Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

Protective effect and mechanism of Xiaoyu Xiezhuo decoction on ischemia-reperfusion induced acute kidney injury based on gut-kidney crosstalk

This study aimed to explore the mechanism of Xiaoyu Xiezhuo decoction (XXD) on ischemia-reperfusion-induced acute kidney injury (IRI-AKI) using network pharmacology methods and gut microbiota analysis. A total of 1778 AKI-related targets were obtained, including 140 targets possibly regulated by AKI in XXD, indicating that the core targets were mainly enriched in inflammatory-related pathways, such as the IL-17 signaling pathway and TNF signaling pathway. The unilateral IRI-AKI animal model was established and randomly divided into four groups: the sham group, the AKI group, the sham + XXD group, and the AKI + XXD group. Compared with the rats in the AKI group, XXD improved not only renal function, urinary enzymes, and biomarkers of renal damage such as Kim-1, cystatin C, and serum inflammatory factors such as IL-17, TNF-α, IL-6, and IL 1-β, but also intestinal metabolites including lipopolysaccharides, d-lactic acid, indoxyl sulfate, p-cresyl sulfate, and short-chain fatty acids. XXD ameliorated renal and colonic pathological injury as well as inflammation and chemokine gene abundance, such as IL-17, TNF-α, IL-6, IL-1β, ICAM-1, and MCP-1, in AKI rats via the TLR4/NF-κB/NLRP3 pathway, reducing the AKI score, renal pathological damage, and improving the intestinal mucosa's inflammatory infiltration. It also repaired markers of the mucosal barrier, including claudin-1, occludin, and ZO-1. Compared with the rats in the AKI group, the α diversity was significantly increased, and the Chao1 index was significantly enhanced after XXD treatment in both the sham group and the AKI group. The treatment group significantly reversed this change in microbiota.

Metagenomic absolute quantification of antibiotic resistance genes and virulence factor genes-carrying bacterial genomes in anaerobic digesters

Sewage treatment works have been considered as hotspots for the dissemination of antibiotic resistance genes (ARGs). Anaerobic digestion (AD) has emerged as a promising approach for controlling the spread of ARGs while destroying biomass in sludge. Evaluating the impact of AD on ARG removal relies on the absolute quantification of ARGs. In this study, we quantified the ARG concentrations in both full-scale and lab-scale AD systems using a cellular spike-ins based absolute quantification approach. Results demonstrated that AD effectively removed 68 ± 18 %, 55 ± 12 %, and 57 ± 19 % of total ARGs in semi-continuous AD digesters, with solid retention times of 15, 20, and 25 days, respectively. The removal efficiency of total ARGs increased as the AD process progressed in the batch digesters over 40 days. A significant negative correlation was observed between digestion time and the concentrations of certain ARG types, such as beta-lactam, sulfonamide, and tetracycline. However, certain potential pathogenic antibiotic resistant bacteria (PARB) and multi-resistant high-risk ARGs-carrying populations robustly persisted throughout the AD process, regardless of the operating conditions. This study highlighted the influence of the AD process and its operating parameters on ARG removal, and revealed the broad spectrum and persistence of PARB in AD systems. These findings provided critical insights for the management of microbial hazards.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

mbDecoda: a debiased approach to compositional data analysis for microbiome surveys

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Bacteria Invade the Brain Following Sterile Intracortical Microelectrode Implantation

Brain-machine interface performance is largely affected by the neuroinflammatory responses resulting in large part from blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings strongly suggest that certain gut bacterial constituents penetrate the BBB and are resident in various brain regions of rodents and humans, both in health and disease. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could amplify dysregulation of the microbiome-gut-brain axis. Here, we report that bacteria, including those commonly found in the gut, enter the brain following intracortical microelectrode implantation in mice implanted with single-shank silicon microelectrodes. Systemic antibiotic treatment of mice implanted with microelectrodes to suppress bacteria resulted in differential expression of bacteria in the brain tissue and a reduced acute inflammatory response compared to untreated controls, correlating with temporary improvements in microelectrode recording performance. Long-term antibiotic treatment resulted in worsening microelectrode recording performance and dysregulation of neurodegenerative pathways. Fecal microbiome composition was similar between implanted mice and an implanted human, suggesting translational findings. However, a significant portion of invading bacteria was not resident in the brain or gut. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.

