Microbiome Studies Research Articles

Gut microbiome dynamics and predictive value in hospitalized COVID-19 patients: a comparative analysis of shallow and deep shotgun sequencing.

The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative results of the reverse transcription polymerase chain reaction in the respiratory tract. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings. Our study demonstrated the diverse effects of various combinations of clinical parameters, microbiome profiles, and patient metadata on the precision of outcome prognostication in patients. It indicates that microbiological data possesses greater reliability in forecasting patient outcomes when contrasted with clinical data or metadata. Furthermore, we established that shallow shotgun sequencing presents a viable and cost-effective diagnostic alternative to deep sequencing within clinical environments.

Caenorhabditis elegans as a Convenient Animal Model for Microbiome Studies.

Microbes constitute the most prevalent life form on Earth, yet their remarkable diversity remains mostly unrecognized. Microbial diversity in vertebrate models presents a significant challenge for investigating host-microbiome interactions. The model organism Caenorhabditis elegans has many advantages for delineating the effects of host genetics on microbial composition. In the wild, the C. elegans gut contains various microbial species, while in the laboratory it is usually a host for a single bacterial species. There is a potential host-microbe interaction between microbial metabolites, drugs, and C. elegans phenotypes. This mini-review aims to summarize the current understanding regarding the microbiome in C. elegans. Examples using C. elegans to study host-microbe-metabolite interactions are discussed.

Fast and space-efficient taxonomic classification of long reads with hierarchical interleaved XOR filters.

Metagenomic long-read sequencing is gaining popularity for various applications, including pathogen detection and microbiome studies. To analyze the large data created in those studies, software tools need to taxonomically classify the sequenced molecules and estimate the relative abundances of organisms in the sequenced sample. Because of the exponential growth of reference genome databases, the current taxonomic classification methods have large computational requirements. This issue motivated us to develop a new data structure for fast and memory-efficient querying of long reads. Here, we present Taxor as a new tool for long-read metagenomic classification using a hierarchical interleaved XOR filter data structure for indexing and querying large reference genome sets. Taxor implements several k-mer-based approaches, such as syncmers, for pseudoalignment to classify reads and an expectation-maximization algorithm for metagenomic profiling. Our results show that Taxor outperforms state-of-the-art tools regarding precision while having a similar recall for long-read taxonomic classification. Most notably, Taxor reduces the memory requirements and index size by >50% and is among the fastest tools regarding query times. This enables real-time metagenomics analysis with large reference databases on a small laptop in the field.

Implementing ITS1 metabarcoding for the analysis of soil microeukariotic diversity in the Mountain Cloud Forest

PurposeIn threatened diversity hotspots, such as mountain cloud forests, microbiome studies have focused essentially on bacteria. Unlike prokaryotic microbiomes, the study of the microeukaryotes has largely been restricted to the visual identification of specific groups. Herein, microeukaryotic edaphic diversity from a pristine mountain cloud forest (MCF) of Mexico was analyzed via the metabarcoding of the ITS1 region of ribosomal DNA.Materials and methodsAn exploratory triangular sampling was conducted in the mountain cloud forest located in El Relámpago Mount, Santiago Comaltepec, Oaxaca, Mexico. Each vertex was located adjacent to a dominant plant species in the ecosystem (Oreomunnea mexicana and Alsophila salvinii). After DNA extraction the ITS1 region (rDNA) was amplified. Microeukaryotic sequences were filtered by computational subtraction against the ITS2 Database. Next, alpha and beta diversity indexes were calculated, and the relationship between abiotic variables and diversity patterns were inferred by means of a Canonical Correspondence Analysis.ResultsOverall, 138 inferred sequence variants were identified, including 87 protists, 35 animals (microfauna), and 16 algae. Within the animals, the nematodes were the dominant group, chlorophytes dominated algae, and in Protista, no dominance patterns were observed given the high diversity and equitability of this group. Soil available carbon, carbon degrading enzymes and the pH play a key role in modeling the community structure. Remarkably, high beta diversity levels were obtained, evidencing a strong spatial heterogeneity at the small scale.ConclusionsThe ITS metabarcoding proved to be a useful tool to conduct multi-taxa diversity assessments for microeukaryotes, allowing the identification of alpha and beta diversity patterns and overcoming limitations of sampling and the direct observation of individuals. The results presented in this work evidenced high microeukaryotic diversity levels in the soil of MCF and encourage future studies aiming to explore the taxonomic diversity of individual taxa.

