How to normalize metatranscriptomic count data for differential expression analysis

Taxanorm: a novel taxa-specific normalization approach for microbiome data

BACKGROUND

In high-throughput sequencing studies, sequencing depth, which quantifies the total number of reads, varies across samples. Unequal sequencing depth can obscure true biological signals of interest and prevent direct comparisons between samples. To remove variability due to differential sequencing depth, taxa counts are usually normalized before downstream analysis. However, most existing normalization methods scale counts using size factors that are sample specific but not taxa specific, which can result in over- or under-correction for some taxa.

RESULTS

We developed TaxaNorm, a novel normalization method based on a zero-inflated negative binomial model. This method assumes the effects of sequencing depth on mean and dispersion vary across taxa. Incorporating the zero-inflation part can better capture the nature of microbiome data. We also propose two corresponding diagnosis tests on the varying sequencing depth effect for validation. We find that TaxaNorm achieves comparable performance to existing methods in most simulation scenarios in downstream analysis and reaches a higher power for some cases. Specifically, it balances power and false discovery control well. When applying the method in a real dataset, TaxaNorm has improved performance when correcting technical bias.

CONCLUSION

TaxaNorm both sample- and taxon- specific bias by introducing an appropriate regression framework in the microbiome data, which aids in data interpretation and visualization. The 'TaxaNorm' R package is freely available through the CRAN repository https://CRAN.R-project.org/package=TaxaNorm and the source code can be downloaded at https://github.com/wangziyue57/TaxaNorm .

Structural and functional analysis of the active cow rumen's microbial community provides a catalogue of genes and microbes participating in the deconstruction of cardoon biomass

BACKGROUND

Ruminal microbial communities enriched on lignocellulosic biomass have shown considerable promise for the discovery of microorganisms and enzymes involved in digesting cell wall compounds, a key bottleneck in the development of second-generation biofuels and bioproducts, enabling a circular bioeconomy. Cardoon (Cynara cardunculus) is a promising inedible energy crop for current and future cellulosic biorefineries and the emerging bioenergy and bioproducts industries. The rumen microbiome can be considered an anaerobic "bioreactor", where the resident microbiota carry out the depolymerization and hydrolysis of plant cell wall polysaccharides (PCWPs) through the catalytic action of fibrolytic enzymes. In this context, the rumen microbiota represents a potential source of microbes and fibrolytic enzymes suitable for biofuel production from feedstocks. In this study, metatranscriptomic and 16S rRNA sequencing were used to profile the microbiome and to investigate the genetic features within the microbial community adherent to the fiber fractions of the rumen content and to the residue of cardoon biomass incubated in the rumen of cannulated cows.

RESULTS

The metatranscriptome of the cardoon and rumen fibre-adherent microbial communities were dissected in their functional and taxonomic components. From a functional point of view, transcripts involved in the methanogenesis from CO2 and H2, and from methanol were over-represented in the cardoon-adherent microbial community and were affiliated with the Methanobrevibacter and Methanosphaera of the Euryarchaeota phylum. Transcripts encoding glycoside hydrolases (GHs), carbohydrate-binding modules (CBMs), carbohydrate esterases (CEs), polysaccharide lyases (PLs), and glycoside transferases (GTs) accounted for 1.5% (6,957) of the total RNA coding transcripts and were taxonomically affiliated to major rumen fibrolytic microbes, such as Oscillospiraceae, Fibrobacteraceae, Neocallimastigaceae, Prevotellaceae, Lachnospiraceae, and Treponemataceae. The comparison of the expression profile between cardoon and rumen fiber-adherent microbial communities highlighted that specific fibrolytic enzymes were potentially responsible for the breakdown of cardoon PCWPs, which was driven by specific taxa, mainly Ruminococcus, Treponema, and Neocallimastigaceae.

