Stabilizing microbial communities by looped mass transfer.

Building and changing a microbiome at will and maintaining it over hundreds of generations has so far proven challenging. Despite best efforts, complex microbiomes appear to be susceptible to large stochastic fluctuations. Current capabilities to assemble and control stable complex microbiomes are limited. Here, we propose a looped mass transfer design that stabilizes microbiomes over long periods of time. Five local microbiomes were continuously grown in parallel for over 114 generations and connected by a loop to a regional pool. Mass transfer rates were altered and microbiome dynamics were monitored using quantitative high-throughput flow cytometry and taxonomic sequencing of whole communities and sorted subcommunities. Increased mass transfer rates reduced local and temporal variation in microbiome assembly, did not affect functions, and overcame stochasticity, with all microbiomes exhibiting high constancy and increasing resistance. Mass transfer synchronized the structures of the five local microbiomes and nestedness of certain cell types was eminent. Mass transfer increased cell number and thus decreased net growth rates µ'. Subsets of cells that did not show net growth µ'SCx were rescued by the regional pool R and thus remained part of the microbiome. The loop in mass transfer ensured the survival of cells that would otherwise go extinct, even if they did not grow in all local microbiomes or grew more slowly than the actual dilution rate D would allow. The rescue effect, known from metacommunity theory, was the main stabilizing mechanism leading to synchrony and survival of subcommunities, despite differences in cell physiological properties, including growth rates.

HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes.

Metagenomics became a standard strategy to comprehend the functional potential of microbial communities, including the human microbiome. Currently, the number of metagenomes in public repositories is increasing exponentially. The Sequence Read Archive (SRA) and the MG-RAST are the two main repositories for metagenomic data. These databases allow scientists to reanalyze samples and explore new hypotheses. However, mining samples from them can be a limiting factor, since the metadata available in these repositories is often misannotated, misleading, and decentralized, creating an overly complex environment for sample reanalysis. The main goal of the HumanMetagenomeDB is to simplify the identification and use of public human metagenomes of interest. HumanMetagenomeDB version 1.0 contains metadata of 69 822 metagenomes. We standardized 203 attributes, based on standardized ontologies, describing host characteristics (e.g. sex, age and body mass index), diagnosis information (e.g. cancer, Crohn's disease and Parkinson), location (e.g. country, longitude and latitude), sampling site (e.g. gut, lung and skin) and sequencing attributes (e.g. sequencing platform, average length and sequence quality). Further, HumanMetagenomeDB version 1.0 metagenomes encompass 58 countries, 9 main sample sites (i.e. body parts), 58 diagnoses and multiple ages, ranging from just born to 91 years old. The HumanMetagenomeDB is publicly available at https://webapp.ufz.de/hmgdb/.

Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy.

In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index q; (2) performance testing of the best entropic indices on new datasets; (3) a comparison made with Shannon entropy and (4) generalized entropies; (5) an investigation of the Tsallis entropy in the context of dimensionality reduction. As a result, our proposal proved to be effective, being superior to Shannon entropy and robust in terms of generalization, and also potentially representative for collecting information in fewer dimensions compared with methods such as Singular Value Decomposition and Uniform Manifold Approximation and Projection.

Recovery of 197 eukaryotic bins reveals major challenges for eukaryote genome reconstruction from terrestrial metagenomes.

As most eukaryotic genomes are yet to be sequenced, the mechanisms underlying their contribution to different ecosystem processes remain untapped. Although approaches to recovering Prokaryotic genomes have become common in genome biology, few studies have tackled the recovery of eukaryotic genomes from metagenomes. This study assessed the reconstruction of microbial eukaryotic genomes using 6000 metagenomes from terrestrial and some transition environments using the EukRep pipeline. Only 215 metagenomic libraries yielded eukaryotic bins. From a total of 447 eukaryotic bins recovered 197 were classified at the phylum level. Streptophytes and fungi were the most represented clades with 83 and 73 bins, respectively. More than 78% of the obtained eukaryotic bins were recovered from samples whose biomes were classified as host-associated, aquatic, and anthropogenic terrestrial. However, only 93 bins were taxonomically assigned at the genus level and 17 bins at the species level. Completeness and contamination estimates were obtained for a total of 193 bins and consisted of 44.64% (σ = 27.41%) and 3.97% (σ = 6.53%), respectively. Micromonas commoda was the most frequent taxon found while Saccharomyces cerevisiae presented the highest completeness, probably because more reference genomes are available. Current measures of completeness are based on the presence of single-copy genes. However, mapping of the contigs from the recovered eukaryotic bins to the chromosomes of the reference genomes showed many gaps, suggesting that completeness measures should also include chromosome coverage. Recovering eukaryotic genomes will benefit significantly from long-read sequencing, development of tools for dealing with repeat-rich genomes, and improved reference genomes databases.

