Top-down and bottom-up microbiome engineering approaches to enable biomanufacturing from waste biomass.

Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted. ONE-SENTENCE SUMMARY: This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.

Microbially mediated climate feedbacks from wetland ecosystems.

Wetlands are crucial nodes in the carbon cycle, emitting approximately 20% of global CH4 while also sequestering 20%-30% of all soil carbon. Both greenhouse gas fluxes and carbon storage are driven by microbial communities in wetland soils. However, these key players are often overlooked or overly simplified in current global climate models. Here, we first integrate microbial metabolisms with biological, chemical, and physical processes occurring at scales from individual microbial cells to ecosystems. This conceptual scale-bridging framework guides the development of feedback loops describing how wetland-specific climate impacts (i.e., sea level rise in estuarine wetlands, droughts and floods in inland wetlands) will affect future climate trajectories. These feedback loops highlight knowledge gaps that need to be addressed to develop predictive models of future climates capturing microbial contributions. We propose a roadmap connecting environmental scientific disciplines to address these knowledge gaps and improve the representation of microbial processes in climate models. Together, this paves the way to understand how microbially mediated climate feedbacks from wetlands will impact future climate change.

Wetlands harbor lactic acid-driven chain elongators.

IMPORTANCE: Wetlands are globally significant carbon cycling hotspots that both sequester large amounts of CO2 as soil carbon as well as emit a third of all CH4 globally. Their outsized role in the global carbon cycle makes it critical to understand microbial processes contributing to carbon breakdown and storage in these ecosystems. Here, we confirm the presence of chain-elongating organisms in freshwater wetland soils. These organisms take small carbon compounds formed during the breakdown of biomass and turn them into larger compounds (six to eight carbon organic acids) that may potentially contribute to the formation of soil organic matter and long-term carbon storage. Moreover, we find that these chain-elongating organisms may be widely distributed in wetlands globally. Future work should identify these organisms' contribution to carbon cycling in wetlands and the potential role of the products they form in carbon sequestration in wetlands.

Microbe-cellulose hydrogels as a model system for particulate carbon degradation in soil aggregates.

Particulate carbon (C) degradation in soils is a critical process in the global C cycle governing greenhouse gas fluxes and C storage. Millimeter-scale soil aggregates impose strong controls on particulate C degradation by inducing chemical gradients of e.g. oxygen, as well as limiting microbial mobility in pore structures. To date, experimental models of soil aggregates have incorporated porosity and chemical gradients but not particulate C. Here, we demonstrate a proof-of-concept encapsulating microbial cells and particulate C substrates in hydrogel matrices as a novel experimental model for soil aggregates. Ruminiclostridium cellulolyticum was co-encapsulated with cellulose in millimeter-scale polyethyleneglycol-dimethacrylate (PEGDMA) hydrogel beads. Microbial activity was delayed in hydrogel-encapsulated conditions, with cellulose degradation and fermentation activity being observed after 13 days of incubation. Unexpectedly, hydrogel encapsulation shifted product formation of R. cellulolyticum from an ethanol-lactate-acetate mixture to an acetate-dominated product profile. Fluorescence microscopy enabled simultaneous visualization of the PEGDMA matrix, cellulose particles, and individual cells in the matrix, demonstrating growth on cellulose particles during incubation. Together, these microbe-cellulose-PEGDMA hydrogels present a novel, reproducible experimental soil surrogate to connect single cells to process outcomes at the scale of soil aggregates and ecosystems.

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems.

Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome's functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute's Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE: Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome's metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

Integrating socio-economic vulnerability factors improves neighborhood-scale wastewater-based epidemiology for public health applications.

