UProC: tools for ultra-fast protein domain classification

Metatranscriptomics: A Tool for Clinical Metagenomics

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics.

Fecal microbiota transplantation: current challenges and future landscapes

SUMMARYGiven the importance of gut microbial homeostasis in maintaining health, there has been considerable interest in developing innovative therapeutic strategies for restoring gut microbiota. One such approach, fecal microbiota transplantation (FMT), is the main "whole gut microbiome replacement" strategy and has been integrated into clinical practice guidelines for treating recurrent Clostridioides difficile infection (rCDI). Furthermore, the potential application of FMT in other indications such as inflammatory bowel disease (IBD), metabolic syndrome, and solid tumor malignancies is an area of intense interest and active research. However, the complex and variable nature of FMT makes it challenging to address its precise functionality and to assess clinical efficacy and safety in different disease contexts. In this review, we outline clinical applications, efficacy, durability, and safety of FMT and provide a comprehensive assessment of its procedural and administration aspects. The clinical applications of FMT in children and cancer immunotherapy are also described. We focus on data from human studies in IBD in contrast with rCDI to delineate the putative mechanisms of this treatment in IBD as a model, including colonization resistance and functional restoration through bacterial engraftment, modulating effects of virome/phageome, gut metabolome and host interactions, and immunoregulatory actions of FMT. Furthermore, we comprehensively review omics technologies, metagenomic approaches, and bioinformatics pipelines to characterize complex microbial communities and discuss their limitations. FMT regulatory challenges, ethical considerations, and pharmacomicrobiomics are also highlighted to shed light on future development of tailored microbiome-based therapeutics.

Reduction of Chromate via Biotic and Abiotic Pathways in the Presence of Three Co-contaminating Electron Acceptors

Bioreduction of Cr(VI) to Cr(III) is a promising technology for removing Cr(VI), but Cr(VI) reduction alone cannot support microbial growth. This study investigated the reduction of Cr(VI) in the presence of three electron acceptors that typically coexist with Cr(VI): NO3-, SO42-, and Fe(III). All three systems could reduce Cr(VI) to Cr(III), but the fate of Cr, its impacts on reduction of the other acceptors, and its impact on the microbial community differed. Although Cr(VI) was continuously removed in the NO3--reduction systems, batch tests showed that denitrification was inhibited primarily through impeding nitrite reduction. The SO42- and Fe(III) reduction systems reduced Cr(VI) using a combination of biotic and abiotic processes. Across all three systems, the abundance of genera capable of reducing Cr(VI) increased following the introduction of Cr(VI). Conversely, the abundance of genera that cannot reduce or resist Cr(VI) decreased, leading to restructuring of the microbial community. Furthermore, the abundance of sulfide oxidizers and Fe(II) oxidizers substantially increased after the introduction of chromate. This study provides fundamental knowledge about how Cr(VI) bioreduction interacts with bioreductions of three other co-contaminating electron acceptors.

16S rDNA profiling of Loach (Misgurnus anguillicus) fed with soybean fermented powder intestinal flora in response to Lipopolysaccharide (LPS) infection

Soybean fermentation has a balancing effect on the regulation of intestinal flora. Relative research between fermented soybeans and intestinal microbiota is limited. Our aim was to explore the effects of soybean fermented fowder on lipopolysaccharide (LPS) induced intestinal microflora and corresponding functions in loach. 16S rDNA high-throughout sequencing was applied to estimate differences in the intestinal microbiota and predict genes function. Analysis of the overall of sequencing data showed that the ratio of Effective Tags in both the control group and the treatment group was greater than 80 %. Based on six major classifications involved in the phylum, class, order, family, genus, and species, we acquired the changes in the composition of intestinal microorganisms after the supplement of soybean fermented powder. These results showed that the dominant bacteria in the two groups were basically distinct at different levels. Alpha diversity analysis indicated that the microbial richness and uniformity of soybean fermented powder decreased compared to the control group. PICRUSt and Taxfun tools analysis of intestinal flora illustrated the functional genes of the six groups were mainly involved in metabolism, genetic information processing, cellular processes, environmental information processing, and human diseases at the level 1. These data clearly demonstrated the effect of soybean fermented powder on the gut microbiome. Not only that, it provides new ideas and insights for achieving high-quality utilization of soybean fermented powder. The potential mechanisms of soybean fermented powder to alter gut flora and intestinal microbiome function can further be explored.

Rational probe design for efficient rRNA depletion and improved metatranscriptomic analysis of human microbiomes

The microbiota that colonize the human gut and other tissues are dynamic, varying both in composition and functional state between individuals and over time. Gene expression measurements can provide insights into microbiome composition and function. However, efficient and unbiased removal of microbial ribosomal RNA (rRNA) presents a barrier to acquiring metatranscriptomic data. Here we describe a probe set that achieves efficient enzymatic rRNA removal of complex human-associated microbial communities. We demonstrate that the custom probe set can be further refined through an iterative design process to efficiently deplete rRNA from a range of human microbiome samples. Using synthetic nucleic acid spike-ins, we show that the rRNA depletion process does not introduce substantial quantitative error in gene expression profiles. Successful rRNA depletion allows for efficient characterization of taxonomic and functional profiles, including during the development of the human gut microbiome. The pan-human microbiome enzymatic rRNA depletion probes described here provide a powerful tool for studying the transcriptional dynamics and function of the human microbiome.

Phenotypic and Transcriptional Responses of Pseudomonas aeruginosa Biofilms to UV-C Irradiation via Side-Emitting Optical Fibers: Implications for Biofouling Control

Biofilms give rise to a range of issues, spanning from harboring pathogens to accelerating microbial-induced corrosion in pressurized water systems. Introducing germicidal UV-C (200-280 nm) irradiation from light-emitting diodes (LEDs) into flexible side-emitting optical fibers (SEOFs) presents a novel light delivery method to inhibit the accumulation of biofilms on surfaces found in small-diameter tubing or other intricate geometries. This work used surfaces fully submerged in flowing water that contained Pseudomonas aeruginosa, an opportunistic pathogen commonly found in water system biofilms. A SEOF delivered a UV-C gradient to the surface for biofilm inhibition. Biofilm growth over time was monitored in situ using optical conference tomography. Biofilm formation was effectively inhibited when the 275 nm UV-C irradiance was ≥8 μW/cm2. Biofilm samples were collected from several regions on the surface, representing low and high UV-C irradiance. RNA sequencing of these samples revealed that high UV-C irradiance inhibited the expression of functional genes related to energy metabolism, DNA repair, quorum sensing, polysaccharide production, and mobility. However, insufficient sublethal UV-C exposure led to upregulation genes for SOS response and quorum sensing as survival strategies against the UV-C stress. These results underscore the need to maintain minimum UV-C exposure on surfaces to effectively inhibit biofilm formation in water systems.

Impact of the fungal pathogen Fusarium oxysporum on the taxonomic and functional diversity of the common bean root microbiome

BACKGROUND

Plants rely on their root microbiome as the first line of defense against soil-borne fungal pathogens. The abundance and activities of beneficial root microbial taxa at the time prior to and during fungal infection are key to their protective success. If and how invading fungal root pathogens can disrupt microbiome assembly and gene expression is still largely unknown. Here, we investigated the impact of the fungal pathogen Fusarium oxysporum (fox) on the assembly of rhizosphere and endosphere microbiomes of a fox-susceptible and fox-resistant common bean cultivar.

RESULTS

Integration of 16S-amplicon, shotgun metagenome as well as metatranscriptome sequencing with community ecology analysis showed that fox infections significantly changed the composition and gene expression of the root microbiome in a cultivar-dependent manner. More specifically, fox infection led to increased microbial diversity, network complexity, and a higher proportion of the genera Flavobacterium, Bacillus, and Dyadobacter in the rhizosphere of the fox-resistant cultivar compared to the fox-susceptible cultivar. In the endosphere, root infection also led to changes in community assembly, with a higher abundance of the genera Sinorhizobium and Ensifer in the fox-resistant cultivar. Metagenome and metatranscriptome analyses further revealed the enrichment of terpene biosynthesis genes with a potential role in pathogen suppression in the fox-resistant cultivar upon fungal pathogen invasion.

