SAMSA2: a standalone metatranscriptome analysis pipeline

Biotic interactions between benthic infauna and aerobic methanotrophs mediate methane fluxes from coastal sediments

Coastal ecosystems dominate oceanic methane (CH4) emissions. However, there is limited knowledge about how biotic interactions between infauna and aerobic methanotrophs (i.e. CH4 oxidizing bacteria) drive the spatial-temporal dynamics of these emissions. Here, we investigated the role of meio- and macrofauna in mediating CH4 sediment-water fluxes and aerobic methanotrophic activity that can oxidize significant portions of CH4. We show that macrofauna increases CH4 fluxes by enhancing vertical solute transport through bioturbation, but this effect is somewhat offset by high meiofauna abundance. The increase in CH4 flux reduces CH4 pore-water availability, resulting in lower abundance and activity of aerobic methanotrophs, an effect that counterbalances the potential stimulation of these bacteria by higher oxygen flux to the sediment via bioturbation. These findings indicate that a larger than previously thought portion of CH4 emissions from coastal ecosystems is due to faunal activity and multiple complex interactions with methanotrophs.

AnimalMetaOmics: a multi-omics data resources for exploring animal microbial genomes and microbiomes

The Animal Meta-omics landscape database (AnimalMetaOmics, https://yanglab.hzau.edu.cn/animalmetaomics#/) is a comprehensive and freely available resource that includes metagenomic, metatranscriptomic, and metaproteomic data from various non-human animal species and provides abundant information on animal microbiomes, including cluster analysis of microbial cognate genes, functional gene annotations, active microbiota composition, gene expression abundance, and microbial protein identification. In this work, 55 898 microbial genomes were annotated from 581 animal species, including 42 924 bacterial genomes, 12 336 virus genomes, 496 archaea genomes and 142 fungi genomes. Moreover, 321 metatranscriptomic datasets were analyzed from 31 animal species and 326 metaproteomic datasets from four animal species, as well as the pan-genomic dynamics and compositional characteristics of 679 bacterial species and 13 archaea species from animal hosts. Researchers can efficiently access and acquire the information of cross-host microbiota through a user-friendly interface, such as species, genomes, activity levels, expressed protein sequences and functions, and pan-genome composition. These valuable resources provide an important reference for better exploring the classification, functional diversity, biological process diversity and functional genes of animal microbiota.

A metatranscriptomics strategy for efficient characterization of the microbiome in human tissues with low microbial biomass

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.

Defining microbial community functions in chronic human infection with metatranscriptomics

IMPORTANCE

The microbial diversity in polymicrobial infections (PMIs) allows for community members to establish interactions with one another, which can result in enhanced disease outcomes such as increased antibiotic tolerance and chronicity. Chronic PMIs result in large burdens on health systems, as they affect a significant proportion of the population and are expensive and difficult to treat. However, investigations into physiology of microbial communities in actual human infection sites are lacking. Here, we highlight that the predominant functions in chronic PMIs differ, and anaerobes, often described as bystanders, may be significant in the progression of chronic infections. Determining the community structure and functions in PMIs is a critical step toward understanding the molecular mechanisms that increase the virulence potential of the microbial community in these environments.

Biofilm formation is correlated with low nutrient and simulated microgravity conditions in a Burkholderia isolate from the ISS water processor assembly

The International Space Station (ISS) Water Processor Assembly (WPA) experiences intermittent dormancy in the WPA wastewater tank during water recycling events which promotes biofilm formation within the system. In this work we aimed to gain a deeper understanding of the impact of nutrient limitation on bacterial growth and biofilm formation under microgravity in support of biofilm mitigation efforts in exploration water recovery systems. A representative species of bacteria that is commonly cultured from the ISS WPA was cultured in an WPA influent water ersatz formulation tailored for microbiological studies. An isolate of Burkholderia contaminans was cultured under a simulated microgravity (SμG) treatment in a vertically rotating high-aspect rotating vessel (HARV) to create the low shear modeled microgravity (LSMMG) environment on a rotating wall vessel (RWV), with a rotating control (R) in the horizontal plane at the predetermined optimal rotation per minute (rpm) speed of 20. Over the course of the growth curve, the bacterial culture in ersatz media was harvested for bacterial counts, and transcriptomic and nutrient content analyses. The cultures under SμG treatment showed a transcriptomic signature indicative of nutrient stress and biofilm formation as compared to the R control treatment. Further analysis of the WPA ersatz over the course of the growth curve suggests that the essential nutrients of the media were consumed faster in the early stages of growth for the SμG treatment and thus approached a nutrient limited growth condition earlier than in the R control culture. The observed limited nutrient response may serve as one element to explain a moderate enhancement of adherent biofilm formation in the SμG treatment after 24 h. While nutrients levels can be modulated, one implication of this investigation is that biofilm mitigation in the ISS environment could benefit from methods such as mixing or the maintenance of minimum flow within a dormant water system in order to force convection and offset the response of microbes to the secondary effects of microgravity.

Long-Term Pollution Does Not Inhibit Denitrification and DNRA by Adapted Benthic Microbial Communities

Denitrification in sediments is a key microbial process that removes excess fixed nitrogen, while dissimilatory nitrate reduction to ammonium (DNRA) converts nitrate to ammonium. Although microorganisms are responsible for essential nitrogen (N) cycling, it is not yet fully understood how these microbially mediated processes respond to toxic hydrophobic organic compounds (HOCs) and metals. In this study, we sampled long-term polluted sediment from the outer harbor of Oskarshamn (Baltic Sea), measured denitrification and DNRA rates, and analyzed taxonomic structure and N-cycling genes of microbial communities using metagenomics. Results showed that denitrification and DNRA rates were within the range of a national reference site and other unpolluted sites in the Baltic Sea, indicating that long-term pollution did not significantly affect these processes. Furthermore, our results indicate an adaptation to metal pollution by the N-cycling microbial community. These findings suggest that denitrification and DNRA rates are affected more by eutrophication and organic enrichment than by historic pollution of metals and organic contaminants.

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.

Insights into bacterial community metatranscriptome and metabolome in river water influenced by palm oil mill effluent final discharge

AIMS

This study aimed to investigate the effect of palm oil mill effluent (POME) final discharge on the active bacterial composition, gene expression, and metabolite profiles in the receiving rivers to establish a foundation for identifying potential biomarkers for monitoring POME pollution in rivers.

