Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling

Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results.

Antagonistic conflict between transposon-encoded introns and guide RNAs

TnpB nucleases represent the evolutionary precursors to CRISPR-Cas12 and are widespread in all domains of life. IS605-family TnpB homologs function as programmable RNA-guided homing endonucleases in bacteria, driving transposon maintenance through DNA double-strand break-stimulated homologous recombination. In this work, we uncovered molecular mechanisms of the transposition life cycle of IS607-family elements that, notably, also encode group I introns. We identified specific features for a candidate "IStron" from Clostridium botulinum that allow the element to carefully control the relative levels of spliced products versus functional guide RNAs. Our results suggest that IStron transcripts evolved an ability to balance competing and mutually exclusive activities that promote selfish transposon spread while limiting adverse fitness costs on the host. Collectively, this work highlights molecular innovation in the multifunctional utility of transposon-encoded noncoding RNAs.

Quantifying compositional variability in microbial communities with FAVA

Microbial communities vary across space, time, and individual hosts, presenting new challenges for the development of statistics measuring the variability of community composition. To understand differences across microbiome samples from different host individuals, sampling times, spatial locations, or experimental replicates, we present FAVA, a new normalized measure for characterizing compositional variability across multiple microbiome samples. FAVA quantifies variability across many samples of taxonomic or functional relative abundances in a single index ranging between 0 and 1, equaling 0 when all samples are identical and equaling 1 when each sample is entirely comprised of a single taxon. Its definition relies on the population-genetic statisticF S T , with samples playing the role of "populations" and taxa playing the role of "alleles." Its convenient mathematical properties allow users to compare disparate data sets. For example, FAVA values are commensurable across different numbers of taxonomic categories and different numbers of samples considered. We introduce extensions that incorporate phylogenetic similarity among taxa and spatial or temporal distances between samples. We illustrate how FAVA can be used to describe across-individual taxonomic variability in ruminant microbiomes at different regions along the gastrointestinal tract. In a second example, a longitudinal analysis of gut microbiomes of healthy human adults taking an antibiotic, we use FAVA to quantify the increase in temporal variability of microbiomes following the antibiotic course and to measure the duration of the antibiotic's influence on microbial variability. We have implemented this tool in an R package, FAVA, which can fit easily into existing pipelines for the analysis of microbial relative abundances.

Qu-omics elucidates the formation and spatio-temporal differentiation mechanism underlying the microecology of high temperature Daqu

Even if fermented in the same qu-room, Daqu will form various microecologies. A gradual differentiation of microbial population niches was observed within different qu-layers, manifesting as variations in the abundance of dominant microorganisms including Bacillus, Saccharopolyspora, Weissella, Kroppenstedtia, Thermoascus, Thermomyces, Saccharomycopsis, and Rasamsonia. Moreover, distinctions were observed in the functional expression of microorganisms, including Aspergillus, Virgibacillus, Oceanobacillus, and Neurospora. The community in middle layer Daqu exhibited characteristics of high compactness and niche diversity, which facilitated efficient substrate utilization. During the initial phase, the upper Daqu community demonstrated heightened activity. However, in the middle and lower layers, fungi and bacteria respectively exhibited greater functional expression. The key environmental factors regulating the assembly of communities in the upper and middle layers were pH and temperature, respectively, and the lower was moisture and acidity. Notably, deterministic assembly exerted a stronger influence on fungi.