Microbiota-gut-brain axis and its therapeutic applications in neurodegenerative diseases

The human gastrointestinal tract is populated with a diverse microbial community. The vast genetic and metabolic potential of the gut microbiome underpins its ubiquity in nearly every aspect of human biology, including health maintenance, development, aging, and disease. The advent of new sequencing technologies and culture-independent methods has allowed researchers to move beyond correlative studies toward mechanistic explorations to shed light on microbiome-host interactions. Evidence has unveiled the bidirectional communication between the gut microbiome and the central nervous system, referred to as the "microbiota-gut-brain axis". The microbiota-gut-brain axis represents an important regulator of glial functions, making it an actionable target to ameliorate the development and progression of neurodegenerative diseases. In this review, we discuss the mechanisms of the microbiota-gut-brain axis in neurodegenerative diseases. As the gut microbiome provides essential cues to microglia, astrocytes, and oligodendrocytes, we examine the communications between gut microbiota and these glial cells during healthy states and neurodegenerative diseases. Subsequently, we discuss the mechanisms of the microbiota-gut-brain axis in neurodegenerative diseases using a metabolite-centric approach, while also examining the role of gut microbiota-related neurotransmitters and gut hormones. Next, we examine the potential of targeting the intestinal barrier, blood-brain barrier, meninges, and peripheral immune system to counteract glial dysfunction in neurodegeneration. Finally, we conclude by assessing the pre-clinical and clinical evidence of probiotics, prebiotics, and fecal microbiota transplantation in neurodegenerative diseases. A thorough comprehension of the microbiota-gut-brain axis will foster the development of effective therapeutic interventions for the management of neurodegenerative diseases.

Hydrogen-independent CO2 reduction dominates methanogenesis in five temperate lakes that differ in trophic states

Emissions of microbially produced methane (CH4) from lake sediments are a major source of this potent greenhouse gas to the atmosphere. The rates of CH4 production and emission are believed to be influenced by electron acceptor distributions and organic carbon contents, which in turn are affected by anthropogenic inputs of nutrients leading to eutrophication. Here, we investigate how eutrophication influences the abundance and community structure of CH4 producing Archaea and methanogenesis pathways across time-resolved sedimentary records of five Swiss lakes with well-characterized trophic histories. Despite higher CH4 concentrations which suggest higher methanogenic activity in sediments of eutrophic lakes, abundances of methanogens were highest in oligotrophic lake sediments. Moreover, while the methanogenic community composition differed significantly at the lowest taxonomic levels (OTU), depending on whether sediment layers had been deposited under oligotrophic or eutrophic conditions, it showed no clear trend in relation to in situ distributions of electron acceptors. Remarkably, even though methanogenesis from CO2-reduction was the dominant pathway in all sediments based on carbon isotope fractionation values, taxonomic identities, and genomes of resident methanogens, CO2-reduction with hydrogen (H2) was thermodynamically unfavorable based on measured reactant and product concentrations. Instead, strong correlations between genomic abundances of CO2-reducing methanogens and anaerobic bacteria with potential for extracellular electron transfer suggest that methanogenic CO2-reduction in lake sediments is largely powered by direct electron transfer from syntrophic bacteria without involvement of H2 as an electron shuttle.

