Functional Potential Of Microbial Communities Research Articles

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches

Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.

The role of ecogenomics in environmental and forensic discoveries

Ecogenomics is the application of genetic information and environmental science to understand the genetic diversity and functional potential of microbial communities in various environments. It involves the analysis of DNA and RNA sequences to gain insights into the structure, function, and dynamics of ecosystems. In the context of the environment, ecogenomics plays a crucial role in understanding the impact of human activities on natural ecosystems. By studying the genetic composition of microbial communities, researchers can assess the health and resilience of ecosystems, identify key species involved in nutrient cycling and energy flow, and monitor the effects of pollution and climate change. Ecogenomics also has significant applications in forensic research. By analyzing the microbial DNA present at crime scenes, forensic scientists can gain valuable information about the individuals involved. Microbes are ubiquitous and can be transferred from person to person, leaving behind a unique microbial signature that can be used for identification purposes. This approach, known as microbial forensics, can complement traditional forensic techniques and provide additional evidence in criminal investigations. By harnessing the power of genomics, researchers can uncover hidden microbial diversity, track ecosystem changes, and provide valuable insights into criminal investigations.

Functional redundancy in response to runoff input upholds microbial community in hydrocarbon-contaminated land-sea continuum

An experimental approach mimicking the land-sea continuum in microcosms was developed in order to determine the effect of the terrigenous inputs by soil runoff on the microbial functional potential in hydrocarbon (HC) contaminated marine coastal sediment. We hypothesized that the coalescent event increases the functional potential of microbial communities in marine coastal sediments, influencing the fate of HC in marine coastal ecosystems.The microbial functional potential including the HC degradation ability was assessed by DNA-array to compare the sediment receiving or not terrigenous inputs. The removal of HC and the functional gene richness in sediment was unchanged with the terrigenous inputs. However, the gene variants (GVs) composition was modified indicating functional redundancy. In addition, functional indicators including GVs related to sulfite reduction, denitrification and polyaromatic degradation were identified in higher proportion in sediment receiving terrigenous inputs. The terrigenous inputs modified the functional co-occurrence networks, showing a reorganization of the GVs associations with an increase of the network complexity. Different keystone GVs ensuring similar functions were identified in networks with or without terrigenous inputs, further confirming functional redundancy.We argue that functional redundancy maintains the structure of microbial community in hydrocarbon-contaminated land-sea continuum mixing zone. Our results provide helpful functional information for the monitoring and management of coastal environment affected by human land-based activities.

Taxonomic diversity and functional potential of microbial communities in salt lakes Gudzhirganskoe and Nukhe-Nur (Barguzin depression, Baikal Rift Zone)

On the territory of the Barguzin depression (Baikal Rift Zone) there are salt lakes, which are unique natural formations formed in exceptional natural conditions and experiencing extreme shifts in seasonal environmental conditions. This paper presents the results of the study of the microbial communities in the sulfate Lake Gudzhirganskoe and the soda Lake Nukhe-Nur in winter and summer seasons: from the assessment of the taxonomic composition to potential metabolic pathways. For the first time, based on the 16S rRNA gene sequencing data, the diversity of the microbial community of bottom sediments in summer and winter was studied. Environmental conditions such as pH, temperature and mineralization mainly determined the microbial community composition and led to noticeable shifts in the composition of the community at the level of families and genera. The change of the "summer" obligate and moderately alkalphilic and halophilic community to the "winter" alkali-, halotolerant/halophilic microbial community is observed in the winter period in Lake Gudzhirganskoe. In Lake Nukhe-Nur, a change from mesophilic-thermophilic community in summer to mesophilic-psychrophilic microbial community in winter was revealed. The totality of the obtained results gives an idea about the main trends in the seasonal dynamics of extremophilic microbial communities in the studied lakes in response to fluctuations in environmental parameters. The prediction of the metabolic pathways of prokaryotic communities using the Tax4Fun program made it possible to reveal similarities and differences in the metabolic potential of the microbial communities of the studied lakes. Potential functional genes have been found for all stages of the C, N and S cycles, with the exception of nitrification and aerobic CH4 oxidation.Only small qualitative and quantitative variations in the relative abundance of predicted functional genes were found between the microbial communities of the studied lakes. We predicted metabolic pathways that play an important role in the adaptation of microorganisms to changing environmental conditions. In general, it has been shown that there is a change in the dominant taxa at the level of families and genera in the microbial community in the summer and winter seasons, however, the predicted functional potential of microbial communities differed slightly by season and between the studied lakes.