METHODS AND RESULTS

The POME final discharge, upstream (unpolluted by POME), and downstream (effluent receiving point) parts of the rivers from two sites were physicochemically characterized. The taxonomic and gene profiles were then evaluated using de novo metatranscriptomics, while the metabolites were detected using qualitative metabolomics. A similar bacterial community structure in the POME final discharge samples from both sites was recorded, but their composition varied. Redundancy analysis showed that several families, particularly Comamonadaceae and Burkholderiaceae [Pr(>F) = 0.028], were positively correlated with biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). The results also showed significant enrichment of genes regulating various metabolisms in the POME-receiving rivers, with methane, carbon fixation pathway, and amino acids among the predominant metabolisms identified (FDR < 0.05, PostFC > 4, and PPDE > 0.95). This was further validated through qualitative metabolomics, whereby amino acids were detected as the predominant metabolites.

CONCLUSIONS

The results suggest that genes regulating amino acid metabolism have significant potential for developing effective biomonitoring and bioremediation strategies in river water influenced by POME final discharge, fostering a sustainable palm oil industry.

Physiological and evolutionary contexts of a new symbiotic species from the nitrogen-recycling gut community of turtle ants

While genome sequencing has expanded our knowledge of symbiosis, role assignment within multi-species microbiomes remains challenging due to genomic redundancy and the uncertainties of in vivo impacts. We address such questions, here, for a specialized nitrogen (N) recycling microbiome of turtle ants, describing a new genus and species of gut symbiont-Ischyrobacter davidsoniae (Betaproteobacteria: Burkholderiales: Alcaligenaceae)-and its in vivo physiological context. A re-analysis of amplicon sequencing data, with precisely assigned Ischyrobacter reads, revealed a seemingly ubiquitous distribution across the turtle ant genus Cephalotes, suggesting ≥50 million years since domestication. Through new genome sequencing, we also show that divergent I. davidsoniae lineages are conserved in their uricolytic and urea-generating capacities. With phylogenetically refined definitions of Ischyrobacter and separately domesticated Burkholderiales symbionts, our FISH microscopy revealed a distinct niche for I. davidsoniae, with dense populations at the anterior ileum. Being positioned at the site of host N-waste delivery, in vivo metatranscriptomics and metabolomics further implicate I. davidsoniae within a symbiont-autonomous N-recycling pathway. While encoding much of this pathway, I. davidsoniae expressed only a subset of the requisite steps in mature adult workers, including the penultimate step deriving urea from allantoate. The remaining steps were expressed by other specialized gut symbionts. Collectively, this assemblage converts inosine, made from midgut symbionts, into urea and ammonia in the hindgut. With urea supporting host amino acid budgets and cuticle synthesis, and with the ancient nature of other active N-recyclers discovered here, I. davidsoniae emerges as a central player in a conserved and impactful, multipartite symbiosis.

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data

Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e. type I error, false discovery rate and sensitivity) of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease (early childhood caries, ECC), whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

Current concepts, advances, and challenges in deciphering the human microbiota with metatranscriptomics

Metatranscriptomics refers to the analysis of the collective microbial transcriptome of a sample. Its increased utilization for the characterization of human-associated microbial communities has enabled the discovery of many disease-state related microbial activities. Here, we review the principles of metatranscriptomics-based analysis of human-associated microbial samples. We describe strengths and weaknesses of popular sample preparation, sequencing, and bioinformatics approaches and summarize strategies for their use. We then discuss how human-associated microbial communities have recently been examined and how their characterization may change. We conclude that metatranscriptomics insights into human microbiotas under health and disease have not only expanded our knowledge on human health, but also opened avenues for rational antimicrobial drug use and disease management.

Fiber supplementation protects from antibiotic-induced gut microbiome dysbiosis by modulating gut redox potential

Antibiotic-induced gut dysbiosis (AID) is a frequent and serious side effect of antibiotic use and mitigating this dysbiosis is a critical therapeutic target. We propose that the host diet can modulate the chemical environment of the gut resulting in changes to the structure and function of the microbiome during antibiotic treatment. Gut dysbiosis is typically characterized by increases in aerobic respiratory bacterial metabolism, redox potential, and abundance of Proteobacteria. In this study, we explore dietary fiber supplements as potential modulators of the chemical environment in the gut to reduce this pattern of dysbiosis. Using defined-diets and whole-genome sequencing of female murine microbiomes during diet modulation and antibiotic treatment, we find that fiber prebiotics significantly reduced the impact of antibiotic treatment on microbiome composition and function. We observe reduced abundance of aerobic bacteria as well as metabolic pathways associated with oxidative metabolism. These metatranscriptomic results are corroborated by chemical measurements of eH and pH suggesting that fiber dampens the dysbiotic effects of antibiotics. This work indicates that fiber may act as a potential therapeutic for AID by modulating bacterial metabolism in the gut to prevent an increase in redox potential and protect commensal microbes during antibiotic treatment.

Human oral mucosa and oral microbiome interactions following supragingival plaque reconstitution in healthy volunteers: a diet-controlled balanced design proof-of-concept model to investigate oral pathologies

Changes in the oral microbiome may contribute to oral pathologies, especially in patients undergoing cancer therapy. Interactions between oral microbiome and oral mucosa may exacerbate inflammation. We determined whether probiotic-controlled plaque formation could impact proximal oral mucosa gene expression profiles in healthy volunteers. A 3-weeks balanced sample collection design from healthy volunteers (HVs) was implemented. At Week-1 plaques samples and labial mucosa brush biopsies were obtained from HVs in the morning (N = 4) and/or in the afternoon (N = 4), and groups were flipped at Week-3. A fruit yogurt and tea diet were given 2-4hrs before sample collection. mRNA gene expression analysis was completed using RNA-Seq and DESeq2. Bacterial taxa relative abundance was determined by 16S HOMINGS. Bacterial diversity changes and metabolic pathway enrichment were determined using PRIMERv7 and LEfSe programs. Alpha- and beta-diversities did not differ morning (AM) vs. afternoon (PM). The most affected KEGG pathway was Toll-like receptor signaling in oral mucosa. Eighteen human genes and nine bacterial genes were differentially expressed in plaque samples. Increased activity for 'caries-free' health-associated calcifying Corynebacterium matruchotii and reduced activity for Aggregatibacter aphrophilus, an opportunistic pathogen, were observed. Microbial diversity was not altered after 8 hours plaque formation in healthy individuals as opposed to gene expression.