CONCLUSIONS

Analysis of 16S rRNA and metatranscriptomic sequencing data revealed that the cow rumen microbiome harbors a repertoire of new enzymes capable of degrading PCWPs. Our results demonstrate the feasibility of using metatranscriptomics of enriched microbial RNA as a potential approach for accelerating the discovery of novel cellulolytic enzymes that could be harnessed for biotechnology. This research contributes a relevant perspective towards degrading cellulosic biomass and providing an economical route to the production of advanced biofuels and high-value bioproducts.

Heterogeneous lineage-specific arginine deiminase expression within dental microbiome species

Arginine catabolism by the bacterial arginine deiminase system (ADS) has anticariogenic properties through the production of ammonia, which modulates the pH of the oral environment. Given the potential protective capacity of the ADS pathway, the exploitation of ADS-competent oral microbes through pre- or probiotic applications is a promising therapeutic target to prevent tooth decay. To date, most investigations of the ADS in the oral cavity and its relation to caries have focused on indirect measures of activity or on specific bacterial groups, yet the pervasiveness and rate of expression of the ADS operon in diverse mixed microbial communities in oral health and disease remain an open question. Here, we use a multivariate approach, combining ultra-deep metatranscriptomic sequencing with paired metataxonomic and in vitro citrulline quantification to characterize the microbial community and ADS operon expression in healthy and late-stage cavitated teeth. While ADS activity is higher in healthy teeth, we identify multiple bacterial lineages with upregulated ADS activity on cavitated teeth that are distinct from those found on healthy teeth using both reference-based mapping and de novo assembly methods. Our dual metataxonomic and metatranscriptomic approach demonstrates the importance of species abundance for gene expression data interpretation and that patterns of differential expression can be skewed by low-abundance groups. Finally, we identify several potential candidate probiotic bacterial lineages within species that may be useful therapeutic targets for the prevention of tooth decay and propose that the development of a strain-specific, mixed-microbial probiotic may be a beneficial approach given the heterogeneity of taxa identified here across health groups.

IMPORTANCE

Tooth decay is the most common preventable chronic disease, affecting more than two billion people globally. The development of caries on teeth is primarily a consequence of acid production by cariogenic bacteria that inhabit the plaque microbiome. Other bacterial strains in the oral cavity may suppress or prevent tooth decay by producing ammonia as a byproduct of the arginine deiminase metabolic pathway, increasing the pH of the plaque biofilm. While the benefits of arginine metabolism on oral health have been extensively documented in specific bacterial groups, the prevalence and consistency of arginine deiminase system (ADS) activity among oral bacteria in a community context remain an open question. In the current study, we use a multi-omics approach to document the pervasiveness of the expression of the ADS operon in both health and disease to better understand the conditions in which ADS activity may prevent tooth decay.

Prevotella copri and microbiota members mediate the beneficial effects of a therapeutic food for malnutrition

Microbiota-directed complementary food (MDCF) formulations have been designed to repair the gut communities of malnourished children. A randomized controlled trial demonstrated that one formulation, MDCF-2, improved weight gain in malnourished Bangladeshi children compared to a more calorically dense standard nutritional intervention. Metagenome-assembled genomes from study participants revealed a correlation between ponderal growth and expression of MDCF-2 glycan utilization pathways by Prevotella copri strains. To test this correlation, here we use gnotobiotic mice colonized with defined consortia of age- and ponderal growth-associated gut bacterial strains, with or without P. copri isolates closely matching the metagenome-assembled genomes. Combining gut metagenomics and metatranscriptomics with host single-nucleus RNA sequencing and gut metabolomic analyses, we identify a key role of P. copri in metabolizing MDCF-2 glycans and uncover its interactions with other microbes including Bifidobacterium infantis. P. copri-containing consortia mediated weight gain and modulated energy metabolism within intestinal epithelial cells. Our results reveal structure-function relationships between MDCF-2 and members of the gut microbiota of malnourished children with potential implications for future therapies.

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.