Wastewater Based Epidemiology (WBE) of COVID-19 is a low-cost, non-invasive, and inclusive early warning tool for disease spread. Previously studied WBE focused on sampling at wastewater treatment plant scale, limiting the level at which demographic and geographic variations in disease dynamics can be incorporated into the analysis of certain neighborhoods. This study demonstrates the integration of demographic mapping to improve the WBE of COVID-19 and associated post-COVID disease prediction (here kidney disease) at the neighborhood level using machine learning. WBE was conducted at six neighborhoods in Seattle during October 2020 - February 2022. Wastewater processing and RT-qPCR were performed to obtain SARS-CoV-2 RNA concentration. Census data, clinical data of COVID-19, as well as patient data of acute kidney injury (AKI) cases reported during the study period were collected and the distribution across the city was studied using Geographic Information System (GIS) mapping. Further, we analyzed the data set to better understand socioeconomic impacts on disease prevalence of COVID-19 and AKI per neighborhood. The heterogeneity of eleven demographic factors (such as education and age among others) was observed within neighborhoods across the city of Seattle. Dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 varied across neighborhood with different levels of demographics. Machine learning models trained with data from the earlier stages of the pandemic were able to predict both COVID-19 and AKI incidence in the later stages of the pandemic (Spearman correlation coefficient of 0·546 - 0·904), with the most predictive model trained on the combination of wastewater data and demographics. The integration of demographics strengthened machine learning models' capabilities to predict prevalence of COVID-19, and of AKI as a marker for post-COVID sequelae. Demographic-based WBE presents an effective tool to monitor and manage public health beyond COVID-19 at the neighborhood level.

Metaproteomics-informed stoichiometric modeling reveals the responses of wetland microbial communities to oxygen and sulfate exposure.

Climate changes significantly impact greenhouse gas emissions from wetland soil. Specifically, wetland soil may be exposed to oxygen (O2) during droughts, or to sulfate (SO42-) as a result of sea level rise. How these stressors - separately and together - impact microbial food webs driving carbon cycling in the wetlands is still not understood. To investigate this, we integrated geochemical analysis, proteogenomics, and stoichiometric modeling to characterize the impact of elevated SO42- and O2 levels on microbial methane (CH4) and carbon dioxide (CO2) emissions. The results uncovered the adaptive responses of this community to changes in SO42- and O2 availability and identified altered microbial guilds and metabolic processes driving CH4 and CO2 emissions. Elevated SO42- reduced CH4 emissions, with hydrogenotrophic methanogenesis more suppressed than acetoclastic. Elevated O2 shifted the greenhouse gas emissions from CH4 to CO2. The metabolic effects of combined SO42- and O2 exposures on CH4 and CO2 emissions were similar to those of O2 exposure alone. The reduction in CH4 emission by increased SO42- and O2 was much greater than the concomitant increase in CO2 emission. Thus, greater SO42- and O2 exposure in wetlands is expected to reduce the aggregate warming effect of CH4 and CO2. Metaproteomics and stoichiometric modeling revealed a unique subnetwork involving carbon metabolism that converts lactate and SO42- to produce acetate, H2S, and CO2 when SO42- is elevated under oxic conditions. This study provides greater quantitative resolution of key metabolic processes necessary for the prediction of CH4 and CO2 emissions from wetlands under future climate scenarios.

Increasing aggregate size reduces single-cell organic carbon incorporation by hydrogel-embedded wetland microbes.

Microbial degradation of organic carbon in sediments is impacted by the availability of oxygen and substrates for growth. To better understand how particle size and redox zonation impact microbial organic carbon incorporation, techniques that maintain spatial information are necessary to quantify elemental cycling at the microscale. In this study, we produced hydrogel microspheres of various diameters (100, 250, and 500 µm) and inoculated them with an aerobic heterotrophic bacterium isolated from a freshwater wetland (Flavobacterium sp.), and in a second experiment with a microbial community from an urban lacustrine wetland. The hydrogel-embedded microbial populations were incubated with 13C-labeled substrates to quantify organic carbon incorporation into biomass via nanoSIMS. Additionally, luminescent nanosensors enabled spatially explicit measurements of oxygen concentrations inside the microspheres. The experimental data were then incorporated into a reactive-transport model to project long-term steady-state conditions. Smaller (100 µm) particles exhibited the highest microbial cell-specific growth per volume, but also showed higher absolute activity near the surface compared to the larger particles (250 and 500 µm). The experimental results and computational models demonstrate that organic carbon availability was not high enough to allow steep oxygen gradients and as a result, all particle sizes remained well-oxygenated. Our study provides a foundational framework for future studies investigating spatially dependent microbial activity in aggregates using isotopically labeled substrates to quantify growth.