Antagonistic conflict between transposon-encoded introns and guide RNAs

TnpB nucleases represent the evolutionary precursors to CRISPR-Cas12 and are widespread in all domains of life, presumably due to the critical roles they play in transposon proliferation. IS605family TnpB homologs function in bacteria as programmable homing endonucleases by exploiting transposon-encoded guide RNAs to cleave vacant genomic sites, thereby driving transposon maintenance through DSB-stimulated homologous recombination. Whether this pathway is conserved in other genetic contexts, and in association with other transposases, is unknown. Here we uncover molecular mechanisms of transposition and RNA-guided DNA cleavage by IS607-family elements that, remarkably, also encode catalytic, self-splicing group I introns. After reconstituting and systematically investigating each of these biochemical activities for a candidate 'IStron' derived from Clostridium botulinum, we discovered sequence and structural features of the transposon-encoded RNA that satisfy molecular requirements of a group I intron and TnpB guide RNA, while still retaining the ability to be faithfully mobilized at the DNA level by the TnpA transposase. Strikingly, intron splicing was strongly repressed not only by TnpB, but also by the secondary structure of ωRNA alone, allowing the element to carefully control the relative levels of spliced products versus functional guide RNAs. Our results suggest that IStron transcripts have evolved a sensitive equilibrium to balance competing and mutually exclusive activities that promote transposon maintenance while limiting adverse fitness costs on the host. Collectively, this work explains how diverse enzymatic activities emerged during the selfish spread of IS607-family elements and highlights molecular innovation in the multi-functional utility of transposon-encoded noncoding RNAs.

High-throughput microbial culturomics using automation and machine learning

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype-genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.

EMBED: Essential MicroBiomE Dynamics, a dimensionality reduction approach for longitudinal microbiome studies

Dimensionality reduction offers unique insights into high-dimensional microbiome dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar ecological perturbations. However, methods providing lower-dimensional representations of microbiome dynamics both at the community and individual taxa levels are not currently available. To that end, we present EMBED: Essential MicroBiomE Dynamics, a probabilistic nonlinear tensor factorization approach. Like normal mode analysis in structural biophysics, EMBED infers ecological normal modes (ECNs), which represent the unique orthogonal modes capturing the collective behavior of microbial communities. Using multiple real and synthetic datasets, we show that a very small number of ECNs can accurately approximate microbiome dynamics. Inferred ECNs reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Moreover, the multi-subject treatment in EMBED systematically identifies subject-specific and universal abundance dynamics that are not detected by traditional approaches. Collectively, these results highlight the utility of EMBED as a versatile dimensionality reduction tool for studies of microbiome dynamics.

Quantitative microbiome profiling reveals the developmental trajectory of the chicken gut microbiota and its connection to host metabolism

Revealing the assembly and succession of the chicken gut microbiota is critical for a better understanding of its role in chicken physiology and metabolism. However, few studies have examined dynamic changes of absolute chicken gut microbes using the quantitative microbiome profiling (QMP) method. Here, we revealed the developmental trajectory of the broiler chicken gut bacteriome and mycobiome by combining high-throughput sequencing with a microbial load quantification assay. We showed that chicken gut microbiota abundance and diversity reached a plateau at 7 days posthatch (DPH), forming segment-specific community types after 1 DPH. The bacteriome was more impacted by deterministic processes, and the mycobiome was more affected by stochastic processes. We also observed stage-specific microbes in different gut segments, and three microbial occurrence patterns including "colonization," "disappearance," and "core" were defined. The microbial co-occurrence networks were very different among gut segments, with more positive associations than negative associations. Furthermore, we provided links between the absolute changes in chicken gut microbiota and their serum metabolite variations. Time-course untargeted metabolomics revealed six metabolite clusters with different changing patterns of abundance. The foregut microbiota had more connections with chicken serum metabolites, and the gut microbes were closely related to chicken lipid and amino acid metabolism. The present study provided a full landscape of chicken gut microbiota development in a quantitative manner, and the associations between gut microbes and chicken serum metabolites further highlight the impact of gut microbiota in chicken growth and development.

An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

BACKGROUND

Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources.

RESULTS

The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

SynMADE: synthetic microbiota across diverse ecosystems

The past two decades have witnessed rapid advances in engineering individual microbial strains to produce biochemicals and biomaterials. However, engineering microbial consortia has been relatively slow. Using systems and synthetic biology approaches, researchers have been developing tools for engineering complex microbiota. In this opinion article, I discuss future directions and visions regarding developing microbiota as a biomanufacturing host. Specifically, I propose that we can develop the soil microbial community itself as a huge bioreactor. Ultimately, researchers will provide a generalizable system that enables us to understand a microbial consortium's interaction and metabolism on diverse temporal and spatial scales to address global problems, including the climate crisis, food inequality, the issue of waste, and sustainable bioproduction.

