 26 252 ± 11 119 ± 28 Fig. 2 Significant differences between the different OLR (a–d) and the additive concentrations (e–i). 3.3 The effect of the additives on VFA, conductivity, and ammonia nitrogen We evaluated the effect of the additives on the AD performance under different OLR and additive concentrations ( Fig. 3 and Table 3 ). When the OLR increased from 1.0 VS per L per day to 4.0 g VS per L per day, VFA remained below 3000 mg L −1 . At this point, AD can continue to run under this load. 35 When the OLR increased from 4.0 g VS per L per day to 5.0 g VS per L per day, the VFA of each group exceeded 3000 mg L −1 , and the methane yield declined ( Fig. 1(d) ). It showed that the methanogen was inhibited. At 3.0 g VS per L per day OLR, the VFA and acetic acid contents of A2 and A3 were higher than the groups without the additives. The higher VFA content indicates that the additive promotes hydrolysis and acidification. From 1.0 g VS per L per day to 4.0 g VS per L per day, the pH improved from 7.5 to 7.7 in A2 and A3, and it remained at 7.5 without additives ( Fig. 3(e) and Table 3 ). Fig. 3 Variation of VFA, acetic acid, pH, conductivity and ammonia nitrogen. (a) Daily VFA, (c) acetic acid, (d) pH, (f) conductivity, and (h) ammonia nitrogen; (b) average VFA, (e) pH, (g) conductivity, and (i) ammonia nitrogen of AD in steady-state under different OLR. Average VFA, acetic acid, pH, conductivity, and ammonia nitrogen of AD in steady-state under different OLR OLR (g VS per L per day) 1 2 3 4 5 VFA (mg L −1 ) No additives 2692 ± 225 2023 ± 713 1240 ± 99 1493 ± 54 5155 ± 1148 A1 2666 ± 202 2462 ± 853 1349 ± 215 1624 ± 132 4150 ± 847 A2 2495 ± 556 2450 ± 761 1898 ± 277 1720 ± 162 4137 ± 849 A3 2420 ± 270 2574 ± 603 1830 ± 315 1792 ± 127 4481 ± 1300 Acetic acid (mg L −1 ) No additives 184 ± 21 243 ± 74 449 ± 77 812 ± 35 2585 ± 569 A1 185 ± 24 324 ± 78 515 ± 151 900 ± 68 1853 ± 272 A2 175 ± 37 373 ± 123 831 ± 189 962 ± 75 1882 ± 258 A3 166 ± 24 385 ± 115 773 ± 227 1011 ± 67 2051 ± 410 pH No additives 7.45 ± 0.04 7.43 ± 0.05 7.53 ± 0.04 7.51 ± 0.02 4.78 ± 0.22 A1 7.48 ± 0.04 7.43 ± 0.07 7.55 ± 0.03 7.61 ± 0.02 5.40 ± 0.32 A2 7.47 ± 0.04 7.46 ± 0.09 7.55 ± 0.06 7.72 ± 0.06 5.84 ± 0.29 A3 7.49 ± 0.04 7.46 ± 0.09 7.56 ± 0.06 7.75 ± 0.05 5.76 ± 0.28 Conductivity (mS cm −1 ) No additives 36.35 ± 1.03 36.70 ± 0.94 35.67 ± 1.34 35.47 ± 0.30 32.68 ± 1.48 A1 36.50 ± 1.03 37.08 ± 0.88 36.14 ± 1.42 37.31 ± 0.25 29.90 ± 0.53 A2 37.13 ± 0.81 37.33 ± 1.06 37.80 ± 1.49 38.06 ± 0.49 36.86 ± 0.58 A3 37.40 ± 0.94 37.50 ± 0.97 37.64 ± 1.62 38.71 ± 0.52 37.30 ± 0.51 Ammonia nitrogen (mg L −1 ) No additives 2115 ± 92 2378 ± 32 2505 ± 30 2585 ± 24 2780 ± 198 A1 2080 ± 99 2340 ± 28 2423 ± 22 2464 ± 24 3220 ± 707 A2 2020 ± 170 2281 ± 1 2418 ± 46 2476 ± 38 4210 ± 495 A3 2030 ± 28 2243 ± 4 2405 ± 64 2468 ± 27 3310 ± 354 Digestate with higher pH values can be used as natural buffers. 36 Therefore, there was no inhibition at OLR 1.0–4.0 g VS per L per day. With the OLR increased from 4.0 g VS per L per day to 5.0 g VS per L per day, the pH value of each group sharply decreased to below 6.0. The decrease in pH indicated the accumulation of VFA ( Fig. 3(a)–(c) ), which inhibited the methanogenesis and led to the decrease in methane yield ( Fig. 1(d) ). 37 At OLR 4.0 g VS per L per day, the conductivity of the A1, A2, and A3 groups was higher than that of the group without the additives. The A3 group was provided with the highest conductivity and methane yield. These results indicated that the conductivity and methane yield increased with increasing additive concentration. The reason for the increased electrical conductivity was that the addition of iron elements resulted in the composite material having a certain degree of conductivity. As mentioned earlier, the XPS results showed the presence of elemental iron in the interior of the additive, and iron oxide on the surface. The increase in methane yield may be attributed to more electrons being used for methanogenesis through DIET. 38 The OLR varied from 1.0 g VS per L per day to 4.0 g VS per L per day, and the concentration of ammonia nitrogen in all groups was maintained below 2500 mg L −1 ( Fig. 3(h), (i) and Table 3 ). Upon increasing from 4.0 g VS per L per day to 5.0 g VS per L per day, the concentrations of ammonia nitrogen in A1, A2, and A3 increased rapidly, exceeding 3000 mg L −1 . At this time, the methane yields significantly decreased. Previous studies have shown that the high ammonia nitrogen concentration inhibited the performance of the methanogens. 39 In summary, the potential promoting effect of the additives increases the OLR to 4.0 g VS per L per day in AD. 3.4 Microbial community analysis \n Fig. 4(a) shows the composition of the methanogen in each group under different OLR. The main archaea in each group were Metanosarcinales and Metanomicrobiales , among which Metanosarcinales was the dominant archaea. When the OLR was below 2.0 g VS per L per day, the composition of archaea in each group was consistent, and the abundance of Methanosarcinales in the group with the additives was slightly higher than that without. At OLR 3.0 g VS per L per day, the abundance of Methanosarcinales in the “no additives”, A1, A2, and A3 groups was 91.31%, 91.49%, 92.26%, and 93.35%, respectively. The abundance of Methanosarcinales in each group increased with increasing additive concentration. At 4.0 g VS per L per day, the abundance of Methanosarcinales in the “no additives”, A1, A2, and A3 groups was 62.24%, 64.83%, 80.83%, and 88.07%, respectively. At 5.0 g VS per L per day, the abundance of Methanosarcinales in the “no additives”, A1, A2, and A3 groups was 98.09%, 95.84%, 86.55%, and 68.39%, respectively. When the OLR increased from 1.0 g VS per L per day to 4.0 g VS per L per day, the abundance of Methanosarcinales in each group decreased to a certain extent with the increase of OLR, although the trend of decline was most evident in the group without the additives. In 2014, Metanosarcina was confirmed to be able to accept electrons and reduce CO 2 to CH 4 through DIET. 9 Furthermore, it can use acetic acid or H 2 /CO 2 to produce CH 4 . In high-load AD systems, it is usually the dominant archaea community. 40 Due to the increased abundance of Methanosarcinales , the additives have the potential to significantly promote DIET in the AD system. Furthermore, additives with mesoporous structures (Fig. S2 † ) can provide a suitable habitat for microorganisms, which was one of the significant reasons for enhancing the abundance of the dominant species. 28 Fig. 4 Compositions of methanogen at the (a) order and (b) family levels under different OLR and the additive concentrations. \n Fig. 4(b) shows the composition of methanogens in each group at the family level under different OLR. The main archaea were Methanosaetaceae and Methanomicrobiaceae, with Methanosaetaceae being the dominant archaea. Methanosaetaceae is one of the two known acetoclastic methanogens. 41 The community structure suggested that the acetoclastic methanogenesis pathway was the main pathway in semicontinuous AD. When OLR was 3.0 g VS per L per day, the abundance of Methanosaetaceae in the groups “no additives”, A1, A2, and A3 was 81.24%, 85.42%, 92.14%, and 93.25%, respectively. The abundance of Methanosaetaceae in each group increased with the additive concentration. At 4.0 g VS per L per day, the abundance of Methanosaetaceae in the groups “No additives”, A1, A2, and A3 was 62.21%, 64.76%, 80.57% and 88.02%, respectively. At 5.0 g VS per L per day, the abundance of Methanosaetaceae in the groups “No additives”, A1, A2 and A3 was 98.09%, 95.84%, 86.55%, and 68.39%, respectively. The abundance of Methanosaetaceae was the highest in the “no additives” group and the lowest in the A3 group. This phenomenon may be due to the rapid increase in VFA when the OLR increases to 5.0 g VS per L per day. At this point, the AD system is already in an unstable state. In summary, at OLR 1.0–4.0 g VS per L per day, the additives maintained the activity of acetoclastic methanogens with the increased OLR, thereby promoting the conversion of acetic acid to methane. The higher the concentration of the additives, the more pronounced the effect. 3.5 ML analysis The correlation between the data was preliminarily explored through PCC. The results showed that the pH value and additive concentration were positively correlated with the methane yield. In contrast, the OLR, VFA, acetic acid, and ammonia nitrogen were negatively correlated with the methane yield ( Fig. 5(a) ). The PCC can find the linear correlation between the data. However, it cannot perform a nonlinear analysis of variables or deeper data relationship mining. Therefore, two ML algorithms, RF and ANN, which are most widely used in AD, are used to predict and analyze the experimental data. 14 Fig. 5 Plot of Pearson's coefficient of variables (a). ML prediction plot (b and c). Feature importance plot (d and e). 1D partial correlation plot (f–h). 2D partial correlation (i and j). The best combination of RF and ANN was obtained by RandomSearchCV. 42 The RF hyperparameter combination is n _estimators = 314, max_depth = 12, and the ANN hyperparameter combination is hidden_layer_sizes = (100 100), solver = “lbfgs”, and activation = ‘relu’. For the prediction of the methane yield, the results show that although the fitting degree of RF in the training set is slightly lower than that of ANN, the fitting degree of RF in the test set is much higher than that of ANN, which may be due to the partial overfitting of ANN. Therefore, compared with the ANN model, RF has superior prediction and generalization performance (higher R 2 and lower RMSE) ( Fig. 5(b) and (c) ). A comparison of the predicted and actual values for each target, based on the RF model, is shown in Fig. 5(a) and (b) . The predicted and actual values are almost always concentrated on the parity line (function y = x ), with closer points to the parity line indicating higher prediction accuracy. The confidence of the training and test data is set at 95%. The probability that the value is in the purple or blue area is high. The smaller the confidence interval area, the lower the data value uncertainty. In addition, the training set has a higher accuracy and density of distribution around the dichotomous line than the test set. This indicates that the predictions of the training set tend to outperform those of the test set due to the inherent characteristics of the ML algorithm. Based on the best RF model, the importance of the input characteristics on the methane yield was investigated using the importance analysis method of the self-contained model characteristics and the SHAP method. The importance ranking of the input features generated by the two feature analysis methods showed highly similar results ( Fig. 5(d) and (e) ). The pH and additive concentration are two key characteristics. The hydrolysis and acidification of organic matter is a crucial limiting step in the AD process, and the composition of VFA in the AD process is mainly affected by pH. 43,44 The additive concentration may mainly affect the abundance of microorganisms, and thus affect the methane yield. As one of the most critical control conditions of the experiment, OLR does not play a significant role in anticipating the methane yield. The relationship between OLR and the methane yield may be explored through partial correlation since OLR is at a fixed state in much of the data, and its range of fluctuation is not as dramatic as other features. Therefore, the relationship between OLR and the methane yield was explored through partial correlation. A one-dimensional partial correlation was carried out using the best RF model to establish the changing relationship between the input variables and methane yield ( Fig. 5(f)–(h) ). When the pH exceeds 7.6, the maximum promotion effect on the methane yield is achieved. When the pH is 7.5 and below, the promotion effect on the methane yield will sharply drop. This may be because the free VFA can cross the cell membrane of the microorganisms, and dissociate within the cell when the pH is low. The H + and ionized VFA acidify the cytoplasm and weaken the cell activity, affecting the efficiency of the methane yield. The inhibition of VFA was attributed to the concentration of free VFA, H + , and ionized VFA in the system. 45 When the additive concentration is 0–1.5 g, the methane yield rises sharply with increasing additive concentration. It is possible that as the abundance of the microorganisms is affected by the increase in the additive concentration, the increased activity of the acetic acid-consuming methanogenic bacteria promotes the conversion of acetic acid to methane, which in turn increases the methane yield. However, when it is more than 1.5 g, the additive concentration has little impact on the methane yield and gradually enters a stable stage. When the OLR increases, the methane yield gradually decreases. This is probably due to the fast VFA production rate and high nitrogen content of FW, which makes it susceptible to the suppression of VFA or ammonia nitrogen in AD. Therefore, to avoid the inhibition of metabolites such as VFA or ammonia nitrogen, the usual AD reactor can only be operated at a low OLR. Two-dimensional partial correlation analysis was carried out on the optimal RF model to determine the optimal variable range and its combination. When the pH is between 7.6 and 8.0 and the additive concentration is more than 1.2 g, the maximum promotion effect on the methane yield can be achieved ( Fig. 5(i) ). When the OLR is between 0 and 3.0 g VS per L per day and the additive concentration is more than 1.1 g, the methane yield can be improved. The ML analysis showed that the experiment achieves optimal daily methane yield in the OLR range of 0–3.0 g VS per L per day, a pH range of 7.6–8.0, and an additive quality of more than 1.2 g (0.5 g per L per day)."
} | 6,834 |
24995002 | PMC4061905 | pmc | 6,773 | {
"abstract": "The microbial conversion of solid cellulosic biomass to liquid biofuels may provide a renewable energy source for transportation fuels. Cellulolytic fungi represent a promising group of organisms, as they have evolved complex systems for adaptation to their natural habitat. The filamentous fungus Myceliophthora thermophila constitutes an exceptionally powerful cellulolytic microorganism that synthesizes a complete set of enzymes necessary for the breakdown of plant cell wall. The genome of this fungus has been recently sequenced and annotated, allowing systematic examination and identification of enzymes required for the degradation of lignocellulosic biomass. The genomic analysis revealed the existence of an expanded enzymatic repertoire including numerous cellulases, hemicellulases, and enzymes with auxiliary activities, covering the most of the recognized CAZy families. Most of them were predicted to possess a secretion signal and undergo through post-translational glycosylation modifications. These data offer a better understanding of activities embedded in fungal lignocellulose decomposition mechanisms and suggest that M. thermophila could be made usable as an industrial production host for cellulolytic and hemicellulolytic enzymes.",
"conclusion": "Conclusions Rapid depolymerization of lignocellulosic material is a distinguishing feature of thermophilic fungi, such as M. thermophila , which was isolated from soil and self-heating masses of composted vegetable matter (Domsch et al., 1993 ). However, the precise biochemical mechanisms and underlying genetics of this procedure are not completely understood. Systematic examination of the M. thermophila genome revealed a unique enzymatic system comprising of an unusual repertoire of auxiliary enzymes, especially those classified to AA9 family, and provided insights into its extraordinary capacity for protein secretion. The current review constitutes, to the best of our knowledge, the first genomic analysis of the lignocellulolytic system of M. thermophila . The genomic data, along with the observed enzymatic activity of several isolated and characterized enzymes suggest that this fungus possesses a complete set of enzymes, including 30 cellulases, 66 hemicellulases, and 35 proteins with auxiliary auxiliary enzymes, covering the most of the recognized CAZy families. From its cellulases to its oxido-reductases and multicopper enzymes, M. thermophila gene complement represents several avenues for further research and its diverse array of enzymatic capabilities will contribute to the study of lignocellulose degradation and the subsequent ethanol biofuel production. Conflict of interest statement The Review Editor Ulrika Rova declares that, despite being affiliated to the same institution as authors Anthi Karnaouri, Io Antonopoulou, and Paul Christakopoulos, the review process was handled objectively and no conflict of interest exists. 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 Ethanol production from lignocellulosic biomass, comprised primarily of cellulose and hemicellulose, appears to evolve as one of the most important technologies for sustainable development. Given its renewable nature, biomass is a potential raw material not only for the production of biofuels, but also chemicals, energy and other materials of main industrial interest (Zhang et al., 2006 ). The monosaccharides contained in the cellulosic (glucose) and hemicellulosic fractions (xylose, arabinose, mannose, and galactose) represent substrates that can be used for ethanol production via fermentation. To initiate the degradation of these fractions, it is necessary to overcome the physical and chemical barriers presented by the cohesive combination of the main biomass components, which hinders the hydrolysis of cellulose and hemicellulose into fermentable sugars. The above include high substrate viscosity, poor mass transfer conditions and long reaction times, during which hydrolysis reactors are susceptible to contamination. Fungi are the main decomposers of lignocellulosic biomass in terrestrial ecosystems and the enzymes they secrete to break down lignocellulose may be useful in industrial processes. Thermophilic fungi provide a potential source of plant cell wall degrading enzymes with higher levels of specific activity and better stability at higher temperatures, thus making it feasible to minimize the hydrolysis time, reduce substrate viscosity and contamination levels (Margaritis and Merchant, 1986 ). Myceliophthora thermophila (synonym Sporotrichum thermophile ) is a thermophilic filamentous fungus, classified as an ascomycete, which was isolated from soil in eastern Russia and constitutes an exceptionally powerful cellulolytic organism, which synthesizes a complete set of enzymes necessary for the breakdown of cellulose. The growth rate and cell density of this microorganism appear to be similar in media containing cellulose or glucose (Bhat and Maheshwari, 1987 ). The 38.7 Mbp genome of M. thermophila , comprising about 9500 genes, organized in 7 chromosomes, has been sequenced and annotated (Joint Genome Institute, University of California, http://genome.jgi-psf.org ; Berka et al., 2011 ). It revealed a large number of genes putatively encoding industrially important enzymes, such as carbohydrate-active enzymes (CAZy), proteases, oxido-reductases, and lipases, while more than 200 sequences have been identified exclusively for plant cell-wall-degrading enzymes. These sequences encode a large number of glycoside hydrolases (GH) and polysaccharide lyases, covering the most of the recognized families (Table 1 ). In addition, M. thermophila was developed into a proprietary mature enzyme production system with easy scaling (C1 strain; Visser et al., 2011 ). The main features of C1 are the high production levels (up to 100 g/L protein), as well as the maintenance of low viscosity levels of the culture medium, thus enabling fermentation process to reach very high densities. Table 1 Number of predicted CAZymes encoded in the genome of M. thermophila . Specific activity CAZy module(s) No. id. seq . Cellulases Endoglucanases GH 5, 7, 12, 45 8 Cellobiohydrolases GH 6, 7 7 β-glucosidases GH 1, 3 8 LPMOs AA9 25 Xylanases Xylanases GH 10, 11 12 Xylosidases GH 3, 43 4 Arabinases Endoarabinases GH 43 3 Exo-arabinases/ arabinofuranosidases GH 43, 51, 62 11 Mannanases Endomannanases GH 5, 26 3 Mannosidases GH 2 2 Pectinases Polygalacturonases GH28 2 Rhamnosidases GH78 1 Pectin lyases PL 1, 3, 4, 20 8 Pectin esterases CE 8, 12 4 Esterases Feruloyl esterases CE 1 4 Acetyl esterases CE 3, 5, 16 8 Acetylmannan esterases CE 12 2 Glycuronoyl esterases CE 15 2 GHs, Glycoside hydrolases; CEs, carbohydrate esterases; and PLs, polysaccharide lyases are included, covering the most of the recognized families. M. thermophila exhibits an impressing number of accessory enzymes belonging to AA9 (previously described as GH61) and family 1 carbohydrate binding modules (CBM), which are the highest found in fungi (Berka et al., 2011 ). Family 1 CBM presents a cellulose-binding function and is almost exclusively found in enzymes of fungal origin ( http://www.cazy.org ; Guillén et al., 2009 ). In addition, M. thermophila distinguishes itself from other cellulolytic fungi, such as Aspergillus niger and Trichoderma reesei by the presence of a relatively high number of (glucurono) arabinoxylan degrading enzymes (Hinz et al., 2009 ). Eleven putative xylanases were found that belong into GH 10 and 11 families compared to five in both A. niger (Broad Institute of Harvard and MIT, http://www.broadinstitute.org ) and T. reesei (Joint Genome Institute, University of California, http://genome.jgi-psf.org ), while 14 arabinofuranosidases belonging to GH 43, 51, and 62 families were found compared to 13 in A. niger and three in T. reesei , rendering M. thermophila a promising source of hemicellulolytic enzymes. Studying the secretome of M. thermophila after 30 h of growth in barley and alfalfa straws, it was found to comprise of 683 predicted proteins, 230 of which are proteins with unknown function (Berka et al., 2011 ). Based on transcriptome analysis, many secreted enzymes including accessory enzymes, hypothetical proteins and proteins with unknown function were upregulated, when the fungus is grown in more complex substrates, such as agricultural straws, compared to glucose, indicating their crucial role in lignocellulose degradation (Berka et al., 2011 ). M. thermophila grows in temperatures between 25 and 55°C, while a relative growth performance study on mycobroth agar plates indicated that the optimum condition is at 45°C (Morgenstern et al., 2012 ). The temperature optima for several enzymes with the same specific activity, characterized from M. thermophila , range from 50 to 70°C. For example, StEG5 endoglucanase, expressed in A. niger , exhibits a T opt of 70°C (Tambor et al., 2012 ), while recombinant MtEG7 expressed in Pichia pastoris exhibited an optimal temperature of 60°C (Karnaouri et al., 2014 ). The same characteristic is also observed for M. thermophila xylanases expressed in A. niger , showing optimal activity at temperatures between 50 and 70°C (Berka et al., 2011 ), underpinning the enzymatic potential that is not only diverse in catalytic activities, but also in properties increasing its efficiency in various temperatures. Individual cellulolytic enzymes exhibit comparable activities on cellulose; however, synthetically composed multienzyme mixtures display a much higher performance than those from other lignocellulolytic thermostable fungi (Szijártó et al., 2011 ; Zhang et al., 2013 ). This can be attributed to synergistic mode of action between the enzymes. For example, synergism between GH 11 xylanase and type C feruloyl esterase has been proved (Moukouli et al., 2010 ), as well as between cellobiohydrolases acting on the reducing and the non-reducing end of cellulose molecules (Gusakov et al., 2007 ). In this review, an overview will be given of the cellulolytic and hemicellulolytic potential of M. thermophila regarding the degradation of plant cell wall material. The genomic potential of this thermophilic fungus demonstrates a strong enzymatic toolbox including hydrolytic, oxidative and accessory activities that may enhance its ability to decompose plant biomass. Many of these enzymes have been isolated from culture supernatant or selectively overexpressed in M. thermophila (C1 strain) or in other heterologous hosts and have been characterized. All sequences used in this study were extracted from Genome Portal database ( http://genome.jgi-psf.org ) and the continually updated CAZy database ( http://www.cazy.org/ ; Lombard et al., 2014 ). The conserved domains were found with Pfam/InterProscan ( http://pfam.sanger.ac.uk/ ; Punta et al., 2012 ), while the theoretical molecular mass and isoelectric point for each protein were calculated using the ProtParam tool of ExPASY ( http://web.expasy.org/protparam/ ). Post-translational glycosylation sites were predicted with NetNGlyc 1.0 server ( http://www.cbs.dtu.dk/services/NetNGlyc/ ) and NetOGlyc 3.1 server ( http://www.cbs.dtu.dk/services/NetOGlyc/ ). Predicted secretome was extracted using SignalP v4.0 ( http://www.cbs.dtu.dk/services/SignalP/ )."
} | 2,876 |
35126327 | PMC8811301 | pmc | 6,777 | {
"abstract": "The anaerobic ammonium oxidation (anammox) by autotrophic anaerobic ammonia-oxidizing bacteria (AnAOB) is a biological process used to remove reactive nitrogen from wastewater. It has been repeatedly reported that elevated nitrite concentrations can severely inhibit the growth of AnAOB, which renders the anammox process challenging for industrial-scale applications. Both denitrifying (DN) and dissimilatory nitrate reduction to ammonium (DNRA) bacteria can potentially consume excess nitrite in an anammox system to prevent its inhibitory effect on AnAOB. However, metabolic interactions among DN, DNRA, and AnAOB bacteria under elevated nitrite conditions remain to be elucidated at metabolic resolutions. In this study, a laboratory-scale anammox bioreactor was used to conduct an investigation of the microbial shift and functional interactions of AnAOB, DN, and DNRA bacteria during a long-term nitrite inhibition to eventual self-recovery episode. The relative abundance of AnAOB first decreased due to high nitrite concentration, which lowered the system’s nitrogen removal efficiency, but then recovered automatically without any external interference. Based on the relative abundance variations of genomes in the inhibition, adaptation, and recovery periods, we found that DN and DNRA bacteria could be divided into three niche groups: type I (types Ia and Ib) that includes mainly DN bacteria and type II and type III that include primarily DNRA bacteria. Type Ia and type II bacteria outcompeted other bacteria in the inhibition and adaptation periods, respectively. They were recognized as potential nitrite scavengers at high nitrite concentrations, contributing to stabilizing the nitrite concentration and the eventual recovery of the anammox system. These findings shed light on the potential engineering solutions to maintain a robust and efficient industrial-scale anammox process.",
"conclusion": "Conclusion In this study, a laboratory-scale anammox bioreactor was used to conduct an investigation of the microbial shift and functional interactions of AnAOB, DN, and DNRA bacteria during a long-term nitrite inhibition to eventual self-recovery episode. The relative abundance of AnAOB first decreased due to high nitrite concentration, which lowered the system’s nitrogen removal efficiency but then recovered automatically without any external interference. By analyzing the relationship between the relative abundances of MAGs and their nitrogen metabolism pathways, we found that type Ia (mainly DN) and type II (mainly DNRA) bacteria outcompeted other bacteria in the inhibition and adaptation periods, respectively. They were capable of living under high nitrite concentration conditions and potentially served as a nitrite scavenger that could trigger the recovery of AnAOB from the nitrite inhibition. Our results provide a possible mechanistic explanation for the performance shift of the anammox bioreactor during a long-term nitrite inhibition to the eventual self-recovery episode and advance the stable control of this promising technology.",
"introduction": "Introduction Anaerobic ammonium oxidization (anammox) has gained significant momentum as a highly efficient, cost-effective, and environment-friendly biological nitrogen removal process compared with the conventional nitrification–denitrification processes ( Ismail et al., 2019 ). Anammox is mediated by the autotrophic anaerobic ammonia-oxidizing bacteria (AnAOB), which oxidize ammonium (NH 4 + ) using nitrite (NO 2 – ) as an electron acceptor under anaerobic conditions and produce nitrogen gas (N 2 ) and nitrate (NO 3 – ) ( Bonassa et al., 2021 ). The anammox process has primarily been used to treat ammonium-rich wastewater ( Alejandro et al., 2018 ), such as anaerobic digestion liquid of sludge, landfill leachate, urban domestic sewage, swine wastewater, or monosodium glutamate wastewater. Due to the extremely high ammonia nitrogen content (800–3,000 mg/L) in these types of wastewater, a preliminary step before the implementation of the anammox process is to oxidize approximately half of the ammonium into nitrite, known as partial nitritation, by ammonium oxidizing bacteria in the wastewater ( Li et al., 2019 ). However, the nitritation process is difficult to control, often resulting in an overproduction of nitrite ( Tao et al., 2012 ), leading to the inhibition of the anammox process. This possible inhibitory effect makes controlling nitrite concentration a focus in implementing the anammox process. The inhibitory nitrite concentrations reported vary from 5 to 750 mg N L –1 ( Zekker et al., 2017 ; Cho et al., 2020 ) in various anammox-based systems. Although the broad range of inhibitory nitrite concentrations can be attributed to variations in experimental conditions and operating modes (pH, temperature, experimental continuity, etc.), it implies that a mechanistic understanding of nitrite inhibition and nitrite resistance of the anammox microbial communities remains to be elucidated. Furthermore, due to the slow growth of AnAOB ( Wang et al., 2016 ), the recovery from inhibition/death caused by prolonged high nitrite shock was proved to be difficult ( van der Star et al., 2007 ) and puzzling even when a successful recovery did happen ( Lotti et al., 2012 ). Denitrifying (DN) and the dissimilatory nitrate reduction to ammonium (DNRA) bacteria can metabolize nitrite and potentially promote the recovery process of the anammox system from the nitrite shock. AnAOB is a key player in nitrogen cycling, whereas DN and DNRA bacteria are also important participants of the anammox system ( Gonzalez-Gil et al., 2015 ; Keren et al., 2020 ). To date, the vast majority of identified AnAOB belongs to Planctomycetes, including six genera that have been reported so far, i.e., Candidatus ( Ca. ) Brocadia, Ca. Jettenia, Ca. Kuenenia, Ca. Scalindua, Ca. Anammoxoglobus, and Ca. Anammoxomicrobium ( Ibrahim et al., 2016 ). DN and DNRA bacteria are mainly heterotrophs from a variety of phyla, for example, Proteobacteria, Chloroflexi, and Actinobacteria ( Zhao et al., 2019 ; Keren et al., 2020 ). Many studies have pointed out that autotrophic organisms such as AnAOB can release soluble microbial products and extracellular polymeric substances. Environmental stimuli can cause the immediate release of these substances from the autotrophs ( Ma et al., 2012 ; Chu et al., 2015 ; Zhang et al., 2016 ). Heterotrophic bacteria, including DN and DNRA bacteria, can thus live on these substances for survival and cater to important nitrogen cycling processes ( Guo et al., 2016 ; Hou et al., 2017 ; Zhu et al., 2017 ). DN bacteria can reduce NO 3 – and NO 2 – to N 2 ( Zhou et al., 2014 ), whereas DNRA bacteria can reduce them to NH 4 + . The function of DNRA bacteria under high nitrite concentrations was not clear. DN bacteria from Proteobacteria, however, were reported to aid in reducing nitrite to nitrogen gas. In short, the nitrite denitrification genes in DN bacteria had higher abundance after the inhibition phase, and their cooperation could prevent the nitrite inhibition of anammox bacteria when the influent nitrite concentration was higher ( Zhao et al., 2019 ). Thus, the DN/DNRA bacteria can potentially reduce the NO 2 – concentration and then balance the NO 2 – /NH 4 + ratio to create a preferable environment for the anammox process ( Wang et al., 2018 ; Zhao et al., 2019 ). There were a number of studies using amplicon sequencing targeting 16S ribosomal RNA (rRNA) gene to decode the bacterial communities ( Ren et al., 2014 ; Liang et al., 2015 ), whereas others have used shotgun metagenomic approaches to understand functional genomes in anammox systems ( Guo et al., 2016 ; Ciesielski et al., 2018 ; Sun et al., 2018 ; Tang et al., 2018 ). Overall, the metagenomic analysis is more powerful in revealing microbial functions at the genus or species level ( Keren et al., 2020 ; Li et al., 2020 ). To better understand the potential roles of nitrogen removal bacteria in membrane bioreactor (MBR) at high nitrite concentrations, we conducted a long-term nitrite inhibition experiment in MBR by progressively reducing the ammonium loading. Remarkably, an anammox self-recovery event was observed under nitrite inhibition, which has provided a potential solution to mitigate the inhibition of AnAOB in its industrial applications. Both 16S rRNA gene sequencing and genome-resolved metagenomic analysis were implemented to decode the shift of community composition and nitrogen metabolism genes during the nitrite-inhibition and recovery processes in the anammox system. Our finding will enable more stable control of anammox technology and facilitate its widespread application in wastewater treatment plants.",
"discussion": "Discussion In this study, we quickly established a laboratory-scale anammox-based nitrogen removal process using the MBR. Overall, a high and stable nitrogen removal rate had been achieved in the MBR after 51 days of operation and lasted in the stabilization period, indicating that high AnAOB activity had been achieved in the MBR. This coincided with the increase and stabilization of the relative abundance of Planctomycetes, especially the genus Ca. Brocadia. In the inhibition period, the low nitrogen removal efficiency and decreased relative abundance of Ca. Brocadia strongly suggested a disrupted anammox system, which is likely attributed to inhibiting AnAOB by a high nitrite concentration ( Li et al., 2019 ; Lin et al., 2020 ; Pradhan et al., 2020 ). The concentration of the MLVSS has no significant change between the stabilization and inhibition periods, suggesting that the microbial biomass was maintained at a stable level. Therefore, we confer that the relative abundance of the microbial community at different time points was comparable. At the phylum level, the relative abundance of Chloroflexi increased, which is highly reasonable. Chloroflexi bacteria are highly active in protein degradation, which caters to the metabolism of soluble microbial products and extracellular polymeric substances derived from autotrophic organisms such as AnAOB in the MBR ( Kindaichi et al., 2012 ). Besides, Chloroflexi bacteria play an essential role in enhancing the particle structure of the sludge by producing filamentous biomass networks on the sludge flocs and granules ( Kragelund et al., 2011 ; Guo and Zhang, 2012 ; Kindaichi et al., 2012 ). At the genus level, Ignavibacterium was the most abundant except Ca. Brocadia, whose relative abundance variation was also related to the nitrite concentration change. Some Ignavibacterium spp. were previously reported contributing to the nitrogen removal through the nitrite or nitrate pathway ( Yang et al., 2017 ), whereas some other Ignavibacterium spp. were capable of heterotrophic denitrification ( Liu et al., 2012 ; Oren, 2014 ). In the recovery period, we may imply the recovery of AnAOB by the restoration of nitrogen removal efficiency ( Zekker et al., 2015 ; Zhang et al., 2016 ). It is critical to explore such a recovery process because it may directly or indirectly be linked with certain bacteria such as DNRA and DN, which can consume the excess nitrite in the inhibition and recovery period. Therefore, we divided these MAGs into three groups: type I separating into two subgroups: types Ia and Ib (mainly DN bacteria), type II (mainly DNRA bacteria), and type III bacteria (mainly DNRA bacteria) ( Figure 4B ). Based on these three groups, we further analyzed the dynamics of microbial population in the event of anammox inhibition and restoration to dissect the potential functions of nitrogen cycling bacteria. The results show the relative abundance of type Ia bacteria increased rapidly along with nitrite concentration, whereas other bacterial groups increased slowly or decreased in the inhibition period. We infer that type Ia bacteria are better adapted to the high nitrite concentration (60.55 ± 29.20 mg N L –1 ). The nitrogen removal efficiency increased from a minimum of 76–89% from days 170 to 188 in the inhibition period ( Figure 1C ), indicating excess nitrite was consumed. Therefore, type Ia bacteria may serve as pioneers to consume the excess nitrite. Such speculation is supported by a previous study, which revealed DN bacteria from Proteobacteria could aid in reducing nitrite to nitrogen gas. Based on the metagenomic analysis, the nitrite denitrification genes in DN bacteria had higher abundance after the inhibition phase, and their cooperation could prevent the nitrite inhibition of anammox bacteria when the influent nitrite concentration was higher ( Zhao et al., 2019 ). Therefore, we assume that type Ia DN bacteria are important nitrite scavengers at high nitrite concentrations. In the adaptation period, nitrite concentration rapidly dropped (15.33 ± 9.52 mg N L –1 ), whereas the relative abundance of AnAOB did not fluctuate significantly, suggesting that the anammox bacteria may not recover under this nitrite concentration. On the other hand, we observed a major increase of type II bacteria, a decrease of type Ia bacteria, and a slow rise in type Ib. This shows that type I bacteria (mainly DN bacteria) were outcompeted by type II bacteria in this period. The competition mechanisms between the two under high nitrite concentration conditions remain to be revealed in future studies. More importantly, nitrite and ammonium concentrations decreased in the adaptation period compared with inhibition. The effluent nitrite concentration maintained at 19 ± 8 mg N L –1 , whereas the ammonium concentration was observed with an increase from 41 to 78 mg N L –1 before day 215 in the adaptation period ( Figure 1B ) (effluent NH 4 + -N/NO 2 -N ratio increased from 2.48 to 3.66). As DNRA can provide NH 4 + for the anammox process in NH 4 + -limiting environments ( Wang et al., 2018 ), the ammonium accumulation might be attributed to the DNRA process, which might be critical in triggering the recovery of AnAOB. Therefore, apart from type Ia bacteria that reduce nitrite concentration, type II bacteria might be another key player in assisting the AnAOB recovery. In the recovery period, the nitrite concentration decreased to a low level of 4.63 ± 3.66 mg N L –1 ( Figure 4C ), whereas AnAOB and nitrogen removal efficiency ( Figure 1C ) were observed to have gradually recovered and stabilized. The relative abundance of type II bacteria again dropped to the same level as in the stabilization period. On the other hand, the relative abundance of type III bacteria decreased steadily, and type Ib bacteria increased rapidly, suggesting that type III DNRA bacteria ( Figure 4B ) were irreversibly stressed under high nitrite concentration ( Figure 4C ) and replaced by type Ib bacteria. The observations discussed earlier imply that the anammox performance was restored along with the shift of the microbial community. In the restored anammox system, although the relative abundance of type II bacteria remained similar to that under the initial conditions, type III bacteria were substituted by type I bacteria (mostly type Ib). It is indicated that different niches of bacteria may occur after inhibition in the MBR system, which contributes to the stability of the micro-ecosystem."
} | 3,820 |
28852625 | PMC5566244 | pmc | 6,779 | {
"abstract": "Storing energy harvested by triboelectric nanogenerators (TENGs) from ambient mechanical motion is still a great challenge for achieving low‐cost and environmental benign power sources. Here, an all‐solid‐state Na‐ion battery with safe and durable performance used for efficient storing pulsed energy harvested by the TENG is demonstrated. The solid‐state sodium‐ion batteries are charged by galvanostatic mode and pulse mode with the TENG, respectively. The all‐solid‐state sodium‐ion battery displays excellent cyclic performance up to 1000 cycles with a capacity retention of about 85% even at a high charge and discharge current density of 48 mA g −1 . When charged by the TENG, an energy conversion efficiency of 62.3% is demonstrated. The integration of TENGs with the safe and durable all‐solid‐state sodium‐ion batteries is potential for providing more stable power output for self‐powered systems."
} | 226 |
38731513 | PMC11085530 | pmc | 6,781 | {
"abstract": "The various wastes generated by silkworm silk textiles that are no longer in use are increasing, which is causing considerable waste and contamination. This issue has attracted widespread attention in countries that use a lot of silk. Therefore, enhancing the mechanical properties of regenerated silk fibroin (RSF) and enriching the function of silk are important directions to expand the comprehensive utilization of silk products. In this paper, the preparation of RSF/Al 2 O 3 nanoparticles (NPs) hybrid fiber with different Al 2 O 3 NPs contents by wet spinning and its novel performance are reported. It was found that the RSF/Al 2 O 3 NPs hybrid fiber was a multifunctional fiber material with thermal insulation and UV resistance. Natural light tests showed that the temperature rise rate of RSF/Al 2 O 3 NPs hybrid fibers was slower than that of RSF fibers, and the average temperature rose from 29.1 °C to about 35.4 °C in 15 min, while RSF fibers could rise to about 40.1 °C. UV absorption tests showed that the hybrid fiber was resistant to UV radiation. Furthermore, the addition of Al 2 O 3 NPs may improve the mechanical properties of the hybrid fibers. This was because the blending of Al 2 O 3 NPs promoted the self-assembly of β-sheets in the RSF reaction mixture in a dose-dependent manner, which was manifested as the RSF/Al 2 O 3 NPs hybrid fibers had more β-sheets, crystallinity, and a smaller crystal size. In addition, RSF/Al 2 O 3 NPs hybrid fibers had good biocompatibility and durability in micro-alkaline sweat environments. The above performance makes the RSF/Al 2 O 3 NPs hybrid fibers promising candidates for application in heat-insulating and UV-resistant fabrics as well as military clothing.",
"conclusion": "4. Conclusions In this study, RSF/Al 2 O 3 NPs hybrid fibers with high strength, heat insulation, and UV resistance were prepared by wet spinning. The addition of Al 2 O 3 NPs could enhance the mechanical properties of RSF/Al 2 O 3 NPs hybrid fibers. The mechanical properties of 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers were the best. The appropriate amount of Al 2 O 3 NPs could increase the β-sheet content of RSF/Al 2 O 3 NPs hybrid fibers. Moreover, RSF/Al 2 O 3 NPs hybrid fibers and fabrics had excellent heat insulation and UV resistance. Thus, the data from this study on Al 2 O 3 NPs in enhanced RSF are expected to be helpful in the future for the design and production of more innovative functional fibers. Overall, the present research provides a method for manufacturing fibers with ultraviolet resistance and thermal insulation capabilities that can be used in multifunctional fibers.",
"introduction": "1. Introduction Natural protein fiber has a wide range of applications in many aspects [ 1 ]. Silk fibroin is a natural protein secreted by insects, such as silk. It has excellent performance characteristics, such as lightness, softness, durability, and high tensile strength. These characteristics make silk fibroin widely used in the field of textiles [ 2 , 3 ]. However, the poor thermal insulation and UV resistance of silk fibroin limit its application in some special application scenarios [ 4 , 5 ]. Therefore, researchers have begun to focus on how to improve the thermal insulation and UV resistance of silk fibroin [ 6 , 7 , 8 , 9 ]. Through an in-depth study of the structure and characteristics of silk fibroin, researchers have found that the heat insulation and UV resistance of silk fibroin can be enhanced by changing the composition, structure, and treatment of silk fibroin. For example, researchers altered the composition and proportion of silk fibroin by changing the feeding methods, regulating the crystal structure of silk fibroin fiber, and improving its UV resistance [ 10 ]. In addition, the researchers also used chemical modification and functional addition methods to introduce materials with thermal insulation properties into silk fibroin to enhance its thermal insulation performance [ 11 ]. Therefore, the research background of silk fibroin in heat insulation and ultraviolet resistance is mainly to improve and enhance its performance to meet the needs of material properties in different application fields. These studies help to develop more functional and diverse silk fibroin textiles and expand their application. α-phase nano-alumina (α-Al 2 O 3 NPs) is a kind of high-temperature stable nanoparticle [ 12 ], with excellent thermal conductivity [ 13 , 14 ] and a high specific surface area [ 15 , 16 ], which makes it have potential in the fields of thermal insulation and UV resistance. The research background of α-Al 2 O 3 NPs in thermal insulation and UV resistance mainly involves the following aspects: It has good stability at high temperatures and can maintain its thermal insulation performance in high temperature environments [ 17 ]. Ji et al. successfully prepared alumina aerogel nanosheets with silica sol as a high-temperature binder with excellent ultra-high temperature and thermal insulation. Even after calcination at up to 1600 °C, it exhibits excellent thermal and chemical stability [ 18 ]. Al 2 O 3 NPs co-catalyst could act as a multifunctional finishing material. The flame-retardant property of the fabric, besides wrinkle resistance, was improved [ 19 ]. It was found that an approximately 2-nm-thick Al 2 O 3 insulating layer was introduced between ZnO NWs and Au NPs, causing the quenching phenomenon to disappear [ 20 ]. As a material with excellent thermal insulation and ultraviolet resistance, α-Al 2 O 3 NPs had broad application prospects in the fields of textiles, construction, and so on. In this study, α-Al 2 O 3 NPs were used to modify RSF, and RSF/Al 2 O 3 NPs hybrid fibers were prepared by wet spinning. The structure and properties of RSF were modified by Al 2 O 3 NPs in order to prepare a high-toughness RSF/Al 2 O 3 NPs hybrid fiber with heat insulation and ultraviolet resistance under sunlight ( Figure 1 ).",
"discussion": "2. Results and Discussion 2.1. Morphology of RSF/Al 2 O 3 NPs Hybrid Fibers Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) component spectrum of the 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers is shown in Figure 2 a–d. It illustrates the distribution of Al element in the RSF/Al 2 O 3 NPs hybrid fibers, with the Al evenly distributed throughout, indicating an effective and uniform distribution of the Al 2 O 3 NPs in the RSF/Al 2 O 3 NPs hybrid fibers. As illustrated in Figure 2 e,f, the surfaces of 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers were shown to be smooth and homogeneous, free of any cavities or fractures in the interior structure. Therefore, adding Al 2 O 3 NPs to RSF fibers did not affect their initial structure or qualities, and it was these traits that contributed to the excellent mechanical performance of the RSF/Al 2 O 3 NPs hybrid. We discovered that the fibers had an average diameter of 37.55 ± 0.62 μm. The total spectrum of elements in the 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers is shown in Figure 2 g. It can be observed that all elements were detected in the hybrid fibers, confirming the successful distribution of Al 2 O 3 NPs. Among them, in the 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers, the elements C, N, O, S, and Al made up 42.14%, 20.70%, 36.23%, 0.26%, and 0.67% of the total, respectively ( Figure 2 g). 2.2. Mechanical Properties of RSF/Al 2 O 3 NPs Hybrid Fibers Figure 3 a shows the stress-strain curves of RSF fibers and RSF/Al 2 O 3 NPs hybrid fibers. The 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers exhibited the best mechanical properties when compared with RSF fibers. Its breaking strength (300.04 ± 17.25 MPa) and elongation at break (40.88 ± 12.45%) are higher than those of the blank group ( Figure 3 a), respectively, indicating that Al 2 O 3 NPs could enhance the mechanical properties of the hybrid fibers to a certain extent. This phenomenon suggested that Al 2 O 3 NPs had a significant toughening effect on the RSF fibers. Further analysis of the effect of Al 2 O 3 NPs on the mechanical properties of RSF/Al 2 O 3 NPs hybrid fibers was conducted by plotting the breaking strength and elongation at the break curve of RSF/Al 2 O 3 NPs hybrid fibers with varying Al 2 O 3 NPs concentrations ( Figure 3 b). Results indicated that with increasing Al 2 O 3 NPs content, both the breaking strength and elongation at break initially increased, reaching a maximum at 0.8 wt% Al 2 O 3 NPs, before decreasing. Notably, the increase in breaking strength was more pronounced compared with elongation at break. In summary, the excellent mechanical properties make RSF/Al 2 O 3 NPs hybrid fibers potential fibers for heat-insulated fabrics. 2.3. Structure Analysis of RSF/Al 2 O 3 NPs Hybrid Fibers The secondary structures of silk were strongly associated with their mechanical qualities, such as tensile modulus, elongation at break, and breaking strength [ 21 ]. To elucidate the mechanism underlying the enhanced mechanical properties, the secondary structure and crystal structure of RSF fibers and RSF/Al 2 O 3 NPs hybrid fibers were investigated by fourier transform infrared spectroscopy (FTIR) and wide-angle X-ray diffraction (WAXD) [ 22 ]. As illustrated in Figure 3 c, both RSF fibers and RSF/Al 2 O 3 NPs hybrid fibers show peak locations almost identical to their corresponding FTIR spectra. It suggests that Al 2 O 3 NPs have no impact on the secondary structural components of RSF fibers. We de-convoluted the amide III spectral area of both the RSF fibers and RSF/Al 2 O 3 NPs hybrid fibers for probing into what was in the secondary structural components of both materials [ 23 ]. The relative content of α-helix/random coil (1230 cm −1 ) and β-sheet structure (1260 cm −1 ) in RSF fibers and RSF/Al 2 O 3 NPs hybrid fibers was analyzed ( Figure 3 d). It was observed that RSF/Al 2 O 3 NPs hybrid fibers with varying Al 2 O 3 NPs content exhibited higher β-sheet structures and lower α-helix/random coil structures compared with RSF fibers, indicating that Al 2 O 3 NPs promoted the formation of β-sheet structures in RSF/Al 2 O 3 NPs hybrid fibers. Specifically, the content of β-sheet structure initially increased and then decreased with increasing Al 2 O 3 NPs content, with the highest content observed in 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers, thereby explaining the superior mechanical properties, especially breaking strength. The β-sheet is considered to be a highly ordered structure, having interactions that are arranged in an extremely organized manner to form a distribution known as the β-sheet crystal, which is widespread throughout the silk fibroin network. This unique structure effectively absorbs and cushions external stresses, thus conferring excellent mechanical properties to silk fibroin. As the content of β-sheet increases, the structure of silk fibroin fibers becomes stronger and more robust [ 24 ]. These results indicated that the integration of Al 2 O 3 NPs accelerated the transition from the α-helix/random coil of RSF fibers to the β-sheet. Furthermore, the crystal structure of 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers was analyzed, as it significantly influences the mechanical properties of RSF materials [ 25 ]. WAXD revealed characteristic peaks corresponding to crystal faces (200), (210), and (002), indicating the axes a, b, and c of the crystallite, respectively ( Figure 3 e,f). The crystallinity of 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers was higher than that of RSF fibers, consistent with the secondary structure results ( Table 1 ). Moreover, the crystallite size [ 26 ] of 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers was smaller than that of RSF fibers, suggesting that the addition of Al 2 O 3 NPs promoted the crystallization of RSF and the formation of smaller crystallite sizes ( Table 1 ). Nova et al. [ 27 ] considered that the ultimate strength of spider silk is controlled by the strength of β-sheet nanocrystal, and the strength of β-sheet nanocrystal is directly related to its size. The smaller the crystal inside the fiber, the better its toughness. Keten et al. [ 25 ] found that the β-crystallite size had a great influence on the mechanical properties of fibers. The larger β-crystallite in the fiber will be destroyed under lower force, while the smaller β-crystallite can provide the ability to resist deformation and fracture. In summary, Al 2 O 3 NPs facilitated the formation of β-sheet structures in RSF fibers, contributing to improved mechanical properties, while the addition of Al 2 O 3 NPs enhanced the crystallinity and suppressed the crystallite size of RSF, further enhancing the mechanical strength of RSF/Al 2 O 3 NPs hybrid fibers. 2.4. UV Resistance of RSF/Al 2 O 3 NPs Hybrid Fibers The UV absorption and refractive index of 0.5 mg/mL of RSF and RSF/Al 2 O 3 NPs solutions in the wavelength range from 200 to 800 nm are shown in Figure 4 a,b. The UV absorption of RSF/Al 2 O 3 NPs mixed solution and RSF solution is the same ( Figure 4 a). The UV refractive indices of the RSF/Al 2 O 3 NPs solutions were all increased compared with the RSF solutions ( Figure 4 b). With the increase in Al 2 O 3 NPs content, the UV refractive index of the RSF/Al 2 O 3 NPs solution also increased. When the content of Al 2 O 3 NPs was between 0.8 and 1.0 wt%, the UV refractive index of the RSF/Al 2 O 3 NPs solution increased obviously. Combined with the mechanical properties of RSF/Al 2 O 3 NPs hybrid fibers, 0.8 wt% Al 2 O 3 NPs could be used as the optimal concentration of the hybrid fibers. 2.5. Thermal Insulation Properties of RSF/Al 2 O 3 NPs Hybrid Fibers Through the photothermal performance test under natural sunlight irradiation, we explored the difference in thermal insulation performance between RSF/Al 2 O 3 NPs hybrid fibers and RSF fibers. We used an infrared imager to observe the change in fiber surface temperature for 15 min [ 28 ]. Before natural light irradiation, there was almost no difference in temperature between the blank RSF fiber and the RSF/Al 2 O 3 NPs hybrid fiber ( Figure 4 c). However, after illumination, the temperature rise rate of the blank RSF fiber was faster than that of the hybrid fiber. The temperature of the blank RSF fiber increased from 29.2 °C to 36.1 °C in only 5 min and increased to 40.1 °C in 15 min ( Figure 4 f,i). In contrast, the temperatures of the hybrid fibers only increased to 33.0 °C and 35.4 °C, respectively. This indicates that the RSF/Al 2 O 3 NPs hybrid fiber had better thermal insulation performance and could effectively reduce the temperature rise rate of the fiber surface. This finding provided a scientific basis for the application of the new hybrid fiber in the field of thermal insulation fabrics. 2.6. Perspiration Resistance of RSF/Al 2 O 3 NPs Hybrid Fibers The degradation rate and mechanical properties of RSF fibers and RSF/Al 2 O 3 NPs hybrid fibers after wetting in alkaline simulated sweat were observed. As illustrated in Figure 5 b, the degradation residual rate of RSF fiber and RSF/Al 2 O 3 NPs hybrid fiber remained above 90%, which indicated that the damage degree of alkaline simulated sweat to RSF/Al 2 O 3 NPs hybrid fiber was low. The low degradation rate might be due to the partial disintegration or dissolution of silk fibroin in an alkaline environment. The morphology and quality of RSF fibers and RSF/Al 2 O 3 NPs hybrid fibers were well maintained. It could be inferred that the degree of damage to RSF/Al 2 O 3 NPs hybrid fibers by alkaline simulated sweat was only small, indicating that RSF/Al 2 O 3 NPs hybrid fibers had good stability and durability in alkaline environments such as sweat. Figure 5 a,b shows that the mechanical properties of RSF fibers decreased after 4 h of wetting treatment. The breaking strength and elongation at break of RSF fibers decreased by 48.26% and 6.95%, respectively, reaching 70.25 ± 4.98 MPa and 33.46 ± 3.77%, respectively. In contrast, the breaking strength and elongation at break of RSF/Al 2 O 3 NPs hybrid fibers decreased by 6.17% and 3.97%, reaching 285.20 ± 4.33 MPa and 51.09 ± 3.23%, respectively, under the same wetting conditions. It could be seen that the mechanical properties of RSF/Al 2 O 3 NPs hybrid fibers had very little change and higher retention. This difference might be due to the influence of the micro-alkali sweat environment. The RSF/Al 2 O 3 NPs hybrid fibers had a higher density of crystalline regions than the RSF fibers under micro-alkali conditions, so they were not easily destroyed by the micro-alkali environment [ 29 ]. The internal crystalline region of RSF fiber contained more hydrogen bonds, which were easy to combine with alkaline substances, affecting the orderliness of the crystalline region and thus reducing the mechanical properties of RSF fiber. For the effect of Al 2 O 3 NPs on the thermal stability of RSF fibers, we further investigated the thermal stability of the 0.8 wt% RSF/Al 2 O 3 NPs hybrid fibers using a thermal gravimetric analyzer (TGA). The thermogravimetric (TG) and derivative thermogravimetric (DTG) curves of the RSF fibers were obtained ( Figure 5 c,d), revealing similar thermal degradation processes for both fibers. As illustrated in Figure 5 c,d, it demonstrated that the addition of Al 2 O 3 NPs only had a little effect on the thermal decomposition process of RSF fibers and maintained the thermal stability of the RSF fibers. In general, RSF/Al 2 O 3 NPs hybrid fibers showed high stability under alkaline sweat conditions, which was beneficial to the application of RSF/Al 2 O 3 NPs hybrid fibers in the fields of heat insulation and anti-ultraviolet fabrics. 2.7. Cell Cytotoxicity and Antioxidant of RSF/Al 2 O 3 NPs Hybrid Fibers As a fiber material used to contact the skin, its biological toxicity and antioxidant properties are important indicators. In this study, we selected rat cardiomyocyte cells (H9C2) and mouse fibroblast cells (L929) as the cell models. H9C2 and L929 cells were treated with RSF and RSF/Al 2 O 3 NPs solutions at a concentration of 0.5 mg/mL, and the cytotoxicity was evaluated by CellTiter-Blue reagent. Figure 5 e shows the cytotoxicity results of RSF and RSF/Al 2 O 3 NPs solution-treated H9C2 and L929 cells. It was observed that concentrations of 0.5 mg/mL of RSF and RSF/Al 2 O 3 NPs solutions were non-toxic to H9C2 and L929 cells, and the cell survival rate remained above 90%. It also had a certain proliferation effect on the growth of H9C2 and L929 cells. This indicated that the RSF/Al 2 O 3 NPs solution had good biocompatibility for H9C2 and L929 cells. Moreover, the RSF/Al 2 O 3 NPs solution had good antioxidant properties and could effectively scavenge ROS in H9C2 and L929 cells ( Figure 5 f)."
} | 4,688 |
31098412 | PMC6513846 | pmc | 6,782 | {
"abstract": "Although metagenomics researches have illuminated microbial diversity in numerous biospheres, understanding individual microbial functions is yet difficult due to the complexity of ecosystems. To address this issue, we applied a metagenome-independent, de novo assembly–based metatranscriptomics to a complex microbiome, activated sludge, which has been used for wastewater treatment for over a century. Even though two bioreactors were operated under the same conditions, their performances differed from each other with unknown causes. Metatranscriptome profiles in high- and low-performance reactors demonstrated that denitrifiers contributed to the anaerobic degradation of heavy oil; however, no marked difference in the gene expression was found. Instead, gene expression-based nitrification activities that fueled the denitrifiers by providing the respiratory substrate were notably high in the high-performance reactor only. Nitrifiers—small minorities with relative abundances of <0.25%—governed the heavy-oil degradation performances of the reactors, unveiling an unexpected linkage of carbon- and nitrogen-metabolisms of the complex microbiome.",
"introduction": "Introduction Understanding the individual microbial roles in ecosystems is a great challenge in microbial ecology, because community function is expressed as the sum of the metabolic activities and interactions of various microbes 1 – 3 . A powerful tool to address this issue is metatranscriptome analysis; 4 however, its capability and reliability are reduced by an insufficiency of reference metagenome sequences 5 . Accordingly, the roles of rare microorganisms tend to be masked, which is problematic because functional importance does not always correspond to population abundance 6 – 8 . Recently, de novo assembly–based transcriptome analysis (de novo RNA-seq) was developed to overcome the dependency on reference genome data size 9 . Here, we applied de novo RNA-seq to decipher individual microbial functions in the complex ecosystem of activated sludge 10 . Even though activated sludge bioreactors have been used for wastewater treatment for over a century, the complexity of the sludge microbiome has hindered our precise understanding of the microbial processes (Fig. 1 ) 3 , 10 . It is difficult to stably manage the microbiomes in bioreactors, in part because of the unpredictable behaviors commonly observed in such complex microbial ecosystems 11 . Therefore, bioreactor performance occasionally deteriorates because of unknown causes; such problems are currently solved by impromptu means. Industrial and domestic wastewaters are often contaminated by heavy oil, which inhibits microbial activities and decreases reactor performance 12 . Although the degradation of heavy oil, which contains toxic aromatic hydrocarbons, by cultured microorganisms has been well studied 13 , 14 , the mechanisms underlying the degradation by complex microbiomes remain largely unknown. Here, we focused on membrane bioreactor (MBR), which is a representative activated sludge bioreactor 15 . The two replicate bioreactors were run under the same operational conditions for 37 days, during which heavy oil was spiked-in with increasing concentrations from day 20; yet, their performances became different from each other. We investigated these two reactors, one (reactor 1) with high heavy-oil degradation activity and one (reactor 2) with low activity, by metatranscriptome analysis based on de novo transcript assembly, referred to as de novo RNA-seq 9 . The results suggested that the small but important minorities of nitrifiers governed the heavy-oil degradation activities of the activated sludge bioreactors. Fig. 1 Heavy-oil degradation model in the sludge microbiome. Because the complete metabolic map of the ecosystem was unduly complex, several vital metabolic pathways were extracted by evaluating the microbial transcript diversity of the expressed genes (i.e., the number of microbial species expressing a gene) as well as the expression level (upper figure; Supplementary Table 2 ). On the basis of the extracted pathways, we proposed heavy-oil degradation mechanisms in the sludge microbiome, in which denitrifiers and nitrifiers indirectly cooperate. Red arrows indicate the metabolic pathways highly enriched. Blue and red boxes emphasize pathways possibly performed by denitrifier and nitrifiers, respectively. Gray lines in the background denote the representatives of metabolic pathways that were common but not directly related to the targeted key metabolisms. The lower figure shows the predicted interaction between denitrifier and nitrifiers. AOB and NOB denote ammonia-oxidizing bacteria and nitrite oxidizing bacteria, respectively",
"discussion": "Discussion Whether or not aromatic compounds in heavy oil were detoxified by denitrifying bacteria seemed to differentiate the nitrification activities of the respective reactors. Because of their low abundances, nitrifiers could be easily washed out from the sludge microbiome through growth inhibition by the toxic aromatic compounds remaining (Fig. 2g, h , Supplementary Fig. 8 ). Denitrifiers’ genes encoding degradation enzymes for aromatic hydrocarbons showed high relative expression levels in reactor 1 only (Fig. 3d ), presumably removing the toxic compounds to maintain the activity of the susceptible nitrifiers 20 . In turn, nitrifiers helped denitrifiers by providing nitrate. In fact, relative expression levels of AOB-derived rRNA were notably high only in reactor 1 even at the beginning of the heavy-oil addition (Supplementary Fig. 4e, f ). This indirect cooperation in the sludge microbiome was vital for sustaining reactor performance. Ammonia monooxygenase and degradation enzymes for aromatic hydrocarbons are generally functional under oxic conditions, whereas enzymes involved in denitrification and anaerobic alkane degradation are activated under anoxic conditions. Dissolved oxygen concentrations in the reactors decreased after the heavy oil addition (Fig. 2c ), possibly due to the increase in viscosity of the sludge and the resulting oxygen mass transfer limitation 21 , 22 . Consequently, both oxic and anoxic conditions existed locally and temporally even in the continuously aerated activated sludge. We proposed that the co-existence of aerobic and anaerobic environments enabled a link between carbon metabolism (alkane and aromatic hydrocarbon degradation) and nitrogen metabolism (nitrification and denitrification), leading to the heavy-oil degradation performances of reactor 1 (Fig. 1 ). Here, de novo RNA-seq was proven effective for deciphering functioning metabolisms in a complex sludge microbiome. However, determining which metabolic pathways or reactions cause a phenomenon observed in an ecosystem is challenging, because the phenomenon is usually the sum of diverse biological processes 1 – 3 . This is especially difficult when the reaction of interest is not identical to, or has little relationship with, the phenomenon, e.g., when these reactions are located far from each other on the metabolic map (Fig. 1 ). To bypass this drawback, we extracted the vital pathways on the metabolic map by evaluating diversity of the expressed genes (Supplementary Fig. 7d , Table 1 ). De novo metatranscriptome assembly generated several to dozens of homologous sequences for most assemblies, identifying the higher priority genes that a greater number of microbial species expressed. Given the importance of microbial richness to maintaining ecosystem functionality 23 , the information on the transcript diversity of functioning key genes would be of tremendous benefit. By contrast, RNA-seq studies have so far prioritized genes on the basis of their expression levels alone, because only a few homolog sequences have been available from the limited number of reference genomes or metagenomes. This advantage of de novo RNA-seq can also unmask the ecophysiology of rare microorganisms. The big contribution of minorities, such as AOB and NOB, might not have been identified by a metagenome-based RNA-seq approach. The results by de novo RNA-seq suggested that the small but important minorities of nitrifiers, AOB and NOB, the relative abundances of which were <0.15% and <0.25%, respectively, governed the heavy-oil degradation activities of the activated sludge bioreactors. Nitrifiers and denitrifiers indirectly cooperated with each other by supplying the respiratory substrate nitrate and by detoxifying heavy-oil components, respectively, thereby helping maintain the reactor performance. Taken together, our de novo RNA-seq strategy deciphers the unexpected linkage between carbon- and nitrogen-metabolisms in the complex microbiome."
} | 2,177 |
34671223 | PMC8452181 | pmc | 6,784 | {
"abstract": "Significant progress in lignins valorization and development of high-performance sustainable materials have been achieved in recent years. Reports related to lignin utilization indicate excellent prospects considering green chemistry, chemical engineering, energy, materials and polymer science, physical chemistry, biochemistry, among others. To fully realize such potential, one of the most promising routes involves lignin uses in nanocomposites and nanohybrid assemblies, where synergistic interactions are highly beneficial. This review first discusses the interfacial assembly of lignins with polysaccharides, proteins and other biopolymers, for instance, in the synthesis of nanocomposites. To give a wide perspective, we consider the subject of hybridization with metal and metal oxide nanoparticles, as well as uses as precursor of carbon materials and the assembly with other biobased nanoparticles, for instance to form nanohybrids. We provide cues to understand the fundamental aspects related to lignins, their self-assembly and supramolecular organization, all of which are critical in nanocomposites and nanohybrids. We highlight the possibilities of lignin in the fields of flame retardancy, food packaging, plant protection, electroactive materials, energy storage and health sciences. The most recent outcomes are evaluated given the importance of lignin extraction, within established and emerging biorefineries. We consider the benefit of lignin compared to synthetic counterparts. Bridging the gap between fundamental and application-driven research, this account offers critical insights as far as the potential of lignin as one of the frontrunners in the uptake of bioeconomy concepts and its application in value-added products.",
"introduction": "1. Introduction In the last ten years the subject of “lignin” has re-surfaced in the fora of renewable polymers and associated materials. A reason for such interest relates to the impending availability of lignins from the next generation bioproduct mills or biorefineries, whose economic viability will hinge on finding new uses for this abundant biomacromolecule. 1,2 Lignins are in fact one of the most important streams in the forest industry. 3,4 Not surprisingly, the most recent publications related to lignin utilization include widespread opportunities going from chemical engineering, energy and fuels, green sustainable chemistry and technology, polymer science, physical chemistry, biochemistry and materials science. 5 In such diverse fields, consideration has been given to lignin depolymerization, 6 e.g. , to produce platform chemicals in large volumes. 7,8 The opposite extreme, equally relevant, proposes the uses of lignin as a colloidal material or in the form of a polymeric matrix that can lead to lignin-based functional materials. 9 In both cases, the so-called lignin-centered biomass fractionation and transformation or modification, using green chemistry routes or otherwise, are central aspects under scrutiny. Recent investments to pulp mills signal lignin extraction in addition to its utilization as a renewable option to energy generation. Such efforts are aimed at reducing the dependency on fossil carbon feedstocks and to fully utilize the plant-based resources, for example, in products relevant to the resin and plastic industries. Many applications such as biomedical and nutritional materials under development strive to take advantage of lignins’ antioxidant activity, 10–13 or its high carbon content attractive to carbonaceous materials in the form of fibers and electroactive components. 14 Owing to its amphiphilic structure, lignins have been shown to be surface-active and are able to improve the strength at interfaces, for example, in adhesives. 4 In these and other applications, however, there is the challenge of substituting molecules that are in current use, in stablished processing units, which should be adapted or retooled. In addition, a major roadblock is the chemical and structural variability of technical lignins, depending on the plant source, the nature of the process used in their extraction and other variables. 15–17 Lignin complexity, variability and its elusive chemical structure, which remains a subject of discussion, compounds such challenges. On the other hand, the expected availability and low cost of lignin represent an incentive, together with the fact that lignin's property profiles can be tailored by controlling the variables listed before, paradoxically the same that have delayed lignin's wider adoption. The complex phenolic makeup of lignin is quite challenging, if not impossible to achieve synthetically and one has to wonder why such complexity has not been frequently exploited in polymer architecture and functional groups. Along with its high density in hydroxyl and carboxyl substituents, there are plenty of reasons to consider when taking lignin as a precursor and as a reactive scaffold that can unleash development in the next generation materials. This consideration is particularly timely under the emerging concept of the circular bioeconomy. Lignin's own features are further expanded if one considers the bonding patterns and linkages with carbohydrates, for instance, in lignin-carbohydrate complexes, 18,19 which are finding traction in exploiting the advantage that each component bring in given applications. Lignin has attracted and increasing attention from both fundamental and applied fields as it is available in large quantities from renewable sources and can be potentially applied in many fields thanks to its special physico-chemical properties. In this review, as schematically summarized in Fig. 1 , we focus on the fundamentals and prospective application of lignin as an added value material. As requirements and mechanistic of lignin-based materials significantly vary, the discussion is structured by lignin fundamentals, lignin-nanocomposites and lignin-nanohybrids, to finalize with the most common applications of those materials. Besides its presentation in the so-called black liquors or in solution, lignin solids are produced upon drying, as amorphous powder. Moreover, recent efforts have been applied in the production of lignin in other forms, such as spherical particles, responding to the possibility of capitalizing the interrelation that is known to exist between shape and function. Together with the exquisite opportunities expected from lignin, as a polyphenolic molecule, one should add its self-assembly and supramolecular organization, alone or in combination with polysaccharides, for example in composites. These aspects are discussed in this review in the section “Lignin fundamentals”. In the next section, “Lignin-nanocomposite approach”, we consider lignin synergies with polysaccharides, proteins, other biopolymers and synthetic polymers in the form of the most traditional types of lignin-based materials, where lignin is applied as a reinforcing phase to develop (nano)composites. We also consider the recent works highlighting the many new opportunities arising from the formation of hybrid-materials involving lignin interactions at the nano- to micro-scale with a secondary organic or inorganic component at the nanometer or molecular level. Accordingly, in the section “Lignin-nanohybrid approach” we show how lignin is used as a renewable matrix to host either inorganic or organic nanoparticles, are addressed. Due to the sheer volume of work in this area, lignin-nanohybrids are here grouped into those bearing metal oxides, zero-valent metals, carbonaceous nanoparticles and biobased nanoparticles. Hereafter, the more imminent realm of applications is highlighted, covering the reduced flammability of lignin-based materials, food packaging applications, plant and crop protection ability, energy storage, and health. Finally, the full utilization of lignin, in these and other applications, is justified considering the environmental benefits that can be gained, as shown by gains in net carbon storage and in the reduction of greenhouse gases. This specially applies when lignin fractionation and associated bioproduct developments are integrated under the biorefinery concept, following the purpose of fully utilizing the component of plants that are sustainably harvested to fulfill most of our current and future material needs. Fig. 1 Schematic representation of the valorization of lignin in the form of nanocomposites and nanohybrids, as well as its associated production and fundamental considerations and applications. The main sections of the Review are summarized, with specific subsections for the main chapters. During the recent years, there have been many reviews on lignin nanoparticles and their applications, 9,20–28,29 chemical and microbial transformation of lignin to chemicals and polymer precursors, 30,31 and chemical functionalization and processing of lignin into various materials (not in the nano-scale). 2,32–40 Though many of the prior reviews discuss formulation of lignin-based polymers and composite systems, 41–49 the literature is lacking a review on nanocomposites and hybrid materials based on lignin. These two classes of materials are quickly gaining momentum. The present work is a comprehensive review of lignin building on the recent progress that links (1) progresses in fundamental understanding of the structure and properties of technical lignins as a function of their isolation and modifications and (2) their assembly to nano- and miscroscaled building blocks with (3) the formation of lignin-based composites, inclusive of hybrid nano- and microparticles. We thereafter thoroughly review recent progresses in lignin-based nanocomposites and nanohybrids when lignin is compounded with the main classes of inorganic and organic materials."
} | 2,454 |
36349407 | PMC10107361 | pmc | 6,786 | {
"abstract": "Summary \n Ecological corridors promote species coexistence in fragmented habitats where dispersal limits species fluxes. The corridor concept was developed and investigated with macroorganisms in mind, while microorganisms, the invisible majority of biodiversity, were disregarded. We analyzed the effect of corridors on the dynamics of endospheric fungal assemblages associated with plant roots at the scale of 1 m over 2 years (i.e. at five time points) by combining an experimental corridor‐mesocosm with high‐throughput amplicon sequencing. We showed that plant root endospheric mycobiota were sensitive to corridor effects when the corridors were set up at a small spatial scale. The endospheric mycobiota of connected plants had higher species richness, lower beta‐diversity, and more deterministic assembly than the mycobiota of isolated plants. These effects became more pronounced with the development of host plants. Biotic corridors composed of host plants may thus play a key role in the spatial dynamics of microbial communities and may influence microbial diversity and related ecological functions.",
"introduction": "Introduction Anthropogenic activities have caused habitat destruction and degradation leading to habitat fragmentation and threatening biodiversity (Diamond, 1976 ; Hilty et al ., 2006 ; Haddad et al ., 2015 ). Ecological corridors are one way to mitigate the negative effect of habitat fragmentation (Rosenberg et al ., 1997 ; Haddad, 2015 ). When dispersal among habitat patches is limited, corridors facilitate the movement of animals and dispersal of plants between habitat patches (Tewksbury et al ., 2002 ) and reduce the probability of local extinction of isolated populations (Brown & Kodric‐Brown, 1977 ). Corridors therefore have mostly positive effects on species richness at the patch scale (Damschen et al ., 2006 ) although there may be some negative effects, such as facilitation of dispersal by highly competitive species or pathogens (Haddad et al ., 2014 ). In metacommunities, sets of connected patches linked by dispersal fluxes, local community assembly involves both deterministic and stochastic processes such as competition, drift, and dispersal (Leibold et al ., 2004 ). The metacommunity framework predicts that local species diversity will increase when the dispersal rate is intermediate (a hump‐shaped relationship among dispersal effects and local diversity). Patches with high dispersal may be less affected by drift and therefore more similar in species composition (Mouquet & Loreau, 2003 ). Beta‐diversity among local communities is reduced (Jamoneau et al ., 2012 ) due to high migration rates (Arévalo et al ., 2010 ). To date, research into the effect of ecological corridors on maintaining biodiversity has focused on macroorganisms, while microorganisms, the invisible majority of biodiversity (Staddon et al ., 2010 ), have largely been overlooked. Existing knowledge on ecological corridors applied to microorganisms focuses either on key pathogen species or on specific taxonomic groups such as mycorrhizal fungi (Groppe et al ., 2001 ; Yuen & Mila, 2015 ; Chagnon et al ., 2020 ) but does not account for the diversity, composition, and assembly process of microbial assemblages as a whole. Microbial distribution has long been assumed to be subject to very little dispersal limitation due to the small size and high propagule production of many microbes (Baas‐Becking's hypothesis; Baas Becking, 1934 ). If microbes indeed do not suffer from dispersal limitation, then community assembly would depend only on local conditions. However, microbe communities demonstrate biogeographical patterns (Martiny et al ., 2006 ; Hanson et al ., 2012 ; Nemergut et al ., 2013 ; Xu et al ., 2022 ) and strong distance–decay relationships (Xu et al ., 2021 ). Furthermore, mechanistic approaches, based on the metacommunity framework, have been used to quantify the importance of dispersal for species coexistence in microbes (Miller et al ., 2018 ; Langenheder & Lindström, 2019 ). Most studies that have analyzed microbial dispersal have focused on biogeographic distribution patterns based on metacommunity theory at a large scale (Powell et al ., 2015 ; Mansour et al ., 2018 ; Zhang et al ., 2018 ; Brown et al ., 2020 ), even dispersal studies of mycorrhizal fungal propagules (Correia et al ., 2019 ; Paz et al ., 2021 ). However, local dispersal limitation has been shown to play a critical role in shaping the distribution and diversity of microorganisms (Telford et al ., 2006 ). Some of these microorganisms, including endospheric fungi, are preferentially associated with certain plants (Wagner et al ., 2016 ). The host preference effect results from close interactions among plants and their associated microbiota leading to the selective recruitment of particular microorganisms. Host plants are thereby the preferential microhabitats of these microorganisms, and microbial dispersal limitation may be linked to the distance between host plants (Mony et al ., 2020b ). In endospheric fungi associated with plant roots, dispersal limitation seems to occur at scales of < 1 m (Mony et al ., 2020a ), likely because of their low dispersal capacity. Local microbial dispersal among host plants is therefore assumed to be at least partially achieved via the dispersal of spores or hyphal fragments by soil fauna (Lilleskov & Bruns, 2005 ; Vašutová et al ., 2019 ), by the dispersal of stolons or rhizomes (Vannier et al ., 2018 ), or by inoculation of roots through contact with neighboring plant roots (Mony et al ., 2021 ). The effect of corridors provided by connected host plants may thus promote dispersal of microorganisms among plants. In the initial stages of plant development, the colonization of microorganisms is mostly driven by priority effects (Debray et al ., 2022 ). Fungal species in the immediate vicinity colonize hosts sequentially rather than simultaneously with the first species to arrive possibly preventing further colonization by latecomers (e.g. as with arbuscular mycorrhiza; Werner & Kiers, 2015 ). This early microbial colonization process is determined by the composition of local microbial reservoirs and results in stochastic species dynamics in homogeneous environments (Bell, 2001 ). After this first phase of colonization, the host plants then regulate the colonization of microorganisms through selective recruitment of particular microorganisms potentially reducing the influence of stochasticity on colonization. Such root‐associated microbial recruitment can be achieved by emitting secondary metabolites such as coumarins (Voges et al ., 2019 ) and volatile compounds (Schulz‐Bohm et al ., 2018 ), and by rewarding the most beneficial symbionts in the endosphere (Kiers et al ., 2011 ). As such, the effect of corridors might be more pronounced over time as root‐associated microbial communities become more specific to the host plant species. Plant‐associated microorganisms influence their host's performance (Trivedi et al ., 2020 ). Thus, if corridor connections can affect the distribution patterns of plant‐associated microorganisms, then corridors might be important to consider as a driver for providing microbial functions (Mony et al ., 2022 ). Corridors between plants may enable favorable microorganisms to colonize plants such as mycorrhizal fungi which are particularly important in plant nutrition (Smith & Read, 2008 ), or detrimental microorganisms such as pathogens which may alter plant health. Understanding the dynamics of such corridors from the perspective of the microbes is therefore likely to contribute to building up barriers of pathogen dispersion or channels of beneficial microorganisms in agrosystems (Ampt et al ., 2022 ). In this study, we analyzed the effect of corridors on the dynamics of plant root endospheric mycobiota at the scale of 1 m. We used carefully designed mesocosm systems to test two experimental treatments: two isolated patches of Trifolium repens growing in a matrix of Brachypodium pinnatum , one with a corridor of T. repens and one without (Fig. 1 ). After checking that B. pinnatum and T. repens contained distinct microbiota, we characterized root endospheric mycobiota using high‐throughput amplicon sequencing for T. repens in connected or isolated patches and analyzed sequence cluster composition, richness, and dissimilarity under each treatment. In theory, in a community of sympatric organisms sharing the same habitat, species are neutral if the success of one is not dependent on the other. In this case, changes in the community composition are expected to be random. Alternatively, species can interact with one another, and these deterministic species interactions can alter community assembly. In natural communities, both neutral and deterministic processes take place simultaneously and can be partitioned. In this study, we used neutral community models (NCMs) (Sloan et al ., 2006 ) to predict whether connection would facilitate species fluxes between patches, thereby promoting species coexistence. To test for the effects of a corridor on fungal groups with particular ecological functions, we parsed fungal sequence clusters by ecological guilds or traits using an existing fungal trait database (Nguyen et al ., 2016 ; Zanne et al ., 2020 ). Fig. 1 Experimental design of a patch‐matrix mesocosm. (a) Photograph of the designed mesocosms used in this study. Two patches (D: 0.40 m; H: 0.15 m) of Trifolium repens were embedded in a matrix (L: 1.30 m × W: 1.30 m × H: 0.25 m) of Brachypodium pinnatum ; in the treatment including a corridor, T. repens patches were connected by a narrow corridor (L: 0.90 m × W: 0.15 m × H: 0.15 m) of T. repens . (b) Plant sampling design for mesocosms with no connecting corridor. (c) Plant sampling design for mesocosms with a connecting corridor. Triangles denote T. repens roots and rectangles denote B. pinnatum roots. Yellow dashed lines indicate potential belowground microbial interactions through plants. Data analyses were conducted at individual and patch scales. At the individual scale, the fungal structure of each individual plant was analyzed at each sampling time point. At the patch scale, the fungal structure of three T. repens plants in the same T. repens patch was pooled at each sampling time point (total sequence clusters were pooled with their mean abundances using the R function aggregate ). In all, 10 mesocosms with a corridor and nine mesocosms with no corridor were created. The experiment was launched with the transplantation of T. repens plantlets in patches with or without a corridor in June 2017 (the first year), with five sampling time points: October 2017 (t0 + 4 months), May 2018 (t0 + 11 months), June 2018 (t0 + 12 months), October 2018 (t0 + 16 months), and May 2019 (t0 + 23 months). The corresponding growth stages of T. repens are indicated in red below each sampling time point. Red dashed lines denote the end of each year. Based on metacommunity theory, we made three specific predictions: (1) connected host plants display higher root endospheric mycobiota species richness than isolated host plants; (2) the root endospheric mycobiota of connected host plants are more similar than those of isolated host plants (i.e. lower beta‐diversity); (3) the root endospheric mycobiota of connected host plants are less subject to stochasticity than the mycobiota of isolated host plants. Overrepresented and underrepresented sequence clusters detected in the NCM of connected plants are assumed to be actively promoted or filtered within the root mycobiota and to correspond to deterministic processes of assembly. We further expect that these patterns will become more pronounced over time with the development of host plants, because of the reduced importance of stochastic colonization of roots by prior species, compared with deterministic selection by plants in the assembly of root‐associated mycobiota.",
"discussion": "Discussion Plant root endospheric mycobiota were shaped by the presence of corridors While the structure of sequence clusters of the T. repens root endosphere was marked by strong temporal dynamics, we demonstrated that the plant mycobiota community was shaped by connectivity between patches of individual plants. Depending on the sampling time points, the presence of a corridor influenced root endospheric fungal structure, richness and/or evenness, and fungal diversity at all scales from the individual plant to plant patches and between patches. Our experimental design was based on the assumption that two phylogenetically distant plants such as B. pinnatum and T. repens were associated with different root fungal communities and could thus be used to simulate the isolation or connectivity of fungal assemblages associated with T. repens individuals. This assumption was confirmed throughout the experiment with contrasted fungal assemblages in the two plant species, but similar assemblages between T. repens mycobiota originating from patches or corridors, suggesting a strong host preference effect. Edge effects (Tscharntke et al ., 2012 ) were limited with little species spillover from the B. pinnatum matrix to T. repens patches despite the likely existence of contacts between the roots of the two plant species. Such a spillover edge effect was also absent in corridors, despite their longer edges and linear forms that result in a higher ratio between the length of edges to the amount of habitat that might have made them more prone to the influence of B. pinnatum growing outside the corridors. The rooting system of the host plants may provide available niches for fungal species with a certain host preference effect and correspond to particular habitats to colonize, or through which to disperse. Dispersal may involve not only a given organism or species but also small or large parts of communities leading to community coalescence (Rillig et al ., 2015 ), that is, the mixing of different communities, from which homogenization of species composition and community structure would be expected. The expected homogenization influence of corridors was not detected at the very beginning of the experiment but was detected 10–12 months later. This time‐lag response may be explained by the succession process involved in the assembly of fungi associated with plants throughout the course of their development from germination to the mature stage. Indeed, newly developing roots are initially randomly colonized by the microbial species present in the soil where the roots are developing (Dini‐Andreote & Raaijmakers, 2018 ; Hu et al ., 2020 ). This colonization can take place through fungal propagules, hyphal fragments, or spores that are present locally in the soil (Smith & Read, 2008 ). Then, during the course of their development, individual plants progressively select among the fungi colonizing the endosphere those that are the most favorable for their own development through active recruitment or filtration processes, such as the carbon rewarding process (Kiers et al ., 2011 ). As found in this study, plant filtering modifies species composition over time, with a decrease in fungal species evenness in fungal assemblages associated with individual plants. Our results suggest that in addition to this plant filtering process, the spatial configuration of the plant influences the process of fungal succession. The influence of the host plant configuration on root‐associated mycobiota has already been demonstrated in B. pinnatum plant species (Mony et al ., 2020b ), especially due to the functional connectivity it provided among plants (Mony et al ., 2020a ). The present study clearly demonstrates that this connectivity effect exists. To validate the assumptions about the dispersal and colonization process, it would be interesting to undertake complementary experiments with tagged fungi that could be surveyed over time in the presence and absence of corridors. This could be carried out using a range of fungi selected for their contrasted colonization abilities in order to obtain more detailed information to explain the community‐level dispersal we addressed here. Connected host plants displayed higher root endospheric mycobiota species richness and more similar species structure than isolated host plants Confirming our first prediction, we demonstrated that the presence of a corridor increased the endospheric fungal sequence cluster richness associated with T. repens roots, and that this effect was more pronounced with increasing age and in later phenological stages, notably after the plants had flowered. This effect on fungal species richness was accompanied by a change in species composition that was already detected at the first sampling time point and remained significant until the end of the experiment. Changes were observed earlier at the individual scale (in composition in October 2017, in richness in June 2018) than at the patch scale (in composition and richness in October 2018), reflecting a progressive process that eventually reached the entire population of host plants. In addition, we confirmed our second prediction of a more similar endospheric mycobiota among T. repens individuals in connected patches than in isolated ones. This was evidenced by a decrease in root endospheric mycobiota dissimilarity associated with connected T. repens individuals and patches compared with isolated ones. This effect on beta‐diversity was detected after June 2018, that is, 1 yr after the start of the experiment, and until the end of the experiment. This result confirms that, despite starting from a different and stochastic root endospheric mycobiota, corridors tend to homogenize root‐associated fungal composition among connected plants. Such time‐lag effects in response to landscape structure are referred to as a colonization credit and have been repeatedly demonstrated in the case of macroorganisms (Kuussaari et al ., 2009 ). There have only been a few demonstrations of such processes in microorganisms, especially in response to habitat connectivity (Mony et al ., 2022 ), probably because of the limited number of studies based on time‐series analysis of microbial composition. The present work demonstrates the importance of analyzing temporal dynamics in microbial response to host landscapes. In landscape ecology, the positive effect of the presence of corridors on diversity in the landscape is often explained by two main processes: facilitated dispersal among habitat patches (Rosenberg et al ., 1997 ; Beier & Noss, 1998 ) and/or an increase in habitat availability, which refers to the habitat amount hypothesis (Fahrig, 2013 ). Our results suggest that these processes may also apply to fungi, at least at the metric scale considered in this study. Dispersal of mycorrhizal fungi among patches via our experimental corridor may be driven by root contacts (Smith & Read, 2008 ), but endophytic fungi also disperse through the clonal development of stolons (Vannier et al ., 2018 , 2019 ). Additional dispersal processes may involve litter decomposition and a legacy effect on the soil reservoir after the plant leaf senescence stage that influenced the microbial composition of the sampling campaign in October, showing an obvious seasonal effect. Indeed, litter composition has a strong impact on soil fungal composition (Habtewold et al ., 2020 ). In addition to improved species coexistence, corridors led to species homogenization among patches by promoting preferential dispersal of particular fungi along the corridor, leading to more deterministic assemblages. As we predicted, root endospheric mycobiota of T. repens in connected patches were subjected to more deterministic processes than in isolated T. repens patches, suggesting a buffering effect of drift through corridors. Despite an existing fungal host plant preference confirmed herein, the drift effect in isolated patches could result from a priority effect. The first fungal colonizers have a major influence on subsequent community assembly, leading to divergent root endospheric mycobiota composition, with the strength of the drift effect expected to be positively correlated with the heterogeneity of available fungal propagules among disconnected patches. This is in line with the observation that among the most underrepresented fungi in patches with no corridor, we found an arbuscular mycorrhizal fungus ( Ambispora fennica , Fig. 5d ) for which an existing priority effect has already been demonstrated (Werner & Kiers, 2015 ). The other underrepresented fungi in the unconnected patches were mainly presumed saprotrophs (Table S3 ). The importance of habitat availability for microscopic fungal species is questionable, as many processes that define the carrying capacity of the roots of individual plants for fungi remain unknown. Interactions among microbial components (e.g. fungus–fungus and fungus–bacteria) that constitute the plant microbiota may indeed impact the carrying capacity of plants for microorganisms. Interactions include competition for habitat, available nutrients, exclusion through antimicrobial compounds and reciprocally, via facilitation processes such as cross‐feeding, cometabolism, evolution of dependencies (Mataigne et al ., 2021 ), but these ideas are still mostly theoretical and empirical support is needed. Beyond these microbial interactions that putatively at least partially explain microbial community assembly, we cannot exclude possible indirect effects of T. repens on soil characteristics and thereby habitat conditions for fungi. It has long been known that, like other Fabaceae species associated with a rhizobium, T. repens could enrich the soil in bioavailable nitrogen (Ruschel et al ., 1979 ). Changes in soil characteristics, and especially in nitrogen, are known to affect both fungal and bacterial activity in soil (Demoling et al ., 2008 ; Wang et al ., 2021 ), which possibly result in a change in root endospheric microbiota (Mareque et al ., 2018 ). Further work is necessary to disentangle the direct effect of T. repens corridors on microbial dispersal from changes in soil abiotic conditions resulting in changes in the soil microorganism reservoir. The presence of a corridor affected the fungal species that form the plant root endospheric mycobiota differently Corridors increased fungal richness in all phyla except Glomeromycota, both at the individual scale (except Basidiomycota) and at the patch scale, despite a wide range of dispersal strategies among phyla. The absence of an effect on Glomeromycota for the first four sampling occasions may result from a less pronounced host preference effect on this phylum, thereby increasing the influence of plants developing in the vicinity (Hausmann & Hawkes, 2009 ; Mony et al ., 2020a ). Because the roots of focal and neighboring plants may intermingle, exchanges of the entire set of the fungal assemblages may occur between the two habitats illustrating a potential coalescence process among host plants (Rillig et al ., 2015 ). At the last sampling occasion, the reduced richness in Glomeromycota in patches with a corridor may be related to a strong effect of competitive interactions among fungi belonging to Glomeromycota within roots (Maherali & Klironomos, 2007 ) driving species assembly. By contrast, symbiotroph richness increased over time in the presence of a corridor, probably because the disappearance of some Glomeromycota species released ecological niches for other symbiotic species. Certain components of the plant mycobiota at small spatial scales could be important, considering the wide range of functions fulfilled by fungi for plants (Vandenkoornhuyse et al ., 2015 ). The general results we obtained based on functional guilds revealed no significant effect of connectivity on plant pathogens, and only a weak effect on symbiotrophs and saprotrophs; however, these results should be interpreted with caution due to the lack of available information for almost half of the sequence clusters in our dataset. We found that specific taxa were depleted in isolated (four closely related sequence clusters and four fungi taxa) or selected in connected conditions (11 closely related sequence clusters and four fungi taxa). These taxa that were promoted by corridor connections were pathotrophs, symbiotrophs, and saprotrophs, while those that were disfavored by isolation were symbiotrophs and saprotrophs. This may be a consequence of competitive exclusion and competitive behavior for root colonization. More specifically, bad competitors may escape from their competitive superiors through better dispersal and can persist by finding new habitat; this could explain the coexistence of functionally redundant fungi (Smith et al ., 2018 ). Fungal competition–colonization trade‐offs could be seen as an important hypothesis to be further explored to link ecological functions and traits to community assembly and dynamics, possibly through experiments using fungal synthetic fungal communities to colonize plants. Future studies may focus more on specific taxa that are selected by connection or depleted by isolation, and develop in vitro complementary analysis to better describe their biological traits in order to understand their specific responses to connectivity. Corridors as a key to understanding plant‐associated mycobiota assembly: toward a concept of biotic corridors Through this work, which was based on a simple but robust experimental design with two patches of plants isolated or connected by a linear corridor of the same plants, we demonstrated that plants provide biotic corridors for fungi, with effects on alpha‐ and beta‐diversity of root‐associated fungal communities. Our results followed the same predictions as those concerning corridors for macroorganisms (Pardini et al ., 2005 ). Importantly, we detected such corridor effects at the community level despite the wide range of modes of dispersal. Community‐wide assessment of corridor effects is a recent topic in landscape ecology for macroorganisms (Uroy et al ., 2019 ), while this is one of the very first pieces of evidence for microorganisms (see Mony et al ., 2022 for a review on habitat corridor studies for microbes). Because such effects are demonstrated at the community level, these results also demonstrate that most fungi are limited to short spatial scales for their dispersal. In addition, ecological corridors are often defined by a habitat (Beier & Noss, 1998 ; Gilbert‐Norton et al ., 2010 ; Fletcher et al ., 2016 ). Here, we demonstrated that the corridors provided by host plants may provide suitable habitats for certain fungi to promote their exchanges between two connected patches, and mitigate the negative influence of isolation on the structure of endospheric mycobiota. The corridors could comprise biotic corridors that differ from the classical corridor concept because of the tight interactions of a host and its microbiota that override the effect of the characteristics of the physical environment (e.g. here habitat is better defined by the host plants than by the abiotic conditions). Such a biotic corridor concept could widely apply to microbes due to the large number of host‐associated microbes. Whether the corridor affects dispersal independently of a potential indirect effect of T. repens on soil characteristics or not remains an open question. Plants are known to be associated with preferential fungal assemblages. Here, we demonstrated that, for fungi, a host preference effect can also influence processes at the larger spatial scale of the micro‐landscape, thereby promoting connectivity of fungi from one patch of host plants to another. Such an effect could explain the marked spatial heterogeneity of fungi recorded even at small spatial scales (Bahram et al ., 2015 ). This paper illustrates a new concept of biotic corridors for microorganisms that offers opportunities to advance our theoretical understanding of fungal assembly, and for the manipulation of in situ mycobiota structure and its application in agriculture through plant configuration."