Medium-chain carboxylates production from plant waste: kinetic study and effect of an enriched microbiome

BackgroundThe need for addition of external electron donors such as ethanol or lactate impairs the economic viability of chain elongation (CE) processes for the production of medium-chain carboxylates (MCC). However, using feedstocks with inherent electron donors such as silages of waste biomass can improve the economics. Moreover, the use of an appropriate inoculum is critical to the overall efficiency of the CE process, as the production of a desired MCC can significantly be influenced by the presence or absence of specific microorganisms and their metabolic interactions. Beyond, it is necessary to generate data that can be used for reactor design, simulation and optimization of a given CE process. Such data can be obtained using appropriate mathematical models to predict the dynamics of the CE process.ResultsIn batch experiments using silages of sugar beet leaves, cassava leaves, and Elodea/wheat straw as substrates, caproate was the only MCC produced with maximum yields of 1.97, 3.48, and 0.88 g/kgVS, respectively. The MCC concentrations were accurately predicted with the modified Gompertz model. In a semi-continuous fermentation with ensiled sugar beet leaves as substrate and digestate from a biogas reactor as the sole inoculum, a prolonged lag phase of 7 days was observed for the production of MCC (C6–C8). The lag phase was significantly shortened by at least 4 days when an enriched inoculum was added to the system. With the enriched inoculum, an MCC yield of 93.67 g/kgVS and a productivity of 2.05 gMCC/L/d were achieved. Without the enriched inoculum, MCC yield and productivity were 43.30 g/kgVS and 0.95 gMCC/L/d, respectively. The higher MCC production was accompanied by higher relative abundances of Lachnospiraceae and Eubacteriaceae.ConclusionsEnsiled waste biomass is a suitable substrate for MCC production using CE. For an enhanced production of MCC from ensiled sugar beet leaves, the use of an enriched inoculum is recommended for a fast process start and high production performance.

Clinical studies of the human microbiome. Strategies for applying methods and translating results into clinical practice: A review

Clinical studies of the human microbiome. Strategies for applying methods and translating results into clinical practice: A review

A systematic comparison of natural product potential, with an emphasis on RiPPs, by mining of bacteria of three large ecosystems

The implementation of several global microbiome studies has yielded extensive insights into the biosynthetic potential of natural microbial communities. However, studies on the distribution of several classes of ribosomally synthesized and post-translationally modified peptides (RiPPs), non-ribosomal peptides (NRPs) and polyketides (PKs) in different large microbial ecosystems have been very limited. Here, we collected a large set of metagenome-assembled bacterial genomes from marine, freshwater and terrestrial ecosystems to investigate the biosynthetic potential of these bacteria. We demonstrate the utility of public dataset collections for revealing the different secondary metabolite biosynthetic potentials among these different living environments. We show that there is a higher occurrence of RiPPs in terrestrial systems, while in marine systems, we found relatively more terpene-, NRP-, and PK encoding gene clusters. Among the many new biosynthetic gene clusters (BGCs) identified, we analyzed various Nif-11-like and nitrile hydratase leader peptide (NHLP) containing gene clusters that would merit further study, including promising products, such as mersacidin-, LAP- and proteusin analogs. This research highlights the significance of public datasets in elucidating the biosynthetic potential of microbes in different living environments and underscores the wide bioengineering opportunities within the RiPP family.

Quantifying variation across 16S rRNA gene sequencing runs in human microbiome studies

Recent microbiome research has incorporated a higher number of samples through more participants in a study, longitudinal studies, and metanalysis between studies. Physical limitations in a sequencing machine can result in samples spread across sequencing runs. Here we present the results of sequencing nearly 1000 16S rRNA gene sequences in fecal (stabilized and swab) and oral (swab) samples from multiple human microbiome studies and positive controls that were conducted with identical standard operating procedures. Sequencing was performed in the same center across 18 different runs. The simplified mock community showed limitations in accuracy, while precision (e.g., technical variation) was robust for the mock community and actual human positive control samples. Technical variation was the lowest for stabilized fecal samples, followed by fecal swab samples, and then oral swab samples. The order of technical variation stability was inverse of DNA concentrations (e.g., highest in stabilized fecal samples), highlighting the importance of DNA concentration in reproducibility and urging caution when analyzing low biomass samples. Coefficients of variation at the genus level also followed the same trend for lower variation with higher DNA concentrations. Technical variation across both sample types and the two human sampling locations was significantly less than the observed biological variation. Overall, this research providing comparisons between technical and biological variation, highlights the importance of using positive controls, and provides semi-quantified data to better understand variation introduced by sequencing runs.Key points• Mock community and positive control accuracy were lower than precision.• Samples with lower DNA concentration had increased technical variation across sequencing runs.• Biological variation was significantly higher than technical variation due to sequencing runs.