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

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.

Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities

Multi-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptomes. At its core, we designed a method for condition-specific metabolic modeling of microbial communities through the integration of metatranscriptomic data. Using this approach, we explored the behavior of anaerobic digestion consortia driven by hydrogen availability and human gut microbiota dysbiosis associated with Crohn's disease, identifying condition-dependent amino acid requirements in archaeal species and a reduced short-chain fatty acid exchange network associated with disease, respectively. Our approach can be applied to complex microbial communities, allowing a mechanistic contextualization of multi-omics data on a metagenome scale.

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.

A Framework for Comparison and Assessment of Synthetic RNA-Seq Data

The ever-growing number of methods for the generation of synthetic bulk and single cell RNA-seq data have multiple and diverse applications. They are often aimed at benchmarking bioinformatics algorithms for purposes such as sample classification, differential expression analysis, correlation and network studies and the optimization of data integration and normalization techniques. Here, we propose a general framework to compare synthetically generated RNA-seq data and select a data-generating tool that is suitable for a set of specific study goals. As there are multiple methods for synthetic RNA-seq data generation, researchers can use the proposed framework to make an informed choice of an RNA-seq data simulation algorithm and software that are best suited for their specific scientific questions of interest.

An interolog-based barley interactome as an integration framework for immune signaling

The barley MLA nucleotide-binding leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many fungal diseases, including powdery mildew, stem-, and stripe rust. We used interolog inference to construct a barley protein interactome (Hordeum vulgare predicted interactome, HvInt) comprising 66,133 edges and 7,181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared with the experimentally validated Arabidopsis interactome of 11,253 proteins and 73,960 interactions, verifying that the 2 networks share scale-free properties, including a power-law distribution and small-world network. Then, by successive layering of defense-specific "omics" datasets, HvInt was customized to model cellular response to powdery mildew infection. Integration of HvInt with expression quantitative trait loci (eQTL) enabled us to infer disease modules and responses associated with fungal penetration and haustorial development. Next, using HvInt and infection-time-course RNA sequencing of immune signaling mutants, we assembled resistant and susceptible subnetworks. The resulting differentially coexpressed (resistant - susceptible) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to mildew resistance locus a (Mla) through trans eQTL associations. Lastly, we anchored HvInt with new and previously identified interactors of the MLA coiled coli + nucleotide-binding domains and extended these to additional MLA alleles, orthologs, and NLR outgroups to predict receptor localization and conservation of signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity.

Disruption of barley immunity to powdery mildew by an in-frame Lys-Leu deletion in the essential protein SGT1

Barley (Hordeum vulgare L.) Mla (Mildew resistance locus a) and its nucleotide-binding, leucine-rich-repeat receptor (NLR) orthologs protect many cereal crops from diseases caused by fungal pathogens. However, large segments of the Mla pathway and its mechanisms remain unknown. To further characterize the molecular interactions required for NLR-based immunity, we used fast-neutron mutagenesis to screen for plants compromised in MLA-mediated response to the powdery mildew fungus, Blumeria graminis f. sp. hordei. One variant, m11526, contained a novel mutation, designated rar3 (required for Mla6 resistance3), that abolishes race-specific resistance conditioned by the Mla6, Mla7, and Mla12 alleles, but does not compromise immunity mediated by Mla1, Mla9, Mla10, and Mla13. This is analogous to, but unique from, the differential requirement of Mla alleles for the co-chaperone Rar1 (required for Mla12 resistance1). We used bulked-segregant-exome capture and fine mapping to delineate the causal mutation to an in-frame Lys-Leu deletion within the SGS domain of SGT1 (Suppressor of G-two allele of Skp1, Sgt1ΔKL308-309), the structural region that interacts with MLA proteins. In nature, mutations to Sgt1 usually cause lethal phenotypes, but here we pinpoint a unique modification that delineates its requirement for some disease resistances, while unaffecting others as well as normal cell processes. Moreover, the data indicate that the requirement of SGT1 for resistance signaling by NLRs can be delimited to single sites on the protein. Further study could distinguish the regions by which pathogen effectors and host proteins interact with SGT1, facilitating precise editing of effector incompatible variants.