} | 7,097 |
30123641 | PMC6059151 | pmc | 6,787 | {
"abstract": "ABSTRACT Endolithic true fungi and fungus-like microorganisms penetrate calcareous substrates formed by living organisms, cause significant bioerosion and are involved in diseases of many host animals in marine ecosystems. A theoretical interactive model for the ecology of reef-building corals is proposed in this review. This model includes five principle partners that exist in a dynamic equilibrium: polyps of a colonial coelenterate, endosymbiotic zooxanthellae, endolithic algae (that penetrate coral skeletons), endolithic fungi (that attack the endolithic algae, the zooxanthellae and the polyps) and prokaryotic and eukaryotic microorganisms (which live in the coral mucus). Endolithic fungi and fungus-like boring microorganisms are important components of the marine calcium carbonate cycle because they actively contribute to the biodegradation of shells of animals composed of calcium carbonate and calcareous geological substrates.",
"conclusion": "General comments/conclusions Endoliths are important components of the marine calcium carbonate cycle because they actively contribute to the biodegradation of shells of dead animals composed of calcium carbonate and calcareous geological substrates. They have been implicated as a causative agent of shell diseases in live corals, molluscs and other invertebrate animals which have shells composed of calcium carbonate (Golubic 1969 ; Kohlmeyer 1969 ; Che et al. 1996 ; Golubic et al. 2005 ; Zuykov et al. 2014 ). Endolithic microorganisms have important roles as saprotrophs in bio-erosion of many calcium carbonate substrates, as parasites on the production of commercially important animal species, regulate biodiversity in marine ecosystems, and they respond to environmental factors which are involved significant components of global climate change. Heterotrophic endoliths can destroy the shells of animal species living in marine ecosystems or bioerode dead shells buried in the sediment (Kendrick et al. 1982 ; Raghukumar and Lande 1988 ). The dissolution of calcium carbonate in bioerosion causes the release of carbon dioxide into the marine environments, which increases acidification. Calcareous substrates contain large amount of carbon. Therefore, heterotrophic endoliths are key players in the marine calcium carbonate cycle. The total amount of global calcareous substrates in sediments in the ocean has not been accurately estimated. However, as carbon dioxide from bioerosion of calcium carbonate in the ocean eventually enters the atmosphere, large losses in calcareous substrates in carbon sinks would be expected to result in increased heat retention by the atmosphere, increasing global mean temperatures. If rising temperatures and acidity in the ocean increase the rate of growth of endolithic fungi, this could provide a positive feed-back mechanism potentially accelerating the rate of climate change. Many studies suggest that the prevalence of emerging infectious diseases is currently increasing in all ecosystems including coral reefs (e.g. Fisher et al. 2012 ; Burge et al. 2013 ). Heterotrophic fungal endoliths comprise a group which contains parasites and which can cause diseases in corals. Since fungal endoliths are present in both healthy and diseased corals, they are considered to be opportunist parasites, which can cause disease but only under certain environmental conditions. Probably stress, depression of immune responses and global climate change factors are all important in triggering disease. Nonetheless, if emerging infectious disease causes large losses in population sizes and in biodiversity, the lost carbon will undoubtedly enter the atmosphere as carbon dioxide produced by respiration and further contribute to global climate change.",
"introduction": "Introduction Importance of this research topic During the past three decades, the prevalence and the rate of transmission of emerging infectious diseases, and the frequency of epizootics increased significantly in both terrestrial and aquatic ecosystems, primarily due to social, demographic and environmental transformations (Wilcox and Gubler 2005 ; Fisher et al. 2012 ; Burge et al. 2013 ). It is extremely important to thoroughly understand host–parasite interactions in these times of environmental and climate change so that better management practices for preserving both wild and cultivated species and species diversity can be designed and implemented. Species of endolithic fungi are known to cause significant bioerosion and diseases of many host animals in marine ecosystems. Unfortunately, our knowledge of the ecological functions of these microorganisms is only superficial. This and the subsequent reviews focus on current knowledge of true fungal, algal, stramenopilian (eukaryotic) and cyanobacterial (prokaryotic) endolithic parasites in marine environments and their ecological functions. The basic concepts of ecology related to rock penetrating microorganisms including lichens and mycorrhizal fungi must be discussed first. Rock penetrating or rock boring microorganisms can be divided into two groups: those which penetrate calcareous substrates formed by living organisms and those which penetrate substrates formed by geological processes. Endoliths offer excellent model systems for the study of the interaction between physical and biological factors in microbial ecology, geobiology and astrobiology. A theoretical interactive model for the ecology reef-building corals According to Bentis et al. ( 2000 ), reef-building corals appear to exist in dynamic equilibrium with four principal partners: (1) interconnected polyps of a colonial coelenterate, (2) endosymbiotic dinoflagellate zooxanthellae residing in the host’s endoderm, (3) endolithic algae that penetrate coral skeletons and (4) endolithic fungi that attack one or more of the endolithic algae, the zooxanthellae and the polyps. In our opinion, the collection of prokaryotic and eukaryotic microorganisms in the coral mucus needs to be included as a partner as well (number 5) ( Table 1 ). These can be either beneficial or harmful (Harel et al. 2008 ). In this revised model, the five principal partners are actually populations including many different genotypes. The composition and interactions of these five partners are controlled by environmental factors. 10.1080/21501203.2017.1352049-T0001 Table 1. Interactions between components of the bottom part of a generalised coral reef food web. Partners Site Trophic type Trophic level Direction of energy flow 1) Interconnected polyps Inside Skeleton Heterotrophic Primary consumer From external food, Zooxanthellae and Endolithic Algae to coral tissues 2) Zooxanthellae Endoderm Polyp Tissue Autotrophic Producer Provides carbon nutrients for polyp tissues 3) Endolithic algae Within skeleton Autotrophic Producer Provides carbon nutrients for polyp tissues and Endolithic fungi 4) Endolithic fungi Within skeleton Heterotrophic Primary consumer From endolithic algae and proteins inside the skeleton 5) Fungi and bacteria in coral mucus Outside skeleton Heterotrophic Primary consumers From secreted nutrients or from coral tissue when parasitic 6) Zooxanthellae Free-living in water Autotrophic Producers Provides nutrients for parasites and predators when released 7) Parasitic dinoflagellates Free-living in water Heterotrophic Primary consumers From living and atrophied zooxanthellae when released from coral tissues 8) Zooplankton protists and small animal predators Free-living in water Heterotrophic Primary or secondary consumers From all dinoflagellates, other phytoplankton and fungi 9) Endolithic algae In environmentoutside corals Calcareous sediments /substrates Autotrophic Producers Provides nutrients for Parasites and Predators 10) Endolithic fungiin coral reefenvironment Calcareous sediments/ substrates Heterotrophic Primary consumer From endolithic algae and proteins inside calcareous structures Finally, the dynamics of populations of heterotrophic dinoflagellate parasites of the zooxanthellae, other protists and small animals which are parasites, predators or grazers on any of the other partners, as well as their predators (numbers 6, 7 and 8), and endolithic fungi and algae in the environment outside corals (numbers 9 and 10) needs to be considered as parts of this model. This extends the model to include the entire coral reef food web ( Table 1 ). Kendrick et al. ( 1982 ) isolated into pure culture a number of bioeroding fungi from the interior of the aragonite skeleton of living corals in the Caribbean and South Pacific, most of which were dikaryomycotan anamorphs. These fungi are thought to be a major cause of bioerosion in coral reef ecosystems. Although fungi undoubtedly play many important ecological roles in coral ecosystems, they have been largely ignored in the past (Bentis et al. 2000 ). Our current and very limited knowledge of the roles of fungi in corals and coral reef ecosystems has been reviewed in detail by Raghukumar and Ravindran ( 2012 ). Corals, lichens and mycorrhizae are all symbiotic relationships involving fungi. Primary objectives of this review Many species of true fungi, fungus-like microorganisms and algae are known to bore into solid rock, sand grains and shells. In this review, we discuss what is known about the different types of rock penetrating endoliths with emphasis on marine species of true fungi which bore into corals and briefly describe their morphology, life history, mechanisms of infection, general roles in ecology, host substrate interactions, participation in the marine calcium carbonate cycle and the possible effects of global climate change on growth."
} | 2,412 |
23417095 | PMC3659820 | pmc | 6,788 | {
"abstract": "Efforts to improve the production of a compound of interest in Saccharomyces cerevisiae have mainly involved engineering or overexpression of cytoplasmic enzymes. We show that targeted expression of metabolic pathways to mitochondria can increase production levels compared with expression of the same pathways in the cytoplasm. Compartmentalisation of the Ehrlich pathway into mitochondria increased isobutanol production by 260%, whereas overexpression of the same pathway in the cytoplasm only improved yields by 10%, compared with a strain overexpressing only the first three steps of the biosynthetic pathway. Subcellular fractionation of engineered strains reveals that targeting the enzymes of the Ehrlich pathway to the mitochondria achieves higher local enzyme concentrations. Other benefits of compartmentalization may include increased availability of intermediates, removing the need to transport intermediates out of the mitochondrion, and reducing the loss of intermediates to competing pathways.",
"introduction": "Introduction Metabolic engineering of cytoplasmic biosynthetic pathways to create industrial strains of S. cerevisiae is commonplace, whereas engineering of biosynthetic pathways that function in mitochondria has largely been ignored. Yet, mitochondria have many potential advantages for metabolic engineering, including the sequestration of diverse metabolites, such as heme, tetrahydrofolate, ubiquinone, α-ketoacids, steroids, aminolevulinic acid, biotin, and lipoic acid 1 - 15 . In addition, mitochondria contain intermediates of many central metabolic pathways, including the tricarboxylic acid (TCA) cycle, amino acid biosynthesis and fatty acid metabolism 3 , 8 , 16 , 17 . The environment within the mitochondrial matrix differs from the cytoplasm, including higher pH, lower oxygen concentration, and a more reducing redox potential 18 - 20 . This environment may more closely match the optimal for maximal activity of many enzymes such as the iron-sulfur clusters (ISC), which are essential cofactors of enzymes in diverse pathways including branched chain amino acid and isoprenoid biosynthetic pathways, and which are synthesized exclusively in mitochondria 21 . Although ISCs can be exported to the cytoplasm, the molecular machinery that loads ISCs onto extramitochondrial enzymes is likely to be incompatible with most exogenous ISC-apoenzymes, especially those of bacterial, or archaeal origin 22 , 23 . The smaller volume of mitochondria, could concentrate substrates favoring faster reaction rates and productivity and confine metabolic intermediates avoiding repressive regulatory responses, diversion of intermediates into competing pathways or even toxic effects of intermediates to cytoplasmic or nuclear processes. To take advantage of the potential attributes of the mitochondrial environment, we engineered yeast mitochondria to produce three advanced biofuels, namely isobutanol, isopentanol and 2-methyl-1-butanol (collectively called fusel alcohols). Isobutanol is synthesized in yeast by the valine Ehrlich degradation pathway 24 , but can also be produced from pyruvate in a biosynthetic pathway that recruits the upstream pathway of valine biosynthesis ( Fig. 1 ). The upstream isobutanol pathway, between pyruvate and α-ketoisovalerate (α-KIV), comprises acetolactate synthase (ALS, ILV2 ), ketolacid reductoisomerase (KARI, ILV5 ) and dehydroxyacid dehydratase (DADH, ILV3 ), ( Fig. 1 ). The downstream isobutanol pathway comprises α-ketoacid decarboxylase (α-KDC) and alcohol dehydrogenase (ADH). The complete biosynthetic pathway for isobutanol production has been engineered in bacteria 25 - 30 . In yeast, the isobutanol biosynthetic pathway is complicated by subcellular compartmentalization: the upstream part of the pathway is confined to mitochondria 3 , whereas the downstream part of the pathway is confined to the cytoplasm (Ehrlich pathway 24 ) ( Fig. 1A ). Therefore the simple overexpression of all the enzymes in the isobutanol pathway could create a significant bottleneck in which the transport of intermediates across membranes reduces productivity and enables these intermediates to be consumed by competing pathways. Nevertheless, the isobutanol pathway has been partially constructed in yeast, by overexpressing only some of the enzymes, and in their natural compartments, to increase isobutanol production 31 - 33 . Although efforts have been made to transfer the isobutanol pathway, partly or completely, to either compartment 34 (upstream to cytoplasm 35 , 36 , or downstream to mitochondria 37 , 38 ), there has been no direct comparison of the effects of mitochondrial versus cytoplasmic engineering of downstream enzymes in fully assembled pathways. Yeast mitochondria have been exploited through gene targeting to produce hydrocortisone 39 and plant terpenoids 40 ; however, these pathways were split across multiple subcellular compartments. In this study we engineer strains with complete isobutanol pathways overexpressed in mitochondria to avoid pathway sub-compartmentalization ( Fig. 1B ), and compare their fusel alcohol production levels with those of strains in which all enzymes of the isobutanol pathway are overexpressed in their natural compartments (divided into an upstream mitochondrial and downstream cytoplasmic pathways; Fig. 1A ). We show that moving the complete pathway into a single compartment (mitochondria) results in a substantial increase in the production of fusel alcohols, and provide evidence that this increase is at least partly due to a higher availability of the key α-KIV intermediate, and increased local enzyme concentrations due to mitochondrial compartmentalization. To reconstruct multiple isoforms of the isobutanol pathway we developed a standard, flexible set of vectors (pJLA vector series) that facilitates targeting of identical pathways to either the cytoplasm or mitochondria. These new tools enabled rapid assembly and comparison of eighteen isoforms (or “isopathways”) of the complete isobutanol pathway, in which their downstream enzymes are targeted to either the mitochondria or the cytoplasm, but are in every other way identical. This approach enabled us to measure the effect of mitochondrial engineering of isobutanol pathways for the production of isobutanol, isopentanol and 2-methyl-1-butanol.",
"discussion": "Discussion Our results show that targeting the entire isobutanol pathway to mitochondria significantly improves its titer, yield and productivity, compared with a partly cytoplasmic equivalent. Isobutanol production is substantially increased when the overexpression of the downstream enzymes (α-KDC and ADH) is targeted to mitochondria rather than to the cytoplasm. To measure the effect of mitochondrial engineering, we analyzed the isobutanol titers obtained in high cell-density fermentations in minimal media when downstream enzymes, supplementing the overexpression of ILV genes, are targeted to either mitochondria or the cytoplasm ( Fig. 2a and b ). In this manner, JAy153 (with mitochondrial Ll-kivd and Sc-adh7) produces 486 ± 36 mg/L of isobutanol, whereas JAy166 (with the same downstream enzymes targeted to cytoplasm) produces 151 ± 34 mg/L. Since JAy38 (which overexpresses only the ILV genes) produces 136 ± 23 mg/L of isobutanol, the effect of targeting Ll-kivd and Sc-adh7 to mitochondria is approximately a 260% improvement in isobutanol titers, as opposed to a maximum improvement of 10% seen when the same enzymes are targeted to the cytoplasm (JAy166 isobutanol titers are the highest of all strains overexpressing cytoplasmic downstream enzymes). ( Fig. 2a and b ). Our combinatorial constructs permitted a rapid comparison of the efficacy of native and heterologous enzymes for isobutanol production. The choice of decarboxylase in the downstream isobutanol pathway produced significant effects, whereas the activity of the three selected dehydrogenases seemed equivalent ( Fig. 2a and b ). The specific α-KDC homologue used has an important impact on titers of isobutanol, with Ll-kivd and Sc-aro10 being substantially more active than Sc-kid1. By contrast, the three ADHs selected in this study are roughly equally active, despite the reported NADPH dependence of Sc-adh7 42 , the improved affinity for isobutyraldehyde of Ll-adhA RE1 26 , and the ability of Ec-fucO to reduce relatively large aldehydes 43 . Isopentanol and 2-methyl-1-butanol titers are also increased in strains containing mitochondrial isobutanol pathways. These increased titers are likely owing to: (1) expression of the upstream ILV genes ( ILV2 , ILV3 and ILV5 ), also involved in the biosynthetic pathways of leucine and isoleucine, resulting in the production of the relevant α-ketoacids (α-ketoisocaproate and α-keto-3-methylvalerate, respectively), ( Fig. 4C ); (2) the two α-ketoacid precursors to these alcohols being suitable substrates for the overexpressed α-KDCs, with Ll-kivd having a bias for isopentanol production, Sc-aro10 higher activity for 2-methyl-1-butanol production, and Sc-kid1 no detectable activity; and (3) the aldehydes they produce being potential substrates for the three overexpressed ADHs, which have no apparent preference for the production of either alcohol ( Fig. 4 and Supplementary Table 4 ). In common with isobutanol production, the overexpression of α-KDCs and ADHs increases the production of isopentanol and 2-methyl-1-butanol but only when these enzymes are targeted to mitochondria. Moreover, the effects of mitochondrial engineering on the production of isopentanol and 2-methyl-1-butanol in high cell-density fermentations are even larger than those observed for isobutanol. Overexpression of downstream enzymes of the isobutanol pathway in mitochondria increased isopentanol and 2-methyl-1-butanol titers (from the overexpression of ILV genes alone) by as much as 370% and 500%, respectively. In the case of 2-methyl-1-butanol (as with isobutanol), this is probably due to the fact that its α-ketoacid precursor, α-keto-3-methylvalerate, is synthesized in mitochondria, and thus has higher availability in this organelle. However, the case of isopentanol is paradoxical since its α-ketoacid precursor, α-ketoisocaproate, is synthesized in the cytoplasm. It is possible that mitochondrial pathways, by consuming α-ketoisovalerate in mitochondria, mitigate the repression that ILV gene overexpression is likely to effect on α-ketoisocaproate biosynthesis (and thus isopentanol production), due to the tight regulation of leucine biosynthesis 3 . Mitochondrial engineering offers a more frugal way to direct metabolic flux towards isobutanol production, compared with other possible isobutanol pathway configurations. Pathways in which the downstream enzymes are expressed in the cytoplasm, and separated from the mitochondrial upstream pathway (as in strains JAy166-JAy174) might benefit from the overexpression of branched-chain aminoacid aminotransferases ( BAT1 and BAT2 , Fig1A ), as suggested by the overexpression of cytoplasmic BAT2 31 . However, in order to exploit the increased abundance of α-ketoisovalerate provided by the upstream pathway in this configuration, it would be necessary to overexpress at least one, and most likely two additional genes ( BAT1 and BAT2 ), compared to fully mitochondrial pathways. This not only increases the engineering burden, but also increases the chances of off-target effects, such as the possibility of overproducing valine as a side product. Thus, mitochondrial engineering offers a more efficient way to direct the increased metabolic flux provided by overexpressed ILV genes towards isobutanol, without invoking valine as an intermediate. Expression of the complete isobutanol pathway in the cytoplasm by overexpression of ILV2 , ILV3 and ILV5 simply lacking mitochondrial localization signals does not increase isobutanol production 33 , 35 , 36 , because these enzymes themselves are tailored for the mitochondrial environment. Increased isobutanol production in the cytoplasm might be achieved by other means but requires, among other things, extensive modifications of all upstream enzymes (all of heterologous origin), overexpression of native and heterologous iron-sulfur cluster assembly and insertion machineries (to obtain cytosolically active ILV3 homologues), and overexpression of multiple native and heterologous chaperones 36 . These numerous manipulations contrast with the modest changes required to achieve an approximately 5-fold increase in isobutanol production by overexpressing the native ILV genes in their natural environment of mitochondria. The titers, yields and productivities of isopentanol and 2-methyl-1-butanol we achieved ( Table 1 ) with JAy161, are the highest ever reported. Our isobutanol productivity is more than double than the highest reported in the literature 35 , and our titers are as high as the highest ever reported, but our strains required higher sugar concentrations. Furthermore, our strains produced substantial yields ofisobutanol in complete media containing valine, whereas engineering the complete pathway for overexpression in the cytoplasm rendered it sensitive to inhibition by valine 35 , a nutrient that is likely to be present in industrial feedstocks. While these alcohols are known to be toxic, even our best isobutanol producers did not show any reduced fitness, measured by growth rate or maximal cellular density. The challenge to further improve yields, titers and productivities will require diverting more carbon flux, from ethanol to branched-chain alcohols production 32 , 44 . The strategy of organelle targeting has been applied in plants, where portions of the isoprenoid pathway have been targeted to plastids of the tobacco plant 45 . Unlike our study, the pathways compared in tobacco plant cytoplasm (mevalonate pathway) and chloroplast (methylerythritol pathway) were not identical, and only their downstream enzymes were overexpressed. Nevertheless, the targeting of a metabolic pathway to the plant plastid, as with our redirection of the isobutanol pathway to yeast mitochondria, resulted in marked increases in the production levels of the desired end-products. The sequestration of the isoprenoid pathway in plastids, in common with the confinement of the isobutanol pathway to mitochondria in yeast, might benefit from higher concentrations of enzymes, substrates and cofactors, which would favor higher productivities. We have shown that for isobutanol production in yeast, sequestration of the pathway in the mitochondrion results in higher enzyme concentrations, probably due to their confinement in the relatively smaller volume of the mitochondrial compartment. Moreover, the key α-KIV intermediate is limiting in the cytosol, but not in mitochondria; thus targeting the full pathway to mitochondria benefits from increased α-KIV availability, eliminates the bottleneck of exporting α-KIV to the cytoplasm, and reduces the loss of α-KIV to competing reactions. Pathways targeted to mitochondria may be further enhanced by mutagenesis. Pathways that are naturally cytoplasmic might also benefit from mitochondrial compartmentalization, as the confinement of enzymes and metabolites to subcellular compartments may result not only in an increase in their local concentrations and proximities, but also in the ability to reduce the toxicity of pathway intermediates, bypass inhibitory regulatory networks, or avoid competing pathways. Thus, subcellular metabolic engineering has the potential to provide multiple mechanisms to improve the performance of engineered pathways."
} | 3,909 |
35264928 | PMC8900719 | pmc | 6,789 | {
"abstract": "In bottom-up neuroscience, questions on neural information processing are addressed by engineering small but reproducible biological neural networks of defined network topology in vitro . The network topology can be controlled by culturing neurons within polydimethylsiloxane (PDMS) microstructures that are combined with microelectrode arrays (MEAs) for electric access to the network. However, currently used glass MEAs are limited to 256 electrodes and pose a limitation to the spatial resolution as well as the design of more complex microstructures. The use of high density complementary metal-oxide-semiconductor (CMOS) MEAs greatly increases the spatial resolution, enabling sub-cellular readout and stimulation of neurons in defined neural networks. Unfortunately, the non-planar surface of CMOS MEAs complicates the attachment of PDMS microstructures. To overcome the problem of axons escaping the microstructures through the ridges of the CMOS MEA, we stamp-transferred a thin film of hexane-diluted PDMS onto the array such that the PDMS filled the ridges at the contact surface of the microstructures without clogging the axon guidance channels. This method resulted in 23 % of structurally fully connected but sealed networks on the CMOS MEA of which about 45 % showed spiking activity in all channels. Moreover, we provide an impedance-based method to visualize the exact location of the microstructures on the MEA and show that our method can confine axonal growth within the PDMS microstructures. Finally, the high spatial resolution of the CMOS MEA enabled us to show that action potentials follow the unidirectional topology of our circular multi-node microstructure.",
"introduction": "Introduction How networks of biological neurons process, store and retrieve information remains an unanswered question to this day, despite a vast amount of tools available to establish neural interfaces. Patch-clamp technique has enabled highly sensitive recordings and stimulation of isolated neurons down to the level of single ion channels (Johansson and Arhem, 1994 ). However, the patch-clamp technique requires a high level of technical expertise and the requirement for sequential patching of each individual neuron highly restrict the throughput and application of the technique. On the other hand, functional magnetic resonance imaging (fMRI) allows for an analysis of neuronal dynamics in large areas of brain tissue at the cost of limited temporal and spatial resolution (Lewis et al., 2016 ). To overcome those limitations, microelectrode arrays (MEAs) have been developed and applied to study the behavior of neural networks in vitro (Bakkum et al., 2008 ), ex vivo (Ha et al., 2020 ), and even in vivo (Wei et al., 2015 ). Such devices allow for simultaneous extracellular recording and stimulation at up to several thousand electrodes across the spatial extent of the electrode array. However, in all cases the vast amount of interference of the measured neural circuit with other neurons and the high degree of variability between brains and brain explants of even the same species prevented true understanding on how even small neural networks operate. A true understanding of neural networks would mean that we can combine neural elements with defined connectivity to generate a network with a predictable output. This approach to reverse engineer the brain from its fundamental unit—the single neuron—is termed bottom-up neuroscience. To enable bottom-up neuroscience research, it is essential to gain electrical modulatory access to individual neurons and have full control over the network topology. A distinct network topology can be achieved by confining the adhesion sites of neurons and influence the growth-direction of axons by chemical surface patterning (Jang and Nam, 2012 ), the usage of microstructures (Renault et al., 2016 ; Ming et al., 2021 ), and other techniques involving electrokinetic (Honegger et al., 2013 ) or optical guidance (Stevenson et al., 2006 ). From all mentioned techniques, the physical confinement of cells in microstructures is considered the most reliable method to establish engineered biological neural networks in vitro (Aebersold et al., 2016 ). Using polydimethylsiloxane (PDMS) microstructures, any network topology consisting of multiple compartments interconnected with thin axon guidance channels can be generated. Thus, PDMS microstructures allow full control over topology-relevant parameters such as the distance and directionality of connections between neurons. Previously, this method was used to implement engineered biological neural networks on 60-channel glass MEAs (Forró et al., 2018 ; Girardin et al., 2021 ; Ihle et al., 2022 ). The high compatibility of PDMS microstructures with the flat surface of standard glass MEAs allows the alignment of channels and microcompartments on top of each of the 60 electrodes (Forró et al., 2018 ). The combination of MEAs with PDMS microstructures defines the connectivity of small neural populations and allows for selective electrical access to the neural culture. One key limitation of this approach is the limited number of electrodes that restricts PDMS design options and requires a tedious process of structure-to-electrode alignment. More importantly, the high electrode pitch in the order of 100 μm present in these MEAs only allows the detection of electrical activity of multiple axons growing within the channels of the microstructure ( Figure 1A ). Figure 1 Comparison of recording electrode densities on 60-channel glass MEAs and CMOS MEAs. (A) Commonly used 60-channel glass MEAs allow for simultaneous recording from an engineered biological neural network at 4 electrodes (blue dots) located at the microstructure channels. (B) The same microstructure on a CMOS MEA is covered with more than 700 electrodes, allowing for both axonal and somatic signal acquisition. (C) Electron micrograph of the chip surface. While the glass MEAs are flat, the surface of the CMOS MEA is non-planar due to recessed electrodes and trenches between columns of electrodes. Such a surface topology impairs the adhesion of the PDMS microstructure to the surface compromising its cell and axon guidance characteristics. In recent years, the advent of complementary metal-oxide-semiconductor (CMOS) microelectrode array technology has led to a massive expansion in the number of electrodes per array (Obien et al., 2015 ). By including filtering and amplification stages directly on the chip in the vicinity of each microelectrode, wiring of the electrode to external signal processing elements becomes less susceptible to noise and high electrode densities can be realized (Heer et al., 2006 ). Commercially available chips offer up to 26,400 electrodes confined within a sensing area of 3.85 × 2.10 mm 2 (Müller et al., 2015 ). The high electrode density enables action potential propagation tracking with subcellular resolution (Bakkum et al., 2013 ) as well as the stimulation of single neurons (Ronchi et al., 2019 ). Upcoming, experimental stage CMOS MEAs offer the opportunity to record the electrical activity from neural cultures at even up to 19584 recording sites simultaneously (Yuan et al., 2021 ). Combining CMOS arrays with PDMS microstructures would enhance the number of recording sites by orders of magnitude ( Figure 1B ) with respect to 60-channel glass MEAs and enable high resolution recording from neurons of defined connectivity. In the past, CMOS MEAs were used in combination with PDMS microstructures to isolate cell bodies from axons and record axonal signals along microchannels running in parallel to electrode columns of the CMOS MEA (Lewandowska et al., 2015 ). While neuron confining microstructures work very well on standard glass MEAs, so far it was very difficult to transfer this methodology to CMOS MEAs due to its complex 3D topography ( Figure 1C ). Here, we show the feasibility to use such microstructures with CMOS MEAs for the first time, but without a direct comparison to glass MEAs. In particular present a method to glue PDMS microstructures with feature sizes down to 5 × 4 μm 2 onto the uneven surface of a non-planar CMOS MEA and show that this method confines cell bodies and axons within the microstructures. Moreover, we developed an impedance-based protocol to identify the exact location of all microstructure elements, which enables selective routing of almost all underlying electrodes. Finally, we show that our method enables us to track the spatiotemporal spread of action potentials within our directional circular four-node microstructure networks at a very high signal-to-noise ratio.",
"discussion": "Discussion Combining PDMS microstructures with glass MEAs enables axon guidance across electrodes and the formation of any desired network topology. In order to increase the electrode density, we aimed to transfer this technology to high density CMOS microelectrode arrays. However, mounting PDMS microstructures onto CMOS MEAs has so far resulted in axons escaping from the channels due to the non-planar surface of the CMOS chip. In this study, we demonstrated a stamp transfer method using hexane diluted PDMS (1:9, PDMS:hexane) to bond PDMS microstructures onto CMOS chips. Successful stamp transfer depended on a thin PDMS film to be transferred from the PDMS-wafer contact surface onto the CMOS chip without clogging the PDMS microchannels, while still providing excess PDMS to fill the ridges on the CMOS to prevent axonal escape. Successful stamp transfer of PDMS microstructures with 10 μm tall channels onto flat surfaces has already been shown by Wu et al. ( 2005 ). In their work, a 400-nm thick PDMS film was ideal for a permanent bond without clogging the channels. The presented dilution of 1:9 (PDMS:hexane) does not rely on any pressure to be applied to seal off the uneven CMOS surface. Instead reliable stamp transfer is achieved by (1) creating a very thin homogeneous layer of PDMS on the wafer (Con and Cui, 2013 ; Lee et al., 2019 ) and (2) by avoiding any horizontal movement of the PDMS microstructure on the wafer and CMOS surface during the transfer. Diluting PDMS in a solvent enables spincoating of a very thin layer of PDMS at feasible rotation speeds. Using undiluted PDMS would require much higher rotation speeds that are hard to achieve with most spincoaters. Since hexane evaporates within seconds after spincoating (Abbott, 2018 ) we are most likely stamp transferring a thin layer of undiluted PDMS. Using other solvents with similar evaporation rates would most likely not improve the reliability of our method. However, it might be worth testing solvents with a lower evaporation rate that would enable stamp transfer of solvent diluted PDMS that might fill the ridges more reliably due to the expected lower viscosity of the diluted solution. Small axon guidance channels have the advantage that axons will not spontaneously turn around and grow in reverse direction. Moreover, they enable a wider repertoire of network architectures in a more compact arrangement. We have successfully bonded channels with sizes down to 10 × 4 μm onto the CMOS chip. However, channels smaller than 10 μm width frequently got clogged. The stamp transfer method relies on a balance between transferring a layer of PDMS that is thin enough to not clog the small PDMS microchannels but sufficiently thick to fill and seal the ridges on the CMOS array. Thus, we believe it would be difficult to further optimize our method for smaller channels. The microstructures in this work are from Forró et al. ( 2018 ) and as such, these were not optimized for the use on high density MEAs. We believe that the width of the 5 × 4 μm channel should not have an effect on the redirection capabilities of axons. Hence, increasing the size of these thin channels in future designs adapted for the use on CMOS MEAs may prevent clogging which would increase the yield of the presented method while ensuring that the directionality induced by the microstructure design is not compromised. In our previous publication (Forró et al., 2018 ), we have already demonstrated how the asymmetric design of the PDMS microstructures translates into a more directional signal propagation. We expect that by controlling the morphology and directionality of axonal growth the neural networks become more predictable compared to a random culture. The circular 4-node network presented in this work represents one example of a defined neural network but can be changed to create directional circuits that exist in vivo , e.g., retinothalamic circuits or feedback loops. The main aim of using our asymmetric PDMS microstructures however is to control how neurons connect with each other and thereby increase reproducibility of the flow of neuronal information. Indeed multicompartment models without guided axon growth are already used on CMOS arrays to distinguish input and output neurons to interact and control an, e.g., virtual reality setup (Kagan et al., 2021 ). The limited number of electrodes on the glass MEA restricted the measurement of spikes to few locations between the nodes resulting in a low electrode-to-neuron ratio. This makes spike sorting more difficult and assumptions on signal propagation speed and synaptic delays had to be made to assess how the signal travels through the microstructure (Forró et al., 2018 ). The high density of electrodes and the increased signal-to-noise ratio of the CMOS arrays however enables us to identify the origin and measure the propagation direction and speed of action potentials within each circuit (Emmenegger et al., 2019 ). Although we have not done any spike sorting in this work, the higher ratio of electrodes per neuron should facilitate classification of spikes to each neuron (Diggelmann et al., 2018 ). Altogether these features will enable to answer questions such as how potential drug candidates affect signal propagation and/or synaptic transmission on a single neuron level (Emmenegger et al., 2019 ). Finally the high density of electrodes on the CMOS array enables more flexible PDMS microstructure designs with a higher number of single PDMS circuits per array thereby increasing the throughput. Gaining high reproducibility in experiments using random in vitro neural networks has been a big challenge so far (Wagenaar et al., 2006 ; Keren, 2014 ; Napoli et al., 2014 ) and is the reason why the development of central nervous system (CNS)-related drugs has a longer development time and a lower success rate (Gribkoff and Kaczmarek, 2017 ). Using our directional PDMS microstructures mounted on CMOS arrays enables the design of multiple reproducible neural circuits with more predictable activity patterns that were shown to be highly stable under repeated stimulation (Ihle et al., 2022 ). One main reason why only 5 circuits showed the expected activity pattern is that we did not yet gain full control on the number of neurons per well. We expect that combining our approach with single cell placing techniques (Martinez et al., 2016 ) to seed ideally only one neuron per well will increase reproducibility and predictability of our small neural networks. Any drug-induced changes on neural network activity will thus be easier to interpret which should enhance the predictive power on how drug candidates will perform in clinical trials (Charvériat et al., 2021 ). In this work, we have shown an easy-to-apply method to combine high density CMOS microelectrode arrays with complex PDMS microstructures. Moreover, we developed a Python script to identify the exact location of the electrodes on a CMOS MEA for targeted recording and stimulation. The high density of electrodes on a CMOS chip will enable the design of more complex circuits. Moreover, the circuit designs don't need to be adapted to the sparse electrode grid of a glass MEA but can be arranged freely to allow for a more dense arrangement of single circuits. The high density of electrodes eliminates the need to align the PDMS microstructures to the electrodes through a time-consuming manual alignment procedure facilitating a wider adoption of this technology for bottom-up neuroscience community. The ability to record from neural networks of any predefined topology on a high resolution CMOS MEA will enable bottom-up neuroscience research to ask new questions on fundamental neuroscience concepts."
} | 4,111 |
34363004 | PMC8776821 | pmc | 6,790 | {
"abstract": "The symbiont “ Candidatus Aquarickettsia rohweri” infects a diversity of aquatic hosts. In the threatened Caribbean coral, Acropora cervicornis , Aquarickettsia proliferates in response to increased nutrient exposure, resulting in suppressed growth and increased disease susceptibility and mortality of coral. This study evaluated the extent, as well as the ecology and evolution of Aquarickettsia infecting threatened corals, Ac. cervicornis , and Ac. palmata and their hybrid (“ Ac. prolifera ”). Aquarickettsia was found in all acroporids, with coral host and geographic location impacting the infection magnitude. Phylogenomic and genome-wide single-nucleotide variant analysis of Aquarickettsia found phylogenetic clustering by geographic region, not by coral taxon. Analysis of Aquarickettsia fixation indices suggests multiple sequential infections of the same coral colony are unlikely. Furthermore, relative to other Rickettsiales species, Aquarickettsia is undergoing positive selection, with Florida populations experiencing greater positive selection relative to other Caribbean locations. This may be due in part to Aquarickettsia proliferating in response to greater nutrient stress in Florida, as indicated by greater in situ replication rates in these corals. Aquarickettsia was not found to significantly codiversify with either the coral animal or the coral’s algal symbiont ( Symbiodinium “ fitti ”). Quantitative PCR analysis showed that gametes, larvae, recruits, and juveniles from susceptible, captive-reared coral genets were not infected with Aquarickettsia . Thus, horizontal transmission of Aquarickettsia via coral mucocytes or an unidentified host is more likely. The prevalence of Aquarickettsia in Ac. cervicornis and its high abundance in the Florida coral population suggests that coral disease mitigation efforts focus on preventing early infection via horizontal transmission.",
"introduction": "Introduction The alpha-proteobacterium “ Ca . Aquarickettsia rohweri” is a recently discovered bacterial symbiont of many aquatic hosts from around the world [ 1 ]. This symbiosis, e.g., a persistent association between two organisms, occurs in reef-building corals (scleractinians), as well as other cnidarians, sponges, and ctenophores [ 1 ]. Although fairly ubiquitous, A. rohweri may have a more pervasive interaction with Caribbean acroporid corals, as Rickettsiales-like organisms, likely to be A. rohweri , have been found in all histological examinations of these coral species since 1975 [ 2 – 5 ]. Among the Caribbean acroporid coral taxa, the highest concentrations of A. rohweri are observed in the coral Acropora cervicornis following prolonged nutrient enrichment, and resulting in reduced growth of the hosts [ 6 ]. In fact, genets of Acropora cervicornis that are more susceptible to outbreaks of white band disease (WBD) were recently shown to contain significantly higher abundances of A. rohweri [ 7 ]. Thus, it is suspected that A. rohweri facilitates either the onset or progression of WBD [ 6 , 8 , 9 ], a disease that has contributed significantly to the decline of the reef-building corals, Ac. cervicornis and Ac. palmata [ 10 , 11 ]. These two coral species are now so rare that they have been listed as threatened under the US Endangered Species Act and are a major target for restoration efforts [ 12 , 13 ]. Thus, interrogating A. rohweri’s , evolution, transmission route, and roles in initiating and/or mediating disease events may be critical for successful reef management. Aspects of this bacterial parasite’s biology that are unknown, but are key to understanding its effects on host population dynamics, are how the parasite moves between coral colonies and whether it can move between different coral species. Transmission mode is a major determinant of symbiont population structure and evolution [ 14 – 16 ]. Symbionts that are transmitted vertically, which is from parent to offspring, commonly have limited functional capacities and a reduced genome as they coevolve with and become more dependent on their host [ 17 ]. The genome of A. rohweri associated with Caribbean acroporids is significantly reduced (1.28 Mbp) and has limited metabolic capacities, including the inability to produce multiple amino acids and ATP [ 1 ]. Thus, A. rohweri is likely an obligate symbiont dependent on a host for nutrition and energy, and therefore may be transmitted vertically, similar to other closely related and obligate Rickettsiales species, including Wolbachia [ 15 , 18 ]. In symbiotic systems where transmission cannot be observed directly, transmission can be deduced by comparing symbiont and host phylogenies. If the reduced genetic capacity of A. rohweri is indeed indicative of vertical transmission by either the coral or the coral’s mutualistic, intracellular algae (Family Symbiodiniaceae), the host and A. rohweri phylogenies would be congruent [ 16 , 19 , 20 ]. Conversely, the absence of significant congruence with either the coral or algal symbiont would indicate that A. rohweri is likely transmitted horizontally, i.e., through an alternative host or through the environment [ 21 , 22 ]. A. rohweri associates with evolutionarily distant hosts [ 1 ], which is comparable to arthropod or plant mediated horizontal transmission of terrestrial Rickettsiales [ 23 , 24 ]. Although secondary hosts have not been identified yet, possible modes of transmission in the Caribbean include the gastropod Coralliophila abbreviata [ 25 ], zooplankton [ 26 ], or other coral associates. In fact, previously conducted fluorescence in situ microscopy of infected coral polyps did not resolve whether A. rohweri is associated with the acroporid coral cells or the coral’s obligate mutualist, Symbiodinium “ fitti .” Rickettsiales-like organisms were observed in the actinopharynx, cnidoglandular bands, gastrodermal mucocytes, oral disk, and tentacles of a healthy Ac. cervicornis [ 8 ], which are spaces shared with the algal mutualist. All Caribbean acroporid species take up their algal mutualists from the environment upon larval settlement [ 27 ], and thus algal mutualists, and perhaps A. rohweri with them, are horizontally transmitted. However, Caribbean acroporids mainly propagate through asexual fragmentation [ 28 ], resulting in an additional albeit clonal dispersal mode for both the algal symbionts and A. rohweri . Regardless of transmission mode, A. rohweri populations may also be structured by coral host species and the environment, although the latter is difficult to disentangle, as one likely co-varies with the other [ 29 ]. For example, the location where the first genome of A. rohweri was characterized, the Florida Keys, has been exposed to increasing anthropogenic inputs [ 30 , 31 ] and wide-spread bleaching events [ 32 , 33 ]. Corals in this area have also experienced multi-year epizootics, including stony coral tissue loss, WBD, and white pox disease [ 11 , 34 , 35 ]. Differential exposure to these stressors may result in dissimilar disease resistance by location, with higher occurrences of disease resistance in Florida (27%) relative to similar populations found in Panama (6%) and USVI (8%) [ 36 ]. This in turn may influence the prevalence of infection of the nutrient-stress responsive A. rohweri [ 6 , 37 ]. Although disentangling the impact of host and environment will require further sampling and experimental efforts, our comparative analysis of A. rohweri populations provides insight into how infection by this parasitic bacterium may be influenced by the different environmental conditions of each sampling location. Though A. rohweri is capable of infecting a variety of marine phyla, the present study focused on infection of Acropora coral found in the Caribbean: Ac. cervicornis , Ac. palmata , and their hybrid, commonly referred to as “ Ac. prolifera .” Our objective was to provide an in-depth analysis of A. rohweri population structure and acroporid coral infections in the Caribbean. We utilized genomic characterization of the host taxa and their intercellular algal mutualist, Symbiodinium “ fitti ,” [ 29 , 38 , 39 ] to investigate possible strain-specific interactions between A. rohweri and members of the holobiont. In addition, we studied the diversity of A. rohweri infecting acroporids across the Caribbean to understand how quickly this parasite has evolved in this ecosystem [ 40 – 42 ]. Finally, we compared A. rohweri genomes to determine the degree of connectivity between populations and the likelihood of reinfection within and between sampling locations, as well as the parasite’s possible mode of transmission.",
"discussion": "Discussion Ca . Aquarickettsia rohweri infection was found in every sample of acroporid corals taken from across their Caribbean and north-west Atlantic geographic range, with Ac. cervicornis corals consistently yielding more A. rohweri reads relative to Ac. palmata and the hybrid “ Ac. prolifera ” (Supplementary Fig S2A and Supplementary Table S1 ). Assuming the proportion of A. rohweri to host reads are indeed reflective of infection status [ 46 – 48 ], the higher numbers of A. rohweri reads in Ac. cervicornis may in part explain the increased disease susceptibility of this taxa relative to Ac. palmata [ 93 ]. The relative resistance of Ac. palmata to A. rohweri infection may be attributed to environmental factors such as depth, innate host immunity, or defenses mounted by the host microbiome [ 94 – 96 ]. Determining which factors may be leading to resistance in Ac. palmata and the hybrid is a valuable area of further research. The coral Ac. cervicornis yielded higher read numbers of A. rohweri , but both Ac. cervicornis and “ Ac. prolifera ” hosted sufficient reads to construct A. rohweri genomes of similar length and quality as the A. rohweri reference genome Acer44. Phylogenomic analysis using orthologous genes and SNPs indicate the bacteria infecting Caribbean acroporids are specific to the collection location and not the host taxon (Fig. 2 ). This is in contrast to the only other acroporid symbiont with population genetic information, the dinoflagellate Symbiodinium “ fitti .” Genomic variation of S. “ fitti ” loosely partitions to host taxa, regardless of reef location [ 29 ]. These contrasting patterns of population structure indicate that the forces shaping the two coral symbionts are different despite their shared host taxa. Similar differences in the population structures between symbionts co-infecting a shared host include terrestrial symbionts populating aphids and whiteflies [ 23 , 97 , 98 ]. A. rohweri populations from the three sampling locations form separate clades in phylogenomic and SNP phylogenies, with Florida and USVI samples likely sharing a closer ancestral lineage than Florida and Belize (Fig. 2 ). The USVI samples, being sister to the Florida samples, suggest that A. rohweri dispersal is not primarily via the Caribbean current that passes from US Virgin Islands through Belize to Florida. A barrier to gene flow has been identified between the eastern and western Caribbean for coral [ 99 , 100 ]. Similar genetic differentiation by location has been observed for the Ac. cervicornis sequences of the same samples used in this analysis [ 38 ]. Even though seasonally variable surface currents connect all sampling locations [ 100 , 101 ], and all samples are genetically similar (relative to the reference genome, all samples were >99% ANI), there was clear differentiation among Florida, the US Virgin Islands, and Belize A. rohweri populations (Figs. 1 and 2 ). A. rohweri collected across the Caribbean have low levels of genetic polymorphism with <2500 SNPs relative to the reference genome of 1.28 Mbp (Supplementary Table S8 ). Thus, A. rohweri may be considered monomorphic [ sensu , [ 102 – 104 ]]. Lower levels of genetic polymorphism are correlated with virulence in some bacteria, such as pathogens Yersinia pestis and Bacillus anthracis [ 102 , 104 ]. However, comparable levels of genetic polymorphisms are found in the bioluminescent mutualists Ca . Photodemus katoptron and Ca . Photodemus blepaharus [ 103 ]. The role of A. rohweri in coral disease is an active area of research, and thus it is difficult to interpret how the low levels of genetic polymorphisms in A. rohweri influence its parasitic role, but it is notably low for a symbiont spanning such a large geographic range. Of the subset of SNPs that impact functional regions, most (62%) resulted in a change to the amino acid identity and therefore likely affect protein function (Supplementary Table S9 ). Although the majority of genes impacted by missense mutations were hypothetical proteins, some gene annotations were identified as transposases. Moreover, the single gene found to have acquired a stop codon in all USVI and Belize samples was within the transposase ISDpr4. The loss of transposases and the eventual loss of these gene regions is characteristic of long-term obligate symbionts [ 15 , 105 ]; therefore, the A. rohweri genome may still be actively reducing through the loss of mobile genetic elements. Although the A. rohweri were closely related to each other and are phylogenetically clustering by sampling location, there were surprisingly high levels of genetic isolation, even within a single reef (Fig. 3 ). Pairwise comparisons of the fixation indices between all samples indicated extreme genetic isolation between A. rohweri populations from distinct coral colonies, indicating that genetic mixing among populations and thus reinfection of coral colonies with A. rohweri is unlikely to occur between or even within a reef. Genetically isolated populations of A. rohweri may be the result of competitive exclusion once a coral is infected or a consequence of subsequent infections being relatively rare. The most extreme case was observed in comparing samples collected from the same reef in Belize, as all pairwise comparisons had an F st of 0.95 or greater. The lowest level of pairwise genetic differences was observed in Florida, which implies a slightly higher probability of reinfection among coral colonies relative to the other locations, but laboratory studies would be needed to evaluate whether this is due to host or environmental factors. Thus, despite low genetic diversity observed overall, genetic diversity was distributed such that locations and samples were highly differentiated suggesting that A. rohweri infection may occur earlier in the coral lifespan and propagate within the host over 30–838 years [ 106 , 107 ] with little to no genetic mixing among A. rohweri populations from distinct coral colonies occurring thereafter. However, at this stage we do not yet know when the parasite establishes infection. Our work also shows that A. rohweri is undergoing greater positive selection relative to closely related parasitic Rickettsiales species, with genes involved in speciating and virulence undergoing the greatest degree of positive selection (Fig. 4A ). While the identity of the coral host did not have an impact, sampling location did affect the degree of positive selection (Fig. 4B ). The average dN/dS of Florida samples is 2.7 times greater than samples from USVI and 2.8 times greater than those in Belize. Although differences in dN/dS were not observed for all samples at each location, these trends were observed in a subset of the comparisons between all sampling locations. The higher positive selection in Florida populations may be due in part to the higher estimated rates of replication observed in those samples (Fig. 5 ), but further study would be need to evaluate this trend. Genes that were associated with ribosomal assembly, L13 and GTPase ERA, which assemble 50S and 30S ribosomal proteins, respectively, were undergoing positive selection in a subset of the samples. The consequence of ribosomal-associated genes undergoing positive selection is unknown, but it may be indicative of speciation occurring between the different sampling locations. Another gene undergoing positive selection in a subset of samples across locations was the Type IV secretion system-coupling protein VirD4. VirD is essential to T4SS and is involved in substrate recruitment, which plays a role in oncogenic DNA transfer and virulence in Agrobacterium [ 108 – 111 ]. Thus, positive selection in VirD may be affecting how A. rohweri in Florida populations interact with their host species. Though microscopy found A. rohweri living in close proximity to coral cells and S. “ fitti ” [ 8 ], neither are likely transmitting the parasite vertically (Supplementary Table S5 ). Coral larvae seemed the most likely method for transmission across the Caribbean, as larvae can travel long distances as plankton (>500 km) [ 100 ]. Similarly, algal symbionts could provide the necessary nutrients to A. rohweri and facilitate parasitic infection when S. “ fitti ” is acquired by juvenile coral hosts [ 112 – 114 ]. It is also possible that the parasite could be carried alongside either member of the coral holobiont as they reproduce asexually, however, similar to sexual reproduction, this would have resulted in congruence between parasite and host phylogenies and significant codiversification. Yet, codiversification analysis of both SNPs and gene-based phylogenies resulted in neither coral nor S. “ fitti ” having clear codivergence with A. rohweri . In addition, qPCR evaluation of early life phases (<1 week to 1 year) from disease susceptible coral genotypes known to harbor A. rohweri [ 7 ] failed to detect the bacteria (Supplementary Fig. S7 ). The reduced metabolic capabilities of A. rohweri [ 1 ] and the lack of evidence for a dormancy pathway also suggests that the bacteria is unlikely to survive long periods in the environment as free-living bacteria. However, A. rohweri has retained some flagellar genes [ 1 ] and flagellum maybe involved in some aspect of transmission or symbiosis. It is therefore most likely that the bacteria are transmitted via an alternative method that would provide the necessary nutrients. One such method may be through the movement of coral mucocytes coupled with some abrasion or inoculation event. In histology studies of Ac. cervicornis , Rickettsiales are very commonly found in coral mucocytes that are released into the environment [ 8 ]. Mucocytes filled with the parasites would provide a potential source of A. rohweri to the surrounding water and available for ingestion by non-infected conspecifics. In addition, coral predation may influence infection, as corallivores such as ciliates, fireworms, or fishes may leave abrasions for entry of the parasite or they may serve as a vector for transmission. Future studies are needed to identify the mode of A. rohweri transmission, but the results will further inform Acroporid management efforts in the Caribbean. Overall, the results of this study show that A. rohweri infection differs among coral hosts and locations, is evolving at different rates across its host’s range, and is horizontally transmitted. While this work has revealed the population structure of both a bacterial parasite and its marine host along with new insights into the transmission mechanisms of the parasite, several questions remain unknown and should be investigated in the future. For example, the mode of parasite is transmitted remains unknown. Is mucocyte release and subsequent consumption a primary mechanism, are there secondary vectors, or is there a mobile stage of the parasite yet undiscovered. In addition, we do not know when the transmission of parasite occurs during coral ontogeny. However, these findings do suggest new pathways to the study of A. rohweri and its potential contribution to coral diseases in the Caribbean. For example, exploring possible host or microbiome-based deterrents of A. rohweri infection of Ac. palmata [ 115 ] through cross-taxon reinfection or transcriptomic studies may be valuable to the conservation of Caribbean acroporids. In addition, Florida may be a unique focal point for the study of how A. rohweri infection impacts coral disease progression. Several Ac. cervicornis and a hybrid “ Ac. prolifera ” from the Florida Keys host high concentrations of A. rohweri that tend to be less isolated, undergo greater selection in speciation and virulence genes, and are propagating faster than in other sampling locations. Thus, further research into environmental stressors and host responses in this population will be invaluable to our understanding of pathogen evolution, its role in coral disease, and the restoration and recovery of this fragile ecosystem."
} | 5,216 |
23190039 | null | s2 | 6,791 | {
"abstract": "Bacterial flagella are highly conserved molecular machines that have been extensively studied for assembly, function and gene regulation. Less studied is how and why bacteria differ based on the number and arrangement of the flagella they synthesize. Here we explore the cell biology of peritrichous flagella in the model bacterium Bacillus subtilis by fluorescently labelling flagellar basal bodies, hooks and filaments. We find that the average B. subtilis cell assembles approximately 26 flagellar basal bodies and we show that basal body number is controlled by SwrA. Basal bodies are assembled rapidly (< 5 min) but the assembly of flagella capable of supporting motility is rate limited by filament polymerization (> 40 min). We find that basal bodies are not positioned randomly on the cell surface. Rather, basal bodies occupy a grid-like pattern organized symmetrically around the midcell and that flagella are discouraged at the poles. Basal body position is genetically determined by FlhF and FlhG homologues to control spatial patterning differently from what is seen in bacteria with polar flagella. Finally, spatial control of flagella in B. subtilis seems more relevant to the inheritance of flagella and motility of individual cells than the motile behaviour of populations."
} | 322 |
33329631 | PMC7717983 | pmc | 6,792 | {
"abstract": "Intensive agriculture and horticulture heavily rely on the input of fertilizers to sustain food (and feed) production. However, high carbon footprint and pollution are associated with the mining processes of P and K, and the artificial nitrogen fixation for the production of synthetic fertilizers. Organic fertilizers or recovered nutrients from different waste sources can be used to reduce the environmental impact of fertilizers. We tested two recovered nutrients with slow-release patterns as promising alternatives for synthetic fertilizers: struvite and a commercially available organic fertilizer. Using these fertilizers as a nitrogen source, we conducted a rhizotron experiment to test their effect on plant performance and nutrient recovery in lupine plants. Plant performance was not affected by the fertilizer applied; however, N recovery was higher from the organic fertilizer than from struvite. As root architecture is fundamental for plant productivity, variations in root structure and length as a result of soil nutrient availability driven by plant–bacteria interactions were compared showing also no differences between fertilizers. However, fertilized plants were considerably different in the root length and morphology compared with the no fertilized plants. Since the microbial community influences plant nitrogen availability, we characterized the root-associated microbial community structure and functionality. Analyses revealed that the fertilizer applied had a significant impact on the associations and functionality of the bacteria inhabiting the growing medium used. The type of fertilizer significantly influenced the interindividual dissimilarities in the most abundant genera between treatments. This means that different plant species have a distinct effect on modulating the associated microbial community, but in the case of lupine, the fertilizer had a bigger effect than the plant itself. These novel insights on interactions between recovered fertilizers, plant, and associated microbes can contribute to developing sustainable crop production systems.",
"conclusion": "Conclusion The elucidation of plant–rhizosphere–soil interactions is necessary for understanding and improving fertilizer efficiency. Hence, the use of recovered products such as struvite and organic fertilizers needs to be accompanied by specific rhizosphere analyses to increase plant nutrient use efficiency and therefore, yields. Based on our results, both N sources applied increased plant biomass to a similar level; however, the N dynamic in the growing medium showed significant differences between struvite and organic fertilizer treated plants. After N balances, it was shown that the N from the organic fertilizer was still in the form of organic N or was used by another system such as the native microbial community, or was lost to the atmosphere due to mineralization, hindering its quantification in the soil; however, most of the N released from struvite remained immobilized in the soil. Still, similar N mineralization was observed with organic and struvite treatments. That might explain the comparable root growth between struvite- and organic-treated lupine plants contrary to what was observed in the rhizosphere of other species such as tomato where the mineralization of ammonium from struvite did not occur. Consequently, higher root length was observed in the organic fertilizer vs. struvite treated plants. Also, even if the N recovery was higher with the organic fertilizer compared to struvite, no significant differences in biomass were observed. Furthermore, the low N recovery (in both cases, less than 5%) was enough to show significant differences between the growth of fertilized and unfertilized plants. With lupine plants, the fertilizer applied seems to have a significant impact on the associations and functionality of the bacteria inhabiting the growing medium used, suggesting that fertilizer influenced the interindividual dissimilarities in the most abundant genera between treatments. This means that different species have a distinct effect on modulating the associated microbial community, but in the case of lupine, the fertilizer had a more significant effect than the plant itself. It was shown that the products recovered can substitute the use of mineral fertilizers and therefore have a commercial value. The research, therefore, promotes the recycling of recovered products to a greater extent. Furthermore, the use of lupine as an economically relevant crop was of central interest. There is no doubt that generating a detailed understanding of rhizosheath–rhizosphere-related microbial community, their assembly over time and activity will be essential to manipulate root–soil interactions and to ensure sustainable fertilizer use efficiency and soilless crop production in the future.",
"introduction": "Introduction Intensive agriculture and horticulture heavily rely on the input of fertilizers to sustain food (and feed) production ( Vaneeckhaute et al., 2013 ). The global demand for fertilizers amounts to an estimated 110 million tons (Mt) of N, 47.0 Mt P 2 O 5 , and 37.5 Mt K 2 O per year ( Spanoghe et al., 2020 ). However, high carbon footprint and pollution are associated with the mining processes of P and K, and the artificial nitrogen fixation for the production of synthetic fertilizers ( Pikaar et al., 2018 ). Organic fertilizers or recovered nutrients from different waste sources can be used to reduce the environmental impact of fertilizers ( Burnett et al., 2016 ; Tittarelli et al., 2016 ). Also, these nutrient recovery techniques can yield high-performance, flexible, and concentrated mineral fertilizers such as struvite (MAP, NH 4 MgPO 4 ⋅6H 2 O), with a demonstrated high fertilizer use efficiency, also considering the plants’ nutrient release and uptake strategies ( Sigurnjak et al., 2016 ; Vaneeckhaute et al., 2016 ; Robles-Aguilar et al., 2019a ). Commercial inorganic fertilizers are high in nutrient content, easily soluble, rapidly available, and have low and competitive prices, rendering them in principle more effective and efficient than organic or recycled fertilizers. However, organic fertilizers release nutrients slowly ( Dion et al., 2020 ), lowering P fixation and N losses via leaching. Moreover, they enhance root growth and improve soil structure and water holding capacity, reducing soil acidification ( Chen, 2006 ; Paungfoo-Lonhienne et al., 2012 ). Similarly, many studies have demonstrated struvite (recycled fertilizer) as a crystalline mineral free of contaminants that can be used as a slow-releasing fertilizer ( El Diwani et al., 2007 ; Robles-Aguilar et al., 2019b ). Nitrogen is generally considered the main factor limiting plant growth. Struvite, generally seen merely as a P fertilizer, also contains NH 4 + (6.5%). The N cycle (dominated mainly by microbial processes) has a significant impact on soil chemistry and, consequently, on soil fertility ( Jetten, 2008 ). Therefore, analyzing the N dynamics after struvite application is crucial in order to define the overall fertilizer efficiency. Organic fertilizers, next to synthetic fertilizers, are an important supplier to the modern horticultural industry. The combination of organic fertilizers in growing media is not always easy, as the delivery of nutrients depends on microbial breakdown and interaction. Similarly, in order to assure optimal plant performance, the effect of incorporating recovered fertilizers such as struvite in the growing media needs to be analyzed taking into account its influence in the bacterial community ( Van Gerrewey et al., 2020 ). Narrow-leafed lupine ( L. angustifolius L.) is a native European legume, with a high seed protein content (up to 44%) ( Lucas et al., 2015 ). Plant proteins have been presented as a sustainable alternative to animal protein; however, Europe still depends on the import for 70% of its plant protein requirements (mainly import of soybean). Therefore, the growth of lupine contributes to the sustainability of cropping systems ( Lucas et al., 2015 ), representing an effective alternative to other crops like soy. In our previous studies, we could demonstrate that L. angustifolius , by exudation of organic acids, allowed for an improved P release and uptake from struvite ( Robles-Aguilar et al., 2019a ). The combination of its high protein quality, its ability to mobilize nutrients, and the capacity to take up nutrients from recycled fertilizers makes lupine a particularly promising crop to meet sustainability and circular economy goals. Lupine is known for its high physiological root plasticity, related to exudation of large amounts of organic acids that, for example, can free P from insoluble forms ( Lambers et al., 2012 ; Robles-Aguilar, 2018 ). However, lupine root systems can influence not only the nutrient turnover but also the rhizosphere microbial composition and pH. The significance of the rhizosphere ( Hiltner, 1904 ) arises from the volume of soil influenced by exudates from plant root tissues and colonized by rhizobacteria and subsequently altering microbial activity, nutrient cycling, and plant growth. Consequently, the concentration of nutrients with a cycle highly affected by the microbial activity like N may differ between rhizosphere and the bulk growing medium. Particularly the growing medium directly attached to the roots named the rhizosheath is dynamically influenced by plant–microbiome interactions as observed in a previous investigation on tomato ( Grunert et al., 2019 ). In that study, it was shown that the presence of a tomato plant leads to a convergence toward a similar microbial community, regardless of fertilization ( Grunert et al., 2019 ), indicating that the plant rather than the fertilizer shifted the microbial community in the growing medium. However, the microbial composition can also be affected by the type of growing media used ( Grunert et al., 2016b ) or by the cultivation practice, meaning the type of fertilization, as shown by Edwards et al. (2015) in field conditions. Plant growth performance counts substantially on the availability of nutrients at the soil–root interface, which in turn is shaped by a wide range of factors including the soil or growing medium characteristics, environmental conditions, and the microbial community and its structure ( Grunert et al., 2016a ). The function of the microbial community as fertilizer can be direct and indirect ( Sakarika et al., 2019 ). The first is associated with the use of dead biomass, with its inherent nitrogen/phosphorus/potassium content. Dead microbial biomass having a direct fertilizer function can be used by plants as nitrogen/phosphorus/potassium (N/P/K) source. Living biomass can also boost the nutrient acquisition, due to microbial activities such as nitrogen fixation and P solubilization. Biological N 2 fixation can occur primarily in soil by either free-living or plant-associated diazotrophs ( Galloway et al., 2008 ). Symbiotic bacteria (e.g., Rhizobia) fix nitrogen inside nodules and are hence endosymbionts. Non-symbiotic nitrogen fixing microbes are generally referred as diazotrophs and are identified as free-living within soil or associated with plant roots ( Vadakattu et al., 2019 ) and include Cyanobacteria, Proteobacteria, Archaea, and Firmicutes ( Pii et al., 2015 ). Plants can use a wide array of chemical N forms, ranging from simple inorganic N compounds such as ammonium (NH 4 + ), nitrate (NO 3 – ) to polymeric N forms such as proteins ( Paungfoo-Lonhienne et al., 2008 , 2012 ). The primary nitrogen forms taken up by the plant are ammonium and nitrate ( Marschner, 2011 ). As in soils with a large organic N pool and growing media supplemented with organic fertilizers, it is indisputable that the organic nitrogen is only made accessible to the plants by the decomposition carried out by bacteria with first the ammonium as a side product and subsequent nitrification. Microorganisms like mycorrhizal fungi and plant growth-promoting rhizobacteria mineralize organic nitrogen by releasing hydrolytic enzymes and thus enhancing the nutrient availability in soil ( Miransari, 2011 ; Ollivier et al., 2011 ) and most likely also in other culture systems such as soilless growing medium. This study aims to elucidate the effect of organic fertilizer, and a mineral fertilizer recovered from wastewater, i.e., struvite, on the below and aboveground plant development of lupine, its root architecture, and nutrient turnover. Furthermore, our current understanding of the factors affecting microbial communities inhabiting growing media and how, in turn, it influences plant nitrogen availability, is still limited. Hence, this study also aims to determine the effect of an organic fertilizer and an inorganic recovered fertilizer on the root-associated microbial community structure and its functionality. We hypothesized that (a) the cultivation of lupine will be affected differently by both fertilizer sources of nitrogen (organic vs. inorganic) due to different N release dynamics in the rhizosphere. The organic fertilizers have in common that the major part of the N and P is bounded in complex molecules such as proteins; consequently, their release is related to the decomposition by the microbial community associated with the soil. Therefore, we hypothesized that (b) the microbial community in the rhizosphere (volume of growing medium influenced by the root)/rhizosheath (growing medium directly attached to the roots) will have a more significant influence on the nutrient turnover of the organic fertilizer compared with the struvite. Finally, we hypothesized that (c) lupine plants will influence the microbial community structure in the growing medium compared with the medium with no plants.",
"discussion": "Discussion Plant N recovery (% of the applied N that is taken up) was higher from the organic fertilizer than from struvite, indicating that N from the organic fertilizer was more easily available to plants, contrary to what we hypothesized. The nutrient content of a plant not only varies among its various tissue parts but also changes with age and stage of development. Therefore, analyzing nutrient concentration rather than content might allow for a more precise diagnosis of plant nutritional state. Nutrient deficiency or toxic values are typically described in % of dry weight ranges ( Marschner, 2011 ). For leguminous plants, such as lupine, the critical N concentration is less than 3% ( Marschner, 2011 ). The N concentration in lupine was at the expected level (from 3.9 to 6%), with no significant differences between fertilizers. This indicates that lupine plants fulfilled their N needs under both fertilizer treatments. N recovery from struvite and organic fertilizer was very low, but still, this small N recovery made a significant difference in the N shoot concentration and plant biomass compared to the unfertilized plants. The small recovery might be explained by the high fertilization rate and the short period of the experiment (27 DAS). Furthermore, nitrogen concentration in shoots decreased during growth, as indicated by the lower N concentration in lupine plants in the second harvest. This can be explained as the N uptake is assumed to be lower than the crop growth rate. Also, N concentration decreases with plant growth as there is a higher amount of metabolic tissues with higher nitrogen concentration in the juvenile plant stage ( Marschner, 2011 ). Our results indicate that physiological changes may play a secondary role in the reduction of the concentrations of nitrogen in plant tissue at the second harvest. The efficiency of nitrogen use by the lupine is probably a size-dependent phenomenon resulting from the accelerated plant growth. Struvite was compared with an organic fertilizer made of organic matter that first required breakdown into amino acids, whose application as a fertilizer has been demonstrated to have a beneficial effect on leaf mineral status ( Garcia et al., 2011 ). It was hypothesized that struvite would have a beneficial effect as N source, as it delivers the ammonium directly in the rooting medium. On the other hand, the organic fertilizers need a supplementary conversion first from the organic N to ammonium to be plant available. Trying to demonstrate this, the calculation of N balance in the plant-growing medium was performed. The ammonium concentration in the growing medium with lupine but without fertilizer had an increase of 4 mg L –1 in the first harvest, 2 mg L –1 more than what was measured in the growing medium unfertilized and without plants. Next to other factors, watering of a growing medium stimulates the release of nutrients through decomposition and mineralization, explaining the increased ammonium concentrations in the non-fertilized setup without plants. The higher ammonium concentration in the medium with plants but without fertilizer might be explained by increased microbial activity due to root C inputs ( Clarholm, 1985 ) or decreased microbial immobilization resulting from more effective competition for N by plants ( Griffiths and Robinson, 1992 ). At this time point, the concentration of ammonium with organic fertilizer was 22 and 70 mg L –1 with struvite ( Table 4 ). Even though the ammonium from the struvite in the growing medium was indeed higher compared to the organic fertilizer, this was not reflected in a higher N uptake or higher biomass. The oxidation of ammonium to nitrate in the second harvest was higher in the organic fertilizer treatment, indicating that lupine plants associated bacteria are more effective mineralizing ammonium from organic fertilizer but also still can successfully perform nitrification from struvite, contrary to what was observed in previous experiments with other species such as tomato ( Grunert et al., 2019 ). PH changes might be related to the initial ammonium dissolved from the struvite and organic fertilizer that is still present in the growing medium and not taken up by the plant. Under no fertilizer conditions, the increase of the pH was the highest (5.5–6.7). This might be explained by the liming effect of [(Ca, Mg)CO 3 ] 2 , which was added to the growing medium to adjust the pH to the desired level. The use of rhizotrons allowed us to analyze root growth over time and to observe changes in root architecture at different stages of root development. The non-fertilized plants had higher total root length than the fertilized plants, independently of the N source. This might be explained by the nutrient content in the seed and the successful nodulation observed in the no N treatment that provided the plant with the extra N needed to establish the seedlings and initial plant growth. Moreover, it was observed that these non-fertilized lupines increased primary root growth and decreased the lateral root density, contrary to what was observed in the fertilized plants. These variations in the root morphology might affect the percentage of visible roots in the rhizotrons. Usually, this value is ∼20%, but it decreased with an increasing average root diameter of the plant species ( Nagel et al., 2012 ). That might explain the final higher root length measured in lupine plants under no fertilizer (with higher root diameter, i.e., higher % of visible roots) compared with the secondary-thinner roots in organic fertilizer and struvite treatments. In a previous study with tomato ( Grunert et al., 2019 ), organic fertilizer led to a higher root length compared to struvite. In that case, the rhizosphere microbial community of tomato plant was not able to mineralize the ammonium from the struvite, which entailed significant differences in the ammonium concentration between both fertilizers in the growing medium that probably affected root architecture ( Liu and von Wirén, 2017 ). In our study, lupine plants were able to mineralize ammonium from struvite in a similar way than from the organic fertilizer. It is known that the root morphology of lupine is modulated by the N source (ammonium or nitrate) present in the soil ( Robles-Aguilar et al., 2019b ). Therefore, similar concentrations of ammonium and nitrate, in this case, could be related to the similar root morphology of lupine under both N fertilization regimes. However, this explains only a part of the difference between lupine and tomato, as it seems that plant species also have a big impact on the root/shoot ratio. Struvite treated growing medium showed in general 10–20 times higher P concentrations compared to the organic fertilizer and the no fertilizer treatment. According to preceding research, under low plant-available phosphorous concentrations, nodules would mainly decrease the utilization of atmospheric nitrogen as primary nitrogen source and utilize more readily plant-available nitrogen sources such as nitrate and ammonium ( Valentine et al., 2017 ). Furthermore, root nodulation is accelerated by low concentrations of nitrogen and significantly suppressed by high concentrations of nitrogen. Minchin and Witty (2005) showed that nitrate itself is a strong inhibitor of nodulation and hence nitrogen fixation but also that the utilization of carbon as an energy source for nitrogen uptake from the soil is less than that for nitrogen fixation. That might explain why N fixation was not the preferred path to take up nitrogen in the fertilized plants. Ammonium availability from a fertilizer might be related not only to specific plant nutrient turnover strategies or to the form that the nitrogen is delivered in (organic or inorganic) but also to other factors like the microbial activity. The activity is associated with each species that might prefer one source of N over another, affecting the nitrification of ammonium differently regarding the N source ( Grunert et al., 2016b , 2019 ). The active, competitive and resilient native community has been observed in the type of organic growing medium used in this experiment. Previous work from our team reported that tomato plants modify the structure and function of the bacterial community rather than the applied fertilizer ( Grunert et al., 2019 ). We hypothesized that this would also be the case with lupine plants. However, fertilizer had a significant impact on the associations and functionality of the bacteria inhabiting the growing medium used, suggesting that fertilizer influenced the interindividual dissimilarities in the most abundant genera between treatments. As symbiotic bacteria support legumes in meeting their nutrient demands ( Velázquez et al., 2017 ), less competition for nutrients between lupine and its bacterial community potentially occurred in our study. Microbial richness, evenness, and diversity were significantly higher in the rhizosheath at the first harvest when no fertilizer was supplied; however, these metrics were not significantly different at the second harvest and in the rhizosphere. No differences were found between the organic N and the inorganic N source. Time and soil pretreatment significantly impacted the mean bacterial relative abundances of the communities in the rhizosphere ( Supplementary Figure 1B ). Bacterial communities were dissimilar at the start of the experiment, in comparison with those at the end of the second harvest. This may indicate that time plays an essential role in the bacterial colonization of the rhizosheath of lupine plants. Indeed, the structure and function of the microbial communities in the rhizosphere are cooperatively orchestrated by plant and growing medium ( Berg and Smalla, 2009 ). These results are in agreement with earlier research showing reduced biodiversity upon N supplementation ( He et al., 2007 ; Geisseler and Scow, 2014 ; Ling et al., 2017 ; Zhou et al., 2017 ); however, earlier-mentioned research studied the long-term effects of inorganic N sources, while in our study short-term and inorganic and organic N supplementations were examined. The structure of the rhizosphere associated microbial community is gardened by a complex trade of compounds between the microorganisms and the plant ( Werner et al., 2014 ), which can have plant growth-promoting effects for the plants ( Pii et al., 2015 ). Indeed, microorganisms can alter nutrient availability in the rhizosphere ( Pii et al., 2015 ). Differences in the microbial community structure of the rhizosphere were mainly a result of time, which has been described for maize ( Baudoin et al., 2002 ), tomato ( Grunert et al., 2019 ) and now also for a legume. Therefore, we concluded that the change of microbial community structure in the rhizosheath of lupine could be attributed to the direct influence of plant rather than the fertilizer. We identified Rhodanobacter , Rhizomicrobium , Acidobacteria , Microbacterium , Chitinophaga , Actinomadura , Mucilaginibacter , Nocardioides , Burkholderia , Streptomyces , and TM7 genus incertae sedis as the bacterial genera with the highest relative abundance in the rhizosheath ( Figure 3A ). The relative abundance of the N-cycle Rhizomicrobium increased over time, regardless of fertilizer applied, while Actinomadura increased in the growing medium supplemented with fertilizers. Actinobacterium and Rhizobium replaced Burkholderia and Nocardioides in the rhizosphere ( Figure 3B ), regardless of plant presence. Acidobacteria are one of the most general and abundant phyla on earth ( Kielak et al., 2016 ). Cultured Acidobacteria are heterotrophic, they can use multiple carbon sources, and they are able to use nitrite as a nitrogen source. However, there is no clear proof for the role of Acidobacteria in N-cycle processes ( Kielak et al., 2016 ). We reported positive associations between ammonium and Streptomyces in the rhizosphere when struvite was supplied. Plant growth-promoting rhizobacteria (PGPR), such as Burkholderia , can enhance nutrient acquisition through nitrogen fixation, phosphate solubilization, sulfur oxidation, and iron acquisition. Different Streptomyces strains, such as Streptomyces thermoautotrophicus ( Ribbe et al., 1997 ), showed plant growth-promoting effects in Arabidopsis ( Cordovez et al., 2015 ), rice, wheat, sorghum, and tomato ( Gopalakrishnan et al., 2013 , 2014 ). Rhodanobacter is a genus known to reduce nitrate, playing a key role in the nitrogen cycle ( Kostka et al., 2012 ). We observed that ammonium nitrogen concentration was significantly high both in the rhizosphere and in the rhizosheath. This variable may have been correlated with the relative abundance of Rhodanobacter . Indeed, Rhodanobacter was positively associated with sulfates, Na, and Cl when organic fertilizer as applied and with low EC when struvite was applied. For this reason, Rhodanobacter may be one of the main genera impacting pH in the growing medium, regardless of fertilizer supplementation. For this reason, Rhodanobacter may be one of the main genera impacting pH in the growing medium, regardless of fertilizer supplementation. These results suggest that the rhizosheath-associated bacterial community evolved into copiotrophic populations ( Ho et al., 2017 ) as a result of the increased N and carbon sources available in the growing medium ( Fierer et al., 2012 ; Ramirez et al., 2012 ; Männistö et al., 2016 ). Rhizobium showed a decreased relative abundance in the rhizosheath. Five different OTUs represented this genus, and OTU02, OTU0190, and OTU0798 displayed significant variation on their relative abundances ( Figure 4 ). These results confirm the high phenotypic diversity between representatives of the genus Rhizobium , known for its nitrogen-fixing capabilities ( Beijerinck, 1901 ), inhabiting the rhizosheath of lupine plants supplemented with different fertilizers. Additionally, Arthrobacter , a heterotrophic nitrifier ( Verstraete and Alexander, 1973 ), was significantly abundant when organic fertilizer was supplied. This genus can inhibit the growth of phytopathogenic fungi and enhance salt tolerance in plants ( Velázquez-Becerra et al., 2013 ). We confirmed that Xanthomonadaceae OTUs were significantly more abundant in rhizosheath supplied with struvite and organic fertilizer. Endophytic Xanthomonas ribotypes have been recorded in L. angustifolius nodules ( Ferchichi et al., 2019 ). PGPRs such as Sphingomonas and Burkholderia ( Compant et al., 2005 ; Khan et al., 2014 , 2017 ) and Phenylobacterium ( Lingens et al., 1985 ), a Gram-negative bacterium that degrades xenobiotic compounds, were positively associated with pH when struvite was supplied. Strains of rhizobia can gain access through cracks from new roots and not only colonize the roots but also migrate upward into the stem base, leaf base, leaf sheaths, and some leaves of the plant ( Chi et al., 2005 ). We detected that Prosthecobacter , which use ammonium as the preferential nitrogen source ( Takeda et al., 2008 ), Labrys ( Inui et al., 2012 ), and uncultured Aquificae were positively associated with leaf area when struvite was supplied. Alkanibacter was also associated with this plant variable. Our results suggest that PGPR and other bacteria present in the rhizosheath may migrate to the root nodules of lupine later in life. Our study addresses some limitations of previous studies and extended our knowledge about the effect of applying recovered nutrients (such as organic fertilizer and struvite) in growing media on belowground microbiology. To do that, (1) rhizosphere and rhizosheath samples were studied in addition to bulk growing media samples; (2) the bacterial community was assessed using Illumina sequencing; and (3) rhizotrons were used to test their effects on root length and morphology, plant performance, and nutrient recovery in lupine plants. Compared with aboveground plant parts, roots are not easily accessible by non-invasive analyses and research is still based mainly on destructive methods at harvest. Plants were grown in growing medium-filled rhizotrons, allowing for simultaneous quantitative measurements of root architecture parameter and shoot biomass in 2D over time, and rhizotrons are helpful instruments for guided accurate sampling. Still, a major drawback of working with rhizotrons is the limited volume and hence limitations in time. This means that lupine plants grown for profit are likely grown longer than the 27 days of the experiment. For this reason, the nutrient and microbial dynamics observed in a longer-term experiment might be different than described here. As root architecture is fundamental for plant productivity, variations in root structure and length as a result of soil nutrient availability driven by plant–bacteria interactions were compared, showing also no differences between fertilizers. However, they were different in the root length and morphology compared with the no fertilized plants. To the best of our knowledge, the presented study is the first study to use amplicon sequencing to assess the effect of struvite and organic fertilizer on the rhizosphere and the rhizosheath microbiome. We identified spp. belonging to Rhodanobacter , Rhizomicrobium , Acidobacteria , Microbacterium , Chitinophaga , Actinomadura , Mucilaginibacter , Nocardioides , Burkholderia , Streptomyces and TM7 genus incertae sedis as the bacterial genera with the highest relative abundance in the rhizosheath. Moreover, we showed that addition of struvite or an organic fertilizer to a growing medium influenced the microbial composition, in which oligotrophic microorganisms such as Acidobacteria decreased their relative abundance over time. These results suggest that the rhizosheath-associated bacterial community evolved into copiotrophic populations as a result of the increased N available in the growing medium. Our results confirm the high phenotypic diversity between representatives of the genus Rhizobium inhabiting the rhizosheath of lupine plants supplemented with different fertilizers. Moreover, analysis of the significant differences in taxa abundance uncovered a log2 fold increase in Phenylobacterium in growing medium supplemented with an organic fertilizer when compared with medium without fertilizer. In contrast, this same genus was significantly more abundant in the growing medium supplemented with struvite. It is very well known from the literature that amplicon sequencing gives accurate information on microbial taxonomy ( Poretsky et al., 2014 ), and in addition, different community metrics can be calculated, such as species richness, evenness, and diversity. However, this technique does not give data on the actual microbial biomass."