Quantitative Amplicon Sequencing Is Necessary to Identify Differential Taxa and Correlated Taxa Where Population Sizes Differ

High-throughput, multiplexed-amplicon sequencing has become a core tool for understanding environmental microbiomes. As researchers have widely adopted sequencing, many open-source analysis pipelines have been developed to compare microbiomes using compositional analysis frameworks. However, there is increasing evidence that compositional analyses do not provide the information necessary to accurately interpret many community assembly processes. This is especially true when there are large gradients that drive distinct community assembly processes. Recently, sequencing has been combined with Q-PCR (among other sources of total quantitation) to generate "Quantitative Sequencing" (QSeq) data. QSeq more accurately estimates the true abundance of taxa, is a more reliable basis for inferring correlation, and, ultimately, can be more reliably related to environmental data to infer community assembly processes. In this paper, we use a combination of published data sets, synthesis, and empirical modeling to offer guidance for which contexts QSeq is advantageous. As little as 5% variation in total abundance among experimental groups resulted in more accurate inference by QSeq than compositional methods. Compositional methods for differential abundance and correlation unreliably detected patterns in abundance and covariance when there was greater than 20% variation in total abundance among experimental groups. Whether QSeq performs better for beta diversity analysis depends on the question being asked, and the analytic strategy (e.g., what distance metric is being used); for many questions and methods, QSeq and compositional analysis are equivalent for beta diversity analysis. QSeq is especially useful for taxon-specific analysis; QSeq transformation and analysis should be the default for answering taxon-specific questions of amplicon sequence data. Publicly available bioinformatics pipelines should incorporate support for QSeq transformation and analysis.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Accounting for 16S rRNA copy number prediction uncertainty and its implications in bacterial diversity analyses

16S rRNA gene copy number (16S GCN) varies among bacterial species and this variation introduces potential biases to microbial diversity analyses using 16S rRNA read counts. To correct the biases, methods have been developed to predict 16S GCN. A recent study suggests that the prediction uncertainty can be so great that copy number correction is not justified in practice. Here we develop RasperGade16S, a novel method and software to better model and capture the inherent uncertainty in 16S GCN prediction. RasperGade16S implements a maximum likelihood framework of pulsed evolution model and explicitly accounts for intraspecific GCN variation and heterogeneous GCN evolution rates among species. Using cross-validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms other methods in both precision and recall. We have predicted GCN for 592605 OTUs in the SILVA database and tested 113842 bacterial communities that represent an exhaustive and diverse list of engineered and natural environments. We found that the prediction uncertainty is small enough for 99% of the communities that 16S GCN correction should improve their compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on beta-diversity analyses such as PCoA, NMDS, PERMANOVA and random-forest test.

Role of the Microbiome in Gut-Heart-Kidney Cross Talk

Homeostasis is a prerequisite for health. When homeostasis becomes disrupted, dysfunction occurs. This is especially the case for the gut microbiota, which under normal conditions lives in symbiosis with the host. As there are as many microbial cells in and on our body as human cells, it is unlikely they would not contribute to health or disease. The gut bacterial metabolism generates numerous beneficial metabolites but also uremic toxins and their precursors, which are transported into the circulation. Barrier function in the intestine, the heart, and the kidneys regulates metabolite transport and concentration and plays a role in inter-organ and inter-organism communication via small molecules. This communication is analyzed from the perspective of the remote sensing and signaling theory, which emphasizes the role of a large network of multispecific, oligospecific, and monospecific transporters and enzymes in regulating small-molecule homeostasis. The theory provides a systems biology framework for understanding organ cross talk and microbe-host communication involving metabolites, signaling molecules, nutrients, antioxidants, and uremic toxins. This remote small-molecule communication is critical for maintenance of homeostasis along the gut-heart-kidney axis and for responding to homeostatic perturbations. Chronic kidney disease is characterized by gut dysbiosis and accumulation of toxic metabolites. This slowly impacts the body, affecting the cardiovascular system and contributing to the progression of kidney dysfunction, which in its turn influences the gut microbiota. Preserving gut homeostasis and barrier functions or restoring gut dysbiosis and dysfunction could be a minimally invasive way to improve patient outcomes and quality of life in many diseases, including cardiovascular and kidney disease.