Resource availability governs polyhydroxyalkanoate (PHA) accumulation and diversity of methanotrophic enrichments from wetlands.

Aquatic environments account for half of global CH4 emissions, with freshwater wetlands being the most significant contributors. These CH4 fluxes can be partially offset by aerobic CH4 oxidation driven by methanotrophs. Additionally, some methanotrophs can convert CH4 into polyhydroxyalkanoate (PHA), an energy storage molecule as well as a promising bioplastic polymer. In this study, we investigate how PHA-accumulating methanotrophic communities enriched from wetlands were shaped by varying resource availability (i.e., C and N concentrations) at a fixed C/N ratio. Cell yields, PHA accumulation, and community composition were evaluated in high (20% CH4 and 10 mM NH4 +) and low resource (0.2% CH4 and 0.1 mM NH4 +) conditions simulating engineered and environmental settings, respectively. High resource availability decreased C-based cell yields, while N-based cell yields remained stable, suggesting nutrient exchange patterns differed between methanotrophic communities at different resource concentrations. PHA accumulation was only observed in high resource enrichments, producing approximately 12.6% ± 2.4% (m/m) PHA, while PHA in low resource enrichments remained below detection. High resource enrichments were dominated by Methylocystis methanotrophs, while low resource enrichments remained significantly more diverse and contained only a minor population of methanotrophs. This study demonstrates that resource concentration shapes PHA-accumulating methanotrophic communities. Together, this provides useful information to leverage such communities in engineering settings as well as to begin understanding their role in the environment.

Mainstream nitrogen removal from low temperature and low ammonium strength municipal wastewater using hydrogel-encapsulated comammox and anammox.

Application of partial nitritation (PN)-anammox to mainstream wastewater treatment faces challenges in low water temperature and low ammonium strength. In this study, a continuous flow PN-anammox reactor with hydrogel-encapsulated comammox and anammox was designed and operated for nitrogen removal from mainstream wastewater with low temperature. Long-term operation with synthetic and real wastewater as the feed demonstrated nearly complete ammonium and total inorganic nitrogen (TIN) removal by the reactor at temperatures as low as 10 °C. A significantly decreased nitrogen removal performance and biomass activity was observed in the reactor at 4 °C before a selective heating strategy was employed. A novel heating technology using radiation to heat carbon black co-encapsulated in the hydrogel matrix with biomass was used to selectively heat biomass but not water in the treatment system. This selective heating technology enabled nearly complete ammonium removal and 89.4 ± 4.3 % TIN removal at influent temperature of 4 °C and reactor temperature 5 °C. Activity tests suggested selective heating brought the biomass activity at influent temperatures of 4 °C and reactor temperature 5 °C to a level comparable to that at 10 °C. Comammox and anammox were consistently present in the system and spatially organized in the hydrogel beads as revealed by qPCR and fluorescence in-situ hybridization (FISH). The abundance of comammox largely decreased by 3 orders of magnitude during the operation at 4 °C, and rapidly recovered after the application of selective heating. The anammox-comammox technology tested in this study essentially enabled mainstream shortcut nitrogen removal, and the selective heating ensured good performance of the technology at temperature as low as 5 °C.

Trophic interactions shape the spatial organization of medium-chain carboxylic acid producing granular biofilm communities.

Granular biofilms producing medium-chain carboxylic acids (MCCA) from carbohydrate-rich industrial feedstocks harbor highly streamlined communities converting sugars to MCCA either directly or via lactic acid as intermediate. We investigated the spatial organization and growth activity patterns of MCCA producing granular biofilms grown on an industrial side stream to test (i) whether key functional guilds (lactic acid producing Olsenella and MCCA producing Oscillospiraceae) stratified in the biofilm based on substrate usage, and (ii) whether spatial patterns of growth activity shaped the unique, lenticular morphology of these biofilms. First, three novel isolates (one Olsenella and two Oscillospiraceae species) representing over half of the granular biofilm community were obtained and used to develop FISH probes, revealing that key functional guilds were not stratified. Instead, the outer 150-500 µm of the granular biofilm consisted of a well-mixed community of Olsenella and Oscillospiraceae, while deeper layers were made up of other bacteria with lower activities. Second, nanoSIMS analysis of 15N incorporation in biofilms grown in normal and lactic acid amended conditions suggested Oscillospiraceae switched from sugars to lactic acid as substrate. This suggests competitive-cooperative interactions may govern the spatial organization of these biofilms, and suggests that optimizing biofilm size may be a suitable process engineering strategy. Third, growth activities were similar in the polar and equatorial biofilm peripheries, leaving the mechanism behind the lenticular biofilm morphology unexplained. Physical processes (e.g., shear hydrodynamics, biofilm life cycles) may have contributed to lenticular biofilm development. Together, this study develops an ecological framework of MCCA-producing granular biofilms that informs bioprocess development.