Endometrial microbial dysbiosis and metabolic alteration promote the development of endometrial cancer.

Emerging evidence suggests that the endometrial microbiome plays important roles in the development of endometrial cancer (EC). Here, we evaluate stage-specific roles of microbial dysbiosis and metabolic disorders in patients with EC, patients with endometrial hyperplasia (EH), and patients afflicted with benign uterine conditions (CK). This prospective cohort study included 33 women with EC, 15 women with endometrial EH, and 15 women with benign uterine conditions (CK) from November 2022 to September 2023. Different typical endometrial samples were imaged with a scanning electron microscope and a transmission electron microscope. The endometrial microbiome was assessed by sequencing the V3-V4 region of the 16S rRNA gene and the ITS1 to fill the gap in relation to the study of the uterine fungal microbiome. Moreover, liquid chromatography-mass spectrometry-based metabolomics was used to identify and quantify metabolic changes among these groups. The endometrial microbiome revealed that there is a structural microbiome shift and an increase in the α-diversity in the EC and EH cases, distinguishable from the benign cases, especially the fungal community structure. The fungal microbiome from patients with EC and EH was altered relative to controls and dominated by Penicillium sp. By contrast, Sarocladium was more abundant in controls. Significant differences were observed in the composition and content of compounds between benign cases and EC, especially estradiol-like metabolism-related substances. Altered microbiota was correlated with the concentrations of interleukin-6 (IL-6), IL-11, transforming growth factor-beta, and β-glucuronidase activity especially the relative abundance increase of Penicillium sp. This study suggested that the endometrial microbiome is complicit in modulating the development of EC such as estrogen activity and a pro-inflammatory response. Our work provides a new insight into the endometrial microbiome from a perspective of stages, which opens up new avenues for EC prognosis and therapy.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Integrative study of transcriptome and microbiome to reveal the response of Rhododendron decorum to cadmium stress

The anomalies of cadmium (Cd) in karst region pose a severe threat to plant growth and development. In this study, the responses of Rhododendron decorum to Cd stress were investigated at physiological, molecular, and endophytic microbial levels, and the potential correlation among these responses was assessed. The Cd stress impeded R. decorum growth and led to an increase in malondialdehyde (MDA) and hydrogen peroxide (H2O2) levels, as well as enhanced superoxide dismutase (SOD) and catalase (CAT) activities. Meanwhile, Cd stress increased the Cd (up to 80 times compared to the control), sodium (Na), aluminum (Al), and zinc (Zn) contents, while decreased the magnesium (Mg) and manganese (Mn) contents in R. decorum leaves. Transcriptome suggested that Cd significantly regulated the pathways including “protein repair”, “hormone-mediated signaling pathway”, and “ATP-binding cassette (ABC) transporters”. Additionally, q-PCR analysis showed that Cd stress significantly up-regulated the expressions of ABCB19-like and pleiotropic drug resistance, while down-regulated the expressions of indole-3-acetic acid-amido synthetase and cytokinin dehydrogenase. The Cd stress influenced the composition of endophytic microbial communities in R. decorum leaves and enhanced the interspecific bacterial associations. Furthermore, the bacterial genera Achromobacter, Aureimonas and fungal genus Vishniacozyma exhibited a high degree of connectivity with other nodes in networks constructed by the metal element contents, differentially expressed genes (DEGs), and microbial communities, respectively. These findings provide a comprehensive insight into the response of R. decorum to Cd-induced stress, which might facilitate the breeding of the Cd-tolerant R. decorum.

TENSOR REGRESSION FOR INCOMPLETE OBSERVATIONS WITH APPLICATION TO LONGITUDINAL STUDIES.