Abundant fungi dominate the complexity of microbial networks in soil of contaminated site: High-precision community analysis by full-length sequencing

During the past decade, the characterization of microbial community in soil of contaminated sites was primarily done by high-throughput short-read amplicon sequencing. However, due to the similarity of 16S rRNA and ITS genes amplicon sequences, the short-read approach often limits the microbial composition analysis at the species level. Here, we simultaneously performed full-length and short-read amplicon sequencing to clarify the community composition and ecological status of different microbial taxa in contaminated soil from a high-resolution perspective. We found that (1) full-length 16S rRNA gene sequencing gave better resolution for bacterial identification at all levels, while there were no significant differences between the two sequencing platforms for fungal identification in some samples. (2) Abundant taxa were vital for microbial co-occurrences network constructed by both full-length and short-read sequencing data, and abundant fungal species such as Mortierella alpine, Fusarium solani, Mrakia frigida, and Chaetomium homopilatum served as the keystone species. (3) Heavy metal correlated with the microbial community significantly, and bacterial community and its abundant taxa were assembled by deterministic process, while the other taxa were dominated by stochastic process. These findings contribute to the understanding of the ecological mechanisms and microbial interactions in site soil ecosystems and demonstrate that full-length sequencing has the potential to provide more details of microbial community.

Gut microbiome studies in CKD: opportunities, pitfalls and therapeutic potential

Interest in gut microbiome dysbiosis and its potential association with the development and progression of chronic kidney disease (CKD) has increased substantially in the past 6 years. In parallel, the microbiome field has matured considerably as the importance of host-related and environmental factors is increasingly recognized. Past research output in the context of CKD insufficiently considered the myriad confounding factors that are characteristic of the disease. Gut microbiota-derived metabolites remain an interesting therapeutic target to decrease uraemic (cardio)toxicity. However, future studies on the effect of dietary and biotic interventions will require harmonization of relevant readouts to enable an in-depth understanding of the underlying beneficial mechanisms. High-quality standards throughout the entire microbiome analysis workflow are also of utmost importance to obtain reliable and reproducible results. Importantly, investigating the relative composition and abundance of gut bacteria, and their potential association with plasma uraemic toxins levels is not sufficient. As in other fields, the time has come to move towards in-depth quantitative and functional exploration of the patient's gut microbiome by relying on confounder-controlled quantitative microbial profiling, shotgun metagenomics and in vitro simulations of microorganism-microorganism and host-microorganism interactions. This step is crucial to enable the rational selection and monitoring of dietary and biotic intervention strategies that can be deployed as a personalized intervention in CKD.

Benefits of Huang Lian mediated by gut microbiota on HFD/STZ-induced type 2 diabetes mellitus in mice

BACKGROUND

Huang Lian (HL), one of the traditional Chinese medicines (TCMs) that contains multiple active components including berberine (BBR), has been used to treat symptoms associated with diabetes for thousands of years. Compared to the monomer of BBR, HL exerts a better glucose-lowering activity and plays different roles in regulating gut microbiota. However, it remains unclear what role the gut microbiota plays in the anti-diabetic activity of HL.

METHODS

In this study, a type 2 diabetes mellitus (T2DM) mouse model was induced with a six-week high-fat diet (HFD) and a one-time injection of streptozotocin (STZ, 75 mg/kg). One group of these mice was administrated HL (50 mg/kg) through oral gavage two weeks after HFD feeding commenced and continued for four weeks; the other mice were given distilled water as disease control. Comprehensive analyses of physiological indices related to glycolipid metabolism, gut microbiota, untargeted metabolome, and hepatic genes expression, function prediction by PICRUSt2 were performed to identify potential mechanism.

RESULTS

We found that HL, in addition to decreasing body fat accumulation, effectively improved insulin resistance by stimulating the hepatic insulin-mediated signaling pathway. In comparison with the control group, HL treatment constructed a distinct gut microbiota and bile acid (BA) profile. The HL-treated microbiota was dominated by bacteria belonging to Bacteroides and the Clostridium innocuum group, which were associated with BA metabolism. Based on the correlation analysis, the altered BAs were closely correlated with the improvement of T2DM-related markers.