The Gut Commensal Escherichia coli Aggravates High-Fat-Diet-Induced Obesity and Insulin Resistance in Mice.

Changes in the gut microbiota have been linked to metabolic endotoxemia as a contributing mechanism in the development of obesity and type 2 diabetes. Although identifying specific microbial taxa associated with obesity and type 2 diabetes remains difficult, certain bacteria may play an important role in initiating metabolic inflammation during disease development. The enrichment of the family Enterobacteriaceae, largely represented by Escherichia coli, induced by a high-fat diet (HFD) has been correlated with impaired glucose homeostasis; however, whether the enrichment of Enterobacteriaceae in a complex gut microbial community in response to an HFD contributes to metabolic disease has not been established. To investigate whether the expansion of Enterobacteriaceae amplifies HFD-induced metabolic disease, a tractable mouse model with the presence or absence of a commensal E. coli strain was established. With an HFD treatment, but not a standard-chow diet, the presence of E. coli significantly increased body weight and adiposity and induced impaired glucose tolerance. In addition, E. coli colonization led to increased inflammation in liver and adipose and intestinal tissue under an HFD regimen. With a modest effect on gut microbial composition, E. coli colonization resulted in significant changes in the predicted functional potential of microbial communities. The results demonstrated the role of commensal E. coli in glucose homeostasis and energy metabolism in response to an HFD, indicating contributions of commensal bacteria to the pathogenesis of obesity and type 2 diabetes. The findings of this research identified a targetable subset of the microbiota in the treatment of people with metabolic inflammation. IMPORTANCE Although identifying specific microbial taxa associated with obesity and type 2 diabetes remains difficult, certain bacteria may play an important role in initiating metabolic inflammation during disease development. Here, we used a mouse model distinguishable by the presence or absence of a commensal Escherichia coli strain in combination with a high-fat diet challenge to investigate the impact of E. coli on host metabolic outcomes. This is the first study to show that the addition of a single bacterial species to an animal already colonized with a complex microbial community can increase severity of metabolic outcomes. This study is of interest to a wide group of researchers because it provides compelling evidence to target the gut microbiota for therapeutic purposes by which personalized medicines can be made for treating metabolic inflammation. The study also provides an explanation for variability in studies investigating host metabolic outcomes and immune response to diet interventions.

Enriched bacterial community efficiently degrade polycyclic aromatic hydrocarbons in soil ecosystem: Insights from a mesocosms study

The comprehensive study on PAHs bioremediation together with microbial communities and their functional potential analysis is limited. PAHs biodegradation through mesocosm studies using polluted sediment from Alang-Sosiya ship breaking yard was performed to compare the impact of 3 different bioremediation approaches (natural attenuation, combined treatment of bioaugmentation-biostimulation using enriched consortia and chemical fertilizers and combined treatment using enriched consortia and organic manures). The highest degradation of naphthalene, phenanthrene, fluoranthene, pyrene and total PAHs after 60d of incubation were 94.3%, 88.9%, 59.2%, 60.7% and 76.0%, respectively in bioaugmented-biostimulated mesocosms using eight consortia and chemical fertilizers. Microbial community structure analysis indicated that combined treatments of bioaugmentation-biostimulation were able to replace the native microflora with PAHs degraders. Proteobacteria and Firmicutes phyla were dominant in all mesocosms. Chelatococcus genus was absent as native microflora in natural attenuation treatment, which was dominant in bioaugmented-biostimulated mesocosms. The functional potential of microbial communities was estimated using the PICRUSt.