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.

Insights into rumen microbial biosynthetic gene cluster diversity through genome-resolved metagenomics

Ruminants are critical to global food security as they transform lignocellulosic biomass into high-quality protein products. The rumen microbes ferment feed to provide necessary energy and nutrients for the ruminant host. However, we still lack insight into the metabolic processes encoded by most rumen microbial populations. In this study, we implemented metagenomic binning approaches to recover 2,809 microbial genomes from cattle, sheep, moose, deer, and bison. By clustering genomes based on average nucleotide identity, we demonstrate approximately one-third of the metagenome-assembled genomes (MAGs) to represent species not present in current reference databases and rumen microbial genome collections. Combining these MAGs with other rumen genomic datasets permitted a phylogenomic characterization of the biosynthetic gene clusters (BGCs) from 8,160 rumen microbial genomes, including the identification of 195 lanthipeptides and 5,346 diverse gene clusters for nonribosomal peptide biosynthesis. A subset of Prevotella and Selenomonas BGCs had higher expression in steers with lower feed efficiency. Moreover, the microdiversity of BGCs was fairly constant across types of BGCs and cattle breeds. The reconstructed genomes expand the genomic representation of rumen microbial lineages, improve the annotation of multi-omics data, and link microbial populations to the production of secondary metabolites that may constitute a source of natural products for manipulating rumen fermentation.

Anderson and Fernando use metagenomic binning approaches to reconstruct 2,809 microbial metagenome-assembled genomes from ruminants, and perform phylogenomic analyses on the biosynthetic gene clusters from over 8,000 total rumen microbial genomes. These genomes provide insight into the relationship between microbial populations and the production of secondary metabolites that may be important for manipulating rumen fermentation.

Systems biology of acidophile biofilms for efficient metal extraction

Society’s demand for metals is ever increasing while stocks of high-grade minerals are being depleted. Biomining, for example of chalcopyrite for copper recovery, is a more sustainable biotechnological process that exploits the capacity of acidophilic microbes to catalyze solid metal sulfide dissolution to soluble metal sulfates. A key early stage in biomining is cell attachment and biofilm formation on the mineral surface that results in elevated mineral oxidation rates. Industrial biomining of chalcopyrite is typically carried out in large scale heaps that suffer from the downsides of slow and poor metal recoveries. In an effort to mitigate these drawbacks, this study investigated planktonic and biofilm cells of acidophilic (optimal growth pH < 3) biomining bacteria. RNA and proteins were extracted, and high throughput “omics” performed from a total of 80 biomining experiments. In addition, micrographs of biofilm formation on the chalcopyrite mineral surface over time were generated from eight separate experiments. The dataset generated in this project will be of great use to microbiologists, biotechnologists, and industrial researchers.

Measurement(s)	biofilm formation • Proteome • protein expression data • RNA • transcriptome
Technology Type(s)	fluorescence microscopy • mass spectrometry • RNA sequencing
Sample Characteristic - Organism	Leptospirillum ferriphilum • Sulfobacillus thermosulfidooxidans • Acidithiobacillus caldus
Sample Characteristic - Environment	mine

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12173637

Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

BACKGROUND

Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications.

METHODS

We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation.

RESULTS

The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context.

CONCLUSIONS

The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.

Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis

PURPOSE

Diabetic retinopathy (DR) especially proliferative diabetic retinopathy (PDR) is a serious eye disease. We aimed to identify key pathway and hub genes associated with PDR by analyzing the expression of retinal fibrovascular tissue in PDR patients.