CONCLUSION

Collectively, these results revealed a cultivar-dependent enrichment of specific bacterial genera and the activation of putative disease-suppressive functions in the rhizosphere and endosphere microbiome of common bean under siege.

Integrated de novo gene prediction and peptide assembly of metagenomic sequencing data

Metagenomics is the study of all genomic content contained in given microbial communities. Metagenomic functional analysis aims to quantify protein families and reconstruct metabolic pathways from the metagenome. It plays a central role in understanding the interaction between the microbial community and its host or environment. De novo functional analysis, which allows the discovery of novel protein families, remains challenging for high-complexity communities. There are currently three main approaches for recovering novel genes or proteins: de novo nucleotide assembly, gene calling and peptide assembly. Unfortunately, their information dependency has been overlooked, and each has been formulated as an independent problem. In this work, we develop a sophisticated workflow called integrated Metagenomic Protein Predictor (iMPP), which leverages the information dependencies for better de novo functional analysis. iMPP contains three novel modules: a hybrid assembly graph generation module, a graph-based gene calling module, and a peptide assembly-based refinement module. iMPP significantly improved the existing gene calling sensitivity on unassembled metagenomic reads, achieving a 92-97% recall rate at a high precision level (>85%). iMPP further allowed for more sensitive and accurate peptide assembly, recovering more reference proteins and delivering more hypothetical protein sequences. The high performance of iMPP can provide a more comprehensive and unbiased view of the microbial communities under investigation. iMPP is freely available from https://github.com/Sirisha-t/iMPP.

Coremoval of Energetics and Oxyanions via the In Situ Coupling of Catalytic and Enzymatic Destructions: A Solution to Ammunition Wastewater Treatment

Ammunition wastewater contains toxic nitrated explosives like RDX and oxyanions like nitrate and perchlorate. Its treatment is challenged by low efficiency due to contaminant recalcitrance and high cost due to multiple processes needed for separately removing different contaminant types. This paper reports a H₂-based low-energy strategy featuring the treatment of explosives via catalytic denitration followed by microbial mineralization coupled with oxyanion reduction. After a nitrate- and perchlorate-reducing biofilm incapable of RDX biodegradation was coated with palladium nanoparticles (Pd⁰NPs), RDX was rapidly denitrated with a specific catalytic activity of 8.7 g_cat^-1 min^-1, while biological reductions of nitrate and perchlorate remained efficient. In the subsequent 30-day continuous test, >99% of RDX, nitrate, and perchlorate were coremoved, and their effluent concentrations were below their respective regulation levels. Detected intermediates and shallow metagenome analysis suggest that the intermediates after Pd-catalytic denitration of RDX ultimately were enzymatically utilized by the nitrate- and perchlorate-reducing bacteria as additional electron donor sources.

Exploring Gut Microbiome in Predicting the Efficacy of Immunotherapy in Non-Small Cell Lung Cancer

We performed various analyses on the taxonomic and functional features of the gut microbiome from NSCLC patients treated with immunotherapy to establish a model that may predict whether a patient will benefit from immunotherapy. We collected 65 published whole metagenome shotgun sequencing samples along with 14 samples from our previous study. We systematically studied the taxonomical characteristics of the dataset and used both the random forest (RF) and the multilayer perceptron (MLP) neural network models to predict patients with progression-free survival (PFS) above 6 months versus those below 3 months. Our results showed that the RF classifier achieved the highest F-score (85.2%) and the area under the receiver operating characteristic curve (AUC) (95%) using the protein families (Pfam) profile, and the MLP neural network classifier achieved a 99.9% F-score and 100% AUC using the same Pfam profile. When applying the model trained in the Pfam profile directly to predict the treatment response, we found that both trained RF and MLP classifiers significantly outperformed the stochastic predictor in F-score. Our results suggested that such a predictive model based on functional (e.g., Pfam) rather than taxonomic profile might be clinically useful to predict whether an NSCLC patient will benefit from immunotherapy, as both the F-score and AUC of functional profile outperform that of taxonomic profile. In addition, our model suggested that interactive biological processes such as methanogenesis, one-carbon, and amino acid metabolism might be important in regulating the immunotherapy response that warrants further investigation.

Fermented soybean meal modified the rumen microbiome to enhance the yield of milk components in Holstein cows

The study was conducted to evaluate the rumen microbiota as well as the milk composition and milk component yields of Holstein cows supplemented with fermented soybean meal (FSBM). Eighteen Holstein cows in their 2nd parity with 54.38 ± 11.12 SD days in milking (DIM) were divided into two dietary groups (CON and TRT) of nine cows per group. The cows in the TRT group received 300 g of FSBM per cow per day in addition to the conventional diet, while each cow in the CON group was supplemented with 350 g of soybean meal (SBM) in their diet daily throughout the 28-day feeding trial. Rumen bacterial composition was detected via 16S rRNA sequencing, and the functional profiles of bacterial communities were predicted. Milk composition, milk yield, as well as rumen fermentation parameters, and serum biochemistry were also recorded. The inclusion of FSBM into the diets of Holstein cows increased the milk urea nitrogen (MUN), milk protein yield, fat corrected milk (FCM), and milk fat yield while the milk somatic cell count (SCC) was decreased. In the rumen, the relative abundances of Fibrobacterota, and Spirochaetota phyla were increased in the TRT group, while the percentage of Proteobacteria was lower. In addition, the supplementation of FSBM to Holstein cows increased the acetate percentage, rumen pH, and acetate to propionate ratio, while the proportion of propionate and propionate % was observed to decrease in the TRT group. The KEGG pathway and functional prediction revealed an upregulation in the functional genes associated with the biosynthesis of amino acids in the TRT group. This enrichment in functional genes resulted in an improved synthesis of several essential amino acids including lysine, methionine, and branch chain amino acids (BCAA) which might be responsible for the increased milk protein yield. Future studies should employ shotgun metagenomics, transcriptomics, and metabolomics technology to investigate the effects of FSBM on other rumen microbiomes and milk protein synthesis in the mammary gland in Holstein cows. KEY POINTS: • The supplementation of fermented soybean meal (FSBM) to Holstein cows modified the proportion of rumen bacteria. • Predicted metabolic pathways and functional genes of rumen bacteria revealed an enrichment in pathway and genes associated with biosynthesis of amino acids in the group fed FSBM. • The cows supplemented with FSBM record an improved rumen fermentation. • Cows supplemented with FSBM recorded an increased yield of milk protein and milk fat.

Interpreting alignment-free sequence comparison: what makes a score a good score?

Alignment-free methods are alternatives to alignment-based methods when searching sequence data sets. The output from an alignment-free sequence comparison is a similarity score, the interpretation of which is not straightforward. We propose objective functions to interpret and calibrate outputs from alignment-free searches, noting that different objective functions are necessary for different biological contexts. This leads to advantages: visualising and comparing score distributions, including those from true positives, may be a relatively simple method to gain insight into the performance of different metrics. Using an empirical approach with both DNA and protein sequences, we characterise different similarity score distributions generated under different parameters. In particular, we demonstrate how sequence length can affect the scores. We show that scores of true positive sequence pairs may correlate significantly with their mean length; and even if the correlation is weak, the relative difference in length of the sequence pair may significantly reduce the effectiveness of alignment-free metrics. Importantly, we show how objective functions can be used with test data to accurately estimate the probability of true positives. This can significantly increase the utility of alignment-free approaches. Finally, we have developed a general-purpose software tool called KAST for use in high-throughput workflows on Linux clusters.