Cross-Feedings, Competition, and Positive and Negative Synergies in a Four-Species Synthetic Community for Anaerobic Degradation of Cellulose to Methane.

Complex interactions exist among microorganisms in a community to carry out ecological processes and adapt to changing environments. Here, we constructed a quad-culture consisting of a cellulolytic bacterium (Ruminiclostridium cellulolyticum), a hydrogenotrophic methanogen (Methanospirillum hungatei), an acetoclastic methanogen (Methanosaeta concilii), and a sulfate-reducing bacterium (Desulfovibrio vulgaris). The four microorganisms in the quad-culture cooperated via cross-feeding to produce methane using cellulose as the only carbon source and electron donor. The community metabolism of the quad-culture was compared with those of the R. cellulolyticum-containing tri-cultures, bi-cultures, and mono-culture. Methane production was higher in the quad-culture than the sum of the increases in the tri-cultures, which was attributed to a positive synergy of four species. In contrast, cellulose degradation by the quad-culture was lower than the additive effects of the tri-cultures which represented a negative synergy. The community metabolism of the quad-culture was compared between a control condition and a treatment condition with sulfate addition using metaproteomics and metabolic profiling. Sulfate addition enhanced sulfate reduction and decreased methane and CO2 productions. The cross-feeding fluxes in the quad-culture in the two conditions were modeled using a community stoichiometric model. Sulfate addition strengthened metabolic handoffs from R. cellulolyticum to M. concilii and D. vulgaris and intensified substrate competition between M. hungatei and D. vulgaris. Overall, this study uncovered emergent properties of higher-order microbial interactions using a four-species synthetic community. IMPORTANCE A synthetic community was designed using four microbial species that together performed distinct key metabolic processes in the anaerobic degradation of cellulose to methane and CO2. The microorganisms exhibited expected interactions, such as cross-feeding of acetate from a cellulolytic bacterium to an acetoclastic methanogen and competition of H2 between a sulfate reducing bacterium and a hydrogenotrophic methanogen. This validated our rational design of the interactions between microorganisms based on their metabolic roles. More interestingly, we also found positive and negative synergies as emergent properties of high-order microbial interactions among three or more microorganisms in cocultures. These microbial interactions can be quantitatively measured by adding and removing specific members. A community stoichiometric model was constructed to represent the fluxes in the community metabolic network. This study paved the way toward a more predictive understanding of the impact of environmental perturbations on microbial interactions sustaining geochemically significant processes in natural systems.

Tailoring polyvinyl alcohol-sodium alginate (PVA-SA) hydrogel beads by controlling crosslinking pH and time.

Hydrogel-encapsulated catalysts are an attractive tool for low-cost intensification of (bio)-processes. Polyvinyl alcohol-sodium alginate hydrogels crosslinked with boric acid and post-cured with sulfate (PVA-SA-BS) have been applied in bioproduction and water treatment processes, but the low pH required for crosslinking may negatively affect biocatalyst functionality. Here, we investigate how crosslinking pH (3, 4, and 5) and time (1, 2, and 8 h) affect the physicochemical, elastic, and process properties of PVA-SA-BS beads. Overall, bead properties were most affected by crosslinking pH. Beads produced at pH 3 and 4 were smaller and contained larger internal cavities, while optical coherence tomography suggested polymer cross-linking density was higher. Optical coherence elastography revealed PVA-SA-BS beads produced at pH 3 and 4 were stiffer than pH 5 beads. Dextran Blue release showed that pH 3-produced beads enabled higher diffusion rates and were more porous. Last, over a 28-day incubation, pH 3 and 4 beads lost more microspheres (as cell proxies) than beads produced at pH 5, while the latter released more polymer material. Overall, this study provides a path forward to tailor PVA-SA-BS hydrogel bead properties towards a broad range of applications, such as chemical, enzymatic, and microbially catalyzed (bio)-processes.