METHODS

First raw data were downloaded from the Gene Expression Omnibus database. Median normalization was subsequently applied to preprocess. Differentially expressed genes (DEGs) analyzed with the Limma package. Weighted correlation network analysis (WGCNA) was utilized to build the co-expression network for all genes. Then, we compared the DEGs and modules filtered out by WGCNA. A protein-protein interaction network based on the STRING web site and the Cytoscape software was constructed by the overlapping DEGs. Next, the Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. Finally, we used the Comparative Toxicogenomics Database to identify some important pathways and hub genes tightly related to PDR.

RESULTS

Functional enrichment analysis showed that the pathway of cytokine-cytokine receptor interaction was significantly related to PDR eight hub genes which were associated with pathway including tumor necrosis factor (TNF), tumor necrosis factor receptor superfamily member 12A (TNFRSF12A), C-C chemokine 20 (CCL20), chemokine (C-X-C motif) ligand 2 (CXCL2), oncostatin M (OSM) interleukin 10 (IL10), interleukin 15 (IL 15), and interleukin 1B (IL1B).

CONCLUSIONS

We identified one pathway and eight hub genes, which were associated with PDR. The pathway provided references that will advance the understanding of mechanisms of PDR. Moreover, the hub genes may serve as therapeutic targets for precise diagnosis and treatment of PDR in the future.

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.

Determining Microbial Roles in Ecosystem Function: Redefining Microbial Food Webs and Transcending Kingdom Barriers

Microorganisms can have a profound and varying effect on the chemical character of environments and, thereby, ecological health. Their capacity to consume or transform contaminants leads to contrasting outcomes, such as the dissipation of nutrient pollution via denitrification, the breakdown of spilled oil, or eutrophication via primary producer overgrowth.

Microorganisms can have a profound and varying effect on the chemical character of environments and, thereby, ecological health. Their capacity to consume or transform contaminants leads to contrasting outcomes, such as the dissipation of nutrient pollution via denitrification, the breakdown of spilled oil, or eutrophication via primary producer overgrowth. Recovering the genomes of organisms directly from the environment is useful to gain insights into resource usage, interspecies collaborations (producers and consumers), and trait acquisition. Microbial data can also be considered alongside the broader biological character of an environment through the co-recovery of eukaryotic DNA. The contributions of individual microorganisms (bacteria, archaea, and protists) to snapshots of ecosystem processes can be determined by integrating genomics with functional methods. This combined approach enables a detailed understanding of how microbial communities drive biogeochemical cycles, and although currently limited by scale, key attributes can be effectively extrapolated with lower-resolution methods to determine wider ecological relevance.

Meta-transcriptomics reveals a diverse antibiotic resistance gene pool in avian microbiomes

BACKGROUND

Antibiotic resistance is rendering common bacterial infections untreatable. Wildlife can incorporate and disperse antibiotic-resistant bacteria in the environment, such as water systems, which in turn serve as reservoirs of resistance genes for human pathogens. Anthropogenic activity may contribute to the spread of bacterial resistance cycling through natural environments, including through the release of human waste, as sewage treatment only partially removes antibiotic-resistant bacteria. However, empirical data supporting these effects are currently limited. Here we used bulk RNA-sequencing (meta-transcriptomics) to assess the diversity and expression levels of functionally viable resistance genes in the gut microbiome of birds with aquatic habits in diverse locations.

RESULTS

We found antibiotic resistance genes in birds from all localities, from penguins in Antarctica to ducks in a wastewater treatment plant in Australia. Comparative analysis revealed that birds feeding at the wastewater treatment plant carried the greatest resistance gene burden, including genes typically associated with multidrug resistance plasmids as the aac(6)-Ib-cr gene. Differences in resistance gene burden also reflected aspects of bird ecology, taxonomy, and microbial function. Notably, ducks, which feed by dabbling, carried a higher abundance and diversity of resistance genes than turnstones, avocets, and penguins, which usually prey on more pristine waters.