Decreased Gut Microbiome Tryptophan Metabolism and Serotonergic Signaling in Patients With Persistent Mental Health and Gastrointestinal Symptoms After COVID-19

INTRODUCTION

An estimated 15%-29% of patients report new gastrointestinal (GI) symptoms after coronavirus-19 disease (COVID-19) while 4%-31% report new depressive symptoms. These symptoms may be secondary to gut microbiome tryptophan metabolism and 5-hydroxytryptamine (5-HT)-based signaling.

METHODS

This study used specimens from 2 patient cohorts: (i) fecal samples from patients with acute COVID-19 who participated in a randomized controlled trial testing prebiotic fiber and (ii) blood samples from patients with acute COVID-19. Six months after recovering from COVID-19, both cohorts answered questions related to GI symptoms and anxiety or depression. Microbiome composition and function, focusing on tryptophan metabolism-associated pathways, and plasma 5-HT were assessed.

RESULTS

In the first cohort (n = 13), gut microbiome L-tryptophan biosynthesis during acute COVID-19 was decreased among those who developed more severe GI symptoms (2.0-fold lower log activity comparing those with the most severe GI symptoms vs those with no symptoms, P = 0.06). All tryptophan pathways showed decreased activity among those with more GI symptoms. The same pathways were also decreased in those with the most severe mental health symptoms after COVID-19. In an untargeted analysis, 5 additional metabolic pathways significantly differed based on subsequent development of GI symptoms. In the second cohort (n = 39), plasma 5-HT concentration at the time of COVID-19 was increased 5.1-fold in those with GI symptoms alone compared with those with mental health symptoms alone ( P = 0.02).

DISCUSSION

Acute gut microbiome-mediated reduction in 5-HT signaling may contribute to long-term GI and mental health symptoms after COVID-19. Future studies should explore modification of 5-HT signaling to reduce post-COVID symptoms.

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.

Microbiota imbalance induced by dietary sugar disrupts immune-mediated protection from metabolic syndrome

How intestinal microbes regulate metabolic syndrome is incompletely understood. We show that intestinal microbiota protects against development of obesity, metabolic syndrome, and pre-diabetic phenotypes by inducing commensal-specific Th17 cells. High-fat, high-sugar diet promoted metabolic disease by depleting Th17-inducing microbes, and recovery of commensal Th17 cells restored protection. Microbiota-induced Th17 cells afforded protection by regulating lipid absorption across intestinal epithelium in an IL-17-dependent manner. Diet-induced loss of protective Th17 cells was mediated by the presence of sugar. Eliminating sugar from high-fat diets protected mice from obesity and metabolic syndrome in a manner dependent on commensal-specific Th17 cells. Sugar and ILC3 promoted outgrowth of Faecalibaculum rodentium that displaced Th17-inducing microbiota. These results define dietary and microbiota factors posing risk for metabolic syndrome. They also define a microbiota-dependent mechanism for immuno-pathogenicity of dietary sugar and highlight an elaborate interaction between diet, microbiota, and intestinal immunity in regulation of metabolic disorders.

The systemic anti-microbiota IgG repertoire can identify gut bacteria that translocate across gut barrier surfaces

Unique gut microbiota compositions have been associated with inflammatory diseases, but identifying gut bacterial functions linked to immune activation in humans remains challenging. Translocation of pathogens from mucosal surfaces into peripheral tissues can elicit immune activation, although whether and which gut commensal bacteria translocate in inflammatory diseases is difficult to assess. We report that a subset of commensal gut microbiota constituents that translocate across the gut barrier in mice and humans are associated with heightened systemic immunoglobulin G (IgG) responses. We present a modified high-throughput, culture-independent approach to quantify systemic IgG against gut commensal bacteria in human serum samples without the need for paired stool samples. Using this approach, we highlight several commensal bacterial species that elicit elevated IgG responses in patients with inflammatory bowel disease (IBD) including taxa within the clades Collinsella, Bifidobacterium, Lachnospiraceae, and Ruminococcaceae. These and other taxa identified as translocating bacteria or targets of systemic immunity in IBD concomitantly exhibited heightened transcriptional activity and growth rates in IBD patient gut microbiomes. Our approach represents a complementary tool to illuminate interactions between the host and its gut microbiota and may provide an additional method to identify microbes linked to inflammatory disease.