Synergy of Dietary Quercetin and Vitamin E Improves Cecal Microbiota and Its Metabolite Profile in Aged Breeder Hens

In the present study, the synergistic effects of quercetin (Q) and vitamin E (E) on cecal microbiota composition and function, as well as the microbial metabolic profile in aged breeder hens were investigated. A total of 400 (65 weeks old) Tianfu breeder hens were randomly allotted to four experimental groups (four replicates per group). The birds were fed diets containing quercetin at 0.4 g/kg, vitamin E (0.2 g/kg), quercetin and vitamin E (QE; 0.4 g/kg and 0.2 g/kg), and a basal diet for a period of 10 wks. After the 10 week experimental period, the cecal contents of 8 aged breeder hens per group were sampled aseptically and subjected to high-throughput 16S rRNA gene sequencing and untargeted metabolomic analysis. The results showed that the relative abundances of phyla Bacteroidota, Firmicutes, and Actinobacteriota were the most prominent among all the dietary groups. Compared to the control group, the relative abundance of the families Bifidobacteriaceae, Lachnospiraceae, Tannerellaceae, Mathonobacteriaceae, Barnesiellaceae, and Prevotellaceae were enriched in the QE group; and Bacteroidaceae, Desulfovibrionaceae, Peptotostretococcaceae, and Fusobacteriaceae were enriched in the Q group, whereas those of Lactobacillaceae, Veillonellaceae, Ruminococcaceae, Akkermansiaceae, and Rikenellaceae were enriched in the E group compared to the control group. Untargeted metabolomics analyses revealed that Q, E, and QE modified the abundance of several metabolites in prominent pathways including ubiquinone and other terpenoid-quinone biosynthesis, regulation of actin cytoskeleton, insulin secretion, pancreatic secretion, nicotine addiction, and metabolism of xenobiotics by cytochrome P450. Furthermore, key cecal microbiota, significantly correlated with important metabolites, for example, (S)-equol positively correlated with Alistipes and Chlamydia in E_vs_C, and negatively correlated with Olsenella, Paraprevotella, and Mucispirillum but, a contrary trend was observed with Parabacteroides in QE_vs_C. This study establishes that the synergy of quercetin and vitamin E alters the cecal microbial composition and metabolite profile in aged breeder hens, which lays a foundation for chicken improvement programs.

Exploring changes in bacterioplankton community structure in response to tannic acid, a major component of mangrove litterfall of Sundarbans mangrove ecosystem: a laboratory mesocosm approach

Tannic acid is a secondary compound produced by vascular plants and is a major component of mangrove litterfall. Tannic acid is water soluble, leaches out from mangrove litterfall and contributes to DOC and DON pools in adjacent estuaries. About 50% of the litterfall may be degraded and channelized into the marine microbial loop. The influence of tannic acid on bacterioplankton community structure was tested by setting up laboratory-based barrel experiments. Estuarine water from Stn3 of Sundarbans Biological Observatory Time Series (SBOTS) was enriched with tannic acid, and the change in concentration of dissolved nutrients was determined on a daily basis over a span of 15 days. Concentrations of tannic acid, gallic acid and other dissolved nutrients such as nitrate and ortho-phosphate were determined using a UV-Vis spectrophotometer. Tannic acid significantly affected the concentrations of gallic acid and dissolved nitrate in the barrels. Degradation of tannic acid was tracked by a decrease in concentration of tannic acid and generation of gallic acid. The influence of tannic acid on bacterioplankton community structure was analysed on the start (day 0), intermediate (day 3, day 5, day 7 and day 9) and end (day 15) of the experiment. Bacterioplankton community structure was elucidated by sequencing the V3-V4 region of 16S ribosomal RNA on an Illumina MiSeq platform. Proteobacteria was found to be the most dominant bacterial phylum in control and tannic acid-enriched barrels (barrels 1 and 2) on day 0. With the progression of experiment, the abundance of Proteobacteria altered significantly in the control barrel indicating the possible role of this phylum in the breakdown of tannic acid within estuarine mangroves. The abundance of Proteobacteria in the tannic acid-enriched barrels remained high, indicating that members of Proteobacteria may be capable of using tannic acid as a source of carbon and nitrogen. Tannic acid appeared to inhibit most of the other bacterioplankton phyla including Actinobacteria, Acidobacteria and Verrucomicrobia that existed in large abundance in the control barrel on day 15 but were almost absent in the tannic acid-enriched barrels. At class level, Bacteroides was found to be present in highest abundance in the tannic acid-enriched barrels. Tannic acid appeared to strongly influence the abundant bacterioplankton phyla and families as indicated by Pearson's correlation coefficient and non-metric multidimensional scaling ordination plots. Gallic acid is one of the final products of tannic acid degradation. Breakdown of tannic acid could influence the marine nitrogen and carbon cycling by releasing DON and DOC, respectively, into the adjacent estuaries. Information of breakdown and remineralization of components of litterfall such as tannic acid would also be important for calculation of carbon and nitrogen budgets of coastal ecosystems including in mangroves.

Legacy Metal Contaminants and Excess Nutrients in Low Flow Estuarine Embayments Alter Composition and Function of Benthic Bacterial Communities

Coastal systems such as estuaries are threatened by multiple anthropogenic stressors worldwide. However, how these stressors and estuarine hydrology shape benthic bacterial communities and their functions remains poorly known. Here, we surveyed sediment bacterial communities in poorly flushed embayments and well flushed channels in Sydney Harbour, Australia, using 16S rRNA gene sequencing. Sediment samples were collected monthly during the Austral summer-autumn 2014 at increasing distance from a large storm drain in each channel and embayment. Bacterial communities differed significantly between sites that varied in proximity to storm drains, with a gradient of change apparent for sites within embayments. We explored this pattern for embayment sites with analysis of RNA-Seq gene expression patterns and found higher expression of multiple genes involved in bacterial stress response far from storm drains, suggesting that bacterial communities close to storm drains may be more tolerant of localised anthropogenic stressors. Several bacterial groups also differed close to and far from storm drains, suggesting their potential utility as bioindicators to monitor contaminants in estuarine sediments. Overall, our study provides useful insights into changes in the composition and functioning of benthic bacterial communities as a result of multiple anthropogenic stressors in differing hydrological conditions.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Microbial community transcriptional patterns vary in response to mesoscale forcing in the North Pacific Subtropical Gyre

The physical and biological dynamics that influence phytoplankton communities in the oligotrophic ocean are complex, changing across broad temporal and spatial scales. Eukaryotic phytoplankton (e.g., diatoms), despite their relatively low abundance in oligotrophic waters, are responsible for a large component of the organic matter flux to the ocean interior. Mesoscale eddies can impact both microbial community structure and function, enhancing primary production and carbon export, but the mechanisms that underpin these dynamics are still poorly understood. Here, mesoscale eddy influences on the taxonomic diversity and expressed functional profiles of surface communities of microeukaryotes and particle-associated heterotrophic bacteria from the North Pacific Subtropical Gyre were assessed over 2 years (spring 2016 and summer 2017). The taxonomic diversity of the microeukaryotes significantly differed by eddy polarity (cyclonic versus anticyclonic) and between sampling seasons/years and was significantly correlated with the taxonomic diversity of particle-associated heterotrophic bacteria. The expressed functional profile of these taxonomically distinct microeukaryotes varied consistently as a function of eddy polarity, with cyclones having a different expression pattern than anticyclones, and between sampling seasons/years. These data suggest that mesoscale forcing, and associated changes in biogeochemistry, could drive specific physiological responses in the resident microeukaryote community, independent of species composition.

Wastewater effluents cause microbial community shifts and change trophic status

Widespread wastewater pollution is one of the greatest challenges threatening the sustainable management of rivers globally. Understanding microbial responses to gradients in environmental stressors, such as wastewater pollution, is crucial to identify thresholds of community change and to develop management strategies that protect ecosystem integrity. This study used multiple lines of empirical evidence, including a novel combination of microbial ecotoxicology methods in the laboratory and field to link pressure-stressor-response relationships. Specifically, community-based whole effluent toxicity (WET) testing and environmental genomics were integrated to determine real-world community interactions, shifts and functional change in response to wastewater pollution. Here we show that wastewater effluents above moderate (>10%) concentrations caused consistent significant shifts in bacterial community structure and function. These thresholds of community shifts were also linked to changes in the trophic state of receiving waters in terms of nutrient concentrations. Differences in the community responses along the effluent concentration gradient were primarily driven by two globally relevant bacterial indicator taxa, namely Malikia spp. (Burkholderiales) and hgcI_clade (Frankiales). Species replacement occurred above moderate effluent concentrations with abundances of Malikia spp. increasing, while abundances of hgcI_clade decreased. The responses of Malikia spp. and hgcI_clade matched gene patterns associated with globally important nitrogen cycling pathways, such as denitrification and nitrogen fixation, which linked the core individual taxa to putative function and ecosystem processes, rarely achieved in previous studies. This study has identified potential indicators of change in trophic status and the functional consequences of wastewater pollution. These findings have immediate implications for both the management of environmental stressors and protection of aquatic ecosystems.