Single-cell genomics for resolution of conserved bacterial genes and mobile genetic elements of the human intestinal microbiota using flow cytometry

ABSTRACT As our understanding of the importance of the human microbiota in health and disease grows, so does our need to carefully resolve and delineate its genomic content. 16S rRNA gene-based analyses yield important insights into taxonomic composition, and metagenomics-based approaches reveal the functional potential of microbial communities. However, these methods generally fail to directly link genetic features, including bacterial genes and mobile genetic elements, to each other and to their source bacterial genomes. Further, they are inadequate to capture the microdiversity present within a genus, species, or strain of bacteria within these complex communities. Here, we present a method utilizing fluorescence-activated cell sorting for isolation of single bacterial cells, amplifying their genomes, screening them by 16S rRNA gene analysis, and selecting cells for genomic sequencing. We apply this method to both a cultured laboratory strain of Escherichia coli and human stool samples. Our analyses reveal the capacity of this method to provide nearly complete coverage of bacterial genomes when applied to isolates and partial genomes of bacterial species recovered from complex communities. Additionally, this method permits exploration and comparison of conserved and variable genomic features between individual cells. We generate assemblies of novel genomes within the Ruminococcaceae family and the Holdemanella genus by combining several 16S rRNA gene-matched single cells, and report novel prophages and conjugative transposons for both Bifidobacterium and Ruminococcaceae. Thus, we demonstrate an approach for flow cytometric separation and sequencing of single bacterial cells from the human microbiota, which yields a variety of critical insights into both the functional potential of individual microbes and the variation among those microbes. This method definitively links a variety of conserved and mobile genomic features, and can be extended to further resolve diverse elements present in the human microbiota.

Metagenomic insights into the effects of submerged plants on functional potential of microbial communities in wetland sediments

Submerged plants in wetlands play important roles as ecosystem engineers to improve self-purification and promote elemental cycling. However, their effects on the functional capacity of microbial communities in wetland sediments remain poorly understood. Here, we provide detailed metagenomic insights into the biogeochemical potential of microbial communities in wetland sediments with and without submerged plants (i.e., Vallisneria natans). A large number of functional genes involved in carbon (C), nitrogen (N) and sulfur (S) cycling were detected in the wetland sediments. However, most functional genes showed higher abundance in sediments with submerged plants than in those without plants. Based on the comparison of annotated functional genes in the N and S cycling databases (i.e., NCycDB and SCycDB), we found that genes involved in nitrogen fixation (e.g., nifD/H/K/W), assimilatory nitrate reduction (e.g., nasA and nirA), denitrification (e.g., nirK/S and nosZ), assimilatory sulfate reduction (e.g., cysD/H/J/N/Q and sir), and sulfur oxidation (e.g., glpE, soeA, sqr and sseA) were significantly higher (corrected p < 0.05) in vegetated vs. unvegetated sediments. This could be mainly driven by environmental factors including total phosphorus, total nitrogen, and C:N ratio. The binning of metagenomes further revealed that some archaeal taxa could have the potential of methane metabolism including hydrogenotrophic, acetoclastic, and methylotrophic methanogenesis, which are crucial to the wetland methane budget and carbon cycling. This study opens a new avenue for linking submerged plants with microbial functions, and has further implications for understanding global carbon, nitrogen and sulfur cycling in wetland ecosystems.

Disproportionate microbial responses to decadal drainage on a Siberian floodplain.