Multivariate longitudinal data are frequently encountered in practice such as in our motivating longitudinal microbiome study. It is of general interest to associate such high-dimensional, longitudinal measures with some univariate continuous outcome. However, incomplete observations are common in a regular study design, as not all samples are measured at every time point, giving rise to the so-called blockwise missing values. Such missing structure imposes significant challenges for association analysis and defies many existing methods that require complete samples. In this paper we propose to represent multivariate longitudinal data as a three-way tensor array (i.e., sample-by-feature-by-time) and exploit a parsimonious scalar-on-tensor regression model for association analysis. We develop a regularized covariance-based estimation procedure that effectively leverages all available observations without imputation. The method achieves variable selection and smooth estimation of time-varying effects. The application to the motivating microbiome study reveals interesting links between the preterm infant's gut microbiome dynamics and their neurodevelopment. Additional numerical studies on synthetic data and a longitudinal aging study further demonstrate the efficacy of the proposed method.

The microbiome landscape of early-onset colorectal cancer: A systematic review.

e15646 Background: The rise of early-onset colorectal cancer (EOCRC, age < 50 years old) necessitates the examination of potential risk factors to enhance prevention and control strategies. Microbial dysbiosis has been hypothesized as a potential contributor, yet studies are limited. To guide future research, we systematically reviewed studies of the EOCRC microbiome, emphasizing methodological heterogeneities. Methods: We systematically reviewed available literature for microbiome studies involving EOCRC and synthesized findings according to comparison groups (healthy/later-onset CRC [LOCRC]), sample type (fecal /tumor), and analyses (taxonomic/metabolomic). Results: Collection, storage, extraction, and analytical methods varied across the 10 studies included out of 2073 studies. There were 377 unique EOCRC patients, of which most originated from China (193 EOCRC cases), the United States (52 EOCRC cases), and Japan (42 EOCRC cases). Six studies analyzed 224 unique EOCRC fecal samples (4 performed 16S, 5 performed WGSS, 4 performed metabolomic); five compared EOCRC against healthy individuals and five to LOCRC. Four studies utilized 153 unique EOCRC tumor samples (3 performed 16S, 1 performed WGSS, 1 performed metabolomic); two compared with tumor-adjacent normal tissue, one to colonic tissue of healthy individuals, and four to LOCRC. Among studies comparing fecal samples of EOCRC to healthy individuals, EOCRC exhibited increased levels of Fusobacterium and Escherichia-Shigella, known for metabolizing 5-Fluorouracil, while butyrate-producing taxa like Faecalibacterium, protective against CRC, were diminished; EOCRC also showed decreased lung-associated pathogens and Clostridium difficile, but increased oral pathogens. Among studies comparing fecal samples of EOCRC to LOCRC, EOCRC fecal samples were more abundant in oral/gastrointestinal pathogens, with varying findings on Fusobacterium nucleatum. Faecalibacterium prausnitzii was more abundant in EOCRC than LOCRC, whereas Escherichia-Shigella was less abundant. Fecal metabolomic studies displayed increased LPS-related pathways and bile acids in EOCRC compared to healthy individuals. Compared to tumor-adjacent normal tissues or colonic tissues of healthy individuals, EOCRC tumors were characterized by higher levels of Fusobacterium, Actinomyces, and Escherichia coli ( E. coli), fewer butyrate producers, and more Bacteroides fragilis. Conclusions: While data is limited and methodologies varied, emerging evidence suggests that EOCRC may have unique microbial signatures compared to both healthy individuals and LOCRC. This may signal unique pathogenesis for EOCRC and present potential for prevention, early detection, and intervention. It is essential to harmonize study methods and analytical approaches to advance understanding of the role of microbiome in EOCRC.

Uses of Fecal Microbiota Transplantation in Neurodegenerative Disease: A Scoping Review.

