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ABSTRACT Soil microorganisms play a key role in driving major biogeochemical cycles and in global responses to climate change. However, understanding and predicting the behavior and function of these microorganisms remains a grand challenge for soil ecology due in part to the microscale complexity of soils. It is becoming increasingly clear that understanding the microbial perspective is vital to accurately predicting global processes. Here, we discuss the microbial perspective including the microbial habitat as it relates to measurement and modeling of ecosystem processes. We argue that clearly defining and quantifying the size, distribution and sphere of influence of microhabitats is crucial to managing microbial activity at the ecosystem scale. This can be achieved using controlled and hierarchical sampling designs. Model microbial systems can provide key data needed to integrate microhabitats into ecosystem models, while adapting soil sampling schemes and statistical methods can allow us to collect microbially-focused data. Quantifying soil processes, like biogeochemical cycles, from a microbial perspective will allow us to more accurately predict soil functions and address long-standing unknowns in soil ecology. The microbial perspective, including microscale microbial interactions, is a crucial component of the soil environment that must be considered when measuring soil processes and developing predictive ecosystem models.

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.

The most important operational expense during wastewater treatment is electricity for pumping and aeration. Therefore, this work evaluated operational parameters and contaminant removal efficiency of a microbial fuel cell stack system (MFCSS) that uses no electricity. This system consists of (i) septic tank primary treatment, (ii) chamber for secondary treatment containing 18 MFCs, coupled to an energy-harvesting circuit (EHC) that stores the electrons produced by anaerobic respiration, and (iii) gravity-driven disinfection (sodium hypochlorite 5%). The MFCSS operated during 60 days (after stabilization period) and it was gravity-fed with real domestic wastewater from a house (5 inhabitants). The flow rate was 600 ± 100 L∙d−1. The chemical oxygen demand, biological oxygen demand, total nitrogen and total phosphorous were measured in effluent, with values of 100 ± 10; 12 ± 2; 9.6 ± 0.5 and 4 ± 0.2 mg∙L−1, and removal values of 86%, 87%, 84% and 64%, respectively. Likewise, an EHC (ultra-low energy consumption) was built with 6.3 V UCC® 4700 µF capacitors that harvested and stored energy from MFCs in parallel. Energy management was programmed on a microcontroller Atmega 328PB®. The water quality of the treated effluent complied with the maximum levels set by the Mexican Official Standard NOM-001-SEMARNAT-1996-C. A cost analysis showed that MFCSS could be competitive as a sustainable and energy-efficient technology for real domestic wastewater treatment.

This chapter contains sections titled: General Introduction Mature Market and Established Technology (Citric Acid) Chemicals Obtained from Citric Acid Coproduct Stream Conversion Chemicals Currently Produced on a Commercial Scale with Markets that Still Need Development Emerging Technology for Sustainable Production of Chemicals that can Feed into Existing Markets Conclusions References

Recent advances focusing on the metabolic interactions within and between cellular populations have emphasized the importance of microbial communities for human health. Constraint-based modeling, with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena) to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena.

Microbial oils are lipids produced by oleaginous microorganisms, which can be used as a potential feedstock for oleochemical production. The oleaginous yeast Rhodotorula toruloides can co-produce microbial oils and high-value compounds from low-cost substrates, such as xylose and acetic acid (from hemicellulosic hydrolysates) and raw glycerol (a byproduct of biodiesel production). One step towards economic viability is identifying the best conditions for lipid production, primarily the most suitable carbon-to-nitrogen ratio (C/N). Here, we aimed to identify the best conditions and cultivation mode for lipid production by R. toruloides using various low-cost substrates and a range of C/N ratios (60, 80, 100, and 120). Turbidostat mode was used to achieve a steady state at the maximal specific growth rate and to avoid continuously changing environmental conditions (i.e., C/N ratio) that inherently occur in batch mode. Regardless of the carbon source, higher C/N ratios increased lipid yields (up to 60% on xylose at a C/N of 120) but decreased the specific growth rate. Growth on glycerol resulted in the highest specific growth and lipid production (0.085 g lipids/gDW*h) rates at C/Ns between 60 and 100. We went on to study lipid production using glycerol in both batch and fed-batch modes, which resulted in lower specific lipid production rates compared with turbisdostat, however, fed batch is superior in terms of biomass production and lipid titers. By combining the data we obtained in these experiments with a genome-scale metabolic model of R. toruloides, we identified targets for improvements in lipid production that could be carried out either by metabolic engineering or process optimization.