Metagenome-assembled genomes indicate that antimicrobial resistance genes are highly prevalent among urban bacteria and multidrug and glycopeptide resistances are ubiquitous in most taxa.

Introduction: Every year, millions of deaths are associated with the increased spread of antimicrobial resistance genes (ARGs) in bacteria. With the increasing urbanization of the global population, the spread of ARGs in urban bacteria has become a more severe threat to human health. Methods: In this study, we used metagenome-assembled genomes (MAGs) recovered from 1,153 urban metagenomes in multiple urban locations to investigate the fate and occurrence of ARGs in urban bacteria. Additionally, we analyzed the occurrence of these ARGs on plasmids and estimated the virulence of the bacterial species. Results: Our results showed that multidrug and glycopeptide ARGs are ubiquitous among urban bacteria. Additionally, we analyzed the deterministic effects of phylogeny on the spread of these ARGs and found ARG classes that have a non-random distribution within the phylogeny of our recovered MAGs. However, few ARGs were found on plasmids and most of the recovered MAGs contained few virulence factors. Discussion: Our results suggest that the observed non-random spreads of ARGs are not due to the transfer of plasmids and that most of the bacteria observed in the study are unlikely to be virulent. Additional research is needed to evaluate whether the ubiquitous and widespread ARG classes will become entirely prevalent among urban bacteria and how they spread among phylogenetically distinct species.

StandEnA: a customizable workflow for standardized annotation and generating a presence-absence matrix of proteins.

Motivation: Several genome annotation tools standardize annotation outputs for comparability. During standardization, these tools do not allow user-friendly customization of annotation databases; limiting their flexibility and applicability in downstream analysis. Results: StandEnA is a user-friendly command-line tool for Linux that facilitates the generation of custom databases by retrieving protein sequences from multiple databases. Directed by a user-defined list of standard names, StandEnA retrieves synonyms to search for corresponding sequences in a set of public databases. Custom databases are used in prokaryotic genome annotation to generate standardized presence-absence matrices and reference files containing standard database identifiers. To showcase StandEnA, we applied it to six metagenome-assembled genomes to analyze three different pathways. Availability and implementation: StandEnA is an open-source software available at https://github.com/mdsufz/StandEnA. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Exploring the Potential Molecular Mechanisms of Interactions between a Probiotic Consortium and Its Coral Host.

Beneficial microorganisms for corals (BMCs) have been demonstrated to be effective probiotics to alleviate bleaching and mitigate coral mortality in vivo. The selection of putative BMCs is traditionally performed manually, using an array of biochemical and molecular tests for putative BMC traits. We present a comprehensive genetic survey of BMC traits using a genome-based framework for the identification of alternative mechanisms that can be used for future in silico selection of BMC strains. We identify exclusive BMC traits associated with specific strains and propose new BMC mechanisms, such as the synthesis of glycine betaine and ectoines. Our roadmap facilitates the selection of BMC strains while increasing the array of genetic targets that can be included in the selection of putative BMC strains to be tested as coral probiotics. IMPORTANCE Probiotics are currently the main hope as a potential medicine for corals, organisms that are considered the marine "canaries of the coal mine" and that are threatened with extinction. Our experiments have proved the concept that probiotics mitigate coral bleaching and can also prevent coral mortality. Here, we present a comprehensive genetic survey of probiotic traits using a genome-based framework. The main outcomes are a roadmap that facilitates the selection of coral probiotic strains while increasing the array of mechanisms that can be included in the selection of coral probiotics.