CONCLUSIONS

These transcriptome data suggest that human waste, even if it undergoes treatment, might contribute to the spread of antibiotic resistance genes to the wild. Differences in microbiome functioning across different bird lineages may also play a role in the antibiotic resistance burden carried by wild birds. In summary, we reveal the complex factors explaining the distribution of resistance genes and their exchange routes between humans and wildlife, and show that meta-transcriptomics is a valuable tool to access functional resistance genes in whole microbial communities.

Weak Iron Oxidation by Sulfobacillus thermosulfidooxidans Maintains a Favorable Redox Potential for Chalcopyrite Bioleaching

Bioleaching is an emerging technology, describing the microbially assisted dissolution of sulfidic ores that provides a more environmentally friendly alternative to many traditional metal extraction methods, such as roasting or smelting. Industrial interest is steadily increasing and today, circa 15-20% of the world's copper production can be traced back to this method. However, bioleaching of the world's most abundant copper mineral chalcopyrite suffers from low dissolution rates, often attributed to passivating layers, which need to be overcome to use this technology to its full potential. To prevent these passivating layers from forming, leaching needs to occur at a low oxidation/reduction potential (ORP), but chemical redox control in bioleaching heaps is difficult and costly. As an alternative, selected weak iron-oxidizers could be employed that are incapable of scavenging exceedingly low concentrations of iron and therefore, raise the ORP just above the onset of bioleaching, but not high enough to allow for the occurrence of passivation. In this study, we report that microbial iron oxidation by Sulfobacillus thermosulfidooxidans meets these specifications. Chalcopyrite concentrate bioleaching experiments with S. thermosulfidooxidans as the sole iron oxidizer exhibited significantly lower redox potentials and higher release of copper compared to communities containing the strong iron oxidizer Leptospirillum ferriphilum. Transcriptomic response to single and co-culture of these two iron oxidizers was studied and revealed a greatly decreased number of mRNA transcripts ascribed to iron oxidation in S. thermosulfidooxidans when cultured in the presence of L. ferriphilum. This allowed for the identification of genes potentially responsible for S. thermosulfidooxidans' weaker iron oxidation to be studied in the future, as well as underlined the need for new mechanisms to control the microbial population in bioleaching heaps.

Automated Microscopic Analysis of Metal Sulfide Colonization by Acidophilic Microorganisms

Industrial biomining processes are currently focused on metal sulfides and their dissolution, which is catalyzed by acidophilic iron(II)- and/or sulfur-oxidizing microorganisms. Cell attachment on metal sulfides is important for this process. Biofilm formation is necessary for seeding and persistence of the active microbial community in industrial biomining heaps and tank reactors, and it enhances metal release. In this study, we used a method for direct quantification of the mineral-attached cell population on pyrite or chalcopyrite particles in bioleaching experiments by coupling high-throughput, automated epifluorescence microscopy imaging of mineral particles with algorithms for image analysis and cell quantification, thus avoiding human bias in cell counting. The method was validated by quantifying cell attachment on pyrite and chalcopyrite surfaces with axenic cultures of Acidithiobacillus caldus, Leptospirillum ferriphilum, and Sulfobacillus thermosulfidooxidans. The method confirmed the high affinity of L. ferriphilum cells to colonize pyrite and chalcopyrite surfaces and indicated that biofilm dispersal occurs in mature pyrite batch cultures of this species. Deep neural networks were also applied to analyze biofilms of different microbial consortia. Recent analysis of the L. ferriphilum genome revealed the presence of a diffusible soluble factor (DSF) family quorum sensing system. The respective signal compounds are known as biofilm dispersal agents. Biofilm dispersal was confirmed to occur in batch cultures of L. ferriphilum and S. thermosulfidooxidans upon the addition of DSF family signal compounds.IMPORTANCE The presented method for the assessment of mineral colonization allows accurate relative comparisons of the microbial colonization of metal sulfide concentrate particles in a time-resolved manner. Quantitative assessment of the mineral colonization development is important for the compilation of improved mathematical models for metal sulfide dissolution. In addition, deep-learning algorithms proved that axenic or mixed cultures of the three species exhibited characteristic biofilm patterns and predicted the biofilm species composition. The method may be extended to the assessment of microbial colonization on other solid particles and may serve in the optimization of bioleaching processes in laboratory scale experiments with industrially relevant metal sulfide concentrates. Furthermore, the method was used to demonstrate that DSF quorum sensing signals directly influence colonization and dissolution of metal sulfides by mineral-oxidizing bacteria, such as L. ferriphilum and S. thermosulfidooxidans.