Fecal microbiota transplantation (FMT) is the administration of fecal bacteria from a healthy donor into the intestinal tract of a recipient in order to directly change the recipient's gut microbial composition and confer a health benefit. The relationship between the gut microbiome and the central nervous system, termed the gut-brain axis, has been a frequent topic of gut microbiome studies. Commensal gut bacteria communicate with the central nervous system through various hormones, cytokines, and neural pathways. Therefore, influencing the gut microbiome via FMT may have the potential in treating symptoms of neurodegenerative conditions. This study aims to identify current uses of FMT in treating neurodegenerative diseases and highlight areas of future investigation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a literature search was conducted of peer-reviewed sources on September 27, 2022, from Embase, MEDLINE, Web of Science, and Cochrane Central. Searchterms were utilized that were related to the application of FMT and neurodegenerative disorders and limited those human studies, those that were published in English, and those that were published between 2017 and 2022. The initial search yielded 450 unique articles, and after the assessment of the title and abstract for inclusion and exclusion criteria, six articles were identified for full-text review. Studies that focused on either Parkinson's disease (PD) or multiple sclerosis (MS) demonstrated improvements in both motor symptoms and non-motor symptoms. FMT was also shown to provide significant relief of constipation and general gastrointestinal (GI) symptoms in all conditions studied. The studies related to MS showed the most mixed results with regard to symptomatic improvement. The data on the use of FMT as a treatment for neurodegenerative disorders is limited; however, studies have shown not only improvement in GI symptoms but also improvement in the cognitive symptoms of PD and dementia. The data on FMT as a treatment to improve the motor symptoms of PD is both more complete and more compelling than the data on the motor symptoms of MS. The studies that were reviewed showed no major adverse effects of FMT and generally promising results. There is a strong case to be made for larger, more well-controlled studies to be done on FMT and its potential use as a treatment not only for GI symptoms but for the motor and cognitive symptoms of neurodegenerative diseases.

Pinpointing the microbiota of tardigrades: What is really there?

Microbiota are considered significant in the biology of tardigrades, yet their diversity and distribution remain largely unexplored. This is partly due to the methodological challenges associated with studying the microbiota of small organisms that inhabit microbe-rich environments. In our study, we characterized the microbiota of 31 species of cultured tardigrades using 16S rRNA amplicon sequencing. We employed various sample preparation strategies and multiple types of controls and estimated the number of microbes in samples using synthetic DNA spike-ins. We also reanalysed data from previous tardigrade microbiome studies. Our findings suggest that the microbial communities of cultured tardigrades are predominantly composed of bacterial genotypes originating from food, medium, or reagents. Despite numerous experiments, we found it challenging to identify strains that were enriched in certain tardigrades, which would have indicated likely symbiotic associations. Putative tardigrade-associated microbes rarely constituted more than 20% of the datasets, although some matched symbionts identified in other studies. We also uncovered serious contamination issues in previous tardigrade microbiome studies, casting doubt on some of their conclusions. We concluded that tardigrades are not universally dependent on specialized microbes. Our work underscores the need for rigorous safeguards in studies of the microbiota of microscopic organisms and serves as a cautionary tale for studies involving samples with low microbiome abundance.

Microbiome and floral associations of a wild bee using biodiversity survey collections.

The health of bees can be assessed through their microbiome, which serves as a biomarker indicating the presence of both beneficial and harmful microorganisms within a bee community. This study presents the characterisation of the bacterial, fungal, and plant composition on the cuticle of adult bicoloured sweat bees (Agapostemon virescens). These bees were collected using various methods such as pan traps, blue vane traps and sweep netting across the northern extent of their habitat range. Non-destructive methods were employed to extract DNA from the whole pinned specimens of these wild bees. Metabarcoding of the 16S rRNA, ITS and rbcL regions was then performed. The study found that the method of collection influenced the detection of certain microbial and plant taxa. Among the collection methods, sweep net samples showed the lowest fungal alpha diversity. However, minor differences in bacterial or fungal beta diversity suggest that no single method is significantly superior to others. Therefore, a combination of techniques can cater to a broader spectrum of microbial detection. The study also revealed regional variations in bacterial, fungal and plant diversity. The core microbiome of A. virescens comprises two bacteria, three fungi and a plant association, all of which are commonly detected in other wild bees. These core microbes remained consistent across different collection methods and locations. Further extensive studies of wild bee microbiomes across various species and landscapes will help uncover crucial relationships between pollinator health and their environment.