Improving protein domain classification for third-generation sequencing reads using deep learning

BACKGROUND

With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads.

RESULTS

In this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification.

CONCLUSIONS

In summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.

Gut Microbial Profile Is Associated With Residential Settings and Not Nutritional Status in Adults in Karnataka, India

Undernutrition is a leading contributor to disease and disability in people of all ages. Several studies have reported significant association between nutritional status and gut microbiome composition but other factors such as demographic settings may also influence the adult microbiome. The relationship between undernourishment and gut microbiome in adults has not been described to date. In this study, we compared the gut microbiome in fecal samples of 48 individuals, from two demographic settings (rural and urban slum) in Karnataka, India using 16S rRNA sequencing. Nutritional status was assessed based on BMI, with a BMI of < 18.5 kg/m2 classified as undernourished, and a BMI in the range 18.5-25 kg/m2 as nourished. We analyzed 25 individuals from rural settings (12 undernourished and 13 nourished) and 23 individuals from urban slum settings (11 undernourished and 12 nourished). We found no significant difference in overall gut microbial diversity (Shannon and Unweighted UniFrac) between undernourished and nourished individuals in either geographical settings, however, microbial taxa at the phylum level (i.e., Firmicutes and Proteobacteria) and beta diversity (unweighted UniFrac) differed significantly between the rural and urban slum settings. By predicting microbial function from 16S data profiling we found significant differences in metabolic pathways present in the gut microbiota from people residing in different settings; specifically, those related to carbohydrate and lipid metabolism. The weighted sum of the KEGG Orthologs associated with carbohydrate metabolism (Spearman's correlation coefficient, ρ = -0.707, p < 0.001), lipid metabolism (Spearman's correlation coefficient, ρ = -0.330, p < 0.022) and biosynthesis of secondary metabolites (Spearman's correlation coefficient, ρ = -0.507, p < 0.001) were decreased in the urban slum group compared to the rural group. In conclusion, we report that the geographical location of residence is associated with differences in gut microbiome composition in adults. We found no significant differences in microbiome composition between nourished and undernourished adults from urban slum or rural settings in India.

Metagenomic Profiling of Ocular Surface Microbiome Changes in Meibomian Gland Dysfunction

Purpose

Ocular surface microbiome changes can affect meibomian gland dysfunction (MGD) development. This study aimed to delineate differences among the microbiome of eyelid skin, conjunctiva, and meibum in healthy controls (HCs) and patients afflicted with MGD.

Methods

Shotgun metagenomic analysis was used to determine if there are differences between the microbial communities in ocular sites surrounding the meibomian gland in healthy individuals and patients afflicted with MGD.

Results

The meibum bacterial content of these microbiomes was dissimilar in these two different types of individuals. Almost all of the most significant taxonomic changes in the meibum microbiome of individuals with MGD were also present in their eyelid skin, but not in the conjunctiva. Such site-specific microbe pattern changes accompany increases in the gene expression levels controlling carbohydrate and lipid metabolism. Most of the microbiomes in patients with MGD possess a microbe population capable of metabolizing benzoate. Pathogens known to underlie ocular infection were evident in these individuals. MGD meibum contained an abundance of Campylobacter coli, Campylobacter jejuni, and Enterococcus faecium pathogens, which were almost absent from HCs. Functional annotation indicated that in the microbiomes of MGD meibum their capability to undergo chemotaxis, display immune evasive virulence, and mediate type IV secretion was different than that in the microbiomes of meibum isolated from HCs.

Conclusions

MGD meibum contains distinct microbiota whose immune evasive virulence is much stronger than that in the HCs. Profiling differences between the meibum microbiome makeup in HCs and patients with MGD characterizes changes of microbial communities associated with the disease status.

Cyanobacterial blooms in wastewater treatment facilities: Significance and emerging monitoring strategies

Municipal wastewater treatment facilities (WWTFs) are prone to the proliferation of cyanobacterial species which thrive in stable, nutrient-rich environments. Dense cyanobacterial blooms frequently disrupt treatment processes and the supply of recycled water due to their production of extracellular polymeric substances, which hinder microfiltration, and toxins, which pose a health risk to end-users. A variety of methods are employed by water utilities for the identification and monitoring of cyanobacteria and their toxins in WWTFs, including microscopy, flow cytometry, ELISA, chemoanalytical methods, and more recently, molecular methods. Here we review the literature on the occurrence and significance of cyanobacterial blooms in WWTFs and discuss the pros and cons of the various strategies for monitoring these potentially hazardous events. Particular focus is directed towards next-generation metagenomic sequencing technologies for the development of site-specific cyanobacterial bloom management strategies. Long-term multi-omic observations will enable the identification of indicator species and the development of site-specific bloom dynamics models for the mitigation and management of cyanobacterial blooms in WWTFs. While emerging metagenomic tools could potentially provide deep insight into the diversity and flux of problematic cyanobacterial species in these systems, they should be considered a complement to, rather than a replacement of, quantitative chemoanalytical approaches.

Metformin elicits antitumour effect by modulation of the gut microbiota and rescues Fusobacterium nucleatum-induced colorectal tumourigenesis

BACKGROUND

The effect of metformin on gut microbiota has been reported, but whether metformin can suppress colorectal cancer (CRC) by affecting gut microbiota composition and rescue F. nucleatum-induced tumourigenicity remains unclear.

METHODS

To identify microbiota associated with both CRC occurrence and metformin treatment, first, we reanalyzed the gut microbiome of our previous data on two human cohorts of normal and CRC individuals. Subsequently, we summarized microbiota altered by metformin from published literatures. Several taxa, including Fusobacterium, were associated with both CRC occurrence and metformin treatment. We investigated the effect of metformin on APCMin/+ mice given with or without F. nucleatum. 16S rRNA gene sequencing was performed.

FINDINGS

We summarized 131 genera altered by metformin from 18 published literatures. Five genera reported to be changed by metformin, including Bacteroides, Streptococcus, Achromobacter, Alistipes and Fusobacterium, were associated with CRC in both of our human cohorts. Metformin relieved the symptoms caused by F. nucleatum administration in APCMin/+ mice, and showed promise in suppressing intestinal tumour formation and rescuing F. nucleatum-induced tumourigenicity. Administration of F. nucleatum and/or metformin had effect on gut microbiome structure, composition and functions of APCMin/+ mice.

INTERPRETATION

This study pioneers in predicting critical CRC-associated taxa contributing to the antitumour effect of metformin, and correlating gut microbiome with the antitumour effect of metformin in experimental animals. We presented a basis for future investigations into metformin's potential effect on suppressing F. nucleatum-induced tumor formation in vivo.

FUNDING

This work was supported by grants from the National Natural Science Foundation of China (31701250).