} | 8,179 |
35423606 | PMC8695991 | pmc | 6,793 | {
"abstract": "Wetting of electrospun mats plays a huge role in tissue engineering and filtration applications. However, it is challenging to trace the interrelation between the wetting of individual nano-sized fibers and the macroscopic electrospun mat. Here we measured the wetting of different nylon-11 samples – solution-cast films, electrospun fibers deposited onto a substrate, and free-standing mats. With electrospun nylon-11 on aluminium foil, we traced the dependence of the wetting contact angle on the fibers' surface density (substrate coverage). When the coverage was low, the contact angle increased almost linearly with it. At ∼17–20% coverage, the contact angle achieved its maximum of 124 ± 7°, which matched the contact angle of a non-woven electrospun mat, 126 ± 2°. Our results highlight the importance of the outermost layer of fibers for the wetting of electrospun mats.",
"conclusion": "Conclusions As we deposit electrospun fibers onto a substrate, they form a mesh with a gradually increasing thickness and decreasing pore size. In our experiments with nylon-11, we observed a change in the samples' wetting behaviour when the surface coverage achieved ∼17–20%, and the pore size decreased down to 2.5 ± 1.3 μm. This change can be regarded as the transition from a set of individual fibers to a mat. Further research is needed to find the dependence of this threshold coverage on the diameter of the fibers and their packing. A deeper analysis shows that the surface coverage measured using SEM seems overestimated if compared with the surface coverage in the outmost layer of the mat. Estimating the exact number of the fibers which contribute to the interaction with the liquid is challenging. A possible approach to this problem can use optical microscopy (confocal microscopy or one of its far-field super-resolution modifications 42 ) to control the penetration of the liquid into the pores. 40 Hopefully, with this setup, one can estimate the penetration of the drop into the mat and verify the applicability of certain theoretical models. The results obtained in the current study can be used to prepare hydrophobic coatings on household goods, personal protective equipment, and other applications. Indeed, we demonstrate that relatively low surface coverage is enough to ensure surface hydrophobicity. This result can help us to prepare hydrophobic coatings made of stable constructional polymers, such as polypropylene or poly(ether ether ketone).",
"introduction": "Introduction How many nanofibers do we need to make a non-woven mat? Is it enough to have ten fibers? Or a hundred? Or a thousand? These questions might seem speculative; however, they become crucial when we try to relate the properties of electrospun mats with the properties of individual nanofibers. This relation is essential when we focus on the wetting of non-woven mats. 1,2 Wetting of electrospun mats influences their performance as filters, 3 separators, 4 wound dressings, 5 sorbents, 6 and tissue engineering scaffolds. 7 Wetting depends on the chemical composition of the fibers, their diameter, spatial arrangement, and post-treatment (chemical functionalization, 8 thermal treatment, 9 and others). Controlling and predicting wetting properties relies on understanding the liquid–polymer interaction at different scales from individual fibers to a mat. The theoretical and experimental studies of the macroscopic fibers give a detailed description of the interaction force and the shape of the drop placed onto a fiber 10–13 or a set of fibers. 14–16 Since electrospun fibers are usually far smaller than the liquid drops, their wetting is studied using sophisticated experimental procedures, such as attaching an individual nanofiber to an AFM tip. 1,17 Based on the wetting properties of individual fibers and general theoretical models, one can explain the wetting properties of mats. It is usually performed using the Cassie–Baxter model. 1,18–20 When a drop of liquid is placed onto an electrospun mat, it interacts with thousands of nanofibers, as well as with the air entrapped between them. The importance of air was highlighted in several studies. 2 Due to the small radius of the nanofibers and their low packing density, the electrospun mats demonstrate superhydrophobic 19 or super-oleophobic 1,21 behaviour. Let us reframe the above-mentioned question about the minimum number of fibers that form a mat using the “surface coverage” term. Since electrospun fibers are usually formed on a grounded collector, we can ask: “Which minimum surface coverage of the collector makes supported fibers behave as a free-standing mat?” To answer this question, we prepared non-uniform samples with a gradual change in surface coverage by electrospun nylon-11 fibers. These samples allowed us to trace the relation between the surface coverage and the sample's contact angle (CA). We have found a certain threshold (∼17–20% coverage) at which the CA of the sample becomes equal to the CA of an electrospun mat. This can be regarded as the transition from individual fibers to a mat.",
"discussion": "Discussion Measurements of the CA of electrospun samples The experiment described above allowed us to trace the relation between the fraction of the substrate occupied by the fibers and the CA. The typical diameter of the gradient samples was ∼200 mm, with a region of bare substrate at the edge. The examined points were located at a distance of at least d macro = 7 mm from each other. The typical lateral size of the water drop was d drop ∼ 1 mm < d macro . We estimated the maximum gradient of the surface coverage as 1% per 1 mm, so within a single experimental point (the contact area between a single water drop and the substrate), the surface coverage can be regarded as constant. The size of the SEM frame was in the range from 16.5 μm × 12.4 μm (at ×10 000 magnification) to 110.4 μm × 82.8 μm (the ×1500 magnification). In the whole indicated range, the typical frame size was smaller than the drop size d drop , so we captured several frames to estimate the surface coverage. The meaning of the error bars in Fig. 4 is worth an explanation. Each vertical error bar was calculated as the standard deviation over N = 60 measurements recorded during 1 minute of observation. Each horizontal error bar was obtained as the standard deviation of at least four surface coverage values calculated as described in the ESI. † The variations in local surface coverage were more significant than the variations of the contact angles. SEM imaging of polymer samples requires coating them with a thin metal layer. In our experiments, we carried out the CA measurements prior to SEM imaging because we did not want the metal coating to influence the CA. We implied that the fibers' diameters and the sample's overall morphology remained intact after the CA measurement. This assumption seems reasonable since nylon-11 demonstrates low swelling in water; the typical swelling in ambient humidity is typically 0.245 wt% (ref. 25 ); nylon-11 – based films can be used as barriers for water. 26 Electrospun mats usually retain residual electrostatic charge after manufacturing. 27–29 The charge facilitates the wetting of the sample surface with water, regardless of the charge sign. 30 In our experiments, we neglected the charge-related effects for two reasons. First, the typical time between the sample preparation and CA measurement was 1–2 weeks, which is usually enough to eliminate the residual charge. 28 Second, the aluminium substrate used for the gradient samples facilitated the charge removal. The relation between the substrate coverage and the CA was previously examined for the nylon-6 fibers. 31 The variation of the surface coverage was achieved by varying the electrospinning time; the fibers were deposited onto a polymer-coated glass slide. In contrast with the current study, the authors of ref. 31 did not observe the transition from the monotonous growth to a plateau. The CA of poly(methyl methacrylate) electrospun mats were found to be independent of the thickness of the mat. 2 This result does not contradict the data presented above because it was obtained for the mats with a thickness between 35 and 134 μm, far larger than the thickness of our gradient sample ( h ≤ 4 μm). Even at the left point of this range, the mats were macroscopic, and the surface coverage was relatively large (45.20 ± 15.55%). In the context of our experiments, it means that the samples described in ref. 2 were at the plateau of the graph shown in Fig. 4 . Theoretical considerations Let us regard a one-dimensional system that consists of infinite fibers located near a flat surface ( Fig. 5 A ). Placement of the fibers at a distance above the surface accounted for the growth of the mat thickness – as the mat thickness increased, the top layer position shifted upward from the substrate. The height of the water drop (typically ∼1 mm) is small, so we can neglect the gravity. 32,33 It is a common assumption used for the analysis of the interaction between a liquid and fibers. 14–16,34 Fig. 5 Schematic presentation of the model water drop on top of the parallel fibers. (A) The overall scheme, (B) the “full spreading” case, (C) the “non-contact” (Cassie–Baxter case). (D), (E), and (F) show different shapes of the water meniscus between the adjacent fibers. We can assess two possible states of the drop. First, if the fibers are sparse and hydrophilic, the drop penetrates between them and touches the substrate ( Fig. 5 B ). This “full spreading” state is likely to emerge at low surface coverage. Second, if the fibers are dense and hydrophobic, they do not let the drop touch the substrate ( Fig. 5C ). Both states can be described using the central angle φ , which relates to the CA of the sample θ : 1 We calculated the energies of the two cases ( Fig. 5B and C ) as described in the ESI † and minimized them using a Python script to determine the equilibrium angle φ and the corresponding energy. The model relies on the five surface energies of the contacts within the system: water–air, water–substrate, water–nylon, substrate–air, and nylon–air. The calculations were carried out for different values of the distance d , which accounted for the variations of coverage. If we neglect the overlapping of the fibers, the 2 r / d ratio is equal to the surface coverage measured using SEM. If the drop is in the “full spreading” state, the CA should increase linearly with coverage, as shown in Fig. 6A (blue dots). This can explain the growth of the CA observed experimentally at low coverage ( Fig. 4 ). Fig. 6 The contact angles (A) and the energies (B) calculated by the energy minimization for the “non-contact” and the “full spreading” cases (green and blue, respectively). The angles calculated using the Cassie–Baxter formula are shown in red (A). The “full spreading” state was analyzed at a 300 nm gap. However, as the surface density of the fibers increases, we should come to the “non-contact” state ( Fig. 5C ). If the bottom part of the drop is considered flat ( Fig. 5 D ), we can determine the angle α between the bottom part of the drop and a single fiber as the CA at a curved surface (the CA of the film is θ f ): 32,33 2 In this case, we can use the classical Cassie–Baxter model, 18,35 which describes the contact between a liquid drop and a two-phase substrate (in our case, the two phases are nylon-11 and air). When this model is applied to a set of parallel fibers, 1,18–20 the contact angle θ CB can be calculated as 3 The CA of the film θ f is often referred to as Young's contact angle. 1,18,19,36 Based on this relation, we can expect that the observed contact angle θ CB should decrease as the coverage increases ( Fig. 6A , the red and green markers). A deviation of the experimental CA values from the ones calculates using the Cassie–Baxter model was observed previously for the electrospun mats made of nylon-6. 31 The system switches from the “non-contact” state to the “full spreading” state if the drop penetrates the pores and touches the substrate. However, if the bottom part of the drop is flat ( Fig. 5 D ), it will not allow the formation of the contact between water and the foil substrate. Indeed, the water level is always higher than the bottom plane of the fibers, so the water does not touch the surface even if the distance d between the fibers is macroscopically large. To make the model more realistic, we can assume that the water surface between the fibers has a fixed curvature radius ( Fig. 5 E ), which is equal to the macroscopic drop radius R . In this case, the angle α is not fixed; it can be varied to minimize the system energy. The change from the flat surface to the curved one slightly increases the contribution of the lower part of the drop into the total system energy. However, energy estimation shows that this contribution is still small, and it is typically accompanied by a less than 1% relative change in the CA. We can estimate the minimum distance between the fibers, which will allow the drop to penetrate down to the bottom plane of the fibers ( Fig. 5F ). Using the geometrical constraints, we get 4 where r is the fiber radius, and the angle α ≈ π − θ f . If we assume r = 100 nm, R = 1 mm, and θ f = 90°, we get d crit = 28 μm. If the surface coverage is low, and the distance between the fibers is larger than the critical value d > d crit , the drop easily penetrates between them. However, at low d < d crit , the drop does not penetrate below the top layer of the fibers. Similar critical behaviour was previously described for several systems. 34,37 This can explain the plateau observed in Fig. 4 at high coverage and the deviation from the Cassie–Baxter model. When we use SEM to estimate the size of the pores, we cannot distinguish between the pores in the outmost layer and the deeper ones. It seems that at the threshold coverage of 17–20%, the typical pore size in the outmost layer becomes so small that the fibers do not let the drop get into the deeper layers. In other words, as we increase the surface coverage, we go from d > d crit down to d < d crit . The coverage calculated based on the SEM images becomes different from the “contact” coverage, which determines the actual drop behaviour. The critical pore size d crit is overestimated since at 17–20% coverage, we observed pores with the mean size of 2.5 ± 1.3 μm (ESI † ). Thus, we can qualitatively explain the two different experimental trends – the linear growth of the CA and plateau in Fig. 4 . However, the transition between the “full spreading” and “non-contact” states cannot be explained by this deterministic approach. From the energetical point of view, the “non-contact” state should never be achieved because the energy of the “full spreading” state is always lower ( Fig. 6 B ). The reason for this inconsistency is the stochastic nature of the wetting process, 38–40 which is not captured by the deterministic models. For each pore, water either penetrates through it or is pinned ( Fig. 5 D–F ) at a certain probability. Further theoretical research is needed to incorporate this probability into the wetting model for a liquid drop on a non-woven material. Besides, in the described models, we neglected several aspects of the interaction between the water drop and the fibers. First, we neglect the water vapor, which is captured in the pores between the fibers. 40 Since the pore volume is low and the top part of the pore is sealed by the water drop, we can expect a large local humidity inside the pores. It will facilitate the condensation of a water film on the fibers' surface and the transition from the “non-contact” state to the “full spreading” one. Second, we regard a two-dimensional system instead of a three-dimensional one, and this simplification allows us to capture the phenomena only at a qualitative level. Finally, the measurements of the pores using SEM are usually prone to errors. 41 These reasons can explain the large value of d crit calculated above."
} | 4,027 |
19772347 | null | s2 | 6,794 | {
"abstract": "No abstract available"
} | 5 |
39259789 | PMC11389779 | pmc | 6,796 | {
"abstract": "The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured doping of inorganic nanoparticles to enhance the local electric field, coupled with advanced deep learning algorithms, achieving a Δ V /Δ d sensitivity of 14.2, surpassing benchmarks. This enables precise remote control of surveillance systems and robotic manipulators. Our long short-term memory–based adaptive pulse identification achieves 99.56% accuracy in material identification with accelerated processing speeds. In addition, we demonstrate the feasibility of using a two-dimensional (2D) sensor matrix to integrate real object scan data into a convolutional neural network to accurately discriminate the shape and material of 3D objects. This promises transformative advances in human-computer interaction and neuromorphic computing.",
"introduction": "INTRODUCTION Humans perceive the world through multi-sensory (touch, sight, sound, taste, and smell) integration. Recent advances in humanoid robots ( 1 – 3 ) and human-machine interface (HMI) ( 4 , 5 ) underscore an urgent imperative to amplify the human sensory apparatus ( 6 – 8 ) ( Fig. 1A ) and potentially extend beyond the conventional five senses to encompass the elusive sixth sense. Tele-perception represents a paradigm shift in human cognition, with considerable potential for enhancing situational awareness, decision-making, and environmental interaction. By surpassing the limitations of traditional senses, tele-perception provides a pathway to unlock innovative dimensions of human perception and cognition. Current tactile sensors ( 9 – 11 ), essential for intelligent perception and control, such as pressure-sensitive arrays ( 12 – 14 ), pliant optical strain sensors ( 15 ), magnetic micro-electro-mechanical sensors ( 16 – 18 ), capacitive arrays ( 19 – 21 ), and piezoelectric sensors ( 22 – 24 ), rely on physical contact and encounter limitations in perceiving objects without direct interaction, constraining human-robot interaction framework ( 25 , 26 ). Efforts have focused on enhancing precontact capabilities through innovations in surface structuring ( 27 , 28 ), the employment of composite materials ( 29 – 31 ), ion injection ( 32 ), and the creation of electrets imbued with surface charges ( 33 ). However, the deficient charge capture capacity inherent in the dielectric layer further compromises sensitivity ( 34 , 35 ). While precontact somatosensation, as defined through simulation modeling, can achieve three-dimensional (3D) shape identification, the limited sensitivity of electroreceptors inhibits simultaneous identification of both material and shape ( 36 ), which persists as a formidable challenge. Therefore, integrating tactile perception and tele-perception functionalities into advanced sensors is pivotal ( 37 , 38 ), leveraging progress in materials science, nanotechnology, and deep learning algorithms ( 39 – 41 ). Key strategies involve enhancing charge capture capacity to advancing tele-perception somatosensation ( 42 , 43 ). Fig. 1. Multi-receptor skin perception system. ( A ) Human brain function area. ( B ) Schematic demonstration of the dual receptor system that is distributed on the platypus’s bill for environmental perception. ( C ) Schematic diagram of intelligent perception system based on the multi-receptor skin and deep learning. ( D ) The applicability of the multi-receptor (bionic electroreceptor and bionic mechanoreceptor) skin for different materials. Biological systems present an abundance of exemplary templates, particularly within domains associated with perception and tactile discernment. Notably, the platypus ( 44 ) serves as an exemplar, possessing specialized mechanoreceptors and electroreceptors orderly arranged on its bills ( 45 , 46 ), creating a series of parallel stripes. This elaborate arrangement of thousands of dual receptors adeptly responds to both mechanical and electric stimuli, enabling a comprehensive reaction to natural stimuli. For instance, when a shrimp’s tail initiates muscle contractions, concurrently generating electric signals and mechanical disturbances, the platypus’s distributed electroreceptors within the soft dermal layer discern faint bioelectric signals correlated with prey muscle contractions, while mechanoreceptors promptly respond to stimuli associated with physical contact. The dynamic sensitivity to efficacious stimuli is intricately regulated by the voltage drop across the skin. The pulse activation process of the receptor involves the polarization of the axon membrane, and these consequential changes in membrane potential are transmitted as pulse signals to the brain via the trigeminal nerve. This intricate mechanism facilitates the platypus in various tasks, including detection, communication, hunting, and navigation ( Fig. 1B ). Inspired from the dual sensory system and structured arrangement of receptors observed in the platypus for detection and navigation, we propose the concept of tele-perception to expand human perception and cognition, surpassing the constraints of noncontact sensors and traditional sensory modalities. Our designed bio-inspired multi-receptor skin achieves superior tele-perception and tactile perception through structured incorporation of inorganic nonmetal nanoparticles and integration with advanced deep learning algorithms. On the basis of experiments and COMSOL (Comsol Multiphysics) simulation, the charge trap mechanism is proposed, and it is initiated by induced polarization facilitated by the structured doping of inorganic nonmetal nanoparticles to amplify local electric fields. This mechanism enhances tele-perception somatosensation and establishes a record-high sensitivity benchmark (Δ V /Δ d = 14.2), surpassing previously reported standards by an order of magnitude, which enables precise remote control over surveillance system and robotic manipulators. Another breakthrough involves the development of an enhanced artificial intelligence algorithm designed for harsh environments, aimed at improving its robustness, adaptability, and accuracy. This algorithm integrates adaptive pulse identification technology with the inherent memory efficiency of the long short-term memory (LSTM) algorithm. When conjoined with the multi-receptor skin, tactile perception achieves 99.56% accuracy on materials identification, accompanied by accelerated processing speeds. It achieves an impressive CPU inference time of just 0.04392 s—25 times faster than conventional CNN models. Furthermore, we have demonstrated the feasibility of integrating data obtained from real object scans using a 2D sensor matrix into a convolutional neural network (CNN), thereby facilitating the development of a multi-receptor skin with tele-perception somatosensation for real-world applications. The flexible AI-driven e-skin composed of nano-materials ushers in capabilities in exteroceptive tele-perception and proprioceptive tactile perception, promising transformative advancements in various fields such as human-machine interaction and neuromorphic computing.",
"discussion": "DISCUSSION Our investigation unveils the use of tele-perception as a means to unlock innovative dimensions of human perception and cognition, thereby surpassing the constraints of noncontact sensors and traditional sensory modalities. Our designed bio-inspired multi-receptor skin achieves superior tele-perception and tactile perception through structured incorporation of inorganic nonmetal nanoparticles and integration with advanced deep learning algorithms. On the basis of experimental observations and COMSOL simulations, we propose a charge trap mechanism initiated by induced polarization, facilitated by the structured doping of inorganic nonmetal nanoparticles. This mechanism augments tele-perception somatosensation and sets a record-high sensitivity. This strategic shift effectively addresses the prior challenge of disorganized doping of inorganic nonmetal nanoparticles within elastic substrates, mitigating confusion in the direction of the local electric fields ( 61 ). Furthermore, we demonstrate that our enhanced adaptive pulse identification, facilitated by the LSTM algorithm, further elevates the tactile perception for material identification, achieving an impressive accuracy of 99.56% alongside accelerated processing speeds. Compared to the intricate process that humans must undergo to integrate multiple senses (such as sight and touch) for tactile perception, our tactile perception system exhibited highly efficient material identification for real applications. In addition, we have represented the feasibility of incorporation of data derived from real object scans using a 2D sensor matrix into a CNN, thereby facilitating the development of a multi-receptor skin with both tele-perception somatosensation and material identification. In contrast, traditional 3D identification via charge-coupled device and infrared cameras entails more complex circuits and higher energy consumption. These distinctive advantages render our multi-receptor skin more competitive in certain applications, including the following: (i) independence from external power sources; (ii) high level of signal stability; (iii) highly sensitive characteristics; (iv) our proposed concept of tele-perception surpasses the limitations of traditional noncontact sensors in terms of operating distance and sensing modes; and (v) the design incorporates a doped elastomer film treated with high-voltage polarization, enabling multi-receptor skin to accurately detect changes in electrical signals from a distance without direct contact with the object’s surface. By integrating multiple sensory inputs, the multi-receptor skin enhances human perception in HMIs and humanoid robotics, akin to a sixth sense. Looking forward, intelligent fingertip interfaces with robots or even the human body hold promise for advancing human-machine interaction and sensory immersion, facilitating more sophisticated and practical application scenarios."
} | 2,570 |
40108200 | PMC11923131 | pmc | 6,799 | {
"abstract": "Electrochemical random-access memory devices are promising for analog cross-point array-based artificial intelligence accelerators due to their high stability and programmability. However, understanding their switching mechanism is challenging due to complex multilayer structures and the high resistivity of oxide materials. Here, we fabricate multi-terminal Hall-bar devices and conduct alternating current magnetic parallel dipole line Hall measurements to extract transport parameters. Through variable-temperature Hall measurements, we determine the oxygen donor level at approximately 0.1 eV in tungsten oxide and reveal that conductance potentiation even at low temperatures results from increased mobility and carrier density. This behavior is linked to reversible electronic and atomic structure changes, supported by density functional theory calculations. Our findings enhance the understanding of electrochemical random-access memory switching mechanisms and provide insights for improving high-performance, energy-efficient artificial intelligence computation in analog hardware.",
"introduction": "Introduction Deep neural network technology, commonly known as deep learning, has achieved a series of breakthroughs in AI capabilities 1 , 2 . These models progressively improve their ability to solve complex problems by learning from data 3 . Further propelled by advances in algorithms, improved hardware platforms, and extensive datasets, AI now facilitates a range of functions including automation, content generation, and predictive maintenance across numerous sectors 4 . As the demand for computational power surges to support the expanding scope of AI applications, both specialized and conventional hardware developments are advancing to manage the billions of parameters and simple but data-intensive and repetitive computations required. In response to these growing demands, analog AI computation architectures using cross-point arrays of non-volatile memory (NVM) devices have been proposed, promising significant increases in processing speeds and energy efficiency, thereby reducing time and energy costs 5 . Analog AI computation involves physically executing vector-matrix multiplication in deep learning by arranging tunable conductive NVM in a cross-point array architecture. This approach minimizes the need for data transfer between memory and processor units and performs massively parallel computation in analog, enabling large computation acceleration while consuming less energy 6 . However, realizing this concept demands prioritizing the selection of appropriate NVM candidates. Furthermore, manipulating numerical values in the format of analog physical quantities poses a challenge. In particular, if the vulnerability to variation and noise in analog signals remains unresolved, the advantage of analog AI computation will ultimately be limited to the application working with limited accuracies such as approximation 7 , 8 . Electrochemical random-access memory (ECRAM) stands as a promising cross-point element for performing analog AI computation. With a transistor-like three-terminal structure, ECRAM’s channel conductance can be modulated by applied gate bias which induces ion migration into or out of the channel material 9 , 10 . In contrast to two terminal counterparts, the device structure with the third terminal separates the read and write pathways to provide improved controllability and ease for device optimization. Such devices based on the electrochemical movement of ions offer excellent programmability and broaden versatility with multilevel, low cycle-to-cycle, and device-to-device variation. Among various ions considered, oxygen ion-based ECRAM (O-ECRAM) devices have attracted many researchers due to their excellent programmability and foundry-friendly material compositions. Transition metal oxide materials such as WO 3 11 , 12 , Pr 1- x Ca x MnO 3 13 , TiO 2 14 , and MoO 3 15 , 16 have been considered as channel layers for O-ECRAM. Recently, various reports have highlighted ECRAM-based cross-point array architecture 17 including in-situ training 18 , 19 , demonstrating excellent switching characteristics 20 . Understanding and tracking the changes in materials within a device is essential. To advance towards a technology specialized in analog AI computation with high energy efficiency and training capabilities, insight from fundamental studies on atomic and electronic behavior of materials during the switching process of ECRAM are necessary to enable appropriate material selection and optimization. Although numerous studies have investigated how electrochemical reactions and ion diffusion underlie various switching mechanisms in resistive random-access memory (ReRAM) and other memory devices, these mechanisms are not necessarily identical to those in ECRAM. For example, filamentary-based processes (e.g., electrochemical metallization, valence change mechanism, thermochemical mechanism) rely on localized conduction paths and Joule heating 21 , whereas ECRAM exhibits a bulk switching mechanism driven by electrochemical redox reactions in the channel and electrolyte, along with the electric double layer (EDL) effect and electrochemical doping 22 . Nevertheless, the underlying mechanisms responsible for the switching characteristics of ECRAM remain to be revealed. This deficiency can be attributed to three primary factors: the challenge of observing oxygen vacancies or oxygen ion migration, the intricate multilayer structure of ECRAM, and the intermixing of the conduction and switching mechanisms resulting from the separation of read/write pathways. Therefore, advanced measurement techniques with high-sensitivity is required to shed light on revealing the switching mechanism in ECRAM. In this work, we develop ECRAM Hall-bar devices using WO 3- x as the channel material to explore their essential transport properties, including carrier type, mobility, carrier density, and operational principles. Our method, leveraging an AC magnetic parallel dipole line (PDL) Hall system, overcomes a longstanding measurement barrier that was previously nearly impossible to address for high-resistance channels. Specifically, we perform Hall measurements by applying a time-varying magnetic field and a lock-in technique, which allows for the detection of weak Hall signals in WO 3- x channels, effectively eliminating DC background and noise 23 – 25 . Furthermore, we conduct the comparative study on resistive switching in ECRAM devices at low temperatures, capturing crucial physical parameters through Hall measurements at various conductance states. By employing first-principle calculations, we quantitatively analyze the observed physical properties, providing insights into optimizing ECRAM performance for use as an analog AI computation accelerator. Building on these insights, we assess the robust cycle-to-cycle performance of ECRAM and investigate its potential impact on the training of neural networks."
} | 1,749 |
30270573 | PMC6528606 | pmc | 6,802 | {
"abstract": "Abstract The discovery of secondary metabolites from marine microorganisms is beset by numerous challenges including difficulties cultivating and subsequently eliciting expression of biosynthetic genes from marine microbes in the laboratory. In this paper, we describe a method of culturing three species from the marine bacterial genus Pseudoalteromonas using cotton scaffold supplemented liquid media. This simple cultivation method was designed to mimic the natural behavior of some members of the genus wherein they form epibiotic/symbiotic associations with higher organisms such as sponges and corals or attach to solid structures as a biofilm. Our scaffolded cultivation is highly effective at stimulating an attachment/biofilm phenotype and causes large changes to metabolite profiles for the microbes investigated. Metabolite changes include alteration to the production levels of known molecules such as violacein, thiomarinol A, and the alterochromide and prodiginine families of molecules. Finally and critically, our technique stimulates the production of unknown compounds that will serve as leads for future natural product discovery. These results suggest our cultivation approach could potentially be used as a general strategy for the activation of silent gene clusters in marine microbes to facilitate access to their full natural product biosynthetic capacity.",
"conclusion": "4 CONCLUSIONS We have found that addition of a cotton scaffold to standard marine bacterial growth media is an effective technique to promote attachment and biofilm formation. This technique may lead to insights into the chemical ecology of the biofilm mode of life in marine bacteria. Cotton settlement appears to be associated with metabolic changes in these Pseudoalteromonads including increases in the proportion of brominated alterochromides produced in relation to their non‐brominated analogues, increased production of thiomarinol A, violacein and potentially prodiginine variants, and the stimulation of synthesis of secondary metabolites not observed for bacteria of the same strain grown in identical media without cotton. It is particularly noteworthy to see secondary metabolite changes observed not only for noted biofilm forming bacterium P. luteoviolacea 2ta16 , but also P. piscicida JCM 20779 and P. rubra DSM‐6842; organisms that were initially found in their planktonic form and whose biofilms are not described in the literature. Scanning electron microscopy images show that these three strains are forming biofilms on the surface of cotton fibers from our cultures, which is likely the primary driver of the observed changes in secondary metabolite production. However, it is worthy to note that at least two strains of Pseudoalteromonas have been isolated that have cellulase activity (Kim et al., 2009 ; Violot et al., 2005 ). It is unknown whether the three strains used in this study have any cellulase activity, so further study will be required to determine whether this activity exists and whether it has an effect on secondary metabolite production. Further investigations will focus on elucidating the structure of the unknown metabolites identified in this study and will seek the cause of changes to the metabolite profiles observed here. The question remains whether new metabolites are being observed due to modification to the expression of silent biosynthetic gene clusters or other reasons such as increased cell mass. Regardless of the mechanism, this method, cotton‐supported culturing, stimulates the production of secondary metabolites not always produced under conventional liquid media growth conditions. This method presents a simple and effective approach for natural product discovery that can likely be applied to many bacterial species.",
"introduction": "1 INTRODUCTION Mining of microbes for bioactive natural products has resulted in the discovery of a plethora of valuable pharmaceutically relevant compounds (Newman & Cragg, 2016 ). The incentive to discover or develop novel bioactive molecules from nature has increased significantly in recent years with the rise of antibiotic‐resistant pathogens (Davies & Davies, 2010 ). Natural product discovery, however, is faced with a number of challenges including activation of silent gene clusters. Natural products discovered through laboratory fermentation of wild‐type strains represent a small fraction of the genetically encoded molecules that exist in nature, as many biosynthetic genes are not expressed at detectable levels under laboratory conditions (Reddy et al., 2012 ). Furthermore, only a small fraction of microbial strains have been successfully cultivated in the laboratory (Rapp & Giovannoni, 2003 ; Staley, 1985 ). Marine microorganisms have received a good deal of attention in recent years as sources of natural product molecules with structural motifs and biosynthetic mechanisms not commonly found among terrestrial bacteria (Montaser & Luesch, 2011 ; Timmermans, Paudel, & Ross, 2017 ). However, many marine microorganisms are obligate symbionts with other marine organisms or form biofilms when settled on surfaces. Species adapted to these modes of life can be challenging to culture in vitro , and they may differentially express natural product biosynthetic gene clusters under standard laboratory conditions (Berrue, Withers, Haltli, Withers, & Kerr, 2011 ; Stewart, 2012 ). It is generally accepted that biofilm formation and quorum sensing are intimately linked for many microbes; however, there are multiple instances where secondary metabolite production is also altered as a result of these behaviors. (Atkinson, Cámara, & Williams, 2007 ; Barnard et al., 2007 ; Beauvais & Latgé, 2015 ; Bleich, Watrous, Dorrestein, Bowers, & Shank, 2015 ; Braga, Dourado, & Araujo, 2016 ; Busetti, Maggs, & Gilmore, 2017 ; Cude & Buchan, 2013 ; Cude et al., 2015 ; Favre et al., 2017 ; Harrington et al., 2014 ; Johnson, Kido Soule, & Kujawinski, 2016 ; Nickzad & Déziel, 2014 ; Othmani, Briand, Ayé, Molmeret, & Culioli, 2016 ; Zhou, Lyu, Richlen, Anderson, & Caia, 2016 ). For example, regulation of the biosynthesis of violacein, an antibacterial secondary metabolite produced by several species of Pseudoalteromonas , is highly sensitive to acyl‐homoserine lactone (AHL) quorum sensing (Ayé et al., 2015 ; Wang et al., 2008 ). We believe that by exploiting the dynamics between biofilm formation, AHL‐mediated quorum sensing, and secondary metabolite production, we can expand laboratory access to microbial bioactive natural products. Here, we report a set of simple culture conditions that stimulate natural product production by three species of marine gammaproteobacteria of the genus Pseudoalteromonas while simultaneously instigating their settlement on a cotton scaffold. Pseudoalteromonas is a genus of gram‐negative marine bacteria whose members are found in marine sediment, seawater, and frequently in association with other marine organisms (Skovhus, Holmström, Kjelleberg, & Dahllöf, 2007 ). Pseudoalteromonads commonly form biofilms for at least part of their life cycle (Rao, Webb, & Kjelleberg, 2005 ; Sneed, Sharp, Ritchie, & Paul, 2014 ) and produce a large range of bioactive secondary metabolites including violacein, thiomarinol, pentabromopseudilin, prodigiosin, indolmycin, and bromoalterochromide A (Bowman, 2007 ; Holmstrom & Kjelleberg, 1999 ; Vynne, Månsson, Nielsen, & Gram, 2011 ). In this paper, we detail the effect of cultivation with a cotton scaffold on biofilm formation and the production of the known metabolites violacein, thiomarinol A, the alterochromides, and the prodiginines (Figure 1 ) by three bacterial species, Pseudoalteromonas luteoviolacea 2ta16 (Maansson et al., 2016 ; Yang, Xiong, Lee, Qi, & Qian, 2007 ), P. piscicida JCM 20779 (Ross, Gulland, Dorrestein, & Moore, 2015 ; Speitling, Smetanina, Kuznetsova, & Laatsch, 2007 ), and P. rubra DSM‐6842 (Fehér, Barlow, Lorenzo, & Hemscheidt, 2008 ; Gerber & Gauthier, 1979 ). We also describe global changes in metabolite profiles for the microbes investigated. Figure 1 Structures of known secondary metabolites analyzed in this study. Violacein and thiomarinol A, produced by P. luteoviolacea 2ta16; the prodiginines, produced by P. rubra \n DSM ‐6842; and the alterochromides, produced by P. piscicida \n JCM 20779",
"discussion": "3 RESULTS AND DISCUSSION To obtain greater access to the encoded secondary metabolites of marine bacteria such as Pseudoalteromonas luteoviolacea , we sought to grow bacteria in conditions resembling those in their native habitat by making a simple modification to laboratory culturing conditions. A cotton scaffold in the form of cotton balls was added to the standard liquid culturing conditions to imitate the macroarchitecture of marine invertebrates such as sponges and corals, upon which many microbes reside. Organic extracts of culture supernatants and biomass were then compared for bacteria grown under standard liquid conditions and the modified cotton scaffold‐liquid conditions. Scanning electron microscopy was used to determine the presence of bacterial biofilms on the cotton scaffold, and ultraperformance liquid chromatography coupled to photodiode array detection with in‐line mass spectrometry (UPLC‐PDA‐MS) was used to determine a metabolite profile for each microbe under each culture condition. Three strains of marine bacteria from the genus Pseudoalteromonas were investigated, and the changing production of their known secondary metabolites was documented ( Supporting Information Tables S2 and S3 ). 3.1 \n Pseudoalteromonas luteoviolacea 2ta16 \n Pseudoalteromonas luteoviolacea 2ta16 is a γ‐proteobacterial isolate from a coral specimen, Montastrea anularis, collected in the Florida Keys (Rypien, Ward, & Azam, 2010 ). P. luteoviolacea 2ta16 is a known producer of several natural products including the bright purple‐coloured antibiotic, violacein (Yang et al., 2007 ), and the thiomarinol family of dithiolopyrrolone antibiotics (Maansson et al., 2016 ). Analysis of the P. luteoviolacea 2ta16 genome using bioinformatic prediction tool antiSMASH 4.0 (Blin et al., 2017 ) identified 13 putative biosynthetic gene clusters ( Supporting information Table S1 ), indicating that a number of molecules remain to be discovered from this microbe. In an effort to mimic its native environment, and in the process potentially activate silent natural product biosynthetic gene clusters, we cultivated P. luteoviolacea 2ta16 in standard liquid media in the presence and absence of a cotton scaffold. The phenotypic difference between the two cultivations is readily discernible. As seen in Figure 2 , when P. luteoviolacea is grown in liquid media without cotton, it displays a non‐pigmented phenotype and the bacterial population appears to be entirely planktonic. By contrast, bacteria associated with the cotton in scaffold‐containing cultures demonstrate an intense purple pigmentation and show a mucoid phenotype that is indicative of biofilm formation. SEM images of cotton fibers from this culture show structures consistent with biofilm formation, with clusters of cells forming visible microcolonies on the surface of the fibers (Figure 3 and Supporting information Figure S1 ). Coverage of cotton fibers was consistent throughout multiple portions of the cotton, with no apparent differences in adherence between the areas of the cotton that were imaged. No obvious morphological differences could be observed between cultures that were grown for 24 h and those grown for 96 h. These images are consistent with the initial phenotypic observations that P. luteoviolacea 2ta16 forms a biofilm on the surface of the cotton fibers within 24 hr post‐inoculation. Figure 2 \n P. luteoviolacea 2ta16 grown in Difco Marine Media 2216 in the absence (a) and presence (b) of cotton scaffold. (c): UPLC ‐ PDA chromatograms of ethyl acetate extracts of cell‐free supernatants of P. luteoviolacea 2ta16 cultures showing total UV –Vis absorbance. The peak at 16.1 min has an m/z of 344.1 (M+H) + confirming the molecule is violacein. The peak at 19.00 min has an m/z of 641.4 (M+H) + confirming the molecule is thiomarinol A Figure 3 Scanning electron microscope ( SEM ) images of un‐inoculated cotton balls (a) and cotton balls cultured in the presence of P. luteoviolacea 2ta16. In this image, a microcolony of bacterial cells can be seen adhering to the surface of two cotton fibers UPLC‐PDA‐MS analysis of the cell‐free supernatant, planktonic biomass, and cotton‐associated biomass shows distinct differences in the metabolite profiles of P. luteoviolacea grown in the two culturing conditions. A peak with mass and UV–Vis profile matching that of violacein appears in significant quantity in the cell‐free supernatant of the cotton scaffold culture ( p ‐value of 0.014) but was undetectable in the supernatant of the non‐cotton‐containing culture as seen in Figure 2 . Violacein was also detectable in the planktonic biomass of both cultures but was present in greater quantity in the cotton‐associated biomass as seen in Supporting Information Figure S4 . As described in the Introduction , it has previously been reported that violacein biosynthesis in Pseudoalteromonas is regulated by quorum sensing molecules including acyl‐homoserine lactones (Wang et al., 2008 ) , and there is an‐established interdependency of quorum sensing and epibiotic biofilm formation in marine bacteria (Zhou et al., 2016 ). Our SEM images and UPLC‐MS data taken together confirm that the presence of the cotton is facilitating biofilm formation and violacein biosynthesis. These findings support work by Yang and coworkers who observed that cultures of an unidentified strain of P. luteoviolacea produced violacein when grown in a stationary manner in which biofilm formation was possible. Contrastingly, cultures grown with agitation, which would inhibit biofilm formation and the associated quorum sensing, did not make violacein. (Yang et al., 2007 ). The potent dithiolopyrrolone antibiotic thiomarinol A was also readily detected in the cotton‐containing cultures of P. luteoviolacea 2ta16 and was undetectable in standard cultures as seen in Figure 2 ( p ‐value 6.0 × 10 −4 , deemed significant) The thiomarinols are produced by a number of Pseudoalteromonas strains including P. luteoviolacea 2ta16 (Maansson et al., 2016 ; Murphy et al., 2014 ; Shiozawa et al., 1993 ) and although there has been considerable investigation of their biosynthesis (Dunn, Wever, Economou, Bowers, & Li, 2015 ; Qin, Huang, Yu, & Deng, 2013 ; Zhai et al., 2016 ), to the best of our knowledge there have not been reports on the regulation of this biosynthetic pathway or exploration of thiomarinol chemical ecology. Using cotton scaffolds to stimulate molecule production may facilitate examination of these unanswered questions. In our experiments, we also observed that settlement of P. luteoviolacea on cotton stimulated the production of a number of other metabolites at levels higher than baseline noise. Initial in‐line UPLC‐MS analysis of these compounds indicates that they are not known secondary metabolites of P. luteoviolacea 2ta16. Work is ongoing to isolate and characterize these new natural products. We believe our results culturing P. luteoviolacea 2ta16 with a cotton scaffold establish a simple technique for accessing a greater proportion of the encoded biosynthetic potential of marine bacteria. To demonstrate the utility of the approach, we have applied the same cotton scaffold culturing condition to several other Pseudoalteromonas species that are not known to be marine invertebrate epibionts. 3.2 \n Pseudoalteromonas piscicida JCM20779 \n Pseudoalteromonas piscicida JCM20779 is a γ‐proteobacterium first isolated from a seawater sample collected during a “red tide” event off the west coast of Florida (Bein, 1954 ). P . piscicida JCM 20779 is predicted to encode nine biosynthetic gene clusters based on antiSMASH 4.0 analysis ( Supporting Information Table S1 ); however, the only natural products characterized for this strain are the alterochromide family of lipopeptides (Ross et al., 2015 ). Another strain, P . piscicida S2040, was recently reported to produce several siderophores (pseudochelin and myxochelins) and the anti‐cancer molecule, alteramide A (Sonnenschein et al., 2017 ). To assess whether secondary metabolite production by P . piscicida JCM 20779 is affected by adding a solid support, we grew the strain in liquid media in the presence and absence of a cotton scaffold. As seen in Figure 4 , the addition of cotton balls to liquid culturing conditions results in a phenotypic change to the P . piscicida JCM 20779. While there is a noticeable orange pigmentation of cells grown in liquid media, growth with added cotton appears to stimulate a considerable increase in pigmentation, especially for cells associated with the cotton. Cells associated with the cotton scaffold also display a mucoid phenotype on air‐exposed surfaces. Based on these observations, it appears that P. piscicida JCM 20779 is adhering to the cotton and forming a biofilm. SEM images of P. piscicida JCM 20779 can be found in Supporting Information Figure S2 showing a dense coverage of biofilm on cotton fibers from these cultures. Figure 4 \n P. piscicida \n JCM 20779 cultured in Difco Marine Media 2216 in the absence (a) and presence (b) of cotton scaffold. (c): Overlaid UPLC ‐ PDA chromatograms showing absorbance at 390 nm for the methanol extract of the planktonic cell pellet of P. piscicida \n JCM 20779 grown without cotton and the methanol extract of biomass associated with the cotton scaffold for P. piscicida \n JCM 20779 grown with cotton. Known alterochromide variants are indicated. A = alterochromide A, B = alterochromide B, BrA = bromoalterochromide A, BrB = bromoalterochromide B etc Alterochromide‐like molecules can be catalogued by viewing absorbance chromatograms at 390 nm (Ross et al., 2015 ). Supporting Information Figure S8 shows UPLC‐PDA chromatograms of ethyl acetate extracts from the cell‐free supernatant, and Supporting Information Figure S9 shows methanol extracts of planktonic cell pellets of cotton‐ and non‐cotton‐containing cultures. These figures show that while it was possible to extract alterochromides from the cell‐free supernatant of the non‐cotton culture, the molecules were not detected in the cell‐free supernatant of the cotton ball cultures. However, alterochromide molecules are clearly detectable in the methanol extracts of biomass from both cotton‐ and non‐cotton‐containing cultures as seen in Figure 4 . Indicating that while alterochromides are synthesized in both culture conditions, they do not appear to be secreted from the cells in the cotton treatments. Although alterochromides are produced under both culture conditions, the relative amounts of different alterochromides differ between cotton and non‐cotton cultures (Figure 4 ). In‐line mass spectrometry allows for the identification of eight known alterochromide family members. There is a significant increase in the ratios of bromoalterochromide A/A′, bromoalterochromide A″, and bromoalterochromide B/B′ in relation to their non‐brominated analogues when the microbe is cultured with a cotton scaffold ( p values of 0.024 and 0.028 for the A alterochromides and B alterochromides, respectively). Changes to the alterochromide profile are very consistent between biological replicates. In addition to the known alterochromides, there are several additional molecules detected only in the cotton ball‐containing cultures that strongly absorb light at 390 nm and show a bromine isotope pattern in their mass spectra. We believe that our new culturing technique allows detection of new alterochromide analogues in addition to changing the relative ratios of known alterochromide variants. 3.3 \n Pseudoalteromonas rubra DSM‐6842 \n Pseudoalteromonas rubra DSM‐6842 was originally isolated from a seawater sample from the Mediterranean Sea near Nice, France (Gauthier, 1976 ). P. rubra strains including P. rubra DSM‐6842 are known producers of several prodigiosin analogues (Fehér et al., 2008 ; Gauthier, 1976 ; Johnson, de Rond, Lindsay, Keasling, & Sarpong, 2015 ) and analysis of the P. rubra DSM‐6842 genome with antiSMASH 4.0 identified 11 putative biosynthetic gene clusters ( Supporting Information Table S1 ). It is important to note that the prodigiosin gene cluster itself is not detected by antiSMASH 4.0, demonstrating a limitation of this prediction tool and emphasizing that the “true” number of encoded secondary metabolites may be underestimated by these types of analyses. Following our success modifying and increasing secondary metabolite production by Pseudoalteromonads, we cultivated P. rubra DSM‐6842 in liquid conditions with and without an added cotton scaffold and analyzed their relative prodiginine production and the global metabolome. Prodiginines are characteristically red in color with an absorbance maximum at 535 nm (Gerber & Gauthier, 1979 ). Both cultures had visible red pigmentation concentrated in the biomass after centrifugation, clearly indicating prodiginine production and the isolated cotton balls were light pink. Interestingly, the cotton cultures developed a golden yellow coloured supernatant (Figure 5 ); however, the pigment was not extracted into ethyl acetate, and thus, further experiments will be required to isolate and characterize the corresponding molecule. Figure 5 \n P. rubra \n DSM ‐6842 grown in Difco Marine Media 2216 in the absence (a) and presence (b) of cotton scaffold. (c, d): Partial UPLC ‐ PDA chromatograms showing absorbance at 250–600 nm for organic extracts of P. rubra \n DSM ‐6842 grown in the presence and absence of cotton (c) supernatant and biomass extracts, and (d) planktonic and cotton‐associated biomass extracts. The peak at 13.6 min has an m/z of 324.1 (M+H) + , identifying the compound as prodigiosin In addition to the phenotypic colour differences between cultures, SEM images show the formation of cellular aggregates of P. rubra on the surface of cotton fibers (see Supporting Information Figure S3 ), and changes in the metabolic profiles were also apparent when extracts were analyzed by UPLC‐PDA‐MS. There is a distinct increase in the absorbance at 535 nm for several peaks in the cotton‐associated biomass extract as seen in Supporting Information Figure S13 , that were linked to known prodigiosin analogues through mass spectral data ( Supporting Information Table S2 ) (Fehér et al., 2008 ). The addition of cotton to the culture caused a limited increase in the relative abundance of prodigiosin, 4″‐( n ‐heptyl) prodigiosin, and cycloprodigiosin production. The cotton scaffold modified not only production of known prodiginines, but also production of unknown compounds as well. When looking at the global metabolic changes in the supernatant extracts, two peaks at 1.15 min and 3.02 min in the non‐cotton supernatant appeared to increase in intensity in the cotton culture supernatant (Figure 5 ). Additionally, two initially negligible peaks at 12.27 min and 12.96 min from the planktonic biomass appear to be produced in higher relative quantities in the cotton‐associated biomass (Figure 5 ). However, it is worth noting that these apparent changes in metabolite production in P. rubra were observed in only two out of three biological replicates, demonstrating more variability than was seen for the metabolite profiles of P. luteoviolacea 2ta16 or P. piscicida JCM 20779 ( Supporting Information Figure S4 ) and affecting statistical analysis ( Supporting Information Table S4 ). The abundance of a bacterial secondary metabolite is a critical factor in the ease of its isolation and identification. With the addition of a cotton scaffold, we display a simple method to upregulate natural product production and therefore facilitate their isolation and characterization."
} | 6,033 |
32118096 | null | s2 | 6,804 | {
"abstract": "The recent demonstration of neuromorphic computing with spin-torque nano-oscillators has opened a path to energy efficient data processing. The success of this demonstration hinged on the intrinsic short-term memory of the oscillators. In this study, we extend the memory of the spin-torque nano-oscillators through time-delayed feedback. We leverage this extrinsic memory to increase the efficiency of solving pattern recognition tasks that require memory to discriminate different inputs. The large tunability of these non-linear oscillators allows us to control and optimize the delayed feedback memory using different operating conditions of applied current and magnetic field."
} | 170 |
35193559 | PMC8864926 | pmc | 6,806 | {
"abstract": "Background Butyl acetate is a versatile compound that is widely used in the chemical and food industry. The conventional butyl acetate synthesis via Fischer esterification of butanol and acetic acid using catalytic strong acids under high temperature is not environmentally benign. Alternative lipase-catalyzed ester formation requires a significant amount of organic solvent which also presents another environmental challenge. Therefore, a microbial cell factory capable of producing butyl acetate through fermentation of renewable resources would provide a greener approach to butyl acetate production. Result Here, we developed a metabolically engineered strain of Escherichia coli that efficiently converts glucose to butyl acetate. A modified Clostridium CoA-dependent butanol production pathway was used to synthesize butanol which was then condensed with acetyl-CoA through an alcohol acetyltransferase. Optimization of alcohol acetyltransferase expression and redox balance with auto-inducible fermentative controlled gene expression led to an effective titer of 22.8 ± 1.8 g/L butyl acetate produced in a bench-top bioreactor. Conclusion Building on the well-developed Clostridium CoA-dependent butanol biosynthetic pathway, expression of an alcohol acetyltransferase converts the butanol produced into butyl acetate. The results from this study provided a strain of E. coli capable of directly producing butyl acetate from renewable resources at ambient conditions. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-022-01755-y.",
"conclusion": "Conclusion While most of the microbial butyl acetate synthesis available in the literature required externally added butanol or butyric acid, and have resulted in lower titers, in this study, we presented an inducer-free strain of E. coli for butyl acetate production directly from glucose. A proper vector with medium copy number was selected for alcohol acetyltransferase ATF1 expression to achieve highest butyl acetate titer. In addition, we investigated the stoichiometry of NADH involved in butyl acetate synthesis by fdh expression. The final butyl acetate producing strain was constructed by expressing ATF1 together with CoA-dependent butanol synthesis pathway without fdh . After substituting the IPTG inducible promoter with self-regulated fermentative regulatory elements to drive the gene expression, the highest butyl acetate effective titer achieved 22.8 ± 1.8 g/L in a bench top bioreactor. To our knowledge, this result represents the highest butyl acetate production titer from glucose currently reported in the literature.",
"introduction": "Introduction Butyl acetate is an industrially important chemical used as a solvent for various coatings and paints involved in the production of consumer products such as automobiles, wood furniture, artificial leather, printing inks, nail polish and adhesives [ 1 – 3 ]. In addition, it is a fruity odorant that is used as a synthetic fruit flavoring agent in food and beverages and as an odor enhancer to perfumes [ 4 ]. It is also considered as a biofuel additive for enhancing biodiesel properties [ 5 ]. These diverse applications of butyl acetate makes it a high volume production chemical with a production capacity of around 800,000 tonnes per year in Asia and 200,000 tonnes per year in the U.S. with price around $ 1.5 USD/kg [ 6 ]. Butyl acetate is currently manufactured through Fischer esterification of butanol and acetic acid under elevated temperature with sulfuric acid for catalysis [ 7 ], which is not environmentally benign for the volume at which butyl acetate is produced. To address this difficulty, mild methods for preparing butyl acetate such as using lipase for catalysis have been developed. However, lipase-catalyzed condensation requires a significant amount of organic solvent which presents another environmental challenge [ 8 , 9 ]. An ideal reaction system would directly condense butanol and acetic acid in aqueous solution under mild condition. However, under mild ambient conditions, the thermodynamics for esterification is unfavorable. Previously, Atsumi group overcame this challenge by using an alcohol acetyltransferase that condenses alcohols with acyl-CoA and achieved ester production from glucose using metabolically engineered Escherichia coli [ 10 ]. Acyl-CoA represents an activated carboxylic acid and is synthesized through ATP-dependent ligation of coenzyme A with a carboxylic acid. This ATP-dependent activation provided the energy required for the condensation. Furthermore, using metabolically modified microorganisms to produce esters also allows for carbon feedstock flexibility as conventional carbon resources such as glucose, xylose, and glycerol have been extensively studied for various bio-productions. To date, the bio-production of isobutyl acetate [ 11 , 12 ], isoamyl acetate [ 13 , 14 ], ethyl acetate [ 15 – 17 ], geraniol acetate [ 18 ], tetradecyl acetate [ 10 ] and other esters [ 19 – 21 ] have been demonstrated. Previous studies on the production of butyl acetate have primarily been relying on feeding butanol to an alcohol acetyltransferase expressing strain [ 10 , 22 , 23 ]. Only a few recent studies reported direct butyl acetate synthesis from glucose in Clostridial strains [ 24 , 25 ], which are natural butanol producers. However, as Clostridia are strict anaerobes with complex physiology and less tools for genetic manipulations, construction of butyl acetate producing strains using conventional hosts such as E. coli may offer more rapid development and easier cultivation. Therefore, here we metabolically engineered E. coli for direct butyl acetate production from glucose. Butyl acetate synthesis was achieved by co-expressing a modified Clostridium CoA-dependent butanol pathway [ 26 ] and an alcohol acetyltransferase, ATF1, from Saccharomyces cerevisiae s288c. Since the ATF1 protein has been reported to form aggregates in E. coli under high expression levels [ 27 ], we first screened for plasmids with different copy number to express ATF1 for higher butyl acetate titer. We then investigated the stoichiometry of the whole pathway for converting glucose to butyl acetate by taking out the formate dehydrogenase (encoded by fdh ) expression (Fig. 1 ), which has been previously shown to be important for butanol and butyraldehyde production [ 26 , 28 ]. Finally, we replaced the IPTG-inducible promoter with self-regulated fermentation regulatory elements (FRE) [ 29 ] to construct an inducer-free butyl acetate production strain. The resulting strain was able to achieve a butyl acetate titer of 22.8 g/L using a bench-top bioreactor, representing the highest titer of butyl acetate produced by a recombinant microorganism. Fig. 1 Butyl acetate production pathway from glucose. Blue arrows represent the overexpressed genes for butyl acetate production. The butyl acetate synthesis pathway is separated into A , B glycolysis, C formate dehydrogenase reaction, D butanol synthesis and E alcohol acetyltransferase reaction. The black box shows the stoichiometry of each pathway as well as the overall scenario of either fdh expressed or not. ATP equivalents are not shown in this figure",
"discussion": "Results and discussions Identification of proper plasmid copy number to drive alcohol acetyltransferase expression The most essential enzyme in developing a recombinant butyl acetate production pathway is the alcohol acetyltransferase. We previously constructed recombinant strains of E. coli that are efficient in producing butyraldehyde and butanol [ 26 , 28 ]. Therefore, to initiate the construction of a recombinant butyl acetate producer, we focused on the expression of alcohol acetyltransferase. Alcohol acetyltransferase ATF1 from S. cerevisiae has previously been selected for the production of isobutyl acetate and isoamyl acetate [ 12 , 13 ] at relatively high titers. Considering the chemical similarity of butyl acetate with isobutyl acetate and isoamyl acetate, ATF1 should be a good candidate for butyl acetate biosynthesis. First, we tested the in vivo activity of ATF1 by expressing it on a high copy plasmid with butanol feeding. Surprisingly, only trace amounts of butyl acetate were observed in the resulting culture medium, indicating minimal ATF1 activity. This result was likely due to inclusion body formation as ATF1 has previously been shown to aggregate in E. coli [ 27 ]. Therefore, we varied the plasmid origin of replication from high copy to medium and low copy in order to assess the effect of plasmid copy number on ATF1 expression. We confirmed the relative copy number of ATF1 on these vectors by qRT-PCR (Additional file 1 : Fig. S1A). As shown in Fig. 2 A, upon switching the expression vector from high copy to medium and low copy plasmids, recombinant E. coli strain was able to convert butanol to butyl acetate with no significant effect on growth (Fig. 2 B). Expressing ATF1 gene on medium copy number plasmid with ColA origin outperformed the other expression plasmids. Further analysis of protein expression using SDS-page (Additional file 1 : Fig. S1B) showed that similar ATF1 expression was observed when using pSC101 and ColA origins. Therefore, subsequent experiments will express ATF1 on a medium copy plasmid. Fig. 2 Evaluating the effect of expressing ATF1 on different plasmid vectors for converting butanol to butyl acetate and butyl acetate toxicity to E. coli . A Product concentration in the culture broth and B cellular growth of E. coli JCL16 carrying ATF1 gene on different copy number plasmid vectors. The cultures were incubated at 37 °C anaerobically for 24 h with butanol supplemented. The origin of replication, ColE1, ColA and pSC101 represent the relative copy number high, medium and low, respectively. C Growth of E. coli culture after 24 h with different concentration of butyl acetate added to the culture. T-test was performed to the experimental data. A p value larger than 0.05 is labeled as n.s Construction of recombinant butyl acetate producing strains To achieve butyl acetate production directly from glucose, we next combined the CoA-dependent butanol production pathway, encoded by atoB , hbd , crt , ter , and adhE2 , and the alcohol acetyltransferase, ATF1 , into a single E. coli strain. Previously, we showed that the removal of competing fermentative pathways was essential for achieving high flux production of butanol [ 26 ]. Therefore, we used the same host strain JCL299 in this study for butyl acetate production. Here, we used the same three plasmid system, pEL11/pIM8/pCS138 (Table 1 ), previously shown to efficiently produce butanol. As the results shown in the above section, ATF1 gene expressed on a medium copy number plasmid yielded the highest butyl acetate production. Therefore, we modified pIM8 by expressing ATF1 together with ter in an operon. Furthermore, the chloramphenicol resistance gene (Cm R ) in pCS138 was replaced by a spectinomycin resistance gene (Spec R ) to avoid ethyl acetate production from ethanol added to medium as chloramphenicol is typically dissolved in ethanol. The resulting strain YA2 produced 0.39 ± 0.01 g/L of butyl acetate with no detectable ethyl acetate (Table 2 ) in a 24 h batch fermentation in test tube. Its control strain YA1 which lacks the expression of ATF1 showed no butyl acetate production. Comparing the biochemical production profile of strains YA2 and YA1, significant differences in by-product pyruvate and formate secretion, glucose consumption, and cell growth were observed. Butyl acetate producing strain YA2 resulted in significantly slower growth with OD600 of 0.87 and is accompanied by lower glucose consumption of 8.0 ± 0.4 g/L compared to the butanol producing strain YA1. This level of growth retardation is unlikely to be due to butyl acetate toxicity as only minor inhibitory effect of butyl acetate at 0.5 g/L concentration was observed (Fig. 2 C). Table 1 Strains and plasmids used in this study Relevant genotype References Strain BW25113 rrnB T14 Δ lacZ WJ16 \n hsd R514 Δ araBAD AH33 Δ rhaBAD LD78 XL1-blue recA1 endA1 gyrA96 thi - 1 hsdR17 supE44 relA1 lac [F’ proAB lacI q ZΔM15 Tn10 (Tet R )] Agilent Technologies JCL16 BW25113/F’ [ traD36 proAB + \n lacI qZΔM15 (Tet R )] [ 39 ] JCL299 JCL16 ΔadhE ΔldhA ΔfrdBC Δpta [ 39 ] YA1 JCL299 transformed with pEL11, pIM8, pBA1 This work YA2 JCL299 transformed with pEL11, pBA5, pBA1 This work YA3 JCL299 transformed with pEL11, pBA5 This work YA5 JCL299 transformed with pRW13, pYA2 This work Plasmid pMW1 P LlacO1 :: ATF1 ; ColA ori; Kan R This work pMW4 P LlacO1 :: ATF1 ; ColE1 ori; Amp R This work pBA1 P LlacO1 :: fdh ; pSC101 ori; Spec R This work pBA3 P LlacO1 :: ATF1 ; pSC101 ori; Spec R This work pBA5 P LlacO1 :: ter , ATF1 ; ColA ori; Kan R This work pYA2 P adhE :: ter, ATF1 ; ColA ori; Kan R This work pRW13 P ack :: atoB, adhE2, crt, hbd ; ColE1 ori; Amp R [ 29 ] pRW18 P adhE :: fdh ; pSC101 ori; Cm R [ 29 ] pRW22 P adhE :: ter ; ColA ori; Kan R [ 29 ] pEL11 P LlacO1 :: atoB, adhE2, crt, hbd ; ColE1 ori; Amp R [ 26 ] pIM8 P LlacO1 :: ter ; ColA ori; Kan R [ 26 ] pCS138 P LlacO1 :: fdh ; pSC101 ori; Cm R [ 26 ] Table 2 Butyl acetate production using different E. coli strains Strain Genes expressed Acetate esters (g/L) By-products (g/L) Glucose consumption (g/L) Cell optical density (OD600) Butanol biosynthesis ATF1 fdh Promoter used Butyl acetate Ethyl acetate Ethanol Butanol Acetate Pyruvate Formate YA1 O O LlacO1 0 ± 0 n.d. 0.05 ± 0.01 1.8 ± 0.2 0.92 ± 0.08 1.6 ± 0.3 0 ± 0 9.7 ± 0.8 2.0 ± 0.2 YA2 O O O LlacO1 0.39 ± 0.1 n.d. 0.05 ± 0.01 0.42 ± 0.05 1.02 ± 0.03 0.50 ± 0.08 0.38 ± 0.02 8.0 ± 0.4 0.87 ± 0.05 YA3 O O LlacO1 1.4 ± 0.1 n.d. 0.12 ± 0.01 0.33 ± 0.02 1.28 ± 0.04 1.4 ± 0.2 0.59 ± 0.19 12 ± 0.4 1.3 ± 0.1 YA5 O O FRE 1.5 ± 0.1 n.d. 0.18 ± 0.01 0.53 ± 0.04 1.42 ± 0.03 1.8 ± 0.2 0.63 ± 0.06 13 ± 0.1 1.5 ± 0.1 n.d. not detected * FRE represents promoters of native E. coli fermentative genes. See Table 1 for detailed plasmid used Next, we analyzed the stoichiometry of the butyl acetate production and compared it with butanol production hoping to identify potential causes for the reduced cell growth. As depicted in Fig. 1 , when fdh is expressed, butyl acetate production strain results in NADH excess. Since the native fermentation pathways were deleted in strain YA2, the excess NADH generation during butyl acetate synthesis may potentially decrease the intracellular NAD + concentration, leading to lower glycolysis rate and growth. To test this, we removed fdh from strain YA2, resulting in strain YA3. As the results shown in Table 2 , strain YA3 has improved growth and glucose consumption compared to strain YA2 expressing fdh . Formate secretion was also increased in strain YA3 upon the removal of fdh . More interestingly, production of butyl acetate significantly improved to 1.4 ± 0.1 g/L. This result suggests that expression of fdh is less favorable for butyl acetate production and more favorable for butanol production. We reasoned that this behavior is due to fdh expression causing an increase in intracellular NADH levels [ 26 ]. Increased level of NADH drives acetyl-CoA flux towards butanol biosynthesis, potentially leaving less acetyl-CoA for butyl acetate production. This is in part supported by a higher butanol production by strain YA2 compared to YA3. While the production titer of butyl acetate is higher when fdh is not expressed, pyruvate secretion is significantly increased. This result is most likely due to a shortage of NADH. Butyl acetate production pathway is the only fermentation pathway available. 4 NADH are required for each butyl acetate synthesized and 3 NADH are produced by glycolysis to generate the 3 acetyl-CoA molecules for producing butyl acetate. The 1 NADH short is made up by oxidation of 0.5 glucose to pyruvate. Since pyruvate can be secreted from the cell while acetyl-CoA cannot, pyruvate is secreted to avoid acetyl-CoA accumulation. Analysis of intracellular redox state and ATP concentrations As described above, NADH is in excess when fdh is expressed, which causes a decrease in the intracellular NAD + concentration, leading to lower glycolysis rate and growth. We measured the NADH to NAD + ratio in each butyl acetate producing strain to investigate the relationship between butyl acetate titer and intracellular redox state. As the result shown in Fig. 3 A, NADH/NAD ratio of fdh -expressing strain YA2 is significantly higher than that of strain YA3 which does not express fdh . Previous study showed that high NAD + availability benefits high glycolytic rate [ 30 ]. The high NADH/NAD + ratio in strain YA2 limited NAD + availability, resulting in lowered glycolytic rate, potentially explaining the low butyl acetate titer, cell density, and glucose consumption in strain YA2 (Table 2 ). Analysis of intracellular ATP concentration (Fig. 3 B) also indicated a lower glycolytic activity in strain YA2. Since phosphate acetyltransferase (Pta) was knocked out in all strains, the primary source of ATP generation is through glycolysis. Strain YA2 had the lowest ATP level when compared to butanol producing strain YA1 and butyl acetate producing strain without fdh YA3, potentially explaining its slower growth rate. Interestingly, we noticed that although butyl acetate production without fdh expression is theoretically short of NADH, it achieved a similar NADH/NAD ratio to that of the butanol producing strain YA1 which has a balanced NADH production and consumption. Strain YA3 achieved this similar NADH/NAD ratio as YA1 likely through the secretion of pyruvate. Strain YA3 secreted pyruvate with a normalized titer of 1.07 g/L/OD of while strain YA1 secreted 0.8 g/L/OD. Together, these results provided intracellular explanation to why fdh expression is unfavorable for butyl acetate biosynthesis. Fig. 3 Intracellular redox level and ATP concentration. A Relative NADH/NAD + ratio and B ATP level normalized by the value in butanol producing strain YA1. Positive and negative value for NADH produced represents net NADH produced for a molecule butanol or butyl acetate production. T-test was performed to the experimental data. A p value larger than 0.05 is labeled as n.s. Using native fermentative regulatory elements to construct an inducer-free butyl acetate producing strain Next, we wanted to establish an inducer-free butyl acetate production system to avoid the necessity of IPTG inducer which is cost prohibitive in large scale production. Since our butyl acetate production is primarily performed under anaerobic conditions, the oxygen levels and its absence can serve as a switch for expression of butyl acetate production genes. Previously, genetic sequences upstream of native E. coli fermentative pathway genes, including their promoters and regulatory protein binding sites, were used to drive synthetic operons for butanol production genes [ 29 ]. These sequences termed fermentative regulatory elements (FRE) enabled a higher butanol production compared to using the LlacO1 promoter and allowed inducer-free production. Here we recruited these FRE and used them on butyl acetate similar to what has been done in butanol production. Genes atoB , adhE2 , crt , and hbd were grouped into a synthetic operon under the control of native acetate kinase ack FRE. Genes ter and ATF1 were grouped into another synthetic operon under the control of native alcohol/aldehyde dehydrogenase adhE FRE. These two plasmids were subsequently transformed into strain JCL299, resulting in strain YA5. As the production results shown in Table 2 , metabolite secretion profiles were similar to that of strain YA3 using LlacO1 promoter with IPTG as inducer. Furthermore, NADH/NAD ratio and ATP levels in strain YA5 were also similar to that of strain YA3. These results indicated the successful construction of an inducer-free butyl acetate production strain. Scale up butyl acetate production using bioreactor with in situ product removal To examine the fermentation time-course behavior of the inducer-free strain YA5 for butyl acetate production, we conducted a fed-batch fermentation using a bench-top bioreactor. Since butyl acetate is toxic and inhibits E. coli growth at 4 g/L concentration (Fig. 2 C), in situ product removal may be necessary to properly assess the butyl acetate production ability of our strain. Butyl acetate is volatile and can be removed from production vessel using gas stripping. Therefore, we used a continuous N 2 purging fermentation coupled to water traps for product removal. Continuous bubbling of N 2 gas carries the volatile butyl acetate out of the fermentation vessel and into sequential water traps connected by a Graham condenser. The first water trap was placed under room temperature, whereas the other two water traps were placed under ice bath. The schematics of this setup is shown in Fig. 4 A. As shown in Fig. 4 B, the effective titer of butyl acetate produced reached up to 22.8 ± 1.8 g/L in 96 h. The productivity for the first 72 h was nearly constant at a rate of around 0.28 g/L/hour. The cellular growth of the fermentation decreased significantly after switching to anaerobic condition for butyl acetate production (Fig. 4 C). This growth behavior was similar to that reported in previous study of butanol production [ 26 ]. To our knowledge, this result represents the highest butyl acetate production titer from glucose currently reported in the literature. Fig. 4 Bench-top fermentation of butyl acetate production. A Schematics for the fed-batch bioreactor fermentation with in situ product removal with gas striping setup. During the production phase, the bioreactor was connected to N 2 gas cylinder to maintain the anaerobic condition. The gas outlet of the fermenter was connected to three water trap in series to capture the evaporated butyl acetate. The first water trap is placed under room temperature as indicated by RT. The second and third trap, Cold trap 1 and 2, were placed under ice bath. The Graham condenser was connected to a refrigerated chiller with circulating water at 4 °C. B Alcohols and ester production, C cellular growth, and D byproduct secretion profile of strain YA5 during the fermentation. E Butyl acetate distribution and F butyl acetate to butanol ratio in fermentation broth, trap (room temperature) and 2 cold traps. The time point zero indicates the time switching culture into anaerobic condition by pumping nitrogen gas Our butyl acetate production achieved a final yield of 0.12 g/g glucose, representing 37% of maximum theoretical yield. Notably, we previously showed that glycerol in TB medium has very little contribution towards product titer [ 26 ]. Therefore, it is not considered in the yield calculation. Solvent byproduct titers were about 1 order of magnitude lower than that of butyl acetate, where ethanol and butanol titer accumulated to around 2 g/L each by 96 h post anaerobic switch. Nonetheless, the notable amount of butanol secretion suggested insufficient ATF1 activity in the cell. ATF1 expressed in E. coli has been previously shown with a specific activity of around 0.01 μmol/min/mg [ 27 ], while the activities of the enzymes from butanol production pathway were around 0.1 to 100 μmol/min/mg [ 31 ]. Ethyl acetate, the ATF1-catalyzed product of ethanol and acetyl-CoA, resulted in about 1 g/L. Since previous studies showed that ATF1 is more favorable on catalyzing butyl ester formation than that of ethyl ester [ 32 , 33 ], the significant lower amount of ethyl acetate than butyl acetate is reasonable. Furthermore, AdhE2 enzyme is more specific towards butanol production than ethanol as it is the enzyme involved in Clostridium butanol fermentation [ 26 ]. No butyl butyrate was observed, indicating low activity of ATF1 for using butyryl-CoA as a substrate. Other major byproducts were formate, acetate, and pyruvate (Fig. 4 D). As the stoichiometry analysis illustrated in Fig. 1 , the theoretical ratio of BA:formate:pyruvate should be 1:3:1. However, our fermentation results showed a ratio of roughly 1:0.88:0.21, showing that the expected byproducts were produced at significantly lower concentrations. This may be due to several contributing factors. First, E. coli contains three native formate dehydrogenases that convert formate into CO 2 and H 2 , potentially explaining the lower formate observed in our fermentation. In particular, fdhF encoded an anaerobically expressed formate dehydrogenase induced by formate [ 34 ]. Second, a notable amount of carbon went to biosynthesis of butanol, ethanol, and ethyl acetate (Fig. 4 B). The production of these compounds is currently inevitable due to the enzyme promiscuity of AdhE2 [ 35 ] and ATF1 proteins [ 10 ]. As ethanol and butanol are more reduced products, their biosynthesis lead to a lower stoichiometric formate production. Detailed stoichiometric balance is shown in Additional file 1 : Fig. S2. Lastly, pyruvate is likely converted to acetate, which is also a major by-product in this fermentation, through acetyl-CoA. Although phosphate acetyltransferase (Pta) was knocked out, which should’ve blocked acetate formation, acetate secretion may result from non-specific activity of endogenous thioesterases in which E. coli has numerous homologues. Furthermore, ATF1 was previously reported to also contain thioetserase activity which can hydrolyze acetyl-CoA [ 36 ], potentially explaining the higher acetate production in this study. Formate was also produced with a high concentration up to 12 g/L, which is usually inhibitory to cell growth [ 37 , 38 ]. To evaluate if formate is toxic to our culture, we measured the cell growth under different formate concentrations. The result (Additional file 1 : Fig. S3) showed that formate concentration up to 15 g/L inhibited approximately 50% cell growth while 10 g/L resulted in minor inhibition. Therefore, we believe that 12 g/L of formate should not significantly affect cell growth and viability. Interestingly, while acetate and pyruvate secretion gradually increased with time, we noticed that formate started to decline after 48 h, which may due to conversion to CO 2 and H 2 by native formate dehydrogenases. Initially most of the gas striped butyl acetate were found in water trap 1. As fermentation progresses, amount of butyl acetate in trap 1 remained around 3 g/L (Fig. 4 E), indicating its saturation in water under room temperature. Majority of butyl acetate was found in the cold traps. By 96 h post anaerobic switch, more than 85% of the butyl acetate were in the cold traps. In particular, cold trap 1 was saturated with about 12 g/L of butyl acetate. Butyl acetate to butanol ratio in fermenter and the water traps gradually decreases (Fig. 4 F). This result was due to gradual accumulation of butanol produced by our strain. Nonetheless, the butyl acetate to butanol ratio observed in fed-batch fermentation outperformed test tube production (Table 2 ), indicating in situ product removal helps to drive butyl acetate formation as butyl acetate is more volatile than butanol. Ethyl acetate showed a similar distribution in the in situ removal system to that of butyl acetate. Although ethanol (Additional file 1 : Fig. S4A) accumulated a higher concentration in fermenter than butanol (Additional file 1 : Fig. S4B), the ethyl acetate titer was significantly lower than butyl acetate (Additional file 1 : Fig. S4C), indicating ATF1 is more favorable on using butanol as substrate instead of ethanol. Together, the results presented here showed an efficient conversion of glucose to butyl acetate."