Short-Term Dietary Intervention with Whole Oats Protects from Antibiotic-Induced Dysbiosis

Antibiotic-induced gut microbiome dysbiosis (AID) is known to be influenced by host dietary composition. However, how and when diet modulates gut dysbiosis remains poorly characterized. Thus, here, we utilize a multi-omics approach to characterize how a diet supplemented with oats, a rich source of microbiota-accessible carbohydrates, or dextrose impacts amoxicillin-induced changes to gut microbiome structure and transcriptional activity. We demonstrate that oat administration during amoxicillin challenge provides greater protection from AID than the always oats or recovery oats diet groups. In particular, the group in which oats were provided at the time of antibiotic exposure induced the greatest protection against AID while the other oat diets saw greater effects after amoxicillin challenge. The oat diets likewise reduced amoxicillin-driven elimination of Firmicutes compared to the dextrose diet. Functionally, gut communities fed dextrose were carbohydrate starved and favored respiratory metabolism and consequent metabolic stress management while oat-fed communities shifted their transcriptomic profile and emphasized antibiotic stress management. The metabolic trends were exemplified when assessing transcriptional activity of the following two common gut commensal bacteria: Akkermansia muciniphila and Bacteroides thetaiotaomicron. These findings demonstrate that while host diet is important in shaping how antibiotics effect the gut microbiome composition and function, diet timing may play an even greater role in dietary intervention-based therapeutics. IMPORTANCE We utilize a multi-omics approach to demonstrate that diets supplemented with oats, a rich source of microbiota-accessible carbohydrates, are able to confer protection against antibiotic-induced dysbiosis (AID). Our findings affirm that not only is host diet important in shaping antibiotics effects on gut microbiome composition and function but also that the timing of these diets may play an even greater role in managing AID. This work provides a nuanced perspective on dietary intervention against AID and may be informative on preventing AID during routine antibiotic treatment.

Diversity of virulence and antibiotic resistance genes expressed in Class A biosolids and biosolids-amended soil as revealed by metatranscriptomic analysis

Class A biosolids is a treated sewage sludge, commonly applied to agricultural fields, home lawns/gardens, golf courses, forests, and remediation sites around the world. This practice is of public and agricultural concern due to the possibility that biosolids contain antibiotic-resistant bacteria and fungal pathogens that could persist for extended periods in soil. This possibility was determined by metatranscriptomic analysis of virulence, antibiotic resistance, and plasmid conjugation genes, a Class A biosolids, organically managed soil, and biosolids-amended soil under realistic conditions. Biosolids harbored numerous transcriptionally active pathogens, antibiotic resistance genes, and conjugative genes that annotated mostly to Gram-positive pathogens of animal hosts. Biosolids amendment to soil significantly increased the expression of virulence genes by numerous pathogens and antibiotic-resistant genes that were strongly associated with biosolids. Biosolids amendment also significantly increased the expression of virulence genes by native soil fungal pathogens of plant hosts, which suggests higher risks of crop damage by soil fungal pathogens in biosolids-amended soil. Although results are likely to be different in other soils, biosolids, and microbial growth conditions, they provide a more holistic, accurate view of potential health risks associated with biosolids and biosolids-amended soils than has been achievable with more selective cultivation and PCR-based techniques.

MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities

BACKGROUND

Whole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze, and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles.

RESULTS

Here, we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single-end or paired-end reads) and processes them through a tiered series of filtering, assembly, and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state-of-the-art pipelines and demonstrate improved performance and functionality.

CONCLUSIONS

MetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes, is made freely available at https://github.com/ParkinsonLab/MetaPro . The software is freely available under the GNU general public license v3. Video Abstract.

Defining Microbial Community Functions in Chronic Human Infection with Metatranscriptomics

Chronic polymicrobial infections (cPMIs) harbor complex bacterial communities with diverse metabolic capacities, leading to competitive and cooperative interactions. Although the microbes present in cPMIs have been established through culture-dependent and-independent methods, the key functions that drive different cPMIs and the metabolic activities of these complex communities remain unknown. To address this knowledge gap, we analyzed 102 published metatranscriptomes collected from cystic fibrosis sputum (CF) and chronic wound infections (CW) to identify key bacterial members and functions in cPMIs. Community composition analysis identified a high prevalence of pathogens, particularly Staphylococcus and Pseudomonas, and anaerobic members of the microbiota, including Porphyromonas, Anaerococcus, and Prevotella. Functional profiling with HUMANn3 and SAMSA2 revealed that while functions involved in bacterial competition, oxidative stress response, and virulence were conserved across both chronic infection types, >40% of the functions were differentially expressed (padj < 0.05, fold-change >2). Higher expression of antibiotic resistance and biofilm functions were observed in CF, while tissue destructive enzymes and oxidative stress response functions were highly expressed in CW samples. Of note, strict anaerobes had negative correlations with traditional pathogens in both CW (P = -0.43) and CF (P = -0.27) samples and they significantly contributed to the expression of these functions. Additionally, we show microbial communities have unique expression patterns and distinct organisms fulfill the expression of key functions in each site, indicating the infection environment strongly influences bacterial physiology and that community structure influences function. Collectively, our findings indicate that community composition and function should guide treatment strategies for cPMIs.

Mapping human pathogens in wastewater using a metatranscriptomic approach

The monitoring of cities' wastewaters for the detection of potentially pathogenic viruses and bacteria has been considered a priority during the COVID-19 pandemic to monitor public health in urban environments. The methodological approaches frequently used for this purpose include deoxyribonucleic acid (DNA)/Ribonucleic acid (RNA) isolation followed by quantitative polymerase chain reaction (qPCR) and reverse transcription (RT)‒qPCR targeting pathogenic genes. More recently, the application of metatranscriptomic has opened opportunities to develop broad pathogenic monitoring workflows covering the entire pathogenic community within the sample. Nevertheless, the high amount of data generated in the process requires an appropriate analysis to detect the pathogenic community from the entire dataset. Here, an implementation of a bioinformatic workflow was developed to produce a map of the detected pathogenic bacteria and viruses in wastewater samples by analysing metatranscriptomic data. The main objectives of this work was the development of a computational methodology that can accurately detect both human pathogenic virus and bacteria in wastewater samples. This workflow can be easily reproducible with open-source software and uses efficient computational resources. The results showed that the used algorithms can predict potential human pathogens presence in the tested samples and that active forms of both bacteria and virus can be identified. By comparing the computational method implemented in this study to other state-of-the-art workflows, the implementation analysis was faster, while providing higher accuracy and sensitivity. Considering these results, the processes and methods to monitor wastewater for potential human pathogens can become faster and more accurate. The proposed workflow is available at https://github.com/waterpt/watermonitor and can be implemented in currently wastewater monitoring programs to ascertain the presence of potential human pathogenic species.