Combined pigment and metatranscriptomic analysis reveals highly synchronized diel patterns of phenotypic light response across domains in the open oligotrophic ocean

Sunlight is the most important environmental control on diel fluctuations in phytoplankton activity, and understanding diel microbial processes is essential to the study of oceanic biogeochemical cycles. Yet, little is known about the in situ temporal dynamics of phytoplankton metabolic activities and their coordination across different populations. We investigated diel orchestration of phytoplankton activity in photosynthesis, photoacclimation, and photoprotection by analyzing pigment and quinone distributions in combination with metatranscriptomes in surface waters of the North Pacific Subtropical Gyre (NPSG). We found diel cycles in pigment abundances resulting from the balance of their synthesis and consumption. These dynamics suggest that night represents a metabolic recovery phase, refilling cellular pigment stores, while photosystems are remodeled towards photoprotection during daytime. Transcript levels of genes involved in photosynthesis and pigment metabolism had synchronized diel expression patterns among all taxa, reflecting the driving force light imparts upon photosynthetic organisms in the ocean, while other environmental factors drive niche differentiation. For instance, observed decoupling of diel oscillations in transcripts and related pigments indicates that pigment abundances are modulated by environmental factors extending beyond gene expression/regulation reinforcing the need to combine metatranscriptomics with proteomics and metabolomics to fully understand the timing of these critical processes in situ.

Differential responses of gut microbiota of male and female fathead minnow (Pimephales promelas) to a short-term environmentally-relevant, aqueous exposure to benzo[a]pyrene

In addition to aiding in digestion of food and uptake of nutrients, microbiota in guts of vertebrates are responsible for regulating several beneficial functions, including development of an organism and maintaining homeostasis. However, little is known about effects of exposures to chemicals on structure and function of gut microbiota of fishes. To assess effects of exposure to polycyclic aromatic hydrocarbons (PAHs) on gut microbiota, male and female fathead minnows (Pimephales promelas) were exposed to environmentally-relevant concentrations of the legacy PAH benzo[a]pyrene (BaP) in water. Measured concentrations of BaP ranged from 2.3 × 10^-3 to 1.3 μg L^-1. The community of microbiota in the gut were assessed by use of 16S rRNA metagenetics. Exposure to environmentally-relevant aqueous concentrations of BaP did not alter expression levels of mRNA for cyp1a1, a "classic" biomarker of exposure to BaP, but resulted in shifts in relative compositions of gut microbiota in females rather than males. Results presented here illustrate that in addition to effects on more well-studied molecular endpoints, relative compositions of the microbiota in guts of fish can also quickly respond to exposure to chemicals, which can provide additional mechanisms for adverse effects on individuals.

Graph based analysis for gene segment organization In a scrambled genome

DNA recombinant processes can involve gene segments that overlap or interleave with gene segments of another gene. Such gene segment appearances relative to each other are called here gene segment organization. We use graphs to represent the gene segment organization in a chromosome locus. Vertices of the graph represent contigs resulting after the recombination and the edges represent the gene segment organization prior to rearrangement. To each graph we associate a vector whose entries correspond to graph properties, and consider this vector as a point in a higher dimensional Euclidean space such that cluster formations and analysis can be performed with a hierarchical clustering method. The analysis is applied to a recently sequenced model organism Oxytricha trifallax, a species of ciliate with highly scrambled genome that undergoes massive rearrangement process after conjugation. The analysis shows some emerging star-like graph structures indicating that segments of a single gene can interleave, or even contain all of the segments from fifteen or more other genes in between its segments. We also observe that as many as six genes can have their segments mutually interleaving or overlapping.

Impact of acid mine drainage chemistry and microbiology on the development of efficient Fe removal activities

Coal mine derived acid mine drainage (AMD) is formed when oxygenated water infiltrates mine voids and oxidizes FeS phases, generating acidic fluid rich in heavy metals, polluting thousands of miles of streams. Existing remediation options are cost-prohibitive and difficult to sustain. In some cases, AMD flows over previously pristine soil in thin sheets over terrestrial surface, enhancing AMD aeration and Fe(II) oxidizing activities, leading to oxidative Fe(II) precipitation from AMD, without any human intervention. Since robust Fe(II) biooxidation occurs in the mixture of intruding AMD and pristine soil, understanding the effects of chemically variant AMD can be exploited for effective Fe(II) removal. We hypothesized that chemistry and microbiology of AMD intruding pristine soil on surface would influence the development of Fe(II) oxidizing capabilities. Therefore, to investigate the response of pristine soil to the addition of AMD varying in chemical and microbial characteristics, we mixed soil with a near-neutral and moderately acidic AMD, in separate incubations. Incubations with near-neutral AMD developed microbial Fe(II) oxidation activities after 10 days. However, Fe(II) oxidation in moderately acidic AMD incubations was mostly abiotic. 16S rRNA gene sequences and metabolic functional prediction (Tax4Fun) analysis of near-neutral AMD and soil mixture indicated development of taxonomically different communities capable of activities similar to microorganisms in a mine void. In conclusion, results indicate that AMD chemistry and microbiology affects development of Fe(II) biooxidation. Therefore, understanding of the effect of AMD chemistry on the development of FeOB activities in soil can be exploited to design site-specific and sustainable solutions.

Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences

BACKGROUND

Sequencing of 16S rRNA genes has become a powerful technique to study microbial communities and their responses towards changing environmental conditions in various ecosystems. Several tools have been developed for the prediction of functional profiles from 16S rRNA gene sequencing data, because numerous questions in ecosystem ecology require knowledge of community functions in addition to taxonomic composition. However, the accuracy of these tools relies on functional information derived from genomes available in public databases, which are often not representative of the microorganisms present in the studied ecosystem. In addition, there is also a lack of tools to predict functional gene redundancy in microbial communities.

RESULTS

To address these challenges, we developed Tax4Fun2, an R package for the prediction of functional profiles and functional gene redundancies of prokaryotic communities from 16S rRNA gene sequences. We demonstrate that functional profiles predicted by Tax4Fun2 are highly correlated to functional profiles derived from metagenomes of the same samples. We further show that Tax4Fun2 has higher accuracies than PICRUSt and Tax4Fun. By incorporating user-defined, habitat-specific genomic information, the accuracy and robustness of predicted functional profiles is substantially enhanced. In addition, functional gene redundancies predicted with Tax4Fun2 are highly correlated to functional gene redundancies determined for simulated microbial communities.

CONCLUSIONS

Tax4Fun2 provides researchers with a unique tool to predict and investigate functional profiles of prokaryotic communities based on 16S rRNA gene sequencing data. It is easy-to-use, platform-independent and highly memory-efficient, thus enabling researchers without extensive bioinformatics knowledge or access to high-performance clusters to predict functional profiles. Another unique feature of Tax4Fun2 is that it allows researchers to calculate the redundancy of specific functions, which is a potentially important measure of how resilient a community will be to environmental perturbation. Tax4Fun2 is implemented in R and freely available at https://github.com/bwemheu/Tax4Fun2.

Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome

Microorganisms living inside plants can promote plant growth and health, but their genomic and functional diversity remain largely elusive. Here, metagenomics and network inference show that fungal infection of plant roots enriched for Chitinophagaceae and Flavobacteriaceae in the root endosphere and for chitinase genes and various unknown biosynthetic gene clusters encoding the production of nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs). After strain-level genome reconstruction, a consortium of Chitinophaga and Flavobacterium was designed that consistently suppressed fungal root disease. Site-directed mutagenesis then revealed that a previously unidentified NRPS-PKS gene cluster from Flavobacterium was essential for disease suppression by the endophytic consortium. Our results highlight that endophytic root microbiomes harbor a wealth of as yet unknown functional traits that, in concert, can protect the plant inside out.