Standards for Collection, Preservation, and Transportation of Fecal Samples in TCM Clinical Trials

Background

Unlike chemical drugs with a single or a few kinds of active compounds, traditional Chinese medicines (TCMs)uses herbal formulas composed of numerous kinds of chemical constituents. Therefore, TCM clinical trials require unique and stricter standards for collecting, preserving, and transporting fecal samples than those used for chemical drugs. Unfortunately, there are no special standards for processing fecal samples in TCM clinical trials.

Methods

We invited interdisciplinary experts within TCM clinical trials and gut microbiome research to help formulate this standard. After more than a year’s in-depth discussion and amendments, we achieved a standard via expert interviews, literature research, questionnaire surveys, and public opinion solicitation. This standard has been reviewed and approved by the Standards Office of China of the Association of Chinese medicine.

Results

We established a sample information processing method prior to TCM clinical sample collection, which is adapted to the unique features of TCM. The method formulates detailed processing requirements for TCM information in addition to the factors that may disturb the gut microbiome. We also constructed a set of methods for collecting, preserving, and transporting fecal samples that meet the characteristics of TCM. These methods formulate detailed operating specifications on the collection approaches, storage conditions, transportation requirements, and management of fecal samples.

Conclusions

This standard guides the information processing prior to sample collection and the standard operating procedures for the collection, preservation, and transportation of fecal samples in TCM clinical trials, which also can be used as a reference by clinicians and researchers in modern medicines.

Engineering living and regenerative fungal-bacterial biocomposite structures

Engineered living materials could have the capacity to self-repair and self-replicate, sense local and distant disturbances in their environment, and respond with functionalities for reporting, actuation or remediation. However, few engineered living materials are capable of both responsivity and use in macroscopic structures. Here we describe the development, characterization and engineering of a fungal-bacterial biocomposite grown on lignocellulosic feedstocks that can form mouldable, foldable and regenerative living structures. We have developed strategies to make human-scale biocomposite structures using mould-based and origami-inspired growth and assembly paradigms. Microbiome profiling of the biocomposite over multiple generations enabled the identification of a dominant bacterial component, Pantoea agglomerans, which was further isolated and developed into a new chassis. We introduced engineered P. agglomerans into native feedstocks to yield living blocks with new biosynthetic and sensing-reporting capabilities. Bioprospecting the native microbiota to develop engineerable chassis constitutes an important strategy to facilitate the development of living biomaterials with new properties and functionalities.

Growth promotion and antibiotic induced metabolic shifts in the chicken gut microbiome

Antimicrobial growth promoters (AGP) have played a decisive role in animal agriculture for over half a century. Despite mounting concerns about antimicrobial resistance and demand for antibiotic alternatives, a thorough understanding of how these compounds drive performance is missing. Here we investigate the functional footprint of microbial communities in the cecum of chickens fed four distinct AGP. We find relatively few taxa, metabolic or antimicrobial resistance genes similarly altered across treatments, with those changes often driven by the abundances of core microbiome members. Constraints-based modeling of 25 core bacterial genera associated increased performance with fewer metabolite demands for microbial growth, pointing to altered nitrogen utilization as a potential mechanism of narasin, the AGP with the largest performance increase in our study. Untargeted metabolomics of narasin treated birds aligned with model predictions, suggesting that the core cecum microbiome might be targeted for enhanced performance via its contribution to host-microbiota metabolic crosstalk.

This study compares the functional profiles of the cecal microbiome among chickens fed four different antimicrobial growth promoters. Chickens receiving narasin exhibited the largest performance increase via apparent nitrogen recycling by the core cecal microbiome.