} | 6,965 |
25894589 | PMC4403855 | pmc | 6,808 | {
"abstract": "The organization of cells, emerging from cell–cell interactions, can give rise to collective properties. These properties are adaptive when together cells can face environmental challenges that they separately cannot. One particular challenge that is important for microorganisms is migration. In this study, we show how flagellum-independent migration is driven by the division of labor of two cell types that appear during Bacillus subtilis sliding motility. Cell collectives organize themselves into bundles (called “van Gogh bundles”) of tightly aligned cell chains that form filamentous loops at the colony edge. We show, by time-course microscopy, that these loops migrate by pushing themselves away from the colony. The formation of van Gogh bundles depends critically on the synergistic interaction of surfactin-producing and matrix-producing cells. We propose that surfactin-producing cells reduce the friction between cells and their substrate, thereby facilitating matrix-producing cells to form bundles. The folding properties of these bundles determine the rate of colony expansion. Our study illustrates how the simple organization of cells within a community can yield a strong ecological advantage. This is a key factor underlying the diverse origins of multicellularity.",
"introduction": "Introduction Many properties of biological systems come about through the interactions of the parts that compose such systems. These so-called collective properties are said to “emerge” from these interactions, because they cannot be produced by the parts separately [ 1 – 3 ]. The most remarkable collective properties are found in multicellular organisms, where cell–cell interactions result in a bewildering diversity of forms and functions that cannot be generated by the cells in isolation [ 4 – 7 ]. Cell differentiation is an important factor underlying this diversity [ 8 , 9 ]. Cell types that differ in their adhesive properties, motility, or shape interact with each other and thereby guide developmental change [ 5 , 10 ]. When a collective property is adaptive, cell types that give rise to this property can be favored by selection [ 11 – 13 ]. The evolution of cell differentiation and collective properties can therefore go hand in hand [ 5 ]. Collective properties are often studied in species where cells can live independently, but often choose not to. These species are ideal for studying why and when cells form collectives and how these collectives come about. One of the most remarkable examples of such voluntary cell collectives comes from the soil-dwelling bacterium Myxoccocus xanthus [ 3 , 14 ]. During predation of other bacteria, thousands of M . xanthus cells coordinate their behavior to lyse and degrade prey [ 15 ]. When nutrient levels decrease, M . xanthus cells aggregate and assemble into a fruiting body filled with many thousands of spores [ 16 , 17 ]. The aerial projections of the fruiting body are thought to aid in spore dispersal [ 18 ]. Whereas it is a major challenge for individual cells to disperse, the cell collectives solve this problem by sticking out from the soil [ 1 , 2 , 8 , 9 , 19 ]. Dispersal is a major challenge for many soil-dwelling microorganisms. As a result, aerial spore-containing structures evolved independently in a number of bacterial and eukaryotic species, through the process of convergent evolution [ 20 – 22 ]. Another major challenge for soil-dwelling organisms is migration: how to get from one soil particle to the next. Without the possibility of swimming through liquid, cells have to find alternative ways to migrate [ 9 ]. These are often studied by examining colony growth patterns [ 19 , 23 – 27 ]. For example, Paenibacillus vortex migrates by making vortices that consist of millions of cells that swirl around over agar surfaces, producing beautiful fractal growth patterns [ 19 , 28 , 29 ]. A closely related species, Bacillus mycoides , forms chiral branching patterns that orient clockwise or counterclockwise while expanding over the agar surface [ 30 ]. A number of other species from the same bacterial families, Bacillaceae and Paenibacillaceae, have been studied as well with respect to colony growth patterns [ 26 , 31 – 35 ]. In all cases, cells solve the challenge of migration by migrating together. Yet how cells coordinate migration is often unknown: which cell types drive migration and how do they interact? This lack of knowledge is partly because little is known about the cell types that are expressed during colony growth. Interestingly, one species from the Bacillaceae family, B . subtilis , produces a number of different cell types and has been intensely studied with respect to cell differentiation [ 36 ]. The phenotypes of these cell types are well characterized [ 37 ]. B . subtilis therefore is the ideal species to examine if and how different cell types guide the migration of cell collectives. Furthermore, it gives a unique opportunity to examine how adaptations at the cell level relate to the collective properties that emerge from them. \n B . subtilis can express at least five distinct cell types, which are often studied in the context of biofilm formation. Each of these cell types is associated with a unique set of phenotypes: motility, surfactin production, matrix production, protease production, and sporulation [ 36 – 39 ]. Motile cells synthesize flagella that can be used for swimming. Surfactin-producing cells secrete surfactin, a surfactant that reduces water surface tension [ 21 , 40 ], functions as a communication signal [ 41 , 42 ], and acts as an antimicrobial [ 43 ]. Matrix-producing cells secrete an extracellular polysaccharide (EPS) and the structural protein TasA [ 44 , 45 ]. EPS acts as a “glue” that surrounds cells inhabiting the biofilm. In addition, colony wrinkling requires EPS, and under some conditions, colony expansion also depends on EPS [ 46 – 48 ]. TasA assembles into amyloid-like fibers that attach to the cell wall and, like EPS, is required for colony wrinkling [ 45 , 49 , 50 ]. Since tasA and eps mutants complement each other when cocultured, TasA and EPS are considered common goods that are shared between cells [ 45 , 51 ]. In addition to EPS and TasA, matrix-producing cells secrete antimicrobial compounds that can kill sibling cells and other soil-dwelling organisms [ 52 ]. Protease-producing cells secrete proteases that facilitate nutrient acquisition [ 53 , 54 ]. Finally, cells can differentiate into spores: stress-resistant cells that can survive long periods of desiccation and nutrient limitation [ 55 ]. The regulatory mechanisms underlying cell differentiation in B . subtilis are well-characterized [ 37 ]. In addition, most cell types have been associated with some colony-level properties, although a detailed causal relation is often lacking [ 56 ]. Here we study how cell differentiation affects the migration of cell collectives during B . subtilis colony expansion via sliding motility. We grow bacteria on a specific medium that prevents cells from swimming and swarming (which both rely on flagella), but still allows for colony expansion. In this way, we can examine whether colony expansion depends on cell differentiation, and if so, how the interactions between cell types drive migration. We show that migration depends critically on two cell types: surfactin-producing and matrix-producing cells. Together they drive migration through a mechanism in which cell collectives form highly organized bundles at the colony edge, which we have termed “van Gogh bundles.” Van Gogh bundles are formed from many tightly aligned filaments consisting of chains of cells. They appear elastic and fold into filamentous loops that push themselves away from the colony. Surfactin-producing and matrix-producing cells divide labor during the formation of van Gogh bundles. We propose that surfactin-producing cells reduce the friction between cells and their substrate, which facilitates formation of the van Gogh bundles by the matrix-producing cells. Whereas EPS production is necessary for the formation of these bundles, TasA seems to fine-tune their biophysical properties. Finally, as a complement to the experiments, a mathematical model illustrates how simple cellular properties can affect a bundle’s folding properties and hence the migration rate.",
"discussion": "Discussion In this study we analyzed sliding motility in B . subtilis to determine the factors that allow for the collective migration of cells. We found that cells organize themselves into bundles that spread by forming expanding filamentous loops at the colony edge. These cell collectives, which we call van Gogh bundles, are distinct from previously described filaments in B . subtilis due to their strong alignment and functionality [ 21 , 67 ]. The folding properties of the filamentous loops determine the migration rate and, in part, depend on the products secreted by matrix-producing cells. The development and expansion of van Gogh bundles depend critically on the synergic interaction of surfactin-producing and matrix-producing cells. To our knowledge, this is the first example of bacterial cells dividing labor in order to overcome one of the major ecological challenges: migration ( Fig 11 ). 10.1371/journal.pbio.1002141.g011 Fig 11 Schematic overview of cell differentiation and collective properties in B . subtilis colony expansion. Red and green cells represent, respectively, surfactin- and matrix-producing cells. Dendrites predominantly consist of surfactin-producing cells interspersed with clumps of matrix-producing cells. The petals of the colony consist predominantly of matrix-producing cells that form van Gogh bundles. We propose that surfactin mediates the expansion of van Gogh bundles by reducing the friction between the van Gogh bundles and substrate and that van Gogh bundle expansion is driven by cell division. The elastic and folding properties—dependent on matrix-producing cells—of the van Gogh bundles allow for an efficient colony expansion and prevent the bundles from breaking under increased compression. We show that colony expansion is characterized by up-regulation of srfA expression (i.e., the surfactin-producing cell type) followed by an increase in tapA expression (i.e., the matrix-producing cell type). The two expression phases correspond to the two growth periods that are apparent at the macroscopic level: dendrite formation and petal-shaped colony outgrowth [ 57 ]. The temporal dynamics in gene expression correspond to the regulatory pathways controlling cell differentiation in B . subtilis . For example, srfA expression is regulated by quorum sensing [ 41 , 68 ]; at high cell density, the expression of srfA increases, which explains the gradual up-regulation of srfA at the onset of colony growth. In addition, surfactin can function as a signal that triggers matrix production [ 41 , 42 ]. It is therefore not surprising that the peak in srfA expression is followed by a peak in tapA expression. This regulatory link between cell differentiation of surfactin-producing cells and matrix-producing cells corresponds closely to the functional link we describe in this study: van Gogh bundles, consisting of matrix-producing cells, can develop only in the presence of surfactin ( Fig 11 ). Thus, surfactin-producing and matrix-producing cells divide labor in order to facilitate colony expansion (see also [ 69 ]). The division of labor typically evolves in response to strong phenotypic trade-offs [ 70 , 71 ]. For example, cyanobacteria divide labor between photosynthetic cells and heterocysts, because photosynthesis and nitrogen fixation are incompatible [ 72 , 73 ]. Likewise, there might be a trade-off between the formation of van Gogh bundles by matrix-producing cells and the production of surfactin. Unfortunately, it is unclear what this trade-off might be; perhaps the cell-to-cell attachment of matrix-producing cells would be harmed if cells simultaneously produced surfactin. The fact that eps tasA + srfA chimeras—colonies in which different strains perform different tasks—can expand further than WT colonies suggests there may indeed be a trade-off at play. Besides surfactin, matrix production can also be triggered by environmental stressors like starvation, hypoxia, and osmotic stress [ 36 , 74 , 75 ]. Environmental changes during colony growth might therefore also be responsible for the temporal up-regulation of matrix production and the transition from the dendrite to the petal growth phase. We showed that cells isolated from the petal growth phase readily switch back to dendrite formation, when re-inoculated on a fresh growth medium. This indicates that the environment is indeed an important determinant for the different growth phases. When van Gogh bundles first appear, the matrix-producing cells are surrounded by surfactin-producing cells. Given their proximity, the co-occurring cell types probably sense nearly identical environmental conditions, yet they behave differently [ 69 , 76 , 77 ]. This indicates that—besides depending on the environment—cell differentiation also depends on inherent stochasticity. A recent study showed that under constant environmental conditions, cells can spontaneously differentiate into matrix-producing cell chains [ 78 ] that are preserved for a number of generations due to a regulatory feedback loop that creates a bi-stable switch [ 79 – 81 ]. A similar switch might also be important for the first cell chains that appear in the formation of van Gogh bundles. While previous studies have shown that surfactin production and EPS production can affect colony expansion [ 47 , 48 , 58 , 59 ], these studies did not show a synergistic interaction between cell types. In addition, the colony expansion in our study is of a different nature than the ones described in previous studies. For example, EPS production has been shown to have a relatively small effect on biofilm colony expansion, and that effect was hypothesized to depend on osmotic pressures [ 47 , 48 ]. Here we show that EPS has an all-or-none effect on colony expansion during sliding motility. The migration of van Gogh bundles does not directly rely on osmotic gradients, but instead results from mechanic force (although osmotic gradients can affect cell differentiation [ 74 ]). Hence, EPS stimulates migration by allowing for the organization of van Gogh bundles. How EPS exactly guides bundle formation requires further examination. Our results suggest that EPS is required for side-to-side attachment of cell chains. However, EPS might also affect the pole-to-pole interactions. Besides being essential in the formation of van Gogh bundles, EPS production was also essential for dendrite formation. At this early growth phase, matrix-producing cells do form multicellular clumps, but these clumps lack the tight alignment of cells that characterizes the van Gogh bundles. Thus, the mere presence of surfactin-producing and matrix-producing cells does not guarantee the formation of van Gogh bundles. It would be interesting to examine why matrix-producing cells are essential for dendrite formation, while forming van Gogh bundles only in the petal growth phase. The functions of EPS and TasA inside the van Gogh bundle are different. Whereas EPS is absolutely necessary for the formation of van Gogh bundles, TasA seems to fine-tune the folding properties of the van Gogh bundles. TasA specifically localizes to the pole-to-pole interaction zones of TasA-producing cells inside the van Gogh bundle. Our mathematical model shows that the folding properties of van Gogh bundles determine the efficiency of migration: when the filament is less likely to fold, it can expand farther in space. We suggest that TasA might affect folding, by manipulating the bending rigidity at the pole-to-pole interactions between cells. Although this claim awaits further biophysical quantification, our study suggests that both EPS and TasA have specialized functions that guide the development of van Gogh bundles [ 64 – 66 ]. In this way, matrix-producing cells can organize themselves into multicellular structures that facilitate migration. \n B . subtilis is not the only species that switches to a multicellular lifestyle to accomplish migration. Filamentous structures also occur during the colony growth of P . vortex and B . mycoides , whose growth patterns are described in the Introduction [ 28 – 30 ]. Furthermore, an impressive study by Vilain and colleagues [ 34 ] showed that the closely related species B . cereus switches to a multicellular lifestyle when grown on filter-sterilized soil-extracted soluble organic matter (SESOM) or artificial soil microcosm (ASM)—media that mimic the environmental conditions cells encounter in the soil. They showed that the lifestyle switch to multicellularity allows for migration. Interestingly, B . mycoides and B . subtilis show the same lifestyle switch when exposed to SESOM or ASM. This strongly supports our hypothesis that the collective properties that emerge from the interaction between surfactin-producing and matrix-producing cells—van Gogh bundles—evolved to facilitate migration. This hypothesis is further supported by the fact that the domesticated lab strain, B . subtilis 168, which is known to be defective in surfactin production, cannot make the switch to a multicellular lifestyle when grown on SESOM or ASM [ 34 , 82 ]. It would be interesting to examine whether SESOM and ASM indeed induce surfactin and matrix production and hence the development of van Gogh bundles in the wild isolate of B . subtilis . Like other forms of bacterial multicellularity [ 9 ], van Gogh bundles illustrate how the organization of cells can help to overcome important ecological challenges. Ultimately, we hope that the study of such simple forms of organization can improve our understanding on how evolution constructs [ 10 , 83 – 88 ]: how cells can evolve to become integrated collectives that, together, form a new organizational unit."
} | 4,527 |
39958774 | PMC11830346 | pmc | 6,809 | {
"abstract": "Highlights • Biodegradable polymers can be synthesized from phenol-contaminated effluents. • Phenol degradation occurs through multiple pathways aerobically and anaerobically. • Acetyl-CoA produced during phenol degradation can be directed towards PHA synthesis. • Mixed microbial cultures improve phenol degradation and enhance PHA accumulation.",
"conclusion": "6 Conclusion and future outlooks Comprehending the metabolic conversion of phenol to PHA unlocks the potential for developing microbes with tailored functionality and improved efficiency. This has been demonstrated through the utilization of MMC, where interactions between different species enhance phenol degradation and improve PHA accumulation. By employing toxic substances like phenol for PHA formation, two environmental issues – phenol pollution and the high substrate cost for PHA production – can be addressed, promoting a circular economy and enhancing environmental sustainability. While this review primarily focuses on the conversion mechanism of phenol into PHA, phenol, as one of the simplest aromatic compounds, can also serve as a model for degrading other aromatic compounds and converting them into PHA. Given the variety of toxic substances present in industrial wastewater, other contaminants capable of generating acetyl-CoA could also serve as potential carbon sources for PHA formation, paving the way for the bioconversion of numerous aromatic compounds into PHA.",
"introduction": "1 Introduction Petroleum-derived plastic is extensively produced due to its high demand and wide applicability. Over the past century, the global production of petroleum-derived plastic has reached 320 million tons (Mt) annually ( Ragusa et al., 2021 ). However, this high production rate is alarming due to the extremely low degradability of plastics, posing a serious environmental threat. Until today, recycling rates for plastic waste remain discouraging, with a significant portion ending up in landfills. Plastics can take up to two thousand years to degrade, and while in landfills, they can contaminate the groundwater sources through the leaching of toxic additives. Furthermore, the use of fossil fuels as raw materials for plastic production has raised serious concerns due to the emission of greenhouse gases into the atmosphere, contributing to climate change and global warming ( Naser et al., 2021 ). As a result, much attention has shifted towards bioplastic as an alternative to the issues associated with petroleum-derived plastics. Atiwesh et al. (2021) defined bioplastic as an environmentally sustainable polymeric substance with similar functionality to petroleum-derived plastics, meanwhile Park et al. (2024) added that bioplastics are synthesized from renewable resources and can biodegrade. However, it's important to note that not all bioplastics synthesized from renewable resources are biodegradable, and conversely, not all biodegradable bioplastics are produced from renewable resources, as shown in Fig. 1 . The ideal bioplastics should be both biodegradable and derived from renewable resources, such as polylactic acid (PLA), polyhydroxyalkanoate (PHA), and polyvinyl alcohol (PVA). Only this type of plastic can be regarded as “environmentally friendly bioplastics”. Fig. 1 Classification of polymers based on their raw materials and their degradation capability. Fig 1 Polyhydroxyalkanoate (PHA) is a microbial bioplastic produced by various species of microorganisms as intracellular inclusion bodies for carbon and energy storage under stressful environments ( McAdam et al., 2020 ). It is considered an environmentally friendly bioplastic as it can be degraded and synthesized from renewable feedstock ( Liao et al., 2018 ). PHA can easily be degraded by microbial enzymatic activity, generating carbon dioxide (CO 2 ), water, and microbial biomass as the final products ( Sirohi et al., 2020 ). In addition, PHA is biocompatible with humans, and its physical properties are generally similar to those of petroleum-derived plastics ( Khamkong et al., 2022 ). For example, the tensile strength, melting temperature, Young's modulus, and crystallinity degree of polyhydroxybutyrate (PHB), a type of PHA, are comparable to polypropylene (PP) ( Abate et al., 2024 ). However, PHA is expensive, primarily due to the cost of its raw materials, which account for 40 – 48 % of the total production costs ( Sirohi et al., 2020 ). Common feedstocks used for PHA production include sugars and fatty acids extracted from crops such as corn starch, sugarcane, and vegetable oil, constituting more than 50 % of the total production costs ( Zytner et al., 2023 ). The price of PHA could be up to 16 times higher than that of petroleum-derived plastics ( Alvarez Chavez et al., 2022 ). Despite being the most effective way to reduce plastic pollution in the environment, the high production costs of PHA limit its commercialization. Therefore, the utilization of a cheaper carbon substrate is anticipated to reduce overall production costs and enhance its applicability in everyday use. The field of PHA research is expanding into exploring various waste resources as potential raw materials. Utilizing waste for PHA synthesis is considered a viable option as it meets the ideal raw material requirements of being abundant, affordable, renewable, and carbon-rich. Furthermore, converting waste materials into PHA will help mitigate their negative effects on living organisms and reduce the emission of hazardous compounds into the environment. Previous studies have documented PHA production from various waste streams such as animal waste ( Shahzad et al., 2017 ), cheese whey ( Pais et al., 2016 ), olive mill wastewater ( Bacha et al., 2023 ), waste cooking oil ( Ruiz et al., 2019 ), municipal wastewater ( Bengtsson et al., 2017 ), paper mill wastewater ( Munir et al., 2015 ), food waste ( Colombo et al., 2017 ), and crude glycerol ( Luo et al., 2016 ). Shah and Kumar (2021) supported the idea that using cheap, abundant, and renewable waste materials as substrates might be the solution to reduce the price of PHA. Furthermore, according to Liao et al., 2018 , using renewable waste materials as substrates is expected to halve PHA production costs. Industrial effluents rich in toxic compounds are constantly generated and continuously discarded at very high volumes, making them a potentially remarkable source of feedstock for PHA production. The presence of toxic contaminants will exert stress on microorganisms, diverting their metabolic responses toward PHA accumulation ( Saharan et al., 2014 ). However, research on PHA production from toxic compounds has been limited to very few studies ( Zhang et al., 2018 ), possibly due to the toxic nature of the compounds, which can hamper bacterial growth. One of the most prevalent toxic contaminants, phenol, is typically found in the environment through the discharge of industrial effluents. Since its discovery in 1834, phenol has been widely used to synthesize many other chemical compounds such as acetylsalicylic acid, phenolic resins, bisphenols, polycarbonates, aniline, alkylphenols, diphenols, and salicylic acid ( Weber et al., 2020 ). Phenol can exert mutagenic, teratogenic, and carcinogenic effects on living organisms ( Reddy et al., 2015a ; Saputera et al., 2021 ). Concentrations of phenol ranging between 9 – 25 mg/L are fatal to fish, and between 10–24 mg/L are hazardous to humans ( Hamad, 2021 ). Hence, the United States Environmental Protection Agency (US EPA) and the National Pollutant Release Inventory (NPRI) of Canada have designated phenol as a priority pollutant, ranked 11th out of 126 harmful chemicals ( Liu et al., 2020 ; Naguib and Badawy, 2020 ; Villegas et al., 2016 ). The US EPA has also set a limit for phenol concentration at 0.001 mg/L in surface waters ( Mohd, 2020 ). Therefore, removing phenol from industrial effluent before discharge is crucial to mitigate its harmful effects on the environment and living organisms. The integration of bioremediation technology with the production of value-added products is advantageous in addressing two environmental issues at once and can simultaneously offer an economically viable solution, specifically by coupling phenol bioremediation with PHA production. The utilization of phenol for PHA production has been documented previously ( Chen et al., 2018 ; Kanavaki et al., 2021 ; Maskow and Babel, 2000 ; Nair et al., 2009 ; Reddy et al., 2015a ). Additionally, the interaction between different species of bacteria in microbial mixed culture (MMC) can exert a synergistic effect in enhancing these processes. Nevertheless, to the best of our knowledge, comprehensive studies that discuss the mechanism of phenol conversion into PHA are very limited. It is important to deeply understand the mechanisms of both processes, as various improvements can be made possible, such as identifying the limiting factor and re-engineering the metabolic pathways for enhanced efficiency of phenol degradation and PHA synthesis. Therefore, this review aims to define the underlying mechanisms of phenol degradation and its transformation into PHA, while assessing the enhancement of these processes using MMC."
} | 2,304 |
27311560 | PMC4911592 | pmc | 6,810 | {
"abstract": "Baeyer-Villiger monooxygenases (BVMOs) are able to catalyze regiospecific Baeyer-Villiger oxygenation of a variety of cyclic and linear ketones to generate the corresponding lactones and esters, respectively. However, the enzymes are usually difficult to express in a functional form in microbial cells and are rather unstable under process conditions hindering their large-scale applications. Thereby, we investigated engineering of the BVMO from Pseudomonas putida KT2440 and the gene expression system to improve its activity and stability for large-scale biotransformation of ricinoleic acid ( 1 ) into the ester (i.e., ( Z )-11-(heptanoyloxy)undec-9-enoic acid) ( 3 ), which can be hydrolyzed into 11-hydroxyundec-9-enoic acid ( 5 ) (i.e., a precursor of polyamide-11) and n -heptanoic acid ( 4 ). The polyionic tag-based fusion engineering of the BVMO and the use of a synthetic promoter for constitutive enzyme expression allowed the recombinant Escherichia coli expressing the BVMO and the secondary alcohol dehydrogenase of Micrococcus luteus to produce the ester ( 3 ) to 85 mM (26.6 g/L) within 5 h. The 5 L scale biotransformation process was then successfully scaled up to a 70 L bioreactor; 3 was produced to over 70 mM (21.9 g/L) in the culture medium 6 h after biotransformation. This study demonstrated that the BVMO-based whole-cell reactions can be applied for large-scale biotransformations.",
"conclusion": "Conclusion This study demonstrated that the hexa-glutamate tag played a critical role to improve functional expression and probably structural stability of the BVMO from P. putida KT2440 in E. coli BL21(DE3). By applying the polyionic tag (E6)-based BVMO engineering and the synthetic promoter-driven constitutive gene expression system, the biotransformation activity of the E. coli -based whole-cell biocatalyst was significantly enhanced under process conditions. Furthermore, the biocatalyst system was shown to be applicable for large scale biotransformations. This study will contribute to industrial application of BVMO-based whole-cell biocatalysis.",
"discussion": "Discussion Since the BVMOs have been identified in 1976 34 , numerous studies have reported the type of catalytic reactions, the structural properties and reaction mechanisms, and the protein engineering to improve their catalytic activities and structural stability 1 2 3 4 5 35 36 37 38 . In addition, biotransformation process engineering such as whole-cell reactions (at high cell density) 28 , bioprocess optimization 33 , in situ product recovery 39 40 41 42 , and bioreactor design 39 40 has been intensively investigated to enhance the final product concentrations in the reaction medium and volumetric productivities. One of the milestones was biotransformation of bicyclo[3.2.0]hept-2-ene-6-one to a mixture of the two corresponding lactones by recombinant E. coli expressing the cyclohexanone monooxygenase from Acinetobacter calcoaceticus NCIMB 9871 39 41 . The whole-cell biotransformation process allowed high product concentration and bioconversion yield of ca. 16 g/L and 83%, respectively, by attenuating product toxicity via in situ product recovery with 100 g/L hydrophobic absorbent resins. In this study, the BVMO of P. putida KT2440 and the gene expression system was engineered for the large-scale biotransformation of ricinoleic acid ( 1 ) into the ester ( 3 ) ( Fig. 1 ). With an aim to improve the catalytic activity and stability under process conditions, the BVMO was subjected not only to directed evolution but also rational protein engineering. Since the BVMO of P. putida KT2440 was able to catalyze the regiospecific oxygenation of 4-hydroxy-2-decanone ( 11 ) ( Supplementary Fig. S10 ), the screening method, which was based on Baeyer-Villiger monooxygenation of 4-hydroxy-2-decanone 22 , was used in the study. Thousands colonies, which had been produced via error-prone PCR, were examined. However, the variant, which is more active than the native enzyme, was not isolated. The BVMO from P. putida KT2440 12 was engineered by assuming that negatively charged residues in the N- or C-terminal play a critical role in thermal stability of proteins 23 . Remarkably, functional expression and probably structural stability of the BVMO in E. coli under room temperatures up to 30 °C was significantly improved via fusion with a polyionic peptide tag (i.e., hexa-glutamate (E6)) ( Fig. 2 and Supplementary Fig. S3 ,4). Introduction of the E6 tag into N-terminal of the enzyme appears to enhance proper folding of the newly synthesized polypeptides and structural stability of the active enzymes at the high temperatures. Although the mechanism(s) remain to be investigated, one of the reasons might be similar to that of Group II chaperonins, which thermal stability was dependent upon the number of Glu residues rather than Lys residues in the C-terminal 23 . One possibility would be generation of salt bridge between Glu residues of the tag and the Arg residue sitting around N-terminal of the BVMO ( Supplementary Fig. S11 , structure prediction was performed as previously reported 44 ). Since the BVMOs were reported to undergo a great deal of conformational change during catalysis 45 , the resulting salt bridge might contribute to stabilization of the transition states of the enzyme-NADP(H)-substrate complex in addition to correct folding at high temperatures. Overall, it was assumed that fusion of a polyionic tag (e.g., hexa-glutamate (E6)) to the target enzymes is one of the strategies to enhance functional expression and probably structural stability of the enzymes and proteins in E. coli at high temperatures. The further engineering of the E6-BVMO was conducted by using Rosetta 43 . For all the residues in BVMO sequence, in silico construction of single mutation and the calculation of ΔΔG were performed. Top five putative thermostable mutants were constructed and subjected to the E. coli -based whole-cell biocatalysis (see Experiment 1 in the Supporting information for details). However, this approach did not generate any superior variant to the native (i.e., E6-BVMO) in terms of the catalytic activity at high temperatures. Engineering of the gene expression system for the cascade enzymes (i.g., ADH and E6-BVMO opt ) to overexpress the target proteins without any inducer allowed the biotransformation rate to increase up to 21.6 mM/h (6.7 g/L/h); 60 mM ricinoleic acid ( 1 ) was converted into 54 mM ester ( 3 ) in the culture medium within 2.5 h ( Fig. 4 and Table 1 ). This value is ca. 3-fold higher as compared to the ricinoleic acid biotransformation rate of E. coli BL21(DE3) pACYC-ADH, pJOE-BVMO under comparable conditions (see the Biotransformation 1 and 4 in Table 1 ). The bioprocess was also successfully scaled up to a 70 L bioreactor ( Fig. 5 ). This is the highest productivity of BVMO-based whole-cell biocatalysts with rather high bioconversion yield to our knowledge. In particular, the biotransformation process was very simple; the biotransformation was done by adding ca. 30 g/L reaction substrate into an aqueous culture broth in a conventional bioreactor without applying any complex systems such as in situ product recovery. One of the factors to influence the whole-cell biotransformations could be product toxicity toward E. coli cells. Most of the ester product ( 3 ) was found in cell mass fraction rather than in extracellular space ( Supplementary Fig. S7 ). Since its chemical structure is similar to the constituent of phospholipids of cellular membranes, it may accumulate in the cell membranes. If the ester ( 3 ) accumulates in the cell membranes, it may affect permeability and structural properties of cell membranes as shown with hydrocarbons 30 31 32 46 . This may in turn disturb cellular metabolic reactions including NADPH regeneration essential for the BVMO reactions and enzyme turn-over in the whole-cell biocatalyst. Thereby, our future research will focus on extraction and recovery of the ester products from cell mass. If the product is isolated from the biomass without metabolic stress or damage, the whole-cells could be further used as biocatalysts for the next round of biotransformations."
} | 2,056 |
38066117 | PMC10709317 | pmc | 6,811 | {
"abstract": "Climate change modifies environmental conditions, resulting in altered precipitation patterns, moisture availability and nutrient distribution for microbial communities. Changes in water availability are projected to affect a range of ecological processes, including the decomposition of plant litter and carbon cycling. However, a detailed understanding of microbial stress response to drought/flooding is missing. In this study, an intermittent lake is taken up as a model for changes in water availability and how they affect the functional pathways in microbial communities of the decomposing Phragmites australis litter. The results show that most enriched functions in both habitats belonged to the classes of Carbohydrates and Clustering-based subsystems (terms with unknown function) from SEED subsystems classification. We confirmed that changes in water availability resulted in altered functional makeup of microbial communities. Our results indicate that microbial communities under more frequent water stress (due to fluctuating conditions) could sustain an additional metabolic cost due to the production or uptake of compatible solutes to maintain cellular osmotic balance. Nevertheless, although prolonged submergence seemed to have a negative impact on several functional traits in the fungal community, the decomposition rate was not affected.",
"conclusion": "Conclusions To conclude, shifts in the environmental conditions due to changing precipitation patterns will alter functional microbial communities. Our results indicate that microbial communities under more frequent water stress due to fluctuating conditions could bear an additional metabolic cost. In addition, prolonged submergence seemed to have a negative impact on several functional traits in the fungal community, although the decomposition rate was not affected.",
"introduction": "Introduction Wetlands play an essential role in the global carbon cycle, which depends on their primary productivity and decomposition rate 1 . The accumulation and decomposition of plant litter from vegetation in wetlands are very variable, as plant material and environmental conditions may be highly heterogeneous 2 . This variability of environmental conditions will become even more pronounced due to climate change, leading to increased precipitation and atmospheric moisture 3 due to changes in atmospheric circulation, a more active hydrological cycle, and increased water-holding capacity of the atmosphere 4 . The hydrological cycle intensification will likely increase the intensity of extreme precipitation events and the risk of flooding 5 . However, local low precipitation extremes can also result from climate change 3 . Such changes in moisture availability are believed to affect a range of ecological processes, including the decomposition of plant litter and carbon cycling. Wetlands present an important carbon sink; however, climate change and human interventions can turn them into carbon sources 6 . Lake Cerknica (Slovenia) is an intermittent wetland with pronounced water level fluctuations 2 . Water level fluctuations severely affect decomposition and microbial colonization in such intermittent ecosystems 7 , 8 . Periodic flooding may cause hypoxic or anoxic conditions and thus impact microbial activity and the decomposition of plant litter 2 . On the other hand, water and carbon may be limiting factors for bacteria and fungi in wetlands with low soil water content 6 ; thus, severe drying during the low precipitation phase also has adverse effects on the decomposition rates 9 . Therefore, Lake Cerknica presents a unique system to obtain insight into the impact of different water regimes on microbial activity and their involvement in carbon cycling. Wetland communities, including common reed ( Phragmites australis ), characterize the vegetation of Lake Cerknica 10 . P. australis contributes significantly to the primary production of the area and, consequently, to the production of litter in the ecosystem 7 . Its high biomass production makes it a good candidate for decomposition studies of plant litter in wetland conditions. Microbial colonization and decomposition of P. australis leaves starts already during the vegetation season 11 – 13 , with the phyllosphere community forming a significant portion of decomposers in the early decomposition phase 13 . Decomposition theories describe fungi as primary decomposers of plant litter 14 , as they break down the lignocellulose matrix 15 , followed by bacteria that are regarded as decomposers of detrital particulate and dissolved organic matter 16 . In contrast, recent observations revealed that both microbial groups are essential players in all stages of the decomposition process 17 . However, the precise contributions of fungi and bacteria to the decomposition process remain uncertain, and this can depend on a variety of factors, including the quality of carbon (C) inputs from plants (e.g. 18 , 19 ). Fungi often prefer to utilize more resistant and complex C sources, while bacteria respond rapidly to increased resource availability and colonize readily available, labile C resources 20 , 21 . On the other hand, when more easily degradable substrates are available, the dominance within the microbial community can shift from fungi to bacteria 22 . Nevertheless, the relationship between substrate C quality and the microbial community is not straightforward, and both fungi and bacteria can employ mixed strategies to exploit available substrates 23 , 24 . Furthermore, both microbial groups seem to respond differently to changes in water availability, with bacteria typically responding faster than fungi to changes in soil water availability 25 . Yang et al. 26 observed that the bacterial community was more sensitive to changes in precipitation than the fungal community, which was more sensitive to inter-annual differences in precipitation but not to the treatment-induced changes in precipitation. Shifting precipitation regimes in the future climate could thus alter the stability and functions of microbial communities. With this in mind, parallel analysis of both microbial groups is vital to fully understand the dynamics of microbial communities in the decomposing litter. Our study aimed to evaluate the effect of changed environmental conditions (dry and wet) due to fluctuating water levels on the functional traits of microbial communities of P. australis litter. To predict litter decomposition dynamics in changing environments, we must understand changes in microbial functional pathways under fluctuating water conditions. With this aim, we analysed the functional traits of microbial communities in fresh leaves and leaves after a 45-day decomposition phase in either wet or dry habitats characterized by different flooding patterns. First, we hypothesized that functional traits in the microbial communities from fresh leaves would change due to the advancement of the decomposition processes. Second, we hypothesized that the response of communities developed on leaf litter deposited in different habitats would differ relative to their submergence level, showing differences in the metabolic processes. Our last hypothesis was that differences in the water regimes between both habitats would affect the functional traits of bacteria and fungi in the decomposing litter differently.",
"discussion": "Discussion Metabolic changes in microbial communities during transition to decomposition We found distinctive functional signatures of microbial communities growing on the decomposing P. australis leaves. Bacterial and fungal communities showed an increase in functional α–diversity during the transition from fresh leaves to plant litter with the exception of fungal communities in the wet habitat. As plants gradually undergo senescence, the variability in phyllosphere microbes tends to incrementally rise 30 , with changes in microbial communities on leaves undergoing decomposition 31 , 32 increasingly influenced by the leaves' physicochemical properties and competition between the microbes. Nevertheless, the similarity of functional α–diversity of fungal communities observed in our study between fresh leaves and leaves decomposing in the wet habitat could be specific for fungal communities on submerged plant litter, as Koivusaari et al. 33 observed that fresh leaves and submerged litter shared 65% of fungal taxa. Although many biological functions seem redundantly distributed within decomposer communities 34 , fungal decomposer communities on submerged litter follow a unique pattern. Most taxa of Basidiomycota are absent in aquatic environments 35 , and aquatic hypomycetes that decompose the plant material in freshwater environments are suggested to have a terrestrial plant-associated life cycle phase 36 , 37 and could be transfered from the leaves to the litter. As such, these specifics could reduce the rise of functional α–diversity in leaves decomposing in the wet habitat, as observed in our study. Clustering-based subsystems and Carbohydrate metabolism had the largest quantity of annotated reads. Our observations are similar to the observations of 38 in natural grassland soils or 39 on maize roots, which indicates that this is probably a pattern characteristic of many biological systems and/or metagenomic studies. Many of the functions enriched in decomposing leaves were linked to carbohydrate metabolism, especially mono-, di- and oligosaccharides such as L-rhamnose, trehalose, maltose and maltodextrin, D-galacturonate, etc. These can represent the housekeeping functions, although increased expression of genes linked to the metabolism of sugars like trehalose could suggest an adaptive mechanism of compatible solute accumulation to maintain cellular osmotic balance 40 . Enrichment of genes connected with trehalose metabolism in the dry habitat further supports this. Nevertheless, the enrichment of several of these functions is probably connected to the decomposition of P. australis litter. Enrichment of L-rhamnose and D-galacturonate utilization functions can be related to the degradation of rhamnogalacturonans that comprise pectin molecules of land plants 41 . Similarly, xylose is the most abundant pentose sugar (up to 25%) in lignocellulosic biomass 42 , and its efficient conversion by microbe is a major step in plant litter decomposition. Structural carbohydrates can persist in plant litter even after the first year of decay due to their close association with lignin and thus provide important substrates for the microbe communities in the litter. In fungal functional communities, we observed a shift from a high number of Respiration connected reads to a low number of reads and an opposite shift for Carbohydrate metabolism when comparing decomposing and fresh leaves. This change in functions could be associated with changes in the fungal environment that happen during the transition from fresh to senescent and decomposing leaves. The phyllosphere is considered a low-nutrient environment, with carbon-containing nutrients acting as a major determinant of epiphytic colonization 43 . A low number of Carbohydrates annotated genes would confirm a restriction on investments into metabolic pathways that are not feasible in such a carbon-starved environment. Furthermore, the shift from fresh to decomposing leaves also has profound changes in the fungal taxonomic communities 13 . These changes in the composition of the fungal community could mean that more decomposers with higher expression of hydrolytic enzymes pointed against lignocellulosic biomass colonize plant material and explain the increase in carbohydrate metabolism. Many functional indicators for Carbohydrates and metabolism connected to Proteins, and Amino Acids and derivatives show increased investment in substrate degradation, uptake and assimilation in microbial communities. Microbial communities in the litter may have increased investment in these resource acquisition traits to degrade the more chemically complex and diverse substrates. In addition, the Clustering-based Subsystems category showed a high number of reads in bacterial functional communities. Clustering-based subsystem is a subsystem in which there is evidence for functional connection among the proteins, but their exact roles in the metabolic pathways are yet unknown. The high number of reads for this category in the metagenomes reveals the lack of knowledge about the microbial proteome that still exists and thus warrants further screening and orientation towards proteomic analyses. Community functions unique to the habitat of decomposition Bacterial functional communities showed a high degree of separation, indicating profound differences based on the habitat where common reed leaves decompose. Fungal functional communities, in contrast, showed far fewer differences between the habitats. This lack of differences could be a result of fungal and bacterial communities responding differently to the environmental factors and their interactions 44 , 45 or a larger impact of the surrounding vegetation that could serve as a source for dispersal and thus provide a unification effect between different treatments. Redondo et al. 46 observed that vegetation may be able to maintain similar fungal communities across distances and is likely the driving factor of fungal spore deposition at the landscape level. Carbohydrates were part of the Subsystems category that showed the highest contribution to the differences between fresh leaves and leaves decomposing in dry or wet habitat and dissimilarity between leaves decomposing in both habitats and were selected for further analysis. A comparison of individual Subsystems functions in Carbohydrates showed less difference in leaves decomposing in different habitats than when decomposing leaves were compared with fresh leaves. Nevertheless, several differences in enrichments of functional traits were observed between litter decomposing in wet and dry habitats. Berlemont et al. 45 observed that reduced precipitation decreased bacterial abundance and cellulolytic potential. As microbes convert polysaccharides into vital metabolites, such as acetate, glycerol, pyruvate, succinate, and esters, a decrease in cellulose degradation would affect several metabolic pathways. Furthermore, several differences in enrichments of functions in Central carbohydrate metabolism and Monosaccharides could be linked to different degrees of litter fragmentation in wet and dry habitats 47 and thus changed the availability of primary substrates in the litter in both habitats. As starch and cellulose-degrading microbes lyse the initial polysaccharides, they release oligosaccharides that are accessible for many other lineages 48 . Therefore changes in the availability of the initial substrate could be felt through different metabolic pathways as changes in enrichment of functional traits. In fungal functional communities, we observed a similar pattern of under-enriched functions in leaves decomposing in wet and dry habitats. Nevertheless, the number of under-enriched functions was higher under wet conditions. Berlemont et al. 45 also observed that fungal and bacterial communities reacted differently to seasonal patterns, with fungi preferring the dry season, which could lead to the observed under-enrichment of different functions in the wet habitat in our study. In addition to Carbohydrates, Respiration was an additional functional trait that contributed to dissimilarity in the microbial functional communities in both habitats. Environmental moisture can significantly impact the decomposition of organic matter by affecting the diffusion of oxygen and the availability of substrates for microorganisms 49 , which is especially true for fully submerged systems. In bacterial functional communities, Nitrogen metabolism and Metabolism of aromatic compounds functions were enriched in the wet habitat when compared to the dry habitat. In submerged environments, such as aquatic ecosystems or waterlogged soils, nitrogen may become more available for plants and microorganisms due to reduced oxygen levels. Furthermore, polyphenols that are one of the main inhibitors of microbial decomposition, were found to be less effective in binding nitrogen in submerged conditions due to their solubility and leaching from the litter 50 , 51 . In addition, polyphenols can be degraded as part of metabolic pathways by some anaerobic microbes 52 , which would explain the enrichment of functional traits connected to metabolism of aromatic compound in the wet habitat. Similarly, to Respiration Protein metabolism, and Amino acids and Derivatives functions were also under-enriched in the wet habitat in the fungal community, which would suggest that drier conditions were favourable for the fungal community, as already reported by Berlemont et al. 45 . Nevertheless, similarly to Grašič et al. 53 , we also observed higher decomposition rates of common reed litter under wet conditions. As such it seems that more frequent or prolonged flooding does not affect the decomposition process even if there were several negative changes in the functional traits in the fungal community. On the contrary, more frequent flooding increased the decomposition rate. This contradiction could be due to a fast recovery of fungal communities in the intermittent dry periods and/or higher decomposition by bacteria under flooded condition. In addition, we must emphasise that only 4% of all the fungal sequences were annotated with functional traits. This means that many fungal functional traits are probably still not identified and that improved knowledge of them could shift our understanding of fungal communities' behaviour under the studied conditions."
} | 4,446 |
23167984 | PMC3538563 | pmc | 6,812 | {
"abstract": "Background A modified laboratory-scale upflow anaerobic sludge blanket (UASB) reactor was used to obtain methane by treating hydrous ethanol vinasse. Vinasses or stillage are waste materials with high organic loads, and a complex composition resulting from the process of alcohol distillation. They must initially be treated with anaerobic processes due to their high organic loads. Vinasses can be considered multipurpose waste for energy recovery and once treated they can be used in agriculture without the risk of polluting soil, underground water or crops. In this sense, treatment of vinasse combines the elimination of organic waste with the formation of methane. Biogas is considered as a promising renewable energy source. The aim of this study was to determine the optimum organic loading rate for operating a modified UASB reactor to treat vinasse generated in the production of hydrous ethanol from sugar cane molasses. Results The study showed that chemical oxygen demand (COD) removal efficiency was 69% at an optimum organic loading rate (OLR) of 17.05 kg COD/m 3 -day, achieving a methane yield of 0.263 m 3 /kg COD added and a biogas methane content of 84%. During this stage, effluent characterization presented lower values than the vinasse, except for potassium, sulfide and ammonia nitrogen. On the other hand, primers used to amplify the 16S-rDNA genes for the domains Archaea and Bacteria showed the presence of microorganisms which favor methane production at the optimum organic loading rate. Conclusions The modified UASB reactor proposed in this study provided a successful treatment of the vinasse obtained from hydrous ethanol production. Methanogen groups (Methanobacteriales and Methanosarcinales) detected by PCR during operational optimum OLR of the modified UASB reactor, favored methane production.",
"conclusion": "Conclusions The modified UASB reactor proposed in this study provided a successful treatment of the vinasse obtained from hydrous ethanol production. The optimum organic loading rate found experimentally was 17.05 kg COD/m 3 -day corresponding to a HRT of 7.5 days and a methane yield of 0.263 m 3 /kg COD added . During operational optimum organic loading of the modified UASB reactor, the group of methanogenic archaea belonging to the Methanobacteriales and Methanosarcinales orders favored methane production.",
"discussion": "Results and discussion Vinasse characterization The properties of the vinasse used in this study are shown in Table\n 1 . Its composition can be seen to be acidic, with a high COD (121,000 mg/L) and sulfate (5,336 mg/L) content. Kumar et al .\n[ 8 ] report that in hydrous alcohol vinasse, the COD is found to be between 90,000 and 130,000 mg/L, whilst the sulfate content is between 6,000 and 6,500 mg/L. Table 1 Vinasse characterization Parameter Value * pH 4 COD 121000 SO 4 2- 5336 S - 168 N T 1341 N-NH 3 160 PO 4 3- 141 K + 7262 Ethanol 21007 Acetic Acid 2237 Propionic Acid 4304 Suspended solids 20273 Dissolved solids 45543 * All values except pH are expressed in mg/L. Ammonia nitrogen and sulfide exert inhibitory effects on anaerobic digestion and consequently affect methane yield. The literature reports wide ranges for ammonia nitrogen from 1,700 to 14,000 mg/L and between 30 and 250 mg/L for sulfide\n[ 12 , 13 ]. However, in the case of ammonia nitrogen, results have been reported to be beneficial for anaerobic digestion at concentrations of around 200 mg/L\n[ 12 ]. In this study, the ammonia nitrogen concentration of the vinasse was 160 mg/L and the sulfide concentration was 169 mg/L, meaning that the ammonia nitrogen value was found to be beneficial for anaerobic digestion, whilst sulfide had an inhibitory effect. Carboxylic acids such as acetic, propionic and butyric acids are substrates for the anaerobic digestion process. However, Parawira et al .\n[ 13 ] has demonstrated that values above 10,000 mg/L of total volatile fatty acids (VFAs) can also cause an inhibitory effect by reducing pH, which without sufficient buffering capacity inhibits the initiation of methane production. The vinasse in this study had a total VFA concentration of approximately 7,000 mg/L, which is approaching the inhibitory level, and therefore required the addition of NaHCO 3 as a buffer to prevent a sharp drop in system pH. Reactor start-up This study used granular inoculum from a UASB reactor operated to treat vinasse of banana waste, so, the start-up of the modified UASB reactor was subjected to an acclimatization period with an OLR of 0.34 kg COD/m 3 -day using 200 ml/day of Synthetic Wastewater (SW) during the first six days after inoculation, 17% CH 4 and 72% COD removal was reached. On the seventh day, the loading rate was increased to 5.9 kg COD/m 3 -day (150 ml/day of hydrous ethanol vinasse) and biogas production of 2 L per day was obtained (38% of CH 4 and 84% of COD removal). Finally, on the ninth day and with the same loading rate, biogas methane concentration was 58% and 97% COD removal. Molina et al .\n[ 14 ] obtained a start-up time of 60 days in a hybrid reactor for treating wine vinasse, using flocculent sludge which were collected from two anaerobic digesters for processing wastewater from a sugar factory and the fiberboard production process as the inoculum. These authors used an initial organic loading rate of 0.5 kg COD/m 3 -day, which was increased to reach 5 kg COD/m 3 -day, and obtained removal of 98% of the COD and a biogas methane content of between 70 and 74%. Similarly, Gao et al .\n[ 15 ] obtained a start-up time of 40 days in a UASB reactor for the anaerobic treatment of vinasse from wine production under mesophilic conditions using flocculent sludge from the anaerobic treatment of residential wastewater as the inoculum. The UASB reactor was operated with an initial organic loading rate of 0.42 kg COD/m 3 -day, which was increased to reach 5.6 kg COD/m 3 -day, and obtained COD removal of 93.8% and a biogas methane content of 60%. The COD removal values obtained in this study agrees with the results obtained by Molina et al .\n[ 14 ] and Gao et al. [ 15 ] when they evaluated nearly the same OLR. Nevertheless, it is clear that start-up times were longer, attributed to the flocculent state of the inoculation sludge. The authors considered their start-up stage to be complete when granules could be distinguished in the reactor bed. A longer start-up time benefitted Molina et al. [ 14 ], who obtained a higher biogas methane concentration. On the other hand, Wolmarans and de Villiers\n[ 16 ] studied the start-up period of a UASB reactor for the treatment of vinasse from sugar cane molasses using granular inoculum from a UASB operated to treat wastewater from a brewery. This process was stabilized in 7 days with an organic loading rate of 8 kg COD/m 3 -day and COD removal of over 90% was obtained. These results match the ones in this study, given that the use of granular inoculum increases methane production as a result of their high metabolic activity. This causes the process to reach higher yields in shorter time periods, thereby reducing start-up time. Another important factor is the fact that the inocula in both studies were previously obtained from anaerobic reactors for the treatment of vinasses. Vadlani and Ramachandran\n[ 17 ] showed that by using sludge from the anaerobic treatment of vinasses as the inoculum in the start-up of a UASB reactor, the time can be reduced by up to 40% compared to anaerobic sludge from residential wastewater treatment, given that specific methanogenic activity is greater in sludge from vinasse treatment. Modified UASB performance The organic loading rates, hydraulic retention times (HRT), COD influent, COD effluent and % COD removal, are shown in Figure\n 1a . During this study the vinasse introduced was not subjected to any dilution process. The optimum loading rate selected in this study was 17.05 kg COD/m 3 -day, which corresponded to the highest biogas methane content of 84%, methane yield of 0.263 m 3 CH 4 / kg COD added and 69% COD removal (the level of COD removal increased in this stage). Finally, the system collapsed at an OLR of 22.16 kg COD/m 3 -day (Figure\n 1 a and b). Figure 1 Evaluation of performance. The theoretical methane yield expressed in cubic meters per kilograms of COD consumed should be 0.35, assuming that all of the incoming COD is transformed into methane, and considering that the biomass growth and cell maintenance is null\n[ 18 ]. The methane yield value obtained in the present work (0.263 m 3 CH 4 / kg COD added ) could be explained by the presence of significant sulfate concentrations (5,336 mg/L)\n[ 3 ]. The reduction in reactor performance is attributed to the reduction of the sulfate present in the vinasse to sulfide. Inhibitory sulfide levels reported in the literature were in the range of 100–800 mg/L dissolved sulfide or approximately 50–400 mg/L undissociated H 2 S. The latter it can diffuse into the cell membrane. Once inside the cytoplasm, H 2 S may be inhibitory by denaturing native proteins through the formation of sulfide and disulfide cross-links between polypeptide chains, interfering with the various coenzyme sulfide linkages, and interfering with the assimilatory metabolism of sulfur and therefore reduces COD removal and methane yield\n[ 19 ]. As can be seen in Figure\n 1 b and c, the methane yield increases when the sulfide concentration is reduced. It is important to highlight that the sulfide was present in the modified UASB reactor throughout the experimental period, demonstrating a significant negative effect on methane yield for concentrations of 360 mg/L. Table\n 2 compares the modified UASB reactor in this study with the results of other authors. This study can be placed in the mid-to-high range within the literature with respect to methane yield of 0.263 m 3 /kg COD added . Molina et al .\n[ 14 ], who worked with winery effluent (less complex vinasses), obtained a high methane yield (0.33 m 3 CH 4 / kg COD added ), due to the fact that these authors used an USBF reactor (UASB + anaerobic filter), developing granular biomass with suitable specific methanogenic activity and very good settling characteristics. An adequate biogas quality was also obtained (70–74% CH 4 ). Likewise, the works which presented high COD removal performance are found to be related to the treatment of vinasses originating from the production of alcoholic beverages such as beer and wine. COD values in the literature for brewery effluents are found to range between 1,000 – 6,000 mg/L and between 26,000 – 50,200 mg/L for winery wastewaters\n[ 3 , 20 ]. These values are lower than for the vinasses used in this study (121,000 mg/L). Furthermore, vinasses obtained from hydrous ethanol production from sugar cane molasses present high sulfate, potassium and iron concentrations compared to vinasses from alcoholic beverages\n[ 3 , 7 ]. This leads us to assume that the low percentage COD removal achieved in this study is a result of the high complexity of the vinasse used, which can be corroborated with other similar studies where the substrate used was hydrous alcohol vinasse\n[ 6 , 21 - 23 ]. Likewise, the HRT in this study is found amongst the higher values obtained by previous studies, due to the fact that the vinasse was fed undiluted into the modified UASB reactor, which causes a high organic loading rate at low flow rates (L/day), thereby saving water resources which permit a reduction in the discharge volumes of the modified UASB reactor. Table 2 Comparison of performance parameters of UASB by different types of Vinasses OLR kg COD/m 3 -day HRT days COD removed % CH 4 % M.Y. m 3 CH 4 /kg COD added Reference Vinasse from hydrous alcohol distillery plant, using UASB (laboratory scale) 24.00 4.0 75 58 0.217 [ 21 ] Cane molasses vinasse from hydrous alcohol distillery plant diluted ten-fold, using UASB (pilot scale) 19.00 0.5 40 na 0.210 [ 6 ] Cane molasses hydrous alcohol stillage, using UASB (laboratory scale) 14.49 9.0 65 na 0.055 [ 22 ] Diluted brewery wastewater, using UASB (laboratory scale) 1.53 0.75 91 67 0.209 [ 7 ] Winery effluent treatment in an anaerobic hybrid USBF (pilot scale) 12.00 7.0 96 74 0.330 [ 14 ] Wheat straw vinasse, using a UASB (laboratory scale) 17.10 2.0 76 64 0.155 [ 23 ] Vinasse from hydrous ethanol distillation, using a modified UASB reactor (laboratory scale) 17.05 7.5 69 84 0.263 This Study na: data not available. Characterization of the effluent obtained at the optimum organic loading rate Table\n 3 shows the physicochemical characterization of the influent and the effluent obtained after anaerobic treatment at the optimum organic loading rate. Table 3 Physicochemical characterization of influent and effluent at the optimum organic loading rate Parameter Influent * Effluent * Removal percentage ** pH 4.51 7.22 - COD 125600 39810 69 SO 4 2- 5433 0 100 S - 175 275 - N T 1377 1160 16 N-NH 3 113 230 - N organic 1263 930 26 PO 4 3- 147 117 21 K + 6706 6838 - Ethanol 19901 232 99 Acetic acid 2697 331 88 Propionic acid 3009 2283 24 Butyric acid 0 0 - * All values except pH are in mg/L. ** (−): Not applicable. Potassium (K + ) was not removed; this corroborates the finding that anaerobic digestion does not favor elimination of this element. Information on the effect of application of vinasses on physical properties of soil is limited. However, application of wastewaters with high potassium levels has been found to increase the overall level of soil fertility, with the exception of alkaline effluents which can dissolve soil organic carbon\n[ 24 ]. In anaerobic reactors, sulfate is reduced to sulfide by the sulfate reducing bacteria (SRB). Sulfate reduction is performed by two major groups of SRB including incomplete oxidizers, which reduce compounds such as lactate to acetate and CO 2 , and complete oxidizers, which completely convert acetate to CO 2 and HCO 3 [ 19 ]. Kumar et al .\n[ 8 ] showed that once removal of 80% of the sulfate present in hydrous ethanol vinasse was obtained, the sulfide concentration rose to 400 mg/L, which inhibited the microorganisms and led to a reduction in methane yield. Two stage of inhibition exist for methanogenic bacteria because of the sulfate reduction; primary inhibition is due to competition for common organic and inorganic substrates from SRB, which suppresses methane production, the sequence of the affinity of SRB for reduced substrates is Hydrogen > propionate > other organic electron donors. Because of the variety in substrate utilization exhibited by SRB, they compete with several different types of microorganisms involved in anaerobic digestion. Secondary inhibition results from the toxicity of sulfide, the inhibitory sulfide levels reported in the literature were in the range of 100–800 mg/L dissolved sulfide or approximately 50–400 mg/L undissociated H 2 S, Fermentative microorganisms which are responsible for the breakdown of monomers into smaller products were less affected by sulfide toxicity than SRB, or Methane producing bacteria; toxicity thresholds for acetogens were comparable with those of the SRB. Sulfur is a required nutrient for methanogens. It has been shown that the sulfur content of methanogens was higher than in other groups of microorganisms generally found in anaerobic systems. The optimal level of sulfur reported in the literature varies from 1 to 25 mg/L. The levels reported in the literature for inhibition of Methane producing bacteria also vary, with IC50 values of 50–125 mg H 2 S/L at pH 7–8 for suspended sludge and 250 mg H 2 S/L and 90 mg H 2 S/L at pH 6.4–7.2 and pH 7.8–8.0, respectively\n[ 19 ]. In this study, sulfate removal was 100%; the quantity of sulfide in the effluent was 275 mg/L at the optimum organic loading rate. Harada et al .\n[ 6 ] obtained a maximum concentration of 300 mg/L of acetic acid, 1,200 mg/L of propionic acid, a methane yield of 0.21 m 3 CH 4 /kg COD added and COD removal of 40% with a UASB reactor operated at 19 kg COD/m 3 -day employing hydrous ethanol vinasse. Likewise, during treatment of whiskey distillery wastewater at 18 kg COD/m 3 -day, Goodwin and Stuart\n[ 5 ] found that biogas production was 6 L/day (50% less than at the previous loading rate), obtaining acetic and propionic acid levels of 900 mg/L and 6,000 mg/L respectively and COD removal of 50%. Ethanol and acetic acid removal are undoubtedly greater than propionic acid removal, given that these two compounds have a methane conversion rate of 3.56 and 3.92 mmol CH 4 /g VS-day respectively. In contrast, the rate for propionic acid is 0.55 mmol CH 4 /g VS-day\n[ 25 ]. This suggests that propionic acid is one of the VFAs which microorganisms have difficulty breaking down during anaerobic digestion. In this study, trough out the optimum organic loading rate (45 to 55 days) the VFA were acetic and propionic acids; but, since the begging in the vinasse the acetic (2,697 mg/L) and propionic (3,009 mg/L) acids were present. In the VFA profile of the effluent, the acetic acid changed from 0 mg/L to 331 mg/L, while the propionic acid the values began at 1,429 mg/L and ended with 2,283 mg/L. Although, the acetic acid level was similar to that obtained by Harada et al .\n[ 6 ], the methane yield (0.263 m 3 CH 4 /kg COD added ) and COD removal (69%) were higher, this suggests that the greater quantity of acetic acid present was transformed into methane. Microbial identification In previous research, the microorganisms found in anaerobic reactors are wide-ranging and vary depending on the substrate and operating conditions employed in the bioreactors. Table\n 4 shows the amplification results of the 16S-rDNA genes of the domains Bacteria and Archaea obtained at the optimum organic loading rate in this study. Table 4 Microbial groups evaluated by 16S-rDNA amplification at the optimum organic loading rate in the modified UASB reactor Domain Group Amplicon* Archaea + Archaea Methanogens + Methanobacteriales + Methanosarcinales + α -Proteobacteria + β -Proteobacteria + Bacteria γ -Proteobacteria + δ -Proteobacteria + High GC Gram-positive Bacteria - Low GC Gram-positive Bacteria + Bacillus + Sulfate Reducing Bacteria (SRB) + Clostridium + *Presence (+) or absence (−) of the specific amplification product. The sulfate concentration was above 5,000 mg/L in the hydrous ethanol vinasse in this study, the presence of species from Methanobacteriales and Methanosarcinales orders in the domain Archaea were present in the optimum OLR. The Methanobacterium and Methanosaeta species belong to these orders respectively, which suggests that they were present in the bioreactor. This result was similar to the one obtained by Sarti et al .\n[ 26 ], who performed the characterization of methanogenic archaea in an anaerobic reactor under mesophilic conditions for the treatment of wastewater rich in sulfates (between 1,000 and 3,000 mg/L) with a COD/SO 4 2- ratio of 1.8 and 1.5. Considering the operational condition of 1,000 and 2,000 mg SO 4 2- /L, it was observed the presence of methanogenic archaea (99% of similarity with Methanosaeta spp.). At concentration of 3,000 mg SO 4 2- /L the methanogenesis was inhibited and methanogenic organisms were not detected in the clone library. Likewise, Oude et al .\n[ 11 ] indicated that Methanosaeta spp. were the dominant acetate degraders, and Methanobacterium spp. the dominant hydrogen- and formate-consuming methanogens in the treatment of wastewater with a COD/SO 4 2- ratio of 9.5, while Desulfobulbus spp. and Syntrophobacter spp. were important for propionate degradation (sulfate reduction). The bacterial groups found in this study, were similar to other bacterial identification research in anaerobic digesters\n[ 10 , 11 , 26 ], where the group of Gram-positive bacteria with low GC (included in the Firmicutes phylum) is composed of a large number of bacterial genera including Bacillus, Clostridium, Enterococcus, Lactobacillus and Lactococcus , which perform the stages of hydrolysis and acidogenesis\n[ 10 ]. The detection of the δ-Proteobateria subclass in this reactor suggests the presence of bacterial genera capable of using sulfate as an inorganic substrate, which achieve removal of over 95% of initial sulfates in the hydrous ethanol vinasse\n[ 11 , 26 ]. It is important to highlight that the absence of Gram-positive Bacteria with a high GC content, a well-known group due to its inclusion of different pathogenic species (independently of the γ-Proteobacteria subclass), could benefit the use of this type of effluent in combined fertilization and irrigation systems."
} | 5,164 |
28970817 | PMC5609546 | pmc | 6,814 | {
"abstract": "The Mariana region exhibits a rich array of hydrothermal venting conditions in a complex geological setting, which provides a natural laboratory to study the influence of local environmental conditions on microbial community structure as well as large-scale patterns in microbial biogeography. We used high-throughput amplicon sequencing of the bacterial small subunit (SSU) rRNA gene from 22 microbial mats collected from four hydrothermally active locations along the Mariana Arc and back-arc to explore the structure of lithotrophically-based microbial mat communities. The vent effluent was classified as iron- or sulfur-rich corresponding with two distinct community types, dominated by either Zetaproteobacteria or Epsilonproteobacteria, respectively. The Zetaproteobacterial-based communities had the highest richness and diversity, which supports the hypothesis that Zetaproteobacteria function as ecosystem engineers creating a physical habitat within a chemical environment promoting enhanced microbial diversity. Gammaproteobacteria were also high in abundance within the iron-dominated mats and some likely contribute to primary production. In addition, we also compare sampling scale, showing that bulk sampling of microbial mats yields higher diversity than micro-scale sampling. We present a comprehensive analysis and offer new insights into the community structure and diversity of lithotrophically-driven microbial mats from a hydrothermal region associated with high microbial biodiversity. Our study indicates an important functional role of for the Zetaproteobacteria altering the mat habitat and enhancing community interactions and complexity.",
"conclusion": "Conclusions This study offers insights into the community structure and biodiversity of lithotrophically-driven bacterial mat communities from iron- and sulfur-rich hydrothermal venting along the Mariana Arc and back-arc. Although local geochemistry (e.g., ratio of Fe/H 2 S and availability of H 2 ) was the primary driver that correlated with community structure, geographic patterns in OTU abundance were also apparent, especially in the iron-dominated systems. Our study indicates an important functional role of Zetaproteobacteria at all sites with iron-dominated vent effluent and Epsilonproteobacteria at sites with sulfur-dominated fluids. Gammaproteobacteria were also high in abundance within the iron-dominated mats and likely had a heterotrophic role as secondary consumers, though some show the potential to grow lithotrophically as well. Higher bacterial diversity was observed in Zetaproteobacterial-dominated mats, which supports the hypothesis that Zetaproteobacteria function as ecosystem engineers altering the mat habitat and enhancing community interactions and complexity. In addition, sampling technique is an important consideration when attempting to assess the spatial heterogeneity associated with hydrothermal microbial mat communities. The high diversity observed among and within microbial communities encourages further research into the ecology, metabolic potential, and biodiversity of microbial mats fueled by Mariana Arc and back-arc submarine volcanism.",
"introduction": "Introduction Recognizing and cataloging the microbial biodiversity at extant hydrothermal vents is critical to gain a better understanding of current and ancient ecosystem functions and how the taxa present play a role in global geochemical processes (Gilbert et al., 2011 ; Reed et al., 2014 ). The steep redox gradients and high concentration of reduced substrates [e.g., Fe(II), H 2 S, and H 2 ] in hydrothermal vent habitats provide energetically favorable conditions that support luxuriant microbial mats with phylogenetically diverse lithoautotrophic microbes (Emerson and Moyer, 2010 ; Amend et al., 2011 ). This spectrum of geochemistry is thought to be similar to that of early Earth and as such, hydrothermal vents are a compelling system to study early life on Earth and may provide insights into other potentially habitable zones such as Saturn's moon, Enceladus (Martin et al., 2008 ; McKay et al., 2008 ). Early Earth may have been drastically modified by iron-oxidizing bacteria as they are thought to have been partially, if not fully, responsible for the global pattern of banded iron formations deposited during the Precambrian (Konhauser et al., 2002 ; Chan et al., 2016a ). Iron is the second most abundant metal in Earth's crust (Kappler et al., 2015 ), and it represents a large and ancient energy source for iron-oxidizing bacteria (Planavsky et al., 2009 ). Therefore, hydrothermal vent systems allow for investigations into the fundamentals of microbial ecology and biogeography as well as planetary processes such as global carbon and mineral cycling (Nakagawa and Takai, 2008 ; Dick et al., 2013 ; Resing et al., 2015 ). The hydrothermally active regions of the Mariana Arc and back-arc systems are formed by differential volcanic activity from the subduction and melting of the Pacific plate beneath the Philippine plate (Fryer, 1996 ). Relative to mid-ocean ridge hydrothermal systems, the geochemistry of the hydrothermal vent effluent (e.g., concentrations of reduced metals, H 2 , H 2 S, CO 2 , and NH 4 + ) across the Mariana region is highly heterogeneous due to the wide range in magmatic volatile content and magma chemistry of the Island Arc and back-arc. Fluid chemistry may be dominated by magmatic CO 2 , as at NW Eifuku (Lupton et al., 2006 , 2008 ), or by active volcanism and magmatic SO 2 , as at NW Rota-1 (Butterfield et al., 2011 ), or may be more rock-buffered as at Urashima and Snail sites on the back-arc (Nakamura et al., 2013 ; Ishibashi et al., 2015 ). Significant variation in fluid chemistry within a vent field on a single submarine volcano is not uncommon, due to sub-seafloor reaction between host rock and fluids enriched in magmatic gases (e.g., Champagne and Yellow Cone vents from NW Eifuku). This high variability in vent effluent geochemistry compounded over the expansive geographic area of the Mariana region harbors disparate habitats that offer the opportunity for niche differentiation, a wide range of metabolic potential, and diverse microbial communities (Davis and Moyer, 2008 ). Hydrothermal vent microbial community structure in the Mariana region was shown to be extremely diverse and has been split into three groups dominated either by Zetaproteobacteria, Epsilonproteobacteria, or putative heterotrophic phylotypes (Davis and Moyer, 2008 ). The Zetaproteobacteria are a more recently described class of iron-oxidizing bacteria (Emerson et al., 2007 ), and a complex picture of biogeography has begun to emerge as they are found globally in an array of iron-rich environments including hydrothermal vent microbial mats from the South Tonga Arc; Iwo-Jima, Japan; Tutum Bay, Papua New Guinea; and the Mid Atlantic Ridge (Forget et al., 2010 ; Meyer-Dombard et al., 2013 ; Hoshino et al., 2015 ; Scott et al., 2015 ). Zetaproteobacteria have also been detected in hydrothermal borehole fluids (Kato et al., 2009b ), continental subsurface water (Emerson et al., 2015 ), near shore estuaries (McBeth et al., 2013 ), estuarine oxygen minimum zones (Field et al., 2016 ), and non-venting deep continental margins (Rubin-Blum et al., 2014 ). Recent studies hypothesize that Zetaproteobacteria are microbial ecosystem engineers because they have the genetic potential for the production of organic carbon and the capacity to shape the environment by producing iron oxyhydroxide minerals and exopolysaccharides, which in turn provide structure to the mats and can alter the local geochemistry, enhancing microbial diversity (Forget et al., 2010 ; Fleming et al., 2013 ; Meyer-Dombard et al., 2013 ; Jesser et al., 2015 ; Chan et al., 2016b ). Due to the variability of the vent effluent composition, the Mariana region also has sulfur-rich hydrothermal habitats. Sulfur- and hydrogen-oxidizing Epsilonproteobacteria have been detected in microbial mats and hydrothermal fluids as the primary lithotrophic drivers at these locations (Davis and Moyer, 2008 ; Huber et al., 2010 ; Meyer and Huber, 2014 ). In addition to the observed geographic-scale variation across vent habitats, community structures can be can be examined on a spatial scale of millimeters within individual microbial mats. Sampling at smaller spatial scales has received much attention in studies of soils and photosynthetic mats (Fike et al., 2008 ; Harris et al., 2013 ; Raynaud and Nunan, 2014 ; Cordero and Datta, 2016 ), but only recently has high-resolution sampling of deep-sea hydrothermal mats been highlighted (Breier et al., 2012 ; Teske et al., 2016 ). Unlike easily accessible photosynthetic mats, microbial mats in the deep sea are difficult to study on a fine scale due to the limitations of sample collection with remotely operated vehicles. Systematic, fine-scale sampling of iron mats from Lō'ihi Seamount, Hawai'i has revealed different abundances of functional genes and extracellular structures from visibly different mat morphologies (Fleming et al., 2013 ; Jesser et al., 2015 ; Fullerton et al., 2017 ) that occur in microbial mats that can be over a meter deep (Edwards et al., 2011 ) exhibiting millimeter scale redox gradients, especially at their surface (Glazer and Rouxel, 2009 ). Further, microscopy studies of iron-oxidizing bacteria reveal that cells are not evenly distributed in mats, but rather they develop into actively growing fronts (Chan et al., 2016b ) that can oxidize iron at a rate of up to 52 μmole hr −1 and can accrete mat material at ~2.2 cm yr −1 (Emerson et al., 2017 ). These data confirm the importance of addressing spatial heterogeneity through fine-scale sampling of hydrothermal microbial mats. The Mariana Arc and back-arc hydrothermal vent microbial communities have been described with high microbial biodiversity using small subunit (SSU) rRNA gene clone libraries and community fingerprinting analyses (Davis and Moyer, 2008 ). For this study, we used high-throughput, second generation SSU rRNA gene amplicon sequencing (Caporaso et al., 2012 ; Pedrós-Alió, 2012 ) in an effort to comprehensively investigate and better understand the community structure of microbial mats along the Mariana Arc and back-arc. We further expand on the importance of sampling scale by juxtaposing the microbial diversity of mats collected with a fine-scale sampling device (e.g., biomat sampler) to more commonly used scoop samplers. These results provide novel insights into patterns of biogeography, ecology, and microbial biodiversity of the lithotrophic drivers of these hydrothermal communities.",
"discussion": "Discussion Community structure comparisons Two distinct community types were identified based on the prevalent taxa. The first of these are the Zetaproteobacterial-dominated communities that were present only in association with the iron-dominated vent effluent. The second community type lacked Zetaproteobacteria; however, they had high levels of Epsilonproteobacteria and were found only in association with the sulfur-dominated fluids. These sulfur-dominated communities were present only at the Mariana Arc sites Champagne, NW Eifuku and Iceberg, NW Rota-1. Both community types contain an abundance of bacteria related to known lithoautotrophs to drive community primary production, and no mats were composed primarily of putative heterotrophic taxa as seen previously (Davis and Moyer, 2008 ). The majority of the microbial mats we examined were of the Zetaproteobacteria-dominated community type and exhibited a high level of variability among the remaining community members (Figure 2 ). These Zetaproteobacterial-dominated mats were collected from all four dive locations (Table 1 ), and their respective community structure does not appear to correlate with location when all taxa are included (Figure 2 ). Only 18.5% of the abundant OTUs were found across all iron-dominated mats, which can lend explanation to the low Yue-Clayton similarities and high beta diversity in community structure observed between mat communities (Figure 2 ). Biogeographic patterns emerged only when the variation of less abundant classes and the unclassified OTUs were removed from cluster analysis. The Zeta-, Epsilon-, and Gamma-proteobacteria, often the lithotrophic drivers of hydrothermal microbial mats, were found in high abundance in the iron mats here, and are likely the most ecologically significant members in these communities (Figure 2 ). With only the abundant OTUs belonging to the Zeta-, Epsilon-, and Gamma-proteobacteria, the Zetaproteobacterial-dominated mats of NW Eifuku all clustered together in one group (Figure 3 ). The two other groups are composed of communities from Snail, Urashima, and NW Rota-1, which are all relatively close to one another whereas NW Eifuku is hundreds of km north of these three vent fields (Figure 1A ). These data support previous research showing community structure variability corresponding with vent location (Opatkiewicz et al., 2009 ; Huber et al., 2010 ; Makita et al., 2016 ). Although there were OTUs with significantly different abundances among all three groups of iron-dominated mats as determined via Metastats (Figure 3 ), there were no genera unique to one grouping, which suggests that a common ecosystem function is shared among the abundant community members of the Zeta-, Epsilon-, and Gamma-proteobacterial OTUs in all iron-dominated mats of the Mariana Arc and back-arc. This indicates that local fluid geochemistry (e.g., sulfur- vs. iron-dominated) rather than large-scale geography is more influential in determining the bacterial community composition and function, although there are observable patterns in biogeography based on differential OTU abundance among sites. Zetaproteobacteria All characterized Zetaproteobacteria strains are obligate iron-oxidizing lithoautotrophs belonging to the genus Mariprofundus (Emerson et al., 2007 ; McBeth et al., 2011 ; Makita et al., 2017 ). Members of this genus have been previously detected in flocculent mats from the back-arc Snail and Urashima sites and from NW Eifuku (Davis and Moyer, 2008 ; Kato et al., 2009a ; Makita et al., 2016 ); in addition to these locations, we have now identified Zetaproteobacteria at NW Rota-1 (Olde Iron Slides). After years of documented eruptions from 2004 to 2010 (Schnur et al., 2017 ), it is possible the hydrothermal fluids are undergoing a transition from sulfur- to iron-dominated effluent at this location as hypothesized by Butterfield et al. ( 1997 ). This geochemical succession is likely mirrored by the change in community structure that we are observing here as Zetaproteobacteria colonize a previously Epsilonproteobacteria-dominated vent field. We hypothesize a more recent transition from high sulfur to iron conditions might also explain why the orange mats at Olde Iron Slides were thinner and patchier in comparison to those found at Snail, Urashima, and Yellow Cone (Figure 1 ). Because all Zetaproteobacteria isolates belong to the same genus, RDP classifies each Zetaproteobacterial sequence as identical; however, this underrepresents the actual diversity of this class. To further resolve the taxonomic diversity of the Zetaproteobacteria, McAllister et al. ( 2011 ) focused on their biogeography throughout the Pacific Ocean and found zOTUs 1 and 2 to be globally distributed, or cosmopolitan. In our study, zOTU 1 was found in relatively high abundance in all iron-dominated mats (Figure 3 ); however, zOTU 2 does not appear to be ecologically relevant or play a large role in ecosystem function, though it was detected throughout at low levels. Single cell amplified genomes from Lō'ihi Seamount show greater genetic diversity in zOTU 1 than zOTU 2 (Field et al., 2015 ). This higher genetic diversity may result in more phenotypic plasticity in zOTU 1, providing an advantage in the Mariana hydrothermal systems, where vent habitats are more heterogeneous (in terms of temperature and chemical composition) than locations such as Lō'ihi Seamount, where zOTU 2 is more abundant (McAllister et al., 2011 ; Field et al., 2015 ; Fullerton et al., 2017 ). The presence of zOTUs 3 and 4 in the Mariana raises their status to cosmopolitan across the Pacific. The higher depth of sequencing obtained here is probably responsible for their detection rather than a recent colonization event (although these are not mutually exclusive). The type strains Mariprofundus ferrooxidans and Mariprofundus micogutta belong to zOTUs 11 and 18, respectively. Though detected, these zOTUs were not found to be abundant in any mat communities investigated (Figure 3 ). Therefore, the present type strains are likely poor representatives of the most ecologically important and ubiquitously distributed zOTUs in the Mariana Arc and back-arc microbial mat communities. The diversity of the Zetaproteobacteria has been under investigation due to their prominence and ecological importance in iron-rich hydrothermal habitats. Evidence suggests that Zetaproteobacteria not only act as primary producers (with respect to carbon cycling), but also produce an extensive physical environment that would otherwise not exist, thereby providing a habitat for diverse microbial communities. In a comparison between the two community types, all the Zetaproteobacterial-dominated mats had higher microbial diversity than the Epsilonproteobacteria-dominated community types (Table 3 ). This increased biodiversity is potentially due to the Fe-oxyhydroxides produced by the Zetaproteobacteria. These extracellular structures (e.g., stalks and sheaths) are well-documented from both isolates and naturally occurring microbial mats (Fleming et al., 2013 ; Bennett et al., 2014 ; Chan et al., 2016b ; Makita et al., 2016 ). This high surface-area architecture can be colonized by an array of secondary consumers (e.g., Bacteroidetes, Chloroflexi, Gammaproteobacteria, and Planctomycetes) that were also found to be present (Figure 2 ). Epsilonproteobacteria The Epsilonproteobacteria have broad metabolic capabilities, at hydrothermal vents they are typically represented by lithoautotrophic isolates energetically using reduced sulfur compounds and/or H 2 , they are motile and capable of quorum-sensing (Takai et al., 2005a ; Campbell et al., 2006 ; Pérez-Rodríguez et al., 2015 ; Waite et al., 2017 ). Epsilonproteobacteria were detected in all samples; however, there are two distinct types of Epsilonproteobacterial communities containing OTUs either from the genera Thioreductor and Lebetimonas or Sulfurovum and Sulfurimonas . The sulfur-rich fluids at Champagne Vent were also high in H 2 and supported a Thioreductor/Lebetimonas-dominated microbial mat. This was the only mat community composed of a high percentage of Thioreductor (Figure 3 ), which is represented by the type strain Thioreductor micantisoli (Nakagawa et al., 2005a ). Cultured representatives in this genus are mesophilic, strictly anaerobic, and utilize H 2 as their electron donor and S 0 or NO 3 - as electron acceptors (Nakagawa et al., 2005b ). Champagne Vent was also the only mat community that contained a high percentage of reads classified as Lebetimonas (Figure 3 ). The type strain for this group is Lebetimonas acidophila , also a strictly anaerobic H 2 -oxidizer and S 0 -reducer (Takai et al., 2005b ). Previous work found both Thioreductor and Lebetimonas sequences in hydrothermal fluids sampled from NW Eifuku and NW Rota-1 as well as other Mariana Arc and back-arc vent fields (Huber et al., 2010 ; Meyer and Huber, 2014 ). These two genera appear to be highly prevalent in fluids, but as shown here are often rare in microbial mats as abundances of Thioreductor/Lebetimonas OTUs were <0.05% in all mats other than at Champagne Vent. This is likely due to their metabolic requirement for H 2 , which strongly correlates with the concentration of H 2 (Figure 4 ). The low diversity and evenness in the Champagne Vent mat (Table 3 ) was due to the dominance of four putative H 2 -oxidizing OTUs accounting for 71.5% of the total reads. This indicates that H 2 was likely the primary energy source for this microbial mat community. Epsilonproteobacteria found in the diffuse-flow, sulfur-dominated mat at Iceberg Vent contained abundant OTUs identified as Sulfurimonas and Sulfurovum. Type strain Sulfurimonas autotrophica is a strict aerobe oxidizing H 2 S, S 0 , and S 2 O 3 2 - (Inagaki et al., 2004 ). Sulfurovum lithotrophicum also oxidizes S 0 and S 2 O 3 2 - aerobically or anaerobically with NO 3 - as a terminal electron acceptor (Inagaki et al., 2003 ). We also detected a high abundance of Sulfurovum and Sulfurimonas OTUs in Zetaproteobacterial-dominated mats in addition to iron-oxidizing Zetaproteobacteria. Despite the low concentrations of H 2 S in iron-dominated mats, the Epsilonproteobacteria likely play an important role in biogeochemical cycling of sulfur and carbon in iron mats, and their ecological importance should not be overlooked. Gammaproteobacteria The Gammaproteobacteria were also present in high abundance in mats with iron-dominated vent fluids, but the predicted metabolic potential is difficult to determine for the majority of them due to lack of classification beyond the class level. Six abundant Gammaproteobacterial OTUs classified as Methylococcaceae most likely obtain energy and carbon from methane (Hanson and Hanson, 1996 ; Gulledge et al., 2001 ). Methanotrophic Gammaproteobacteria in the order Methylococcaceae have been detected on the Mariana back-arc and other hydrothermal systems previously (Brazelton et al., 2006 ; Kato et al., 2009a ). The other classified OTU belongs to the heterotrophic genus Thalassomonas (Macián et al., 2001 ). The remaining unclassified Gammaproteobacterial OTUs are likely common at marine hydrothermal habitats as indicated by high similarity to environmental sequences obtained from hydrothermal vents (Supplemental Table 1 ). When these same OTUs are compared to isolated strains the top representatives are heterotrophic or have sulfur-oxidizing metabolisms (Supplemental Table 1 ). Globally, vent systems are rich in chemolithotrophic sulfur-oxidizing Gammaproteobacteria including free-living and invertebrate endosymbiont taxa (Wirsen et al., 1998 ; Reed et al., 2014 ). Known sulfur-oxidizing Gammaproteobacteria were present in low abundances in the mats in this study; e.g., six Thiomicrospira OTUs were detected, but the most abundant of these was only 0.18% of the total reads in sample 797B3 from Urashima. Recently, it has been shown that some Thiomicrospira spp. are also capable of switching between iron and sulfur oxidation (Barco et al., 2017 ). This metabolic switching could potentially cause some of the Gammaproteobacteria OTUs to more tightly cluster with those representing Zetaproteobacteria (Figure 4 ). Still, we hypothesize the abundant unclassified Gammaproteobacterial OTUs are predominantly heterotrophic (i.e., secondary consumers) in these Mariana, Zetaproteobacterial-dominated mats due to ecosystem engineering. The Gammaproteobacterial OTUs generally do not share a similar distribution pattern with the Sulfurovum/Sulfurimonas or Thioreductor/Lebetimonas OTUs, which suggests they do not have an energetic dependence on H 2 S or H 2 , respectively; however, there were two Gammaproteobacterial OTUs (055 and 153) that did share a similar distribution pattern with the Sulfurovum/Sulfurimonas, indicating that they may also share a potential energetic dependence on H 2 S (Figure 4 ). Making assumptions about the role of the unclassified Gammaproteobacteria in these habitats, however, must be done with caution because of the wide array of energy yielding metabolisms utilized by this phylogenetically diverse class (Williams et al., 2010 ). Sampling scale In addition to a large geographic scale, we also compared communities of fine-scale biomat samples and bulk scoop samples taken from the same microbial mat on three occasions. Rarefaction analysis showed higher OTU richness in the scoop sample than in the corresponding biomat sample from all three mat communities (Supplemental Figure 1 ). Observation of the high abundance OTUs in these sample pairings revealed different OTU enrichments based on sample type (Supplemental Figure 2 ). The scoop and biomat pairing from NW Rota-1, Olde Iron Slides (800Sc8 and 800B12456) is a clear example where targeted, fine-scale sampling had a significant impact on the measured microbial diversity and community structure. The biomat sampler specifically targeted thin, orange tufts on the surface of the mat, and the scoop sampler was less selective and collected underlying sediment as well as the orange and white microbial mats. Correspondingly, the biomat sample had over twice as many Zetaproteobacterial reads (Figure 2 ) and was enriched in zOTUs 1 and 8 (Supplemental Figure 2 ). The higher diversity and richness exhibited in scoops (Table 3 , Supplemental Figure 1 ) from the other two microbial mats (Snap-Snap, Urashima and Yellow Cone, NW Eifuku) is likely a result of sampling multiple microhabitats within these mats, e.g., oxygenated surface layers and microaerophilic to anoxic zones deeper in the architecture of the mat (Chan et al., 2016b ). These data are similar to that of more accessible systems such as in photosynthetic microbial mats, where depth profiles have shown considerable changes in community structure at the millimeter scale (Harris et al., 2013 ). Sampling scale affected estimated bacterial diversity and should be considered in future assessments of hydrothermal microbial mat communities."