Metatranscriptomic analysis of the gut microbiome of black soldier fly larvae reared on lignocellulose-rich fiber diets unveils key lignocellulolytic enzymes

Recently, interest in the black soldier fly larvae (BSFL) gut microbiome has received increased attention primarily due to their role in waste bioconversion. However, there is a lack of information on the positive effect on the activities of the gut microbiomes and enzymes (CAZyme families) acting on lignocellulose. In this study, BSFL were subjected to lignocellulose-rich diets: chicken feed (CF), chicken manure (CM), brewers' spent grain (BSG), and water hyacinth (WH). The mRNA libraries were prepared, and RNA-Sequencing was conducted using the PCR-cDNA approach through the MinION sequencing platform. Our results demonstrated that BSFL reared on BSG and WH had the highest abundance of Bacteroides and Dysgonomonas. The presence of GH51 and GH43_16 enzyme families in the gut of BSFL with both α-L-arabinofuranosidases and exo-alpha-L-arabinofuranosidase 2 were common in the BSFL reared on the highly lignocellulosic WH and BSG diets. Gene clusters that encode hemicellulolytic arabinofuranosidases in the CAZy family GH51 were also identified. These findings provide novel insight into the shift of gut microbiomes and the potential role of BSFL in the bioconversion of various highly lignocellulosic diets to fermentable sugars for subsequent value-added products (bioethanol). Further research on the role of these enzymes to improve existing technologies and their biotechnological applications is crucial.

Soil microbial gene expression in an agricultural ecosystem varies with time and neonicotinoid seed treatments

Neonicotinoids, a class of systemic insecticides, have been widely used for decades against various insect pests. Previous studies have reported non-target effects of neonicotinoids on some beneficial macro- and micro-organisms. Considering the crucial role the soil microbiota plays in sustaining soil fertility, it is critical to understand how neonicotinoid exposure affects the microbial taxonomic composition and gene expression. However, most studies to date have evaluated soil microbial taxonomic compositions or assessed microbial functions based on soil biochemical analysis. In this study, we have applied a metatranscriptomic approach to quantify the variability in soil microbial gene expression in a 2 year soybean/corn crop rotation in Quebec, Canada. We identified weak and temporally inconsistent effects of neonicotinoid application on soil microbial gene expression, as well as a strong temporal variation in soil microbial gene expression among months and years. Neonicotinoid seed treatment altered the expression of a small number of microbial genes, including genes associated with heat shock proteins, regulatory functions, metabolic processes and DNA repair. These changes in gene expression varied during the growing season and between years. Overall, the composition of soil microbial expressed genes seems to be more resilient and less affected by neonicotinoid application than soil microbial taxonomic composition. Our study is among the first to document the effects of neonicotinoid seed treatment on microbial gene expression and highlights the strong temporal variability of soil microbial gene expression and its responses to neonicotinoid seed treatments.

Trophic level and proteobacteria abundance drive antibiotic resistance levels in fish from coastal New England

BACKGROUND

The natural marine environment represents a vast reservoir of antimicrobial resistant bacteria. The wildlife that inhabits this environment plays an important role as the host to these bacteria and in the dissemination of resistance. The relationship between host diet, phylogeny, and trophic level and the microbiome/resistome in marine fish is not fully understood. To further explore this relationship, we utilize shotgun metagenomic sequencing to define the gastrointestinal tract microbiomes of seven different marine vertebrates collected in coastal New England waters.

RESULTS

We identify inter and intraspecies differences in the gut microbiota of these wild marine fish populations. Furthermore, we find an association between antibiotic resistance genes and host dietary guild, which suggests that higher trophic level organisms have a greater abundance of resistance genes. Additionally, we demonstrate that antibiotic resistance gene burden is positively correlated with Proteobacteria abundance in the microbiome. Lastly, we identify dietary signatures within the gut of these fish and find evidence of possible dietary selection for bacteria with specific carbohydrate utilization potential.

CONCLUSIONS

This work establishes a link between host lifestyle/dietary guild, and microbiome composition and the abundance of antibiotic resistance genes within the gastrointestinal tract of marine organisms. We expand the current understanding of marine organism-associated microbial communities and their role as reservoirs of antimicrobial resistance genes.

Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly

BACKGROUND

Diverse communities of microbial eukaryotes in the global ocean provide a variety of essential ecosystem services, from primary production and carbon flow through trophic transfer to cooperation via symbioses. Increasingly, these communities are being understood through the lens of omics tools, which enable high-throughput processing of diverse communities. Metatranscriptomics offers an understanding of near real-time gene expression in microbial eukaryotic communities, providing a window into community metabolic activity.

RESULTS

Here we present a workflow for eukaryotic metatranscriptome assembly, and validate the ability of the pipeline to recapitulate real and manufactured eukaryotic community-level expression data. We also include an open-source tool for simulating environmental metatranscriptomes for testing and validation purposes. We reanalyze previously published metatranscriptomic datasets using our metatranscriptome analysis approach.

CONCLUSION

We determined that a multi-assembler approach improves eukaryotic metatranscriptome assembly based on recapitulated taxonomic and functional annotations from an in-silico mock community. The systematic validation of metatranscriptome assembly and annotation methods provided here is a necessary step to assess the fidelity of our community composition measurements and functional content assignments from eukaryotic metatranscriptomes.

The Colorectal Cancer Gut Environment Regulates Activity of the Microbiome and Promotes the Multidrug Resistant Phenotype of ESKAPE and Other Pathogens

The human gut microbiota in colorectal cancer patients have a distinct population compared to heathy counterparts. However, the activity (gene expression) of this community has not been investigated.