CONCLUSION

These results indicated that the anti-diabetic activity of HL was achieved, at least partly, by regulating the structure of the gut microbiota and the composition of BAs.

Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease

BACKGROUND

Biomarker discovery exploiting feature importance of machine learning has risen recently in the microbiome landscape with its high predictive performance in several disease states. To have a concrete selection among a high number of features, recursive feature elimination (RFE) has been widely used in the bioinformatics field. However, machine learning-based RFE has factors that decrease the stability of feature selection. In this article, we suggested methods to improve stability while sustaining performance.

RESULTS

We exploited the abundance matrices of the gut microbiome (283 taxa at species level and 220 at genus level) to classify between patients with inflammatory bowel disease (IBD) and healthy control (1,569 samples). We found that applying an already published data transformation before RFE improves feature stability significantly. Moreover, we performed an in-depth evaluation of different variants of the data transformation and identify those that demonstrate better improvement in stability while not sacrificing classification performance. To ensure a robust comparison, we evaluated stability using various similarity metrics, distances, the common number of features, and the ability to filter out noise features. We were able to confirm that the mapping by the Bray-Curtis similarity matrix before RFE consistently improves the stability while maintaining good performance. Multilayer perceptron algorithm exhibited the highest performance among 8 different machine learning algorithms when a large number of features (a few hundred) were considered based on the best performance across 100 bootstrapped internal test sets. Conversely, when utilizing only a limited number of biomarkers as a trade-off between optimal performance and method generalizability, the random forest algorithm demonstrated the best performance. Using the optimal pipeline we developed, we identified 14 biomarkers for IBD at the species level and analyzed their roles using Shapley additive explanations.

CONCLUSION

Taken together, our work not only showed how to improve biomarker discovery in the metataxonomic field without sacrificing classification performance but also provided useful insights for future comparative studies.

Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes

The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.

Absolute quantification and genome-centric analyses elucidate the dynamics of microbial populations in anaerobic digesters

Anaerobic digestion (AD) relies on myriads of functions performed by complex microbial communities in customized settings, thus, a comprehensive investigation on the AD microbiome is central to the fine-tuned control. Most current AD microbiome studies are based on relative abundance, which hinders the interpretation of microbes' dynamics and inter-sample comparisons. Here, we developed an absolute quantification (AQ) approach that integrated cellular spike-ins with metagenomic sequencing to elucidate microbial community variations and population dynamics in four anaerobic digesters. Using this method, 253 microbes were defined as decaying populations with decay rates ranging from -0.05 to -5.85 d^-1, wherein, a population from Flavobacteriaceae family decayed at the highest rates of -3.87 to -5.85 d^-1 in four digesters. Meanwhile, 25 microbes demonstrated the growing trend in the AD processes with growth rates ranging from 0.11 to 1.77 d^-1, and genome-centric analysis assigned some of the populations to the functional niches of hydrolysis, short-chain fatty acids metabolism, and methane generation. Additionally, we observed that the specific activity of methanogens was lower in the prolonged digestion stage, and redundancy analysis revealed that the feedstock composition and the digestion duration were the two key parameters in governing the AD microbial compositions.

Disentangling the genetic basis of rhizosphere microbiome assembly in tomato

Microbiomes play a pivotal role in plant growth and health, but the genetic factors involved in microbiome assembly remain largely elusive. Here, we map the molecular features of the rhizosphere microbiome as quantitative traits of a diverse hybrid population of wild and domesticated tomato. Gene content analysis of prioritized tomato quantitative trait loci suggests a genetic basis for differential recruitment of various rhizobacterial lineages, including a Streptomyces-associated 6.31 Mbp region harboring tomato domestication sweeps and encoding, among others, the iron regulator FIT and the water channel aquaporin SlTIP2.3. Within metagenome-assembled genomes of root-associated Streptomyces and Cellvibrio, we identify bacterial genes involved in metabolism of plant polysaccharides, iron, sulfur, trehalose, and vitamins, whose genetic variation associates with specific tomato QTLs. By integrating 'microbiomics' and quantitative plant genetics, we pinpoint putative plant and reciprocal rhizobacterial traits underlying microbiome assembly, thereby providing a first step towards plant-microbiome breeding programs.