} | 6,456 |
33143657 | PMC7640387 | pmc | 6,815 | {
"abstract": "Background Ferula sinkiangensis is an increasingly endangered medicinal plant. Arbuscular mycorrhiza fungi (AMF) are symbiotic microorganisms that live in the soil wherein they enhance nutrient uptake, stress resistance, and pathogen defense in host plants. While such AMF have the potential to contribute to the cultivation of Ferula sinkiangensis , the composition of AMF communities associated with Ferula sinkiangensis and the relationship between these fungi and other pertinent abiotic factors still remains to be clarified. Results Herein, we collected rhizosphere and surrounding soil samples at a range of depths (0–20, 20–40, and 40–60 cm) and a range of slope positions (bottom, middle, top). These samples were then subjected to analyses of soil physicochemical properties and high-throughput sequencing (Illumina MiSeq). We determined that Glomus and Diversispora species were highly enriched in all samples. We further found that AMF diversity and richness varied significantly as a function of slope position, with this variation primarily being tied to differences in relative Glomus and Diversispora abundance. In contrast, no significant relationship was observed between soil depth and overall AMF composition, although some AMF species were found to be sensitive to soil depth. Many factors significantly affected AMF community composition, including organic matter content, total nitrogen, total potassium, ammonium nitrogen, nitrate nitrogen, available potassium, total dissolvable salt levels, pH, soil water content, and slope position. We further determined that Shannon diversity index values in these communities were positively correlated with total phosphorus, nitrate-nitrogen levels, and pH values (P < 0.05), whereas total phosphorus, total dissolvable salt levels, and pH were positively correlated with Chao1 values ( P < 0.05). Conclusion In summary, our data revealed that Glomus and Diversispora are key AMF genera found within Ferula sinkiangensis rhizosphere soil. These fungi are closely associated with specific environmental and soil physicochemical properties, and these soil sample properties also differed significantly as a function of slope position ( P < 0.05). Together, our results provide new insights regarding the relationship between AMF species and Ferula sinkiangensis , offering a theoretical basis for further studies of their development. Supplementary Information The online version contains supplementary material available at 10.1186/s12866-020-02024-x.",
"conclusion": "Conclusion In summary, our results provide new insights regarding the composition and diversity of rhizosphere AMF communities associated with Ferula sinkiangensis . We found that Glomus and Diversispora were enriched in our samples, and that rhizosphere AMF diversity and richness varied significantly among slope positions, as evidenced by differences in Glomus and Diversispora abundance. In contrast, rhizosphere soil depth did not significantly affect overall AMF diversity, although certain AMF species were found to be sensitive to depth. In addition, the physical and chemical properties of soil varied significantly as a function of slope position ( P < 0.05), potentially explaining the differences in AMF community composition across these slope positions. Together, we hope that our results will help guide efforts to improve soil structure and AMF communities associated with Ferula sinkiangensis in order to improve the protection and cultivation of this valuable medicinal plant.",
"discussion": "Discussion In this study, the relative diversity of Glomus, Diversispora, and Ambispora fungi varied significantly, with these genera accounting for 83.46, 14.73, and 1.21% of the fungi in analyzed samples, respectively (Fig. 2 a). Owing to their adaptability, Glomus species are abundant in many ecosystems, consistent with our findings. As Glomus and Diversispora were the two dominant genera detected in soil samples in the present study, this suggests that these fungi are better adapted to the desert environment at this study site. In addition, we were unable to identify certain AMF species, and Paraglomus species were detected in only a few samples. Some molecular studies of AMF communities have reported difficulties in the detection of Paraglomerales and Archaeosporales species [ 35 , 36 ], potentially due to PCR primer-related issues [ 37 ], owing to the use of different target genes or genomic regions and primer combinations that exhibit differences in specificity and efficacy across fungal genera [ 38 , 39 ]. Maarten et al. demonstrated the complementary specificity of AMV4.5NF–AMDGR with AML1–AML2 primer sets, and found that a greater number of high-quality AMF sequences were obtained for the AMV4.5NF–AMDGR primers when evaluating six primer pairs targeting the nuclear rRNA operon as a means of characterizing AMF communities [ 38 ]. However, their results also suggested that this primer pair favored the amplification of Glomeraceae sequences at the expense of Ambisporaceae , Claroideoglomeraceae, and Paraglomeraceae sequences. As such, future efforts to identify more reliable primer pairs may be warranted. Our petal chart analysis identified 12 core OTUs shared among different soil sample groups (Fig. 1 ), including 10 Glomus OTUs and 2 Diversispora OTUs that were present at all soil depths and slope positions. Given their universality among collected samples, we hypothesize that these fungi are closely associated with Ferula sinkiangensis growth, potentially suggesting that further study of these fungi may offer key insights into soil microbiology that can support artificial Ferula sinkiangensis cultivation. In LEFse analyses, we determined that biomarkers [ 40 – 42 ] differed significantly as a function of soil depth and slope position, with decreasing biomarker levels as soil depth increased, suggesting that certain AMF species are sensitive to soil depth (Fig. 5 b). We also found that most of these soil depth-sensitive AMF biomarkers were located in the lower slope position. This finding, together with the data shown in Fig. 5 a, indicated that most AMF biomarkers were enriched at a soil depth of 0–20 cm in samples collected from the bottom of the slope, which may be a consequence of the fact that plant residues typically accumulate on the soil surface [ 33 , 41 , 42 ], particularly on relatively flat regions like those found at the bottom of a given slope. Such residues are associated with high soil nutrient contents, good ventilation, and favorable hydrothermal conditions that are conducive to the growth of soil microorganisms. Moreover, microorganisms can function synergistically with other AMF species [ 43 , 44 ] to promote Ferula sinkiangensis growth. Spearman correlation analyses revealed that soil physicochemical properties were significantly associated with AMF alpha diversity indices, with TP and pH being positively correlated with Shannon and Chao1 index values ( P < 0.05). Soil phosphorus levels are one of the most important factors regulating AMF community diversity [ 44 , 45 ], with certain studies having found AMF diversity to be significantly negatively correlated with AP levels [ 45 , 46 ]. Herein, we found AMF diversity to be significantly positively correlated with soil TP ( P < 0.05), whereas it was not significantly related to levels of AP. This may be due to the low levels of AP in these soil samples (1.67–6.85 mg/kg). It has been shown that the function whereby AMF species provide phosphorus to their host plants is phylogenetically conserved [ 47 ], such that different AMF phylogenetic groups would exhibit significant differences in availability. For example, the diversity of AMF communities associated with soybean roots was significantly influenced by P application [ 48 ], whereas such application did not affect AMF root colonization or the diversity/structure of AMF communities associated with tomato plants [ 49 ]. As such, it is possible that low P availability may select for functionally similar AMF species exhibiting highly efficient P uptake. In contrast, TP contents varied from 0.49–0.85 g/kg in the soil samples in the present study, suggesting a high potential phosphorus abundance in these soil samples. Ferula sinkiangensis growth is dependent upon the absorption of available soil phosphorus, and AMF species can facilitate such phosphorus uptake [ 46 , 47 ]. This thus explains the increase in TP content, which was consistent with the substantial enrichment of AMF species adapted to low AP levels within the rhizosphere. Soil pH is another key parameter that influences AMF community diversity, with AMF diversity often being significantly negatively correlated with pH [ 50 ]. In contrast, in the present study, we found that AMF community diversity was significantly positively correlated with soil pH, potentially due to unique local environmental factors. Some studies have shown that the tolerance of different AMF species to soil pH varies greatly [ 50 , 51 ], and that the diversity and community composition of AMF species in soils with different pH values were significantly different [ 52 – 54 ]. The soil pH range in the present study was from 7.80–8.81, with only certain Glomus and Diversispora AMF species being able to survive in this pH range. As pH values rose, we found that the richness and diversity values corresponding to these AMFs also increased. We detected significant differences in AMF diversity and richness as a function of slope position but not as a function of soil depth. This may be because the physical and chemical properties of soil at different slope locations differed significantly, whereas these properties did not vary as a function of soil depth. Cluster analyses (Fig. 3 ) clearly separated soil samples into three categories, which revealed that AMF community composition at a given slope level was similar, whereas this composition varied significantly as a function of slope level. We additionally observed no significant differences in soil properties as a function of soil depth, whereas these properties did differ at different slope positions, with significant differences being observed in OM, TP, TK, AN, pH, and SM ( P < 0.05, Table 2 ). AMF diversity and richness were closely associated with environmental factors, and CCA analyses revealed that OM, TN, TK, NN, AN, AK, TDS, pH, SM, and AE all had a significant impact on AMF community composition (Fig. 4 b). AE was found to be positively correlated with TDS and DE, and to be negatively correlated with other environmental factors (Fig. 4 b). These factors were also correlated with AMF community composition, with OM, TN, TP, TK, AN, NN, TDS, pH, and SM all being positively correlated with the abundance of many OTUs, whereas AE and TDS were negatively correlated with the abundance of several OTUs (Fig. 6 ). Soil composition thus differed significantly as a function of slope position, in turn affecting AMF community diversity and richness."
} | 2,771 |
35479177 | PMC9032199 | pmc | 6,816 | {
"abstract": "Biodegradable natural polymers and macromolecules for transient electronics have great potential to reduce the environmental footprint and provide opportunities to create emerging and environmentally sustainable technologies. Creating complex electronic devices from biodegradable materials requires exploring their chemical design pathways to use them as substrates, dielectric insulators, conductors, and semiconductors. While most research exploration has been conducted using natural polymers as substrates for electronic devices, a very few natural polymers have been explored as dielectric insulators, but they possess high dielectric constants. Herein, for the first time, we have demonstrated a natural polyphenol-based nanomaterial, derived from tannic acid as a low- κ dielectric material by introducing a highly nanoporous framework with a silsesquioxane core structure. Utilizing natural tannic acid, porous “raspberry-like” nanoparticles (TA-NPs) are prepared by a sol–gel polymerization method, starting from reactive silane unit-functionalized tannic acid. Particle composition, thermal stability, porosity distribution, and morphology are analyzed, confirming the mesoporous nature of the nanoparticles with an average pore diameter ranging from 19 to 23 nm, pore volume of 0.032 cm 3 g −1 and thermal stability up to 350 °C. The dielectric properties of the TA-NPs, silane functionalized tannic acid precursor, and tannic acid are evaluated and compared by fabricating thin film capacitors under ambient conditions. The dielectric constants ( κ ) are found to be 2.98, 2.84, and 2.69 (±0.02) for tannic acid, tannic acid-silane, and TA-NPs, respectively. The unique chemical design approach developed in this work provides us with a path to create low- κ biodegradable nanomaterials from natural polyphenols by weakening their polarizability and introducing high mesoporosity into the structure.",
"conclusion": "Conclusions The work describes herein is the first demonstration of creating low- κ dielectric nanomaterials (TA-NPs) from tannic acid by modifying tannic acid's pyrogallol units with alkoxy silane moieties followed by introducing silsesquioxane framework via base-catalysed sol–gel polymerization. Controlling the size and shape of TA-NPs have attempted by varying the molar ratio between the silane precursor and the base. The lowest base concentration has yielded spherical raspberry-like particles with an average diameter of 150 nm. The porosity distribution, confirmed by the BET analysis reveals that nanoparticles are mesoporous in nature with average pore diameter, ranging from 19 to 23 nm and pore volume of 0.032 cm 3 g −1 . Weakening the polarizability and introducing a porous framework into the tannic acid structure allow us to lower the dielectric properties of tannic acid from its bulk κ of 2.98 to 2.69 for the nanomaterials. Thus, the unique chemical design approach developed in this work offers a novel and versatile path to create novel low- κ biodegradable nanomaterials from natural polyphenols. Our future work will focus on fabricating patterned low- κ dielectric platforms using TA-NPs and utilize them in organic field effect transistors.",
"introduction": "Introduction Biodegradable, abundant natural macromolecules and polymers have the ability to reduce the environmental footprint of electronic devices, enabling technologies to interact with nature without leaving long half-life toxic pollutants. They offer a path to mitigate the growth of electronic waste (e-waste) and address the growing demand for flexible electronics. 1–4 Biomass polymers can act as a natural bridge between electronic and soft materials for conducting, semiconducting, and dielectric substrates and offer a vast chemical design space for tunability of electronic, mechanical, and transient properties. 5–8 The tunability of natural soft materials' intrinsic properties is beneficial for the development of complex biodegradable electronics, which have a major impact on the biomedical field, especially in basic research, therapeutics, advanced health monitoring, and drug delivery. 9,10 For such transience devices, numerous biodegradable natural materials have been explored and exhibit practicable electronic properties as substrates, dielectrics, and semiconductors. 5,11–14 However, to overcome their challenges in device compatibility and processing, materials design approaches to natural degradable materials for electronic components, particularly use in semiconductors, electrodes, and dielectrics are required. Among a wide variety of biodegradable materials and engineered processing methods, natural polymers such as cellulose, lignin, and sugars like glucose and lactose are widely research as promising biodegradable dielectrics. 5,12–14 However, their bulk form exhibits high dielectric constants ( κ ), ranging from 6.5 to 17. 12–14 With continuous scaling down of field-effect transistors (FETs), the demand for low- κ dielectric materials have grown with the increased need in faster integrated-circuits (ICs). Increasing transistor speed, reducing transistor size, and packing more transistors onto a single chip improve the ICs performance. 15,16 For FETs, biodegradable dielectric with low-dielectric constant (low- κ ) is desired to obtain a lower capacitance per area, thereby enabling lower operation voltage and reducing power consumption. 5 Capacitance per area is directly proportional to κ and inversely proportional to the insulator thickness of a defect-free film. Thus, intrinsic material properties and processing methods are essential to select a proper dielectric material. In order to use in capacitors and FETs, natural dielectric materials should exhibit polarizable properties by an electric field, as κ depends on the number of polarizable groups in a material and density of the material. 5 A low- κ dielectric materials should possess weak polarization when subjected to an externally applied electric field whereas high- κ materials should exhibit strong polarizable properties. One way to design low- κ dielectric materials is to choose materials with chemical bonds of lower polarizability than Si–O. However, most natural polymers are rich in free hydroxyl groups, contributing to high polarity, resulting in high- κ values. Thus, to design low- κ biodegradable dielectric materials from biomass products, either it is necessary to select natural polymers with virtually non-polar bonds such as C–C or C–H or modify hydroxyl groups to weaken the polarity or create a highly porous structure that can result in lower effective κ 's, contributing the κ of the air, which is equal to unity. By introducing porosity, also one can manage to increase the free volume and as a result it decreases the density of a material. Polyphenols serve as versatile building blocks for the preparation of various functional materials, including porous structures by coordinating to metal ions and tailored polarizable substrates by functionalization of hydroxyl groups with non-polar moieties. Owing to these fascinating structural processability and properties, natural polyphenols show the potential of utilizing as versatile platforms for designing environmentally benign engineered dielectric soft materials and surface functionalized substrates. They can also circumvent the need to use complex architectures and patterns to achieve desired properties for electronic devices due to their high-density functionality and selective chemical reactivity. Besides polyphenols' research advancements as capsules, antibacterial and antioxidant films, micro/nanostructures, membranes, components in FETs and energy storage materials, hydrogels, and cell encapsulants, 17–32 a very little research has been conducted on exploring polyphenols as dielectric platforms to use in ICs. 33 To develop polyphenol-based low- κ dielectric materials, it is necessary to: (1) weaken the polarity by modifying the hydroxyl functionality, (2) introduce porosity into the structure, (3) impart adequate thermal, mechanical, and electrical characteristics, and (4) improve compatibility with the inorganic counterparts of the interconnect structure. Complying with these prerequisites, herein we create a highly nanoporous biodegradable low- κ dielectric nanomaterial from natural tannic acid. Its five pyrogallol and five catechol groups provide multiple bonding sites with diverse interactions, including hydrogen bonds, ionic bonds, coordination bonds, and hydrophobic interactions as well as rich in oxygen sites for selective metal ion binding. 17,18,25–28 In our studies, the porous and robust framework is introduced by randomly functionalizing tannic acid's hydroxyl group (pyrogallol units) with silica network (SiO 1.5 ) followed by sol–gel polymerization to produce spherical nanoparticles. The dielectric properties of nanoparticles are reported by fabricating thin film capacitors. The design approach developed in this work to create low- κ biodegradable dielectric nanomaterials can be broadly applied to other types of polyphenols to produce low- κ dielectric natural soft materials on a larger scale. Despite the utilization of tannic acid in components of FETs and energy storage devices, 20–22,24,34 this work is the first demonstration of creating nanoporous low- κ dielectric nanomaterials from tannic acid.",
"discussion": "Results and discussion In this work, base-catalyzed sol–gel process 35 is applied to create nanoporous tannic acid nanoparticles (TA-NPs). Tannic acid's pyrogallol units can be easily modified with an alkyl halide silane precursor to introduce sol–gel reactive sites. The chemistry of making TA-NPs is depicted in Scheme 1 . Starting from the silane precursor of tannic acid, nanoporous particles with silsesquioxane core structure of tannic acid was prepared by augmenting a sol–gel polymerization method. 35 The synthesis, particle composition, thermal stability, and morphology analysis were performed. The thin film capacitors were fabricated to evaluate dielectric properties of biodegradable nanoparticles and compared with the dielectric properties of tannic acid, tannic acid-silane, and other literature published biodegradable natural polymers, such as cellulose, glucose, and lactose. Scheme 1 Chemistry of making nanoporous tannic acid derived nanoparticles (TA-NPs). TA-silane and TA-NPs syntheses and composition analysis The sol–gel reactive sites were introduced randomly into the tannic acid backbone upon alkylating hydroxyl groups of pyrogallol units with the organosilane precursor using Williamson ether synthesis ( Scheme 1 ). The crude product, tannic acid-silane precursor, (TA-silane) collected by concentrating under vacuum was purified by washing with ethanol followed by de-ionized water to remove unreacted tannic acid and other products. The FTIR analysis was conducted to confirm the successful incorporation of silane units into the tannic acid structure. As depicted in Fig. 1(a) , the FTIR spectrum of TA-silane shows a weak broadening in the range of 3000–3500 cm −1 , confirming the modification of hydroxyl groups with silane units. The presence of characteristics bands for Si–O (1120–1025 cm −1 ) and Si–C (1207 cm −1 ) further confirms the chemical attachment of silane moieties. The ester carbonyls and aromatic C–C stretching vibrations are observed at 1692 and 1602–1511 cm −1 , respectively, confirming the intact structure of tannic acid. The shift in the carbonyl stretching of TA-silane from 1708 cm −1 to 1691 cm −1 compared to tannic acid also evidences the successful incorporation of alkoxy silane units into the tannic acid structure. Fig. 1 (a) FTIR spectra and (b) UV-visible spectra of tannic acid (TA), TA-silane, and TA-NPs. The UV-vis absorption spectrum collected for TA-silane in ethanol is depicted in Fig. 1(b) along with the UV-vis absorption traces of tannic acid and TA-NPs. In general, the tannic acid's absorption spectrum exhibits two absorption maxima at 212 nm and 277 nm with a weak shoulder peak at 240 nm. In comparison to tannic acid absorption, in the TA-silane absorption spectrum, the maximum absorption at 212 nm is disappeared. It also exhibits a growth in the absorption peak at 246 nm, which is ∼6 nm red shifted compared to the shoulder peak of tannic acid at 240 nm. While the absorption at 277 nm was less pronounced compared to that of in tannic acid, the additional absorption peak at 344 nm in TA-silane confirms the functionalization of pyrogallol hydroxy groups with benzyl units of organoalkoxy silanes. The elemental composition analysis along with the XPS survey spectrum of TA-silane depicted in Fig. 2(a) confirms the incorporation of silane units without disrupting the tannic acid glucose core structure. The atomic ratio between Si 4+ to tannic acid is found to be 6 : 1, indexing the TA-silane empirical formula to C 136 H 136 O 64 Si 6 . Fig. 2(b)–(d) shows the binding energy spectra for C 1s, O 1s, and Si 2p, respectively. As shown in Fig. 2(b) , the presence of well-resolved three characteristics binding energy peaks for C 1s at 284.86 eV, 286.58 eV, and 288.88 eV, which correspond to C–C, C–O–C, and O–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 bonding, confirms the modified tannic acid structure. In the O 1s spectrum in Fig. 2(c) , C O, Si–O, and C–O bonds are confirmed from a poorly resolved broad peak with two shoulder peaks at 531.61, 532.54, and 533.22 eV. The FWHM is 4.29 eV, which further supports the oxygen chemical bonding state of O 2− . The binding energies of 531.61 eV and 533.22 eV, which correspond to sp 2 C O and sp 3 C–O confirm the presence of tannic acid core. The binding energy spectrum of Si 2p, depicted in Fig. 2(d) , exhibits one major peak at 102.22 eV along with a weakly resolved shoulder peak at 103.58 eV, which are characteristic to the binding energy for Si–C (organic) and Si–O, respectively, confirming alkoxy silyl units. Fig. 2 (a) XPS survey spectrum and binding energy spectra of TA-silane for: (b) C 1s, (c) O 1s, and (d) Si 2p. Utilizing the previous developed modified Stöber method by our group, 35 TA-NPs were prepared from the base-catalysed direct hydrolysis and condensation of the TA-silane with absence of silica sols as nucleation seeds. In a typical procedure, the silane precursor was added slowly dropwise into a solution of 28% NH 4 OH in an anhydrous ethanol. The drop-wise addition is needed to maintain the homogeneity of the particles' formation. The addition of precursors plays a crucial role in maintaining the uniformity of the size and shape of nanoparticles. After 24 hours of reaction time, particles were collected by centrifugation and repeated washing with water and ethanol to yield a brownish solid. A series of controlled experiments were conducted to adjust the reaction parameters, such as base concentration, solvent volume, and reaction time. The effect of base concentration on the shape and size of the nanoparticles were also investigated by varying the base concentration with respect to the TA-silane precursor. Table 1 summarizes the particle size distribution with respect to the three different base concentrations. The lowest base concentration (8.20 mmol) results considerably uniform spherical particles with the average size of 150 nm. The highest base concentration is also yielded a mix of smaller particles with sizes, ranging from 50–300 nm and larger particles with a wider size distribution from 500 nm to up to ∼1 μm. The particles formed with respect to middle base concentration also exhibit a wider size distribution in the range of 100 nm to 500 nm with a few larger particles in >500 nm <1 μm size range. The base concentration dependent studies suggest that there is no significant effect of the base concentration range that we selected on the particle size distribution. However, 8.20 mmol of base amount yields rather uniform particle size distribution with a considerably smaller size range compared to two higher base concentrations. The effect of base concentration on particles size and shape Trail # 28% NH 4 OH (mmol) Shape and dimension 1 8.20 Spherical raspberry-like particles; size distribution: 50–300 nm; average size −150 nm 2 16.40 Spherical, raspberry-like particles; size distribution: 100–500 nm; average size −300 nm 3 32.80 Oval and spherical shape particles; size distribution ranged from 50 nm–1 μm The structural composition of TA-NPs is characterized by FTIR, UV-vis, and XPS analysis. The respective FTIR spectrum shown in Fig. 1(a) confirms the presence of characteristics bands for Si–O–Si (1120–1025 cm −1 ) and Si–C (1376–1317 cm −1 ) upon the base-catalysed hydrolysis and condensation. The existence of Si–O–Si bonds evidence the formation of silsesquioxane network. The aromatic C C, ester carbonyl (C O), and O–H stretching vibrations are observed at 1589, 1693, and 3590 cm −1 respectively. The hydroxyl stretching at 3590 cm −1 is an indicative of partially condensed residual Si–OH groups, compared to the weak broader peak at 3000–3500 cm −1 in TA-silane precursor. The UV-visible spectrum of TA-NPs collected by dispersing ethanol is depicted in Fig. 1(b) and follows the similar characteristics peaks as of TA-silane, evidencing that the tannic acid core structure and silane-ether linkages between silane units and tannic acid are intact. The elemental composition obtained from the XPS survey analysis confirms the partial hydrolysis and condensation of alkoxy group, yielding silsesquioxane network with suggested empirical formula of C 125 H 109 O 64 Si 6 . The XPS survey graph and binding energy graphs, depicted in Fig. 3 , further reveal the elemental composition, chemical bonding type, and chemical environments of modified tannic acid within the TA-NP's core structure. The binding energies for C 1s and O 1s spectra follow the same trend as the TA-silane precursor, confirming the intact tannic acid core structure. The binding energy spectrum for Si 2p exhibits a slight peak shift with broadening for Si–C bonding environment to higher binding energy, confirming the changes in the chemical environment due to the formation of Si–O–Si network. Fig. 3 (a) XPS survey spectrum and binding energy spectra of TA-NPs for: (b) C 1s, (c) O 1s, and (d) Si 2p. In order to utilize TA-NPs in biodegradable dielectrics, they need to possess adequate thermal stability. Therefore, we investigated the thermal stabilities of TA-NPs and the silane precursor using thermogravimetric analysis (TGA). The TGA graphs (Fig. S1 † ) reveal that first significant weight loss for both TA-NPs and TA-silane take place at 350 °C, suggesting that nanoparticles and silane precursor are thermally stable up to 350 °C. Compared to the TA-silane's TGA curve, TA-NPs exhibits multiple small weight losses, evidencing the degradation of partially hydrolysed alkoxy groups during the polymerization process. Comparing to the thermal stability of tannic acid, which is 240 °C, 36 functionalized tannic acid with silane units and TA-NPs exhibit better thermal stability due to the introduction of silane units and silsesquioxane network into the tannic acid structure. Morphology and porosity analysis of TA-NPs Morphologies of TA-NPs prepared by varying the base concentrations were visualized using SEM and are depicted in Fig. 4 . At lower base concentration, particles are uniform and spherical in shape with a raspberry-like surface morphology ( Fig. 4(a) ). The particles formed at the medium base concentration are also somewhat spherical but average particle diameter is larger than the lowest and the highest base concentration ( Fig. 4(b) ). At the highest base concentration, both significantly larger and somewhat oval in shape particles were formed and exhibit also a raspberry-like surface. TEM images of TA-NPs in Fig. 5(a) reveal the condensed silsesquioxane core structure (dark core) and interparticle large pores, creating mesoporous microstructures. The selective area electron diffraction (SAED) pattern ( Fig. 5(b) ) evidences the crystallinity of particles, confirming the packing of phenyl rings of tannic acid in the framework. The pore size distribution and pore volume, obtained from the N 2 -adsorption isotherm using Barrett–Joyner–Halenda (BJH) analysis, particles are mesoporous, having average diameter of pores ranged from 19 to 23 nm and pore volume of 0.032 cm 3 g −1 . The TEM images shown in Fig. 5(c) and (d) further supports the mesoporous structures within and in-between particles along with a textural nanoporosity within the particle's framework. The isotherms of TA-NPs (Fig. S2 † ) exhibit hysteresis between adsorption/desorption curves that resemble isotherm type IV, indicating that the capillary condensation takes place, resembling to mesoporous materials. Fig. 4 SEM images of TA-NPs prepared with respect to different base concentrations; (a) At 8.20 mmol, (b) 16.40 mmol, and (c) 32.80 mmol. Fig. 5 (a) A TEM image of TA-NPs with large interparticle pores; (b) The SAED pattern of TA-NPs evidencing crystallinity; and (c) and (d) TEM images of TA-NPs showing mesoporous structure. Evaluation of dielectric properties of tannic acid, TA-silane and TA-NPs We hypothesize that creating biodegradable porous framework with silsesquioxane structure may lower the dielectric constant of tannic acid, yielding low- κ bio-based nanomaterials. To test our hypothesis, dielectric properties of tannic acid, TA-silane, and TA-NPs were analyzed and compared each other by fabricating thin film capacitors from material. The dielectric constants were calculated from the test capacitor devices, having the active device area of 4.75 cm 2 and film thickness ranged from 850 nm to 880 nm (Fig. S3 † ). The detailed experimental procedure for the device fabrication is described in the Experimental section. A representative device schematic is shown in the inset of Fig. 6 . The dielectric constants were calculated using the eqn (1) . Repetitive capacitance was measured for each sample at more than 8 to 12 points with high consistency, confirming the film uniformity and continuity throughout the active area of the thin films. Fig. 6 represents comparison graphs of average dielectric constants calculated for tannic acid, TA-silane, and TA-NPs. The average dielectric constant for tannic acid was found to be 2.98 ± 0.02, which is significantly lower than the dielectric constants of cellulose, glucose, and lactose, which range from 6.5 to 17. 5,12–14 Also, this is the first time of reporting the dielectric constant for tannic acid. Comparing to tannic acid's κ , TA-silane yields a slightly lower κ ( κ = 2.84 ± 0.02). As we expected, the κ of TA-NPs is much lower ( κ = 2.69 ± 0.02). Previously, we have demonstrated that the silsesquioxane framework can result mesoporous low- κ polymeric nanomaterials. 35 Similarly, in this work, the silsesquioxane framework and mesoporosity have affected to lower the dielectric constant of tannic acid by creating porous silsesquioxane framework of tannic acid. Fig. 6 The comparison graph for average dielectric constants calculated for tannic acid, TA-silane, and TA-NPs; inset – a schematic diagram of the capacitance device."
} | 5,934 |
30006515 | PMC6045665 | pmc | 6,817 | {
"abstract": "Achieving a desirable combination of solid-like properties and fast self-healing is a great challenge due to slow diffusion dynamics. In this work, we describe a design concept that utilizes weak but abundant coordination bonds to achieve this objective. The designed PDMS polymer, crosslinked by abundant Zn(II)-carboxylate interactions, is very strong and rigid at room temperature. As the coordination equilibrium is sensitive to temperature, the mechanical strength of this polymer rapidly and reversibly changes upon heating or cooling. The soft–rigid switching ability σ, defined as G’ max /G’ min , can reach 8000 when ΔT = 100 °C. Based on these features, this polymer not only exhibits fast thermal-healing properties, but is also advantageous for various applications such as in orthopedic immobilization, conductive composites/adhesives, and 3D printing.",
"introduction": "Introduction Self-healing abilities are an important survival feature in nature, as these properties allow living beings to repair damage when wounded. The self-healing abilities of living beings have inspired scientists to invent various methods for restoring the functionality of damaged materials 1 – 7 . To date, many synthetic polymers have been designed to self-heal by encapsulating healing agents (in microcapsules 8 – 10 or microvascular 11 – 13 networks) or incorporating dynamic covalent bonds (such as alkoxyamine 14 – 16 , disulfide 17 – 21 , boronic ester and boroxine 22 – 24 bonds or bonds formed by Diels-Alder reaction 25 – 27 ) or non-covalent interactions (such as hydrogen bonds 28 – 30 , π–π stacking interactions 31 , 32 , host–guest interactions 33 , 34 , ionic interactions 35 – 37 and metal-ligand interactions 38 – 43 ) into the polymer matrix. However, for most self-healing materials, there is often a trade-off between the mechanical properties and the dynamic healing; strong bonds result in mechanically robust but less dynamic systems, precluding autonomous healing, while weak bonds afford dynamic healing, but yield relatively soft materials. Therefore, it is quite challenging to achieve self-healing in strong and solid-like materials 27 , 29 , 42 . Nature has given us hints to solve this conundrum. Hydrogen bonds are considerably weaker than other interactions, but these weak interactions can form very strong materials in some situations. For example, chitin, which consists of polysaccharides (sugars) assembled through extensive hydrogen bonding, has amazing high mechanical strength and serves as a protective shell (such as lobsters claws, beetle carapaces, and tree bark) for living organism 44 , 45 . As another example, at low temperature, water molecules aggregate into solid ice via ordered hydrogen bonding. The compressive strength of ice can be up to 5–25 MPa over the temperature range −10 °C to −20 °C 46 . Recently, Aida et al. reported a mechanically robust yet readily repairable polymer that was cross-linked by a dense hydrogen bonding network 47 . These phenomena indicate that weak bonds that are sufficiently abundant and arranged in an orderly manner, can lead to materials with excellent mechanical strength. Inspired by nature, herein we describe a design concept that utilizes weak but abundant coordination bonds to achieve rigid and healable materials. The coordination bonds used in our study are weak but still significantly stronger than hydrogen bonds. Therefore, the resulting polymer is very strong (with flexural Young’s modulus as high as 480 MPa) and rigid (with an elongation at break smaller than 4%) at room temperature. The coordination equilibrium is sensitive to temperature; thus, the mechanical strength of our polymer exhibits distinct (as high as almost 4 orders of magnitude in a narrow temperature range (∆T < 100 °C)), fast (within tens of seconds) and reversible change upon heating or cooling. Such features make our polymer applicable in various situations. For example, due to its rapid softening and hardening property, our polymer can be used in orthopedic immobilization to replace traditional plaster casting, and it also has the advantages of being lightweight, removable and recyclable. Our polymer can also be used for 3D printing since it turns into a viscous liquid upon heating to 120 °C and quickly forms a rigid solid upon cooling. With its thermal healing properties, objects made of our polymer using 3D printing can be healed when damaged. We can also obtain large or complex objects with only a small 3D printer by taking advantage of the healing processes of this material. Thus we can combine the advantages of modern 3D-printing processes and traditional brick-and-mortar operation using our materials. Moreover, our polymer can be used to prepare conductive composites/adhesives that are reshapable, healable, and 3D printable.",
"discussion": "Discussion In order to overcome the incompatibility between mechanical rigidity and dynamic healing, we herein proposed a strategy for the design of rigid and healable polymers. By incorporating weak but abundant Zn(II)-carboxylate coordination interactions into a PDMS backbone, a designed polymer, that is very rigid and has a high Young’s modulus (up to 480 MPa) and low elongation-at-break ( < 4%), was obtained. Increasing the temperature shifted the Zn 2+ + PDMS-COO - ↔ Zn 2+ ( − OOC-PDMS) equilibrium toward the dissociated state, making the polymer softer and more fluid. Therefore, the rigid PDMS-COO-Zn polymer became plastic, healable, reconfigurable, and re-processable upon heating. Moreover, the change in the mechanical strength upon heating or cooling is rapid and fully reversible. Based on these features, we further demonstrate that our polymer is advantageous for various applications such as orthopedic immobilization, conductive composites/adhesives, and 3D printing. We believe that the design concepts presented here represent a general approach to the preparation of rigid and healable functional materials, and we envisage that the material reported in this work will be promising for practical applications. The properties of the PDMS-COO-M polymer are highly tunable by varying the content of ionic repeating units, metal-to-ligand molar ratio, and metal ions. Therefore, this system is available for further optimization based on the different requirements of various applications, and such optimizations will be the subject of future studies."
} | 1,597 |
35273458 | PMC8822226 | pmc | 6,818 | {
"abstract": "In the current research landscape, microbiota composition studies are of extreme interest, since it has been widely shown that resident microorganisms affect and shape the ecological niche they inhabit. This complex micro-world is characterized by different types of interactions. Understanding these relationships provides a useful tool for decoding the causes and effects of communities’ organizations. Next-Generation Sequencing technologies allow to reconstruct the internal composition of the whole microbial community present in a sample. Sequencing data can then be investigated through statistical and computational method coming from network theory to infer the network of interactions among microbial species. Since there are several network inference approaches in the literature, in this paper we tried to shed light on their main characteristics and challenges, providing a useful tool not only to those interested in using the methods, but also to those who want to develop new ones. In addition, we focused on the frameworks used to produce synthetic data, starting from the simulation of network structures up to their integration with abundance models, with the aim of clarifying the key points of the entire generative process.",
"conclusion": "CONCLUSION In this review, we have summarized different approaches related with the microbial community network inference problem, specifically from 16S sequencing data. We first introduced several methods based on pairwise association metrics that mainly differ in the strategy of assigning edge significance, in the use of information on the topological structure, and in considering the compositional nature of data. Then, we presented some multivariate models with the aim of estimating the entire interaction structure. The main distinction is between regression-based models, where the estimation of the covariance matrix of real abundances is obtained by solving a Lasso problem, and those that rely on the notion of conditional independence, where the estimation process based on Glasso involves the precision matrix. Other multivariate approaches, on the other hand, infer associations using probability theory. In addition, we presented some methods with the aim of reconstructing the evolution of microbial interactions exploiting the temporal information of the longitudinal data. We saw the concept of local association between time profiles as a metric of cause-effect relationship that can be established in different time-lagged windows. Finally, we have defined the different formulations of models that estimate the overall dynamic community. Another aspect that we have covered is the importance of using simulated data as a necessary evaluation tool in the development of new reliable methods. For this purpose, we provided an overview of some simulation framework used by the methods to generate synthetic count data. We have not only seen how to generate several network structures, but also how to integrate them into the models used to produce synthetic abundance data. Although the developed approaches have shown encouraging results in several applications, further efforts are still needed to ensure greater reliability of the inferred microbial interactions. To achieve this goal, not only new statistical or computational methods, but also a solid and reliable simulation framework must be improved. The lack of direct observation of biological ground truth makes it difficult to validate the many interactions that can arise in a complex community. However, if the method is robust, the results obtained are more trusted, even if in contrast with previously observed biological results. Unfortunately, modeling the biological ground truth is a difficult task to propose, since it is based on hypotheses that may not correspond to the reality of the phenomenon. For this reason, a simulation framework that considers different network structures, different count matrix generation approaches and finally, different parameters is desirable. The main idea is that if the inferred networks are robust not only with respect to the simulation parameters, but also with respect to the different possible biological scenarios. With these two objectives achieved, interaction networks will surely be one of the most useful tools for understanding how to control and manipulate the complex micro-world of the microbiota.",
"introduction": "1. INTRODUCTION The microbiota is the set of population microorganisms such as bacteria, archaea, viruses and unicellular fungi that characterize a specific environment and ecosystem, such as human gut or saliva, environmental microecological niches (such as animals and vegetables), water or soil. This complex micro-world is characterized by several interactions that determine its nature. Mainly, two types of relationships are observed in a bacterial community: microbial and ecological. Microbial interactions refer to a number of different kinds of relationships that occur between different taxa, that can bring a positive (+), negative (-) or neutral (0) effect for each of the two taxa involved: (+, +) Mutualism is a common benefit relationship established between biological species. In some cases, microorganisms cooperate in carrying out the same physiological function, in others, they exchange the metabolic products for the mutual sustenance, as in syntrophy or cross-feeding case. (+, -) Parasitism and Predation are two phenomena related to the survival of an organism subject to host or prey’s life, respectively. Parasitic associations can be observed between host and bacteriophage virus that infects bacteria and archaea for the replication purpose. Bdellovibrio is an example of a predator that attacks other bacteria and feeds on the biomolecules produced. (+, 0) Commensalism occurs when a member of the population benefits from the presence of others, without any advantage or harm to them. In the biodegradation process, commensal bacteria feed on others’ products. (-, 0) Amensalism describes a relationship in which an organism harms another component of the community without positive or negative implications for itself. This type of interaction can happen when the metabolic products of one species alter the environment, making it adverse towards another species. (-, -) Competition takes place when two species inhabiting the same environment vie for a common resource. If the resource is in limited supply and the species niches totally overlap, the weaker competitor will be pushed toward extinction or will undergo a gradual shift toward a different ecological niche. This latter phenomenon is summarized in Gause's competitive exclusion principle. (0, 0) Neutralism indicates the absence or irrelevance of relationships. Ecological interactions, on the other hand, occur between taxa and the environment. As an example, in the human gut, host cells live in symbiosis with the microbiota. Molecular signals from bacteria promote many physiological functions and, in the other direction, host cells secrete metabolites that influence the microbial ecosystem [ 1 , 2 ]. Furthermore, age, lifestyle and diet, influence the local environment through hormone and metabolite secretion, thus, in turn, changing the environment in which bacteria live [ 3 ]. Considering the entire time span from pregnancy to birth up to the first months of life, there are different factors that influence microorganisms transfer between the mother and the children: prenatal factors, such as mother’s diet and lifestyle; the delivery mode, i.e ., Caesarean or natural birth; the first contacts that occur between the mother and the skin or the mucous membrane surfaces of the new-born child [ 4 ]; the type of supply, i.e ., breastfeeding or formula-fed [ 5 ]. Recently, airways or lung microbiota has also aroused interest in the study of the causes related to environmental exposures [ 6 ] or smoking habits [ 7 ]. Hanski et al. [ 8 ] have shown results regarding the impact of biodiversity in the natural landscape on the skin microbiota in relation to the allergic predisposition. Deciphering the complex networks of associations among microbial communities, and between them and the environment, tries to shed light on questions like: “how do microbes interact?”, “how does the environment change the microbial population?”, “what is the effect of external perturbations on microbial dynamics?”. These are the main reasons that guide the study of microbial ecosystems, where the answers are sought by exploiting the information contained in sequencing data. The study of microbe-microbe, environment-microbes and host-microbes interactions is extremely important to understand community organization in relation to the factors that determine biodiversity. In addition, microbial networks could provide a powerful predictive and therapeutic tool in the field of human health. Information on how the community is modified due to an introduced stimulus could allow, for example, to act on the network by means of probiotics to restore the correct composition of the community [ 9 ]. To investigate the complex bacterial communities’ landscape, network theory provides useful tools [ 10 ]. Graphs are frequently used in molecular biology to represent the relationships between entities, the nodes of the network, where edges correspond to some interactions between them. Edges may be directed when they link two nodes asymmetrically, from one to the other, or undirected, when they link two nodes symmetrically. Edges can be weighted if there is a strength score associated with the link between the nodes. Indeed, biological networks describe relationships that are established between different actors involved in physiological processes, such as proteins, genes or biomolecules (Table 1 ). In a microbial community landscape, the nodes of the network represent different members, while edges correspond to some of the previously described interactions that occur between them. The presence of a relationship between taxa is inferred from taxa abundance values, using different reverse engineering approaches [ 11 - 14 ] stemming from network theory. Microbial networks can also contain nodes related to ecological or physiological variables that present significant association patterns with the abundance values of microorganisms. In this review, we will focus on the microbes-microbes interaction networks that shape the microbial community. The aim is to give an overview of the literature of microbial networks reconstruction, providing useful information not only for analysts looking for available methods, but also for researchers interested in developing new ones. In section 2, we introduce one of the most popular sequencing techniques used to produce abundance data, 16S rRNA gene sequencing, and into well-known standard analysis tools; in section 3, we summarize the main literature methods for the reconstruction of microbial interaction networks; in section 4, 5 and 6, we consider the need for benchmarking studies that evaluate the performance of the developed methods, the simulation frameworks used to generate the gold standard and the assessment scores. In section 7, we discuss the limits and challenges still open in the field."
} | 2,815 |
36684391 | PMC9858410 | pmc | 6,820 | {
"abstract": "Abstract This feasibility study aimed to develop a new composite material of aligned glass flakes in a polymer resin matrix inspired by the biological composite nacre. The experimental composite was processed by an adapted method of pressing a glass flake and resin monomer system. By pressing and allowing the excess monomer to flow out, the long axis of the flakes was aligned. The resultant anisotropic composite with silanized and non-silanized glass flakes were subjected to fracture toughness tests. We observed increasing fracture toughness with increasing crack extension (Δ a ) known as resistance curve (R-curve) behavior. Silanized specimens had higher stress intensity K R -Δ a over non-silanized specimens, whereas non-silanized specimens had a much lower Young’s modulus, and higher nonlinear plastic-elastic J R -Δ a R-curve. In comparison with conventional composites, flake-reinforced composites can sustain continued crack growth for more significant extensions. The primary toughening mechanism seen in flake-reinforced composites was crack deviation and crack branching. We produced an anisotropic model of glass flake-reinforced composite showing elevated toughening potential and a prominent R-curve effect. The feasibility of flake reinforcement of dental composites has been shown using a relatively efficient method. The use of a biomimetic, nacre-inspired reinforcement concept might guide further research toward improvement of dental restorative materials.",
"conclusion": "Conclusions This study demonstrates the high aspect-ratio glass flake fillers aligned in a resin matrix, producing a composite material showing anisotropy and increasing R-curve. The methodology employed in the study allowed for the processing of a composite which mimics the structure brick-and-mortar structure of nacre. The flake-reinforced composite deviates advancing cracks providing significant energy-consuming processes for increasing R-curve behavior, though in a different mechanism to that of fibers. This work highlights the case for aligned flake-reinforced composites in dentistry and is a potential way forward in dental restorative material development toward structural biomimetic restorations.",
"introduction": "Introduction The current selection of dental restorative resin composites for computer-aided design and manufacturing (CAD/CAM) continues to expand with advances in technology. While restorative dentistry sets its bounds with mechanical, biological and esthetical demands, it is imperative that materials development does not stagnate. Instead, development should intently move toward replicating the mechanical properties of the natural tissues they aim to restore. As the human enamel is a highly textured structure composed of high aspect-ratio reinforcing units, the reinforcing concept is optimized for intraoral loading and maximum damage resistance. Nacre presents a similar concept that should guide us in development of more naturally inspired reinforcing concepts. At this stage, the three-dimensional replication of structures in the micrometric scale remains too high a feat for current CAD/CAM technologies. However, incremental strides can be taken to ensure progress toward bio-inspired materials. Innovative attempts are seen in polymer-infiltrated ceramic scaffolds [ 1 , 2 ]. While exhibiting Young’s modulus matching with dentin, these infiltrated structures fall short in resembling the structural organization and other critical mechanical behavior seen in natural dental tissues [ 3 ]. Hence, the basic mechanical principles are often overlooked. This includes the required arranged configuration of high aspect-ratio microstructural units responsible for complex crack-particulate interactions. Noteworthy innovation is seen with the inclusion of short glass fibers in dental resin composites [ 4 ]. The fibers offer an efficient means of inducing crack bridging toughening mechanisms similar to those seen in human enamel [ 5 , 6 ]. Prism orientation, especially in the inner region of the enamel layer (called Hunter-Schreger-Bands), account for effective energy absorption and toughening during crack propagation from the surface toward the dentin-enamel junction [ 7 ]. The orientation of high aspect-ratio elongated microstructural units, be it fibers in composites or crystal phases in glass-ceramics [ 8 , 9 ], provide the large-scale anisotropic architecture that leads growing cracks to deflect into high-energy-consuming shear loading (mode-II) states [ 10 ]. The forced redirection of advancing cracks toward unfavorable and tortuous paths are toughening strategies commonly seen in biomineralized materials with hierarchical high-ordered structures such as bone [ 11 ], shells [ 12 ] and dental tissues [ 7 ]. Ultimately, crack advance is met with increased resistance for continued extension. This is a highly aspired material property in synthetic structural biomedical materials graphically depicted as rising Resistance curves (or R-curve ). Materials inspiration has been drawn from the inner layer of mollusk shells or nacre ( Figure 1(a) ). Nacre is composed of ordered interdigitated hexagonal aragonite platelets in a plastic interstitial organic matrix ( Figure 1(b,c) ). They form complex ‘brick-and-mortar’ structures that enable multiple toughening mechanisms to act simultaneously [ 13 ]. Notably, platelets in nacre interlock and slide on each other, bridging the crack wake and providing the energy dissipating processes at the microstructural level ( Figure 1(e,f) ) [ 14 ]. Surface nano-asperities on the platelets ( Figure 1(d) ) contribute by increasing frictional resistance [ 15 ], which adds to the bridging stresses that relieve the critical crack tip stress intensity factor needed for further crack advance. Meanwhile, the development of nacre-like artificial materials produced by a variety of techniques continues in engineering labs. These synthetic materials are capable of steep rising R-curves and reach saturation values for the applied stress intensity factor >8.0 MPa.m 0.5 [ 16–18 ]. Figure 1. Structure of nacre; (a) natural nacre in an abalone shell ( Halitosis iris ); (b,c) aragonite platelets in brick-and-mortar structure; (d) arrows showing the nano-asperities on the surface of platelets providing a rough surface and increasing friction; (e) fracture pattern of nacre showing platelet pull out and bridging as well as crack deviations (arrows), more clearly seen in (f), schematic of nacre failure on the microscopic level. Here we demonstrate the feasibility of producing nacre-like architectures in experimental dental composites, leading to brick-and-mortar structures and substantial R-curve mechanical behavior. Combined with our current understanding of fiber-reinforced composites, we explore the nature of mechanistic processes in succession, from one-directional fibers to two-directional microstructural units. We introduce glass flake-reinforced dental composites for indirect CAD/CAM applications.",
"discussion": "Discussion The principal aim of this research was to develop a new composite material composed of glass flake fillers in a polymer resin matrix inspired by the brick-and-mortar structures of nacre. The method employed in this study allowed pressure input while allowing excess monomer flow. This forced flakes into alignment and increased filler percentage. The resulting composite block was anisotropic and matched dimensions for CAD/CAM processing, expanding the future clinical potential. However, under similar processing pressure and heating protocol, vast differences were seen in silanized and non-silanized specimens. After burnout combustion, the filler weight percentage of silanized specimens was 81.26%, and non-silanized was 27.74%. Stark differences between the filler percentages are attributed to the hydrophobic non-silanized flake surfaces, whereas silanized surfaces increase wettability. The same behavior is also seen in fibers where silanized surfaces are seen to improve surface wetting and chemical adhesion [ 23 ]. Furthermore, it has been suggested the silane provides the mechanism of restrained layer theory, which suggests a balanced stress transfer between the high modulus fiber and resin matrix [ 24 ]. The restrained layer is certainly plausible in the case of flake-reinforced composites combined with flake percentage, responsible for the higher Young’s modulus values in silanized specimens (26.9 GPa) One implication of a lower filler percentage, and consequently, a low Young’s modulus is reflected in the resulting R-curve. In K R -Δ a curves, silanized specimens have a more pronounced curve over non-silanized specimens. However, the inverse is true in J R -Δ a curves, where considerable energy is consumed during bending in non-silanized specimens, resulting in high J el . Remarkably, both silanized and non-silanized specimens can arrest crack growth over longer extensions, especially compared to conventional composites. The included values in Figure 3(a) , taken from Shah et al. [ 22 ], show how conventional composites are able to develop toughening mechanisms starting from the same stress intensity range, but fail to continue the arrest over greater distances. In Figure 3(a) , this is demonstrated for another conventional composite, which allowed controlled experiments up to 700 µm in crack extension, with unstable fracture thereafter. Although conventional composites demonstrate the onset of an R-curve effect, their ability to sustain controllable crack growth and crack arrest is limited to very small crack extensions, limiting the benefits of such behavior. The increasing R-curves in flake-filed composites are more comparable to those of fiber-reinforced composites. Both fibers and flakes are high aspect-ratio microstructural units capable of inducing anisotropy when aligned. The aligned commercial composite with 25 wt.% of fibers and 45 wt.% particulates, has been demonstrated to increase resistance to crack growth significantly. Higher values in fiber-reinforced composites are shown in both K R -Δ a and J R -Δ a curves, even with a lower percentage of fibers than the flake percentage in silanized specimens. This is a consequence of a reinforced matrix system with particulates, as opposed to the unfilled matrix in our experimental specimens. In an aligned fiber-reinforced system, the crack front encounters a fiber aligned perpendicular to load direction. Generally, the mismatch in Young’s modulus and sufficiently weak interfacial bonding forces the crack to tilt out of plane and around the fiber circumference, or along the fiber length as it debonds. With continued crack growth, the debonded fiber will begin to pull out behind the crack wake, continuing its toughening contribution through the frictional surfaces between the fiber and matrix. The generated energy consumption of events over a process zone around the crack tip reduces the crack advancing forces felt at the crack tip. Over greater distances, the length of fibers bridging behind the crack increases until the crack opening is displaced enough for fiber bridges to degrade. The toughening mechanisms in flake-reinforced composites are markedly different. Crack advance in flake-reinforced composites is resisted by greater lateral distances from aligned glass flakes. Crack growth is more tortuous, and cracks deviate alongside the flake-resin interface in almost a ‘step-wise’ pattern ( Figure 4 ). The crack is forced to deviate, meander, and in some cases, evidence of branching was seen. Unlike fibers, flakes do not pull out and bridge behind the wake; a toughening process zone is not seen. Instead, toughness is located at the crack tip through tortuous routes. Additionally, the effects of silanized flakes play an essential role in toughening. The significant contribution of crack deviation is easily redirected through the interface of the flake and matrix. Without silane, this path offers the path of least resistance. Apart from increasing the wettability and packing, the silanization of flakes offers a bonded interface for the growing crack to overcome, toughening the system. Still, as seen in Figure 4 , the interface is weak enough to hinder flake cracking and straight – less energetic – crack trajectories. Current dental restoratives lack intricate architectural designs that can significantly improve mechanical properties. This study showed that by adjusting to high aspect-ratio fillers and aligning the long axis perpendicular to the preferential crack growth plane, anisotropic fracture behavior could be fostered. The increasing R-curve behavior exceeds the benchmark for most dental materials, which do not show any significant toughness mechanisms to arrest advancing cracks. Apparently the toughening effect only accounts for a loading perpendicular to the reinforcement and hence the clinical loading szenario has to be taken into account when placing such a highly texturized material. The experimental composite under investigation however, is a significant step into bio-inspired designs in dentistry. Adopting toughening strategies and principles from natural composites opens new possibilities for developing more biologically oriented materials with competing performance. Future iterations through optimization may include particulate-reinforced resin matrices, different aspect-ratio flake fillers, or even a hybrid of fibers and flakes to take advantage of multiple toughening strategies. Material development - as shown here – aims to improve mechanical resistance against clinical fracture. On the other side, material selection by the dentist and acceptance by the patient is also highly influenced by the esthetic appearance and might decide for the economic success of a final product. Whatever innovative dental material befalls us should continue to take a similar path of biomimetic strategies."
} | 3,473 |
37264438 | PMC10233912 | pmc | 6,821 | {
"abstract": "Background 2-Pyrone-4,6-dicarboxylic acid (PDC), a chemically stable pseudoaromatic dicarboxylic acid, represents a promising building block for the manufacture of biodegradable polyesters. Microbial production of PDC has been extensively investigated, but low titers and yields have limited industrial applications. Results In this study, a multi-step biosynthesis strategy for the microbial production of PDC was demonstrated using engineered Escherichia coli whole-cell biocatalysts. The PDC biosynthetic pathway was first divided into three synthetic modules, namely the 3-dehydroshikimic acid (DHS) module, the protocatechuic acid (PCA) module and the PDC module. Several effective enzymes, including 3-dehydroshikimate dehydratase for the PCA module as well as protocatechuate 4,5-dioxygenase and 4-carboxy-2-hydroxymuconate-6-semialdehyde dehydrogenase for the PDC module were isolated and characterized. Then, the highly efficient whole-cell bioconversion systems for producing PCA and PDC were constructed and optimized, respectively. Finally, the efficient multi-step biosynthesis of PDC from glucose was achieved by smoothly integrating the above three biosynthetic modules, resulting in a final titer of 49.18 g/L with an overall 27.2% molar yield, which represented the highest titer for PDC production from glucose reported to date. Conclusions This study lays the foundation for the microbial production of PDC, including one-step de novo biosynthesis from glucose as well as the microbial transformation of monoaromatics. Supplementary Information The online version contains supplementary material available at 10.1186/s13068-023-02350-y.",
"conclusion": "Conclusions In this study, an efficient multi-step biosynthesis method for the microbial production of PDC was demonstrated by smoothly integrating a DHS module, a PCA module and a PDC module. The final PDC titer achieved was 49.19 g/L with a molar yield of 27.2%, which represented the highest titer for PDC production from glucose reported to date. In addition, this work also provides several effective enzymes for the microbial biosynthesis of PDC. Although an efficient multi-step biosynthesis of PDC with a high titer has been achieved, the microbial production process tends to be cumbersome. Future studies in this direction can, therefore, focus on the application of metabolic engineering technologies in an attempt to construct PDC-overproducing E. coli cell factories based on the WJ060 strain as well as the above effective enzymes, especially in view of achieving a one-step production of PDC with higher titers or yields from glucose. Besides, if a higher PDC biosynthesis rate or titer could be achieved, the rate of PCA uptake or PDC efflux could become a new limiting factor. Therefore, the mining PCA- and PDC-specific transporters could be of great significance to the construction of efficient microbial cell factories.",
"discussion": "Results and discussion Establishment of a highly efficient whole-cell biocatalytic system for the conversion of PCA into PDC In biocatalytic processes, different buffers can significantly affect reaction efficiency. Hence, to achieve efficient whole-cell conversion of PCA into PDC, the effects of different reaction buffers on the biocatalytic processes were first examined. In this case, the bioconversion was achieved with LigAB and LigC (Fig. 1 A), both derived from S. paucimobilis SYK-6 and widely used in the study of PDC biosynthesis [ 16 , 22 , 24 , 26 ]. E. coli BL21 (DE3) cells co-expressing the ligABC gene cluster were used as whole-cell biocatalysts. Under shaking flask conditions, approximately 98% of PCA was exhausted within 3 h in M9 medium (M9, pH 7.0), and the titer of PDC reached 5.74 g/L after extending the process to 9 h (Fig. 1 B, C). The resulting PDC was also analyzed by GC–MS (Additional file 1 : Fig. S1). However, when the reaction was performed in 100 mM of sodium phosphate buffer (PB, pH 7.0), the reaction rate and the PDC titer were significantly lower than those of M9 (Fig. 1 B, C). Compared with PB, the M9 medium also contains glucose and NH 4 Cl, hence suggesting that adding small amounts of carbon and nitrogen sources could be helpful for maintaining the catalytic activities of whole-cell biocatalysts and improving the substrate conversion ratio. On the other hand, when the reaction was conducted in 100 mM of Tris–HCl buffer (pH 7.0), the reaction rate was comparable to that of M9 for 1.5 h before decreasing rapidly (Fig. 1 B, C). This result could be attributed to the small size and the diol-like structure of Tris which could have contributed to its ability to bind and occupy the active site of LigAB, thereby resulting in a mild inhibitory effect [ 30 ]. Therefore, for the subsequent experiments, M9 medium was selected as the reaction buffer for the whole-cell bioconversion. Fig. 1 Whole-cell bioconversion of PCA into PDC. A Scheme of bioconversion of PCA into PDC catalyzed by LigAB and LigC. Time courses of PCA ( B ) and PDC ( C ) concentrations catalyzed by LigABC biocatalyst in different reaction buffers. The reactions were conducted in 100 mL shake flasks with 10 mL of 100 mM sodium phosphate buffer (PB, pH 7.0) or 100 mM Tris–HCl (pH 7.0) or M9 medium (pH 7.0), 5 OD 600 of whole-cell biocatalyst, 5 g/L PCA and incubated at 37 °C and 250 rpm. All data and standard errors were derived from three independent biological replicates Screening protocatechuate 4,5-dioxygenase and CHMS dehydrogenase for the efficient conversion of PCA into PDC Based on sequence analysis, the reported ligABC [ 22 ] and pmdABC [ 31 ] exist in the form of gene clusters on chromosomes, and therefore, the entire coding sequence of the three genes can be obtained from any one of them. In this case, the protocatechuate 4,5-dioxygenase α subunit LigA and PmdA were selected as query sequences for a BLAST search against the protein database of NCBI. The results showed that most of the protocatechuate 4,5-dioxygenases exist in Alphaproteobacteria and Betaproteobacteria , with a small fraction also present in Gammaproteobacteria and Actinobacteria . Based on the sequence identities, a total of 86 sequences were selected together with LigA and PmdA to draw the phylogenetic tree, with the number of sequences derived from Alphaproteobacteria , Betaproteobacteria , Gammaproteobacteria and Actinobacteria being 38, 37, 6 and 5, respectively. Through phylogenetic analysis, the proteins clustered within their own classes in the tree according to their evolutionary characteristics (Fig. 2 ), thus indicating that the protocatechuate 4,5-dioxygenases are highly conserved in different clades. A total of 16 sequences (numbered 1–16) were then evenly selected from the tree, and included 1ABC-6ABC from Alphaproteobacteria , 7ABC-12ABC from Betaproteobacteria , 13ABC and 14ABC from Gammaproteobacteria as well as 15ABC and 16ABC from Actinobacteria (Fig. 2 ). By aligning their genome sequences, the entire coding sequences for all ABC gene clusters were finally obtained (Additional file 1 : Table S2). Fig. 2 Phylogenetic tree of the protocatechuate 4,5-dioxygenase α subunits from different bacteria. A total of 86 sequences derived from Alphaproteobacteria , Betaproteobacteria , Gammaproteobacteria and Actinobacteria were selected to draw the phylogenetic tree, and 16 of them (numbered 1–16 and labeled with green circles) were evenly selected for the subsequent studies. The reported LigA and PmdA were labeled with red circles. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test are shown next to the branches The genes of ligAB and ligC derived from different organisms were first cloned into the pRSFDuet-1 plasmid (Additional file 1 : Table S2). After that, these plasmids were transformed into E. coli BL21 (DE3) cells, and protein expression was carried out in shake flasks to obtain whole-cell biocatalysts that were named 1ABC-16ABC, LigABC and PmdABC. When analyzing the bioconversion processes, these whole-cell biocatalysts displayed differences in their ability to convert PCA into PDC, especially in terms of their conversion ratios and reaction rates (Additional file 1 : Table S3). In particular, the PDC titers of 2ABC, 3ABC and LigABC were up to 6.0 g/L with approximately 100% of molar yields at 12 h. In addition, the reaction rate of 2ABC was comparable to that of LigABC, while that of 3ABC was slower compared with LigABC (Fig. 3 A, B and Additional file 1 : Table S3). Unexpectedly, the reaction rates of 14ABC and 15ABC were significantly faster than that of LigABC, with less than 5% of PCA remaining in the medium at 3 h. However, as the reaction continued, the final titer of PDC was only 5.2 g/L for 14ABC or 15ABC with an 87% molar yield, which was significantly lower than those of LigABC or 2ABC (Fig. 3 A, B and Additional file 1 : Table S3). This result suggested that only part of the intermediate CHMS generated by 14AB or 15AB was converted into PDC. It has been reported that in addition to being converted into PDC, CHMS can also spontaneously generate 2,4-pyridine dicarboxylic acid through the ammonia cyclisation reaction in the presence of NH 4 Cl [ 19 , 32 , 33 ]. Thus, avoiding the accumulation of intermediate CHMS by slowing down the reaction catalyzed by 14AB or 15AB (protocatechuate 4,5-dioxygenase), or by increasing the activity of 14C or 15C (CHMS dehydrogenase) may be helpful for further improving the conversion ratio. Fig. 3 Whole-cell bioconversion of PCA into PDC catalyzed by different ABC whole-cell biocatalysts. Time courses of PCA ( A ) and PDC ( B ) concentrations catalyzed by different biocatalysts. The reactions were conducted in 100 mL shake flasks with 10 mL M9 medium, 5 OD 600 of whole-cell biocatalyst, 5 g/L PCA and incubated at 37 °C and 250 rpm. E. coli BL21(DE3) strain transformed with pRSFDuet-1 plasmid was used as the negative control (CK). All data and standard errors were derived from three independent biological replicates Optimizing reaction conditions for efficient conversion of PCA into PDC Changes in temperature and pH can significantly affect the progress of enzymatic reactions. Therefore, these parameters were optimized for the whole-cell biocatalytic reactions. To confirm the optimum temperature, the reaction was performed in the range of 30 °C to 40 °C. With the increase in temperature from 30 to 37 °C, the reaction rate gradually accelerated, but no significant differences in the rates of PCA consumption and PDC synthesis were observed between 37 and 40 °C within 3 h (Fig. 4 A, B). Since the higher temperature (40 °C) was not conducive to maintaining cell viability, 37 °C was selected as the optimum temperature for subsequent whole-cell biocatalytic studies. Fig. 4 Effects of temperature and pH on whole-cell bioconversion of PCA into PDC. Time courses of PCA ( A ) and PDC ( B ) concentrations catalyzed by 14ABC biocatalyst at different temperatures. The reactions were conducted in 100 mL shake flasks with 10 mL M9 medium, 5 OD 600 of whole-cell biocatalyst, 5 g/L PCA and incubated at 250 rpm. C Time courses of PDC production catalyzed by 14ABC biocatalyst in the pH range of 5.5 to 7.0. The reactions were conducted in 5-L bioreactors at 37 °C with 1 L M9 medium and 30 OD 600 of whole-cell biocatalyst. Approximately 10 g/L PCA was supplemented into the bioreactor at one time when the DO level rose. All data and standard errors were derived from three independent biological replicates To confirm the optimum pH, whole-cell biocatalytic reactions were first conducted in the pH range of 5.0 to 8.0 in shake flasks. With increasing pH, the reaction efficiency first increased before subsequently decreasing. In particular, at a pH of 5.5, the fastest reaction rate and highest PDC titer were achieved (Additional file 1 : Fig. S2A, B). However, unexpectedly, when the reaction was conducted in a 5-L bioreactor at this pH, a final titer of only 1.07 g/L was obtained for PDC, even though the biocatalyst load reached 30 OD 600 (Fig. 4 C). This discrepancy in outcomes between shake flasks and the bioreactor prompted us to reconfirm the optimum pH for conversion in bioreactors. In this case, when the pH increased from 5.5 to 7.0, the reaction efficiency initially increased before decreasing, as initially observed. However, the optimum pH for the reaction was changed from 5.5 to 6.5, and the final titer of PDC reached 28.39 g/L, with this value being significantly higher than that of other pH conditions (Fig. 4 C). In phosphate buffers, the optimum pH for protocatechuate 4,5-dioxygenase and CHMS dehydrogenase was reported to be 7.5 and 8.0, respectively, even though the CHMS dehydrogenase was most stable at pH 6.5–7.0 [ 30 , 34 , 35 ]. These results showed that a pH of 6.5 was more conducive for the whole-cell bioconversion process on a large scale. In biocatalytic process requiring two different catalytic enzymes, individual reaction rates may restrict one another. The best reaction efficiency can, therefore, be achieved only when the activities of the two enzymes are reasonably complemented. Highly efficient conversion of PCA into PDC on a large scale To achieve efficient conversion of PCA into PDC on a large scale, whole-cell bioconversion was performed in a 5-L bioreactor. For this experiment, a fed-batch fermentation was first carried out to prepare enough biocatalysts of 2ABC, 14ABC and LigABC. After induction with 0.2 mM IPTG and subsequent growth for an additional 6 h, the 2ABC, 14ABC and LigABC strains reached an OD 600 of 61.6, 50.8 and 59.0, respectively (Additional file 1 : Fig. S3). By analyzing bioconversion processes, the PDC titers of the fresh biocatalysts were approximately five-fold higher than those obtained from the frozen biocatalysts whenever the reactions were conducted at pH 5.5 or 6.5 (Additional file 1 : Fig. S2C). At the end of the fed-batch fermentation, the cells were harvested and directly used as whole-cell biocatalysts for subsequent bioconversion processes. Since the oxidative ring-opening reaction of PCA, catalyzed by protocatechuate 4,5-dioxygenase, is an O 2 -dependent one [ 30 ], the DO level can be used to indicate the residual amount of PCA. In addition, because PCA possesses strong cellular toxicity that hampers both cell growth and intracellular metabolism [ 36 ], a batch feeding strategy was adopted to avoid the influence of high concentrations of PCA on the catalytic activity of the whole-cell biocatalysts. In this case, with each rise in DO levels, approximately 10 g/L PCA was supplemented into the bioreactor to maintain the PCA concentration at a relatively low level throughout the bioconversion process (Fig. 5 A–C). This strategy of batch feeding the PCA can in fact be crucial for efficient conversions. By analyzing whole-cell bioconversion processes, 2ABC was significantly better than 14ABC and LigABC in terms of both reaction rate and PDC titer. When excess PCA was provided, a final PDC titer of 53.46 g/L and a molar yield of 95.2% were obtained for 2ABC with a productivity of 7.64 g/L/h (Fig. 5 A). Under similar conditions, the PDC titer for LigABC was only 49.18 g/L, which was 8.7% lower than that of 2ABC, while the productivity was 7.03 g/L/h (Fig. 5 C). The reaction rate of 14ABC was comparable to that of LigABC within 3 h, but it subsequently decreased rapidly to reach a final PDC titer of only 32.83 g/L, a molar yield of 75.8% as well as a productivity of 4.69 g/L/h (Fig. 5 B). These results showed that 2ABC was the most efficient whole-cell biocatalyst for the conversion of PCA into PDC on a large scale. Fig. 5 Whole-cell bioconversion of PCA into PDC on a large scale. Time courses of the PDC production catalyzed by the biocatalysts of 2ABC ( A ), 14ABC ( B ) and LigABC ( C ). The reactions were conducted in 5-L bioreactors with 1 L M9 medium and 30 OD 600 of whole-cell biocatalysts at 37 °C and pH 6.5. Approximately 10 g/L PCA was supplemented into the bioreactor at one time when the DO level rose (indicated with black arrows). The error bars represent the standard deviation of the three replicates, and the DO curve is the result of one run Efficient multi-step biosynthesis of PDC from glucose Although efficient production of PDC has been achieved using pure PCA as substrate, it is economically unfeasible for industrial scale production due to the high costs associated with PCA. Alternatively, the use of cheap glucose as substrate could significantly reduce the production cost. Since PCA possesses strong cellular toxicity [ 36 ], it is difficult for E. coli to produce high titers of this compound directly from glucose. In our previous work, a DHS-overproducing E. coli strain WJ060 was successfully constructed through systematic metabolic engineering [ 29 ]. Tto achieve efficient whole-cell bioconversion of DHS into PCA, the reaction efficiencies of five different 3-dehydroshikimate dehydratases, derived from Klebsiella pneumoniae (AroZ) [ 37 ], P. putida GB-1 (PpQuiC), Acinetobacter baylyi ADP1 (AbQuiC), Microbacterium foliorum (MfAsbF) and Alteromonas macleodii (AmAsbF), were then compared and analyzed. Using DHS broth as the substrate, the cells expressing the above 3-dehydroshikimate dehydratases could efficiently convert DHS into PCA, with those expressing AbQuiC exhibiting the highest catalytic efficiency (Fig. 6 A). Indeed, with a conversion ratio of 99.0% (mol/mol), AbQuiC was selected for whole-cell bioconversion in follow-up studies. Fig. 6 Whole-cell bioconversion of DHS into PCA. A Bioconversion of DHS into PCA catalyzed by 3 OD 600 of biocatalysts expressing different 3-dehydroshikimate dehydratases. E. coli BL21(DE3) strain transformed with pET-30a plasmid was used as the negative control (CK). B Time courses of the conversion ratio catalyzed by 0.2 OD 600 of AbQuiC biocatalyst in the pH range of 5.5 to 8.0. C Time courses of the conversion ratio catalyzed by AbQuiC biocatalyst with different loads at pH 7.0. The reactions were conducted in 100 mL shake flasks with 60 g/L of DHS and incubated at 37 °C and 250 rpm. All data and standard errors were derived from three independent biological replicates Subsequently, the optimum pH and biocatalyst load for the above reaction was determined. It has been reported that 3-dehydroshikimate dehydratase has an optimal pH within alkaline range [ 38 – 40 ]. In this study, the cells expressing AbQuiC exhibited the highest reaction efficiency at pH 7.0 and pH 7.5, while at a pH lower than 6.5, the conversion ratio decreased significantly (Fig. 6 B). Given that DHS is chemically unstable under alkaline conditions, the subsequent whole-cell bioconversion for AbQuiC was, therefore, carried out at pH 7.0. The amount of whole-cell biocatalysts loaded into the reaction is also an important factor that influences reaction efficiency and cost feasibility. With an increase in the biocatalyst load (0.1 to 2.0 OD 600 ), the reaction rate increased gradually, with a molar yield of 91.1% obtained when AbQuiC cells had an OD 600 of 2.0. However, when the reaction time was extended to 6 h, the conversion ratio of cells with an OD 600 of 0.5 was also equal to that of cells with an OD 600 of 2.0 (Fig. 6 C). Based on the above results, the PDC biosynthetic pathway was divided into three synthetic modules, namely the DHS module, the PCA module and the PDC module, all of which account for the bioconversion of glucose to DHS, DHS to PCA and PCA to PDC, respectively (Fig. 7 A). First, the rationally designed WJ060 strain was grown in minimal medium, and through fed-batch fermentation, it produced 78.28 g/L of DHS from glucose with a molar yield of 31.2%. In addition to DHS, a small amount of by-product GA (4.37 g/L) was also detected in the fermentation broth (Fig. 7 B). Second, the high concentration of DHS (78.28 g/L) in the broth was effectively converted into PCA (74.35 g/L) at pH 7.0 using AbQuiC biocatalyst with an OD 600 of 2.0. During the whole bioconversion process, the amount of GA in the fermentation broth did not change significantly (Fig. 7 C). Finally, the PCA broth was directly used as substrate to produce PDC using the batch feeding strategy. In this case, the conversion of bio-based PCA into PDC was conducted in a bioreactor using cells expressing 2ABC (named PsLigABC) as the biocatalyst. After bioconversion for 16 h, the final PDC titer reached 49.19 g/L with a molar yield of 101.1% (Fig. 7 D). When calculating the production of PDC using glucose as the substrate, the molar conversion ratio was up to 27.2%. In addition to PCA, the protocatechuate 4,5-dioxygenase can also catalyze the oxidative ring-opening of GA to generate 4-oxalomesaconate (OMA), with the latter’s further dehydration into PDC catalyzed by PDC hydrolase (LigI) [ 18 , 41 ]. The GA by-product in the fermentation broth was also completely consumed, suggesting that GA might be converted into PDC, catalyzed by PsligAB and other intracellular hydrolases with similar functions to LigI, to achieve a molar yield of higher than 100%. If the conversion of the GA by-product was also taken into account, the molar yield decreased to 95.8% (PCA + GA). Fig. 7 Multi-step biosynthesis of PDC from glucose. A Scheme of the multi-step biosynthesis of PDC from glucose. EMP Embden–Meyerhof–Parnas pathway, PPP pentose phosphate pathway, PEP phosphoenolpyruvate, E4P \n d -erythrose 4-phosphate, DAHP 3-deoxy- d -arabino-heptulosonate-7-phosphate. B Biosynthesis of DHS from glucose by the WJ060 strain through fed-batch fermentation. C Bioconversion of fermentative DHS into PCA catalyzed by 2 OD 600 of AbQuiC biocatalyst. The reaction was conducted in a 5-L bioreactor at 37 °C and pH 7.0. D Bioconversion of the PCA broth into PDC catalyzed by 2ABC (PsLigABC) biocatalyst. The reaction was conducted in a 5-L bioreactor with ~ 30 OD 600 of whole-cell biocatalyst at 37 °C and pH 6.5. Approximately 15 g/L PCA was supplemented into the bioreactor at one time when the DO level rose (indicated with black arrows). The error bars represent the standard deviation of the three replicates, and the DO curve is the result of one run In this study, the efficient biosynthesis of PDC was achieved using a well-designed whole-cell biocatalytic system. When pure PCA was used as substrate, the 2ABC biocatalyst produced 53.46 g/L PDC within 7 h, with a molar yield of 95.2% (Fig. 5 A and Table 1 ). Johnson et al. [ 19 ] used engineered P. putida as the biocatalysts and achieved the highest PDC titer (58 g/L) reported to date from 4-hydroxybenzoate, with a molar yield of 80.7%. However, the conversion time was as long as 288 h. When glucose was used as substrate, a final PDC titer of 49.19 g/L was achieved using an optimized multi-step biosynthesis system, and interestingly, this represented the highest titer for PDC production from glucose reported to date (Fig. 7 and Table 1 ). However, due to the low conversion ratio of the DHS module (31.2%), the final molar yield from glucose was only 27.2%. It is, therefore, speculated that a one-step production of PDC from glucose in subsequent studies would yield a significantly improved conversion ratio. Table 1 Production of PDC by engineered strains Strain Substrate Cultivation Titer (g/L) Yield Time (h) References P. putida PpY1100 with pDVABC PCA 5-L bioreactor; fed-batch ~ 11 – 36 [ 22 ] E. coli BL21(DE3) with pRSF-2ABC PCA 5-L bioreactor; fed-batch 53.46 0.952 mol/mol 22 (15 + 7) This study P. putida KT2440 Δ pcaGH Δ crc with pSEVA631-ligABC p -Coumarate 5-L bioreactor; fed-batch 22.7 1.0 mol/mol 110 [ 16 ] N. aromaticivorans DSM12444 Δ ligI Δ desCD Vanillin and vanillate 0.25-L bioreactor; fed-batch 4.9 0.103 mol/mol 48 [ 20 ] P. putida KT2440 Δ pcaHG ::Ptac: ligABC (CJ251) 4-Hydroxybenzoate 2.5-L bioreactor; fed-batch 58 0.81 mol/mol 288 [ 19 ] P. putida PpY1100 with pVapoligVABC Vanillin and vanillate derived from lignin Bioreactor; fed-batch 9.21 (50 mM) – 18 [ 23 ] P. putida PpY1100-dHG with pJFVV2AB and pDVZ21X Bamboo extract 1-L bioreactor; fed-batch 8.7 0.936 mol/mol 24 [ 42 ] P. putida KT2440 KT2440 Δ pcaHG ::Ptac: ligABC Δ vanAB ::Ptac: vanAB HR199 Equimolar mixture of syringate, p -coumarate, and ferulate Shake flask 0.79 (4.3 mM) 0.93 mol/mol 24 [ 21 ] E. coli PCA strain and E. coli PDC PCA strain Terephthalic acid derived from PET waste Shake flask 0.57 (3.11 mM) 0.99 mol/mol 6 [ 24 ] E. coli BL21(DE3) harboring three plasmids (pACYC-aroF fbr -aroB, pCDF-ubiC-pobA, and pFT-ligABC-qutC) Algae hydrolysate and glucose Shake flask 1.22 0.161 24 [ 25 ] E. coli GYT1 with pTacFABC and pBBR1G fbr -EcA Glucose 6.6-L bioreactor; fed-batch 16.72 0.201 g/g 96 [ 27 ] E. coli WJ060, E. coli BL21(DE3) with pET30a-AbquiC and E. coli BL21(DE3) with pRSF-2ABC Glucose 5-L bioreactor; fed-batch 49.19 0.272 mol/mol 66 This study"
} | 6,258 |
31306566 | null | s2 | 6,823 | {
"abstract": "The autoinducer-2 (AI-2) quorum sensing system is involved in a range of population-based bacterial behaviors and has been engineered for cell-cell communication in synthetic biology systems. Investigation into the cellular mechanisms of AI-2 processing has determined that overexpression of uptake genes increases AI-2 uptake rate, and genomic deletions of degradation genes lowers the AI-2 level required for activation of reporter genes. Here, we combine these two strategies to engineer an Escherichia coli strain with enhanced ability to detect and respond to AI-2. In an E. coli strain that does not produce AI-2, we monitored AI-2 uptake and reporter protein expression in a strain that overproduced the AI-2 uptake or phosphorylation units LsrACDB or LsrK, a strain with the deletion of AI-2 degradation units LsrF and LsrG, and an \"enhanced\" strain with both overproduction of AI-2 uptake and deletion of AI-2 degradation elements. By adding up to 40 μM AI-2 to growing cell cultures, we determine that this \"enhanced\" AI-2 sensitive strain both uptakes AI-2 more rapidly and responds with increased reporter protein expression than the others. This work expands the toolbox for manipulating AI-2 quorum sensing processes both in native environments and for synthetic biology applications."