Permafrost thaw induces soil hydrological changes which in turn affects carbon cycle processes in the Arctic terrestrial ecosystems. However, hydrological impacts of thawing permafrost on microbial processes and greenhouse gas (GHG) dynamics are poorly understood. This study examined changes in microbial communities using gene and genome-centric metagenomics on an Arctic floodplain subject to decadal drainage, and linked them to CO2 and CH4 flux and soil chemistry. Decadal drainage led to significant changes in the abundance, taxonomy, and functional potential of microbial communities, and these modifications well explained the changes in CO2 and CH4 fluxes between ecosystem and atmosphere-increased fungal abundances potentially increased net CO2 emission rates and highly reduced CH4 emissions in drained sites corroborated the marked decrease in the abundance of methanogens and methanotrophs. Interestingly, various microbial taxa disproportionately responded to drainage: Methanoregula, one of the key players in methanogenesis under saturated conditions, almost disappeared, and also Methylococcales methanotrophs were markedly reduced in response to drainage. Seven novel methanogen population genomes were recovered, and the metabolic reconstruction of highly correlated population genomes revealed novel syntrophic relationships between methanogenic archaea and syntrophic partners. These results provide a mechanistic view of microbial processes regulating GHG dynamics in the terrestrial carbon cycle, and disproportionate microbial responses to long-term drainage provide key information for understanding the effects of warming-induced soil drying on microbial processes in Arctic wetland ecosystems.

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/.

RBUD: A New Functional Potential Analysis Approach for Whole Microbial Genome Shotgun Sequencing.

Whole metagenome shotgun sequencing is a powerful approach to detect the functional potential of microbial communities. Currently, the read-based metagenomics profiling for established database (RBED) method is one of the two kinds of conventional methods for species and functional annotations. However, the databases, which are established based on test samples or specific reference genomes or protein sequences, limit the coverage of global microbial diversity. The other assembly-based metagenomics profiling for unestablished database (ABUD) method has a low utilization rate of reads, resulting in a lot of biological information loss. In this study, we proposed a new method, read-based metagenomics profiling for unestablished database (RBUD), based on Metagenome Database of Global Microorganisms (MDGM), to solve the above problems. To evaluate the accuracy and effectiveness of our method, the intestinal bacterial composition and function analyses were performed in both avian colibacillosis chicken cases and type 2 diabetes mellitus patients. Comparing to the existing methods, RBUD is superior in detecting proteins, percentage of reads mapping and ontological similarity of intestinal microbes. The results of RBUD are in better agreement with the classical functional studies on these two diseases. RBUD also has the advantages of fast analysis speed and is not limited by the sample size.

Community structure and functional genes in radionuclide contaminated soils in Chernobyl and Fukushima.

Chernobyl and Fukushima were subjected to radionuclide (RN) contamination that has led to environmental problems. In order to explore the ability of microorganisms to survive in these environments, we used a combined 16S rRNA and metagenomic approach to describe the prokaryotic community structure and metabolic potential over a gradient of RN concentrations (137Cs 1680-0.4 and 90Sr 209.1-1.9 kBq kg-1) in soil samples. The taxonomic results showed that samples with low 137Cs content (37.8-0.4 kBq kg-1) from Fukushima and Chernobyl clustered together. In order to determine the effect of soil chemical parameters such as organic carbon (OC), Cesium-137 (137Cs) and Strontium-90 (90Sr) on the functional potential of microbial communities, multiple predictor model analysis using piecewiseSEM was carried out on Chernobyl soil metagenomes. The model identified 46 genes that were correlated to these parameters of which most have previously been described as mechanisms used by microorganisms under stress conditions. This study provides a baseline taxonomic and metagenomic dataset for Fukushima and Chernobyl, respectively, including physical and chemical characteristics. Our results pave the way for evaluating the possible RN selective pressure that might contribute to shaping microbial community structure and their functions in contaminated soils.

Tundra microbial community taxa and traits predict decomposition parameters of stable, old soil organic carbon.