Case Study: Impact of Diurnal Variations and Stormwater Dilution on SARS-CoV-2 RNA Signal Intensity at Neighborhood Scale Wastewater Pumping Stations.

Wastewater based epidemiology (WBE) has emerged as a tool to track the spread of SARS-CoV-2. However, sampling at wastewater treatment plants (WWTPs) cannot identify transmission hotspots within a city. Here, we sought to understand the diurnal variations (24 h) in SARS-CoV-2 RNA titers at the neighborhood level, using pump stations that serve vulnerable communities (e.g., essential workers, more diverse communities). Hourly composite samples were collected from wastewater pump stations located in (i) a residential area and (ii) a shopping district. In the residential area, SARS-CoV-2 RNA concentration (N1, N2, and E assays) varied by up to 42-fold within a 24 h period. The highest viral load was observed between 5 and 7 am, when viral RNA was not diluted by stormwater. Normalizing peak concentrations during this time window with nutrient concentrations (N and P) enabled correcting for rainfall to connect sewage to clinical cases reported in the sewershed. Data from the shopping district pump station were inconsistent, probably due to the fluctuation of customers shopping at the mall. This work indicates pump stations serving the residential area offer a narrow time period of high signal intensity that could improve the sensitivity of WBE, and tracer compounds (N, P concentration) can be used to normalize SARS-CoV-2 signals during rainfall.

Chain elongators, friends, and foes.

Bioproduction of medium chain carboxylic acids has recently emerged as an alternative strategy to valorize low-value organic waste and side-streams. Key to this route is chain elongation, an anaerobic microbial process driven by ethanol, lactic acid, or carbohydrates. Because these technologies use wastes as feedstocks, mixed microbial communities are often considered as biocatalysts. Understanding and steering these microbiomes is key to optimize bioprocess performance. From a meta-analysis of publicly available sequencing data, we (i) explore how the current collection of isolated chain elongators compares to microbiome members, (ii) discuss the main beneficial and antagonistic interactions with community partners, and (iii) identify the key research gaps and needs to help understand chain elongation microbiomes, and design/steer these novel bioproduction processes.

Impact of substrate and growth conditions on microbial protein production and composition.

Production of microbial protein (MP) from recovered resources - e.g. CO2-sourced formate and acetate - could provide protein while enabling CO2 capture. To assess the protein quality obtained from this process, pure cultures and enriched communities were selected and characterized kinetically, stoichiometrically and nutritionally. Growth on acetate resulted in up to 5.3 times higher maximum specific growth rate (µmax) than formate (i.e. 0.15-0.41 h-1 for acetate compared to 0.061-0.29 h-1 for formate at pH = 7). The protein content was a function of the growth phase, with the highest values during stationary phase, ranging between 18 and 82%CDW protein depending on the organism and substrate. The negative correlation between biomass productivity and protein content indicated a trade-off between production rate and product quality. The final product (i.e. dried MP) quality was in most cases superior to soybean and all cultures were rich in threonine, phenylalanine and tyrosine, regardless of the carbon source.

Ethanol:propionate ratio drives product selectivity in odd-chain elongation with Clostridium kluyveri and mixed communities.

Microbial production of valerate, a five-carbon carboxylate, can occur from propionate and ethanol through a process called odd-chain elongation. The generation of even-chain compounds in this process lowers product selectivity, forming a key challenge. This study investigated factors determining product selectivity during odd-chain elongation in an odd-chain elongating mixed community and the pure culture Clostridium kluyveri DSM555. Incubations at different ratios of ethanol:propionate showed that increasing ratios (from 0.5 to 7) lowered product specificity, as evidenced by a decrease in the odd:even product ratio from 5.5 to 1.5 for C. kluyveri and from 15 to 0.8 for the mixed community. The consistency of these observations with literature data suggests that control of ethanol:propionate ratio offers a robust tool for process control in odd-chain elongation, while the flexible metabolism can also have implications for efficient use of ethanol during even-chain elongation processes.