} | 324 |
31767843 | PMC6877613 | pmc | 6,824 | {
"abstract": "At high cell density, swimming bacteria exhibit collective motility patterns, self-organized through physical interactions of a however still debated nature. Although high-density behaviours are frequent in natural situations, it remained unknown how collective motion affects chemotaxis, the main physiological function of motility, which enables bacteria to follow environmental gradients in their habitats. Here, we systematically investigate this question in the model organism Escherichia coli , varying cell density, cell length, and suspension confinement. The characteristics of the collective motion indicate that hydrodynamic interactions between swimmers made the primary contribution to its emergence. We observe that the chemotactic drift is moderately enhanced at intermediate cell densities, peaks, and is then strongly suppressed at higher densities. Numerical simulations reveal that this suppression occurs because the collective motion disturbs the choreography necessary for chemotactic sensing. We suggest that this physical hindrance imposes a fundamental constraint on high-density behaviours of motile bacteria, including swarming and the formation of multicellular aggregates and biofilms.",
"introduction": "Introduction When the cell density of a suspension of swimming bacteria increases, collective motion emerges, characterized by intermittent jets and swirls of groups of cells 1 – 3 . This behaviour is observed for many microorganisms not only in artificial but also in natural situations, often at an interface, e.g., when bacteria swarm on a moist surface in the lab 4 – 8 or during infection 9 , or at an air-water interface during formation of pellicle biofilms 1 , 10 . Bacterial collective motion has been extensively studied experimentally 11 – 14 and theoretically 15 – 20 , and it is known to emerge from the alignment between the self-propelled cells 21 . Two alignment mechanisms have been proposed, based either on steric interactions between the rod-like bacteria 22 – 24 or on the hydrodynamics of the flow they create as they swim 15 , 17 , which displays a pusher force dipole flow symmetry 3 , 25 , 26 . However, the relative importance of these two mechanisms has not been clearly established so far 27 . Bacterial collective motion contrasts to the behaviour of individual motile cells in dilute suspension, when bacteria swim in relatively straight second-long runs interrupted by short reorientations (tumbles), resulting at long times in a random walk by which they explore their environment 28 . Bacteria can furthermore navigate in environmental gradients by biasing this motion pattern: they lengthen (resp. shorten) their runs when swimming toward attractive (resp. repulsive) environment 28 . The biochemical signalling pathway controlling this chemotactic behaviour is well understood in E. coli 29 , 30 and it is one of the best modelled biological signalling systems 31 . Bacteria monitor—via their chemoreceptors—the changes in environmental conditions and respond to them by modifying a phosphorylation signal transmitted to the flagellar motors to change the tumbling frequency accordingly 32 , 33 . In E. coli , attractant substances repress the phosphorylation signal, which results in prolonged runs, repellents having the opposite effect. An adaptation module slowly resets the receptor sensitivity for further stimulations, via a negative feedback loop 34 , 35 . This effectively allows the cell to compare its current situation to the recent past while swimming along a given direction 30 , with a memory time scale of a few seconds 36 , 37 . Notably, the rotational Brownian motion of the cell body interferes with this mechanism of sensing by randomly changing the direction of swimming while temporal comparisons are performed 28 , 29 . Although typically seen as a single-cell behaviour, chemotaxis also drives collective behaviours based on chemical interactions, such as autoaggregation 38 , 39 , self-concentration in patches 40 , 41 and travelling band formation 42 , 43 , where the chemotactic response to self-generated gradients of chemoattractants leads to local cell density increases. However, very little is known about how the high-density physical interactions and the resulting collective motion influence the chemotactic navigation of bacteria 44 , 45 : for example, it is unclear whether chemotaxis would be improved by alignments of convective flows with the gradient 19 , 45 or instead compromised by random collisions between cells. This lack of knowledge is in part due to the technical difficulty of measuring the dynamics of cells in a dense suspension 20 . Over the last few years, new image analysis methods have been developed or adapted to bacterial systems 2 , 46 – 49 , which exploit intensity fluctuations 46 or intensity pattern shifts 49 to characterize swimming and chemotaxis in populations of bacteria. These Fourier image analysis methods function at arbitrarily high cell densities, capture the dynamics of all cells without bias, and are at least as fast as the more commonly used particle tracking techniques. In this paper, we use Fourier image analysis methods to investigate how the collective motion developing with increasing cell density affects the ability of E. coli populations to follow controlled chemical gradients in a microdevice. Our experimental results and computer simulations show that, after increasing up to a maximum at intermediate densities, chemotaxis is strongly reduced as collective motion developed in the sample. Collective reorientations act similarly to an active rotational diffusion interfering with the chemosensing mechanism. Additionally, the characteristics of the collective motion are consistent with hydrodynamic interactions being the primary driver of its emergence, additional steric effects being important but secondary. These results have important implications for collective behaviours of motile bacteria at high density.",
"discussion": "Discussion Although the principles of bacterial chemotactic sensing are fairly well understood for a single cell 29 , 31 , little was known about the effects of physical interactions between cells on chemotaxis, despite their frequent occurrence during self-concentration processes 38 – 43 and high-density collective motility 4 , 5 , 10 . While the physical properties of the collective motion, e.g. in a swarming colony, are largely unaffected by activity of the chemotaxis pathway 58 , 60 , 61 , the reverse is not necessarily true. Here, we thus used suspensions of E. coli in a controlled environment as a model system to investigate the effect of collective motion, emerging when cell density increases, on chemotactic sensing. We observed that the size of bacterial flow structures in fully developed collective motion is set by the smallest system size, the channel height, independently of cell length and volume fraction, and that an increasing amount of the kinetic energy of the system gets poured into this flow structure as the cell density increases. This property strongly suggests that hydrodynamics plays the primary role in the emergence of the collective motion, since, in the Hele-Shaw geometry of our system, the channel height sets the reach of the hydrodynamic interaction 8 , fundamentally because of viscous friction on its top and bottom walls. Previous works also predicted that the largest possible flow structure—set by system size—dominates in hydrodynamics-based collective motion 15 , 62 . Also consistent with previous simulations of such motion 63 is the observed reduction of the total kinetic energy at fixed \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Phi }_{{\\rm{c}}}$$\\end{document} Φ c but under increased confinement. Interestingly, similar flow properties are observed for the hydrodynamics-based collective flows during sedimentation of dense suspensions of passive particles 64 , 65 . Consistantly, our numerical simulations show that considering hydrodynamic interactions is key to reproduce the main experimental features, including the dependence of the vortex size on channel height, and its independence of cell length. Although the simulated vortex size is smaller than in the experiments, such shifts were previously observed on other quantities for this type of model 58 . Notably, besides the channel height, hydrodynamic interactions also set another characteristic length in our system, the hydrodynamic dipole length. This might explain the apparent saturation of the vortex size at about \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$20\\ \\upmu$$\\end{document} 20 μ m in the experiments, when the channel height approaches the estimated dipole length ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sim {\\!}6\\ \\upmu$$\\end{document} ~ 6 μ m). Physical interactions between cells at high densities result in a strong reduction and ultimately in the abolishment of the specific ability of E. coli to track chemical gradients, and thus of the chemotactic drift, despite the moderate increase in swimming speed due to collective entrainment. The collective motion is the driver of this decrease, with its amplitude \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E({q}_{{\\rm{str}}})$$\\end{document} E ( q str ) being the sole determinant of reduced chemotactic efficiency. Collective motion acts directly on the mechanism of gradient-sensing since the gradient itself is little affected. Our analysis based on agent-based simulations suggests that this reduction is induced by an increased reorientation rate of cell bodies due to the collective motion, whether it emerges from steric or hydrodynamic interactions, which in this sense acted similarly to an active rotational diffusion. Importantly, we conclude that Eq. 2 derived for non-interacting swimmers can describe the chemotactic drift well at all densities, provided that the time scale of rotational diffusion is set to account not only for Brownian but also for interaction-induced reorientations. A similar reduction of chemotaxis by forced cell reorientations could be expected in other contexts, such as cells swimming in circles near surfaces 66 or during migration through a porous medium where cells collide with obstacles 67 , 68 . In contrast to the inhibition at high densities, the chemotactic drift is enhanced between low and intermediate densities. Although the nature of this increase remains to be elucidated, it is unlikely to result from self-attraction 38 , 39 or other chemical effects, as its extent depends on the degree of confinement and on cell length. In the simulations, a similar enhancement is observed when only steric interactions are considered—but not with the full model including hydrodynamic interactions, although it is not clear whether the nature of the transient enhancement in those simulations is the same as in the experiments. This discrepancy, along with the other quantitative differences between experiments and simulations, could potentially be explained by a number of factors our hydrodynamic simulations do not account for, such as collisions with the top and bottom channel walls and other physical effects neglected by two-dimensional confinement, flagellar entanglements and fluid flows affecting the flagellar bundles stability 69 , as well as the point force approximation. The observed regulation of chemotactic behaviour through physical interactions among motile cells has several important consequences for bacterial high-density behaviours. First, it provides a physical mechanism that might regulate chemotactic accumulation of bacteria near sources of chemoattractants (e.g., nutrients), because gradually increasing cell density 70 would initially promote and subsequently limit the process. Indeed, this effect could explain why the density of cells entering a capillary filled with chemoattractant saturates as a function of the cell density in the suspension 71 . The density for which the chemotactic drift is maximal, \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Phi }_{{\\rm{c}}}\\simeq 0.01$$\\end{document} Φ c ≃ 0.01 , which should play a cut-off role, is indeed the typical maximal cell density reached within travelling chemotactic bands which form through a self-generated gradient 42 , 43 . Thus, the hitherto neglected effects of physical interactions should be taken into account when describling these phenomena, in conditions for which the density gets high. Second, the observed strong reduction in chemotactic drift at cell densities typical of swarming ( \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Phi }_{{\\rm{c}}} \\sim 0.30$$\\end{document} Φ c ~ 0.30 ) 5 suggests that, without specific counteracting mechanisms, chemotactic navigation of bacteria swimming within a swarm is nearly impossible, consistent with recent indications that the swarm expansion rate is set by the cell growth rate rather than motility 8 . Interestingly, we observed that cell elongation, one of the major hallmarks of swarming bacteria 4 , indeed improved chemotaxis at high \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Phi }_{{\\rm{c}}}$$\\end{document} Φ c under moderate confinement. However, it appeared to have little effect under stronger confinement expected in the swarm. Bacterial swarming was already known to be unaffected by the lack of functional chemotactic sensing 60 , 61 . Although more prominent steric interactions within a swarming colony 7 , 8 might potentially improve tracking of gradients at high density, as could other differences in swimming behaviour 72 , 73 , or additional cohesive interactions 44 caused by cell differentiation in a swarm, our results suggest that the emergence of swirling collective motion fundamentally undermines the chemotactic behaviour."
} | 3,832 |
37463898 | PMC10354067 | pmc | 6,826 | {
"abstract": "Conductive hydrogels require tunable mechanical properties, high conductivity and complicated 3D structures for advanced functionality in (bio)applications. Here, we report a straightforward strategy to construct 3D conductive hydrogels by programable printing of aqueous inks rich in poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) inside of oil. In this liquid-in-liquid printing method, assemblies of PEDOT:PSS colloidal particles originating from the aqueous phase and polydimethylsiloxane surfactants from the other form an elastic film at the liquid-liquid interface, allowing trapping of the hydrogel precursor inks in the designed 3D nonequilibrium shapes for subsequent gelation and/or chemical cross-linking. Conductivities up to 301 S m −1 are achieved for a low PEDOT:PSS content of 9 mg mL −1 in two interpenetrating hydrogel networks. The effortless printability enables us to tune the hydrogels’ components and mechanical properties, thus facilitating the use of these conductive hydrogels as electromicrofluidic devices and to customize near-field communication (NFC) implantable biochips in the future.",
"introduction": "Introduction Electronics, such as brain-computer interfaces, are one of the most important materials for connecting electronic functionality and biological systems 1 – 4 . In particular, flexible bioelectronics have attracted extensive attention owing to their tremendous potential in disease treatment, such as Parkinson’s or amyotrophic lateral sclerosis 5 – 8 . Relative to most conventional rigid electronics that are physically and mechanically dissimilar to biological tissues, conductive hydrogel electronics offer both favorable electrical properties and ideal interfaces with the tissues, eliminating immunological responses or the electrochemical instability arising from severe mechanical mismatch 9 – 11 . To mimic the mechanical properties of a wide range of biological tissues from ultrasoft to stiff (with elastic modulus values ranging from 0.1–500 kPa) 12 , conductive hydrogels are required to form mechanically compliant interfaces without compromising their electronic performance. Additionally, the prevailing fabrication of conducting hydrogels has mostly relied on conventional manufacturing techniques for 2D patterns 13 – 15 . To broaden the applications of conductive hydrogels, a transformation of customized hydrogel (bio)electronics from traditional 2D thin films to shape-conformable, integrated 3D structures is in process 16 – 20 . Among the investigated conductive polymers used to make electrically conducting hydrogels, poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) has many advantages owing to its biocompatibility, water solubility, intrinsic electronic conductivity and commercial availability 21 – 24 . With the aid of 3D printing technology that has emerged in recent years, PEDOT:PSS-based hydrogels with 3D microscale structures have been fabricated 25 . Reported examples of 3D PEDOT:PSS hydrogels either rely on inkjet extrusion of conducting polymer inks layer by layer 16 , 26 , 27 or the employment of light-based 3D printing where lasers or light-emitting diodes (LEDs) initiate spatial photopolymerization in liquid 28 – 33 . Unfortunately, the inject extrusion method has a specific rheological requirement for the employed polymer inks, which brings difficulties in ink preparation and thus makes it difficult to selectively tune the mechanical properties of the formed gel. In the light-based 3D printing process, the light-induced curing of a prepolymer or monomer mixture first forms an inert hydrogel matrix. Then, the conductive components are introduced in the hydrogel system by blending PEDOT:PSS with hydrogel-forming precursors before curing 30 , 31 or by in situ polymerization of EDOT within the resulting hydrogel matrix 32 , 33 . Such disconnected aggregates of the conductive polymer network result in a high number of open circuits, making both strategies suffer from low conductivity (0.01–2.2 S m −1 ), even with a relatively high PEDOT:PSS content 34 . Additionally, constructing hydrogels with 3D overhanging structures does not accommodate soft objects in the absence of auxiliary support. Liquid-in-liquid 3D printing is a promising approach that allows for the freeform fabrication of soft materials 35 . In this technique, the ink phase is extruded into a bath phase while a translation stage moves according to a 3D design, shaping the soft material into complex structures. However, suppressing Rayleigh-Plateau instabilities and minimizing the deformation of the extruded ink in the bath phase remain significant challenges in this process. Unlike embedded extrusion 3D printing, which requires specific support bath phases with shear-thinning or solid-fluid transition capabilities 36 , 37 , another liquid-in-liquid printing technology utilizes self-assembly 38 , jamming 39 , and coacervation 40 to stabilize the filament extruded from the jet nozzle. This approach partly eliminates the need for rheological properties in both ink and support bath phases. Despite these advantages, reports on the use of liquid-in-liquid 3D printing for PEDOT:PSS conductive hydrogels are rare. While some cases of fibrillary gelation of PEDOT:PSS by injection into coagulation baths 40 – 42 have been reported, the printing method still requires strict matching of ink concentration, support bath components, and extrusion rate due to the limited ability to overcome Rayleigh-Plateau instabilities. Although considerable efforts have been devoted to 3D printing conductive polymers, constructing 3D hydrogels with tunable mechanical properties, high conductivity, a broad selection of ink materials, and complicated structures remains challenging. To address these limitations, we describe a liquid-in-liquid 3D printing process for the production of highly conductive PEDOT-based hydrogels with tunable stiffness and arbitrary structures (Fig. 1 ). Relying on the interfacial jamming of PEDOT:PSS–PDMS surfactants (PPSs) at the immiscible aqueous and oil-liquid interface, this process provides compelling routes to trap the aqueous inks into a nonequilibrium and 3D programmable shape and physically and/or chemically convert the liquid ink into the hydrogel state inside the defined profile. As such, the mechanical stability of the PEDOT:PSS–surfactant elastic film enables us to implement PEDOT:PSS physical gelation to form a loose gel interpenetrated by a secondary and robust polymer network. Conductivities up to 301 S m −1 are achieved for a low PEDOT:PSS content of 9 mg mL −1 . Importantly, the printable PEDOT:PSS inks are highly versatile, covering a broad range of PEDOT:PSS concentrations (in this study, the investigated concentration is 0.1–20 mg mL −1 ) and viscosity values (the corresponding viscosity of aqueous inks ranges from 6.5–23000 mPa·s, with inks of higher viscosity also being applicable). Therefore, the mechanical properties of the cured hydrogels can be orthogonally controlled by tuning the ink formulation and cross-linking conditions. Given the printed hydrogels’ compatibility with biological systems, such easy printability and designability would allow for the fabrication of stretchable hydrogels without compromising conductivity, multimaterials integrated with 3D hydrogels, electrochemical devices and, notably, conductive hydrogels for implantable chips. Fig. 1 Liquid-in-liquid 3D printing of hydrogels with tunable morphology, mechanical properties and conductivity. a Schematic of the 3D printing of PEDOT:PSS-based aqueous threads in an oil. PEDOT:PSS–PDMS surfactants self-assemble at the liquid-liquid interface, forming an elastic wall that allows the liquid ink architecture to maintain integrity for the subsequent treatments. b Schematic of the soft PEDOT device for wireless sensing and simulation. c Photographic images showing the liquid-in-liquid 3D print with and without interfacial PPSs assembly. The scale bar is 3 mm. d Photographic images showing the cured hydrogels with different stiffnesses. The scale bar is 3 mm. e The combination of high elasticity and conductivity of the hydrogel allows effective current transmission under arbitrary deformation. The scale bar is 1 cm.",
"discussion": "Discussion This is an effective attempt to facilely construct highly conductive hydrogels with tunable mechanical properties and 3D architecture by using liquid-in-liquid 3D printing technology. Polymer inks based on aqueous PEDOT:PSS capable of simultaneously forming PEDOT:PSS colloidal particles and PDMS surfactants assemblies at the liquid-liquid interface are successfully printed into arbitrary 3D structures without additional templates or supports. Additionally, the mechanically stable interfacial assembly allows these all-liquid architectures to be extensively manipulated, such as through ionic liquid-induced gelation and UV curing. This PEDOT:PSS-based ink exhibits broad applicability for various hydrogel-forming precursors and 3D printability over a wide range of PEDOT:PSS concentrations, solution pH values and, most importantly, apparent viscosities. With this capability, we successfully printed stretchable hydrogels (subjected to >200% strain) without compromising their conductivity, integrating multiple materials into advanced 3D objects and fabricating conductive tubular hydrogels for electrosynthesis. The PEDOT gel-based NFC chips enable wireless power harvesting through biological tissues, offering a possible route for electrical signal transmission while preserving the designed 3D architecture and matched mechanical properties when applied to human tissue engineering. Although there may be a lower resolution at the current stage and the long-term stability of the electrodeposited PEDOT:PSS on bioelectrodes in a wet physiological environment needs to be further improved, the ability to spatially program the electronic conductivity, mechanical properties and material functionality of a hydrogel in three dimensions makes this liquid-in-liquid 3D printing technique highly promising for integrating conductive gels into flexible and implantable bioelectronics, directing both matter and energy across biotic-abiotic interfaces."
} | 2,563 |
35889697 | PMC9317797 | pmc | 6,827 | {
"abstract": "Microbial electrosynthesis (MES) can sustainably convert CO 2 to products and significant research is currently being conducted towards this end, mainly in laboratory-scale studies. The high-cost ion exchange membrane, however, is one of the main reasons hindering the industrialization of MES. This study investigates the conversion of CO 2 (as a sole external carbon source) to CH 4 using membraneless MES inoculated with anaerobic granular sludge. Three types of electrodes were tested: carbon cloth (CC) and CC functionalized with Cu NPs, where Cu NPs were deposited for 15 and 45 min, respectively. During the MES experiment, which lasted for 144 days (six cycles), methane was consistently higher in the serum bottles with CC electrodes and applied voltage. The highest CH 4 (around 46%) was found in the second cycle after 16 days. The system’s performance declined during the following cycles; nevertheless, the CH 4 composition was twice as high compared to the serum bottles without voltage. The MES with Cu NPs functionalized CC electrodes had a higher performance than the MES with plain CC electrodes. Microbial profile analysis showed that the Methanobacterium was the most dominant genus in all samples and it was found in higher abundance on the cathodes, followed by the anodes, and then in the suspended biomass. The genus Geobacter was identified only on the anodes regarding relative bacterial abundance at around 6–10%. Desulfovibrio was the most dominant genus in the cathodes; however, its relative abundance was significantly higher for the cathodes with Cu NPs.",
"conclusion": "4. Conclusions This study investigated the conversion of CO 2 (as a sole external carbon source) to CH 4 using membraneless MES inoculated with AGS. Three types of electrodes were independently tested: CC and CC electrodes functionalized with Cu NPs deposited for 15 min and 45 min, respectively. The experiment took place in six cycles for a total of 144 days. The methane was around 2.5 times higher in the serum bottles with CC electrodes with voltage than in serum bottles without voltage. The highest CH 4 (around 46%) was found in the second cycle after 16 days. However, the system’s performance declined during the following cycles probably because of low hydrogen generated from the abiotic process and therefore low methane. The MES with CC electrodes containing Cu NPs exhibited a higher performance than the MES with plain CC electrodes. This is likely due to H 2 generation by electrodes that contain Cu NPs and because of the higher electrical conductivity of those electrodes that probably facilitate the microbial reactions. This difference was more pronounced after the second cycle. During the first cycle, the serum bottles with electrodes and voltage generated a significant amount of H 2 (41–48%). However, in the following cycles, the H 2 was significantly lower. Acetic acid was mainly detected in the first cycle, whereas in the last cycles, propionic acid was the primary carboxylic acid identified in all serum bottles. The efficiency was relatively low for all conditions. However, the efficiency of serum bottles with CC and Cu NPs was higher than for the serum bottles with CC only. The energy recovery efficiency decreased with increasing input voltage from 1 to 2 V for serum bottles. Methanobacterium was the most dominant genus in all samples, and it was detected in higher abundance on the cathodes, then on the anodes, and then in the suspended biomass. The genus Geobacter was identified only in the anodes regarding relative bacterial abundance at around 6–10%. Desulfovibrio was the most dominant genus in the cathodes; however, its relative abundance was significantly higher for the cathodes with Cu NPs.",
"introduction": "1. Introduction Carbon dioxide (CO 2 ) seriously affects the environment by contributing to the rise in the Earth’s surface temperature; 2020 was one of the hottest years in recorded history and extreme weather events occurred more frequently [ 1 ]. International treaties such as the Paris Agreement (with 196 signatories) show anthropogenic climate change as a worldwide public concern. Nations agreed on carbon trading and taxation as one way to view the economics of emissions reduction and society anticipates solutions by scientists and engineers with regards to CO 2 emissions. However, CO 2 is in a high oxidation state (+4) and is considered stable, so highly reducing conditions are required for its reduction. Several promising technologies have already been proposed that are mainly based on physicochemical methods but require extreme conditions, high energy, or expensive materials [ 2 ]. A generally cost-effective and environmentally friendly route for reducing CO 2 , by converting it into other useful organics, is microbial electrosynthesis (MES) [ 3 , 4 ]. MES utilizes anaerobic microbes as biocatalysts for the electricity-driven conversion of CO 2 into products such as methane or volatile fatty acids (VFAs) under mild conditions [ 5 ]. The conversion of CO 2 into CH 4 takes place at the cathode via a biocatalytic reaction that involves electrochemically active microorganisms. The principles of MES in CO 2 -capturing and conversion have been outlined in several recent reviews [ 6 , 7 ]. In most studies for the conversion of CO 2 to CH 4 , the MES systems used double chamber geometries, with the anode and cathode compartments separated by either an anion or cation exchange membrane. The membrane allows separate optimization of the cathode-catholyte and anode-anolyte. The cathode chamber can be inoculated with anaerobic pure chemolithoautotrophic bacteria or enriched with mixed methanogens or homoacetogens. In contrast, the anode chamber is either inoculated with unspecific exoelectrogens or maintained abiotic. The ion exchange membrane prevents oxygen produced at an aerobic anode from diffusing into an anaerobic cathode chamber and inhibits the anaerobic microorganisms in the cathode chamber [ 8 ]. In optimizing electromethanogenesis, reducing its cost, and potentially scaling-up the process, several reactor modification attempts have been employed over the years, the most notable of which relate to the removal of the ion exchange membrane and the modification of the used electrodes. The ion exchange membrane is in fact one of the main reasons hindering the industrialization of this process due to its high cost. It creates several technical challenges such as lowered mass transfer rate associated with membrane fouling, increased internal resistance and considerable voltage losses [ 8 , 9 ]. In addition, the ion exchange membrane contributes to a relatively large distance between electrodes in a double-chambered reactor which is another cause of higher ohmic losses [ 5 , 8 ]. Considering the total cost of reactor fabrication, ≈50–60% of the cost is due to the membrane and membrane maintenance cost [ 10 ]. A membraneless MES reactor, in which the CO 2 -containing gas sparged from the bottom of the reactor such as to prevent oxygen diffusion from the anode towards the cathode, was examined by Giddings et al. [ 9 ] for the production of acetic acid using Sporomusa ovata . A membraneless MES was also reported for biomethane production from CO 2 , achieving a generation rate of 4.7 L/ (m 2 ∙d) [ 8 ]. The second advancement front relates to the development of novel MES electrodes with enhanced properties. According to a recent review [ 7 ], the insufficient electrocatalytic ability of electroactive microorganisms is one of the main bottlenecks restricting the further large-scale application of MES. Research on electrodes is constantly advancing, and a plethora of new materials with excellent performance and breakthrough technologies are emerging that can play a vital role in developing MES at a larger scale. In general MES electrodes should be conductive, chemically inert, biocompatible, and with a high surface area to maximize interaction with the methanogenic bacteria. The materials of choice were traditionally metals, carbon-based and, more recently, metal–carbon hybrids. According to recent studies, conductive carbon materials can stimulate direct or indirect interspecies electron transfer (DIET) in co-culture and the anaerobic digestion process by accelerating rates of anaerobic metabolism [ 11 ]. In addition, Kim et al. [ 12 ] reported that DIET-mediated methanogenesis in MES might be further improved by adding transition metals to carbon electrodes. These metals on the electrode’s surface can serve as highly efficient conduits based on their intrinsic electric properties and catalysts for electromethanogenic reactions. The NPs can improve local electric conductivity while simultaneously reducing the electron transfer activation energy threshold at the surface of the electrodes. Several transition metals and noble catalysts have already being studied such as platinum (Pt), gold (Au), nickel (Ni), palladium (Pd), and iridium (Ir) [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Copper (Cu), which also possesses catalytic characteristics and is a lower-cost alternative compared to the abovementioned metals, has not been used in its particle form. Only two bulk-form applications have been detected: Aryal et al. [ 22 ] synthesized a cathode by reducing graphene oxide coated on copper foam, and it was used for the microbial electrosynthesis of acetate from CO 2 , whereas Thatikayala and Min [ 23 ] developed copper ferrite/reduced graphene oxide nanocomposites using the bio-combustion method. They used this as a cathode catalyst in the microbial reduction of CO 2 to volatile fatty acids (VFAs) in a single chamber MES. This study examines the conversion of CO 2 (as a sole external carbon source) to CH 4 using membraneless MES inoculated with AGS, where novel carbon cloth (CC) electrodes functionalized with copper nanoparticles (Cu NPs) at two different concentrations were used. The MES was inoculated with anaerobic granular sludge (AGS) to prevent the electrolytic oxygen produced on the anode to inhibit anaerobic microorganisms and especially methanogens. According to Tartakovsky et al. [ 24 ], micro-aerobic conditions did not prevent methane production at a laboratory Upflow Anaerobic Sludge Blanket (UASB) treating synthetic wastewater. Since the anaerobic granules are typically larger than 500 μm in diameter and the oxygen penetration depth does not exceed 50 μm, the electrolytic oxygen might be consumed in the outer biofilm layer of granular sludge and does not inhibit methanogens that are positioned in the core of the AGS. The tolerance of methanogens in anaerobic granular sludge to oxygen and their coexistence with facultative bacteria were also previously reported by Kato et al. [ 25 , 26 ]. The novel CC and CC-Cu NPs electrodes synthesized and used within this study were characterized before and after use, and the microbial profile in the cathode and anode biofilm in the AGS was examined at the end of the experiment.",
"discussion": "3. Results and Discussion 3.1. CC Electrodes Functionalized with Cu NPs Figure 1 shows SEM images at various magnifications of the plain CC (a-d) used for the production of electrodes utilized in the MES systems developed within this study. The 90 degrees weaved structure is evident, generated from micron scale fibers, providing a robust material with a significant surface area for interaction with the microorganisms of the AGS in the MES system. The copper nanoparticles deposited in the case of CC-Cu electrodes are shown in the high magnification image (×100,000) in Figure 1 e. More precise geometrical characteristics of the deposited Cu NPs are quantified through AFM measurements. The atomic structure of this material was probed using a Raman Microscope which is an important tool for characterizing carbon allotropes. The spectra for plain CC are shown in Figure 2 with three characteristic peak intensities at 1335 cm −1 , 1580 cm −1 , and 2700 cm −1 that correlate with the D, G and 2D bands of carbon respectively. The D band relates to the disorder of carbon materials, while the G band is primarily an in-plane vibrational mode of sp 2 hybridization states, whereas the 2D band is an overtone of the D band, indicative of long range order. A D band intensity greater than the G band suggests a material with disordered dominance which relates to a moderate mechanical response and moderate electrical conductance carbon, consistent with the manufacturer’s specifications. Consistent with the literature the metallic nanoparticles do not introduce any additional peaks in the Raman spectra. The CC fabrics were functionalized with Cu NPs in an attempt to enhance the interaction between the microorganisms and the electrodes during the MES process. NPs deposited using the Nanogen source had a Gaussian particle size distribution around a mean of 6 nm in diameter, as measured with the MESOQ filter and presented in Figure 3 a. In order to ensure the proper production and deposition of NPs, AFM images of NPs deposited on a silicon wafer were collected and analyzed. Figure 3 b shows an indicative AFM of Cu NPs deposited for 15 min with a cross sectional representation (inset) verifying that the NPs diameters are primarily in the range of 5–8 nanometers. To ensure the proper deposition and effective functionalization of the CC electrodes with Cu NPs, plain CC and CC decorated with Cu NPs (CC-45minsCu) were probed using an energy dispersive X-ray spectrometer. Figure 4 shows the X-ray spectra of the two materials that verify the existence of Cu NPs in the latter case, where Cu peaks are detected in their respective characteristic energies with an estimated atomic percentage of copper on the order of 0.04 at.%. Beyond carbon and copper one can see traces of Si, S and Ca, which are probably residuals of the CC material. Al is related to the stubs used for sample holders. 3.2. MES System Performance Figure 5 a shows the methane production (%) over time at the different serum bottles. During the experiment (six cycles), the methane was consistently higher at the serum bottles with CC electrodes with applied voltage. The highest CH 4 (around 46%) was found at the second cycle after 16 days. However, the system’s performance declined during the following cycles, nevertheless the CH 4 % composition was almost doubled compared to the serum bottles without voltage. The CC electrodes with Cu NPs and voltage exhibited higher methane production than plain CC with voltage for cycles three to six. At the first two cycles, no significant difference was identified for these electrodes; however, their performance was higher than that of the samples without voltage. The serum bottles with CC electrodes (without voltage) showed a higher performance than the control. This is in line with the study of Feng et al. [ 34 ], that showed that the addition of conductive carbon cloth is a feasible strategy to enhance AD performance through the stimulation of DIET in a mixed culture. However, solid evidence for microorganisms participating in DIET is lacking, and the mechanism of the shift from IHT to DIET remains unknown [ 34 ]. The H 2 composition over time is presented in Figure 5 b. During the first cycle, the serum bottles with electrodes and voltage generated a significant amount of H 2 (41–48%). At the beginning of the second cycle, the serum bottle with the voltage generated around 2.5%. At the following days until the end of the experiment, the H 2 was consistently lower than 1%. During the first cycles, it is likely that the cathode potential is low enough to produce water reduction from the electrodes. In addition, at this stage the biofilm was likely not yet developed on the surface of the electrode, resulting in the accumulation of H 2 . Apart from this, during the first cycles it is likely that the iron rods of the CC electrodes were oxidized and generated H 2 . Subsequently, the H 2 was utilized by hydrogenotrophic methanogens and homoacetogens to produce methane and acetic acid, respectively. During the first cycle, the rate of H 2 production was likely higher than the rate of H 2 utilization by hydrogenotrophic microorganisms. However, in the following cycle, the hydrogenotrophic methanogens were enriched and the hydrogen production rate declined, and as a result, no substantial hydrogen was identified. Therefore, the high methane in the first cycles it is likely due to abiotic production of H 2 followed by its utilization by hydrogenotrophic microorganisms. After the first cycle, siderite was created on the surface of iron rod, and this probably blocked further oxidation of iron and subsequent hydrogen generation. However, after the first cycles, despite the increase in the voltage, the oxidized products have not contributed further to H 2 production. The high percentage of hydrogenotrophic methanogens in all the samples from MES system is in line with the findings from this section. In order to better investigate the interaction between microorganisms and electrodes SEM and XRD investigations were performed on the extracted electrodes after they have been used in the MES systems. Figure 6 shows SEM images at two different magnifications for all electrodes used in this study after ~5 months of operation in the MES systems. It is evident that as the Cu NPs content increases on the CC surface the microbial/deposition layer increases, suggesting a more favorable interaction in the presence of Cu NPs. Various microbes attached on the CC surface can be observed at higher magnifications. Furthermore, in order to detect any crystalline phases that probably exist on the surfaces of those materials, we performed XRD. Figure 7 shows an indicative diffraction pattern (we have observed similar trends for all electrodes) after the electrodes were used in the MES system and in interaction with the anaerobic microbes suggesting that the dominant biocorrosion crystalline product of the process is the iron carbonate phase of siderite (FeCO 3 ). The availability of carbon is a result of the metabolic pathway of converting CO 2 to CH 4 and part of it appears to be chemically forming siderite. 3.3. VFAs As shown in Figure 8 a–d, in the first cycle on day 36, the serum bottles exposed to the voltage generated a higher concentration of volatile fatty acids than the other samples. On day 36, the serum bottles exposed to voltage has a higher propionic acid concentration that than the acetic acid and this could be due to the relatively high hydrogen that was produced on the first cycle. It is likely at these conditions, the rate of hydrogen production from the electrolysis was higher than the rate of hydrogen utilization by hydrogenotrophic methanogens or homoacetogens, and this has not facilitated the propionic acid biodegradation that requires low hydrogen partial pressure [ 27 ]. In the next cycles, the acetic acid was the dominant VFAs; however, its concentration was below 100 mg L −1 probably due to its utilization by acetoclastic methanogens or due to the uptake of H 2 at a faster rate by hydrogenotrophic methanogens than the homoacetogens. 3.4. Energy Efficiency The average current density in each cycle and the energy efficiency of the MES system are pointed out in Table 3 and in Figure 9 . As shown in Table 3 , the current for the serum bottle with copper electrodes (CC-45minCu-Volt) is relatively constant, despite the lower current in this higher methane production. This is in line with the higher energy efficiency in this system. The same trend but with a higher fluctuation in current was found for the serum bottle (CC-15min-Volt). The serum bottle with CC-Volt had relatively higher current density compared with the other serum bottles, especially in the first two cycles; however, the produced CH 4 was not high probably due to higher energy losses. This is an indication that the main mechanism for methane production is through hydrogen production and not through DIET, this is in line with the microbial findings that the hydrogenotrophic methanogens was the dominant genus. However, more research can be conducted to elucidate the mechanism in this system. The energy efficiency of the MES system is pointed out in Figure 9 . The efficiency was relatively low for all conditions, less than 2%, more likely due to the absence of proton exchange membrane and the high internal resistance of the system. However, the efficiency of serum bottles with CC and Cu NPs was higher than the serum bottles with CC only. For serum bottles the energy recovery efficiency decreased with increasing input voltage from 1 to 2 V. According to Wang et al. [ 32 ] increasing the applied voltage, although it was able to increase the current density, increased the internal resistance of the system, which resulted in increased energy loss. Yuan et al. [ 35 ] pointed out that increased CH 4 production in an MEC-AD reactor would decrease the electric energy consumption around 20-fold, while the economic benefits of increased CH 4 production could fully cover the input power cost. 3.5. Microbial Profile Analysis The relative microbial abundance of archaea for the three anodes, cathodes and suspended biomass is shown in Figure 10 . The Methanobacterium was the most dominant genus in all samples, and it is found in higher abundance on the cathodes, then on the anodes, and then in the suspended biomass. Methanospirillum was the second most abundant genus and is following the same trend as Methanobacterium —high relative abundance in the cathodes, lower abundance on the anodes and was slightly present in the suspended sludge. Methanobacterium and Methanospirillum are hydrogenotrophic methanogens and utilize the hydrogen produced in the cathodes. The results align with Siegert et al. [ 36 ], who reported cathodes’ attachment mainly by the genus Methanobacterium while studying methane production in acetate-fed MECs systems. In addition, Gatidou et al. [ 33 ] and Li et al. [ 37 ] found a high relative abundance for Methanobacterium on the electrodes at a microbial electrolysis cell compared to the control. On the other hand, Methanosaeta (obligate acetoclastic methanogens) was most dominant in the suspended sludge (35–39%), followed by high abundance in the anode (6–18%) and was little present in the suspended sludge. Methanosaeta is a prominent microbial group responsible for methanogenic granule formation [ 38 ]. Previous work with MECs using iron rod electrodes also revealed a high abundance of Methanosaeta in suspended sludge compared to the electrode surface [ 33 ]. Based on these findings a tentative conclusion can be stated that methane is mainly generated by hydrogenotrophic methanogens from the electrodes and through acetoclastic methanogens from the suspended AGS. The microbial profile of bacteria at the genus level can be seen in Figure 11 . The genus Geobacter was identified only in the anodes regarding relative bacterial abundance at around 6–10%. Geobacter is an electroactive bacterium; previous research found that Geobacter dominated the anode microbial community [ 32 , 39 ]. Geobacter performs DIET, which has a higher electron transfer efficiency than anaerobic digestion’s electronic respiratory transfer chain [ 32 ]. Desulfovibrio was the most dominant genus in the cathodes; however, its relative abundance was significantly higher for the cathodes with Cu NPs. The same trend but with a lower relative abundance than the cathodes electrodes was found for Desulfovibrio on the anodes. According to [ 40 ], several microorganisms within the deltaproteobacteria class have been proved to possess electroactivity. This genus was also identified on iron rod electrodes during MEC treating bilge water [ 33 ]. DEMR64 (family Rikenellaceae , Phylum Bacteroidota ) and Mesotoga was identified at a high relative abundance on the anode and cathode but not in the suspended sludge. Mesotoga bacteria could utilize acetate and it Li et al. [ 37 ] also found an increase of Mesotoga in MEC when potential was applied. A homoacetogenic bacteria ( Acetobacterium ) was identified in relatively high abundance only in the cathodes with Cu NPs, more likely due to the higher concentration of hydrogen produced on these cathodes. On the other hand, the genus Clostridium sensu stricto , Syntrophomonas and Thiobacillus were mostly found in suspended sludge and were slightly identified in the electrodes. Clostridium could reduce CO 2 to produce acetic acid, which might be responsible for the relatively high propionic acid [ 32 ]. Clostridium has been identified as acetogenic bacteria with bioelectrochemical activity, and it could oxidize acetate to produce H 2 /CO 2 and grow symbiotic ally with H2 consuming methanogens [ 41 ]. Longilinea was identified in all samples; however, it had its higher abundance in 39.2% in the suspended biomass in control. Zhang et al. [ 13 ] also found Longilinea on an anode in a microbial electrolysis system treating wastewater."
} | 6,281 |
39900940 | PMC11791102 | pmc | 6,828 | {
"abstract": "Biogas production through the anaerobic digestion (AD) of organic waste plays a crucial role in promoting sustainability and closing the carbon cycle. Over the past decade, this has driven global research on biogas-producing microbiomes, leading to significant advances in our understanding of microbial diversity and metabolic pathways within AD plants. However, substantial knowledge gaps persist, particularly in understanding the specific microbial communities involved in biogas production in countries such as South Korea. The present dataset addresses one of these gaps by providing comprehensive information on the metagenomes of five full-scale mesophilic biogas reactors in South Korea. From 110 GB of raw DNA sequences, 401 metagenome-assembled genomes (MAGs) were created, which include 42,301 annotated genes. Of these, 187 MAGs (46.7%) were classified as high-quality based on Minimum Information about Metagenome-Assembled Genome (MIMAG) standards. The data presented here contribute to a broader understanding of biogas-specific microbial communities and offers a significant resource for future studies and advancements in sustainable biogas production."
} | 292 |
37206335 | PMC10189066 | pmc | 6,829 | {
"abstract": "Modern agriculture is primarily focused on the massive production of cereals and other food-based crops in a sustainable manner in order to fulfill the food demands of an ever-increasing global population. However, intensive agricultural practices, rampant use of agrochemicals, and other environmental factors result in soil fertility degradation, environmental pollution, disruption of soil biodiversity, pest resistance, and a decline in crop yields. Thus, experts are shifting their focus to other eco-friendly and safer methods of fertilization in order to ensure agricultural sustainability. Indeed, the importance of plant growth-promoting microorganisms, also determined as “plant probiotics (PPs),” has gained widespread recognition, and their usage as biofertilizers is being actively promoted as a means of mitigating the harmful effects of agrochemicals. As bio-elicitors, PPs promote plant growth and colonize soil or plant tissues when administered in soil, seeds, or plant surface and are used as an alternative means to avoid heavy use of agrochemicals. In the past few years, the use of nanotechnology has also brought a revolution in agriculture due to the application of various nanomaterials (NMs) or nano-based fertilizers to increase crop productivity. Given the beneficial properties of PPs and NMs, these two can be used in tandem to maximize benefits. However, the use of combinations of NMs and PPs, or their synergistic use, is in its infancy but has exhibited better crop-modulating effects in terms of improvement in crop productivity, mitigation of environmental stress (drought, salinity, etc.), restoration of soil fertility, and strengthening of the bioeconomy. In addition, a proper assessment of nanomaterials is necessary before their application, and a safer dose of NMs should be applicable without showing any toxic impact on the environment and soil microbial communities. The combo of NMs and PPs can also be encapsulated within a suitable carrier, and this method aids in the controlled and targeted delivery of entrapped components and also increases the shelf life of PPs. However, this review highlights the functional annotation of the combined impact of NMs and PPs on sustainable agricultural production in an eco-friendly manner.",
"conclusion": "12. Conclusion Improving food-based crop production is the primary need for a rapidly growing world population. This goal can be achieved through strategies that use agriculturally important microbe and nanomaterial-based fertilizers without relying heavily on agrochemicals. The abundant scientific literature supports the effectiveness of using NMs and microorganisms as PPs in improving plant growth, ameliorating environmental stresses, and improving soil health. In recent years, however, scientists have been keenly interested in investigating the synergistic effects of NMs and PPs in agriculture to maximize crop yields and maintain soil health. In this cocktail of NMs and PPs, nanomaterials serve as effective sources of nutrients for plants, while PPs stimulate plant growth, therefore serving as natural crop vitalizers. According to the recent literature, the synergistic effect of NMs and PPs has played a promising role in achieving the following target: (a) maximization of crop productivity and crop quality, (b) assurance of food security for the rapidly escalating global population, (c) amelioration of the drastic effects of various environmental stresses such as drought, salinity, and cold, as well as biotic stresses, (d) maintenance of soil health, (e) reduction of the massive reliance on chemical-based fertilizers, and (f) strengthening of the bioeconomy by improving grain yield, grain quality, and biomass in a sustainable way without showing negative impact on the environment. The breakthroughs in nanotechnology have also facilitated the inclusion of plant probiotic strains within the ideal nanomaterials or the entrapment of both NMs and PPs within a suitable carrier. In addition to the controlled and consistent supply of both NMs and PPs, this strategy retains the effectiveness and longevity of the PPs and exhibits a positive impact on crop productivity. In addition, the safe dose of NMs must be determined from an environmental perspective, and a risk assessment must be conducted to ensure that NMs are not hazardous to local soil microbial populations. In conclusion, the application of NMs and PPs in a synergistic manner is demonstrated as an efficient way of improving the quality and production of food-based crops and strengthening the bioeconomy. Furthermore, detailed investigations are also required to develop a customized cocktail of NMs and PPs, understanding their controlled and targeted delivery as well as their molecular mechanisms in plants, to pave the way for sustainable agriculture.",
"introduction": "1. Introduction Numerous tactics dealing with the improvement of crop production are essentially required to meet the basic food needs of the rapidly growing human population. The sector of agriculture affected by climate change, where increasing phenomena of abiotic stresses such as drought, salinity, cold, flooding, and biotic stress (attacks by pathogens such as bacteria, fungi, oomycetes, nematodes, and herbivores) negatively affect agricultural production ( Shahzad et al., 2021 ; Upadhayay et al., 2023 ). In addition, the agrochemicals showed a significant increase in crop yield in the last few decades ( Lin et al., 2019 ), but later harmful effects from the over-application of chemical fertilizers became apparent ( Upadhayay et al., 2022a , b ). It led to the degradation of soil quality, disturbance of soil microbial ecology, pollution of soil and water bodies, and harmful effects on human health due to residues of pesticides and herbicides ( Singh et al., 2020 ; Tripathi et al., 2020 ; Boregowda et al., 2022 ). Moreover, the transition to organic agriculture, particularly the use of biofertilizers, provided an environmentally friendly alternative to chemical-based agriculture, as well as improved crop yield and soil quality ( Asghar et al., 2022 ; Elnahal et al., 2022 ). The term “plant probiotics (PPs)” can be used to decode a distinct group of microbial strains with all the necessary characteristics to be classified as biofertilizers that influence plant growth through both direct and indirect mechanisms (microbes that show beneficial attributes for plants in terms of growth and yield; Sarbani and Yahaya, 2022 ; Rai et al., 2023 ). The rhizosphere and the inner regions of plant tissues each serve as a special hub for their respective microbial communities, the rhizomicrobiome ( Jiang G. et al., 2022 ), and the endophytomicrobiome ( Pandey et al., 2022 ). This microbiome is a rich source of plant probiotics due to the multitude of traits it possesses, such as the solubilization of nutrients ( Khan et al., 2022 ), nitrogen fixation ( Abdelkhalek et al., 2022 ), production of plant hormones [indole-3-acetic acid (IAA); Nazli et al., 2020 ], ammonia ( Upadhayay et al., 2022b ), anti-pathogenic compounds ( Mathur et al., 2019 ), hydrogen cyanide (HCN; Kashyap et al., 2021 ), exopolysaccharides ( Latif et al., 2022 ), siderophore ( Mushtaq et al., 2022 ), and lytic enzymes ( Reddy et al., 2022 ). Plant probiotics enhance nutrient uptake and provide protection for plants from environmental stresses, such as biotic and abiotic stresses, and also improve plant health ( Kenawy et al., 2021 ; Pandey et al., 2022 ). Plant probiotics with varying plant growth-stimulating capabilities provide advantages such as improved crop productivity and food security ( Arif et al., 2020 ; Ghoghari et al., 2022 ). In contemporary times, the use of nanotechnology in developing countries is gaining more attention, especially in the field of agriculture ( Neme et al., 2021 ). Due to their greater surface area and solubility, nanomaterials are regarded as superior to conventional agrochemicals when used as nanofertilizers in agriculture ( Fen et al., 2022 ). Nanofertilizers improve the nutrient uptake efficiency of plants, diminish the detrimental effects of environmental stresses, and increase crop productivity ( Guleria et al., 2022 ). It is possible to use a combination of the selective plant probiotics that have been shown to be compatible with the nanoparticles of interest ( Khati et al., 2017 , 2018 ; Agri et al., 2021 ; Chaudhary et al., 2021a , b , c ). NMs and PPs together hold a great promise for sustainable agriculture as better alternatives to agrochemicals and are becoming a popular concept in the agricultural sector. This idea of efficient fertilization can be preferred over chemical-based fertilization because of its higher efficacy in resource utilization, sustained and slow release of nutrients, increase in crop productivity with a lesser dose of fertilizer, and least negative impacts on soil. Moreover, the use of NMs and PPs is economically feasible and poses lesser toxicity to the environment. According to the literature, the “cocktail” of NMs and PPs can be considered a “nanobiofertilizer (NBF),” because it has the effectiveness of both components (i.e., NMs and PPs) and aids in the slow and controlled release of nutrients, improves nutrient use efficiency, and results in a significant increase in crop yield ( Kumari and Singh, 2020 ). The microbial part of this cocktail contributes benefits to the plant system due to its wide array of plant growth-stimulating traits such as the solubilization of nutrients, nitrogen fixation, production of plant hormones, EPS, siderophore, and anti-pathogenic compounds. The improvement in soil fertility, functional enzymatic activities, NPK content, organic carbon content, and soil microbial biomass are reflected under the influence of the effective microbial component. On the contrary, the second and most effective segment, “NMs,” maximize the benefits and contributes to plant growth through the controlled and sustained release of nutrients, a reduction in the fixation of nutrients in the soil, an increase in the bio-availability of nutrients to plants, making plants more tolerant to environmental stress, and the protection of plants from pests. The combination of nanomaterials and plant probiotics can be applied to plants in a variety of ways, including seed treatment, seedling treatment, foliar application, soil application, and other methods. Nanotechnology advancements have also led to the encapsulation of plant probiotic strains within the appropriate nanomaterials ( Panichikkal et al., 2019 , 2021 ; Akhtar et al., 2022 ) or the encapsulation of both NMs and PPs within a suitable carrier ( Moradi Pour et al., 2022 ), depending on the choice of experiments. This concept maintains the efficacy and shelf life of the microbial component (PPs) as well as the controlled and sustained supply of both NMs and PPs. This two-pronged strategy increases nutrient availability directly through the use of nanomaterials, while also stimulating plant growth through effective microbial treatment. The use of such a combination of effective doses of NMs and PPs has the potential to create a big difference in the agricultural sector, which will eventually be fruitful in providing benefits of sustainable agricultural production and as well as food security ( Kumari et al., 2021 ; Agri et al., 2022 ; Akhtar et al., 2022 ). The present review illustrates the impact of the combined use of NM and PP, as an effective but two-way strategy, on food crops in terms of increased crop production, reducing the detrimental effects of environmental stress, improving soil fertility, and strengthening the bioeconomy."
} | 2,920 |
29101362 | PMC5670227 | pmc | 6,830 | {
"abstract": "In 2015/16, a marine heatwave associated with a record El Niño led to the third global mass bleaching event documented to date. This event impacted coral reefs around the world, including in Western Australia (WA), although WA reefs had largely escaped bleaching during previous strong El Niño years. Coral health surveys were conducted during the austral summer of 2016 in four bioregions along the WA coast (~17 degrees of latitude), ranging from tropical to temperate locations. Here we report the first El Niño-related regional-scale mass bleaching event in WA. The heatwave primarily affected the macrotidal Kimberley region in northwest WA (~16°S), where 4.5–9.3 degree heating weeks (DHW) resulted in 56.6–80.6% bleaching, demonstrating that even heat-tolerant corals from naturally extreme, thermally variable reef environments are threatened by heatwaves. Some heat stress (2.4 DHW) and bleaching (<30%) also occurred at Rottnest Island (32°01’S), whereas coral communities at Ningaloo Reef (23°9’S) and Bremer Bay (34°25’S) were not impacted. The only other major mass bleaching in WA occurred during a strong La Niña event in 2010/11 and primarily affected reefs along the central-to-southern coast. This suggests that WA reefs are now at risk of severe bleaching during both El Niño and La Niña years.",
"introduction": "Introduction Coral reefs are in serious decline worldwide due to a combination of increasing local and global anthropogenic pressures 1 , 2 . Rising atmospheric CO 2 -concentrations are causing ocean warming, which leads to more intense and frequent mass coral bleaching events. To date, three global mass bleaching events (1998, 2010, and 2015/16) have been documented since the 1980s and were associated with El Niño-Southern Oscillation (ENSO) driven warming events 3 – 5 , highlighting the sensitivity of corals to climate-driven marine heatwaves 6 . Bleaching most commonly occurs during periods of thermal stress when corals lose their algal dinoflagellate symbionts ( Symbiodinium spp.), resulting in a pale or white appearance of the coral colony 7 – 9 . Given that the majority of scleractinian corals meet most of their metabolic demand from carbon derived from symbiont photosynthesis 10 , bleaching results in severe resource limitation and thus significantly weakens them. While bleached corals can sometimes recover, the physiological damage caused during bleaching often results in extensive coral mortality 11 – 13 . Therefore, warming-related mass bleaching events are among the greatest threats to coral reefs today 5 , 14 . Since these events can lead to mass mortality from regional to global scales, they impact both the diversity and functioning of coral reef ecosystems 15 , and also threaten the socio-economic services on which millions of people worldwide depend 16 . In 2015/16, unusually high ocean temperatures associated with one of the strongest El Niño events on record triggered an unprecedented global coral reef crisis, initiating what would become the third documented global mass bleaching event 17 . This event was estimated to have impacted 38% of the world’s coral reefs and became the longest and most severe mass bleaching event on record 5 , 17 . It has devastated coral reefs across all three major ocean basins 3 , 5 , 11 , 18 – 20 , and caused annually recurring mass bleaching in several locations for the first time 3 , 21 . Coral reefs in the South China Sea, for example, experienced unprecedented mass bleaching with 40% coral mortality in 2015 11 , while in the Maldives live coral cover declined by 75% due to severe bleaching in 2016 19 . Similarly, the Great Barrier Reef experienced the worst bleaching event in its history in 2016 5 , followed by another severe bleaching event just one year later 21 . In late 2015, the U.S. National Oceanic and Atmospheric Administration (NOAA)’s Coral Reef Watch predicted significant coral bleaching and/or mortality (alert levels 1 and 2) for most coral reefs in Western Australia (WA) during the austral summer 2016 17 . NOAA’s bleaching forecasts showed that the greatest heat stress would occur along the northern WA coast, particularly in the remote Kimberley region. The macrotidal Kimberley region is one of the most extreme natural coral reef environments in the world, with tidal ranges up to 12 m, turbid waters associated with strong tidal currents and terrestrial sediment discharge, and sea surface temperatures (SST) exceeding 30 °C for five months per year 22 – 25 . Despite these extreme environmental conditions, highly diverse coral reefs exist throughout the Kimberley 25 . Interestingly, the highly fluctuating temperatures of intertidal reef habitats (up to 7 °C daily) have been shown to enhance coral thermal tolerance 26 , consistent with other work on thermally variable reef environments 27 , 28 . However, Kimberley corals were nevertheless not immune to severe heat stress simulated in a tank experiment 26 , raising the question how they would respond to periods of elevated temperatures associated with marine heat waves. To date, regional-scale mass bleaching (i.e. extending 100 s of kilometres) has been documented in WA only once during a strong La Niña event in 2010/11 29 – 33 , but not during strong El Niño years (e.g. 1997/98, 2010) that caused mass bleaching in many other locations around the world. Although some offshore oceanic coral atolls in northern WA (e.g. Scott Reef) did bleach severely in 1998 34 , the coastal WA region has largely been considered to be at low risk from bleaching during strong El Niño events. During the La Niña-driven heatwave in 2010/11, sea surface temperatures exceeded normal summer temperatures by an average of 3 °C along the WA coast between 22°S (Ningaloo Reef) and 34°S (Cape Leeuwin) 33 (Fig. 1a ). This resulted, for example, in 12–100% bleaching at the Houtman Abrolhos Islands 29 , 35 and 79–92% at Ningaloo Reef 12 . However, coral reefs in northern WA, including the Kimberley, escaped this marine heatwave and associated bleaching. Early forecasts in late-2015 by NOAA’s Coral Reef Watch of an impending heatwave during the austral summer 2016 raised significant concern for northern WA, given that regional-scale mass bleaching has never occurred in WA during a strong El Niño. As a consequence we conducted extensive coral health surveys at five sites in four different bioregions along the WA coast between December 2015 and May 2016 (Fig. 2 ), both prior to and following the peak warming. Since El Niño events will likely increase in frequency and intensity due to climate change 36 , understanding how these extreme climatic events impacts coral reefs in WA is critical to predict their persistence under continued ocean warming and climate change. Figure 1 Large-scale patterns of sea surface temperature (SST) anomalies within the Eastern Indian Ocean along Western Australia, monthly-averaged for the periods when SST anomalies were near maximum: ( a ) Feb 2011 during the La Niña; and ( b ) Apr 2016 during the El Niño. SST anomalies are based on NOAA 1/4° daily optimally interpolated SST version 2 (OISST V2) relative a 1971–2000 climatological mean for the respective month using data available from https://www.ncdc.noaa.gov/oisst/data-access \n 63 . Map figures are plotted using MATLAB R2015b ( http://www.mathworks.com/ ). \n Figure 2 Map of Western Australia showing the locations of all survey sites and corresponding sea surface temperature (SST) plots for ( a ) Montgomery Reef, ( b ) Cygnet Bay Intertidal, ( c ) Cygnet Bay Subtidal, ( d ) Ningaloo Reef, ( e ) Rottnest Island, and ( f ) Bremer Bay. SSTs are in situ temperature data for all sites except Montgomery Reef, for which satellite-derived data were used (NOAA Coral Reef Watch 5-km virtual station North Western Australia). SSTs are shown for the 12 weeks prior to the first survey time point through to the second survey time point at each site. Dashed vertical lines indicate survey time points. MMM = maximum monthly mean, bleaching threshold = local MMM + 1 °C, DHW = Degree Heating Weeks (see Methods for more details). Line art imagery created in Adobe Illustrator (version CS6) based on a map image created in R software (version 3.4.0, R Core Team, 2017, https://www.R-project.org ) by S. Comeau.",
"discussion": "Discussion We show here that marine heatwaves associated with extreme climatic events such as the record-strength 2015/16 El Niño have the potential to cause unprecedented regional-scale mass bleaching, even in coral reef regions that harbour naturally heat-resistant corals that have escaped mass bleaching in previous El Niño years. This occurred in the macrotidal Kimberley region in northwestern Australia during the austral summer of 2016, where highly diverse and naturally stress-resistant coral reef communities have been observed to thrive under conditions that corals from more typical reef environments would usually not survive (e.g. long aerial exposure, daily temperature fluctuations of up to 7 °C and temperature maxima of up to 38 °C during low tide 24 , 26 , 51 ). A recent study showed that these highly fluctuating temperatures enhance the thermal tolerance of Kimberley corals 26 . Nevertheless, Kimberley coral reefs experienced unprecedented mass bleaching in April 2016 in response to severe heat stress (~4–9 DHW, Fig. 2 ), with more than 56% and 71–80% of live coral cover being bleached at our two Kimberley study sites (Montgomery Reef and Cygnet Bay, respectively) (Fig. 4 ). In addition, broader-scale aerial surveys of 25 reefs in the southern Kimberley, conducted over the same time period, further confirmed the regional scale of this mass bleaching event and showed that most reefs in this region had 30–60% bleaching 5 . This demonstrates that even naturally heat-resistant corals from extreme temperature environments such as the Kimberley region are not immune to marine heatwaves and extreme climatic events. These findings are consistent with experimental work on Kimberley corals showing that heat stress equivalent to ~3 DHW resulted in severe bleaching and mortality, although heat stress in that study was applied over a much shorter time period (<2 weeks) 26 . Coral communities in other naturally extreme temperature environments, such as the Persian/Arabian Gulf where corals have the world’s highest known bleaching thresholds (~35–36 °C), are also not immune to severe heat stress and have suffered from multiple episodes of bleaching associated with significant mortality over the last three decades 52 . This suggests that even naturally heat-resistant corals are significantly threatened by periods of sustained ocean warming, as it is currently unclear whether they can increase their heat tolerance over the time scales required to cope with future climate change. The marine heatwave causing the 2016 mass bleaching in the Kimberley was characterized by long-lasting exposure to small positive temperature anomalies that rarely exceeded the local MMM by more than 1 °C (Fig. 2a–c ). SSTs already rose above the local MMM in November 2015, resulting in increasing heat stress and DHW from that point onwards. As a consequence, some coral genera (i.e. Seriatopora and Stylophora ) were already severely bleached in January 2016 but did not substantially influence overall coral community health due to their overall low abundance. Thus, Kimberley reefs experienced cumulative heat stress for ~5 consecutive months, demonstrating that even small positive temperature anomalies can cause severe bleaching and mortality when persisting over a long duration. The severity of the bleaching event was further confirmed by the significant bleaching of massive corals, which are typically more resistant to heat stress 53 . Surprisingly, Montgomery Reef appeared to experience greater heat stress, yet a lower percentage of bleaching was observed compared to Cygnet Bay (Fig. 4a–c ). This could be due to several factors, such as satellite-derived SSTs overestimating in situ heat stress, and/or coral cover being dominated by plate-like Montipora rather than Acropora corals (Table 3 ). Importantly, the choice of the MMM value can have a substantial influence on calculated heat stress and DHW values even when in situ temperature data are available. NOAA’s MMM of 30.827 °C (version 2) for the virtual station North Western Australia shows close agreement with experimentally established bleaching thresholds (MMM + 1 °C) of ~32 °C for Kimberley corals 26 , verifying the applicability of this MMM value for determining the bleaching threshold. However, NOAA’s climatology was recently updated (i.e. in 2017), emphasizing the challenge of accurately defining baseline temperatures that corals are adapted to. The latest version (i.e. version 3) now lists a much lower MMM (29.903 °C) for this station than version 2. This lower MMM value is most likely far too conservative because Kimberley corals do not show any signs of visible bleaching or declines in their photochemical efficiency (Fv/Fm) when exposed to daily average temperatures of ~31 °C for almost two weeks 26 . While we cannot exclude the possibility that Kimberley corals may have acclimatized to rising SSTs over the last few decades, these discrepancies highlight the importance of in situ temperature records and physiological data, especially in complex macrotidal reef environments that create particular challenges for satellite-derived SST monitoring. The large spatial scale of the 2016 mass bleaching event in northern WA was, to the best of our knowledge, unprecedented. Kimberley offshore oceanic atolls (e.g. Scott Reef) have bleached previously 54 , 55 , and bleached again in 2016 5 ; however, the vast inshore Kimberley region has escaped any bleaching prior to 2016, although it is possible that such events may have gone unnoticed or undocumented due to the remoteness of this region. The 2015/16 El Niño coincided with an extremely unusual and dry wet season in the Kimberley 56 , and also with the most extreme tides of the year. This likely resulted in increased temperature, light and UV stress as well as longer aerial exposure of shallow corals. Furthermore, the absence of major storms and cyclones would have prevented mitigation of both heat and light stress 57 . The combination of these factors most likely contributed or exacerbated heat stress 58 , thus resulting in unprecedented bleaching. On finer spatial scales, the bleaching susceptibility of Kimberley coral communities differed significantly depending on small-scale differences in their thermal environment. During peak heat stress, coral health of subtidal coral communities at Cygnet Bay had declined significantly more than in intertidal communities, as indicated by a much higher percentage of severely bleached corals (Table 2 , Fig. 4 ). This was the case despite similar exposure to heat stress (4.3 and 4.5 DHW in the subtidal and intertidal, respectively) and community composition (dominated by Acropora corals). These observations confirm experimental work showing that subtidal Acropora and Dipsastraea corals at Cygnet Bay have a lower thermal tolerance than their intertidal counterparts 26 . Although both intertidal and subtidal environments have similar average temperatures, they differ substantially with regard to daily temperature fluctuations and the frequency of aerial exposure during low tide 26 . Given that symbiont types did not differ between intertidal and subtidal corals 26 , our findings provide further evidence that extreme temperature fluctuations (up to 7 °C daily in the intertidal) represent a mechanism that enhances coral thermal tolerance 26 – 28 . The 2016 mass bleaching event in the Kimberley region is the first such event to occur in WA during a strong El Niño year 32 as regional-scale mass bleaching in WA extending over 100 s of kilometres has to date only occurred once during a strong La Niña year in 2010/11 29 – 33 . The 2010/11 heatwave primarily affected the central-to-southern WA coastal region (Fig. 1a ), devastating coral communities from Ningaloo Reef to Rottnest Island. In the Ningaloo region, for example, the Exmouth Gulf had the highest amount of bleaching (~95%), whereas Coral Bay suffered ~25% bleaching 12 . In the Perth region, ~17% bleaching was observed 12 , which also extended to deeper reef communities at Rottnest Island (24–28 m) 59 . During the heatwave in 2016, satellite SST anomalies were negative (cooler) along the central-to-southern WA, which would suggest that no heat stress should occur (Fig. 1b ). However, in situ temperature records showed that Rottnest Island experienced substantial heat stress (2.4 DHW, Fig. 2e ). These reef-scale temperature anomalies are likely due to the positive local anomalies in air-sea heat fluxes that occurred over large scales across Western Australia at the time, which would have caused coastal warming in shallow reef waters off the coast of Perth 60 . This may explain why we observed 29.1% bleaching in Rottnest Island, although most corals were only moderately bleached (i.e. <50% bleached or colony pale) (Fig. 4 ). In contrast, no heat stress occurred in Coral Bay as the in situ SSTs stayed well below the local MMM for most of the summer period (Fig. 2d ). We therefore consider it unlikely that the large proportion of dead corals observed at Coral Bay (Fig. 4 ), particularly in May 2016, was in some way related to unusual temperatures. Rather, we suspect that coral spawn slicks may have caused significant coral mortality at Coral Bay between our survey time points. Unusual weather conditions can trap coral spawn slicks in shallow bays, which then severely deplete oxygen concentrations in the water, resulting in mass mortality of coral and other reef organisms 61 , 62 . Such events have been documented at Coral Bay and other reefs in WA previously 61 , 62 . The high-latitude site Bremer Bay also did not experience any heat stress (Fig. 2f ) and the small proportion of moderately bleached corals (Fig. 4f ), thus, likely represents naturally lighter pigmentation due to seasonally higher light levels. Furthermore, it is unlikely that any heat stress occurred after early March given that SSTs generally peak in February at this site and remained below the MMM of 21.3 °C during all of March and April (C. Ross, unpublished data). To our knowledge, this site has not been surveyed during previous El Niño or La Niña years and it is therefore unclear whether it was affected by the marine heatwave in 2010/11. This study shows that the geographic footprint of the 2010/11 and 2016 mass bleaching events in WA differed substantially (Fig. 1 ). While the 2010/11 event affected a larger geographic area, impacting coral reefs across 12° of latitude along the WA coast 30 , the 2016 event was regionally restricted to the northwestern parts of the state (although it should be noted that heat stress and bleaching occurred throughout northern Australia, including the Northern Territory and the Great Barrier Reef 30 , 32 ). The 2010/11 event also coincided with a very active cyclone season 30 , which may have mitigated bleaching impacts to some extent 57 , whereas the opposite was the case for the 2016 event 56 , likely exacerbating heat stress. Both the 2010/11 and the 2016 event were prolonged marine heatwaves that lasted for several months, but heat stress peaked earlier (between January and early March 30 ) in 2010/11 compared to 2016 (April-May). Bleaching patterns across these two events suggest that northern WA is particularly at risk of bleaching during strong El Niño years, whereas central-to-southern WA is vulnerable during strong La Niña years 32 . Rottnest Island could be particularly susceptible to frequent bleaching since it is the only site that bleached during both events, which threatens the potential of this high-latitude site to serve as climate change refuge 42 . Moreover, our findings highlight that regional-scale mass bleaching may now occur throughout WA during both El Niño and La Niña events 32 . As El Niño Southern Oscillation events will likely become more frequent and intense with continued climate change 36 , these findings have significant implications for the future resilience of coral reefs in WA."
} | 5,080 |
29658273 | null | s2 | 6,831 | {
"abstract": "Dissimilatory iron-reducing bacteria (DIRB) are known to use humic substances (HS) as electron shuttles for dissimilatory iron reduction (DIR) by transferring electrons to HS-quinone moieties, which in turn rapidly reduce Fe(III) oxides. However, the potential for HS to serve as a source of organic carbon (OC) that can donate electrons for DIR is unknown. We studied whether humic acids (HA) and humins (HM) recovered from peat soil by sodium pyrophosphate extraction could serve as both electron shuttles and electron donors for DIR by freshwater sediment microorganisms. Both HA and HM served as electron shuttles in cultures amended with glucose. However, only HA served as an electron donor for DIR. Metagenomes from HA-containing cultures had an overrepresentation of genes involved in polysaccharide and to a lesser extent aromatic compound degradation, suggesting complex OC metabolism. Genomic searches for the porin-cytochrome complex involved in DIR resulted in matches to Ignavibacterium/Melioribacter, DIRB capable of polymeric OC metabolism. These results indicate that such taxa may have played a role in both DIR and decomposition of complex OC. Our results suggest that decomposition of HS coupled to DIR and other anaerobic pathways could play an important role in soil and sediment OC metabolism."
} | 329 |
25532804 | null | s2 | 6,832 | {
"abstract": "Mammals exhibit marked interindividual variations in their gut microbiota, but it remains unclear if this is primarily driven by host genetics or by extrinsic factors like dietary intake. To address this, we examined the effect of dietary perturbations on the gut microbiota of five inbred mouse strains, mice deficient for genes relevant to host-microbial interactions (MyD88(-/-), NOD2(-/-), ob/ob, and Rag1(-/-)), and >200 outbred mice. In each experiment, consumption of a high-fat, high-sugar diet reproducibly altered the gut microbiota despite differences in host genotype. The gut microbiota exhibited a linear dose response to dietary perturbations, taking an average of 3.5 days for each diet-responsive bacterial group to reach a new steady state. Repeated dietary shifts demonstrated that most changes to the gut microbiota are reversible, while also uncovering bacteria whose abundance depends on prior consumption. These results emphasize the dominant role that diet plays in shaping interindividual variations in host-associated microbial communities."
} | 266 |
32033333 | PMC7074696 | pmc | 6,833 | {
"abstract": "Revealing the unexplored rhizosphere microbiome of plants in arid environments can help in understanding their interactions between microbial communities and plants during harsh growth conditions. Here, we report the first investigation of rhizospheric fungal and bacterial communities of Adenium obesum , Aloe dhufarensis and Cleome austroarabica using next-generation sequencing approaches. A. obesum and A. dhufarensis grows in dry tropical and C. austroarabica in arid conditions of Arabian Peninsula. The results indicated the presence of 121 fungal and 3662 bacterial operational taxonomic units (OTUs) whilst microbial diversity was significantly high in the rhizosphere of A. obesum and A. dhufarensis and low in C. austroarabica. Among fungal phyla, Ascomycota and Basidiomycota were abundantly associated within rhizospheres of all three plants. However, Mucoromycota was only present in the rhizospheres of A. obesum and A. dhufarensis , suggesting a variation in fungal niche on the basis of host and soil types. In case of bacterial communities, Actinobacteria , Proteobacteria , Bacteroidetes , Planctomycetes , Acidobacteria, and Verrucomicrobia were predominant microbial phyla. These results demonstrated varying abundances of microbial structure across different hosts and locations in arid environments. Rhizosphere’s extracellular enzymes analysis revealed varying quantities, where, glucosidase, cellulase, esterase, and 1-aminocyclopropane-1-carboxylate deaminase were significantly higher in the rhizosphere of A. dhufarensis, while phosphatase and indole-acetic acid were highest in the rhizosphere of A. obesum . In conclusion, current findings usher for the first time the core microbial communities in the rhizospheric regions of three arid plants that vary greatly with location, host and soil conditions, and suggest the presence of extracellular enzymes could help in maintaining plant growth during the harsh environmental conditions.",
"introduction": "1. Introduction The arid or semi-arid land covers almost 26% of the earth’s ecosystems, where life is constrained and often confronted with extremely low water and high temperature. The vegetation is either succulent (accumulating water) or non-succulent perennial hard plants. Both are true xerophytes and are well adapted to the low water conditions [ 1 ]. However, in such harsh climatic conditions, endemic micro-symbionts are of great importance for plant survival [ 2 ]. Understanding the role of microbial communities and their association with plants during their growth, development, and extreme conditions in arid environments are of considerable interest to ecologists [ 3 , 4 , 5 ]. The microorganisms that are predominantly present in the rhizosphere have been shown to play a role in the transport of mineral nutrients, secretion of secondary metabolites, and mitigation of abiotic and biotic stresses [ 6 , 7 , 8 , 9 , 10 ]. During microbial association with the host plants, bacteria and fungi produce various extracellular enzymes that convert the macromolecules into transportable simpler products that can be distributed throughout the plant cells [ 11 , 12 , 13 ]. In addition to the initiation of the host-symbiosis process, some of these exozymes hinder the plant pathogenic infections and boost abiotic stress tolerance [ 14 , 15 ]. The plant, on the other hand, facilitates a suitable niche for distinct microbes to grow and reproduce while mutually sharing beneficial exudates and nutrients [ 16 , 17 ]. Such interactions between the microbial communities and medicinal plants have been minimally investigated, particularly in arid ecosystems [ 5 ]. Previous studies [ 18 , 19 , 20 , 21 , 22 , 23 ] have evaluated the microbiome, especially the bacterial communities from arid soil; however, no studies have been performed on the rhizosphere microbiomes of arid plants. Despite the importance of the plant life in the arid environments, little is known about their associated endemic microflora [ 17 ]. Recently, some studies have been performed on the rhizospheric bacterial microbiomes of plants growing in the arid land ecosystems [ 24 , 25 , 26 , 27 ]. The analyses of microbiomes of various cultivated plants, including Agave species, Zea mays, Phaseolus vulgaris, Ainsliaea henryi Diels, Dioscorea opposita , Potentilla discolor Bge, Stellera chamaejasme L., Ophiopogon japonicus (Thunb) Ker-Gawl., Juncus effusus L. var. decipiens Buchen., and Rhizoma arisaematis [ 28 , 29 ] showed remarkably high and diverse rhizosphere colonization with Actinobacteria [ 30 ]. In addition, some of the recent studies have elucidated the rhizosphere communities of Rehmannia glutinosa [ 31 ], Rumex patientia [ 32 ], Polygonum cuspidatum [ 33 ], Aloe vera [ 34 ], Rhododendron arboretum [ 35 ], and Thymus zygis [ 36 ]. These studies have been restricted to the bacterial communities and did not include fungi, and a few studies used high-throughput next-generation sequencing. However, the importance of understanding the microbiome composition of wild plants growing in the arid environments has at least been demonstrated till now. In the present study, we have investigated the microbiomes of three plants ( A. dhufarensis, C. austroarabica, and A. obesum ) collected from different areas of the arid land that have previously not been explored. A. obesum and A. dhufarensis are more concentrated in the tropical arid environments, whereas C. austroarabica inhabits in extremely arid environments ( Figure 1 ). Moreover, these plants are ecologically and medicinally important too. The plants growing in such an environment often experience a wide array of environmental stresses, including UV irradiation, high heat, drought and strong wind. Rainfall in this region is very limited (<80 mm per annum) and occurs for very short periods. A. dhufarensis , an endemic plant to the Dhofar region in Oman [ 37 ], is the least studied but has shown to possess antioxidant potentials [ 38 ]. The crushed leaves of C. austroarabica produce fragrance. A. obesum is often known as arid rose, and local people use it to treat wounds, venereal diseases, skin diseases, tooth decay, headaches, and muscle pain [ 39 ]. These three species are the representative plants in the arid lands of Oman and the Arabian Peninsula and are often exposed to the harsh environmental conditions. However, despite the exposure to the high drought, heat and strong UV conditions, these plants survive for long periods of time. Herein, we investigated for the first time the fungal and bacterial communities associated with the rhizosphere of these three plants species. Comparative studies across the microbiomes allowed us to explore the major and prominent microbial players in the arid plant life.",
"discussion": "4. Discussion The results showed diverse niche of microorganisms in the rhizosphere of three plant species. This was also evidenced from the soil physical and chemical properties suggesting a complete segregation of the two locations i.e., dry tropical to the complete arid land system. Comparing both types of the rhizosphere from arid plant species could be essential to understand the major microbial associations. Although, the majority of the present insights into the interactions and processes of rhizosphere microbiome have come from studies on model plants such as Arabidopsis thaliana and Medicago truncatula and agricultural or horticultural crops [ 10 , 26 , 49 ], nonetheless, a reasonable progress has also being made in elucidating the microbial ecology of non-cultivated plant species [ 16 , 17 , 26 ]. Some studies also showed that how microbial associations impact the resource allocation, biodiversity and above-ground interactions with herbivores and their natural enemies [ 50 , 51 ]. Understanding microbial diversity across different soil types and locations of wild plants could also help in future expansion of agricultural activities in broader ecological niches and wastelands. To some extent, microbial players and their abundances depends not only on the biogeography of the host plant species but also on host genotype, which is still being investigated by comparing microbial communities of the sample plant during varying seasonal conditions [ 52 ]. The present study elucidated the fungal and bacterial association of three medicinal plant species that displayed a varying response in the metagenomics data output as well as the number of OTUs. This finding was also validated in the recent studies that demonstrated host-specific characteristics such as a wide variety of morphology [ 53 ] and genomics [ 54 , 55 ] could convincingly affect the microbiome structure and diversity [ 16 , 56 ]. Although the climatic, soil, and plant growth parameters were quite similar, A. dhufarensis , C. austroarabica and A. obesum possess considerably different features in their growth, morphology, and genetic makeup, resulting in a varying nature of bacterial and fungal communities in the rhizosphere. This substantial difference in the microbial diversity can be attributed to the microsite niche heterogeneity [ 57 , 58 ]. The roots and their exudates can reduce the niche heterogeneity, which in turn affects the diversity and abundance of fungal and/or bacterial communities [ 7 , 59 ]. A. dhufarensis and A. obesum are known as sap-producing plants [ 37 , 60 ] in their phyllosphere continuum, which naturally becomes part of the rhizosphere either by root exudation or by wounding through herbivory. C. austroarabica , and is also rich in the essential oils [ 59 ]. Such host plant potentials can also result in the distribution and occurrence of certain classes of microbial communities. Therefore, a varying composition of OTUs was observed for the three medicinal plants. A similar conclusion was drawn when root exudates of maize and soybean shown drastic effects in the rhizosphere bacterial community structure and composition [ 61 ]. Rasmann and Turlings (2016) [ 62 ] recently suggested that the plant kind and its root exudation could influence the mutualistic interaction in the rhizosphere. In addition, the immediate changes in the soil attributes (pH, water, and C availability) either climatically or by the host itself and it can increase or reduce the abundance of rhizosphere microbiomes [ 27 , 50 ]. In addition, the difference in microbial communities associated with A. dhufarensis suggests that the microbiome of a species or cultivar exhibits both specific microbial lineages with host-specific abundance patterns and a conserved core microbiome [ 57 , 58 ]. In addition to the abundance, the distribution of microbial communities also differed across the three plant species. Although Basidiomycota and Ascomycota were abundant phyla, the contribution of unidentified fungi was still high in the three plants. This suggests the presence of novel fungal diversity in rhizosphere that have yet to be described. This report is consistent with previous studies on semi-arid land plants [ 27 , 34 , 36 ]. Corynascus , which has been classified as thermophilic in arid land ecosystems, was abundant in C. austroarabica, suggesting its dominant role in countering climatic perturbations. In addition, Corynascus kuwaitiensis, Cochliobolus sp. and Ceratobasidium sp. were also abundant in the three rhizosphere samples. Previously, these were also found in the root zones of date palms [ 63 , 64 ], agave [ 27 ] and grasses [ 64 , 65 ] that are widely grown in arid land ecosystem. In case of bacterial communities, Acidobacteria, Actinobacteria , Bacteroidetes, and Proteobacteria were highly abundant bacterial phyla. These are a few of the dominant bacterial species found in metagenomic dataset obtained from various plants and rhizospheres [ 57 ]. Similarly, increased abundance of Proteobacteria and decreased presence of Acidobacteria in the plant-rhizosphere samples with respect to different hosts were previously found with Agave species [ 27 ], suggesting a major community structure associated with the arid land plants. These have also been reported in some of the important medicinal plants, such as Panax ginseng [ 66 ], Thymus zygis [ 36 ], Polygonum cuspidatum [ 33 ], Rhododendron arboretum [ 35 ], Sapindus saponaria [ 53 ], Taxus baccata and Aloe vera [ 34 , 67 ]. Nonetheless, distribution of phyla including Chloroflexi, Planctomycetes, and Firmicutes in C. austroarabica rhizosphere and Cyanobacteria in A. obesum rhizosphere were significantly different, suggesting host-specific microbe management as indicated by Berendsen et al. [ 17 ]. The presence of a considerably higher number of “unidentified” sequences in bacteria might be due to (i) presence of a large number of sequences of uncultured microbes, (ii) presence of less sequenced microbial genomes, and/or (iii) absence of related orthologous nucleotide sequences in NCBI [ 16 , 68 ]. Since these plant species have been analyzed for the first time, unidentified sequences could not be associated with the potential survival of these three plants in harsh environmental conditions. The holobiont (plants and their microbiota) plays a collective role in intergenic function and development of ecological niche. In this reciprocal interaction, production of bioactive metabolites, including extracellular enzymes and phytohormones, can subsequently pave the way for viable growth of the hosts [ 15 , 69 ]. These extracellular enzymes target various macromolecules, such as carbohydrates, lignin, organic phosphate, proteins, and sugar-based polymers, for their degradation into transportable products throughout the cells and to continue heterotopic metabolism [ 70 ]. In addition to establishing an association with host, these enzymes also initiate the action of extracellular hydrolysis to counteract plant pathogenic infection [ 14 ]. We found considerable higher concentrations of cellulases, glucosidases, esterase, and ACC deaminase in the rhizosphere samples of A. dhufarensis . However, phosphatase and IAA were high in A. obesum . Cellulase allows the bioconversion of cellulose and its modification into simple carbohydrates that act as carbon source for microbes [ 71 ]. The glucosidase enzyme hydrolyses starch and glycogen and converts them into monomers of carbohydrates [ 72 ]. Along with cellulase, glucosidase also plays an important role in providing carbon sources for the plants. Similarly, phosphatase degrades phosphoric acid monoesters into phosphate ions and alcohol [ 73 ]. Phosphate is one of the most important macronutrients of plant and plays diverse roles in plant growth and development, including root development and colonization of rhizospheric microbes [ 4 , 74 , 75 ]. Therefore, the presence of cellulase, glucosidase, and phosphatase in rhizosphere possess considerable significance. These enzymes, along with IAA production by associated microflora, have a high impact on the plant health and fitness against abiotic stresses [ 15 , 48 ]. In addition, higher ACC deaminase in A. dhufarensis can be attributed to abundance of Bacteroidetes, which have been shown extensively to produce ACC deaminase in rhizosphere [ 76 , 77 ]. IAA, on the other hand, was high in the rhizosphere of A. obesum, which could contribute to the abundance of Actinobacteria . Actinobacteria are known to produce IAA, as previous studies have shown [ 15 , 78 ]. This could also be attributed to survivability potentials of these three plant species during low water and nutrient availability. In conclusion, the current results provide a genomic basis to enhance our understanding of these complex and dynamic microbial interactions with plants of sub-tropical arid ecosystems. Overall, our results are in parallel with recent metagenomic data on diversity of microbiomes associated with dry tropical to arid land plants. These plant species were studied for the first time. The identification of specific taxa, particularly at species level, can provide a new insight for future research on the associated functions and reciprocation of enriched species in rhizosphere of these medicinally important plants. Furthermore, we also assume that the secretion of exozymes and essential metabolites at microbial community level can enhance the ability of these plant species to withstand harsh sub-tropical environmental conditions."