Melatonin and gut microbiome

In recent years, studies of the human microbiome have led to the development of the gut-brain axis theory, which proposes a relationship between the gut bacterial community and its metabolites and chemical compounds, as well as physiological and pathological processes in the brain. Melatonin is considered one of the potential links in this relationship.The review provides brief information about the synthesis, metabolism and effects of melatonin in the body. General patterns of the effect of melatonin on the intestinal microbiota under various conditions, such as oral and intraperitoneal melatonin administration, sleep deprivation, as well as the results of studying the microbial community with measurement of hormone levels are considered.Results are presented for families and individual genera of microorganisms and are obtained from studies in animal models or in the research of diseases. The article also notes some additional parameters which are supposed to be relevant to the interaction of melatonin with gut microbiome. Special attention is paid to butyrate as one of the most studied possible parts in this interaction. Studying the relationship between melatonin and microbiome is of interest for basic science and practical medicine.Literature search was carried out using electronic information resources PubMed and eLibrary.

A new and widespread group of fish apicomplexan parasites

Apicomplexans are obligate intracellular parasites that have evolved from a free-living, phototrophic ancestor. They have been reported from marine environmental samples in high numbers,1 with several clades of apicomplexan-related lineages (ARLs) having been described from environmental sequencing data (16S rRNA gene metabarcoding).2 The most notable of these are the corallicolids (previously ARL-V), which possess chlorophyll-biosynthesis genes in their relic chloroplast (apicoplast) and are geographically widespread and abundant symbionts of anthozoans.3 Corallicolids are related to the Eimeriorina, a suborder of apicomplexan coccidians that include other notable members such as Toxoplasma gondii.4Ophioblennius macclurei, the redlip blenny, along with other tropical reef fishes, is known to be infected by Haemogregarina-like and Haemohormidium-like parasites5 supposedly belonging to the Adeleorina; however, phylogenetics shows that these parasites are instead related to the Eimeriorina.6,7 Hybrid genomic sequencing of apicomplexan-infected O.macclurei blood recovered the entire rRNA operon of this apicomplexan parasite along with the complete mitochondrion and apicoplast genomes. Phylogenetic analyses using this new genomic information consistently place these fish-infecting apicomplexans, hereby informally named ichthyocolids, sister to the corallicolids within Coccidia. The apicoplast genome did not contain chlorophyll biosynthesis genes, providing evidence for another independent loss of this pathway within Apicomplexa. Based on the 16S rRNA gene found in the apicoplast, this group corresponds to the previously described ARL-VI. Screening of fish microbiome studies using the plastid 16S rRNA gene shows these parasites to be geographically and taxonomically widespread in fish species across the globe with implications for commercial fisheries and oceanic food webs.

Rhodobacteraceae are key players in microbiome assembly of the diatom Asterionellopsis glacialis.

Most, if not all, microeukaryotic organisms harbor an associated microbial community, termed the microbiome. The microscale interactions that occur between these partners have global-scale consequences, influencing marine primary productivity, carbon cycling, and harmful algal blooms to name but a few. Over the last decade, there has been a growing interest in the study of phytoplankton microbiomes, particularly within the context of bloom dynamics. However, long-standing questions remain regarding the process of phytoplankton microbiome assembly. The significance of our research is to tease apart the mechanism of microbiome assembly with a particular focus on identifying bacterial taxa, which may not merely be symbionts but architects of the phytoplankton microbiome. Our results strengthen the understanding of the ecological mechanisms that underpin phytoplankton-bacteria interactions in order to accurately predict marine ecosystem responses to environmental perturbations.

Bayesian Variable Shrinkage and Selection in Compositional Data Regression: Application to Oral Microbiome

Microbiome studies generate multivariate compositional responses, such as taxa counts, which are strictly non-negative, bounded, residing within a simplex, and subject to unit-sum constraint. In presence of covariates (which can be moderate to high dimensional), they are popularly modeled via the Dirichlet-Multinomial (D-M) regression framework. In this paper, we consider a Bayesian approach for estimation and inference under a D-M compositional framework, and present a comparative evaluation of some state-of-the-art continuous shrinkage priors for efficient variable selection to identify the most significant associations between available covariates, and taxonomic abundance. Specifically, we compare the performances of the horseshoe and horseshoe+ priors (with the benchmark Bayesian lasso), utilizing Hamiltonian Monte Carlo techniques for posterior sampling, and generating posterior credible intervals. Our simulation studies using synthetic data demonstrate excellent recovery and estimation accuracy of sparse parameter regime by the continuous shrinkage priors. We further illustrate our method via application to a motivating oral microbiome data generated from the NYC-Hanes study. RStan implementation of our method is made available at the GitHub link: (https://github.com/dattahub/compshrink).