High-throughput identification and quantification of single bacterial cells in the microbiota

The bacterial microbiota works as a community that consists of many individual organisms, i.e., cells. To fully understand the function of bacterial microbiota, individual cells must be identified; however, it is difficult with current techniques. Here, we develop a method, Barcoding Bacteria for Identification and Quantification (BarBIQ), which classifies single bacterial cells into taxa–named herein cell-based operational taxonomy units (cOTUs)–based on cellularly barcoded 16S rRNA sequences with single-base accuracy, and quantifies the cell number for each cOTU in the microbiota in a high-throughput manner. We apply BarBIQ to murine cecal microbiotas and quantify in total 3.4 × 10⁵ bacterial cells containing 810 cOTUs. Interestingly, we find location-dependent global differences in the cecal microbiota depending on the dietary vitamin A deficiency, and more differentially abundant cOTUs at the proximal location than the distal location. Importantly, these location differences are not clearly shown by conventional 16S rRNA gene-amplicon sequencing methods, which quantify the 16S rRNA genes, not the cells. Thus, BarBIQ enables microbiota characterization with the identification and quantification of individual constituent bacteria, which is a cornerstone for microbiota studies.

Here, Jin et al., develop a method called Barcoding Bacteria for Identification and Quantification (BarBIQ), which allows to both characterize the global microbiome and to identify and quantify single-cell bacterial members in a high-throughput manner.

Two Blautia Species Associated with Visceral Fat Accumulation: A One-Year Longitudinal Study

Simple Summary

Intestinal microflora has been associated with obesity. While cardiovascular disorders are more strongly associated with visceral fat than the body mass index (BMI), the link between visceral fat area (VFA) and intestinal microflora has been little studied. In this study, we investigated the association between intestinal microflora and VFA and BMI using a longitudinal study (N = 767). We found that the intestinal microflora composition is significantly associated with VFA or BMI; however, the associated gut microbes differ. Furthermore, two gut species—Blautia hansenii and Blautia producta—were significantly and negatively associated with VFA accumulation.

Abstract

Intestinal microflora has been associated with obesity. While visceral fat is more strongly associated with cardiovascular disorder, a complication linked to obesity, than the body mass index (BMI), the association between intestinal microflora and obesity (as defined in terms of BMI) has been studied widely. However, the link between visceral fat area (VFA) and intestinal microflora has been little studied. In this study, we investigate the association between intestinal microflora and VFA and BMI using a longitudinal study on Japanese subjects with different VFA statuses (N = 767). Principal component analysis of the changes in intestinal microflora composition over the one-year study period revealed the different associations between intestinal microflora and VFA and BMI. As determined by 16S rRNA amplicon sequencing, changes in the abundance ratio of two microbial genera—Blautia and Flavonifractor—were significantly associated with VFA changes and changes in the abundance ratio of four different microbial genera were significantly associated with BMI changes, suggesting that the associated intestinal microbes are different. Furthermore, as determined by metagenomic shotgun sequences, changes in the abundance ratios of two Blautia species—Blautia hansenii and Blautia producta—were significantly and negatively associated with VFA changes. Our findings might be used to develop a new treatment for visceral fat.

A Simple, Cost-Effective, and Automation-Friendly Direct PCR Approach for Bacterial Community Analysis