Collective effects of human genomic variation on microbiome function

Studies of the impact of host genetics on gut microbiome composition have mainly focused on the impact of individual single nucleotide polymorphisms (SNPs) on gut microbiome composition, without considering their collective impact or the specific functions of the microbiome. To assess the aggregate role of human genetics on the gut microbiome composition and function, we apply sparse canonical correlation analysis (sCCA), a flexible, multivariate data integration method. A critical attribute of metagenome data is its sparsity, and here we propose application of a Tweedie distribution to accommodate this. We use the TwinsUK cohort to analyze the gut microbiomes and human variants of 250 individuals. Sparse CCA, or sCCA, identified SNPs in microbiome-associated metabolic traits (BMI, blood pressure) and microbiome-associated disorders (type 2 diabetes, some neurological disorders) and certain cancers. Both common and rare microbial functions such as secretion system proteins or antibiotic resistance were found to be associated with host genetics. sCCA applied to microbial species abundances found known associations such as Bifidobacteria species, as well as novel associations. Despite our small sample size, our method can identify not only previously known associations, but novel ones as well. Overall, we present a new and flexible framework for examining host-microbiome genetic interactions, and we provide a new dimension to the current debate around the role of human genetics on the gut microbiome.

Gut metagenome associations with extensive digital health data in a volunteer-based Estonian microbiome cohort

Microbiome research is starting to move beyond the exploratory phase towards interventional trials and therefore well-characterized cohorts will be instrumental for generating hypotheses and providing new knowledge. As part of the Estonian Biobank, we established the Estonian Microbiome Cohort which includes stool, oral and plasma samples from 2509 participants and is supplemented with multi-omic measurements, questionnaires, and regular linkages to national electronic health records. Here we analyze stool data from deep metagenomic sequencing together with rich phenotyping, including 71 diseases, 136 medications, 21 dietary questions, 5 medical procedures, and 19 other factors. We identify numerous relationships (n = 3262) with different microbiome features. In this study, we extend the understanding of microbiome-host interactions using electronic health data and show that long-term antibiotic usage, independent from recent administration, has a significant impact on the microbiome composition, partly explaining the common associations between diseases.

High-throughput qPCR and 16S rRNA gene amplicon sequencing as complementary methods for the investigation of the cheese microbiota

Background

Next-generation sequencing (NGS) methods and especially 16S rRNA gene amplicon sequencing have become indispensable tools in microbial ecology. While they have opened up new possibilities for studying microbial communities, they also have one drawback, namely providing only relative abundances and thus compositional data. Quantitative PCR (qPCR) has been used for years for the quantification of bacteria. However, this method requires the development of specific primers and has a low throughput. The constraint of low throughput has recently been overcome by the development of high-throughput qPCR (HT-qPCR), which allows for the simultaneous detection of the most prevalent bacteria in moderately complex systems, such as cheese and other fermented dairy foods. In the present study, the performance of the two approaches, NGS and HT-qPCR, was compared by analyzing the same DNA samples from 21 Raclette du Valais protected designation of origin (PDO) cheeses. Based on the results obtained, the differences, accuracy, and usefulness of the two approaches were studied in detail.

Results

The results obtained using NGS (non-targeted) and HT-qPCR (targeted) show considerable agreement in determining the microbial composition of the cheese DNA samples studied, albeit the fundamentally different nature of these two approaches. A few inconsistencies in species detection were observed, particularly for less abundant ones. The detailed comparison of the results for 15 bacterial species/groups measured by both methods revealed a considerable bias for certain bacterial species in the measurements of the amplicon sequencing approach. We identified as probable origin to this PCR bias due to primer mismatches, variations in the number of copies for the 16S rRNA gene, and bias introduced in the bioinformatics analysis.