The susceptibility of soil organic carbon (SOC) in tundra to microbial decomposition under warmer climate scenarios potentially threatens a massive positive feedback to climate change, but the underlying mechanisms of stable SOC decomposition remain elusive. Herein, Alaskan tundra soils from three depths (a fibric O horizon with litter and course roots, an O horizon with decomposing litter and roots, and a mineral-organic mix, laying just above the permafrost) were incubated. Resulting respiration data were assimilated into a 3-pool model to derive decomposition kinetic parameters for fast, slow, and passive SOC pools. Bacterial, archaeal, and fungal taxa and microbial functional genes were profiled throughout the 3-year incubation. Correlation analyses and a Random Forest approach revealed associations between model parameters and microbial community profiles, taxa, and traits. There were more associations between the microbial community data and the SOC decomposition parameters of slow and passive SOC pools than those of the fast SOC pool. Also, microbial community profiles were better predictors of model parameters in deeper soils, which had higher mineral contents and relatively greater quantities of old SOC than in surface soils. Overall, our analyses revealed the functional potential of microbial communities to decompose tundra SOC through a suite of specialized genes and taxa. These results portray divergent strategies by which microbial communities access SOC pools across varying depths, lending mechanistic insights into the vulnerability of what is considered stable SOC in tundra regions.

Comparative microbiome analysis of two different long-term pesticide contaminated soils revealed the anthropogenic influence on functional potential of microbial communities

Microbial communities play a crucial role in bioremediation of pollutants in contaminated ecosystem. In addition to pure culture isolation and bacterial 16S rRNA based community studies, the focus has now shifted employing the omics technologies enormously for understanding the microbial diversity and functional potential of soil samples. Our previous report on two pesticide-contaminated sites revealed the diversity of both culturable and unculturable bacteria. In the present study, we have observed distinct taxonomic and functional communities in contaminated soil with respect to an uncontaminated soil as control by using shotgun metagenomic sequencing method. Our data demonstrated that Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Acidobacteria significantly dominated the microbial diversity with their cumulative abundance percentage in the range of 98.61, 87.38, and 80.52 for Hindustan Insecticides Limited (HIL), India Pesticides Limited (IPL), and control respectively. Functional gene analysis demonstrated the presence of large number of both substrate specific upper pathway and common lower pathway degradative genes. Relatively lower number of genes was found encoding the degradation of styrene, atrazine, bisphenol, dioxin, and naphthalene. When three bacteria were augumentated with rhamnolipid (20–100 μM) and Triton X-100 (84–417 μM) surfactants in HIL soil, an enhanced degradation to 76%, 70%, and 58% of HCH, Endosulfan, and DDT respectively was achieved. The overall data obtained from two heavily contaminated soil suggest the versatility of the microbial communities for the xenobiotic pollutant degradation which may help in exploiting their potential applications in bioremediation.

IVikodak-A Platform and Standard Workflow for Inferring, Analyzing, Comparing, and Visualizing the Functional Potential of Microbial Communities.

Background: The objectives of any metagenomic study typically include identification of resident microbes and their relative proportions (taxonomic analysis), profiling functional diversity (functional analysis), and comparing the identified microbes and functions with available metadata (comparative metagenomics). Given the advantage of cost-effectiveness and convenient data-size, amplicon-based sequencing has remained the technology of choice for exploring phylogenetic diversity of an environment. A recent school of thought, employing the existing genome annotation information for inferring functional capacity of an identified microbiome community, has given a promising alternative to Whole Genome Shotgun sequencing for functional analysis. Although a handful of tools are currently available for function inference, their scope, functionality and utility has essentially remained limited. Need for a comprehensive framework that expands upon the existing scope and enables a standardized workflow for function inference, analysis, and visualization, is therefore felt.Methods: We present iVikodak, a multi-modular web-platform that hosts a logically inter-connected repertoire of functional inference and analysis tools, coupled with a comprehensive visualization interface. iVikodak is equipped with microbial co-inhabitance pattern driven published algorithms along with multiple updated databases of various curated microbe-function maps. It also features an advanced task management and result sharing system through introduction of personalized and portable dashboards.Results: In addition to inferring functions from 16S rRNA gene data, iVikodak enables (a) an in-depth analysis of specific functions of interest (b) identification of microbes contributing to various functions (c) microbial interaction patterns through function-driven correlation networks, and (d) simultaneous functional comparison between multiple microbial communities. We have bench-marked iVikodak through multiple case studies and comparisons with existing state of art. We also introduce the concept of a public repository which provides a first of its kind community-driven framework for scientific data analytics, collaboration and sharing in this area of microbiome research.Conclusion: Developed using modern design and task management practices, iVikodak provides a multi-modular, yet inter-operable, one-stop framework, that intends to simplify the entire approach toward inferred function analysis. It is anticipated to serve as a significant value addition to the existing space of functional metagenomics.iVikodak web-server may be freely accessed at https://web.rniapps.net/iVikodak/.