Bacterial communities in water, soil, and humans play an essential role in environmental ecology and human health. PCR-based amplicon analysis, such as 16S rRNA sequencing, is a fundamental tool for quantifying and studying microbial composition, dynamics, and interactions. However, given the complexity of microbial communities, a substantial number of samples becomes necessary for analyses that parse the factors that determine microbial composition. A common bottleneck in performing these kinds of experiments is genomic DNA (gDNA) extraction, which is time-consuming, expensive, and often biased based on the types of species present. Direct PCR method is a potentially simpler and more accurate alternative to gDNA extraction methods that do not require the intervening purification step. In this study, we evaluated three variations of direct PCR methods using diverse heterogeneous bacterial cultures, including both Gram-positive and Gram-negative species, ZymoBIOMICS microbial community standards, and groundwater. By comparing direct PCR methods with DNeasy Blood and Tissue Kits for microbial isolates and DNeasy PowerSoil Kits for microbial communities, we found that a specific variant of the direct PCR method exhibits an overall efficiency comparable to that of the conventional DNeasy PowerSoil protocol in the circumstances we tested. We also found that the method showed higher efficiency for extracting gDNA from the Gram-negative strains compared to DNeasy Blood and Tissue protocol. This direct PCR method is 1,600 times less expensive ($0.34 for 96 samples) and 10 times simpler (15 min hands-on time for 96 samples) than the DNeasy PowerSoil protocol. The direct PCR method can also be fully automated and is compatible with small-volume samples, thereby permitting scaling of samples and replicates needed to support high-throughput large-scale bacterial community analysis.

IMPORTANCE Understanding bacterial interactions and assembly in complex microbial communities using 16S rRNA sequencing normally requires a large experimental load. However, the current DNA extraction methods, including cell disruption and genomic DNA purification, are normally biased, costly, time-consuming, labor-intensive, and not amenable to miniaturization by droplets or 1,536-well plates due to the significant DNA loss during the purification step for tiny-volume and low-cell-density samples. A direct PCR method could potentially solve these problems. In this study, we developed a direct PCR method which exhibits similar efficiency as the widely used method, the DNeasy PowerSoil protocol, while being 1,600 times less expensive and 10 times faster to execute. This simple, cost-effective, and automation-friendly direct-PCR-based 16S rRNA sequencing method allows us to study the dynamics, microbial interaction, and assembly of various microbial communities in a high-throughput fashion.

Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay

The community of the diverse microorganisms residing in the gastrointestinal tract, known as the gut microbiota, is exceedingly being studied for its impact on health and disease. This community plays a major role in nutrient metabolism, maintenance of the intestinal epithelial barrier but also in local and systemic immunomodulation. A dysbiosis of the gut microbiota, characterized by an unbalanced microbial ecology, often leads to a loss of essential functions that may be associated with proinflammatory conditions. Specifically, some key microbes that are depleted in dysbiotic ecosystems, called keystone species, carry unique functions that are essential for the balance of the microbiota. In this review, we discuss current understanding of reported keystone species and their proposed functions in health. We also elaborate on current and future bioinformatics tools needed to identify missing functions in the gut carried by keystone species. We propose that the identification of such keystone species functions is a major step for the understanding of microbiome dynamics in disease and toward the development of microbiome-based therapeutics.

Measuring the absolute abundance of the microbiome by adding yeast containing 16S rRNA gene from a hyperthermophile

High-throughput sequencing (HTS) of 16S rRNA gene amplicons provides compositional information regarding the microbial community, but not the absolute abundance of the bacteria. We aimed to develop a standardized method for quantifying the absolute abundance of bacteria in microbiome studies. To demonstrate the utility of our approach, we quantified the number of bacteria from the compositional data of the fecal and cecal microbiomes. The 16S rRNA gene of a hyperthermophile, Thermus aquaticus, was cloned into Pichia pastoris (yeast) genome, and an equivalent amount of the yeast was added to the stool and cecal samples of mice before DNA extraction. 16S rRNA gene library construction and HTS were performed after DNA extraction. The absolute abundances of bacteria were calculated using T. aquaticus reads. The average relative abundances of T. aquaticus in the five stool and five cecal samples were 0.95% and 0.33%, respectively, indicating that the number of bacteria in a cecum sample is 2.9 times higher than that in a stool sample. The method proposed for quantifying the absolute abundance of the bacterial population in this study is expected to overcome the limitation of showing only compositional data in most microbiome studies.