Conclusion

As the normalized microbial composition results of NGS and HT-qPCR agreed for most of the 21 cheese samples analyzed, both methods can be considered as complementary and reliable for studying the microbial composition of cheese. Their combined application proved to be very helpful in identifying potential biases and overcoming methodological limitations in the quantitative analysis of the cheese microbiota.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-022-02451-y.

Challenges and future directions for studying effects of host genetics on the gut microbiome

The human gut microbiome is a complex ecosystem that is involved in its host's metabolism, immunity and health. Although interindividual variations in gut microbial composition are mainly driven by environmental factors, some gut microorganisms are heritable and thus can be influenced by host genetics. In the past 5 years, 12 microbial genome-wide association studies (mbGWAS) with >1,000 participants have been published, yet only a few genetic loci have been consistently confirmed across multiple studies. Here we discuss the state of the art for mbGWAS, focusing on current challenges such as the heterogeneity of microbiome measurements and power issues, and we elaborate on potential future directions for genetic analysis of the microbiome.

Gut microbiota can utilize prebiotic birch glucuronoxylan in production of short-chain fatty acids in rats

Birch-derived polyphenol and fiber (glucuronoxylan, GX)-rich extract and highly purified GX-rich extract support the growth of beneficial gut bacteria, suppress the harmful ones, and increase the production of total short-chain fatty acids (SCFA).

Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats

Circadian rhythms in gut microbiota composition are crucial for metabolic function, yet the extent to which they govern microbial dynamics compared to seasonal and lifetime processes remains unknown. Here, we investigate gut bacterial dynamics in wild meerkats (Suricata suricatta) over a 20-year period to compare diurnal, seasonal, and lifetime processes in concert, applying ratios of absolute abundance. We found that diurnal oscillations in bacterial load and composition eclipsed seasonal and lifetime dynamics. Diurnal oscillations were characterised by a peak in Clostridium abundance at dawn, were associated with temperature-constrained foraging schedules, and did not decay with age. Some genera exhibited seasonal fluctuations, whilst others developed with age, although we found little support for microbial senescence in very old meerkats. Strong microbial circadian rhythms in this species may reflect the extreme daily temperature fluctuations typical of arid-zone climates. Our findings demonstrate that accounting for circadian rhythms is essential for future gut microbiome research.

Compositional Data Analysis of Microbiome and Any-Omics Datasets: A Validation of the Additive Logratio Transformation

Microbiome and omics datasets are, by their intrinsic biological nature, of high dimensionality, characterized by counts of large numbers of components (microbial genes, operational taxonomic units, RNA transcripts, etc.). These data are generally regarded as compositional since the total number of counts identified within a sample is irrelevant. The central concept in compositional data analysis is the logratio transformation, the simplest being the additive logratios with respect to a fixed reference component. A full set of additive logratios is not isometric, that is they do not reproduce the geometry of all pairwise logratios exactly, but their lack of isometry can be measured by the Procrustes correlation. The reference component can be chosen to maximize the Procrustes correlation between the additive logratio geometry and the exact logratio geometry, and for high-dimensional data there are many potential references. As a secondary criterion, minimizing the variance of the reference component's log-transformed relative abundance values makes the subsequent interpretation of the logratios even easier. On each of three high-dimensional omics datasets the additive logratio transformation was performed, using references that were identified according to the abovementioned criteria. For each dataset the compositional data structure was successfully reproduced, that is the additive logratios were very close to being isometric. The Procrustes correlations achieved for these datasets were 0.9991, 0.9974, and 0.9902, respectively. We thus demonstrate, for high-dimensional compositional data, that additive logratios can provide a valid choice as transformed variables, which (a) are subcompositionally coherent, (b) explain 100% of the total logratio variance and (c) come measurably very close to being isometric. The interpretation of additive logratios is much simpler than the complex isometric alternatives and, when the variance of the log-transformed reference is very low, it is even simpler since each additive logratio can be identified with a corresponding compositional component.