Neutral Processes Dominate Microbial Community Assembly in Atlantic Salmon, Salmo salar

In recent years, a wealth of studies has examined the relationships between a host and its microbiome across diverse taxa. Many studies characterize the host microbiome without considering the ecological processes that underpin microbiome assembly. In this study, the intestinal microbiota of Atlantic salmon, Salmo salar, sampled from farmed and wild environments was first characterized using 16S rRNA gene MiSeq sequencing analysis. We used neutral community models to determine the balance of stochastic and deterministic processes that underpin microbial community assembly and transfer across life cycle stage and between gut compartments. Across gut compartments in farmed fish, neutral models suggest that most microbes are transient with no evidence of adaptation to their environment. In wild fish, we found declining taxonomic and functional microbial community richness as fish mature through different life cycle stages. Alongside neutral community models applied to wild fish, we suggest that declining richness demonstrates an increasing role for the host in filtering microbial communities that is correlated with age. We found a limited subset of gut microflora adapted to the farmed and wild host environment among which Mycoplasma spp. are prominent. Our study reveals the ecological drivers underpinning community assembly in both farmed and wild Atlantic salmon and underlines the importance of understanding the role of stochastic processes, such as random drift and small migration rates in microbial community assembly, before considering any functional role of the gut microbes encountered.IMPORTANCE A growing number of studies have examined variation in the microbiome to determine the role in modulating host health, physiology, and ecology. However, the ecology of host microbial colonization is not fully understood and rarely tested. The continued increase in production of farmed Atlantic salmon, coupled with increased farmed-wild salmon interactions, has accentuated the need to unravel the potential adaptive function of the microbiome and to distinguish resident from transient gut microbes. Between gut compartments in a farmed system, we found a majority of operational taxonomic units (OTUs) that fit the neutral model, with Mycoplasma species among the key exceptions. In wild fish, deterministic processes account for more OTU differences across life stages than those observed across gut compartments. Unlike previous studies, our results make detailed comparisons between fish from wild and farmed environments, while also providing insight into the ecological processes underpinning microbial community assembly in this ecologically and economically important species.

Advances and Challenges in Metatranscriptomic Analysis

Sequencing-based analyses of microbiomes have traditionally focused on addressing the question of community membership and profiling taxonomic abundance through amplicon sequencing of 16 rRNA genes. More recently, shotgun metagenomics, which involves the random sequencing of all genomic content of a microbiome, has dominated this arena due to advancements in sequencing technology throughput and capability to profile genes as well as microbiome membership. While these methods have revealed a great number of insights into a wide variety of microbiomes, both of these approaches only describe the presence of organisms or genes, and not whether they are active members of the microbiome. To obtain deeper insights into how a microbial community responds over time to their changing environmental conditions, microbiome scientists are beginning to employ large-scale metatranscriptomics approaches. Here, we present a comprehensive review on computational metatranscriptomics approaches to study microbial community transcriptomes. We review the major advancements in this burgeoning field, compare strengths and weaknesses to other microbiome analysis methods, list available tools and workflows, and describe use cases and limitations of this method. We envision that this field will continue to grow exponentially, as will the scope of projects (e.g. longitudinal studies of community transcriptional responses to perturbations over time) and the resulting data. This review will provide a list of options for computational analysis of these data and will highlight areas in need of development.

Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data

Background

Metagenomics caused a quantum leap in microbial ecology. However, the inherent size and complexity of metagenomic data limit its interpretation. The quantification of metagenomic traits in metagenomic analysis workflows has the potential to improve the exploitation of metagenomic data. Metagenomic traits are organisms’ characteristics linked to their performance. They are measured at the genomic level taking a random sample of individuals in a community. As such, these traits provide valuable information to uncover microorganisms’ ecological patterns. The Average Genome Size (AGS) and the 16S rRNA gene Average Copy Number (ACN) are two highly informative metagenomic traits that reflect microorganisms’ ecological strategies as well as the environmental conditions they inhabit.

Results

Here, we present the ags.sh and acn.sh tools, which analytically derive the AGS and ACN metagenomic traits. These tools represent an advance on previous approaches to compute the AGS and ACN traits. Benchmarking shows that ags.sh is up to 11 times faster than state-of-the-art tools dedicated to the estimation AGS. Both ags.sh and acn.sh show comparable or higher accuracy than existing tools used to estimate these traits. To exemplify the applicability of both tools, we analyzed the 139 prokaryotic metagenomes of TARA Oceans and revealed the ecological strategies associated with different water layers.

Conclusion

We took advantage of recent advances in gene annotation to develop the ags.sh and acn.sh tools to combine easy tool usage with fast and accurate performance. Our tools compute the AGS and ACN metagenomic traits on unassembled metagenomes and allow researchers to improve their metagenomic data analysis to gain deeper insights into microorganisms’ ecology. The ags.sh and acn.sh tools are publicly available using Docker container technology at https://github.com/pereiramemo/AGS-and-ACN-tools.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3031-y) contains supplementary material, which is available to authorized users.

Just the Two of Us? A Family of Pseudomonas Megaplasmids Offers a Rare Glimpse into the Evolution of Large Mobile Elements

Pseudomonads are ubiquitous group of environmental proteobacteria, well known for their roles in biogeochemical cycling, in the breakdown of xenobiotic materials, as plant growth promoters, and as pathogens of a variety of host organisms. We have previously identified a large megaplasmid present within one isolate of the plant pathogen Pseudomonas syringae, and here we report that a second member of this megaplasmid family is found within an environmental Pseudomonad isolate most closely related to Pseudomonas putida. Many of the shared genes are involved in critical cellular processes like replication, transcription, translation, and DNA repair. We argue that presence of these shared pathways sheds new light on discussions about the types of genes that undergo horizontal gene transfer (i.e., the complexity hypothesis) as well as the evolution of pangenomes. Furthermore, although both megaplasmids display a high level of synteny, genes that are shared differ by over 50% on average at the amino acid level. This combination of conservation in gene order despite divergence in gene sequence suggests that this Pseudomonad megaplasmid family is relatively old, that gene order is under strong selection within this family, and that there are likely many more members of this megaplasmid family waiting to be found in nature.

iMicrobe: Tools and data-dreaiven discovery platform for the microbiome sciences

BACKGROUND

Scientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community.

FINDINGS

The iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation-supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP).

CONCLUSIONS

iMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.

Nitrification inhibitors effectively target N2 O-producing Nitrosospira spp. in tropical soil

The nitrification inhibitors (NIs) 3,4-dimethylpyrazole (DMPP) and dicyandiamide (DCD) can effectively reduce N2 O emissions; however, which species are targeted and the effect of these NIs on the microbial nitrifier community is still unclear. Here, we identified the ammonia oxidizing bacteria (AOB) species linked to N2 O emissions and evaluated the effects of urea and urea with DCD and DMPP on the nitrifying community in a 258 day field experiment under sugarcane. Using an amoA AOB amplicon sequencing approach and mining a previous dataset of 16S rRNA sequences, we characterized the most likely N2 O-producing AOB as a Nitrosospira spp. and identified Nitrosospira (AOB), Nitrososphaera (archaeal ammonia oxidizer) and Nitrospira (nitrite-oxidizer) as the most abundant, present nitrifiers. The fertilizer treatments had no effect on the alpha and beta diversities of the AOB communities. Interestingly, we found three clusters of co-varying variables with nitrifier operational taxonomic units (OTUs): the N2 O-producing AOB Nitrosospira with N2 O, NO3 - , NH4 + , water-filled pore space (WFPS) and pH; AOA Nitrososphaera with NO3 - , NH4 + and pH; and AOA Nitrososphaera and NOB Nitrospira with NH4 + , which suggests different drivers. These results support the co-occurrence of non-N2 O-producing Nitrososphaera and Nitrospira in the unfertilized soils and the promotion of N2 O-producing Nitrosospira under urea fertilization. Further, we suggest that DMPP is a more effective NI than DCD in tropical soil under sugarcane.

Alignment-free method for DNA sequence clustering using Fuzzy integral similarity

A larger amount of sequence data in private and public databases produced by next-generation sequencing put new challenges due to limitation associated with the alignment-based method for sequence comparison. So, there is a high need for faster sequence analysis algorithms. In this study, we developed an alignment-free algorithm for faster sequence analysis. The novelty of our approach is the inclusion of fuzzy integral with Markov chain for sequence analysis in the alignment-free model. The method estimate the parameters of a Markov chain by considering the frequencies of occurrence of all possible nucleotide pairs from each DNA sequence. These estimated Markov chain parameters were used to calculate similarity among all pairwise combinations of DNA sequences based on a fuzzy integral algorithm. This matrix is used as an input for the neighbor program in the PHYLIP package for phylogenetic tree construction. Our method was tested on eight benchmark datasets and on in-house generated datasets (18 s rDNA sequences from 11 arbuscular mycorrhizal fungi (AMF) and 16 s rDNA sequences of 40 bacterial isolates from plant interior). The results indicate that the fuzzy integral algorithm is an efficient and feasible alignment-free method for sequence analysis on the genomic scale.