Characterizing Microbiomes via Sequencing of Marker Loci: Techniques To Improve Throughput, Account for Cross-Contamination, and Reduce Cost

New approaches to characterizing microbiomes via high-throughput sequencing provide impressive gains in efficiency and cost reduction compared to approaches that were standard just a few years ago. However, the speed of method development has been such that staying abreast of the latest technological advances is challenging. Moreover, shifting laboratory protocols to include new methods can be expensive and time consuming. To facilitate adoption of new techniques, we provide a guide and review of recent advances that are relevant for single-locus sequence-based study of microbiomes—from extraction to library preparation—including a primer regarding the use of liquid-handling automation in small-scale academic settings. Additionally, we describe several amendments to published techniques to improve throughput, track contamination, and reduce cost. Notably, we suggest adding synthetic DNA molecules to each sample during nucleic acid extraction, thus providing a method of documenting incidences of cross-contamination. We also describe a dual-indexing scheme for Illumina sequencers that allows multiplexing of many thousands of samples with minimal PhiX input. Collectively, the techniques that we describe demonstrate that laboratory technology need not impose strict limitations on the scale of molecular microbial ecology studies.

IMPORTANCE New methods to characterize microbiomes reduce technology-imposed limitations to study design, but many new approaches have not been widely adopted. Here, we present techniques to increase throughput and reduce contamination alongside a thorough review of current best practices.

Probiotics-induced changes in gut microbial composition and its effects on cognitive performance after stress: exploratory analyses

Stress negatively affects cognitive performance. Probiotics remediate somatic and behavioral stress responses, hypothetically by acting on the gut microbiota. Here, in exploratory analyses, we assessed gut microbial alterations after 28-days supplementation of multi-strain probiotics (EcologicBarrier consisting of Lactobacilli, Lactococci, and Bifidobacteria in healthy, female subjects (probiotics group n = 27, placebo group n = 29). In an identical pre-session and post-session, subjects performed a working memory task before and after an acute stress intervention. Global gut microbial beta diversity changed over time, but we were not able to detect differences between intervention groups. At the taxonomic level, Time by Intervention interactions were not significant after multiple comparison correction; the relative abundance of eight genera in the probiotics group was higher (uncorrected) relative to the placebo group: Butyricimonas, Parabacteroides, Alistipes, Christensenellaceae_R-7_group, Family_XIII_AD3011_group, Ruminococcaceae_UCG-003, Ruminococcaceae_UCG-005, and Ruminococcaceae_UCG-010. In a second analysis step, association analyses were done only within this selection of microbial genera, revealing the probiotics-induced change in genus Ruminococcaceae_UCG-003 was significantly associated with probiotics' effect on stress-induced working memory changes (rspearman(27) = 0.565; pFDR = 0.014) in the probiotics group only and independent of potential confounders (i.e., age, BMI, and baseline dietary fiber intake). That is subjects with a higher increase in Ruminococcaceae_UCG-003 abundance after probiotics were also more protected from negative effects of stress on working memory after probiotic supplementation. The bacterial taxa showing an increase in relative abundance in the probiotics group are plant fiber degrading bacteria and produce short-chain fatty acids that are known for their beneficial effect on gut and brain health, e.g., maintaining intestinal-barrier and blood-brain-barrier integrity. This study shows that gut microbial alterations, modulated through probiotics use, are related to improved cognitive performance in acute stress circumstances.

Progress and Prospects of Mycorrhizal Fungal Diversity in Orchids

Orchids form mycorrhizal symbioses with fungi in natural habitats that affect their seed germination, protocorm growth, and adult nutrition. An increasing number of studies indicates how orchids gain mineral nutrients and sometime even organic compounds from interactions with orchid mycorrhizal fungi (OMF). Thus, OMF exhibit a high diversity and play a key role in the life cycle of orchids. In recent years, the high-throughput molecular identification of fungi has broadly extended our understanding of OMF diversity, revealing it to be a dynamic outcome co-regulated by environmental filtering, dispersal restrictions, spatiotemporal scales, biogeographic history, as well as the distribution, selection, and phylogenetic spectrum width of host orchids. Most of the results show congruent emerging patterns. Although it is still difficult to extend them to all orchid species or geographical areas, to a certain extent they follow the "everything is everywhere, but the environment selects" rule. This review provides an extensive understanding of the diversity and ecological dynamics of orchid-fungal association. Moreover, it promotes the conservation of resources and the regeneration of rare or endangered orchids. We provide a comprehensive overview, systematically describing six fields of research on orchid-fungal diversity: the research methods of orchid-fungal interactions, the primer selection in high-throughput sequencing, the fungal diversity and specificity in orchids, the difference and adaptability of OMF in different habitats, the comparison of OMF in orchid roots and soil, and the spatiotemporal variation patterns of OMF. Further, we highlight certain shortcomings of current research methodologies and propose perspectives for future studies. This review emphasizes the need for more information on the four main ecological processes: dispersal, selection, ecological drift, and diversification, as well as their interactions, in the study of orchid-fungal interactions and OMF community structure.