Mildly acidic pH selects for chain elongation to caproic acid over alternative pathways during lactic acid fermentation.

Carbohydrate-rich waste streams can be used for bioproduction of medium-chain carboxylic acids (MCCA) such as caproic acid. The carbohydrates in these streams can be converted to lactic acid as the initial fermentation product, which can then be fermented to MCCA by chain elongation. In this process, chain elongators compete for lactic acid with other bacterial groups that, for instance, ferment lactic acid to propionic and acetic acid. Understanding the drivers that control the competition between these two pathways is essential to maximizing MCCA production. This study aimed to investigate the competition between chain elongating and propionic acid producing organisms as a function of operational pH. Operation of long-term lactic acid fermenting reactors with varying pH values showed that pH values above 6 resulted in a propionic acid producing community dominated by Veillonella and Aminobacterium. At pH values below 6, the community moved towards chain elongation, with communities dominated by Caproiciproducens. Short-term incubations showed that rates of lactic acid consumption were strongly reduced at pH below 6 (7.7 ± 1.2 mM lactic acid·h-1 at pH 6.5; 0.74 ± 0.33 mM lactic acid·h-1 at pH 5.5). Similar to observations in long-term reactors, when a chain elongating community adapted to pH 5.5 was used for short-term incubations at pH 6.5, propionic acid was the dominant product. The results of this study show that pH below 6 stimulate lactic acid chain elongators through kinetic effects, and potentially improved energetics, providing a tool for microbial management of MCCA-producing systems.

Enrichment and characterisation of ethanol chain elongating communities from natural and engineered environments.

Chain elongation is a microbial process in which an electron donor, such as ethanol, is used to elongate short chain carboxylic acids, such as acetic acid, to medium chain carboxylic acids. This metabolism has been extensively investigated, but the spread and differentiation of chain elongators in the environment remains unexplored. Here, chain elongating communities were enriched from several inocula (3 anaerobic digesters, 2 animal faeces and 1 caproic acid producing environment) using ethanol and acetic acid as substrates at pH 7 and 5.5. This approach showed that (i) the inoculum's origin determines the pH where native chain elongators can grow; (ii) pH affects caproic acid production, with average caproic acid concentrations of 6.4 ± 1.6 g·L-1 at pH 7, versus 2.3 ± 1.8 g·L-1 at pH 5.5; however (iii) pH does not affect growth rates significantly; (iv) all communities contained a close relative of the known chain elongator Clostridium kluyveri; and (v) low pH selects for communities more enriched in this Clostridium kluyveri-relative (57.6 ± 23.2% at pH 7, 96.9 ± 1.2% at pH 5.5). These observations show that ethanol-consuming chain elongators can be found in several natural and engineered environments, but are not the same everywhere, emphasising the need for careful inoculum selection during process development.

Fruity flavors from waste: A novel process to upgrade crude glycerol to ethyl valerate.

Valeric acid and its ester derivatives are chemical compounds with a high industrial interest. Here we report a new approach to produce them from crude glycerol, by combining propionic acid fermentation with chain elongation. Propionic acid was produced by Propionibacterium acidipropionici (8.49 ±â€¯1.40 g·L-1). In the subsequent mixed population chain elongation, valeric acid was the dominant product (5.3 ±â€¯0.69 g·L-1) of the chain elongation process. Residual glycerol negatively impacted the selectivity of mixed culture chain elongation towards valeric acid, whereas this was unaffected when Clostridium kluyveri was used as bio-catalyst. Valeric acid could be selectively isolated and upgraded to ethyl valerate by using dodecane as extractant and medium for esterification, whereas shorter-chain carboxylic acids could be recovered by using a 10 wt% solution of trioctylphosphine oxide (TOPO) in dodecane. Overall, our work shows that the combined fermentation, electrochemistry and homogeneous catalysis enables fine chemical production from side streams.