Gene expression of indoor fungal communities under damp building conditions: Implications for human health

Dampness and visible mold growth in homes are associated with negative human health outcomes, but causal relationships between fungal exposure and health are not well established. The purpose of this study was to determine whether dampness in buildings impacts fungal community gene expression and how, in turn, gene expression may modulate human health impacts. A metatranscriptomic study was performed on house dust fungal communities to investigate the expression of genes and metabolic processes in chamber experiments at water activity levels of 0.5, 0.85, and 1.0. Fungi at water activities as low as 0.5 were metabolically active, focusing their transcriptional resources on primary processes essential for cell maintenance. Metabolic complexity increased with water activity where communities at 1.0 displayed more diverse secondary metabolic processes. Greater gene expression at increasing water activity has important implications for human health: Fungal communities at 1.0 a_w upregulated a greater number of allergen-, mycotoxin-, and pathogenicity-encoding genes versus communities at 0.85 and 0.5 a_w . In damp buildings, fungi may display increases in secondary metabolic processes with the potential for greater per-cell production of allergens, toxins, and pathogenicity. Assessments in wet versus dry buildings that do not account for this elevated health impact may not accurately reflect exposure.

Novel soil bacteria possess diverse genes for secondary metabolite biosynthesis

In soil ecosystems, microorganisms produce diverse secondary metabolites such as antibiotics, antifungals and siderophores that mediate communication, competition and interactions with other organisms and the environment^1,2. Most known antibiotics are derived from a few culturable microbial taxa ³ , and the biosynthetic potential of the vast majority of bacteria in soil has rarely been investigated ⁴ . Here we reconstruct hundreds of near-complete genomes from grassland soil metagenomes and identify microorganisms from previously understudied phyla that encode diverse polyketide and nonribosomal peptide biosynthetic gene clusters that are divergent from well-studied clusters. These biosynthetic loci are encoded by newly identified members of the Acidobacteria, Verrucomicobia and Gemmatimonadetes, and the candidate phylum Rokubacteria. Bacteria from these groups are highly abundant in soils^5-7, but have not previously been genomically linked to secondary metabolite production with confidence. In particular, large numbers of biosynthetic genes were characterized in newly identified members of the Acidobacteria, which is the most abundant bacterial phylum across soil biomes ⁵ . We identify two acidobacterial genomes from divergent lineages, each of which encodes an unusually large repertoire of biosynthetic genes with up to fifteen large polyketide and nonribosomal peptide biosynthetic loci per genome. To track gene expression of genes encoding polyketide synthases and nonribosomal peptide synthetases in the soil ecosystem that we studied, we sampled 120 time points in a microcosm manipulation experiment and, using metatranscriptomics, found that gene clusters were differentially co-expressed in response to environmental perturbations. Transcriptional co-expression networks for specific organisms associated biosynthetic genes with two-component systems, transcriptional activation, putative antimicrobial resistance and iron regulation, linking metabolite biosynthesis to processes of environmental sensing and ecological competition. We conclude that the biosynthetic potential of abundant and phylogenetically diverse soil microorganisms has previously been underestimated. These organisms may represent a source of natural products that can address needs for new antibiotics and other pharmaceutical compounds.