CowPI: A Rumen Microbiome Focussed Version of the PICRUSt Functional Inference Software.

Metataxonomic 16S rDNA based studies are a commonplace and useful tool in the research of the microbiome, but they do not provide the full investigative power of metagenomics and metatranscriptomics for revealing the functional potential of microbial communities. However, the use of metagenomic and metatranscriptomic technologies is hindered by high costs and skills barrier necessary to generate and interpret the data. To address this, a tool for Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was developed for inferring the functional potential of an observed microbiome profile, based on 16S data. This allows functional inferences to be made from metataxonomic 16S rDNA studies with little extra work or cost, but its accuracy relies on the availability of completely sequenced genomes of representative organisms from the community being investigated. The rumen microbiome is an example of a community traditionally underrepresented in genome and sequence databases, but recent efforts by projects such as the Global Rumen Census and Hungate 1000 have resulted in a wide sampling of 16S rDNA profiles and almost 500 fully sequenced microbial genomes from this environment. Using this information, we have developed “CowPI,” a focused version of the PICRUSt tool provided for use by the wider scientific community in the study of the rumen microbiome. We evaluated the accuracy of CowPI and PICRUSt using two 16S datasets from the rumen microbiome: one generated from rDNA and the other from rRNA where corresponding metagenomic and metatranscriptomic data was also available. We show that the functional profiles predicted by CowPI better match estimates for both the meta-genomic and transcriptomic datasets than PICRUSt, and capture the higher degree of genetic variation and larger pangenomes of rumen organisms. Nonetheless, whilst being closer in terms of predictive power for the rumen microbiome, there were differences when compared to both the metagenomic and metatranscriptome data and so we recommend, where possible, functional inferences from 16S data should not replace metagenomic and metatranscriptomic approaches. The tool can be accessed at http://www.cowpi.org and is provided to the wider scientific community for use in the study of the rumen microbiome.

Predictive functional profiles using metagenomic 16S rRNA data: a novel approach to understanding the microbial ecology of aquaculture systems

AbstractMicro‐organisms are an essential component of natural marine ecosystems, but also play important roles in anthropogenically modified ecosystems, including aquaculture. Although metagenomics is currently used to explore microbial communities, its application has not been as extensive in aquaculture. Although some taxonomic profiles and phylogenetic studies have been deciphered using biomarker genes, the functional potential of microbial communities associated with aquaculture systems is still unknown in most cases. Predicting functional profiles through 16S rRNA gene‐based metagenomics analysis is perhaps the most promising tool to elucidate the metabolic capabilities of microbial communities because there is no need to perform shotgun sequencing to have an idea of these capabilities. Moreover, robust bioinformatics background is not required to assess this information, and so the same data (16S rRNA sequences) are used to estimate both taxonomic and functional profiles, therefore providing deeper insights into these kinds of communities. In this review, we suggest the need to major use of novel bioinformatics tools that construct functional profiles from metagenomic 16S rRNA data, as strategy to obtain a preliminary approach about metabolic capacities of microbes that coexist in aquaculture systems.