} | 4,119 |
34139059 | PMC8596516 | pmc | 6,835 | {
"abstract": "Summary An open question in environmental ecology regards the mechanisms triggered by root chemistry to drive the assembly and functionality of a beneficial microbiome to rapidly adapt to stress conditions. This phenomenon, originally described in plant defence against pathogens and predators, is encompassed in the ‘cry‐for‐help’ hypothesis. Evidence suggests that this mechanism may be part of the adaptation strategy to ensure the holobiont fitness in polluted environments. Polychlorinated biphenyls (PCBs) were considered as model pollutants due to their toxicity, recalcitrance and poor phyto‐extraction potential, which lead to a plethora of phytotoxic effects and rise environmental safety concerns. Plants have inefficient detoxification processes to catabolize PCBs, even leading to by‐products with a higher toxicity. We propose that the ‘cry‐for‐help’ mechanism could drive the exudation‐mediated recruitment and sustainment of the microbial services for PCBs removal, exerted by an array of anaerobic and aerobic microbial degrading populations working in a complex metabolic network. Through this synergistic interaction, the holobiont copes with the soil contamination, releasing the plant from the pollutant stress by the ecological services provided by the boosted metabolism of PCBs microbial degraders. Improving knowledge of root chemistry under PCBs stress is, therefore, advocated to design rhizoremediation strategies based on plant microbiome engineering.",
"conclusion": "Concluding remarks Today, an arsenal of new methodologies is available to unveil the complex crosstalk between plants and the associated microbiome (Park and Ryu, 2021 ). The use of Arabidopsis mutant lines (Huang and Osbourn, 2019 ; Voges et al ., 2019 ) and the ‐omics approaches, including culturomics for setting up microbial synthetic communities (Ziegler et al ., 2013 ; de Souza et al ., 2020 ) and metabolomics to identify the plant exudate compounds (Jaini et al ., 2017 ; Pétriacq et al ., 2017 ; Kawasaki et al ., 2018 ; Dietz et al ., 2020 ) potentially will allow to decipher the messages involved in the ‘cry‐for‐help’ dialogue adopted by plants as response strategy to cope with different environmental stresses (Liu et al ., 2020 ). As recently highlighted by Stringlis and colleagues ( 2018 ), who investigated the plant response to iron deficit condition, the microbiome is not merely a receiving component of this dual system. On the contrary, the ability of certain microbes to promote the synthesis and root release of specific compounds ultimately influences the composition of the microbiome itself. The bacteria‐mediated induction of specific plant genes recalls the fine talk taking place in the establishment of the legume‐ Rhizobium symbiotic relationship, where the bacterium, after rhizoplane colonization, causes morphological changes in the root epidermis related to the expression of the plant early‐noduling genes and, from the other side, in response to the flavonoids exudated by roots initiate the synthesis of the Nod Factors which in turn act as transcriptional factors for the plant (Geurts and Bisseling, 2002 ). A co‐adaptative strategy is established between recruited microorganisms and plants exposed to a particular stress, as observed for drought (Williams and de Vries, 2020 ), metal toxicity (Timm et al ., 2018 ), plant predation (Adaikpoh et al ., 2020 ) and nutrient limited growth conditions (Ham et al ., 2018 ) to support plant growth under these specific abiotic stressors (Liu et al ., 2020 ). The coumarin scopoletin breaks this paradigm, showing that a molecule exudated under iron starvation is also involved in counteracting pathogen proliferation and favouring the recruitment of beneficial microbes (Stringlis et al ., 2019 ). Identifying similarities and peculiarities in the root exudation profile responding to different abiotic stresses will contribute to decipher the mechanistic aspects of microbial communities assembly upon the plant ‘cry‐for‐help’ strategy, and the study of root exudation in contaminated soils will highlight the plant‐microorganisms interplay in pollutant degradation. Improving the knowledge on plant secondary metabolites and root exudates effect in response to PCBs polluted soil paves the way for microbiome manipulation to gain ecological services like rhizoremediation. Though most of the ‘cry‐for‐help’ pioneering studies have been realized using the model plant Arabidopsis thaliana , future research could be addressed on plant species that demonstrated to induce a decrease in PCBs concentration by biostimulating the microbiome of historically and highly polluted soils (e.g., Festuca arundinacea , Medicago sativa , Cucurbita pepo ssp. pepo , Terzaghi et al ., 2019 ). A fine metabolomic characterization of the exudation pattern of these plant species challenged in PCBs contaminated soil, coupled with metagenomic studies aimed at identifying the soil microbiome response in terms of functional traits related to biodegradation, will thus represent a milestone to steer the recruitment of PCB‐degrading microbial populations and design effective rhizoremediation strategy based on microbiome engineering."
} | 1,307 |
27687986 | PMC5056432 | pmc | 6,837 | {
"abstract": "May's celebrated theoretical work of the 70's contradicted the established paradigm by demonstrating that complexity leads to instability in biological systems. Here May's random-matrix modelling approach is generalized to realistic large-scale webs of species interactions, be they structured by networks of competition, mutualism or both. Simple relationships are found to govern these otherwise intractable models, and control the parameter ranges for which biological systems are stable and feasible. Our analysis of model and real empirical networks is only achievable on introducing a simplifying Google-matrix reduction scheme, which in the process, yields a practical ecological eigenvalue stability index. These results provide an insight into how network topology, especially connectance, influences species stable coexistence. Constraints controlling feasibility (positive equilibrium populations) in these systems are found more restrictive than those controlling stability, helping explain the enigma of why many classes of feasible ecological models are nearly always stable.",
"discussion": "Discussion In conclusion, while recent studies of the CM-model have failed to find any stability conditions or ‘particular pattern in how the critical (stability) level of mutualistic strength varies with model parameters' (RSB), the techniques presented here result in strong clear relationships. Moreover, May's 1 early stability predictions equation (5) for large complex random systems and equation (10) , hold surprisingly well for competition, as well as highly networked CM-systems; local stability is lost when disturbances increase beyond a relatively small threshold level ( γ =1). This proneness to instability increases with the number of species, intensity of competition and level of disturbance. Analysis of structural stability, the range of parameter space for which feasibility and stability holds, leads to a different but not contradictory viewpoint, more in line with Elton 28 . Namely, CM-systems have poor structural stability for 0.1< q <0.9, while tightly connected ecological networks have the highest structural stability. Ecosystem stability and vulnerability should be assessed by integrating the results from these two different frameworks. The theory developed here also makes clear that constraints on feasibility are more restrictive than those on stability, and explains why nearly all feasible systems are stable for many classes of ecological models ( Supplementary Note 2E ). Interestingly, loss of feasibility might be viewed as an early warning precursor of the interaction matrix losing stability. Hence, external anomalies from changing climate, resource availability or environmental hazards, may readily lead to species extinctions often well before ecosystem instability can even be identified. The tools presented here, based on the Google matrix, extend the scope of May's study of large complex systems making it possible to untangle other important ecological interaction structures. These techniques can be readily adapted to a wide range of disciplines in network science."
} | 778 |
36964430 | PMC10039194 | pmc | 6,838 | {
"abstract": "Highlights \n A fully self-powered bimodal sensor is designed for patterned-displaying the force trajectories. Outstanding mechanoluminescence is achieved with a stimulation force as low as 0.3 N and 2000 cycles reproducibility. The designed bimodal sensor exhibits good potential for handwriting input to achieve visual intelligent control. \n Supplementary Information The online version contains supplementary material available at 10.1007/s40820-023-01054-0.",
"conclusion": "Conclusion In summary, we propose a visualized bimodal sensor which is easy to be manufactured and completely self-powered. Through the design of a multilayer structure based on deformable LMs combined with a micro-nano-structured mechanoluminescent elastomer, the obtained MTBS can output synergistic optoelectronic signals under stress with good reproducibility. In addition, thanks to the processing of transient optical signals, visualization of handwriting and intelligent control by machine learning are achieved. We also demonstrate the application of the device in gesture recognition. Our research provides ideas for future bimodal sensing mechanisms that have broad application scenarios in intelligent control and visual interaction devices.",
"introduction": "Introduction In nature, biological skin has attracted extraordinary attention due to its fascinating properties of stretchability [ 1 – 5 ], self-healing [ 6 , 7 ] and multimodal sensing ability [ 8 – 12 ]. Biological skin allows organisms to interact with their surroundings and sense changes in external stimuli such as temperature [ 13 – 16 ], pressure [ 13 , 17 , 18 ] and pain. It transmits stimulus information to the brain via nerve conduction, enabling the organisms to respond and control. Especially when exposed to external environmental stimuli, some biological skin can present fluorescent or color responses for hazard avoidance, camouflage, and courtship. For example, once cephalopods are under dangerous situations, they can fluoresce to avoid danger by the muscle-controlled movements of chromatophores filled with pigment sacs [ 19 , 20 ]. Inspired by the biological skin, mechanoluminescent materials are being utilized in the fabrication of electronic skin (e-skin) for increasing the functionality and identifiability of wearable electronics [ 21 , 22 ]. Mechanoluminescence (ML) material can establishes a link between mechanical and optical signals for mechanical visual sensing. Generally, the functional materials are prepared by chemical synthesis [ 23 , 24 ] and nano-microstructure strategies [ 25 , 26 ]. While the former approach usually relies on specific force-sensitive chromophore molecules (e.g., spiropyran based on force-induced breakage of C–N and C–O bonds) [ 27 ], the latter one can reflect natural light through elaborate structures to produce distinct color alternations and fluorescent changes. Craig and his colleagues reported ML e-skin with improved mechanical sensitivity by constructing multilayer nanoparticle microporous structures in ML polymers [ 22 ]. Priya et al. introduced a sulfur vacancy in the zinc sulfide doped with copper (ZnS:Cu) phosphors, which is able to induce the formation of new energy levels to improve the luminescence performance [ 28 ]. However, most work focused on enhancing ML intensity and achieving the display and accurate recognition of motion trajectories based on ML remains a great challenge. Mimicking the multimodal sensing ability of animal skin is another target for next-generation e-skin materials [ 29 – 31 ]. Bao and colleagues have developed an e-skin capable of interactive color change and haptic sensing properties based on stretchable resistive pressure sensors and electrochromic devices [ 32 ]. Zhang and colleagues demonstrated a pressure–temperature bimodal tactile sensor by combining fundamentally different sensing mechanisms of optical and electronic devices, thus enabling simultaneous independent sensing of pressure and temperature [ 33 ]. However, the achievement of multimodal sensing usually requires external energy input and additional devices for the successful operation, which limits their practical applications [ 30 , 33 , 34 ]. The self-powered, multimodal and visualized sensor for intelligent control is urgently needed in the era of Internet of Things (IoT) and fifth-generation wireless networks [ 35 – 37 ]. In this work, we demonstrate a mechanoluminescent-triboelectric bimodal sensor (MTBS) that intuitively detects force signals and enables both intelligent control and human physiological activity detection through force-optical and force-electric response. The ZnS:Cu particles are incorporated into polydimethylsiloxane (PDMS) elastomer to generate ML properties, and meanwhile corresponding software is developed to convert transient luminescence into visual images for intelligent control based on interdisciplinary machine learning algorithms. Furthermore, the stress transfer is stabilized by introducing liquid metals (LMs) with excellent electrical conductivity; thereby, the entire device exhibits outstanding mechanoluminescence. The surface microstructures built on the ML materials achieves excellent output of triboelectric nanogenerator (TENG) for power supply of the sensor. This bioinspired nano-microstructured bimodal sensor provides a new solution for preparing fully self-powered visualized multimodal sensing systems, showing potential applications in wearable electronic devices and human–computer interaction.",
"discussion": "Result and Discussion Materials Design As shown in Fig. 1 a, benefit from its multilayer structure design, the MTBS possesses an optoelectronic dual-signal sensing mode with unique characteristics. Specifically, one single stimulus is able to produce both electrical and optical signals at the same time, which is only drove by mechanical forces (no external power supply is required). The MTBS is a typical layer-by-layer structure which consists of three parts from top to bottom based on the contact-separation mode TENG [ 38 ]. The top layer is ML elastomer consisting of PDMS with well-dispersed ZnS:Cu particles, which can produce optical signal under external force. Moreover, the introduction of sandpaper-generated surface microstructures is able to enhance the charge density on the triboelectric surface and improve the sensitivity of sensing detection [ 39 ]. When the obtained composite elastomer subjected to external forces, the defects (point, line and planar defects) of ZnS crystals start to move, which leads to the breakage and reconstruction of Zn–S chemical bond (Fig. 1 b). The covalently bonded s and p electrons will be redistributed after a Zn–S bond breaking, leading to an energy band bending and the tunneling of trapped electrons to the conduction band. Some of the de-trapped electrons move into the conduction band and recombine with holes. Thus, the released energy during the electron–hole complex excites the doped Cu 2+ ions and then the de-excitation of excited Cu 2+ ions lead to a luminescence [ 40 , 41 ]. In the middle part, gallium-based LMs are selected as stretchable electrodes due to their low melting points, low viscosity, high conductivity and non-toxicity [ 42 – 45 ]. Meanwhile, their fluidity makes the stress transfer more stable through overall device to ensure ML performance. The lower part made of PDMS is used as a triboelectric and encapsulation layer to prevent leakage of the electrodes. It is worth noting that LMs are able to form a dense oxide layer on the surface immediately after contact with air during usage, which is effective enough to prevent further oxidation and limit the penetration into PDMS during prolonged stretching [ 46 , 47 ]. Therefore, the oxide layer of LMs makes the electrical signals of the MTBS more stable and durable. Fig. 1 Structure design of the MTBS. a Schematic of the design for the bimodal self-powered sensor. b ML mechanism of ZnS:Cu particles. c Schematic diagrams of the working principle of the MTBS. d Developed intelligent control system by recognizing handwriting numbers based on interdisciplinary machine learning approach MTBS acts on the coupling of triboelectrification and electrostatic induction, where the skin and PDMS are considered as positive and negative frictional electric material, respectively. As shown in Fig. 1 c, human skin acts as the ground and the MTBS attached to human body acts as a single electrode TENG. Since silicone rubber possesses a strong ability to gain electrons while human skin with a strong tendency to lose electrons, silicone rubber is well suited to be a triboelectric layer material for self-powered human motion sensing [ 48 ]. During the cyclic contact and separation between skin and the MTBS, alternating currents can be generated due to the flow of charges. Based on this visualized bimodal sensor, various human–machine interface scenarios are demonstrated, including intelligent control with high recognition rate through machine learning and human physiological activity monitoring (Fig. 1 d). Mechanoluminescent Sensing The ML layer of the visualized bimodal sensor is achieved by homogeneously embedding the ZnS:Cu phosphors into PDMS (PDMS/ZnS:Cu). According to the cross-sectional scanning electron microscopy (SEM) of the PDMS/ZnS:Cu composite elastomer shown in Fig. 2 a, ZnS:Cu particles with an average particle size of around 20 μm are uniformly embedded in PDMS matrix. Energy-dispersive X-ray (EDX) spectral mapping reveals the elemental distribution of individual ZnS:Cu particles. In addition to the elements Zn, S and Cu, there is also Al on the surface for moisture resistance. X-ray powder diffraction (XRD) confirms the presence of the wurtzite structure of ZnS:Cu phosphors (Fig. 2 b) [ 49 ]. The changes in brightness and color during force luminescence were analyzed according to the standards of the International Commission on Luminescence. The ML performances of the composite elastomer under stretching (30% strain) are shown in Fig. 2 c. The luminescence intensity of the composite elastomer increases with increasing stretching rate (from 100 to 800 mm min −1 ). Grayscale color analysis is used as an efficient and scientific method to accurately indicate the lightness/darkness of the pixel in images of the composite elastomer [ 50 – 52 ]. As shown in Fig. 2 c–d, both the intensity and grayscale value of the luminescence increase with increasing stretching rate. The relationship between stretching rate and grayscale values is approximately linear with a goodness of fit > 97% (Fig. S1). In addition, color variations are quantified by RGB color intensity analysis. RGB is the color representing the three channels of red ( R ), green ( G ) and blue ( B ), and all colors are obtained by the variations of the three RGB color channels and their superposition on each other [ 53 , 54 ]. We observe that the R and B values of elastomer luminescence did not change much with increasing stretching rate, while the G value changes sharply (Fig. 2 e) and linearly (Fig. S2). This color change characteristic lays the foundation for the subsequent signal collection and processing. The stress–strain curves of the composite elastomer are shown in Fig. S3. The results show that the composite material has good mechanical properties and can meet the needs of human motion monitoring. To test the durability and stability of the ML performances of the obtained MTBS, the PDMS/ZnS:Cu composite elastomer is subjected to 2000 times of stretch-released cycles by stretching at a strain of 30% (Fig. S4). As shown in Fig. 2 f, the repeated stretching-releasing processes have almost no effect on the ML performances of the composite elastomer, which is crucial for the sensing of the elastomer in practical visualization. Excellent repeatability of ML is important for multiple writing, intelligent recognition stability, etc. More importantly, the light intensity increases with the number of frictions (Fig. S5). Because the triboelectric effect supplies an external field that can reduce the trap-depth of charges in ZnS:Cu. More trapped charges, especially the deep-trapped charges would be released by the in situ internal piezoelectric field of ZnS:Cu, resulted in the improved luminescence property. Fig. 2 ML output performance of the composite elastomer. a Cross-sectional SEM images of PDMS/ZnS:Cu composite elastomer and EDS mapping of individual ZnS:Cu particles. b XRD patterns of ZnS:Cu particles. c Digital images of mechanoluminescent elastomer at a range of stretch rates. d Changes in grayscale values of mechanoluminescent elastomer at a range of stretch rates. e RGB values of mechanoluminescent elastomer vary with the stretching rate. f Durability of mechanoluminescent elastomer for more than 2000 stretching-releasing cycles Triboelectric Sensing MTBS possesses excellent ML performance and good self-powered TENG performance, which can be used in collecting mechanical energy in different environments and is able to realize a self-powered sensing without battery [ 55 – 58 ]. The output performances of the MTBS were tested by a home-made measurement system consisting of a function generator, a power amplifier, a linear motor, an electrostatic meter, and a signal acquisition computer [ 36 , 59 ] (Fig. 3 a). In order to study the effect of different contact materials on the output performance of TENG, polylactic acid (PLA), gelatin, paper and polyethylene terephthalate (PET) were used as friction materials and the open-circuit voltage ( V oc ) and short-circuit current ( I sc ) were measured (Fig. 3 c). Among these friction materials, both the V oc and I sc values reach a maximum in PET, due to the maximum difference in electron gain and loss between PET and PDMS/ZnS:Cu composite films. In subsequent tests, PET was selected as the friction material. To improve the sensitivity, we construct microstructures on the surface of PDMS/ZnS:Cu layer ( Fig. 3 b). Figure 3 d shows the effect of structured surface on the V oc and I sc . The smooth and rough surfaces were compared separately (rough surface was obtained by sandpaper molds while smooth surface was obtained by smooth Teflon molds). The voltage of devices with smooth surface is only about 8 V, while the voltage of devices with rough surface increases to 24 V (a three-fold increase). Generally, the output of TENG under contact separation mode is highly dependent on the applied pressure and the frequency of contact [ 17 , 60 ]. Under the normal impact force, the device can generate different V oc and I sc and the normal force range is 1–25 N. Both the V oc and I sc increase with the increase in impact force, which can reach a maximum V oc of 27 V and I sc of 0.3 μA under 25 N of applied normal impact force (Fig. 3 e). When the frequency of the applied force increased from 0.5 to 2 Hz, the current is around 0.3 μA, but it still increases slightly. The V oc increased from 20 to 27 V from 0.5 to 2 Hz (Fig. 3 f). The MTBS also possesses a very fast response and recovery speed of only 61 and 70 ms after applying or releasing, which makes it possible to monitor external stimuli in real time (Fig. S6). The use of the obtained MTBS as a sensor for human motion monitoring means that it may be subjected to repeated mechanical forces and shocks during usage, so the durability and durability of the devices is very important. As shown in Fig. 3 g, after 4000 cycles of applied force (5 N), there is no significant drop of output signals of the device. In summary, these results show that the obtained MTBS possesses significant advantages in the practical applications of self-powered sensing. Fig. 3 Electrical output performance of the MTBS. a schematic of TENG test system. b SEM images of the microstructures on the surface of PDMS/ZnS:Cu layer. c Output voltage and current of the MTBS in contact with different cathode materials. d MTBS output voltage and current for smooth and rough surfaces. e MTBS output voltage and current under different contact forces. f MTBS output voltage and current under different frequencies. g Long-term stability of MTBS output under 4000 loading–unloading cycles To demonstrate the capability of the obtained MTBS as a self-powered physiological sensor, several practical applications for its dynamic mapping of human motion and writing are performed separately ( Fig. 4 a). As shown in Fig. 4 c–d, the MTBS was attached to the volunteer’s wrist and knee, and the sensor is able to immediately responds by outputting signals according to the body movement. In addition, the MTBS can also be used as a pedometer for monitoring human movements, based on the fact that each movement with contact-separation action is able to produce a clear output signal (~ 200 nA , Fig. 4 e). Then, the MTBS was mounted on the mouse to differentiate frequencies of mouse clicks (Fig. 4 f). By using the MTBS-attached finger to touch the table in different strength, there is a large difference of the output signals (~ 8 nA vs. 60 nA, Fig. 4 g). The designed MTBS can detect larger-scale human movements as well as subtle human movements. As shown in Fig. 4 h, when we attached the MTBS to the volunteer's throat the sensor can generate regular current signals as the vibration of the throat. In addition, wearable gesture sensors capable of sensing dexterous movements of each finger can play an important role in future human–computer interaction interfaces. As shown in Fig. 4 b, when the volunteer expresses the Arabic numbers 1, 2, 3, 4 and 5 by gestures, the sensor with different signal combination states (every finger movement can be detected separately) can be used to accurately identify the meaning of different gestures. As shown in Fig. 4 i–k, when different numbers are written on the MTBS using a pen, there are different current signal outputs as well as visual pictures of the numbers. Since the luminescent signals generated by the MTBS during handwriting are transient for about 200 ms (Video S1 and Fig. S7), which is almost impossible to accomplish for recognition. A tool has been developed to obtain clear digital images by continuously intercepting multiple frames from the input video and combining them for superimposition. Take the writing of number “2” as an example, by using a pen to write on the composite elastomer and intercepting the consecutive images (Fig. S8), the trajectory of the force applied to the material can be clearly seen, which is important for the visual monitoring of writing process on the obtained composite elastomer. Overall, the MTBS shows excellent stability and sensitivity for human motion recognition and possesses great potential for future applications in wearable electronics. Further, the possibility of bright handwriting on MTBS based on the desired ML properties can expand the scope of its applications. Fig. 4 Self-powered sensor performance of the MTBS. a Demonstration of the system for monitoring human physiological signals based on the obtained MTBS. Signals of the MTBS used as self-powered sensors to monitor b Gestures recognizing, c knee bending, d wrist bending, e running, f finger tapping, g finger touching, h swallowing, i writing Arabic number “1”, j writing Arabic number “2”, k writing Arabic number “3” Signal Identification and Intelligent Control Based on the excellent performances of the obtained MTBS, the clear-cut and bright images for controllable handwriting is acquired (Fig. 5 a), which greatly facilitate the accurate recognition and efficient intelligent control. The frame-by-frame enhancement magnification can be adjusted to improve some of the digital defects during the image composition process (Fig. S9). In addition, to facilitate the recognition process based on machine learning approach, the training process of the machine learning network was performed based on the Mixed National Institute of Standards and Technology database (MNIST), an open-source third-party database of handwriting digits [ 61 , 62 ]. In order to improve recognition efficiency and accuracy, the handwriting digital images based on the obtained MTBS (extracted green color at first to avoid disturbance) and then converted to the digitization matrix (Fig. S10). As shown in Fig. S11, compared with extracting red and blue, extracting green for image synthesis makes the writing content the clearest. Consist with the above characterization about ML performances, the grayscale and G-value of the writing area change significantly with the applied force and a very small force is enough for the achievement of handwriting signal input (Fig. 5 b). The MTBS can be used to distinguish the obtained handwriting patterns and has great potential for intelligent control by recognizing numbers. Fig. 5 Intelligent control system. a Images of Arabic numeral from 1 to 9 processed by the developed software. b Histogram of Gray Scale and B -value in response to the applied force during handwriting and the conversion of generated images to the images with white character with black background grayscale for Arabic numbers 2, 4 and 6. c Schematic diagram of RF machine learning algorithm. d Prediction confusion matrix based on 10,000 test data. e Schematic diagram of handwriting numerically controlled trolley movement system. f Movement of the trolley is controlled by different input digital signals, such as left turn, right turn and straight ahead. (Color figure online) Machine learning can be used for handwritten digit recognition through various algorithms [ 63 , 64 ]. Random forest (RF) algorithm, which integrates multiple decision trees through the idea of integrated learning, was used to recognize the handwriting digital images [ 65 – 67 ]. The correct recognition rate of the three algorithms was compared (random forest, K -nearest neighbor, decision tree), the random forest algorithm has the highest correct rate because it integrates multiple decision trees through the idea of integrated learning (Fig. S12). RF algorithm is to determine the result by category voting after classifying the input data according to their different features, and designate the category with the highest number of votes as the final output result (Fig. 5 c). The correct rates of RF algorithm-based machine learning with training amounts of 1000, 5000, 10,000, 30,000 and 60,000 are compared, and it is found that the correct rate of machine learning increases as the training amount increases. However, the time required for machine learning also increases, so in order to balance the time required for machine learning with the correct rate, we choose the training amount of 60,000 for identification (Fig. S13). The developed RF algorithm-based machine learning network was trained by using 60,000 handwriting digits from the open-source MNIST handwriting digit database (Fig. S14), and the accuracy was later verified by using another 10,000 handwriting digits for test. The results show that the model used for recognizing hand-written digits possesses a high accuracy rate (96.87%, Fig. 5 d), verifying the good reliability of digit recognition system based on machine learning. To further validate this concept, videos of writing 2, 4 and 6 on the MTBS were imported into the developed integrated software (combing data reading, image synthesis, recognition and control), after which the handwriting information recognized by the software is transmitted to the trolley via wireless signals to achieve intelligent remote control of the trolley ( Fig. 5 e). Figure 5 f and Videos S2–S4 show that by defining the moving mode, the trolley can move forward and turn left/right according to the written numbers ( Video S5). This handwriting-controlled motion system can be used to manipulate machines to help humans perform complex operations and has great promise for intelligent control and human–machine interface in the future."
} | 6,012 |
36286994 | PMC9769843 | pmc | 6,839 | {
"abstract": "ABSTRACT The Southwest Indian Ridge (SWIR) is one of the typical representatives of deep-sea ultraslow-spreading ridges, and has increasingly become a hot spot of studying subsurface geological activities and deep-sea mining management. However, the understanding of microbial activities is still limited on active hydrothermal vent chimneys in SWIR. In this study, samples from an active black smoker and a diffuse vent located in the Longqi hydrothermal region were collected for deep metagenomic sequencing, which yielded approximately 290 GB clean data and 295 mid-to-high-quality metagenome-assembled genomes (MAGs). Sulfur oxidation conducted by a variety of Gammaproteobacteria, Alphaproteobacteria, and Campylobacterota was presumed to be the major energy source for chemosynthesis in Longqi hydrothermal vents. Diverse iron-related microorganisms were recovered, including iron-oxidizing Zetaproteobacteria, iron-reducing Deferrisoma , and magnetotactic bacterium. Twenty-two bacterial MAGs from 12 uncultured phyla harbored iron oxidase Cyc2 homologs and enzymes for organic carbon degradation, indicated novel chemolithoheterotrophic iron-oxidizing bacteria that affected iron biogeochemistry in hydrothermal vents. Meanwhile, potential interactions between microbial communities and chimney minerals were emphasized as enriched metabolic potential of siderophore transportation, and extracellular electron transfer functioned by multi-heme proteins was discovered. Composition of chimney minerals probably affected microbial iron metabolic potential, as pyrrhotite might provide more available iron for microbial communities. Collectively, this study provides novel insights into microbial activities and potential mineral-microorganism interactions in hydrothermal vents. IMPORTANCE Microbial activities and interactions with minerals and venting fluid in active hydrothermal vents remain unclear in the ultraslow-spreading SWIR (Southwest Indian Ridge). Understanding about how minerals influence microbial metabolism is currently limited given the obstacles in cultivating microorganisms with sulfur or iron oxidoreduction functions. Here, comprehensive descriptions on microbial composition and metabolic profile on 2 hydrothermal vents in SWIR were obtained based on cultivation-free metagenome sequencing. In particular, autotrophic sulfur oxidation supported by minerals was presumed, emphasizing the role of chimney minerals in supporting chemosynthesis. Presence of novel heterotrophic iron-oxidizing bacteria was also indicated, suggesting overlooked biogeochemical pathways directed by microorganisms that connected sulfide mineral dissolution and organic carbon degradation in hydrothermal vents. Our findings offer novel insights into microbial function and biotic interactions on minerals in ultraslow-spreading ridges.",
"conclusion": "Conclusion. In this study, a metagenomic survey was conducted targeting 2 Longqi hydrothermal vents in the SWIR. Through bulk metagenomic analysis and functional profiling on 295 MAGs, microbial structure and metabolic features in Longqi hydrothermal vents revealed the potential impacts of fluid-water mixing and mineralogical composition on microbial communities. Sulfur oxidation by diverse Gammaproteobacteria, Alphaproteobacteria, and Campylobacterota might be the major energy source for primary production in both active black smoker and diffuse vent chimneys, while support from chimney mineral for microbial chemosynthesis was also emphasized. Novel chemoheterotrophic iron-oxidizing species in 12 phyla were identified, and might use nitrate as electron acceptors to couple with iron. Wide distribution of multi-heme and siderophore transportation genes among MAGs also suggested massive electron transportation network between microbes and chimney minerals. Stronger microbial iron oxidoreduction potential was revealed in Dive96 as more diverse compositions of iron-oxidizing and iron-reducing bacteria were detected. It is possible that pyrrhotite in Dive96 minerals provides more dissoluble iron supply for the growth of iron-related species. Inhabitance of these novel iron-oxidizing chemoheterotrophs could further influence iron biogeochemistry in hydrothermal vent minerals but more efforts are essential to verify and quantitively study their iron-oxidizing capabilities.",
"introduction": "INTRODUCTION Mid-ocean ridges constitute the longest deep-sea mountain chains on Earth, occupying approximately one third of the area of the global ocean. Based on their spreading rate, mid-ocean ridges can be categorized into fast (full spreading rate ≥ 60 mm yr −1 ), intermediate (20 mm yr −1 ≤ spreading rate < 60 mm yr −1 ), slow, and ultraslow (<20 mm yr −1 ) ridges. Notably, ultraslow ridges have been recently acknowledged as a new class of mid-ocean ridges and increasingly become the ideal model for observing subseafloor geological processes for their uniqueness in morphology and crustal characteristics ( 1 ). The Southwest Indian Ridge (SWIR) is a typical ultraslow-spreading center, with a spreading rate as low as 12-15 mm yr −1 ( 2 , 3 ), occupying approximately 10% of the area of the global oceanic ridge system ( 4 ). It is also known as one of the most complicated mid-ocean ridge systems in the global oceans, as its hydrothermal activities are under the interactive control of multiple geological factors such as magma supply, thermal sources of magmatic activities, and tectonic settings ( 5 ). Massive deep-sea research has been performed in the SWIR, seeking novel insights in subsurface hydrothermal systems ( 6 , 7 ), global biogeographic connections ( 8 ), and deep-sea mining management ( 5 , 9 , 10 ). The first active hydrothermal region reported in the SWIR was Longqi, which was discovered during the Chinese DY115-19 cruise in 2007 ( 11 ). Up to now, 9 active black smokers and 5 diffuse vents have been reported in Longqi ( 12 ), with the maximum temperature reaching 381°C ( 13 ). A deep subsurface circulation system featured with a high-temperature, fluid-water mixing process was identified, and may be responsible for the formation of massive sulfide deposits ( 6 , 14 , 15 ) and high Fe but low pH hydrothermal fluids ( 6 ). Hydrothermal activities in Longqi have also been presumed active the last 100 thousand years up to now ( 16 ), and are continually growing at matured stages ( 15 ). Previous investigations have mainly aimed to study microbial community compositions on hydrothermal vents or plumes by means of 16S rRNA amplicon sequencing or denaturing gradient gel electrophoresis (DGGE) ( 17 – 20 ). Ding et al. has revealed that microbial communities in Longqi were highly diverse and comparable to other vents in global oceans ( 17 ). However, little is known about the potential microbial activities in Longqi hydrothermal vent communities. It is unclear how microbial communities interact with venting activities, seawater, and chimney minerals in the Longqi area. Biotic iron oxidation in hydrothermal vents is currently poorly understood but is known to be actively associated with local mineral biogeochemistry and microbial energy conservation ( 21 – 25 ). Several autotrophic iron-oxidizing species have been reported functionally versatile and metabolically active, such as Zetaproteobacteria and Gammaproteobacteria ( Thiomicrospira ) ( 26 – 28 ). Conversely, up to now, heterotrophic iron-oxidizing bacteria have been rarely detected and most of them are presumed to have limited abilities of conducting iron oxidation by subsidiary abiotic processes ( 29 , 30 ). Heterotrophic bacteria that can direct enzymatic iron oxidation are scarcely reported across all nature ecosystems, let alone hydrothermal vent environments ( 29 , 31 ). In fact, strict requirements for growth have consistently composed obstacles in the discovery and cultivation of novel iron-oxidizing species ( 32 ). However, recent progresses associated with molecular mechanisms of microbial iron oxidation, such as the recognition of Cyc2 as an iron oxidase ( 33 – 35 ) and the discovery of multi-heme proteins functioning in extracellular electron transportation ( 36 , 37 ), have facilitated chances of studying microbial iron oxidation through bioinformatic methods. Combined with genome-level data and qualified annotation tools ( 28 , 38 – 40 ), identification of novel iron-oxidizing species would be feasible, as well as evaluation of the microbial interactions with chimney minerals. To deepen the understanding of the potential microbial activities in ultraslow ridges, deep metagenomic sequencing of samples from an active black smoker (DFF12) and a diffuse vent (DFF1) in the Longqi hydrothermal region were performed. Combined with mineralogical analysis, potential reconstruction of microbiome and its interactions with environment were indicated in this study.",
"discussion": "DISCUSSION Mineral-involved chemosynthesis. Sulfur oxidation was the most frequently detected pathway for chemosynthesis in Dive96 and Dive100. The majority of assimilatory RubisCO (Ribulose-bisphosphate carboxylase) sequences (form I and II [ 56 ]) were identified in MAGs carrying the rDSR or Sox pathway, such as Alphaproteobacteria and Gammaproteobacteria ( Fig. 3 ). The reductive citrate cycle (rTCA) pathway was present in putative thiosulfate-oxidizing MAGs associated with Campylobacterota, Nitrospinota, and Aquificota in which a Sox cluster was also detected ( Fig. 3 ). Overall, sulfur-oxidizing pathways were possessed by 64.10% of putative autotrophic MAGs in Dive96, and 88.46% in Dive100. Notably, some of these sulfur-oxidizing MAGs affiliated with Gammaproteobacteria and Alphaproteobacteria carried various combinations of sulfur-related genes such as sqr , Sox, rDSR, and soeABC , which indicated versatile genomic potential for sulfur oxidation using various sulfur substrates (sulfide, thiosulfate, sulfite) in multiple reaction sites (cytoplasmic and periplasmic), connecting energy conservation with cellular growth and primary production ( 57 ). These sulfur-oxidizing bacteria (SOB) such as Thiohalomonadales, Thiotrichaceae, and Hyphomicrobiaceae were enriched in microbial communities, further emphasizing the significance of sulfur oxidation in microbial chemosynthesis ( Fig. 3 ). Some of the sulfur-oxidizing primary producers were also detected in microbial communities on inactive sulfide minerals (Fig. S4). After extinction of hydrothermal vent activities, solidate sulfide mineral was presumed to substantially support chemosynthesis in terms of microbial sulfur-oxidation. Autotrophic sulfur-oxidizing bacteria enriched in inactive sulfide minerals might be experts in utilizing sulfide mineral as energy source, capable of dissolving or extracting reductive sulfide species from sulfide minerals for energy conservation. Members of Thiohalomonadales encoding Sox and rDSR gene clusters were detected in Dive96 and Dive100, which were also previously found enriched in extinct vents in the East Pacific Rise and sulfide mineral in Manus Basin ( 58 , 59 ). Sulfur-oxidizing SZUA-229 members dominant in inactive sulfide minerals or chimney walls ( 58 , 59 ) were also recovered in Dive96, with MAGs encoding Sox gene clusters. Electrode-respiring Cadidatus Tenderia was recovered in Dive96 although the sulfur-oxidizing pathway was absent. Detection of these “mineral-preferred” sulfur-oxidizing Gammaproteobacteria indicated chimney mineral supporting microbial chemosynthesis in Dive96 and Dive100 ( Table 1 , Fig. 3 , and Fig. S4). With the additional assistance of diverse “mineral” Gammaproteobacteria members, microbial community might be able to utilize sulfur from chimney minerals and surrounding fluid for chemosynthesis, simultaneously. TABLE 1 Feature and distribution of several Gammaproteobacteria members found in Dive96 and Dive100 a Class/phylum Members Sample Reference Gammaproteobacteria SZUA-229 Dive96 47 , 58 , 59 Gammaproteobacteria \n Tenderia \n Dive96 58 , 59 Gammaproteobacteria Thiohalomonadales Dive96, Dive100 58 , 59 Gammaproteobacteria Xanthomonadales Dive96, Dive100 48 , 58 a “Mineral” type Gammaproteobacteria are presumed capable of oxidizing insoluble sulfur from chimney minerals except for Tenderia . Phylogenetic positions with reference Gammaproteobacteria genomes from other hydrothermal vents or inactive minerals were presented in Fig. S4. Expanded mineral-microbe interaction. Microbial sulfur and iron metabolisms could be significant in as both chimney samples were composed of sulfide minerals (Table S1 and Fig. S1). As both fccB and sqr participate in the oxidation of sulfide and generation of elemental sulfur product (S 8 or HS-(S n )-SH) in forms of sulfur globules or extracellular organic minerals ( 53 , 54 ), their significant enrichment also highlighted the metabolic potential in oxidizing sulfide and transforming sulfur-related minerals in the microbial community. Through sulfur metabolisms, microbes might participate in sulfide material transformation involved with mineral weathering as a result of energy conservation or cellular detoxification. Electron transportation between cells and minerals was also fascinating as it indicated microorganisms’ capabilities of utilizing mineral for energy conservation. Previous metagenomic surveys have demonstrated that putative extracellular electron transporter such as multi-heme proteins ( 36 , 52 , 56 ) were frequently detected and highly expressed in microbial communities accommodated on inactive sulfide minerals ( 58 ). In Dive96 and Dive100, wide distributions of multi-heme protein sequences were also observed, and their products were mostly assumed to be secreted to periplasmic or extracellular sites where oxidoreduction were reported ( 52 ). This expanded distribution of multi-heme proteins ( Fig. 3 and Table S2) covered both chemolithoautorophs and heterotrophs with different energy conservation strategies such as SOB (Gammaproteobacteria, Campylobacterota), sulfate-reducing bacteria (SRB, Desulfobacterota), nitrate-oxidizing bacteria (NOB, Nitrospinota), and iron-oxidizing bacteria (FeOB, Zetaproteobacteria and Zixibacteria). As potentially serving in establishments of massive networks of extracellular electron transfer (EET) between cells or cells to minerals ( 36 , 52 , 56 ), multi-heme proteins can assist in cooperative energy conservation between syntrophic partners ( 57 ) or enabling microorganisms to access more diverse electron acceptors or donors, such as insoluble sulfide minerals or soluble Fe(III), Mn(IV) ions, etc. ( 36 , 52 , 56 ). The establishment of well-organized electron transport networks by multi-heme proteins might comprehensively enhance microorganisms’ adaptation and survival in extreme hydrothermal conditions ( 37 ), extending their energy sources to solid chimney minerals rather than being constrained by the availability of materials from hydrothermal fluids or seawater. Potential influences of minerals on microbial community. Distinct microbial iron oxidoreduction metabolic potential was observed between Dive96 and Dive100, and more diverse microbial participation was featured in Dive96. Firstly, a more diverse composition of iron-oxidizing Zetaproteobacteria represented by ZetaOTUs was detected in Dive96 (Fig. S5). Secondly, more diverse Cyc2 homologs possessed by MAGs were also detected in Dive96 than in Dive100 ( Fig. 4 and Fig. S9). Thirdly, various iron-related bacteria were also identified in Dive96, such as Defferisomatia, Thermosulfidibacter, Desulfovibrionaceae, and magnetotactic bacteria Nitrospirota ( Fig. 3 , Fig. 5 , Fig. S6, Fig. S7, and Fig. S8) ( 42 , 44 , 45 , 60 , 61 ). This increased diversity of both iron-oxidizing members and iron-reducing members might indicate a more active iron biogeochemical cycle in Dive96 directed by multiple microbial species. FIG 5 Potential scheme of microbial sulfur and iron metabolisms and associated interactions between hydrothermal environment, chimney mineral and microbial communities in Longqi region. One of the underlying reasons could be owed to the difference in mineral compositions between Dive96 and Dive100. Participation of chimney minerals in chemosynthetic sulfur oxidation was already indicated by the detection of abundant mineral-based Gammproteobacteria members in Dive96 and Dive100. In fact, mineral composition could affect local microbial structure ( 62 – 68 ) and cause thermodynamic shifts in the local environment ( 69 ). Previous research has illustrated the significant influence of pyrrhotite on microbial iron-oxidizing metabolism in mineral-based communities ( 70 ). Content of pyrrhotite supplied to the sphalerite mixture provided more dissolved iron to surrounding environments, which subsequently allowed iron-oxidizing members to thrive and enhance biotic iron oxidization and sulfur oxidization in the microbial community ( 70 ) ( Fig. 5 ). In Dive96, it is possible that the augment of Fe(II) and Fe(III) supplied from pyrrhotite enhanced the growth of more diverse FeOBs, such as Zetaproteobacteria. Further dissolution of minerals also released sulfur materials, which could enhance in chemosynthetic sulfur oxidation in Dive96 and allow potential heterotrophic FeOBs to thrive. Accelerated FeOBs further caused dissolution and oxidation of minerals, forming positive feedbacks with increased Fe(III) supply and growth of iron-reducing bacteria. As a result, more diverse microbial iron oxidoreduction indicated by the composition of FeOBs and iron-reducing species was observed in Dive96. Novel iron-oxidizing bacteria. As Cyc2 functions as an iron oxidase in FeOBs ( 34 ), its distribution in microorganisms might be indicative of potential iron-related functions in hydrothermal vent microorganisms. However, as functional understandings about Cyc2 proteins are still very limited, caution should be practiced about connecting the possessions of Cyc2 homologs and biotic iron oxidation among microorganisms. In this study, multiple steps of bioinformatic analysis were conducted to demonstrate the proper annotation of Cyc2 homologs. Firstly, close examination of conserved regions with heme-binding functions ( 71 ) were required. Only sequences with N terminus composition similar to those encoded by function-verified iron-oxidizing bacteria, such as Gallionella , Mariprofundus , Thiomonas, and Acidithiobacillus spp. (Fig. S9) could be identified as Cyc2 homolog candidates ( 33 , 35 , 72 , 73 ). Secondly, checking the destinations of these putative Cyc2 products was also necessary, as current research supported that Cyc2 conducted Fe(II) oxidation on the outer membrane where energy conservation was performed with cytochrome oxidases in FeOBs ( 33 , 74 , 75 ). Analysis in the conserved region compositions, product destinations, and phylogeny diversity revealed that features of novel Cyc2 homologs catered to the current understandings of Cyc2 proteins verified for iron oxidation. Given their similarities, it is theoretically possible that these protein products function as an iron oxidases or electron transporter as previously demonstrated ( 34 , 76 ), further enhancing microorganisms′ capabilities of conducting extracellular electron uptake or even allowing them to access additional electron donors such as Fe(II) for energy conservation ( 77 ). In this way, potential FeOBs encoding Cyc2 were identified in Dive96 and Dive100. Some of the relevant MAGs also encoded carbon fixation pathways and might be potential chemolithoautotrophic iron-oxidizing bacteria. In addition to Zetaproteobacteria discussed above, 2 MAGs related to Gammaproteobacteria (class SZUA-229 and Thiohalomondales) carried both Cyc2 homologs from cluster III and assimilatory form of RubisCO sequences ( Fig. 3 and Fig. 4 ). These autotrophic MAGs also carried metabolic potential for nitrate reduction and thus catered to the definition of NRFeOx ( 29 ), which is capable of utilizing nitrate as electron acceptor coupling with iron oxidation. Our result expands the diversity of uncultured iron-oxidizing Gammaproteobacteria inhabiting in hydrothermal vent regions, in addition to isolated Thiomicrospira ( 26 ). Notably, the majority of novel Cyc2-like sequences were found in putative heterotrophic bacterial groups including DTB120, Calditrichota, Gemmatimonadota, Eisenbacteria, Hydrogenedentota, Krumholzibacteriota, Planctomycetota, CSSED10-310, Acidobacteriota, Bacteroidota, KSB1, and SAR324. Apart from Bacteroidota ( 78 , 79 ) and uncultured phylum DTB120 ( 28 ), other heterotrophic groups detected with Cyc2 here have not been presumed with iron oxidation function previously. However, functional profiles of these MAGs in this study were identical to the prediction of uncultured iron-oxidizing extremophiles featured with functions of versatile organic degradation and utilization of nitrate as electron acceptors ( 31 ). For one thing, these MAGs encoded enriched copies of carbohydrate-active enzymes (CAZymes) while absent of carbon fixation pathways. For example, MAGs related to Gemmatimonadota contained up to 20 copies of PLs (polysaccharide lyases), while members of CSSED10-310, Krumholzibacteriota, Planctomycetota, Hydrogenedentota, Calditrichota, and Bacteroidota contained approximately 30 copies of CEs (carbohydrate esterases), on average. MAGs associated with Eisenbacteria also carried over 50 copies of genes functioning in peptide degradation, suggesting its specialty in utilizing peptide molecules ( Fig. 3 ). Furthermore, genes involved in nitrate or nitrite reduction were also carried by these MAGs, suggesting that nitrate may serve as electron acceptors for microbial iron oxidation. Metabolisms and functions that were practical for microbial iron oxidation were also encoded among these microbial groups. Sixteen MAGs associated with Krumholzibacteriota, Acidobacteriota, Bacteroidota, Calditrichota, CSSED10-310, Hydrogenedentota, Eisenbacteria, Planctomycetota, KSB1, and SAR324 also encoded cytochrome c oxidase for respiration (Table S2), indicating the complete energy conservation path known for microbial iron oxidation ( 35 ) ( Fig. 3 ). Functions that were potentially advantageous for iron oxidation were also found, including EPS production and biofilm generation, which can protect microorganisms from suicidal cell encrustation caused by iron-oxyhydroxide ( 80 , 81 ), and enhance their efficiency in acquisition of organic carbon and iron as aggregates ( 82 ). In fact, microorganisms with phenotypes of heterotrophic iron oxidation have been reported in species of Marinobacter ( 83 – 86 ), Alteromonas , Pseudoalteromonas , Pseudomonas , Halomonas , and Alcanivorax ( 87 – 89 ), which were indicated with important roles in the iron cycle in subsurface fluid and hydrothermal vents. Research targeting microbial mats covering hydrothermal chimneys have also suggested the activity of heterotrophic iron oxidizers DTB120, and play an important role in element cycling ( 28 ). Similarly, our study also indicated the potential function of iron oxidation in diverse heterotrophic bacteria. From the perspective of hydrothermal vent biogeochemistry, a novel biogeochemical pathway connecting biotic dissolution or weathering of sulfide minerals and accessibility of organic carbon was predicted because of the presence of heterotrophic FeOBs. While taking advantage from iron oxidation, heterotrophic FeOBs also cause further dissolution of sulfide minerals as the equilibrium of mineral dissolution is constantly disrupted. Additional sulfur species such as polysulfide, sulfide, and thiosulfate could be supplied into microbial communities ( 90 , 91 ), and further accelerate carbon fixation performed by sulfur-oxidizing bacteria such as Gammaproteobacteria and Campylobacteria ( 26 ). In this way, iron-oxidizing heterotrophs and sulfur-oxidizing autotrophs are mutualistic to each other as they participate in the material replenishment processes for counterparts as a result of cellular energy conservation. A new iron-related biogeochemical pathway influenced by organic carbon availability may be present, which is under the control of the interaction of sulfide-oxidizing chemolithoautotrophs and iron-oxidizing chemoorganotrophs. Conclusion. In this study, a metagenomic survey was conducted targeting 2 Longqi hydrothermal vents in the SWIR. Through bulk metagenomic analysis and functional profiling on 295 MAGs, microbial structure and metabolic features in Longqi hydrothermal vents revealed the potential impacts of fluid-water mixing and mineralogical composition on microbial communities. Sulfur oxidation by diverse Gammaproteobacteria, Alphaproteobacteria, and Campylobacterota might be the major energy source for primary production in both active black smoker and diffuse vent chimneys, while support from chimney mineral for microbial chemosynthesis was also emphasized. Novel chemoheterotrophic iron-oxidizing species in 12 phyla were identified, and might use nitrate as electron acceptors to couple with iron. Wide distribution of multi-heme and siderophore transportation genes among MAGs also suggested massive electron transportation network between microbes and chimney minerals. Stronger microbial iron oxidoreduction potential was revealed in Dive96 as more diverse compositions of iron-oxidizing and iron-reducing bacteria were detected. It is possible that pyrrhotite in Dive96 minerals provides more dissoluble iron supply for the growth of iron-related species. Inhabitance of these novel iron-oxidizing chemoheterotrophs could further influence iron biogeochemistry in hydrothermal vent minerals but more efforts are essential to verify and quantitively study their iron-oxidizing capabilities."
} | 6,454 |
39711482 | PMC11837899 | pmc | 6,840 | {
"abstract": "Abstract To tackle the formidable challenges posed by extreme cold weather events, significant advancements have been made in developing functional surfaces capable of efficiently removing accreted ice. Nevertheless, many of these surfaces still require external energy input, such as electrical power, which raises concerns regarding their alignment with global sustainability goals. Over the past decade, increasing attention has been directed toward photothermal surface designs that harness solar energy−a resource available on Earth in quantities exceeding the total reserves of coal and oil combined. By converting solar energy into heat, these designs enable the transformation of the interfacial solid‐solid contact (ice‐substrate) into a liquid‐solid contact (water‐substrate), significantly reducing interfacial adhesion and facilitating rapid ice removal. This critical perspective begins by emphasizing the advantages of photothermal design over traditional de‐icing methods. It then delves into an in‐depth analysis of three primary photothermal mechanisms, examining how these principles have expanded the scope of de‐icing technologies and contributed to advancements in photothermal surface design. Finally, key fundamental and technical challenges are identified, offering strategic guidelines for future research aimed at enabling practical, real‐world applications.",
"conclusion": "6 Conclusions and Outlook Understanding the underlying photothermal physics holds great promise for achieving highly efficient de‐icing. Despite substantial progress made in this field, there is a pressing need to deepen our understanding of the mechanisms of photothermal conversion. This endeavor should be supported by the discovery of novel materials, innovations in ice detection methods, and the multidisciplinary integration of diverse scientific fields. Beyond exploring fundamental mechanisms, it is equally important to establish standardized approaches for evaluating photothermal and de‐icing performance. For photothermal performance assessment, while the measurement of solar absorbance using spectrophotometers is widely accepted, the evaluation of surface temperature rise has been conducted under diverse substrate temperatures, environmental humidity and temperatures, and even varying light intensities, all of which can influence the results. Moreover, the prevalent use of IR cameras for temperature measurement is prone to uncertainty due to improperly calibrated surface emissivity, ambient temperature, and humidity, as well as differences in measurement locations (ice layer or substrate), leading to potential deviations from the true values. Similarly, assessing de‐icing performance presents challenges. The kinetics of ice melting are affected by multiple parameters, including the intensity and angle of incident light, the illuminated surface area, the size and thickness of ice blocks, the thickness and coating of the test surface, the temperature of the test surface, ambient temperature and humidity, and the insulation properties of the underlying support. The absence of a unified set of testing parameters across studies has significantly impeded the effective comparison and interpretation of photothermal de‐icing results in the literature. Moving toward real‐world applications, six primary challenges emerge for photothermal de‐icing, as summarized in Figure \n \n 7 \n . Figure 7 Schematic summarizing the application challenges to photothermal deicing. Challenges involve mechanical stability, chemical stability, thermal controllability, scalability, shape adaptability, and transparency. 6.1 Mechanical Stability The mechanical stability of photothermal materials poses a primary challenge, as many designs are made of nanomaterials which are prone to damage during operation. This challenge is especially pronounced in photothermal designs coupled with superhydrophobic or slippery properties, which often involve complex manufacturing processes [ \n \n 90 \n , \n 108 \n \n ] or liquid infusion [ \n \n 109 \n \n ] that can suffer from mechanical wear or lubrication depletion. [ \n \n 110 \n \n ] Recent strategies demonstrate that overcoming these challenges can rely on integrating photothermal materials with inherently hydrophobic polymers, such as PDMS [ \n \n 85 \n \n ] and silicon resin, [ \n \n 85 \n \n ] or optimizing the coating geometric and structural design, for instance, crafting micro architectures which act as a protective or sacrificial armor. [ \n \n 111 \n \n ] By doing so, the coatings show strong resistance to mechanical abrasion, chemical corrosion, and thermal variation, all together contributing to durable de‐icing performance. Additionally, previous works on robust superhydrophobic surfaces with photothermal effect further support the potential for durable designs, showing resistance to water injection, sand impact, and even long‐term mechanical abrasion. [ \n \n 84 \n , \n 112 \n \n ] Similarly, the self‐repairing ability of slippery surfaces endows the surface with exceptional durability. [ \n \n 113 \n \n ] However, challenges persist, including achieving thin coatings, scalability, and substrate compatibility. Optimized surface architectures and durable surface chemistry can alleviate these concerns. For example, micro‐thick, scalable coatings developed via physical vapor deposition [ \n \n 114 \n \n ] or combined processes like salinization and impregnation [ \n \n 84 \n \n ] have shown resilience to humid and cold environments for up to three years. Although their photothermal performance remains underexplored, these principles offer a foundation for future robust de‐icing solutions. 6.2 Thermal Controllability While photothermal coatings are valuable for de‐icing, they can have adverse effects on surface temperature in certain climates. In cold, cloudy conditions, reduced heating effectiveness limits surface temperature rises, hampering de‐icing. Conversely, in hot climates, excessive heating can lead to system overheating, material stress, or cracking due to thermal expansion and contraction. Reported temperature rises for photothermal surfaces under one‐sun illumination typically range from 20 to 60 °C (Figures 3 , 4 , and 5e ). However, some materials, such as Fe₃O₄ sponges, achieve rises to 80 °C, and CNT‐PDMS nanoparticles reach 135 °C under laser illumination. [ \n \n 61 \n , \n 70 \n \n ] Such overheating can strain systems, particularly in warm climates where surface temperatures need not exceed 0 °C for de‐icing. Furthermore, overheating can increase cooling demand in hot climates. Given that most outdoor industrial facilities operate above −40 °C and that ice melting necessitates only a surface temperature above 0 °C, it may not be necessary to use photothermal materials with ultra‐high performance. Thus, mitigating the adverse effects on surface temperature can resort to properly choosing the photothermal material that meet the practical thermal need. 6.3 Scalability Achieving effective photothermal de‐icing capabilities at small scales is comparatively straightforward. However, application of photothermal coatings to large‐scale systems is fraught with significant challenges. This is because commercial viability of photothermal coatings is highly dependent on the fabrication techniques employed and the choice of materials used. The fabrication of photothermal surfaces should primarily consider the use of commercially available and economically viable products. To date, a substantial body of nanomaterials, particularly metallic nanoparticles such as Au and Ag, are prohibitively expensive for large‐scale deployment. Carbon‐based materials that meet the criteria may serve as a more suitable alternative. Nonetheless, imparting the required mechanical robustness to carbon materials necessitates the use of polymer binders, which increases the complexity of fabrication. Moreover, scaling up the fabrication process requires the innovative integration of multiple considerations, such as substrate compatibility, the design of the manufacturing process line, and the application of appropriate coating patterns. These challenges must be addressed to achieve the practical implementation of photothermal de‐icing coatings at a large scale. 6.4 Chemical Stability In industrial environments, surfaces are often subjected to a variety of chemically corrosive and reactive substances, such as acidic and alkaline solutions, salt solutions, and organic solvents. [ \n \n 115 \n \n ] Even in relatively benign outdoor environments, exposure to ultraviolet radiation, seawater, or acid rain, can be prevalent. If the surface coating lacks sufficient chemical stability, it can easily experience issues like peeling, cracking, or corrosion. [ \n \n 116 \n \n ] This not only affects the aesthetic appearance of the surface but can also seriously undermine the service life and performance reliability of the equipment. One possible pathway is to rationally choose photothermal materials that show strong resistance to chemical and UV interactions or embed the photothermal materials into the chemically stable composites based on the correspondent working environments. [ \n \n 85 \n \n ] \n 6.5 Shape Adaptability Industries increasingly require photothermal coatings for complex or irregular surfaces, such as those found in heat pumps, [ \n \n 117 \n \n ] aircraft, [ \n \n 118 \n \n ] and flexible solar cells, [ \n \n 119 \n \n ] as they enable specialized functionalities, including expanded heat transfer area, enhanced lift, and improved energy harvesting efficiency. A key challenge in this context is the development of photothermal coatings which can be applied to and conform on the shape of these non‐planar or irregular‐shaped surfaces. A potential solution may lie in the judicious selection of photothermal materials with strong shape‐adaptability, coupled with the employment of intelligent and controllable coating fabrication techniques, such as spray coating, dip coating, or conformal chemical vapor deposition. 6.6 Transparency Most photothermal designs exhibit a black appearance, which hinders their application to systems requiring light transmission, including construction, automobile windshield, and energy sectors. [ \n \n 83 \n \n ] A typical example is solar cell panels which require exceptional optical transparency to maximize the absorption of sunlight and efficient conversion to electrical energy. [ \n \n 120 \n \n ] In cold regions, the accumulation of ice on the surface of solar cell panels can diminish their power generation efficiency, necessitating the integration of light‐induced de‐icing capabilities. To design a coating for such applications while exhibiting multi‐functions as mentioned above, it is essential to strike a balance between the material choice, structural, optical, and thermal properties to meet the specific requirements of efficient de‐icing application. To date, it is extremely challenging for current photothermal de‐icing material designs to meet all these requirements. Most existing solutions address only one or two of these aspects and remain at the laboratory scale. Advancing these technologies to a higher technology readiness level will require multidisciplinary collaboration among engineers and scientists. Developing the next generation of photothermal coatings that combine durability, scalability, and efficiency is critical for realizing their full industrial potential.",
"introduction": "1 Introduction The need for effective de‐icing strategies to combat cold weather has been a persistent challenge throughout human civilization. Conventional active de‐icing efforts include electro‐thermal, [ \n \n 1 \n \n ] pulse electro‐thermal, [ \n \n 2 \n \n ] ultrasonic, [ \n \n 3 \n \n ] mechanical, [ \n \n 4 \n \n ] and chemical approaches, [ \n \n 5 \n \n ] which aim to melt, shake, blow, or scrape off accumulated ice or lower its freezing point. [ \n \n 6 \n \n ] While effective, these active approaches are hindered by significant drawbacks such as high energy input, complicated setups, meticulous maintenance requirements, and limited environmental sustainability. [ \n \n 7 \n \n ] These limitations highlight the urgent need for a paradigm shift in the development of functional de‐icing materials, especially given the increasing frequency and severity of extreme weather events. [ \n \n 8 \n \n ] In response, passive de‐icing strategies, which rely on the inherent properties of functional materials, have gained attention. Common examples include superhydrophobic surfaces, [ \n \n 9 \n \n ] lubricant‐infused surface, [ \n \n 10 \n \n ] polymer brush or gels, [ \n \n 11 \n \n ] low‐modulus elastomers, [ \n \n 12 \n \n ] low interfacial‐toughness materials, [ \n \n 13 \n \n ] or suspended thin metallic surface. [ \n \n 14 \n \n ] These approaches leverage mechanisms such as reducing surface energy and contact area, [ \n \n 9 \n \n ] creating liquid‐like slippery interface, [ \n \n 10 \n , \n 11 \n \n ] or initiating interfacial cavitation, cracks, or buckling. [ \n \n 9 \n , \n 10 \n , \n 11 \n , \n 13 \n , \n 14 \n \n ] Despite their innovative design, most passive methods still require some level of energy input to facilitate de‐icing, [ \n \n 15 \n \n ] which stands in stark contrast to global priorities for sustainability, carbon neutrality, and energy efficiency. Toward this end, remarkable research efforts have been put to the exploitation of solar energy, a resource celebrated for its inherently green and sustainable nature. With an immense amount of energy (105 × 109 TWh) absorbed by Earth's surface−exceeding the combined reserves of coal and oil. [ \n \n 16 \n \n ] Solar energy can be harnessed and converted into diverse energy forms, including electricity, chemical fuels, and thermal energy, facilitated by the underlying photovoltaic, photochemical, and photothermal processes, respectively. [ \n \n 17 \n \n ] Among these, the photothermal process stands out for its ability to directly convert solar energy into thermal energy, achieving the highest conversion efficiency. This photothermal paradigm has found wide‐ranging applications, from distillation to steam generation, [ \n \n 18 \n \n ] desalination, [ \n \n 19 \n \n ] and de‐icing. [ \n \n 20 \n \n ] Specifically, in de‐icing applications, the solar‐to‐heat property enables rapid heating of material surfaces to temperatures above the melting point of ice. This process transforms the solid‐solid interface (ice‐substrate) into a liquid‐solid interface (water‐substrate), allowing bulk ice to easily slide off under gravity. [ \n \n 21 \n \n ] Even in cases where meltwater is not completely shed, continuous surface heating can promote evaporation, leaving behind a dry surface. [ \n \n 11 \n , \n 22 \n \n ] As such, photothermal designs demonstrate great promise in achieving effective de‐icing across a wide range of ice sizes, from microscale to macroscale. In this perspective, we begin by exploring the rationale underlying adopting photothermal design as a viable approach to achieve effective de‐icing. This is followed by an in‐depth analysis of three core photothermal mechanisms inherent to nanomaterials. Building on this foundational understanding, we uncover latest advancements in translating these principles into innovative designs that intricately integrate material interfaces with sophisticated structures, aiming to achieve high‐efficiency de‐icing performance. Finally, we engage in a critical discourse surrounding the key fundamental and technical obstacles that persist, to guide future research efforts toward the realization of practical, real‐world de‐icing technologies. This perspective aims to provide the scientific community with a deeper understanding of the nuanced photothermal properties of diverse materials and offers strategic direction for the design of photothermal nanomaterials, facilitating their application in a wide range of real‐world de‐icing scenarios."
} | 3,967 |
29209281 | PMC5701628 | pmc | 6,844 | {
"abstract": "Extremely acidic and metal-rich acid mine drainage (AMD) waters can have severe toxicological effects on aquatic ecosystems. AMD has been shown to completely halt nitrification, which plays an important role in transferring nitrogen to higher organisms and in mitigating nitrogen pollution. We evaluated the gene abundance and diversity of nitrifying microbes in AMD-impacted sediments: ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB). Samples were collected from the Iron Springs Mining District (Ophir, CO, United States) during early and late summer in 2013 and 2014. Many of the sites were characterized by low pH (<5) and high metal concentrations. Sequence analyses revealed AOA genes related to Nitrososphaera , Nitrosotalea , and Nitrosoarchaeum ; AOB genes related to Nitrosomonas and Nitrosospira ; and NOB genes related to Nitrospira . The overall abundance of AOA, AOB and NOB was examined using quantitative PCR (qPCR) amplification of the amoA and nxrB functional genes and 16S rRNA genes. Gene copy numbers ranged from 3.2 × 10 4 – 4.9 × 10 7 archaeal amoA copies ∗ μg DNA -1 , 1.5 × 10 3 – 5.3 × 10 5 AOB 16S rRNA copies ∗ μg DNA -1 , and 1.3 × 10 6 – 7.7 × 10 7 \n Nitrospira nxrB copies ∗ μg DNA -1 . Overall, Nitrospira nxrB genes were found to be more abundant than AOB 16S rRNA and archaeal amoA genes in most of the sample sites across 2013 and 2014. AOB 16S rRNA and Nitrospira nxrB genes were quantified in sediments with pH as low as 3.2, and AOA amoA genes were quantified in sediments as low as 3.5. Though pH varied across all sites (pH 3.2–8.3), pH was not strongly correlated to the overall community structure or relative abundance of individual OTUs for any gene (based on CCA and Spearman correlations). pH was positivity correlated to the total abundance (qPCR) of AOB 16S rRNA genes, but not for any other genes. Metals were not correlated to the overall nitrifier community composition or abundance, but were correlated to the relative abundances of several individual OTUs. These findings extend our understanding of the distribution of nitrifying microbes in AMD-impacted systems and provide a platform for further research.",
"conclusion": "Conclusion In summary, we found interesting patterns of gene abundance and diversity of AOA, AOB, and NOB in AMD-impacted sediments with low pH and high metal concentrations. Sediment pH was correlated with the total abundance of AOB 16S rRNA genes, but not the total abundance of archaeal amoA or Nitrospira nxrB genes, or to the overall community structure or relative abundance of individual OTUs for any gene. Heavy metals, which had high concentrations in the Iron Springs sediments, were correlated with the relative abundances of several individual OTUs, but not the overall nitrifier community composition or abundance. It is important to note that many other factors not measured here could impact nitrification, including other chemical variables or organismal interactions. In addition, the combined effect of multiple, simultaneous stresses (e.g., acidic pH along with a suite of metals) on gene abundance and community composition is difficult to tease apart. This study provides a foundation for future work to determine rates of nitrification and the role of comammox bacteria in this AMD-impacted system, and to cultivate acidophilic and metal-tolerant AOA and NOB.",
"introduction": "Introduction Nitrification, a central part of the nitrogen cycle, is globally important because it transfers nitrogen to higher organisms and mitigates nitrogen pollution when coupled with denitrification and anammox. Nitrification is the two-step aerobic oxidation of ammonia to nitrate through nitrite. Ammonia oxidation is mediated by ammonia-oxidizing archaea and bacteria (AOA and AOB), while nitrite oxidation is mediated by nitrite-oxidizing bacteria (NOB) commonly from the Nitrobacter and Nitrospira lineages. Recent discoveries documented the complete oxidation of ammonia to nitrate (comammox) by Nitrospira ( Daims et al., 2015 ; van Kessel et al., 2015 ). The microbial communities involved in ammonia oxidation and nitrite oxidation are commonly evaluated using 16S rRNA genes ( Stephen et al., 1998 ) or functional markers such as the amoA gene (encoding the alpha subunit of the ammonia monooxygenase enzyme) ( Stephen et al., 1999 ; Francis et al., 2005 ) and nxrB gene (encoding the beta subunit of the nitrite oxidoreductase enzyme) ( Pester et al., 2014 ). Many nitrifiers are obligate ammonia oxidizers or nitrite oxidizers (based on genomic sequences or physiology in culture), though some have mixotrophic capabilities ( Ward et al., 2011 ; Hatzenpichler, 2012 ). Nitrification has been documented in virtually every environment across the earth (e.g., soil, marine, freshwater) ( Ward et al., 2011 ). Nonetheless, very little is known about nitrification in systems impacted by acid mine drainage (AMD), which refers to acidic and metal-rich waters that flow out of coal and metal mines. One study found that nitrification was completely halted in some AMD-impacted streams when pH dropped below 5.3 ( Niyogi et al., 2003 ). However, it is unknown whether the findings are a general phenomenon in other streams or whether AMD differentially impacts the various groups of microorganisms involved in nitrification. Though the impacts of AMD on nitrification are largely unknown, other research has evaluated individual environmental factors that are often associated with AMD, including acidic pH and high metal concentrations. Nitrification is impacted by acidic pH, in part due to the reduced bioavailability of ammonia and nitrite at low pH ( Suzuki et al., 1974 ; Stein et al., 1997 ). In some aquatic environments, overall nitrification rates were inhibited at pH values lower than 5.7–6 ( Rudd et al., 1988 ; Huesemann et al., 2002 ). Small decreases in pH (by 0.05–0.14) reduced ammonia oxidation rates in the Atlantic and Pacific Oceans ( Beman et al., 2011 ). In wastewater batch reactors, nitrite oxidation rates ceased at pH values lower than 6.5 ( Jiménez et al., 2011 ). Nitrite oxidation rates by neutrophilic Nitrobacter and Nitrospira cultures significantly decreased below pH 6.5 ( Ehrich et al., 1995 ; Grunditz and Dalhammar, 2001 ; Blackburne et al., 2007 ). While acidic pH impacts the overall rates of ammonia oxidation and nitrite oxidation, some nitrifying microbes are capable of withstanding low pH conditions. AOA have been shown to be both abundant and active in some acidic soils, and often numerically dominate the AOB (e.g., Nicol et al., 2008 ; Gubry-Rangin et al., 2010 , 2011 ; He et al., 2012 ; Zhang et al., 2012 ). Both AOA and AOB genes have been found in soils with pH values as low as 3.8 ( He et al., 2007 ; Hu et al., 2013 ; Lu and Jia, 2013 ). AOA amoA genes and transcripts have been detected in acidic fen soil pore water with pH values ranging from 4.6–4.9 ( Herrmann et al., 2012 ). A small number of 16S rRNA gene sequences related to AOA were described in an acid pit lake and in AMD sediments with pH 2–3.5 ( Volant et al., 2012 ; Lucheta et al., 2013 ). Two acidophilic AOA cultured from soils showed growth at pH ranging from 4.0-6.1 ( Lehtovirta-Morley et al., 2011 , 2014 ). High concentrations of metals in AMD-impacted systems may also affect nitrification, as has been shown in other environments. Heavy metals, such as copper, zinc, lead, cadmium, nickel, and metal sulfides, are associated with decreased nitrification rates in soils and freshwater sediments ( Wilson, 1977 ; Broberg, 1984 ; Smolders et al., 2001 ; Cela and Sumner, 2002 ). Studies have demonstrated that AOA and AOB respond differently to heavy metals: AOA seemed more tolerant to copper and arsenic contamination than AOB in soils ( Li et al., 2009 ; Subrahmanyam et al., 2014 ); however, AOB were more active than AOA in zinc contaminated soils ( Mertens et al., 2009 ). The mechanism of metal resistance in ammonia oxidizers and nitrite oxidizers is largely unknown; however, metal resistance genes (e.g., copper, mercury, arsenic, zinc resistance) have been found in some nitrifiers, including Nitrosomonas eutropha , Nitrososphaera gargensis , Nitrospira defluvii , and Nitrobacter hamburgensis ( Stein et al., 2007 ; Starkenburg et al., 2008 ; Lücker et al., 2010 ; Spang et al., 2012 ). In the Colorado Rocky Mountains, AMD is a particularly common threat due to the large number of mines in the area. In the present study, we used high-throughput sequencing and quantitative PCR (qPCR) to examine the abundance and diversity of AOA, AOB, and NOB in AMD-impacted sediments in the Iron Springs Mining District near Ophir, CO, United States.",
"discussion": "Discussion Acid mine drainage is a serious threat to freshwater systems and can devastate a river and its aquatic life through its acidic pH and high metal concentrations. In this study, we determined the abundance and diversity of AOA, AOB, and NOB gene sequences through DNA-based analyses in AMD-impacted sediments at the Iron Springs Mining District. The abundance and diversity of AOA, AOB, and NOB genes in these AMD-impacted sediments may suggest the potential for nitrification activity. Low pH presents physiological challenges to nitrifying microbes through reduced bioavailability of ammonia and nitrite ( Suzuki et al., 1974 ; Stein et al., 1997 ). Nitrification is thought to be energetically challenging at low pH because ammonia (NH 3 ) and nitrite (NO 2 - ) concentrations are very low due to the chemical equilibrium in solution (NH 3 + H + → NH 4 + , pKa ∼9.24; NO 2 - + H + → HNO 2 , pKa ∼3.39). At low pH, the number of protons in solution increases and the equilibrium is shifted away from the energetic substrate (ammonia and nitrite). Here, AOA amoA and Nitrospira nxrB genes were present at most sites and time points regardless of pH. Archaeal amoA and Nitrospira nxrB gene abundances were high (10 3 – 10 7 copies per μg DNA) at sites with pH ≤ 4.5, and genes were detected at sites with pH as low as 3.5. Because pH reduces the bioavailability of ammonia, acidic habitats may favor AOA dominance over AOB due to their high affinity for ammonia ( Martens-Habbena et al., 2009 ; Offre et al., 2014 ). Alternatively, it was recently proposed that some AOA might possess ammonium transporters instead of ammonia transporters found in AOB ( Offre et al., 2014 ; Lehtovirta-Morley et al., 2016 ). Similarly, substrate availability and enzyme activity for nitrite oxidation are reduced as the pH decreases ( Tanaka et al., 1983 ) often leading to greater NOB abundance at higher pH conditions. AOA and NOB could have additional mechanisms of adaptation to low pH, such as increased ureolytic activity, proton efflux proteins, proton consuming metabolisms, and cytoplasmic proteins with buffering capacity. The mixotrophic lifestyle of some nitrifiers (e.g., litho- and organotrophy; Hatzenpichler, 2012 ) may afford an additional means of overcoming energetic stress under low pH conditions through the utilization of alternate electron sources. Metal concentrations (e.g., Al, Cu, Fe, and Zn) at all of the sites exceeded the allowable concentrations as determined by the Colorado Department of Public Health and Environment (CDPHE) Regulation 31. Sites with pH <4 had the highest concentrations of metals, perhaps because low pH can increase metal dissolution. Heavy metals are known to inhibit nitrification in some systems ( Cela and Sumner, 2002 ; Yan et al., 2013 ); yet, we found high numbers of AOA, AOB, and NOB genes at sites with high metal concentrations. The relative abundance of some individual OTUs was negatively correlated with Mn, Zn, Sr and Pb, while other OTUs were positively correlated with Mn. One archaeal amoA OTU was positively correlated with copper, which is heavily involved in ammonia oxidation and electron transport for AOA ( Walker et al., 2010 ). Specific metals did not equally impact gene abundances of AOA, AOB, or NOB, possibly suggesting that metals have different effects on each of these groups as seen in previous studies ( Li et al., 2009 ; Subrahmanyam et al., 2014 ; Ouyang et al., 2016 ). For instance, Mn was negatively correlated with some AOB OTUs, positively correlated with some NOB OTUs, and showed no correlation with AOA OTUs. Other previous studies showed decreased abundance or changes in community composition with increasing metal concentrations ( Mertens et al., 2006 ; Principi et al., 2008 ; Li et al., 2009 ; Cao et al., 2011 ). Though metal resistance genes have been found in some nitrifiers ( Stein et al., 2007 ; Starkenburg et al., 2008 ; Lücker et al., 2010 ; Spang et al., 2012 ), it is currently unknown whether these nitrifiers have specific adaptations to tolerate high metal concentrations (e.g., siderophore production, extracellular sequestration, ATP-dependent efflux systems). Nitrifier gene abundance showed interesting patterns in the Iron Springs mining region. Gene abundance showed a clear seasonal pattern: AOA, AOB, and NOB genes were more abundant in late summer than in early summer (∼5 times more abundant in late summer for ∼80% of samples where abundance was measured in both time points for a given year). Overall, AOA were more abundant than AOB across most sample regions irrespective of varying environmental conditions, which may be due to differences in AOA and AOB physiology. Nitrospira nitrite oxidizers were also abundant across all sample regions. When ammonia oxidizers and nitrite oxidizers were compared together, we observed that the Nitrospira nxrB genes were more abundant than archaeal amoA and AOB 16S rRNA genes at many sites ( Figure 2 ). However, gene copy numbers vary for each organism: AOA have one amoA copy per cell, AOB have one 16S rRNA copy per cell, and Nitrospira have 2-6 nxrB copies per cell ( Klappenbach et al., 2001 ; Lücker et al., 2010 ; Hatzenpichler, 2012 ; Pester et al., 2014 ). At a number of sites, we observed either no amplification or non-specific amplification (for archaeal amoA , AOB 16S rRNA, and Nitrospira nxrB ), possibly suggesting that AOA, AOB and NOB were absent in those selected sites, present in low numbers, or did not properly amplify with the PCR primers. Further analyses would be necessary to confirm low nitrifier abundance and its ecological implications (e.g., accumulation of ammonia, decreased denitrification, changes in N 2 O emissions). The overall bacterial 16S rRNA gene community structure in Iron Springs sediments was similar to microbial communities observed in other AMD environments ( Volant et al., 2014 ; Méndez-García et al., 2015 ), but very little is known about the community structure of nitrifying microbes in AMD environments. In this study, the richness (numbers of observed OTUs) and phylogenetic diversity of the AOA, AOB, and NOB communities was comparable to other more moderate environments (e.g., uncontaminated soils or freshwater systems). Regions with acidic pH had several AOA OTUs related to Nitrosotalea associated with acidic soil environments ( Figure 4 ) ( Lehtovirta-Morley et al., 2011 ; Gubry-Rangin et al., 2011 ; Pester et al., 2012 ), which may suggest that these AOA are adapted to acidic pH conditions. Many of the OTUs found in the Howard Fork River and Caribbeau Mine sites (more neutral pH) were associated with freshwater sequences ( van der Wielen et al., 2009 ; Auguet et al., 2012 ; Mosier et al., 2012 ; Auguet and Casamayor, 2013 ). Ammonia-oxidizing bacteria observed in these AMD-impacted sites belonged to the family Nitrosomonadaceae , which are generally well distributed in both terrestrial and aquatic environments ( Stein et al., 2007 ; Norton et al., 2008 ; Zheng et al., 2013 ) ( Figure 5 ). The majority of OTUs dominating the Caribbeau Mine region clustered with Nitrosospira found in soil environments ( Utåker et al., 1995 ; Jiang and Bakken, 1999 ; Purkhold et al., 2003 ; Norton et al., 2008 ). On the other hand, OTUs dominating the New Dominion Mine region belonged only to the Nitrosomonas cluster and were closely related to Nitrosomonas sp. Is79A3 usually found at sites with low ammonia concentrations ( Bollmann et al., 2013 ). These findings could suggest that the AOB in the New Dominion Mine may be adapted to low substrate availability (ammonia), possibly a result of the low pH compared to the Caribbeau Mine. NOB in these AMD-impacted sites were related to phylogenetically diverse Nitrospira species. Nitrobacter NOB genes did not amplify well in the AMD-impacted sediments (no qPCR amplification with several 16S rRNA and nxrB gene primer sets, and only a very small number of OTUs present in the sequence dataset). Although Nitrobacter are typically observed along with Nitrospira in aquatic systems, Nitrospira are often numerically dominant in sediments ( Altmann et al., 2004 ; Cébron and Garnier, 2005 ; Freitag et al., 2006 ; Satoh et al., 2007 ) and water treatment plants ( Juretschko et al., 1998 ; Daims et al., 2001 ; Huang et al., 2010 ). These findings may suggest that Nitrobacter are less tolerant of the harsh conditions in this system (low pH and high metals), that the available primer sets do not amplify the particular Nitrobacter found at these sites, or that biases in DNA extraction or PCR amplification prevented recovery of Nitrobacter sequences."