Polyphasic Analysis Reveals Potential Petroleum Hydrocarbon Degradation and Biosurfactant Production by Rare Biosphere Thermophilic Bacteria From Deception Island, an Active Antarctic Volcano.

Extreme temperature gradients in polar volcanoes are capable of selecting different types of extremophiles. Deception Island is a marine stratovolcano located in maritime Antarctica. The volcano has pronounced temperature gradients over very short distances, from as high as 100°C in the fumaroles to subzero next to the glaciers. These characteristics make Deception a promising source of a variety of bioproducts for use in different biotechnological areas. In this study, we isolated thermophilic bacteria from sediments in fumaroles at two geothermal sites on Deception Island with temperatures between 50 and 100°C, to evaluate the potential capacity of these bacteria to degrade petroleum hydrocarbons and produce biosurfactants under thermophilic conditions. We isolated 126 thermophilic bacterial strains and identified them molecularly as members of genera Geobacillus, Anoxybacillus, and Brevibacillus (all in phylum Firmicutes). Seventy-six strains grew in a culture medium supplemented with crude oil as the only carbon source, and 30 of them showed particularly good results for oil degradation. Of 50 strains tested for biosurfactant production, 13 showed good results, with an emulsification index of 50% or higher of a petroleum hydrocarbon source (crude oil and diesel), emulsification stability at 100°C, and positive results in drop-collapse, oil spreading, and hemolytic activity tests. Four of these isolates showed great capability of degrade crude oil: FB2_38 (Geobacillus), FB3_54 (Geobacillus), FB4_88 (Anoxybacillus), and WB1_122 (Geobacillus). Genomic analysis of the oil-degrading and biosurfactant-producer strain FB4_88 identified it as Anoxybacillus flavithermus, with a high genetic and functional diversity potential for biotechnological applications. These initial culturomic and genomic data suggest that thermophilic bacteria from this Antarctic volcano have potential applications in the petroleum industry, for bioremediation in extreme environments and for microbial enhanced oil recovery (MEOR) in reservoirs. In addition, recovery of small-subunit rRNA from metagenomes of Deception Island showed that Firmicutes is not among the dominant phyla, indicating that these low-abundance microorganisms may be important for hydrocarbon degradation and biosurfactant production in the Deception Island volcanic sediments.

Electrochemical and Microbial Dissection of Electrified Biotrickling Filters.

Electrified biotrickling filters represent sustainable microbial electrochemical technology for treating organic carbon-deficient ammonium-contaminated waters. However, information on the microbiome of the conductive granule bed cathode remains inexistent. For uncovering this black box and for identifying key process parameters, minimally invasive sampling units were introduced, allowing for the extraction of granules from different reactor layers during reactor operation. Sampled granules were analyzed using cyclic voltammetry and molecular biological tools. Two main redox sites [-288 ± 18 mV and -206 ± 21 mV vs. standard hydrogen electrode (SHE)] related to bioelectrochemical denitrification were identified, exhibiting high activity in a broad pH range (pH 6-10). A genome-centric analysis revealed a complex nitrogen food web and the presence of typical denitrifiers like Pseudomonas nitroreducens and Paracoccus versutus with none of these species being identified as electroactive microorganism so far. These are the first results to provide insights into microbial structure-function relationships within electrified biotrickling filters and underline the robustness and application potential of bioelectrochemical denitrification for environmental remediation.

PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning.

BACKGROUND: Transcription factors (TFs) are proteins controlling the flow of genetic information by regulating cellular gene expression. A better understanding of TFs in a bacterial community context may open novel revenues for exploring gene regulation in ecosystems where bacteria play a key role. Here we describe PredicTF, a platform supporting the prediction and classification of novel bacterial TF in single species and complex microbial communities. PredicTF is based on a deep learning algorithm. RESULTS: To train PredicTF, we created a TF database (BacTFDB) by manually curating a total of 11,961 TF distributed in 99 TF families. Five model organisms were used to test the performance and the accuracy of PredicTF. PredicTF was able to identify 24-62% of the known TFs with an average precision of 88% in our five model organisms. We demonstrated PredicTF using pure cultures and a complex microbial community. In these demonstrations, we used (meta)genomes for TF prediction and (meta)transcriptomes for determining the expression of putative TFs. CONCLUSION: PredicTF demonstrated high accuracy in predicting transcription factors in model organisms. We prepared the pipeline to be easily implemented in studies profiling TFs using (meta)genomes and (meta)transcriptomes. PredicTF is an open-source software available at https://github.com/mdsufz/PredicTF .

Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture.

BACKGROUND: The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities. RESULTS: By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target products n-caproate and n-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predict n-caproate and n-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction of n-caproate and n-caprylate productivities was achieved. Four inferred bioindicators belonging to the genera Olsenella, Lactobacillus, Syntrophococcus and Clostridium IV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production. CONCLUSIONS: Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites. Video Abstract.

Draft Genome Sequences of Two Sphingobium Species Associated with Hexachlorocyclohexane (HCH) Degradation Isolated from an HCH-Contaminated Soil.

The draft genome sequences of two Sphingobium strains that are hexachlorocyclohexane (HCH) degraders are presented. The strains were isolated from HCH-contaminated soil in Kitengela, Kenya. Both genomes possess the lin genes responsible for HCH degradation and gene clusters for degradation of other xenobiotic compounds.

Metagenomics-resolved genomics provides novel insights into chitin turnover, metabolic specialization, and niche partitioning in the octocoral microbiome.

BACKGROUND: The role of bacterial symbionts that populate octocorals (Cnidaria, Octocorallia) is still poorly understood. To shed light on their metabolic capacities, we examined 66 high-quality metagenome-assembled genomes (MAGs) spanning 30 prokaryotic species, retrieved from microbial metagenomes of three octocoral species and seawater. RESULTS: Symbionts of healthy octocorals were affiliated with the taxa Endozoicomonadaceae, Candidatus Thioglobaceae, Metamycoplasmataceae, unclassified Pseudomonadales, Rhodobacteraceae, unclassified Alphaproteobacteria and Ca. Rhabdochlamydiaceae. Phylogenomics inference revealed that the Endozoicomonadaceae symbionts uncovered here represent two species of a novel genus unique to temperate octocorals, here denoted Ca. Gorgonimonas eunicellae and Ca. Gorgonimonas leptogorgiae. Their genomes revealed metabolic capacities to thrive under suboxic conditions and high gene copy numbers of serine-threonine protein kinases, type 3-secretion system, type-4 pili, and ankyrin-repeat proteins, suggesting excellent capabilities to colonize, aggregate, and persist inside their host. Contrarily, MAGs obtained from seawater frequently lacked symbiosis-related genes. All Endozoicomonadaceae symbionts harbored endo-chitinase and chitin-binging protein-encoding genes, indicating that they can hydrolyze the most abundant polysaccharide in the oceans. Other symbionts, including Metamycoplasmataceae and Ca. Thioglobaceae, may assimilate the smaller chitin oligosaccharides resulting from chitin breakdown and engage in chitin deacetylation, respectively, suggesting possibilities for substrate cross-feeding and a role for the coral microbiome in overall chitin turnover. We also observed sharp differences in secondary metabolite production potential between symbiotic lineages. Specific Proteobacteria taxa may specialize in chemical defense and guard other symbionts, including Endozoicomonadaceae, which lack such capacity. CONCLUSION: This is the first study to recover MAGs from dominant symbionts of octocorals, including those of so-far unculturable Endozoicomonadaceae, Ca. Thioglobaceae and Metamycoplasmataceae symbionts. We identify a thus-far unanticipated, global role for Endozoicomonadaceae symbionts of corals in the processing of chitin, the most abundant natural polysaccharide in the oceans and major component of the natural zoo- and phytoplankton feed of octocorals. We conclude that niche partitioning, metabolic specialization, and adaptation to low oxygen conditions among prokaryotic symbionts likely contribute to the plasticity and adaptability of the octocoral holobiont in changing marine environments. These findings bear implications not only for our understanding of symbiotic relationships in the marine realm but also for the functioning of benthic ecosystems at large. Video Abstract.

MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes.