The Amazon rainforest is the Earth’s largest reservoir of plant and animal diversity, and it has been subjected to especially high rates of land use change, primarily to cattle pasture. This conversion has had a strongly negative effect on biological diversity, reducing the number of plant and animal species and homogenizing communities. We report here that microbial biodiversity also responds strongly to conversion of the Amazon rainforest, but in a manner different from plants and animals. Local taxonomic and phylogenetic diversity of soil bacteria increases after conversion, but communities become more similar across space. This homogenization is driven by the loss of forest soil bacteria with restricted ranges (endemics) and results in a net loss of diversity. This study shows homogenization of microbial communities in response to human activities. Given that soil microbes represent the majority of biodiversity in terrestrial ecosystems and are intimately involved in ecosystem functions, we argue that microbial biodiversity loss should be taken into account when assessing the impact of land use change in tropical forests.

Microbial carbon use efficiency (CUE) is a critical regulator of soil organic matter dynamics and terrestrial carbon fluxes, with strong implications for soil biogeochemistry models. While ecologists increasingly appreciate the importance of CUE, its core concepts remain ambiguous: terminology is inconsistent and confusing, methods capture variable temporal and spatial scales, and the significance of many fundamental drivers remains inconclusive. Here we outline the processes underlying microbial efficiency and propose a conceptual framework that structures the definition of CUE according to increasingly broad temporal and spatial drivers where (1) CUE P reflects population-scale carbon use efficiency of microbes governed by species-specific metabolic and thermodynamic constraints, (2) CUE C defines community-scale microbial efficiency as gross biomass production per unit substrate taken up over short time scales, largely excluding recycling of microbial necromass and exudates, and (3) CUE E reflects the ecosystem-scale efficiency of net microbial biomass production (growth) per unit substrate taken up as iterative breakdown and recycling of microbial products occurs. CUEE integrates all internal and extracellular constraints on CUE and hence embodies an ecosystem perspective that fully captures all drivers of microbial biomass synthesis and decay. These three definitions are distinct yet complementary, capturing the capacity for carbon storage in microbial biomass across different ecological scales. By unifying the existing concepts and terminology underlying microbial efficiency, our framework enhances data interpretation and theoretical advances.

In biodesulfurization (BD) at haloalkaline and dO 2 -limited conditions, sulfide-oxidizing bacteria (SOB) effectively convert sulfide into elemental sulfur that can be used in agriculture as a fertilizer and fungicide. Here we show which bacteria are present in this biotechnological process. 16S rRNA gene amplicon sequencing of biomass from ten reactors sampled in 2018 indicated the presence of 444 bacterial Amplicon Sequence Variants (ASVs). A core microbiome represented by 30 ASVs was found in all ten reactors, with Thioalkalivibrio sulfidiphilus as the most dominant species. The majority of these ASVs are phylogenetically related to bacteria previously identified in haloalkaline BD processes and in natural haloalkaline ecosystems. The source and composition of the feed gas had a great impact on the microbial community composition followed by alkalinity, sulfate, and thiosulfate concentrations. The halophilic SOB of the genus Guyparkeria (formerly known as Halothiobacillus ) and heterotrophic SOB of the genus Halomonas were identified as potential indicator organisms of sulfate and thiosulfate accumulation in the BD process. Key points • Biodesulfurization (BD) reactors share a core microbiome • The source and composition of the feed gas affects the microbial composition in the BD reactors • Guyparkeria and Halomonas indicate high concentrations of sulfate and thiosulfate in the BD process

This study aimed to gain insight into the microbial quality, safety and bacterial community composition of black soldier fly larvae (Hermetia illucens) reared at different facilities on a variety of organic waste streams. For seven rearing cycles, both on laboratory-scale and in large-scale facilities at several locations, the microbiota of the larvae was studied. Also samples of the substrate used and the residue (= leftover substrate after rearing, existing of non-consumed substrate, exuviae and faeces) were investigated. Depending on the sample, it was subjected to plate counting, Illumina Miseq sequencing and/or detection of specific food pathogens. The results revealed that the substrates applied at the various locations differed substantially in microbial numbers as well as in the bacterial community composition. Furthermore, little similarity was observed between the microbiota of the substrate and that of the larvae reared on that substrate. Despite substantial differences between the microbiota of larvae reared at several locations, 48 species-level operational taxonomic units (OTUs) were shared by all larvae, among which most belonged to the phyla Firmicutes and Proteobacteria. Although the substrate is assumed to be an important source of bacteria, our results suggest that a variety of supposedly interacting factors-both abiotic and biotic-are likely to affect the microbiota in the larvae. In some larvae and/or residue samples, potential foodborne pathogens such as Salmonella and Bacillus cereus were detected, emphasising that decontamination technologies are required when the larvae are used in feed, just as for other feed ingredients, or eventually in food.