Biochar does not attenuate triclosan's impact on soil bacterial communities

Triclosan, a broad-spectrum antimicrobial, has been widely used in pharmaceutical and personal care products. It undergoes limited degradation during wastewater treatment and is present in biosolids, most of which are land applied in the United States. This study assessed the impact of triclosan (0-100 mg kg^-1) with and without biochar on soil bacterial communities. Very little ¹⁴C-triclosan was mineralized to ¹⁴CO₂ (<7%) over the course of the study (42 days). While biochar (1%) significantly lowered mineralization of triclosan, analysis of 16S rRNA gene sequences revealed that biochar impacted very few OTUs and did not alter the overall structure of the community. Triclosan, on the other hand, significantly affected bacterial diversity and community structure (alpha diversity, ANOVA, p < 0.001; beta diversity, AMOVA, p < 0.01). Dirichlet multinomial mixtures (DMM) modeling and complete linkage clustering (CLC) revealed a dose-dependent impact of triclosan. Non-Parametric Metastats (NPM) analysis showed that 150 of 734 OTUs from seven main phyla were significantly impacted by triclosan (adjusted p < 0.05). Genera harboring opportunistic pathogens such as Flavobacterium were enriched in the presence of triclosan, as was Stenotrophomonas. The latter has previously been implicated in triclosan degradation via stable isotope probing. Surprisingly, Sphingomonads, which include well-characterized triclosan degraders were negatively impacted by even low doses of triclosan. Analyses of published genomes showed that triclosan resistance determinants were rare in Sphingomonads which may explain why they were negatively impacted by triclosan in our soil.

Comprehensive Longitudinal Microbiome Analysis of the Chicken Cecum Reveals a Shift From Competitive to Environmental Drivers and a Window of Opportunity for Campylobacter

Chickens are a key food source for humans yet their microbiome contains bacteria that can be pathogenic to humans, and indeed potentially to chickens themselves. Campylobacter is present within the chicken gut and is the leading cause of bacterial foodborne gastroenteritis within humans worldwide. Infection can lead to secondary sequelae such as Guillain-Barré syndrome and stunted growth in children from low-resource areas. Despite the global health impact and economic burden of Campylobacter, how and when Campylobacter appears within chickens remains unclear. The lack of day to day microbiome data with replicates, relevant metadata, and a lack of natural infection studies have delayed our understanding of the chicken gut microbiome and Campylobacter. Here, we performed a comprehensive day to day microbiome analysis of the chicken cecum from day 3 to 35 (12 replicates each day; final n = 379). We combined metadata such as chicken weight and feed conversion rates to investigate what the driving forces are for the microbial changes within the chicken gut over time, and how this relates to Campylobacter appearance within a natural habitat setting. We found a rapidly increasing microbial diversity up to day 12 with variation observed both in terms of genera and abundance, before a stabilization of the microbial diversity after day 20. In particular, we identified a shift from competitive to environmental drivers of microbial community from days 12 to 20 creating a window of opportunity whereby Campylobacter can appear. Campylobacter was identified at day 16 which was 1 day after the most substantial changes in metabolic profiles observed. In addition, microbial variation over time is most likely influenced by the diet of the chickens whereby significant shifts in OTU abundances and beta dispersion of samples often corresponded with changes in feed. This study is unique in comparison to the most recent studies as neither sampling was sporadic nor Campylobacter was artificially introduced, thus the experiments were performed in a natural setting. We believe that our findings can be useful for future intervention strategies and help reduce the burden of Campylobacter within the food chain.

A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling

BACKGROUND

Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples.

RESULTS

MetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples.

CONCLUSIONS

Learning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.

The metagenomic data life-cycle: standards and best practices

Metagenomics data analyses from independent studies can only be compared if the analysis workflows are described in a harmonized way. In this overview, we have mapped the landscape of data standards available for the description of essential steps in metagenomics: (i) material sampling, (ii) material sequencing, (iii) data analysis, and (iv) data archiving and publishing. Taking examples from marine research, we summarize essential variables used to describe material sampling processes and sequencing procedures in a metagenomics experiment. These aspects of metagenomics dataset generation have been to some extent addressed by the scientific community, but greater awareness and adoption is still needed. We emphasize the lack of standards relating to reporting how metagenomics datasets are analysed and how the metagenomics data analysis outputs should be archived and published. We propose best practice as a foundation for a community standard to enable reproducibility and better sharing of metagenomics datasets, leading ultimately to greater metagenomics data reuse and repurposing.

Nitrosospira sp. Govern Nitrous Oxide Emissions in a Tropical Soil Amended With Residues of Bioenergy Crop

Organic vinasse, a residue produced during bioethanol production, increases nitrous oxide (N₂O) emissions when applied with inorganic nitrogen (N) fertilizer in soil. The present study investigated the role of the ammonia-oxidizing bacteria (AOB) community on the N₂O emissions in soils amended with organic vinasse (CV: concentrated and V: non-concentrated) plus inorganic N fertilizer. Soil samples and N₂O emissions were evaluated at 11, 19, and 45 days after fertilizer application, and the bacterial and archaea gene (amoA) encoding the ammonia monooxygenase enzyme, bacterial denitrifier (nirK, nirS, and nosZ) genes and total bacteria were quantified by real time PCR. We also employed a deep amoA amplicon sequencing approach to evaluate the effect of treatment on the community structure and diversity of the soil AOB community. Both vinasse types applied with inorganic N application increased the total N₂O emissions and the abundance of AOB. Nitrosospira sp. was the dominant AOB in the soil and was correlated with N₂O emissions. However, the diversity and the community structure of AOB did not change with vinasse and inorganic N fertilizer amendment. The results highlight the importance of residues and fertilizer management in sustainable agriculture and can be used as a reference and an input tool to determine good management practices for organic fertilization.

The active microbial community more accurately reflects the anaerobic digestion process: 16S rRNA (gene) sequencing as a predictive tool

BACKGROUND

Amplicon sequencing methods targeting the 16S rRNA gene have been used extensively to investigate microbial community composition and dynamics in anaerobic digestion. These methods successfully characterize amplicons but do not distinguish micro-organisms that are actually responsible for the process. In this research, the archaeal and bacterial community of 48 full-scale anaerobic digestion plants were evaluated on DNA (total community) and RNA (active community) level via 16S rRNA (gene) amplicon sequencing.

RESULTS

A significantly higher diversity on DNA compared with the RNA level was observed for archaea, but not for bacteria. Beta diversity analysis showed a significant difference in community composition between the DNA and RNA of both bacteria and archaea. This related with 25.5 and 42.3% of total OTUs for bacteria and archaea, respectively, that showed a significant difference in their DNA and RNA profiles. Similar operational parameters affected the bacterial and archaeal community, yet the differentiating effect between DNA and RNA was much stronger for archaea. Co-occurrence networks and functional prediction profiling confirmed the clear differentiation between DNA and RNA profiles.

CONCLUSIONS

In conclusion, a clear difference in active (RNA) and total (DNA) community profiles was observed, implying the need for a combined approach to estimate community stability in anaerobic digestion.

ROCker: accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores

Functional annotation of metagenomic and metatranscriptomic data sets relies on similarity searches based on e-value thresholds resulting in an unknown number of false positive and negative matches. To overcome these limitations, we introduce ROCker, aimed at identifying position-specific, most-discriminant thresholds in sliding windows along the sequence of a target protein, accounting for non-discriminative domains shared by unrelated proteins. ROCker employs the receiver operating characteristic (ROC) curve to minimize false discovery rate (FDR) and calculate the best thresholds based on how simulated shotgun metagenomic reads of known composition map onto well-curated reference protein sequences and thus, differs from HMM profiles and related methods. We showcase ROCker using ammonia monooxygenase (amoA) and nitrous oxide reductase (nosZ) genes, mediating oxidation of ammonia and the reduction of the potent greenhouse gas, N2O, to inert N2, respectively. ROCker typically showed 60-fold lower FDR when compared to the common practice of using fixed e-values. Previously uncounted ‘atypical’ nosZ genes were found to be two times more abundant, on average, than their typical counterparts in most soil metagenomes and the abundance of bacterial amoA was quantified against the highly-related particulate methane monooxygenase (pmoA). Therefore, ROCker can reliably detect and quantify target genes in short-read metagenomes.