Characteristics of microbial community functional structure of a biological coking wastewater treatment system

Nitrogenous heterocyclic compounds are key pollutants in coking wastewater; however, the functional potential of microbial communities for biodegradation of such contaminants during biological treatment is still elusive. Herein, a high throughput functional gene array (GeoChip 5.0) in combination with Illumina HiSeq2500 sequencing was used to compare and characterize the microbial community functional structure in a long run (500days) bench scale bioreactor treating coking wastewater, with a control system treating synthetic wastewater. Despite the inhibitory toxic pollutants, GeoChip 5.0 detected almost all key functional gene (average 61,940 genes) categories in the coking wastewater sludge. With higher abundance, aromatic ring cleavage dioxygenase genes including multi ring1,2diox; one ring2,3diox; catechol represented significant functional potential for degradation of aromatic pollutants which was further confirmed by Illumina HiSeq2500 analysis results. Response ratio analysis revealed that three nitrogenous compound degrading genes- nbzA (nitro-aromatics), tdnB (aniline), and scnABC (thiocyanate) were unique for coking wastewater treatment, which might be strong cause to increase ammonia level during the aerobic process. Additionally, HiSeq2500 elucidated carbozole and isoquinoline degradation genes in the system. These findings expanded our understanding on functional potential of microbial communities to remove organic nitrogenous pollutants; hence it will be useful in optimization strategies for biological treatment of coking wastewater.

Functional Gene Diversity and Metabolic Potential of the Microbial Community in an Estuary-Shelf Environment.

Microbes play crucial roles in various biogeochemical processes in the ocean, including carbon (C), nitrogen (N), and phosphorus (P) cycling. Functional gene diversity and the structure of the microbial community determines its metabolic potential and therefore its ecological function in the marine ecosystem. However, little is known about the functional gene composition and metabolic potential of bacterioplankton in estuary areas. The East China Sea (ECS) is a dynamic marginal ecosystem in the western Pacific Ocean that is mainly affected by input from the Changjiang River and the Kuroshio Current. Here, using a high-throughput functional gene microarray (GeoChip), we analyzed the functional gene diversity, composition, structure, and metabolic potential of microbial assemblages in different ECS water masses. Four water masses determined by temperature and salinity relationship showed different patterns of functional gene diversity and composition. Generally, functional gene diversity [Shannon–Weaner’s H and reciprocal of Simpson’s 1/(1-D)] in the surface water masses was higher than that in the bottom water masses. The different presence and proportion of functional genes involved in C, N, and P cycling among the bacteria of the different water masses showed different metabolic preferences of the microbial populations in the ECS. Genes involved in starch metabolism (amyA and nplT) showed higher proportion in microbial communities of the surface water masses than of the bottom water masses. In contrast, a higher proportion of genes involved in chitin degradation was observed in microorganisms of the bottom water masses. Moreover, we found a higher proportion of nitrogen fixation (nifH), transformation of hydroxylamine to nitrite (hao) and ammonification (gdh) genes in the microbial communities of the bottom water masses compared with those of the surface water masses. The spatial variation of microbial functional genes was significantly correlated with salinity, temperature, and chlorophyll based on canonical correspondence analysis, suggesting a significant influence of hydrologic conditions on water microbial communities. Our data provide new insights into better understanding of the functional potential of microbial communities in the complex estuarine-coastal environmental gradient of the ECS.

PALADIN: protein alignment for functional profiling whole metagenome shotgun data.

MotivationWhole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN—a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directly mapping in protein space.ResultsWe compared the accuracy and efficiency of PALADIN against existing tools that employ nucleotide or protein alignment algorithms. Using simulated reads, PALADIN consistently outperformed the popular DNA read mappers BWA and NovoAlign in detected proteins, percentage of reads mapped and ontological similarity. We also compared PALADIN against four existing protein alignment tools: BLASTX, RAPSearch2, DIAMOND and Lambda, using empirically obtained reads. PALADIN yielded results seven times faster than the best performing alternative, DIAMOND and nearly 8000 times faster than BLASTX. PALADIN's accuracy was comparable to all tested solutions.Availability and ImplementationPALADIN was implemented in C, and its source code and documentation are available at https://github.com/twestbrookunh/paladinSupplementary information Supplementary data are available at Bioinformatics online.