BACKGROUND: Metagenomics is an expanding field within microbial ecology, microbiology, and related disciplines. The number of metagenomes deposited in major public repositories such as Sequence Read Archive (SRA) and Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) is rising exponentially. However, data mining and interpretation can be challenging due to mis-annotated and misleading metadata entries. In this study, we describe the Marine Metagenome Metadata Database (MarineMetagenomeDB) to help researchers identify marine metagenomes of interest for re-analysis and meta-analysis. To this end, we have manually curated the associated metadata of several thousands of microbial metagenomes currently deposited at SRA and MG-RAST. RESULTS: In total, 125 terms were curated according to 17 different classes (e.g., biome, material, oceanic zone, geographic feature and oceanographic phenomena). Other standardized features include sample attributes (e.g., salinity, depth), sample location (e.g., latitude, longitude), and sequencing features (e.g., sequencing platform, sequence count). MarineMetagenomeDB version 1.0 contains 11,449 marine metagenomes from SRA and MG-RAST distributed across all oceans and several seas. Most samples were sequenced using Illumina sequencing technology (84.33%). More than 55% of the samples were collected from the Pacific and the Atlantic Oceans. About 40% of the samples had their biomes assigned as 'ocean'. The 'Quick Search' and 'Advanced Search' tabs allow users to use different filters to select samples of interest dynamically in the web app. The interactive map allows the visualization of samples based on their location on the world map. The web app is also equipped with a novel download tool (on both Windows and Linux operating systems), that allows easy download of raw sequence data of selected samples from their respective repositories. As a use case, we demonstrated how to use the MarineMetagenomeDB web app to select estuarine metagenomes for potential large-scale microbial biogeography studies. CONCLUSION: The MarineMetagenomeDB is a powerful resource for non-bioinformaticians to find marine metagenome samples with curated metadata and stimulate meta-studies involving marine microbiomes. Our user-friendly web app is publicly available at https://webapp.ufz.de/marmdb/ .

Genome Mining Reveals High Biosynthetic Potential of Biocontrol Agent Bacillus velezensis B.BV10.

The present study demonstrates the biocontrol potential of a plant growth-promoting bacterial strain using three different approaches: (i) an in vitro evaluation of antagonistic activity against important phytopathogenic fungi; (ii) an evaluation under greenhouse conditions with strawberry plants to assess the control of gray mold; and (iii) an in silico whole genome sequence mining to assign genetic features such as gene clusters or isolated genes to the strain activity. The in vitro assay showed that the B.BV10 strain presented antagonistic activity, inhibiting the mycelial growth in all the phytopathogenic fungi evaluated. The application of the Bacillus velezensis strain B.BV10 under greenhouse conditions reduced the presence of Botrytis cinerea and increased the mean fruit biomass. The genome of B.BV10 was estimated at 3,917,533 bp, with a GC content of 46.6% and 4088 coding DNA sequences, and was identified as B. velezensis. Biosynthetic gene clusters related to the synthesis of the molecules with antifungal activity were found in its genome. Genes related to the regulation/formation of biofilms, motility, and the important properties for the rhizospheric colonization were also found in the genome. The current study offers a comprehensive understanding of the genomic architecture and control activity of phytopathogenic fungi by the B. velezensis strain B.BV10 that may substantiate the industrialization of this strain in the future.

Mining Synergistic Microbial Interactions: A Roadmap on How to Integrate Multi-Omics Data.

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used as the starting point to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions' role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species' contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

Complementary Roles of Wood-Inhabiting Fungi and Bacteria Facilitate Deadwood Decomposition.