The Colorectal Cancer Microbiota Alter Their Transcriptome To Adapt to the Acidity, Reactive Oxygen Species, and Metabolite Availability of Gut Microenvironments

The gut microbiome is implicated in the pathology of colorectal cancer (CRC). However, the mechanisms by which the microbiota actively contribute to disease onset and progression remain elusive. In this pilot study, we sequenced fecal metatranscriptomes of 10 non-CRC and 10 CRC patient gut microbiomes and conducted differential gene expression analyses to assess any changed functionality in disease. We report that oxidative stress responses were the dominant activity across cohorts, an overlooked protective housekeeping role of the human gut microbiome. However, expression of hydrogen peroxide and nitric oxide-scavenging genes was diminished and augmented, respectively, positing that these regulated microbial responses have implications for CRC pathology. CRC microbes enhanced expression of genes for host colonization, biofilm formation, genetic exchange, virulence determinants, antibiotic, and acid resistances. Moreover, microbes promoted transcription of genes involved in metabolism of several beneficial metabolites, suggesting their contribution to patient metabolite deficiencies previously solely attributed to tumor cells. We showed in vitro that expression of genes involved in amino acid-dependent acid resistance mechanisms of meta-gut Escherichia coli responded differently to acid, salt, and oxidative pressures under aerobic conditions. These responses were mostly dictated by the host health status of origin of the microbiota, suggesting their exposure to fundamentally different gut conditions. These findings for the first time highlight mechanisms by which the gut microbiota can either protect against or drive colorectal cancer and provide insights into the cancerous gut environment that drives functional characteristics of the microbiome. IMPORTANCE The human gut microbiota has the genetic potential to drive colorectal cancer onset and progression; however, the expression of this genetic potential during the disease has not been investigated. We found that microbial expression of genes that detoxify DNA-damaging reactive oxygen species, which drive colorectal cancer, is compromised in cancer. We observed a greater activation of expression of genes involved in virulence, host colonization, exchange of genetic material, metabolite utilization, defense against antibiotics, and environmental pressures. Culturing gut Escherichia coli of cancerous and noncancerous metamicrobiota revealed different regulatory responses of amino acid-dependent acid resistance mechanisms in a health-dependent manner under environmental acid, oxidative, and osmotic pressures. Here, for the first time, we demonstrate that the activity of microbial genomes is regulated by the health status of the gut in vivo and in vitro and provides new insights for shifts in microbial gene expression in colorectal cancer.

Over supplementation with vitamin B12 alters microbe-host interactions in the gut leading to accelerated Citrobacter rodentium colonization and pathogenesis in mice

BACKGROUND

Vitamin B12 supplements typically contain doses that far exceed the recommended daily amount, and high exposures are generally considered safe. Competitive and syntrophic interactions for B12 exist between microbes in the gut. Yet, to what extent excessive levels contribute to the activities of the gut microbiota remains unclear. The objective of this study was to evaluate the effect of B12 on microbial ecology using a B12 supplemented mouse model with Citrobacter rodentium, a mouse-specific pathogen. Mice were fed a standard chow diet and received either water or water supplemented with B12 (cyanocobalamin: ~120 μg/day), which equates to approximately 25 mg in humans. Infection severity was determined by body weight, pathogen load, and histopathologic scoring. Host biomarkers of inflammation were assessed in the colon before and after the pathogen challenge.

RESULTS

Cyanocobalamin supplementation enhanced pathogen colonization at day 1 (P < 0.05) and day 3 (P < 0.01) postinfection. The impact of B12 on gut microbial communities, although minor, was distinct and attributed to the changes in the Lachnospiraceae populations and reduced alpha diversity. Cyanocobalamin treatment disrupted the activity of the low-abundance community members of the gut microbiota. It enhanced the amount of interleukin-12 p40 subunit protein (IL12/23p40; P < 0.001) and interleukin-17a (IL-17A; P < 0.05) in the colon of naïve mice. This immune phenotype was microbe dependent, and the response varied based on the baseline microbiota. The cecal metatranscriptome revealed that excessive cyanocobalamin decreased the expression of glucose utilizing genes by C. rodentium, a metabolic attribute previously associated with pathogen virulence.

CONCLUSIONS

Oral vitamin B12 supplementation promoted C. rodentium colonization in mice by altering the activities of the Lachnospiraceae populations in the gut. A lower abundance of select Lachnospiraceae species correlated to higher p40 subunit levels, while the detection of Parasutterella exacerbated inflammatory markers in the colon of naïve mice. The B12-induced change in gut ecology enhanced the ability of C. rodentium colonization by impacting key microbe-host interactions that help with pathogen exclusion. This research provides insight into how B12 impacts the gut microbiota and highlights potential consequences of disrupting microbial B12 competition/sharing through over-supplementation. Video Abstract.

The hitchhikers' guide to RNA sequencing and functional analysis

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

TbasCO: trait-based comparative 'omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

Intraintestinal Analysis of the Functional Activity of Microbiomes and Its Application to the Common Marmoset Intestine

The intestinal microbiome is closely related to host health, and metatranscriptomic analysis can be used to assess the functional activity of microbiomes by quantifying microbial gene expression levels, helping elucidate the interactions between the microbiome and the environment. However, the functional changes in the microbiome along the host intestinal tract remain unknown, and previous analytical methods have limitations, such as potentially overlooking unknown genes due to dependence on existing databases. The objective of this study is to develop a computational pipeline combined with next-generation sequencing for spatial covariation analysis of the functional activity of microbiomes at multiple intestinal sites (biogeographic locations) within the same individual. This method reconstructs a reference metagenomic sequence across multiple intestinal sites and integrates the metagenome and metatranscriptome, allowing the gene expression levels of the microbiome, including unknown bacterial genes, to be compared among multiple sites. When this method was applied to metatranscriptomic analysis in the intestinal tract of common marmosets, a New World monkey, the reconstructed metagenome covered most of the expressed genes and revealed that the differences in microbial gene expression among the cecum, transverse colon, and feces were more dynamic and sensitive to environmental shifts than the abundances of the genes. In addition, metatranscriptomic profiling at three intestinal sites of the same individual enabled covariation analysis incorporating spatial relevance, accurately predicting the function of a total of 10,856 unknown genes. Our findings demonstrate that our proposed analytical method captures functional changes in microbiomes at the gene resolution level. IMPORTANCE We developed an analysis method that integrates metagenomes and metatranscriptomes from multiple intestinal sites to elucidate how microbial function varies along the intestinal tract. This method enables spatial covariation analysis of the functional activity of microbiomes and accurate identification of gene expression changes among intestinal sites, including changes in the expression of unknown bacterial genes. Moreover, we applied this method to the investigation of the common marmoset intestine, which is anatomically and pharmacologically similar to that of humans. Our findings indicate the expression pattern of the microbiome varies in response to changes in the internal environment along the intestinal tract, and this microbial change may affect the intestinal environment.