} | 4,373 |
37801504 | PMC10558118 | pmc | 6,845 | {
"abstract": "A widely assumed, but largely untested, tenet in ecology is that ecosystem stability tends to increase over succession. We rigorously test this idea using 60-year continuous data of old field succession across 480 plots nested within 10 fields. We found that ecosystem temporal stability increased over succession at the larger field scale (γ stability) but not at the local plot scale (α stability). Increased spatial asynchrony among plots within fields increased γ stability, while temporal increases in species stability and decreases in species asynchrony offset each other, resulting in no increase in α stability at the local scale. Furthermore, we found a notable positive diversity-stability relationship at the larger but not local scale, with the increased γ stability at the larger scale associated with increasing functional diversity later in succession. Our results emphasize the importance of spatial scale in assessing ecosystem stability over time and how it relates to biodiversity.",
"introduction": "INTRODUCTION Unraveling the temporal dynamics of plant communities over succession has been a central goal of ecology over the last century ( 1 – 4 ). Classical ecological theories posit that, as succession proceeds, plant communities would develop toward a more steady state where ecosystem functions are relatively constant despite perturbations ( 5 ). As a result, ecosystem temporal stability, a crucial dimension of stability that quantifies the invariability of ecosystem properties over time ( 6 – 8 ), has generally been assumed to increase during succession. This idea holds a central position in theories of succession and the field of restoration ecology ( 9 – 12 ). However, despite its widespread acceptance and intuitive appeal, this assumption remains largely untested, in large part due to a paucity of long-term continuous successional data. Recent advances in succession research have highlighted that trajectories and rates of succession may depend on the spatial scale considered, as the ecological mechanisms responsible for successional processes vary across spatial scales ( 13 – 15 ). At larger spatial scales, community dynamics tend to follow a deterministic and predictable path, converging to steady-state conditions over time ( 9 ). At smaller scales, probabilistic processes, such as dispersal and demographic stochasticity, may dominate community assembly, resulting in considerable community instability, even in later successional stages ( 1 , 14 , 16 ). Thus, we hypothesize that ecosystem temporal stability is less likely to increase with succession at the fine local scale but more likely to increase over succession at sufficiently larger scales. Recently, a hierarchical framework has been developed to partition ecosystem temporal stability to its lower-level components ( 17 , 18 ). Within this framework, community stability at the larger scale (i.e., γ stability) is determined by the average stability of all local communities (i.e., α stability) and spatial asynchrony across local communities. Spatial asynchrony refers to the degree of asynchrony in community dynamics across localities. This compensation effect acts as a stabilizing mechanism, enhancing overall stability at the larger scale. Similarly, α stability at the local scale is determined by two components: species stability and species asynchrony. Species stability reflects the average temporal stability of all species within a local community, while species asynchrony represents the asynchronous population dynamics among these species. This framework has been applied to various ecological systems, facilitating our understanding of the underlying processes of temporal stability across spatial scales ( 19 – 22 ). However, this framework has rarely been applied to understand the stability of successional systems ( 23 ). Diversity has long been considered a major determinant of ecosystem temporal stability ( 24 – 26 ). A growing number of studies have shown that increasing species diversity tends to increase ecosystem stability in various habitats ( 7 , 27 – 31 ), although neutral and negative effects have also been reported ( 32 , 33 ). Classical succession theories predict a temporal increase in diversity potentially leading to increased stability in later successional stages ( 5 ). However, recent research has revealed that plant diversity does not always increase over succession and may vary across spatial scales ( 14 , 34 ). Therefore, it remains largely unknown to which extent changes in plant diversity across scales would contribute to changes in temporal stability over succession. Furthermore, previous studies on biodiversity-stability relationships have focused largely on taxonomic diversity, particularly species richness. Ecologists have recently begun to explore whether phylogenetic and functional diversity are better predictors of community stability than taxonomic diversity, as they are better at capturing the evolutionary and ecological differences among species ( 35 – 37 ). If phylogenetically and functionally dissimilar species tend to co-occur more frequently in later successional stages ( 38 – 40 ), then this could lead to greater asynchronous dynamics among species and, in turn, increased temporal stability. Conversely, if phylogenetically and functionally similar species dominate later in succession ( 41 ), then we might expect reduced asynchrony and temporal stability. However, it remains unknown how changes in taxonomic, phylogenetic, and functional diversity over time relate to changes in ecosystem stability over succession. In this study, we use the long-term data from the Buell-Small old field succession study to investigate how ecosystem temporal stability across spatial scales changes with succession. Our study includes 10 abandoned agricultural fields, each containing 48 regularly distributed plots (of 1 m 2 ) that were continuously monitored for over 60 years (fig. S1). This provides a unique opportunity to examine changes in temporal stability from the local plot scale to the larger field scale. We aim to examine (i) whether plant communities become more stable during succession at both local and larger spatial scales, and (ii) how the changes in taxonomic, phylogenetic, and functional biodiversity affect the temporal stability over succession at the two spatial scales.",
"discussion": "DISCUSSION Ecosystem temporal stability has long been a central topic in ecology, due to its vital role in maintaining sustainable ecosystem functioning and services to humanity. Although there is growing interest in understanding how stability varies across time and space, there remains a dearth of data available to directly assess the changes in temporal stability over long time periods. In addition, much research on temporal stability across spatial scales has relied on the artificial aggregation of separate local communities ( 19 – 21 ), and empirical studies sampling at nested spatial scales data have been rare [but see ( 42 , 43 )]. By leveraging the well-replicated and fully nested time series on 60 years of succession, we provide unequivocal evidence that ecosystem temporal stability increased with succession at the larger field scale but did not systematically increase at the local plot scale. These results highlight the necessity of considering spatial scale to better understand ecosystem dynamics over time. At the larger scale, we found that γ stability monotonically increased with succession ( Fig. 1A ), primarily due to increased spatial asynchrony among local communities ( Fig. 2A ). Early in succession, different plots shared similar pioneer species that formed a single vegetation layer, which synchronized their responses to environmental fluctuations. Therefore, α stability of local plots almost directly scaled up to determine the γ stability of the field ( Fig. 2A ). However, as succession proceeds, the effects of factors such as dispersal limitation, habitat heterogeneity, and demographic stochasticity may become more pronounced over time, causing local plots to develop in progressively divergent trajectories ( 15 ). In our study, the local plots within each field significantly diverged over time, resulting in distinct vegetation assemblages in later successional stages ( 14 ). These dissimilar plots exhibited strong asynchronous responses to environmental fluctuations, characterized by high spatial asynchrony, which predominantly contributed to the increased γ stability during later successional stages ( Figs. 2B and 4C ). These results contrast with recent grassland studies that covered short observational periods (i.e., typically range from 3 to 5 years), which found that α stability has a greater effect on larger scale γ stability than spatial asynchrony ( 19 , 20 , 44 ). This discrepancy may result from our focus on a successional system over a much longer time scale, which enables local plots to diverge in both environmental conditions and community structures, resulting in greater asynchronous responses among them ( 14 ). Recent studies highlighted that the strong stabilizing effects of spatial asynchrony are more likely to emerge in relatively large spatial scales with greater spatial heterogeneity and species turnover ( 42 , 43 , 45 ). Our results extend these findings and suggest that long temporal scales are critical to capture the stabilizing effects of spatial asynchrony in empirical studies. Furthermore, we detected a positive diversity-stability relationship at the larger scale, with functional diversity emerging as a superior predictor of γ stability ( Fig. 3A and table S1). This finding corroborates previous studies which demonstrated that functional diversity explains more variation in ecosystem function and stability than taxonomic diversity ( 37 , 46 , 47 ). In our study, taxonomic γ diversity decreased but functional γ diversity increased in later successional stages (fig. S10, A and C). As a result, fields in later successional stages were characterized by the dominance of fewer yet functionally dissimilar species (figs. S13 and S14). These species, occupying a broader range of ecological niches, could better buffer against environmental fluctuations, consequently contributing to increased γ stability ( 37 , 48 ). Further, we showed that the positive functional diversity-stability relationship at the larger scale was mainly generated by the strong positive effect of functional β diversity on spatial asynchrony ( Fig. 4C ). Specifically, different plots in later successional stages were characterized by functionally dissimilar species (figs. S13 and S14), resulting in greater asynchronous responses of plots to environmental fluctuations, thereby generating increased γ stability at the large scale. These findings underscore that conserving functional diversity at the larger scale should be prioritized during successional restoration. In contrast, α stability at the local scale did not increase over time ( Fig. 1B ), indicating that local plots within a field still showed considerable variability later in succession. Early in succession, local plots were dominated by short-lived herbaceous species and underwent rapid species turnover ( 49 ). These early pioneer species showed low species stability due to their fast growth rates and short life spans but exhibited asynchronous and individualistic population dynamics in response to environmental fluctuations ( 50 ). Therefore, species asynchrony generated by species compensation and replacement within plots mainly contributed to community stability at early successional stages ( Fig. 2A ). As succession proceeded, perennial shrubs and other woody species continued to colonize and ultimately dominate communities ( 49 ). These long-lived species provided great species stability due to their low year-to-year variation in individual growth ( 50 , 51 ). However, they are more likely to share the same ecological strategies adapted to local habitats later in succession ( 52 ) and would therefore exhibit parallel responses to natural environmental fluctuations. Together, the decreased species asynchrony and increased species stability cancelled each other out, resulting in a nonsignificant stability-time relationship at the plot scale. We also detected a nonsignificant diversity-stability relationship at the local scale ( Fig. 3C ). This result can be attributed to the nonsignificant or opposing effects of diversity on species stability and species asynchrony, which nullified each other (fig. S11). These results confirm the recent theoretical predictions, empirical evidence, and meta-analysis that found species stability and species asynchrony are often governed by different ecological processes and exhibit distinct relationships with diversity ( 8 , 53 ). For instance, although functionally dissimilar species co-occurred within the same plot at the later successional stages, these species generally exhibited higher species stability but more synchronous population dynamics, resulting in a nonsignificant functional diversity-stability relationship at the local scale ( Fig. 3F ). Our study thus provides compelling empirical evidence that the effects of functional diversity on stability are scale dependent, with the stabilizing effects of functional diversity being more pronounced at larger spatial scales compared to within local plots. This emphasizes the importance of using sufficiently large sampling areas to capture the stabilizing effects of diversity measures. While being effective in demonstrating scale-dependent changes in ecosystem temporal stability over succession, several limitations of our study are worth noting. First, similar to recent studies ( 21 , 42 ), we used total cover as a surrogate for ecosystem function in calculating temporal stability. Further investigations using alternative metrics, such as biomass, would be beneficial to generalize the observed patterns. Second, our 1-m 2 permanent plots may better capture the dynamics of herbaceous species compared to woody species. While our conclusions remained robust when organizing plots into quadrats of different sizes (fig. S9), future studies could benefit from using multiple nested plots to comprehensively characterize species with different life forms at various spatial scales. Third, we calculated functional diversity based on species-level trait means, and our results could be strengthened by incorporating intraspecific trait variation and plastic responses of species traits over succession in future studies ( 54 ). In addition, factors like dispersal limitation, environmental heterogeneity, and demographic stochasticity likely play crucial roles in regulating temporal stability and synchrony across spatial scales. Unfortunately, data on these relevant covariates (e.g., soil nutrients) over succession were not available in our study. Accounting for these factors in future research could provide valuable insights into the underlying mechanisms driving biodiversity-stability relationships across spatial scales and offer useful implications for ecological restoration during succession ( 55 ). In conclusion, our findings provide strong evidence for the scale dependence of the stability-time relationship and diversity-stability relationship over long-term succession. These findings have important implications for succession theory and restoration practice. First, our study suggests that it is inappropriate to assume that stability would always increase with successional development. It is critical to rigorously evaluate the changes in ecosystem stability using long-term and multiscale approaches. Second, we show that although there was no positive diversity-stability relationship at the local scale, it is critical to preserving biodiversity at larger scales, where functional diversity is important for stabilizing meta-communities. In particular, preserving functional β diversity among local communities should provide stable and sustainable ecosystem functions at broader spatial scales."
} | 4,025 |
39049562 | PMC11299186 | pmc | 6,846 | {
"abstract": "Multipore membranes with nanofluidic diodes show memristive\nand\ncurrent rectifying effects that can be controlled by the nanostructure\nasymmetry and ionic solution characteristics in addition to the frequency\nand amplitude of the electrical driving signal. Here, we show that\nthe electrical conduction phenomena, which are modulated by the interaction\nbetween the pore surface charges and the solution mobile ions, allow\nfor a pH-dependent neuromorphic-like potentiation of the membrane\nconductance by voltage pulses. Also, we demonstrate that arrangements\nof memristors can be employed in the design of electrochemical circuits\nfor implementing logic functions and information processing in iontronics."
} | 175 |
36386613 | PMC9651917 | pmc | 6,847 | {
"abstract": "While metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how their expression levels depend on external conditions. Although the field of metatranscriptomics is relatively young, the number of projects related to metatranscriptome analysis increases every year and the scope of its applications expands. However, there are several problems that complicate metatranscriptome analysis: complexity of microbial communities, wide dynamic range of transcriptome expression and importantly, the lack of high-quality computational methods for assembling meta-RNA sequencing data. These factors deteriorate the contiguity and completeness of metatranscriptome assemblies, therefore affecting further downstream analysis. Here we present MetaGT, a pipeline for de novo assembly of metatranscriptomes, which is based on the idea of combining both metatranscriptomic and metagenomic data sequenced from the same sample. MetaGT assembles metatranscriptomic contigs and fills in missing regions based on their alignments to metagenome assembly. This approach allows to overcome described complexities and obtain complete RNA sequences, and additionally estimate their abundances. Using various publicly available real and simulated datasets, we demonstrate that MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data. The pipeline is implemented in NextFlow and is freely available from https://github.com/ablab/metaGT .",
"introduction": "Introduction Metagenome sequencing gained noticeable popularity in the past decade, as multiple projects shed light on microbial communities in various ecosystems ( Poretsky et al., 2005 ; Nowinski et al., 2019 ) and eukaryotic microbiomes ( Turnbaugh et al., 2007 ; Arumugam et al., 2011 ; Lloyd-Price et al., 2019 ). However, these studies required the development of novel software tools, as the previously designed methods for conventional sequencing data analysis appeared to be underperforming on large and complex metagenomic datasets. Thus, multiple tools, such as de novo assemblers ( Li et al., 2015 ; Nurk et al., 2017 ), sequence binners ( Uritskiy et al., 2018 ; Kang et al., 2019 ; Nissen et al., 2021 ), annotation pipelines ( Seemann, 2014 , Keegan et al., 2016 ) and various pipelines for metagenomic downstream analysis ( Caporaso et al., 2010 ; Mitchell et al., 2020 ) were developed in the past years. Although metagenomic sequencing may provide insights on species abundances and gene content, it does not show which members of the community and which genes are active, and how this activity depends on external conditions. To analyze gene expression in the microbial community researchers perform RNA-Seq experiments, which may include sequencing of samples under different conditions, time series, as well as complementary metagenomic and metatranscriptomic sequencing. As complete genomes of the organisms in the community of interest are often unknown, both metagenomics and metatranscriptomics studies heavily rely on de novo sequence assembly. While assembly of metagenomes is typically performed with community-established tools, such as MEGA-HIT ( Li et al., 2015 ) and metaSPAdes ( Nurk et al., 2017 ), metatranscriptome assembly software remains at an early stage and no pipeline is currently regarded as a golden standard ( Shakya et al., 2019 ). Among available tools one can name IDBA-MT ( Leung et al., 2013 ) and its derivative version IDBA-MTP ( Leung et al., 2015 ), which utilizes a database of known proteins to reconstruct complete transcript sequences. Another tool, TAG ( Ye and Tang, 2016 ), exploits the fact that metatranscriptomes are often sequenced along with the metagenomic data from the same sample. TAG maps RNA-Seq reads onto a metagenome assembly graph using and further restores paths corresponding to transcripts. Unfortunately, all listed tools appear to be unmaintained for several years and challenging to run under modern environments. Thus, some of the current studies exploit conventional RNA-Seq assemblers, such as Trinity ( Grabherr et al., 2011 ) and rnaSPAdes ( Bushmanova et al., 2019 ), performance of which remains under-examined on metatranscriptomic data. In this work we present MetaGT, a user-friendly pipeline for de novo assembly of metatranscriptomes, which follows the concept of TAG assembler by simultaneous usage of both metagenomic and metatranscriptomic sequencing data obtained from the same sample. We demonstrate that using metagenomic data greatly improves completeness of assembled transcripts compared to sequences assembled solely from metatranscriptomic data.",
"discussion": "Discussion While combining metatranscriptomic data with metagenome assemblies obtained from the same sample seems to be intuitive, to the best of our knowledge no modern bioinformatics software implements such an idea, with a solo exception of TAG assembler, support of which has been unfortunately discontinued a long time ago. As described previously, TAG maps RNA-Seq reads using Bowtie2 ( Langmead and Salzberg, 2012 ) and k-mer matching to the metagenomic de Bruijn graph, and further derives transcript sequences from the corresponding alignment paths in the graph. In comparison to MetaGT, the approach implemented in TAG can be useful for reconstructing transcripts with extremely low coverage, i.e., when the number of reads is insufficient for de novo transcriptome assembly. At the same time, TAG may output incomplete and fragmented transcripts when RNA-Seq reads do not cover the entire coding region. In contrast, MetaGT exploits predicted genes to fill in the gaps and restore complete transcripts sequences. In this work we present a pipeline that performs de novo assembly of metagenome and metatranscriptome sequencing data using existing software and combines the results in order to reconstruct and further quantify full-length transcripts. Providing complete coding sequences as the result of the assembly pipeline may significantly improve quality of the downstream analysis, such as functional annotation, gene ontology and differential expression analysis. In the view of growing popularity of metatranscriptomic sequencing we believe that MetaGT will be a useful instrument in the field and will allow researchers to perform high-quality studies without spending time developing custom in-house pipelines."
} | 1,663 |
22905115 | PMC3414520 | pmc | 6,851 | {
"abstract": "Self-facilitation through ecosystem engineering (i.e., organism modification of the abiotic environment) and consumer-resource interactions are both major determinants of spatial patchiness in ecosystems. However, interactive effects of these two mechanisms on spatial complexity have not been extensively studied. We investigated the mechanisms underlying a spatial mosaic of low-tide exposed hummocks and waterlogged hollows on an intertidal mudflat in the Wadden Sea dominated by the seagrass Zostera noltii . A combination of field measurements, an experiment and a spatially explicit model indicated that the mosaic resulted from localized sediment accretion by seagrass followed by selective waterfowl grazing. Hollows were bare in winter, but were rapidly colonized by seagrass during the growth season. Colonized hollows were heavily grazed by brent geese and widgeon in autumn, converting these patches to a bare state again and disrupting sediment accretion by seagrass. In contrast, hummocks were covered by seagrass throughout the year and were rarely grazed, most likely because the waterfowl were not able to employ their preferred but water requiring feeding strategy (‘dabbling’) here. Our study exemplifies that interactions between ecosystem engineering by a foundation species (seagrass) and consumption (waterfowl grazing) can increase spatial complexity at the landscape level.",
"introduction": "Introduction Spatial heterogeneity is important for the functioning of many different ecosystems, because it can enhance primary productivity, increase the biodiversity and carrying capacity, and stabilize the ecosystem [1] – [4] . Studies from a wide range of terrestrial and marine ecosystems have demonstrated that ecosystem engineers, i.e., organisms that significantly modify their abiotic environment [5] , [6] , often determine spatial structuring in ecosystems [2] . An important factor often controlling the extent to which the system is modified is the density of the ecosystem engineer [7] , while the modified environment in turn also positively or negatively affects the engineer again. In many cases, such feedback mechanisms cause an increase in the spatial patchiness of the ecosystem [8] – [11] . Consumer-resource interactions may also cause patchiness. For instance, plant-herbivore interactions ranging from the arctic tundra to tropical savannahs cause irregular patchy ‘landscape mosaics’ of intensively grazed ‘lawns’ of short vegetation alternating with ungrazed patches of tall vegetation in ecosystems ranging from the arctic tundra to tropical savannahs [3] , [12] , [13] . Intense grazing of the lawns combined with increased nutrient input by herbivore excrement facilitates growth of consumable and nutrient rich vegetation, while the nutrient-poor, tall vegetation excludes herbivores [12] . Similar to landscapes dominated by ecosystem engineers, these systems are driven by feedbacks. In contrast, however, these feedbacks are not characterized by biotic-abiotic interactions, but mainly driven by trophic interactions. In this study, we investigated the mechanisms behind a spatial mosaic of low-tide exposed hummocks and waterlogged hollows on an intertidal mudflat dominated by the seagrass Zostera noltii , which is periodically grazed by waterfowl ( Fig. 1A ). Using this system as a model, we tested whether an interplay between ecosystem engineering by a foundation species and herbivore grazing activity can lead to patchiness similar to those observed for habitat modification or consumer-resource interactions alone. Intertidal seagrasses like Z. noltii are density-dependent ecosystem engineers in the sense that they progressively reduce hydrodynamics and accrete sediment with increasing shoot density [10] , [14] – [16] . Grazing by waterfowl is a common phenomenon in seagrass systems. In the Wadden Sea, grazing on Z. noltii mainly takes place in autumn by overwintering brent geese ( Branta bernicla ) and widgeon ( Anas penelope ) that migrate from the arctic tundra in Northern Scandinavia and Siberia [17] . The birds use a number of different feeding techniques that depend on the water level. First, upending can be used when the actual water level is still relatively high; next dabbling is employed in areas with a few centimetres of water, and finally grubbing is the most common strategy on completely exposed parts [17] , [18] . Although waterfowl can consume significant amounts of both above- and belowground biomass, seagrass is generally not completely removed, but reduced to about 5 to 15% cover. This is because below this threshold feeding becomes energetically unprofitable for the birds, regardless of their feeding mode (‘giving-up density’) [17] , [19] . 10.1371/journal.pone.0042060.g001 Figure 1 Low-tide exposed hummocks with seagrass alternate with waterlogged, bare hollows in June (A). Seagrass patch cover changed significantly over time (ANOVA: F \n 2,17 = 66.6, p <0.001) from about 61% in June, to over 93% in August, followed by a sudden decrease again to 44% in November due to waterfowl grazing in September and October (B). Error bars indicate SD (number of replicates = 6). We used a combination of field surveys, a seagrass removal experiment and a spatially explicit model to identify the driving mechanisms behind the observed spatial mosaic. First, we quantified differences in sediment height between hummocks and hollows and measured the patchiness of the system and its change across the season. Second, we performed a seagrass removal experiment to test whether seagrass indeed modified its abiotic environment by sediment accumulation. Third, the effect of grazing by waterfowl on the spatial structure of the system was assessed by bird observations in September and October. Finally, to test whether the identified interactions could indeed explain the observed spatial and temporal patterns, we constructed and analyzed a spatially explicit model based on our field data.",
"discussion": "Discussion Spatial heterogeneity in ecosystems can be caused by abiotic variability, but can also result from feedback mechanisms [8] , [21] . Such feedbacks emerge when organisms significantly modify their environment (i.e., ecosystem engineering) [2] , [21] , but can also be caused by plant-herbivore, predator-prey and host-parasitoid interactions [12] , [13] , [22] , [23] . In this study, we report on a spatial mosaic of low-tide exposed hummocks and waterlogged hollows that results from an interaction between sediment accretion by seagrass and selective grazing by waterfowl, thereby illustrating that interactions between ecosystem engineering by a foundation species and grazing can cause spatial structuring in ecosystems. This finding is important because studies ranging from arctic to tropical environments and from terrestrial to marine ecosystems have demonstrated that spatial heterogeneity is often essential for ecosystem functioning [1] – [4] . Our study exemplifies how an interplay between biotic and abiotic factors can spatially structure intertidal seagrasses. The possibility of patchiness being solely driven by underlying abiotic heterogeneity or seagrass ecosystem engineering can be discarded because seagrasses colonized bare areas in summer and were observed to only retreat again due to waterfowl grazing. Similarly, our observations also reveal that waterfowl grazing alone is not sufficient to explain the observed spatial patchiness and temporal trends, as observations showed that grazing intensity was strongly driven by sediment height, which in turn was dependent on sediment accretion by seagrass. Although our study captured the most important structuring mechanisms, some processes have been disregarded or were simplified in both the model and the field experiments. Examples of factors possibly affecting the observed patchiness are bioturbation by infauna [10] , local differences in current velocity and sedimentation, and stochastic events like storms or desiccation of seagrasses at low tide during days with high temperatures [24] . Another simplification is our description of grazing in the model. Here, grazing is described as a process that only removes seagrasses from the system. In reality, depending on the feeding mode (i.e., dabbling, grubbing), grazing by waterfowl also decreases the cohesiveness of the sediment, thereby increasing erosion in the impacted areas. As dabbling (the preferred feeding strategy) directly results in sediment resuspension in the hollows, this will most likely have the most pronounced effect on sediment erosion. This suggests that we may have underestimated the overall effect of waterfowl grazing on erosion of grazed patches in our model, which would in turn imply that the ‘hollow-state’ is in reality more resilient than in the default setting of our model. Nevertheless, our bifurcation analysis ( Fig. 4 ) clearly demonstrates that alternative stable states exist over a much wider range of grazing intensities and sedimentation-erosion balances than those measured in the field, indicating that an under- or overestimation of grazing and/or erosion does not fundamentally alter our results. The spatial structure of seagrass ecosystems is often attributed to abiotic factors such as wave action, currents, sediment transport and light [25] – [29] . Recent studies, however, have shown that seagrasses are strong ecosystem engineers that often improve their own conditions, for instance by lowering nutrient levels, attenuating hydrodynamics and accumulating sediments [15] , [30] , [31] . Moreover, when such positive feedbacks interact with negative feedbacks, it may lead to spatial self-organization in seagrasses [24] . Furthermore, intensive grazing by waterfowl, turtles, dugongs, manatees and urchins has been demonstrated to have significant effects on the spatiotemporal structure and overall productivity of seagrasses as well [17] , [32] – [34] . Over the last century, seagrass meadows have been increasingly disturbed by human activities (e.g., eutrophication, siltation, dredging), resulting in dramatic and large-scale losses worldwide that were in many cases unexpected [15] , [35] , [36] . Our results support the notion that consideration of biological interactions between seagrasses and associated organisms may be crucial for conservation and restoration efforts in many seagrass ecosystems [37] , [38] . Spatial patchiness caused by ecosystem engineering interacts with both abiotic stress and grazing in various ecosystems [21] , [24] , [39] – [41] . However, grazing in these previously studied systems is not part of the structuring feedback mechanisms, and the disruption of these feedbacks by grazing therefore typically induces loss of spatial structure [9] , [39] , [40] . In contrast, the interaction with grazing is the actual cause of spatial patchiness in our system. Furthermore, in contrast to results from resource-limited systems, our model does not predict a complete collapse of the vegetation above a certain threshold for grazing [39] , but rather a homogeneous state of intense periodic grazing ( Fig. 4 ). The seagrass meadow in our model does not collapse because (1) seagrass growth is not resource-limited and (2) waterfowl grazing is periodic and does not continue below 10% of the maximum seagrass density (‘giving-up density’). These results are in agreement with other studies on waterfowl grazing in intertidal seagrasses that show that seagrass survival and production are either not markedly impacted [17] or even facilitated by waterfowl grazing [33] , [42] . Our study therefore supports the notion that the driving mechanisms behind spatial structuring should be well understood before using patchiness as an indicator of stress in ecosystems [24] , [41] ."
} | 2,959 |
30023793 | PMC6045362 | pmc | 6,852 | {
"abstract": "There\nis a growing appreciation that engineered biointerfaces can\nregulate cell behaviors, or functions. Most systems aim to mimic the\ncell-friendly extracellular matrix environment and incorporate protein\nligands; however, the understanding of how a ligand-free system can\nachieve this is limited. Cell scaffold materials comprised of interfused\nchitosan–cellulose hydrogels promote cell attachment in ligand-free\nsystems, and we demonstrate the role of cellulose molecular weight,\nMW, and chitosan content and MW in controlling material properties\nand thus regulating cell attachment. Semi-interpenetrating network\n(SIPN) gels, generated from cellulose/ionic liquid/cosolvent solutions,\nusing chitosan solutions as phase inversion solvents, were stable\nand obviated the need for chemical coupling. Interface properties,\nincluding surface zeta-potential, dielectric constant, surface roughness,\nand shear modulus, were modified by varying the chitosan degree of\npolymerization and solution concentration, as well as the source of\ncellulose, creating a family of cellulose–chitosan SIPN materials.\nThese features, in turn, affect cell attachment onto the hydrogels\nand the utility of this ligand-free approach is extended by forecasting\ncell attachment using regression modeling to isolate the effects of\nindividual parameters in an initially complex system. We demonstrate\nthat increasing the charge density, and/or shear modulus, of the hydrogel\nresults in increased cell attachment.",
"conclusion": "Conclusions To\nconclude, the regeneration of cellulose hydrogels from organic\nelectrolyte solutions, using a chitosan solution to achieve phase\ninversion, enabled the generation of robust, semi-interpenetrating\nnetwork chitosan–cellulose hydrogels without the need for chemical\ncross-linkers. The presence of the chitosan in the hydrogel scaffold\nenabled cell attachment in protein-free growth media, with cell attachment\nto the plant α-cellulose with low concentrations of low molecular\nweight chitosan hydrogel improved by 3000% compared to pure plant\nα-cellulose after 90 min. The physicochemical cell–material\ninterfacial properties (surface ζ potential, capacitive coupling,\nsurface roughness, and shear modulus) were modified by varying the\ncellulose and chitosan degree of polymerization, and chitosan solution\nconcentration (used in phase inversion). This, in turn, affected cell\nattachment on the hydrogels. The use of regression modeling\nenabled the effects of individual\nparameters to be discerned in an initially complex system, and allowed\nfurther development of an understanding of the interaction between\ncells and their surrounding environment. The developed regression\nmodel indicated that an increase in the shear modulus and surface\ncharge, i.e., number of amine groups, and a decrease in the surface\nroughness were beneficial for MG63 cell attachment within the bounds\nof the experimental data. Thus, it is demonstrated that a readily\napplied procedure for deconvoluting\nthe effect of changes in individual material characteristics on cell\nattachment allows the importance of specific characteristics to be\ndiscerned, thus enabling rational design of these readily fabricated\ntissue scaffold materials, prepared from natural biopolymers available\nfrom nonanimal sources, which promote cell attachment even under ligand-free\nconditions.",
"introduction": "Introduction The development of\ncell scaffolds that successfully mimic the extracellular\nmatrix, ECM, is paramount if tissue engineering is to prove to be\nefficacious. However, the bulk mechanical properties of many synthetic\npolymers, although suitable for osseous tissue, are not suitable for\nsoft tissues, such as muscle or nerve tissues, because the physical\nproperties, such as the tensile strength, are not matched. 1 − 3 The use of ECM and ECM-derived proteins also have associated problems:\nscaffolds require well-defined microenvironments in which animal byproducts\nand contaminants are limited, which is difficult to guarantee with\nanimal-derived scaffold materials, and ECM-based scaffolds are often\ncomplex, with poorly defined compositions. 4 To address these problems, alternative biopolymers that mimic\nthe\nproperties of the ECM have been sought. One area of focus is the use\nof plant-, algae-, and fungi-derived polysaccharides to mimic the\nphysical and chemical properties of hyaluronan (the only naturally\noccuring glycosaminoglycan that is not sulfated, or bound to a protein-based\ncore to form a proteoglycan), which is known to be involved in the\nregulation of cell growth, differentiation, adhesion, and motility. 5 Blends consisting of chitosan and alginate have\nbeen generated and reported to result in improved cell response over\npure alginate (due to the modified chemical properties) and improved\nmechanical properties over pure chitosan. 6 − 8 Alginate, cross-linked\nwith multivalent cations, provides mechanical strength, whereas chitosan\nimparts appropriate chemical functionality to the material. However,\nalginate consists of homopolymeric blocks of two epimers in an arrangement\nthat cannot be controlled, and only one of the epimers is involved\nin cross-linking with the multivalent cations. 9 Therefore, it cannot be guaranteed that the mechanical properties\nof the scaffold will show batch-to-batch consistency, which could\nprove challenging at a commercial level. As an alternative to alginate,\ncellulose has been investigated, with cellulose–chitosan composites\ngenerated following codissolution of the polysaccharides in ionic\nliquids. 10 − 13 However, discoloration of the composites was reported using the\ncurrent methodology, 10 indicating the degradation\nof one of the polymers, or a (currently unidentified) side-reaction. Successful scaffold design requires an understanding of cell response\nto the interdependent material properties. It has previously been\nreported that surface charge, 14 − 16 tensile strength, 17 − 20 and surface topography 21 − 25 affect cell response to a scaffold. Despite this, for the majority\nof scaffolds reported in the literature, cell response is considered\nwith respect to (i) one variable, ignoring others, or assuming that\nthese are constant; 14 , 16 , 20 , 22 or (ii) two or more variables, but without\nrobust testing of their independence, or determination of individual\neffects on cell response. 8 , 15 , 26 Design of experiments, DoE, based on regression modeling, may be\nutilized to enable the effect of individual characteristics to be\ndiscerned in a complex system, even where responses to changes in\nindividual variables are not independent of each other. However, use\nof DoE in tissue engineering has been limited and primarily focused\non the response of cells to multiligand systems, 27 , 28 or to elastic modulus and ligand concentration. 29 , 30 Here, we report on the generation of cellulose–chitosan\nscaffolds that enable cell attachment comparable to that on tissue\nculture plate (polystyrene) (TCP) under ligand-free conditions.\nWe use regression modeling to decouple the effects of scaffold surface\ncharge, surface topography, and mechanical properties on cell attachment.\nWe avoid the reductionist, one variable at a time, approach, which\ncan result in the oversimplification of complex systems, potentially\nresulting in missed interdependence, thus enabling understanding of\nthe interaction between properties of the surface that, upon cell\nattachment, becomes the cell/scaffold interface, allowing scaffolds\nto be designed to maximize cell attachment.",
"discussion": "Results and Discussion The generation of a cellulose–chitosan hydrogel by phase\ninversion of cellulose dissolved in an organic electrolyte solution\n(OES) comprised of 1-ethyl-3-methylimidazolium acetate, [EMIm][OAc],\nand dimethyl sulfoxide (DMSO) in a chitosan solution ( Figure 1 A) enables the production of\nSIPN hydrogels (thickness: 200 μm), without the degradation\nof either polymer. Two different sources of cellulose, i.e., plant\nα-cellulose, AC, and bacterial α-cellulose, BC, are tested,\nas these are known to provide cellulose polymers with a 10-fold difference\nin the degree of polymerization. Cell attachment (MG-63 cells) comparable\nto that on TCP is achieved even in the absence of a fetal bovine serum,\nFBS, usually added to provide complex protein ligands. Figure 1 (A) Schematic of SIPN\nhydrogel generation process. 1. Cellulose\nis dissolved in an organic electrolyte solution consisting of [EMIm][OAc]\nand DMSO before being cast on a glass plate. 2. Cellulose film is\nimmersed in a chitosan solution (0.43 M acetic acid, aqueous). 3.\nAfter 20 min cellulose–chitosan SIPN hydrogel is removed. (B)\nFourier transform infrared (FTIR) of cellulose–chitosan hydrogel\ndemonstrating that both polymers are present. Peaks unique to cellulose\nin orange; unique to chitosan in green; present in both polymers in\nblue; summation of fitted peaks in red; raw data in black. (C) Free\nchitosan content determined by ninhydrin adsorption. Chitosan content\nis increased by increasing the chitosan solution concentration from\n0.12 to 2.1 wt % (xxxL vs xxxH), and decreasing the chitosan molecular\nweight from 109 to 26 kDa (xxMx vs xxLx). No significant difference\nis observed between plant α-cellulose, AC, and bacterial α-cellulose,\nBC, samples. Error ± SE, N = 3. † p < 0.001 compared to ACML; ‡ p < 0.001 compared to ACLH; ○ p < 0.01\ncompared to ACLH; ● p < 0.05 compared to\nACLH. (D) Confocal image of BCLL demonstrating the presence of chitosan\nlayer (brighter, blue region) at the surface of the cellulose hydrogel\n(darker, green region). Scale bar 50 μm. To prove the presence of chitosan within the six hydrogels\n( Table 1 ) and investigate\nits penetration, three techniques are employed: Fourier transform\ninfrared (FTIR) spectroscopy, ninhydrin adsorption, and confocal microscopy.\nDeconvolution of the FTIR spectrum in the fingerprint region (1400–1800\ncm –1 ) and comparison with the spectra of pure cellulose\nand chitosan indicates that the hydrogels contain both cellulose and\nchitosan ( Figure 1 B).\nThe free chitosan loading is determined via ninhydrin adsorption,\nand no significant difference is observed between samples prepared\nfrom AC or BC ( Figure 1 C). Confocal microscopy reveals a chitosan-rich region at the surface\nof the hydrogel, i.e., chitosan is not homogeneously distributed throughout\nthe cast film. For the AC samples, increasing the chitosan solution\nconcentration, and decreasing the chitosan MW, results in an increase\nin the chitosan penetration ( Figure 1 D, Table 2 ). By decreasing the chitosan MW, an increase in chitosan loading\nand penetration is observed due to the lower MW chitosan being able\nto access a greater proportion of hydrogel pores, as previously determined\nusing variable-sized biomolecule probes. 31 Differences in the pore structure previously observed between AC\nand BC hydrogels may account for differences in the chitosan penetration\nbetween the two. 31 Although swelling studies\nare not considered here given that the hydrogels are never dried,\na previous study by Liu and Huang suggests that the low chitosan content\nwill not impact the swelling of the hydrogel. 13 Table 1 Hydrogel Formulations and Their Corresponding\nSample Codes sample celluose source chitosan\nMW (kDa) chitosan solution concentration (wt %) AC plant α-cellulose BC bacterial α-cellulose ACLL plant α-cellulose 26 0.21 ACML plant α-cellulose 109 0.21 ACLH plant α-cellulose 26 2.10 ACMH plant α-cellulose 109 2.10 BCLL bacterial α-cellulose 26 0.21 BCMH bacterial α-cellulose 109 2.10 L 26 M 109 Table 2 Depth from the Hydrogel Surface to\nWhich Chitosan Autofluorescence (443 nm) is Dominant over Cellulose\nAutofluorescence (478 nm), As Determined by Confocal Microscopy a sample ACLL ACML ACLH ACMH BCLL BCMH chitosan depth\n(μm) 8 ± 2 6 ± 2 28 ± 2 19 ± 2 15 ± 1 10 ± 1 a Increased penetration is observed\nat higher chitosan solution concentrations, and decreased molecular\nweight. Error ± stack depth. This novel methodology enables the production of SIPN\nscaffolds,\nas defined by Alemán et al. 32 The\nhighly dispersed chitosan (the autofluorescent signal of which dominates\nup to 30% of the scaffold, yet accounts for less than 1.5 wt % of\nthe biopolymer dry material) with a gradated composition ( Figure S3 ) indicates the penetration of chitosan\nwithin the previously characterized, highly porous cellulose network. 31 This provides opportunities for use in applications\nwhere this would be beneficial, such as membranes that require the\nmaterial properties of cellulose and the functionality of chitosan.\nThe dissolution of chitosan from the hydrogels upon exposure to acid\nsolutions during ninhydrin adsorption experiments confirms that the\npolymers are not chemically cross-linked to each other. We therefore\nexpect the hydrogels to be fully biodegradable when exposed to\ncellulases and chitinases. As the goal is to fabricate scaffolds\nthat would not require addition\nof animal-derived proteinaceous ligands, cell attachment in both the\npresence and absence of FBS is tested. No significant difference is\nobserved between cell attachment in the serum (i.e., protein) positive\nand negative media for the majority of the hydrogels ( Figure 2 A). This demonstrates that\nthe cells do not require ligands to mediate the cell–material\ninterface, leading to protein- and serum-free cell attachment. To\nestablish that the cells are binding directly to the hydrogels, pluronic\nF-127 is used to block nonspecific binding on TCP and selected hydrogels\nunder FBS-negative conditions ( Figure 2 B). Although a significant decrease is observed on\nTCP, no significant difference is observed on the cellulose–chitosan\nhydrogels, confirming that the cells are interacting with the hydrogel\nsurface in a specific manner. Comparison of the hydrogels and TCP\nunder FBS-negative conditions established that ACLL and ACML are not\nsignificantly different from TCP whereas ACLH and BCLL are not significantly\ndifferent from AC ( Figure 2 C). This indicates that there are differences in the physicochemical\ncell–material interfacial properties of the hydrogels, affecting\ncell attachment. Cell morphologies are considered after 24 h in FBS-positive\nmedia, and the analysis is included in the Supporting Information\n( Figures S4–S7 ). Figure 2 (A) Ninety minute MG63\ncell attachment relative to tissue culture\nplate (TCP) in fetal bovine serum positive (FBS+) media. Attachment\nwas performed in FBS+ (gray bars) and fetal bovine serum negative\n(FBS−) (white bars) media. No significant difference between\nFBS+ and FBS– media was observed for most of the cellulose–chitosan\nhydrogels, indicating that they are suitable for ligand-free cell\nattachment. Error ± SE, N = 3. (B) Comparison\nof cell attachment with FBS– media on unmodified TCP, ACML,\nand ACMH, and modified using pluronic F-127 to block nonspecific cell\nattachment (denoted by “b”). Cell attachment decreases\nsignificantly on TCP, but no difference is observed on the hydrogels.\nError ± SE, N = 3. ** p <\n0.01. (C) Cell attachment comparison in growth media not containing\nfetal bovine serum (FBS). No significant difference is observed between\nTCP and ACLL, or ACML. No significant difference is observed between\nAC and ACLH, or BCLL. This suggests that there are significant differences\nin the properties of the hydrogels. Error ± SE, N = 3. * p < 0.05; ** p <\n0.01; *** p < 0.001. To determine the cause of differences in cell attachment\nbetween\nthe hydrogels, four properties are measured: surface zeta-potential,\nζ, which is proportional to the total surface charge; capacitive\ncoupling, d C /d z , which is proportional\nto the dielectric constant, a measure of polarizability; shear modulus, G , an indicator of the mechanical properties of the hydrogel;\nand surface root mean square roughness, R q , an indicator of the surface morphology. The incorporation of chitosan\ninto the hydrogel results in an increase in both ζ ( Figure 3 A) and d C /d z ( Figure 3 B) compared to native cellulose, reflecting the p K a of chitosan (6.2–6.5), 33 which leads to protonation of approximately 10% of the amine groups\nat pH 7.4. A statistically significant increase in G is observed for\nACxL hydrogels ( Figure 3 C), whereas the xxLx samples are significantly more rough than the\nother hydrogels ( Figure 3 D). Figure 3 (A) Surface zeta-potential (ζ) of hydrogels. The presence\nof chitosan results in an increase in ζ compared to pure cellulose.\n(B) Shear modulus ( G ) of hydrogels measured via atomic\nforce microscopy (AFM). Generation of AC hydrogels in low chitosan\nconcentration solutions result in a significant increase in G . † p < 0.001 compared to ACLL;\n‡ p < 0.001 compared to ACML. Error ±\nSE, N = 4. (C) Capacitive coupling (d C /d z ) of hydrogels measured via electric force microscopy\n(EFM). The presence of chitosan results in an increase in d C /d z compared to pure cellulose. † p < 0.001 compared to low-MW chitosan (L); ○ p < 0.05 compared to L; ‡ p <\n0.001 compared to medium-MW chitosan (M); ● p < 0.05 compared to M. Error ± SE; N ≥\n3. (D) Root mean square roughness ( R q )\nof hydrogels measured via EFM. Generation of hydrogels in low-MW chitosan\nconcentration solutions result in a significant increase in R q . † p < 0.001 compared\nto ACLH; ‡ p < 0.01 compared to ACLH; ● p < 0.001 compared to BCLL; * p <\n0.05 compared to BCLL. Error ± SE, N ≥\n3. Cell attachment on the ACxL hydrogels\nis not significantly different\nfrom TCP ( Figure 2 C),\nsuggesting that G is critical in determining the\nextent of cell attachment; G for ACxL hydrogels are\nsignificantly different to the other hydrogel samples ( Figure 3 C). The promotion of cell attachment\nwith an increase in G is in agreement with previous\nreports. 18 , 19 However, despite G for\nACxH samples being greater than the values for the BCxx samples ( Figure 3 C), no difference\nin cell attachment is discerned ( Figure 2 C). To understand this, other factors are\nconsidered: R q is significantly different\nfor the ACLH and BCLL samples ( Figure 3 D), suggesting that an increase in R q negatively impacts cell attachment. The effects of ζ\nand d C /d z on cell attachment are\nnot immediately obvious from direct comparison. To untangle\nthe effects of each of the individual properties on\nthe resulting cell attachment, multivariate regression modeling, using\na “leave-one-out” methodology is employed. All of the\npossible models containing four- or five-terms are investigated. Interaction\nterms between two of the properties are included. Average constant\nvalues and three coefficients of determination ( R 2 , Q 2 , and MV) are calculated,\nmodel validity is determined, and selected models are optimized, as\ndetailed in the Experimental Section . The four-term model with the additional interaction term ζ 2 , and without d C /d z , is\ndetermined to be the optimal model to describe cell attachment to\nthe cellulose–chitosan hydrogels under protein-free conditions; R 2 = 0.88, Q 2 = 0.90,\nand MV = 0.93 ( Figure 4 A,B, Table S2 ). The generation of a three-dimensional\ncontour plot ( Figure 4 C) enables the effect of each property on cell attachment to be determined.\nAn increase in G promotes the MG63 cell attachment,\nas previously reported for stromal and hematopoietic cell lines. 18 , 19 An increase in R q results in a decrease\nin cell attachment, in contrast to previous reports, in which the\nauthors suggested that an increase in R q promotes cell attachment. 22 , 23 Notably, these reports\nfocus on ligand-positive systems, where increasing the hydrophobicity\nof the material, influenced by the surface topography, increases ligand\nbinding to the scaffold and, therefore, the attachment of cells, which\ninteract directly with the ligands. 34 Indeed,\nthis is observed here with the two scaffolds that are significantly\nmore rough than the others; both ACLH and BCLL show a significant\nincrease in cell attachment in the presence of FBS ( Figure 2 A). Given the scale over which\nthe roughness changes, it is hypothesized that there is a trade-off\nbetween the number of adhesion points that a cell can access (presumed\nto increase initially with R q as the surface\narea will also increase) against the size of the adhesion points between\ncell and substrate, which are directly proportional to the force that\na cell can exert on a substrate. 35 It has\npreviously been demonstrated that the percentage of cells that remain\nattached after centrifugation increases with initial cell attachment. 22 , 36 Therefore, as R q increases, the binding\nforce between the cells and scaffold decreases, resulting in poorer\ncell attachment. Figure 4 (A) Calculated constants for the four-term regression\nmodel, CA\n= −3.00ζ + 0.0379 G – 0.889 R q – 0.103ζ 2 . Normalization: B × 10 2 ; C × 10; D × 10. Error ± SE, N = 7. (B)\nPredicted vs actual cell attachment for the four-term regression model.\nCoefficient of determination Q 2 calculated\nrelative to the line y = x ; MV calculated\nrelative to the line y = m·x . (C) Three-dimensional contour plot of predicted cell attachment\ngenerated using the four-term model. Cell attachment increases as\ncolored bands change from red to green. Relative positions of hydrogels\ninvestigated are included. The model suggests that cell attachment\nincreases with increasing ζ and G , which is\nexpected from the literature. Increasing R q , within the bounds of the system investigated, results in a decrease\nin cell attachment. As ζ increases,\nthe cell attachment also increases. Consideration\nof the earlier studies on the hydrogels modified using pluronic F-127\n( Figure 2 B), whereby\nthe blocking of nonspecific binding did not affect the cell attachment\nto the hydrogels, suggests that the cells are binding directly to\nthe amine groups, which can exhibit a positive charge (as ammonium\ngroups, −NH 3 + ). Thus, cell attachment\nappears to be directly proportional to the number of amine groups\navailable at the hydrogel surface."
} | 5,517 |
36417801 | PMC9731265 | pmc | 6,853 | {
"abstract": "Fe(II) clays are common across many environments, making\nthem a\npotentially significant microbial substrate, yet clays are not well\nestablished as an electron donor. Therefore, we explored whether Fe(II)-smectite\nsupports the growth of Sideroxydans lithotrophicus ES-1, a microaerophilic Fe(II)-oxidizing bacterium (FeOB), using\nsynthesized trioctahedral Fe(II)-smectite and 2% oxygen. S. lithotrophicus grew substantially and can oxidize\nFe(II)-smectite to a higher extent than abiotic oxidation, based on\nX-ray near-edge spectroscopy (XANES). Sequential extraction showed\nthat edge-Fe(II) is oxidized before interior-Fe(II) in both biotic\nand abiotic experiments. The resulting Fe(III) remains in smectite,\nas secondary minerals were not detected in biotic and abiotic oxidation\nproducts by XANES and Mössbauer spectroscopy. To determine\nthe genes involved, we compared S. lithotrophicus grown on smectite versus Fe(II)-citrate using reverse-transcription\nquantitative PCR and found that cyc2 genes were highly\nexpressed on both substrates, while mtoA was upregulated\non smectite. Proteomics confirmed that Mto proteins were only expressed\non smectite, indicating that ES-1 uses the Mto pathway to access solid\nFe(II). We integrate our results into a biochemical and mineralogical\nmodel of microbial smectite oxidation. This work increases the known\nsubstrates for FeOB growth and expands the mechanisms of Fe(II)-smectite\nalteration in the environment.",
"introduction": "Introduction Iron (Fe) is abundant in the Earth’s\ncrust, occurring mostly\nin the solid phase in mineral structures. Smectite is a common 2:1\nclay mineral 1 that can contain substantial\nFe and play important roles in contaminant degradation, 2 − 4 heavy metal immobilization, 5 − 7 and nutrient cycling. 8 Trioctahedral Fe(II)-smectite, a major product\nof basalt alteration, 9 , 10 is widespread in the ocean crust, 11 − 13 as well as other subsurface terrestrial and marine settings, 14 − 16 forming a large electron pool that may support neutrophilic Fe(II)-oxidizing\nbacteria (FeOB) growth. Most FeOB cultivation uses aqueous Fe(II), 17 − 19 so it is unclear\nif FeOB can grow on solid Fe(II), including smectite. Fe(II) clays\ncan be oxidized by heterotrophic nitrate-reducing bacteria. 20 − 22 However, these studies used nitrate-reducing bacteria that largely\nrely on organic substrates, producing nitrite that can abiotically\noxidize Fe(II), including in clay, 23 − 25 so it is unclear to\nwhat extent Fe oxidation supports growth. In contrast, the Fe(II)\nphyllosilicate biotite has been shown to support chemolithotrophic\ngrowth of the nitrate-reducing, Fe(II)-oxidizing culture KS. 26 The fact that biotite and smectite are both\n2:1 sheet silicates suggests that FeOB growth on smectite clays should\nalso be possible. To grow on Fe(II)-smectite, microbes need\nmechanisms to be able\nto uptake electrons from the solid electron source. Previous studies\nhave proposed two potential pathways for microbial Fe(II) oxidation:\nCyc2 and MtoAB/PioAB pathways. 27 − 30 Cyc2 is used in oxidizing dissolved Fe(II), as demonstrated\nin our study on the microaerophilic FeOB Sideroxydans\nlithotrophicus ES-1. 31 Genes\nfor MtoAB were expressed at a very low level and not responsive to\nFe(II)-citrate. 31 MtoAB is hypothesized\nto be used to interact with solid Fe(II), similar to the interaction\nof the homologous MtrAB(C) with Fe(III) minerals. As a sparingly soluble\nFe(II) mineral, smectite can be used to reveal the mechanisms of solid\nFe(II) oxidation. Microbial interactions with smectite depend\non the mineral structure\nand reactivity, so we need to carefully choose the substrate and track\nFe(II)/Fe(III). The most commonly used Fe clay in microbial experiments\nis the nontronite NAu-2, 20 − 22 , 32 , 33 a natural Fe(III) clay standard. To test\nmicrobial oxidation, previous studies have reduced Fe(III) in NAu-2\nto Fe(II) chemically or microbially. 20 − 22 The reduced Fe(II) remains\nin the NAu-2 dioctahedral structure, with variable distribution. 34 , 35 In contrast, native Fe(II)-smectites are trioctahedral, and this\nstructural difference could cause variation in reactivity, which depends\non the distribution of Fe in the structure. However, trioctahedral\nsmectite is generally unavailable for experimentation because of its\nsensitivity to oxygen. 36 Studies of dioctahedral\nsmectites show that edge-Fe is more reactive, 35 , 37 but interior-Fe within the crystal lattice could be accessible via\nelectron hopping between Fe. 35 , 38 − 43 To track microbial usage of smectite Fe, it is necessary to monitor\ndifferent Fe fractions. This will tell us whether Fe(II) in smectite\nis dissolved and subsequently oxidized to form secondary minerals\nor if Fe(III) is retained in smectite. This monitoring will also show\nwhether microbes can use interior-Fe, which will control the extent\nto which microbes can grow on smectite. In this study, we investigated S. lithotrophicus ES-1, a type strain of the widely\ndistributed Fe-oxidizing genus Sideroxydans . 44 − 49 Importantly, ES-1 has genes that encode both Cyc2 and MtoAB, 27 , 28 which enables us to differentiate the functions of the two pathways\nin oxidizing aqueous and solid Fe(II) using the same microbe. In this\nwork, we tested ES-1 growth on chemically synthesized trioctahedral\nFe(II)-smectite, characterized the products, and demonstrated the\ndistinct roles of Cyc2 and MtoAB in microbial Fe oxidation. These\nresults help us to better understand how clay minerals support life\nand how microbes drive Fe redox reactions that impact fates of contaminants,\nheavy metals, and nutrients.",
"discussion": "Discussion The constant process of Earth’s crustal\nweathering produces\ntrioctahedral Fe(II)-smectite, 9 , 10 which could represent\na widely available substrate for microbial growth. Yet, its potential\nas an electron donor for microbial Fe oxidation has not previously\nbeen explored because trioctahedral Fe(II)-smectites are not generally\navailable for experimentation. As a result, most knowledge about microbial\nFe(II) clay oxidation comes from artificially reduced dioctahedral\nFe(III)-smectites, 20 − 22 which has yielded initial promising evidence that\nmicrobes can oxidize clays. However, these clays may not behave similarly\nbecause Fe distribution in the structure is significantly different.\nUnlike dioctahedral clays, the octahedral sheet of a trioctahedral\nsmectite is fully occupied, allowing for more adjacent Fe atoms, which\ncould lead to faster electron-transfer rates and better performance\nas an electron donor. Here, we synthesized a trioctahedral Fe(II)-smectite\nand demonstrated substantial growth of the FeOB S.\nlithotrophicus ES-1 using clay-bound Fe(II). Our results\nshowed that S. lithotrophicus ES-1\nuses the solid Fe(II) in smectite to produce biomass ( Figures 1 and 2 ) and may accelerate mineral Fe(II) oxidation under certain conditions\n( Figure 4 ). To\ngrow on smectite, FeOB need mechanisms to interact with minerals.\nPrevious studies on FeRB proposed that bacterial cells can access\nsolid Fe(III)-minerals, including smectite, by direct contact and/or\nelectron shuttles. 72 − 76 Unlike the FeRB Shewanella oneidensis , ES-1 does not have a known organic electron shuttle (riboflavin)\ntransporting system. However, ES-1 does possess the Mto pathway, which\nincludes an outer-membrane multiheme cytochrome 27 , 77 , 78 that could enable direct contact to oxidize\nsmectite-bound Fe(II). MtoAB is a decaheme cytochrome-porin complex\nhomologous to the Fe(III)-reducing MtrAB in S. oneidensis , in which multiple hemes conduct electrons across the outer membrane, 29 , 77 thus enabling interactions with extracellular electron sources/sinks.\nES-1 expressed Mto pathway proteins on smectite but not on Fe(II)-citrate,\nwhich supports a model in which ES-1 directly accesses the solid Fe(II)\nin smectite using MtoAB ( Figure 8 A). Figure 8 (A) Proposed molecular mechanism of ES-1 grown on Fe(II)-smectite;\n(B) plan view of the middle trioctahedral layer in the smectite structure.\nThe cations represent the actual metal composition of the synthetic\nsmectite. In addition to MtoAB, the smectite-grown cells\nalso express Cyc2\n( Table 1 ), including\nsubstantial expression of all three Cyc2 proteins, suggesting that\nthey also play a role in smectite oxidation. Cyc2 has a single heme,\nimplying it is an oxidase of dissolved Fe(II). 77 Dissolved Fe(II) could shuttle electrons from smectite\nto cells. Previous findings in abiotic systems have demonstrated the\ninterfacial electron transfer between Fe(III) and structural Fe in\nclays. 79 , 80 Fe(III) produced from dissolved Fe(II) oxidation\ncould therefore be re-reduced to Fe(II) through interfacial electron\ntransfer, as suggested by a previous study on microbial biotite oxidation. 26 Herein, we propose two possible mechanisms for\nES-1 smectite oxidation: (1) direct contact to acquire electrons from\nmineral-bound Fe(II) via multiheme–porin complex MtoAB and\n(2) indirect oxidation with the involvement of dissolved Fe(II) by\nCyc2 ( Figure 8 A). Microbial growth is likely a function of smectite mineralogy. Fe(II)\nis in the middle octahedral layer, sandwiched by two silicate tetrahedral\nsheets. Previous studies have demonstrated that Fe in edge sites is\nthe most reactive and is accessible to microbes. 37 , 67 , 72 However, it is not well-known whether microbes\ncan oxidize interior-Fe(II) though this would clearly affect the extent\nto which microbes can oxidize smectite. Our results show that ES-1\ncan use some interior-Fe(II) ( Figure 2 ). This suggests electron transfer between the interior-\nand edge-Fe ( Figure 8 B), which has been intensively characterized in abiotic systems. 35 , 38 − 43 Although microbes can use the interior-Fe, we observed only partial\noxidation and a two-stage process (fast to slow reaction) during incubation,\nwhich are consistent with previous observations in abiotic systems. 34 , 35 , 37 , 43 , 59 , 81 The rapid\ndecrease of NaH 2 PO 4 -extractable Fe(II) during\ndays 0–3 is primarily due to the consumption of reactive edge-Fe(II)\nby ES-1 ( Figure 2 ).\nThen, the oxidation rate could be constrained by the electron hopping\nrate from less-reactive interior-Fe to the edge sites ( Figure 8 B). Since the driving force\nof electron hopping is the redox potential gradient between the interior-\nand edge-Fe, 35 , 82 , 83 with the oxidation process, the redox potential gradient could decrease\nuntil reaching an equilibrium, which causes the decrease of reaction\nrate and incomplete oxidation. Partial oxidation results in mixed\nvalent trioctahedral smectite ( Figure 8 B), with only minor structural changes and no detectable\nsecondary mineral formation ( Figures 5 B and 6 ). This mixed valent\nsmectite may then be available for microbial Fe reduction. In this\nway, trioctahedral Fe(II)-smectite can act as a geobattery to connect\nthe redox cycles between FeOB and FeRB. Environmental Implications Iron-bearing smectites are\npractically ubiquitous in soils and sediments, where they can support\nmicrobial life and catalyze environmentally significant processes.\nPrevious studies have established that FeRB can reduce Fe(III) in\nsmectite, 32 , 33 , 84 − 86 and resulting redox changes alter the reactivity of smectites toward\nmetals and organic contaminants. 7 , 32 , 33 , 37 , 87 Our work showed that trioctahedral Fe(II)-smectite can be used as\nan electron source to support the growth of a common, widely distributed\nmicroaerophilic Fe(II) oxidizer. Biotic Fe(II) oxidation may result\nin accelerated smectite Fe(II) oxidation under some conditions but\nnot others, and yet, in either case, smectite oxidation fuels growth.\nThis suggests that mineral Fe(II) measurements cannot always distinguish\nbiotic versus abiotic effects, potentially rendering biotic effects\ninvisible, thus emphasizing the need for gene-based markers of microbe–mineral\ninteractions. As smectite Fe(II) oxidation can support the growth\nof FeOB, this is another way in which clays can fuel nutrient cycling\nin subsurface settings. FeOB and FeRB can coexist in sediment redox\ntransition zones, 88 , 89 with autotrophic FeOB using energy\nfrom smectite Fe(II) oxidation to fix carbon and FeRB respiring these\norganics, coupled to smectite Fe(III) reduction. In this way, smectites\ncan support both FeOB and FeRB growth and drive C and N biogeochemical\ncycling. In turn, FeOB and FeRB can modulate redox and charge of smectites\nand their reactivity, including sorption and degradation of contaminants. Intriguingly, FeOB and FeRB appear to use similar mechanisms to\noxidize and reduce smectite clays. Our work and previous studies have\ndemonstrated that the multiheme cytochrome–porin Mto/MtrAB\nhomologues mediate electron transfer between microbes and Fe minerals.\nMto/MtrAB homologues are widely distributed among bacteria, 90 so Mto/Mtr-based extracellular electron transfer\nmay be a common mechanism that bacteria evolved to access solid electron\nsources/sinks. S. lithotrophicus ES-1\nhas not only Cyc2 and MtoAB but also other multiheme cytochrome genes\nthat may also be useful in accessing additional solid substrates.\nSuch a diverse toolkit may allow FeOB like ES-1 to adapt to changing\nFe dynamics, thus playing an important role in sustaining life on\ngeomaterials across diverse habitats."
} | 3,337 |
30837468 | PMC6401179 | pmc | 6,854 | {
"abstract": "Droplet impacting and bouncing off solid surface plays a vital role in various biological/physiological processes and engineering applications. However, due to a lack of accurate control of force transmission, the maneuver of the droplet movement and energy conversion is rather primitive. Here we show that the translational motion of an impacting droplet can be converted to gyration, with a maximum rotational speed exceeding 7300 revolutions per minute, through heterogeneous surface wettability regulation. The gyration behavior is enabled by the synergetic effect of the asymmetric pinning forces originated from surface heterogeneity and the excess surface energy of the spreading droplet after impact. The findings open a promising avenue for delicate control of liquid motion as well as actuating of solids.",
"introduction": "Introduction Controlling droplet-solid impacting behaviors 1 , 2 is significant in a wide range of applications including self-cleaning 3 , 4 , anti-icing 5 , 6 , and inkjet printing 7 – 9 . The outcomes of a droplet impact on solid surfaces, such as deposition, rebounding, splashing, depend on both the micro/nanostructures and chemical property of the solid. Diverse strategies have been exploited by nature as well as artificial materials to regulate the droplet impact processes as well as the subsequent droplet motions 10 – 13 . For example, various superhydrophobic surfaces are fabricated to accelerate the droplet bouncing off solids after impact 14 – 16 ; topological heterogeneity is utilized for directional transportation of impacting droplets 17 , 18 . However, due to the deformability of the droplet and the milliseconds-scale interaction 19 between the impacting droplet and the solid, it is still a challenge to elaborately manipulate the impacting behaviors. As an important mechanical law, the “Newton’s Law of Impact” depicts the elasticity of two objects collision by considering the approach and recession velocities of the objects. Normally the recession of a ball is in a linear motion if it vertically impacts on a solid wall with translational kinetic energy. The principle is also applicable with the reported researches relating to droplet impact on solid surfaces, where the droplet bounces in a translational type after impacting on a flat surface 15 , 19 , 20 . In this work, we report a droplet rotational bouncing by impacting it on an adhesion-patterned surface, which seems beyond the “ Newton’s Law of Impact ”. Based on the mechanics modeling, we reveal that the angular momentum of the droplet is formed by the asymmetric adhesion forces that accumulate during the liquid film retraction. Under a proper test design, the maximum droplet rotational speed can be larger than 7300 revolutions per minute (rpm). We further demonstrate that the heterogeneous solid–liquid interaction forces during droplet impact can be exploited to drive sophisticated motions of the solids as well as the droplets.",
"discussion": "Discussion In conclusion, we have achieved a distinctive droplet gyration induced by the asymmetric pinning forces arising from the solid–liquid interaction when a water droplet impacts a chemically heterogeneous substrate. More importantly, we demonstrated that exploiting the pinning forces can be a general strategy for attaining sophisticated droplet motions, which opens an avenue in future explorations, such as matter transportation 36 , energy transformation 37 , and object actuation 38 ."
} | 862 |
29920568 | PMC6008153 | pmc | 6,855 | {
"abstract": "The photosynthetic quantum yield (Φ), defined as carbon fixed or oxygen evolved per unit of light absorbed, is a fundamental but rarely determined biophysical parameter. A method to estimate Φ for both net carbon uptake and net oxygen evolution simultaneously can provide important insights into energy and mass fluxes. Here we present details for a novel system that allows quantification of carbon fluxes using pH oscillation and simultaneous oxygen fluxes by integration with a membrane inlet mass spectrometer. The pHOS system was validated using Phaeodactylum tricornutum cultured with continuous illumination of 110 μmole quanta m -2 s -1 at 25°C. Furthermore, simultaneous measurements of carbon and oxygen flux using the pHOS-MIMS and photon flux based on spectral absorption were carried out to explore the kinetics of Φ in P . tricornutum during its acclimation from low to high light (110 to 750 μmole quanta m -2 s -1 ). Comparing results at 0 and 24 hours, we observed strong decreases in cellular chlorophyll a (0.58 to 0.21 pg cell -1 ), Fv/Fm (0.71 to 0.59) and maximum Φ CO2 (0.019 to 0.004) and Φ O2 (0.028 to 0.007), confirming the transition toward high light acclimation. The Φ time-series indicated a non-synchronized acclimation response between carbon uptake and oxygen evolution, which has been previously inferred based on transcriptomic changes for a similar experimental design with the same diatom that lacked physiological data. The integrated pHOS-MIMS system can provide simultaneous carbon and oxygen measurements accurately, and at the time-resolution required to resolve high-resolution carbon and oxygen physiological dynamics.",
"conclusion": "Conclusions Photoacclimation related NPQ response and transcriptional regulation can affect the fate of electrons in the photosystems, resulting in changes in Φ and its response to light at various time-scales from short-term perturbations to steady-state acclimation. Our method successfully detected the change of instantaneous Φ in relation to irradiance variation from 0–2000 μmole quanta m -2 s -1 , allowing simultaneous tracking of carbon uptake and oxygen evolution quantum efficiencies during acclimation, in addition to the classic chlorophyll variable fluorescence measurements. We found the optimal concentration for measurement is between [Chl a] >1.5 μg ml -1 and OD 750 < 0.35, which allows sufficient signal for sensor detection and minimizes light attenuation within the bioreactor by cellular absorption. The system configuration as described is ideal for laboratory algal physiological experiment, yet the modular components can be integrated with other instrument for different purposes. Our results demonstrate that there is a non-synchronous response of carbon uptake and oxygen evolution during the acclimation period from low to high light that is consistent with transcriptomic data [ 14 ]. These physiological observations could provide quantitative data for industrial applications such as production simulation in dynamic light environments, and energy utilization per unit of carbon fixed for life cycle analysis. They also inspired testable hypotheses regarding photosynthetic electron transport efficiency related to cellular mechanisms that could be pursued with the pHOS-MIMS system combined with other measurements, for example transcriptomics, variable chlorophyll fluorescence, mutant strains and stable oxygen isotopes. The novel pHOS system described here allows for the resolution of carbon dynamics with temporal resolution sufficient for diel studies and for resolving acclimation to environmental changes on time-scales of hours to days, and was validated with classical observations of rates of carbon growth in microalgae cultures. The integration of the pHOS with a MIMS system demonstrates an ability to resolve both carbon and oxygen dynamics during light and dark periods that were used to construct P vs. E curves from limiting to super-saturating light within 30 minutes. This type of simultaneous oxygen and carbon data is essential for metabolic modeling of mass fluxes and contributes to the integration between classic photo-physiology and the rapidly emerging interest in computational biology. Furthermore, the ability to resolve physiological responses to changing light conditions at time scales of minutes is highly relevant for understanding growth rates of microalgae within the near surface turbulent mixing layer of aquatic ecosystems or commercial production systems.",
"introduction": "Introduction Microalgae are capable of acclimating to dynamic light environments by reducing or increasing light harvesting capacity depending on light intensity, and implementing various strategies for stress mitigation when absorption of light exceeds photosynthetic carbon fixation capacity [ 1 ]. As a result, short-term photosynthesis vs. irradiance (P vs. E) responses are highly variable for different acclimation light conditions [ 2 ]. Several acclimation mechanisms at different levels of photosynthesis are involved in the physiological response to light, nutrient and temperature stresses. When exposed to super-saturating light that exceeds the photochemical capacity, acclimation mechanisms include, but are not limited to, non-photochemical quenching (NPQ) of excessively absorbed light energy as heat, and alternative electron transport, that redirects the fate of electrons for rebalancing NADPH/ATP ratios to satisfy other cellular energy requirements [ 3 – 7 ]. The quantum yield of photosynthesis (Φ), defined here as moles of inorganic carbon fixed or oxygen evolved per mole of photons absorbed, is an important physiological parameter that represents the photochemical efficiency of light utilization and is regulated by environmental factors that control growth and acclimation. In general, variable chlorophyll fluorescence photon flux relative to the maximum yield under actinic light ( Δ ϕ F / ϕ F m ) has been accepted as a good estimation of Φ, since a linear relation was discovered between them [ 3 ]. Note that the symbol Φ refers to the mass of carbon or oxygen per unit of absorbed photons whereas ϕ is a relative fluorescence flux. The term Δ ϕ F / ϕ F m is mathematically equivalent to the product of photochemical fluorescence quenching and the efficiency of excitation capture by open PS II reaction centers (F v /F m ). Both Δ ϕ F / ϕ F m and F v /F m are widely used because fluorescence measurements are easy, fast, non-destructive, provide biophysical information, and inference about biochemical information, at time scales of seconds or faster which is extremely useful for some research questions. However, fluorescence flux is not directly an indicator of mass flux, and therefore it does not represent the full potential of photosynthesis, especially for the dark reactions [ 4 ]. Furthermore, differences in variable fluorescence protocols can give different values of Δ ϕ F / ϕ F m and Fv/Fm for the same culture [ 5 ]. In contrast, mass exchange measurements can provide quantitative estimates for computing the actual mass fluxes relative to quanta absorbed, however, a larger quantity of biomass is required for these types of methods, their implementation is much more difficult to achieve and they are not able to resolve photosynthetic processes at time-scales of seconds. In algal physiology studies under a dynamic environment, a mass flux-based approach is necessary to be able to explore changes in Φ. For example, Broddrick et al. [ 6 ] used oxygen flux as a constraint in a cyanobacterium genome scale model (GEM) to simulate whole cell metabolic flux, which enabled the discovery of unique light-driven cellular mechanisms. While oxygen evolution was directly measured in that study, carbon uptake was estimated simply in direct stoichiometric proportion to the oxygen evolution. Since previous studies on photosynthetic quotients (PQ, the ratio of oxygen evolved to carbon fixed) have shown that this ratio can be highly variable, depending on environmental factors that regulate physiology and growth, and algal species. [ 7 – 10 ]. Therefore, deviations in the ratio of oxygen evolution and carbon uptake would not have been resolved by the metabolic model of Broddrick [ 11 ]. Despite the well-recognized need for simultaneous and quantitative estimates of carbon and oxygen fluxes to develop a deeper understanding of energy and mass flux in both photosynthesis and respiration, previous studies have not yet established robust methodologies that are capable of generating fast, quantitative and high precision results for both carbon and oxygen [ 11 , 12 ] (details discussed in S1 Text ). Here we describe a novel integrated hardware and software system to monitor carbon and oxygen dynamics at short time scales (minutes) using pH oscillation (pHOS) that is validated relative to an independent estimate of net carbon growth of a well-studied marine diatom Phaeodactylum tricornutum ( P . tricornutum ) [ 13 ]. The pHOS system was integrated to a membrane inlet mass spectrometer (MIMS) and we used the integrated pHOS-MIMS system for a 24 hour acclimation study in which the cultivation light was shifted from sub-saturating to super-saturating at time 0 to resolve the kinetics of Φ for both net carbon and net oxygen during the acclimation. The results were interpreted in the context of a similar study using the same organism that reported the changes in gene expression over 24 hours determined by transcriptomic analysis [ 14 ]. Principles of DIC measurement dynamics using pHOS The dissolved inorganic carbon (DIC) pool in an aqueous medium includes three carbon species (CO 2 , HCO 3 - and CO 3 2- ) whose relative concentrations change significantly between pH 6–10. For a closed microalgal culture system, changes in DIC reflect the dynamics of carbon transport into and out of the algal cells. Early work by Allen and Spence[ 15 ] measured carbon uptake by submerging freshwater plants and microalgae into NaHCO 3 solutions and tracking the pH change over time. Their study was the first to demonstrate the feasibility of using “pH drift” caused by changes in carbonate chemistry as a method for photosynthetic carbon uptake measurement in aqueous media. During photosynthesis, DIC from the aqueous medium enters the algal cells either via passive diffusion of CO 2 or active transport of HCO 3 - . CO 2 is then fixed into organic molecules via the Calvin-Benson cycle. The assimilation of CO 2 ( Eq 1 ), and subsequent formation of OH - ions from the carbonate system re-equilibration ( Eq 2 ), together result in a net pH increase in the medium.\n C O 2 + H 2 O → O r g a n i c c a r b o n + O 2 (1) \n C O 2 + O H − ⇐ H C O 3 − ⇔ C O 3 2 − + H + (2) \nHowever, the change in pH does not track the DIC concentration in a simple linear relationship. To estimate carbon uptake, Allen and Spence [ 15 ] assumed the total alkalinity (A T ) to be a constant value, and calculated DIC using Eq 3 :\n D I C = A T − [ O H − ] + [ H + ] α 1 + 2 α 2 (3) \nWhere α 1 and α 2 are the ionization fractions of HCO 3 - and CO 3 2- in fresh water medium, respectively. Carbonate chemistry in seawater-based culture media is far more complex than the fresh water based system that Allen and Spence [ 15 ] worked with, due to higher salinity and more complex ion composition. Relatively recent advances in understanding the complex carbonate chemistry in seawater [ 16 ], combined with advances in pH sensors and electronic circuits, have made possible the application of a “pH drift” system for microalgae in sea water media. After reviewing prior experimental assumptions and conditions, we modified the original “pH drift” concept to a pH oscillation method, adding the following improvements over those employed by Allen and Spence [ 15 ]: We shortened the measurement time from a few hours to 30 minutes to strengthen the assumption of a constant A T , given that algal cell growth within the time scale of hours can change A T , especially for high cell density lab cultures [ 17 , 18 ]; Using a high sensitivity pH apparatus and oscillating light/dark periods of relatively short duration (2 min), we were able to control the pH using the oscillation method over a very small range (usually less than 0.1 pH units), thus avoiding significant changes in media carbonate chemistry that affects the DIC uptake kinetics; and (3) For measurements in the pHOS-MIMS system we gently centrifuged the cells into a pellet, and re-suspended them in the fresh culture media for which careful calibrations had been carried out in order to minimize changes in chemistry that might affect the cell physiology and for precise understanding of the carbonate chemistry and alkalinity. An example of pH oscillation during a series of measurements over a 30 minute period (1800 s) is demonstrated in S1 Fig . The calculations for carbon speciation were based on the following equations from Dickson et al. [ 19 ]:\n D I C = [ H C O 3 − ] + [ C O 3 2 − ] + [ C O 2 ] (4) \n [ C O 2 ] = A C [ H + ] 2 K 1 ( [ H + ] + 2 K 2 ) (5) \n [ H C O 3 − ] = A C [ H + ] [ H + ] + 2 K 2 (6) \n [ C O 3 2 − ] = A C K 2 [ H + ] + 2 K 2 (7) \nWhere A c is the carbonate alkalinity, and K 1 and K 2 are the dissociation constants of HCO 3 - and CO 3 2- in aquatic solutions as functions of salinity and temperature, respectively (details in S2 Text ). A c is related to A T and varies as a function of pH.",
"discussion": "Results and discussion Carbon uptake detection and validation DIC changes reflect the rate of carbon flux into and out of algal cells, yet these rates might be not the same as carbon uptake and respiration rates, especially for eukaryotic algal cells in which DIC might be concentrated in chloroplasts prior to fixation, resulting in the efflux of DIC from the cell by passive diffusion down the concentration gradient during the post illumination period [ 31 ]. Therefore, it is important to confirm the biological mechanisms associated with the measured DIC changes, and in this study we tested and validated the DIC uptake measurement using the pHOS system. CO 2 released from respiration of cellular organic matter is another important physiological parameter, however due to the lack of methodologies to estimate short term respiration rates, this process could not be quantified independently. The experiment was conducted by establishing the cells in steady-state (continuous light, constant temperature, excess nutrients and mid-log phase exponential growth). Light intensity was 110 ± 10 μmole quanta m -2 s -1 , which is approximately the saturation irradiance for this strain [ 32 ]. The samples for testing were concentrated to the optimal concentration ranges ([Chla] >1.5 μg ml -1 and OD 750 < 0.35), and the P vs. E response was measured and fitted with Eq 17 [ 22 ]. 10.1371/journal.pone.0199125.t002 Table 2 P vs. E parameters with data fitted using equations from Platt et al. [ 22 ], with sensitivity test results. P max (Carbon) α (Carbon) β (Carbon) Baseline 154.79 1.11 0 ±12.97 ±0.15 NA A T + 1% +0.76% +0.75% 0 AT—1% -0.76% -0.75% 0 pH + 0.01 +1.11% +1.12% 0 pH—0.01 -1.10% -1.11% 0 P = P max ( 1 − e − α I P max ) e − β I P max (17) \nP max is the photosynthetic rate at the optimal light condition, α and β (if present) are the parameters that control the initial slope of the curve and the photoinhibition factor, respectively. P vs. E results for carbon are shown in Fig 5 with fitted curves and the replicability of the measurement is shown in Table 1 . Considering the potential error in the pH and alkalinity measurements, we performed a sensitivity analysis to determine the uncertainty in our results by manually adding in errors that could have occurred during the measurement. Our titrated A T results showed less than 1% standard deviation, and the pH calibration drift between measurements were less than 0.01 pH units. Both errors together introduce approximately a 1% uncertainty in the final results ( Table 2 ). 10.1371/journal.pone.0199125.g005 Fig 5 Carbon uptake rates vs. irradiance (n = 3) using pHOS. The estimated net carbon growth rate for a separate culture validation experiment at 110 μmole quanta m -2 s -1 is indicated by the closed square and the corresponding interpolation from the pHOS data at the same irradiance is shown in the inverted triangle (n = 6). Carbon uptake rates determined from these two approaches are not significantly different from each other (p > 0.05). Independent from the samples collected for P vs. E measurements, we grew 6 P . tricornutum cultures under the same culture conditions (excess nutrients, 25°C and 110 μmole quanta m -2 s -1 continuous light) and determined the natural log (base e) exponential growth rates (μ) at steady state of the cultures by measuring their optical density at 750 nm (OD 750 ) over time. During mid-log phase of exponential growth, the OD 750 was well correlated to cell concentration, POC and Chla. The Chla specific carbon uptake rates (P Chl ) for these samples were then calculated using Eq 18 .\n P C h l = μ [ P O C ] [ C h l a ] (18) \nThe estimate for P chl determined with Eq 9 for the bulk culture, and separately the P chl value determined with the pHOS, interpolated to the same growth irradiance, agreed well (62.5 ± 9.5 and 68.0 ± 7.9 μmole carbon mg -1 Chla h -1 , respectively). The pHOS data, the P vs. E curve fit to the pHOS data, and the two estimates at 110 μmole quanta m -2 s -1 for the bulk culture and the pHOS interpolation are shown in Fig 5 . The agreement in the rate of carbon fixation between the pHOS P vs. E interpolated to 110 μmole quanta m -2 s -1 and the mid-log phase batch culture provides the primary validation of our pHOS method. Further support for this is provided by Hopkinson et al. [ 31 ] who demonstrate that the rate of carbon fixation intracellularly is >10 times slower than the exchange rates between the cell and the medium, thus the 2 min measurement duration for pHOS rates for each light level is more than sufficient to ensure balanced CO 2 exchange for carbon fixation. Quantum yield dynamics during low-to-high light acclimation To investigate the dynamics of Φ in a non-steady state environment, we shifted low light acclimated P . tricornutum cultures to super-saturating irradiance (110 to 750 μmole quanta m -2 s -1 ), and measured the Φ of oxygen evolution and DIC uptake during the 24 hour high light acclimation. Significant physiological changes in high light treated cells were observed, and the measured shift in cellular Chla concentrations, particulate carbon and nitrogen (POC/PON), and Fv/Fm responses at time 0 and 24 hours ( Table 3 ) are consistent with previous published studies on P . tricornutum acclimated to different light conditions [ 33 , 34 ], confirming the detected physiological changes are correlated to acclimation to high light stress. From P vs. E results we found similar initial slopes at 0 and 24 hours for both the Chla specific oxygen evolution and carbon uptake, whereas for the high light region > 500 μmole quanta m -2 s -1 the rates increased at the 24 hour time point ( Fig 6 ). 10.1371/journal.pone.0199125.g006 Fig 6 (A) Chlorophyll a specific oxygen evolution, and (B) carbon uptake rates, as a function of light intensities measured with the pHOS-MIMS system at 0 and 24 at the start and end of the 24 hours low-to-high light acclimation. Error bars are standard deviations for n = 3; for values with no error bar shown, the standard deviation was smaller than the symbol in the figure. The initial slopes (μmole carbon or oxygen mg -1 Chla h -1 [μmole quanta m -2 s -1]-1 ) of the fitted curves were determined using equations developed by Jassby and Platt [ 35 ] with values as follows: 2.17 for oxygen evolution at 0 hour; 1.46 for oxygen evolution at 24 hours; 2.12 for carbon uptake at 0 hour; 2.36 for carbon uptake at hour 24 hours. The P vs. E response pattern is consistent with previous studies on a diatom Skeletonema costatum [ 2 , 36 ], for P vs. E determined for low light and high light acclimation conditions (50 and 1200 μmole quanta m -2 s -1 , respectively). This effect is likely the combined result of decreases in cellular Chla concentrations and pigment packaging effects, which is inferred by the increased Chla specific absorption coefficient [ 37 , 38 ] ( Fig 7 ). 10.1371/journal.pone.0199125.g007 Fig 7 Spectral chlorophyll a specific absorption coefficients measured at 0 and 24 hours during the 24 hours low-to-high light acclimation. Note that the calculation of Φ max in our method is determined from the peak value of the Φ vs. E curve, and not from the initial slope of a P vs. E curve, as is common practice [ 39 , 40 ]. At low light, basal respiration can be large relative to photosynthesis and at very low light it can even exceed photosynthesis ( S3 Fig ). Our method does not calculate Φ max at the very low light levels as might be expected from short term 14-C tracer studies that estimate gross photosynthesis [ 2 ], since pHOS is considered to measure net photosynthesis. In our study the observed Φ max dropped significantly between the start and end of the acclimation to high light because both the chla-specific absorption increased ~2x ( Fig 7 ) due to lower pigment packaging effects [ 41 ], and also the light intensity corresponding to the Φ max increased significantly during acclimation ( Fig 8B ). The combination of these factors resulted in a greater than 5x reduction in Φ max by the end of the 24 hour experiment. Dark respiration rates in microalgae have been reported to be positively correlated with growth rates [ 42 , 43 ]. In our study, a higher growth rate would be expected after 24 hours of acclimation to higher light with a concomitant increase in dark respiration rates. The dynamics of Φ in response to light indicate that the acclimation process is non-linear and the changes in carbon uptake and oxygen evolution Φ are not synchronized ( S3 Fig ). The Φ for oxygen evolution dropped rapidly at low light within the first hour of high light treatment, however the carbon uptake did not change until the 3-hour time point. Also, by 24 hours the carbon:oxygen ratio became lower (higher oxygen evolution than carbon uptake). Overall, carbon uptake seemed to lag in response to the light change, compared to the oxygen evolution response. Such a non-synchronized change in Φ for oxygen evolution and carbon uptake might be related to the transcriptional regulations in P . tricornutum , reported by Nymark et al. [ 14 ] who found that genes that regulate carbon metabolism, respiration and the Calvin-Benson cycle changed later in the 24 hour acclimation phase, while the down regulation of light harvesting antenna genes responded immediately. We designed our experiments to be comparable to Nymark et al. [ 14 ] by using the same culturing medium, strain of P . tricornutum , and very similar culturing and sampling protocols. Our data show the utility of the pHOS-MIMS system for physiological validation of independent molecular level regulation of metabolic processes. 10.1371/journal.pone.0199125.g008 Fig 8 Observations during the 24 hours low-to-high light acclimation of (A) Maximal observed quantum yield for net photosynthesis, (B) the corresponding light intensities where maximal net quantum yield was observed, (C) Photosynthetic quotient of P max . All data points were determined from a P vs. E curve fitted to the mean of 3 replicates; see Fig 6 for typical statistics for the replicates that were fitted. For the high light acclimation time-series, Fig 8A shows changes in Φ max for oxygen and carbon, and changes in the irradiance where the Φ max is attained for the same data. Over the 24 hour acclimation to high light the Φ max for both carbon and oxygen drop and the irradiance at which Φ max was observed shifted higher ( Fig 8B ). Furthermore the PQ for the fitted P max for both carbon and oxygen showed an initial drop at the 3 hour time point followed by a steady increase, which is consistent with our previous inference that oxygen shifts start sooner than carbon shifts ( Fig 8C ). Photorespiration might be important in the shift to super-saturating light, reducing the oxygen evolved and hence leading to a lower PQ during the first few hours ( Fig 8C ). The increase of PQ in the later acclimation period might have resulted from stronger alternative electron transport [ 44 , 45 ]. The overall trend of a strong decrease in Φ max for both oxygen and carbon following the shift to super-saturating light is likely correlated to the reduction of cellular light harvesting pigments, rebalancing of PSII to PSI, and reaction center damage that has not been repaired [ 46 , 47 ]; These physiological changes are also reflected as decreases in Fv/Fm ( Table 3 ). As these shifts occurred there were also strong changes over 24 hours in cellular properties, as shown by observed decreases in Chla cell -1 , and increases in C:Chla and the chlorophyll specific absorption coefficient (a* ph ). These cellular property changes are consistent with a physiological transition from low light to high light acclimation. System operation and implementation We found the integration of our pHOS and the MIMS to the ALG instruments bioreactor system provided robust data. Although the pHOS system has some novel circuitry and software and we optimized the membrane for the MIMS, all of these novel elements and improvements are well documented here and in the SI. Therefore, based on our current system design, hardware, software and integration, and documentation in the manuscript and the SI, the system could be easily replicated. The pHOS system to estimate carbon uptake from an aqueous medium can be run independent of oxygen measurements. All of the electronic parts required for the pHOS system can be sourced on-line using the information provided in the methods section, and the software for operation is provided in the SI. We used the MIMS for oxygen in part because we intend to use it for other gases in the future but the pHOS system can also be easily used with other oxygen measuring systems such as a Clark electrode or an optode. 10.1371/journal.pone.0199125.t003 Table 3 Physiological parameters measured at 0 and 24 hours during the low-to-high light acclimation (n = 3 for all samples). Time C (pg cell -1 ) N (pg cell -1 ) Chla (pg cell -1 ) C:N C:Chl Fv/Fm 0 hour 11.44 ± 1.45 1.77 ± 0.07 0.58 ± 0.05 6.47 21.07 0.71±0.007 24 hours 15.46 ± 2.10 2.08 ± 0.21 0.21 ± 0.02 7.43 77.32 0.59±0.005"
} | 6,664 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.