Forests accumulate and store large amounts of carbon (C), and a substantial fraction of this stock is contained in deadwood. This transient pool is subject to decomposition by deadwood-associated organisms, and in this process it contributes to CO2 emissions. Although fungi and bacteria are known to colonize deadwood, little is known about the microbial processes that mediate carbon and nitrogen (N) cycling in deadwood. In this study, using a combination of metagenomics, metatranscriptomics, and nutrient flux measurements, we demonstrate that the decomposition of deadwood reflects the complementary roles played by fungi and bacteria. Fungi were found to dominate the decomposition of deadwood and particularly its recalcitrant fractions, while several bacterial taxa participate in N accumulation in deadwood through N fixation, being dependent on fungal activity with respect to deadwood colonization and C supply. Conversely, bacterial N fixation helps to decrease the constraints of deadwood decomposition for fungi. Both the CO2 efflux and N accumulation that are a result of a joint action of deadwood bacteria and fungi may be significant for nutrient cycling at ecosystem levels. Especially in boreal forests with low N stocks, deadwood retention may help to improve the nutritional status and fertility of soils.IMPORTANCE Wood represents a globally important stock of C, and its mineralization importantly contributes to the global C cycle. Microorganisms play a key role in deadwood decomposition, since they possess enzymatic tools for the degradation of recalcitrant plant polymers. The present paradigm is that fungi accomplish degradation while commensalist bacteria exploit the products of fungal extracellular enzymatic cleavage, but this assumption was never backed by the analysis of microbial roles in deadwood. This study clearly identifies the roles of fungi and bacteria in the microbiome and demonstrates the importance of bacteria and their N fixation for the nutrient balance in deadwood as well as fluxes at the ecosystem level. Deadwood decomposition is shown as a process where fungi and bacteria play defined, complementary roles.

OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes.

The high complexity found in microbial communities makes the identification of microbial interactions challenging. To address this challenge, we present OrtSuite, a flexible workflow to predict putative microbial interactions based on genomic content of microbial communities and targeted to specific ecosystem processes. The pipeline is composed of three user-friendly bash commands. OrtSuite combines ortholog clustering with genome annotation strategies limited to user-defined sets of functions allowing for hypothesis-driven data analysis such as assessing microbial interactions in specific ecosystems. OrtSuite matched, on average, 96% of experimentally verified KEGG orthologs involved in benzoate degradation in a known group of benzoate degraders. We evaluated the identification of putative synergistic species interactions using the sequenced genomes of an independent study that had previously proposed potential species interactions in benzoate degradation. OrtSuite is an easy-to-use workflow that allows for rapid functional annotation based on a user-curated database and can easily be extended to ecosystem processes where connections between genes and reactions are known. OrtSuite is an open-source software available at https://github.com/mdsufz/OrtSuite.

Draft Genome Sequence of Fusarium equiseti K3, a Fungal Species Isolated from Hexachlorocyclohexane-Contaminated Soil.

We present the draft genome sequence of Fusarium equiseti strain K3, a fungus isolated from a hexachlorocyclohexane (HCH)-contaminated soil (Kitengela, Kenya). The 37.88-Mb draft genome sequence consists of 206 contigs, 12,311 predicted protein-coding sequences, and 261 tRNA sequences. This genome sequence contributes to our understanding of fungal-bacterial interactions during hexachlorocyclohexane degradation.

Community RNA-Seq: multi-kingdom responses to living versus decaying roots in soil.

Roots are a primary source of organic carbon input in most soils. The consumption of living and detrital root inputs involves multi-trophic processes and multiple kingdoms of microbial life, but typical microbial ecology studies focus on only one or two major lineages. We used Illumina shotgun RNA sequencing to conduct PCR-independent SSU rRNA community analysis ("community RNA-Seq") and simultaneously assess the bacteria, archaea, fungi, and microfauna surrounding both living and decomposing roots of the annual grass, Avena fatua. Plants were grown in 13CO2-labeled microcosms amended with 15N-root litter to identify the preferences of rhizosphere organisms for root exudates (13C) versus decaying root biomass (15N) using NanoSIMS microarray imaging (Chip-SIP). When litter was available, rhizosphere and bulk soil had significantly more Amoebozoa, which are potentially important yet often overlooked top-down drivers of detritusphere community dynamics and nutrient cycling. Bulk soil containing litter was depleted in Actinobacteria but had significantly more Bacteroidetes and Proteobacteria. While Actinobacteria were abundant in the rhizosphere, Chip-SIP showed Actinobacteria preferentially incorporated litter relative to root exudates, indicating this group's more prominent role in detritus elemental cycling in the rhizosphere. Our results emphasize that decomposition is a multi-trophic process involving complex interactions, and our methodology can be used to track the trajectory of carbon through multi-kingdom soil food webs.