Dynamic metabolic interactions and trophic roles of human gut microbes identified using a minimal microbiome exhibiting ecological properties

Microbe–microbe interactions in the human gut are influenced by host-derived glycans and diet. The high complexity of the gut microbiome poses a major challenge for unraveling the metabolic interactions and trophic roles of key microbes. Synthetic minimal microbiomes provide a pragmatic approach to investigate their ecology including metabolic interactions. Here, we rationally designed a synthetic microbiome termed Mucin and Diet based Minimal Microbiome (MDb-MM) by taking into account known physiological features of 16 key bacteria. We combined 16S rRNA gene-based composition analysis, metabolite measurements and metatranscriptomics to investigate community dynamics, stability, inter-species metabolic interactions and their trophic roles. The 16 species co-existed in the in vitro gut ecosystems containing a mixture of complex substrates representing dietary fibers and mucin. The triplicate MDb-MM’s followed the Taylor’s power law and exhibited strikingly similar ecological and metabolic patterns. The MDb-MM exhibited resistance and resilience to temporal perturbations as evidenced by the abundance and metabolic end products. Microbe-specific temporal dynamics in transcriptional niche overlap and trophic interaction network explained the observed co-existence in a competitive minimal microbiome. Overall, the present study provides crucial insights into the co-existence, metabolic niches and trophic roles of key intestinal microbes in a highly dynamic and competitive in vitro ecosystem.

Microbial functional genes are driven by gradients in sediment stoichiometry, oxygen, and salinity across the Baltic benthic ecosystem

BACKGROUND

Microorganisms in the seafloor use a wide range of metabolic processes, which are coupled to the presence of functional genes within their genomes. Aquatic environments are heterogenous and often characterized by natural physiochemical gradients that structure these microbial communities potentially changing the diversity of functional genes and its associated metabolic processes. In this study, we investigated spatial variability and how environmental variables structure the diversity and composition of benthic functional genes and metabolic pathways across various fundamental environmental gradients. We analyzed metagenomic data from sediment samples, measured related abiotic data (e.g., salinity, oxygen and carbon content), covering 59 stations spanning 1,145 km across the Baltic Sea.

RESULTS

The composition of genes and microbial communities were mainly structured by salinity plus oxygen, and the carbon to nitrogen (C:N) ratio for specific metabolic pathways related to nutrient transport and carbon metabolism. Multivariate analyses indicated that the compositional change in functional genes was more prominent across environmental gradients compared to changes in microbial taxonomy even at genus level, and indicate functional diversity adaptation to local environments. Oxygen deficient areas (i.e., dead zones) were more different in gene composition when compared to oxic sediments.

CONCLUSIONS

This study highlights how benthic functional genes are structured over spatial distances and by environmental gradients and resource availability, and suggests that changes in, e.g., oxygenation, salinity, and carbon plus nitrogen content will influence functional metabolic pathways in benthic habitats. Video Abstract.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Inter-species Metabolic Interactions in an In-vitro Minimal Human Gut Microbiome of Core Bacteria

Knowledge of the functional roles and interspecies interactions are crucial for improving our understanding of the human intestinal microbiome in health and disease. However, the complexity of the human intestinal microbiome and technical challenges in investigating it pose major challenges. In this proof-of-concept study, we rationally designed, assembled and experimentally tested a synthetic Diet-based Minimal Microbiome (Db-MM) consisting of ten core intestinal bacterial species that together are capable of efficiently converting dietary fibres into short chain fatty acids (SCFAs). Despite their genomic potential for metabolic competition, all ten bacteria coexisted during growth on a mixture of dietary fibres, including pectin, inulin, xylan, cellobiose and starch. By integrated analyses of metabolite production, community composition and metatranscriptomics-based gene expression data, we identified interspecies metabolic interactions leading to production of key SCFAs such as butyrate and propionate. While public goods, such as sugars liberated from colonic fibres, are harvested by non-degraders, some species thrive by cross-feeding on energetically challenging substrates, including the butyrogenic conversion of acetate and lactate. Using a reductionist approach in an in-vitro system combined with functional measurements, our study provides key insights into the complex interspecies metabolic interactions between core intestinal bacterial species.

MTD: a unique pipeline for host and meta-transcriptome joint and integrative analyses of RNA-seq data

Ribonucleic acid (RNA)-seq data contain not only host transcriptomes but also nonhost information that comprises transcripts from active microbiota in the host cells. Therefore, joint and integrative analyses of both host and meta-transcriptome can reveal gene expression of the microbial community in a given sample as well as the correlative and interactive dynamics of the host response to the microbiome. However, there are no convenient tools that can systemically analyze host-microbiota interactions through simultaneously quantifying the host and meta-transcriptome in the same sample at the tissue and the single-cell level. This poses a challenge for interested researchers with limited expertise in bioinformatics. Here, we developed a software pipeline that can comprehensively and synergistically analyze and correlate the host and meta-transcriptome in a single sample using bulk and single-cell RNA-seq data. This pipeline, named meta-transcriptome detector (MTD), can extensively identify and quantify microbiome, including viruses, bacteria, protozoa, fungi, plasmids and vectors, in the host cells and correlate the microbiome with the host transcriptome. MTD is easy to install and run, involving only a few lines of simple commands. It offers researchers with unique genomics insights into host responses to microorganisms.

Transcriptional responses of Trichodesmium to natural inverse gradients of Fe and P availability

The filamentous diazotrophic cyanobacterium Trichodesmium is responsible for a significant fraction of marine di-nitrogen (N₂) fixation. Growth and distribution of Trichodesmium and other diazotrophs in the vast oligotrophic subtropical gyres is influenced by iron (Fe) and phosphorus (P) availability, while reciprocally influencing the biogeochemistry of these nutrients. Here we use observations across natural inverse gradients in Fe and P in the North Atlantic subtropical gyre (NASG) to demonstrate how Trichodesmium acclimates in situ to resource availability. Transcriptomic analysis identified progressive upregulation of known iron-stress biomarker genes with decreasing Fe availability, and progressive upregulation of genes involved in the acquisition of diverse P sources with decreasing P availability, while genes involved in N₂ fixation were upregulated at the intersection under moderate Fe and P availability. Enhanced N₂ fixation within the Fe and P co-stressed transition region was also associated with a distinct, consistent metabolic profile, including the expression of alternative photosynthetic pathways that potentially facilitate ATP generation required for N₂ fixation with reduced net oxygen production. The observed response of Trichodesmium to availability of both Fe and P supports suggestions that these biogeochemically significant organisms employ unique molecular, and thus physiological responses as adaptations to specifically exploit the Fe and P co-limited niche they construct.