Sediment microbial taxonomic and functional diversity in a natural salinity gradient challenge Remane's "species minimum" concept

Several models have been developed for the description of diversity in estuaries and other brackish habitats, with the most recognized being Remane’s Artenminimum (“species minimum”) concept. It was developed for the Baltic Sea, one of the world’s largest semi-enclosed brackish water body with a unique permanent salinity gradient, and it argues that taxonomic diversity of macrobenthic organisms is lowest within the horohalinicum (5 to 8 psu). The aim of the present study was to investigate the relationship between salinity and sediment microbial diversity at a freshwater-marine transect in Amvrakikos Gulf (Ionian Sea, Western Greece) and assess whether species composition and community function follow a generalized concept such as Remane’s. DNA was extracted from sediment samples from six stations along the aforementioned transect and sequenced for the 16S rRNA gene using high-throughput sequencing. The metabolic functions of the OTUs were predicted and the most abundant metabolic pathways were extracted. Key abiotic variables, i.e., salinity, temperature, chlorophyll-a and oxygen concentration etc., were measured and their relation with diversity and functional patterns was explored. Microbial communities were found to differ in the three habitats examined (river, lagoon and sea) with certain taxonomic groups being more abundant in the freshwater and less in the marine environment, and vice versa. Salinity was the environmental factor with the highest correlation to the microbial community pattern, while oxygen concentration was highly correlated to the metabolic functional pattern. The total number of OTUs showed a negative relationship with increasing salinity, thus the sediment microbial OTUs in this study area do not follow Remane’s concept.

Bacterial community analysis in upflow multilayer anaerobic reactor treating high-solids organic wastes

A novel anaerobic digestion configuration, the upflow multi-layer anaerobic reactor (UMAR), was developed to treat high-solids organic wastes. The UMAR was hypothesized to form multi-layer along depth due to the upflow plug flow; use of a recirculation system and a rotating distributor and baffles aimed to assist treating high-solids influent. The chemical oxygen demand (COD) removal efficiency and methane (CH₄ ) production rate were 89% and 2.10 L CH₄ /L/d, respectively, at the peak influent COD concentration (110.4 g/L) and organic loading rate (7.5 g COD/L/d). The 454 pyrosequencing results clearly indicated heterogeneous distribution of bacterial communities at different vertical locations (upper, middle, and bottom) of the UMAR. Firmicutes was the dominant (>70%) phylum at the middle and bottom parts, while Deltaproteobacteria and Chloroflexi were only found in the upper part. Potential functions of the bacteria were discussed to speculate on their roles in the anaerobic performance of the UMAR system. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1226-1234, 2017.

Statin therapy causes gut dysbiosis in mice through a PXR-dependent mechanism

BACKGROUND

Statins are a class of therapeutics used to regulate serum cholesterol and reduce the risk of heart disease. Although statins are highly effective in removing cholesterol from the blood, their consumption has been linked to potential adverse effects in some individuals. The most common events associated with statin intolerance are myopathy and increased risk of developing type 2 diabetes mellitus. However, the pathological mechanism through which statins cause these adverse effects is not well understood.

RESULTS

Using a murine model, we describe for the first time profound changes in the microbial composition of the gut following statin treatment. This remodelling affected the diversity and metabolic profile of the gut microbiota and was associated with reduced production of butyrate. Statins altered both the size and composition of the bile acid pool in the intestine, tentatively explaining the observed gut dysbiosis. As also observed in patients, statin-treated mice trended towards increased fasting blood glucose levels and weight gain compared to controls. Statin treatment affected the hepatic expression of genes involved in lipid and glucose metabolism. Using gene knockout mice, we demonstrated that the observed effects were mediated through pregnane X receptor (PXR).

CONCLUSION

This study demonstrates that statin therapy drives a profound remodelling of the gut microbiota, hepatic gene deregulation and metabolic alterations in mice through a PXR-dependent mechanism. Since the demonstrated importance of the intestinal microbial community in host health, this work provides new perspectives to help prevent the statin-associated unintended metabolic effects.

Fast and accurate phylogeny reconstruction using filtered spaced-word matches

Motivation

Word-based or ‘alignment-free’ algorithms are increasingly used for phylogeny reconstruction and genome comparison, since they are much faster than traditional approaches that are based on full sequence alignments. Existing alignment-free programs, however, are less accurate than alignment-based methods.

Results

We propose Filtered Spaced Word Matches (FSWM), a fast alignment-free approach to estimate phylogenetic distances between large genomic sequences. For a pre-defined binary pattern of match and don’t-care positions, FSWM rapidly identifies spaced word-matches between input sequences, i.e. gap-free local alignments with matching nucleotides at the match positions and with mismatches allowed at the don’t-care positions. We then estimate the number of nucleotide substitutions per site by considering the nucleotides aligned at the don’t-care positions of the identified spaced-word matches. To reduce the noise from spurious random matches, we use a filtering procedure where we discard all spaced-word matches for which the overall similarity between the aligned segments is below a threshold. We show that our approach can accurately estimate substitution frequencies even for distantly related sequences that cannot be analyzed with existing alignment-free methods; phylogenetic trees constructed with FSWM distances are of high quality. A program run on a pair of eukaryotic genomes of a few hundred Mb each takes a few minutes.

Availability and Implementation

The program source code for FSWM including a documentation, as well as the software that we used to generate artificial genome sequences are freely available at http://fswm.gobics.de/

Supplementary information

Supplementary data are available at Bioinformatics online.

Complementary Metaproteomic Approaches to Assess the Bacterioplankton Response toward a Phytoplankton Spring Bloom in the Southern North Sea

Annually recurring phytoplankton spring blooms are characteristic of temperate coastal shelf seas. During these blooms, environmental conditions, including nutrient availability, differ considerably from non-bloom conditions, affecting the entire ecosystem including the bacterioplankton. Accordingly, the emerging ecological niches during bloom transition are occupied by different bacterial populations, with Roseobacter RCA cluster and SAR92 clade members exhibiting high metabolic activity during bloom events. In this study, the functional response of the ambient bacterial community toward a Phaeocystis globosa bloom in the southern North Sea was studied using metaproteomic approaches. In contrast to other metaproteomic studies of marine bacterial communities, this is the first study comparing two different cell lysis and protein preparation methods [using trifluoroethanol (TFE) and in-solution digest as well as bead beating and SDS-based solubilization and in-gel digest (BB GeLC)]. In addition, two different mass spectrometric techniques (ESI-iontrap MS and MALDI-TOF MS) were used for peptide analysis. A total of 585 different proteins were identified, 296 of which were only detected using the TFE and 191 by the BB GeLC method, demonstrating the complementarity of these sample preparation methods. Furthermore, 158 proteins of the TFE cell lysis samples were exclusively detected by ESI-iontrap MS while 105 were only detected using MALDI-TOF MS, underpinning the value of using two different ionization and mass analysis methods. Notably, 12% of the detected proteins represent predicted integral membrane proteins, including the difficult to detect rhodopsin, indicating a considerable coverage of membrane proteins by this approach. This comprehensive approach verified previous metaproteomic studies of marine bacterioplankton, e.g., detection of many transport-related proteins (17% of the detected proteins). In addition, new insights into e.g., carbon and nitrogen metabolism were obtained. For instance, the C1 pathway was more prominent outside the bloom and different strategies for glucose metabolism seem to be applied under the studied conditions. Furthermore, a higher number of nitrogen assimilating proteins were present under non-bloom conditions, reflecting the competition for this limited macro nutrient under oligotrophic conditions. Overall, application of different sample preparation techniques as well as MS methods facilitated a more holistic picture of the marine bacterioplankton response to changing environmental conditions.