MEDUSA: A Pipeline for Sensitive Taxonomic Classification and Flexible Functional Annotation of Metagenomic Shotgun Sequences

Metagenomic studies unravel details about the taxonomic composition and the functions performed by microbial communities. As a complete metagenomic analysis requires different tools for different purposes, the selection and setup of these tools remain challenging. Furthermore, the chosen toolset will affect the accuracy, the formatting, and the functional identifiers reported in the results, impacting the results interpretation and the biological answer obtained. Thus, we surveyed state-of-the-art tools available in the literature, created simulated datasets, and performed benchmarks to design a sensitive and flexible metagenomic analysis pipeline. Here we present MEDUSA, an efficient pipeline to conduct comprehensive metagenomic analyses. It performs preprocessing, assembly, alignment, taxonomic classification, and functional annotation on shotgun data, supporting user-built dictionaries to transfer annotations to any functional identifier. MEDUSA includes several tools, as fastp, Bowtie2, DIAMOND, Kaiju, MEGAHIT, and a novel tool implemented in Python to transfer annotations to BLAST/DIAMOND alignment results. These tools are installed via Conda, and the workflow is managed by Snakemake, easing the setup and execution. Compared with MEGAN 6 Community Edition, MEDUSA correctly identifies more species, especially the less abundant, and is more suited for functional analysis using Gene Ontology identifiers.

Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling

Background

With over 350,000 estimated deaths worldwide in 2018, prostate cancer (PCa) continues to be a major health concern and a significant cause of cancer-associated mortality among men. While cancer in general is considered a disease of the human genome, there is a growing body of evidence suggesting that changes to the healthy microbiota could play a vital role in cancer development, progression, and/or treatment outcome.

Methods

Using a metatranscriptomic approach, we annotated the microbial reads obtained from total RNA sequencing of 106 prostate tissue samples from 94 PCa patients (discovery cohort). We investigated microbial dysbiosis associated with PCa by systematically comparing the microbiomes between benign and malignant tissue samples, between less vs. more-aggressive PCa, and between patients who had biochemical recurrence as opposed to those who did not. We further performed differential gene expression and cell type enrichment analysis to explore the host transcriptomic and cellular responses to selected microbial genera. A public dataset (GSE115414) of total RNA sequencing reads from 24 prostate tissue samples (8 benign and 16 malignant) served as the validation cohort.

Results

We observed decreased species diversity and significant under-representation of Staphylococcus saprophyticus and Vibrio parahaemolyticus, as well as significant over-abundance of Shewanella in malignant as compared to benign prostate tissue samples in both the discovery (p < 0.01) and validation (p < 0.05) cohorts. In addition, we identified Microbacterium species (p < 0.01) to be significantly over-abundant in pathologically advanced T3 tumors compared to T2 in the discovery cohort. Malignant samples having high vs. low Shewanella counts were associated with downregulated Toll-like receptor signaling pathways and decreased enrichment of dendritic cells. Malignant samples having low vs. high V. parahaemolyticus counts were enriched for olfactory transduction and drug metabolism pathways. Finally, malignant samples were enriched for M1 and M2 macrophages as compared to benign tissue samples.

Conclusions

The results from this exploratory study support the existence of an important biological link between the prostate microbiota and PCa development/progression. Our results highlight Shewanella, V. parahaemolyticus, and Microbacterium sp. as interesting candidates for further investigation of their association with PCa.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01011-3.

Dysbiotic microbes and how to find them: a review of microbiome profiling in prostate cancer

The role of the microbiota in human health and disease is well established, including its effects on several cancer types. However, the role of microbial dysbiosis in prostate cancer development, progression, and response to treatment is less well understood. This knowledge gap could perhaps be implicated in the lack of better risk stratification and prognostic tools that incorporate risk factors such as bacterial infections and inflammatory signatures. With over a decade’s research investigating associations between microbiome and prostate carcinogenesis, we are ever closer to finding the crucial biological link between the two. Yet, definitive answers remain elusive, calling for continued research into this field. In this review, we outline the three frequently used NGS based analysis methodologies that are used for microbiome profiling, thereby serving as a quick guide for future microbiome research. We next provide a detailed overview of the current knowledge of the role of the human microbiome in prostate cancer development, progression, and treatment response. Finally, we describe proposed mechanisms of host-microbe interactions that could lead to prostate cancer development, progression or treatment response.

Role of Probiotics in the Management of COVID-19: A Computational Perspective

Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2.

Streptozotocin-induced hyperglycemia alters the cecal metabolome and exacerbates antibiotic-induced dysbiosis

It is well established in the microbiome field that antibiotic (ATB) use and metabolic disease both impact the structure and function of the gut microbiome. But how host and microbial metabolism interacts with ATB susceptibility to affect the resulting dysbiosis remains poorly understood. In a streptozotocin-induced model of hyperglycemia (HG), we use a combined metagenomic, metatranscriptomic, and metabolomic approach to profile changes in microbiome taxonomic composition, transcriptional activity, and metabolite abundance both pre- and post-ATB challenge. We find that HG impacts both microbiome structure and metabolism, ultimately increasing susceptibility to amoxicillin. HG exacerbates drug-induced dysbiosis and increases both phosphotransferase system activity and energy catabolism compared to controls. Finally, HG and ATB co-treatment increases pathogen susceptibility and reduces survival in a Salmonella enterica infection model. Our data demonstrate that induced HG is sufficient to modify the cecal metabolite pool, worsen the severity of ATB dysbiosis, and decrease colonization resistance.

DiTing: A Pipeline to Infer and Compare Biogeochemical Pathways From Metagenomic and Metatranscriptomic Data

Metagenomics and metatranscriptomics are powerful methods to uncover key micro-organisms and processes driving biogeochemical cycling in natural ecosystems. Databases dedicated to depicting biogeochemical pathways (for example, metabolism of dimethylsulfoniopropionate (DMSP), which is an abundant organosulfur compound) from metagenomic/metatranscriptomic data are rarely seen. Additionally, a recognized normalization model to estimate the relative abundance and environmental importance of pathways from metagenomic and metatranscriptomic data has not been organized to date. These limitations impact the ability to accurately relate key microbial-driven biogeochemical processes to differences in environmental conditions. Thus, an easy-to-use, specialized tool that infers and visually compares the potential for biogeochemical processes, including DMSP cycling, is urgently required. To solve these issues, we developed DiTing, a tool wrapper to infer and compare biogeochemical pathways among a set of given metagenomic or metatranscriptomic reads in one step, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and a manually created DMSP cycling gene database. Accurate and specific formulae for over 100 pathways were developed to calculate their relative abundance. Output reports detail the relative abundance of biogeochemical pathways in both text and graphical format. DiTing was applied to simulated metagenomic data and resulted in consistent genetic features of simulated benchmark genomic data. Subsequently, when applied to natural metagenomic and metatranscriptomic data from hydrothermal vents and the Tara Ocean project, the functional profiles predicted by DiTing were correlated with environmental condition changes. DiTing can now be confidently applied to wider metagenomic and metatranscriptomic datasets, and it is available at https://github.com/xuechunxu/DiTing.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Metatranscriptomics for the Human Microbiome and Microbial Community Functional Profiling

Shotgun metatranscriptomics (MTX) is an increasingly practical way to survey microbial community gene function and regulation at scale. This review begins by summarizing the motivations for community transcriptomics and the history of the field. We then explore the principles, best practices, and challenges of contemporary MTX workflows: beginning with laboratory methods for isolation and sequencing of community RNA, followed by informatics methods for quantifying RNA features, and finally statistical methods for detecting differential expression in a community context. In thesecond half of the review, we survey important biological findings from the MTX literature, drawing examples from the human microbiome, other (nonhuman) host-associated microbiomes, and the environment. Across these examples, MTX methods prove invaluable for probing microbe-microbe and host-microbe interactions, the dynamics of energy harvest and chemical cycling, and responses to environmental stresses. We conclude with a review of open challenges in the MTX field, including making assays and analyses more robust, accessible, and adaptable to new technologies; deciphering roles for millions of uncharacterized microbial transcripts; and solving applied problems such as biomarker discovery and development of microbial therapeutics.

Statistical approaches for differential expression analysis in metatranscriptomics

Motivation

Metatranscriptomics (MTX) has become an increasingly practical way to profile the functional activity of microbial communities in situ. However, MTX remains underutilized due to experimental and computational limitations. The latter are complicated by non-independent changes in both RNA transcript levels and their underlying genomic DNA copies (as microbes simultaneously change their overall abundance in the population and regulate individual transcripts), genetic plasticity (as whole loci are frequently gained and lost in microbial lineages) and measurement compositionality and zero-inflation. Here, we present a systematic evaluation of and recommendations for differential expression (DE) analysis in MTX.

Results

We designed and assessed six statistical models for DE discovery in MTX that incorporate different combinations of DNA and RNA normalization and assumptions about the underlying changes of gene copies or species abundance within communities. We evaluated these models on multiple simulated and real multi-omic datasets. Models adjusting transcripts relative to their encoding gene copies as a covariate were significantly more accurate in identifying DE from MTX in both simulated and real datasets. Moreover, we show that when paired DNA measurements (metagenomic data) are not available, models normalizing MTX measurements within-species while also adjusting for total-species RNA balance sensitivity, specificity and interpretability of DE detection, as does filtering likely technical zeros. The efficiency and accuracy of these models pave the way for more effective MTX-based DE discovery in microbial communities.

Availability and implementation

The analysis code and synthetic datasets used in this evaluation are available online at http://huttenhower.sph.harvard.edu/mtx2021.

Supplementary information

Supplementary data are available at Bioinformatics online.

The relationship between the gut microbiome and host gene expression: a review

Despite the growing knowledge surrounding host-microbiome interactions, we are just beginning to understand how the gut microbiome influences-and is influenced by-host gene expression. Here, we review recent literature that intersects these two fields, summarizing themes across studies. Work in model organisms, human biopsies, and cell culture demonstrate that the gut microbiome is an important regulator of several host pathways relevant for disease, including immune development and energy metabolism, and vice versa. The gut microbiome remodels host chromatin, causes differential splicing, alters the epigenetic landscape, and directly interrupts host signaling cascades. Emerging techniques like single-cell RNA sequencing and organoid generation have the potential to refine our understanding of the relationship between the gut microbiome and host gene expression in the future. By intersecting microbiome and host gene expression, we gain a window into the physiological processes important for fostering the extensive cross-kingdom interactions and ultimately our health.

Advances in Microbiome Detection Technologies and Application in Antirheumatic Drug Design

Rheumatic diseases are a kind of chronic inflammatory and autoimmune disease affecting the connection or supporting structures of the human body, such as the most common diseases Ankylosing spondylitis (AS), gout and Systemic lupus erythematosus (SLE). Although the precise etiology and pathogenesis of the different types of rheumatic diseases remain mostly unknown, it is now commonly believed that these diseases are attributed to some complex interactions between genetics and environmental factors, especially the gut microbiome. Altered microbiome showed clinical improvement in disease symptoms and partially restored to normality after prescribing disease-modifying antirheumatic drugs (DMARDs) or other treatment strategies. Recent advances in next-generation sequencing-based microbial profiling technology, especially metagenomics, have identified alteration of the composition and function of the gut microbiota in patients. Clinical and experimental data suggest that dysbiosis may play a pivotal role in the pathogenesis of these diseases. In this paper, we provide a brief review of the advances in the microbial profiling technology and up-to-date resources for accurate taxonomic assignment of metagenomic reads, which is a key step for metagenomics studies. In addition, we review the altered gut microbiota signatures that have been reported so far across various studies, upon which diagnostics classification models can be constructed, and the drug-induced regulation of the host microbiota can be used to control disease progression and symptoms.

A protocol combining breath testing and ex vivo fermentations to study the human gut microbiome

This protocol describes the application of breath testing and ex vivo fermentations to study the association between breath methane and the composition and functionality of the gut microbiome. The protocol provides a useful systems biology approach for studying the gut microbiome in humans, which combines standardized methods in human breath testing and fecal sampling. The model described is accessible and easy to repeat, but its relative simplicity means that it can deviate from human physiological conditions.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN)

Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: https://github.com/RVanDamme/MUFFIN.