Reanalysis of the Mars500 experiment reveals common gut microbiome alterations in astronauts induced by long-duration confinement

Maintaining astronaut health throughout long-duration spaceflight is essential to the feasibility of a manned mission to Mars. The ground-based Mars500 experiment investigated long-duration health by isolating six astronauts for 520 days, the longest controlled human confinement study conducted to date. After 520 days, astronauts had uniform strength and lean body mass losses, and increased fasting plasma glucose, calprotectin, and neutrophil levels characteristic of intestinal inflammation but previous analyses revealed no common significant changes in gut microbiota. This study reanalysed data from early (days 7–45) and late (days 420–520) faecal samples and identified 408 exact sequence variants (ESVs), including 213 shared by all astronauts. Thirty-two ESVs were significantly differentially abundant over time, including depletion of keystone resistant starch degrading, anti-inflammatory and insulin sensitivity-associated species, such as Faecalibacterium prausnitzii, Ruminococcus bromii, Blautia luti, Anaerostipes hadrus, Roseburia faecis, and Lactobacillus rogosae, and enrichment of yet-to-be-cultured bacteria. Additionally, the extraordinary experimental confinement allowed observation of microbiota potentially shared between astronauts and their habitat. Forty-nine species were shared, representing 49% and 12% of the human and environmental microbiome diversity, respectively. These findings reveal the microbiota which significantly altered in relative abundance throughout confinement, including species known to influence inflammation and host glucose homeostasis consistent with astronaut symptoms. Identification of microbiome alterations after 520 days of isolation represents a missing piece connecting Mars500 astronaut physiological studies. Knowledge of the impact of long-term confinement upon the human microbiome helps to improve our understanding of how humans interact with their habitats and is a valuable step forward towards enabling long-duration spaceflight.

Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data

A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions.

IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention

Quantitatively understanding and predicting spatiotemporal dynamics of microbiota is imperative for development of tailored microbiome-directed therapeutics treatments. However, the complexity of microbial variations, due to interactions with the host, other microbes, and environmental factors, makes it challenging to identify how microbiota colonize in the human gut. Here, we describe a novel multiscale framework for COmputing the DYnamics of the gut microbiota (CODY), which enables the quantification of spatiotemporal-specific variations of gut microbiome abundance profiles, without a prior knowledge of microbiome interactions. Importantly, the predictive power of CODY is demonstrated using cross-sectional data from two longitudinal metagenomics studies—the microbiota development during early infancy and during short-term diet intervention of obese adults.

Microbial variations in the human gut are harbored in temporal and spatial heterogeneity, and quantitative prediction of spatiotemporal dynamic changes in the gut microbiota is imperative for development of tailored microbiome-directed therapeutics treatments, e.g. precision nutrition. Given the high-degree complexity of microbial variations, subject to the dynamic interactions among host, microbial, and environmental factors, identifying how microbiota colonize in the gut represents an important challenge. Here we present COmputing the DYnamics of microbiota (CODY), a multiscale framework that integrates species-level modeling of microbial dynamics and ecosystem-level interactions into a mathematical model that characterizes spatial-specific in vivo microbial residence in the colon as impacted by host physiology. The framework quantifies spatiotemporal resolution of microbial variations on species-level abundance profiles across site-specific colon regions and in feces, independent of a priori knowledge. We demonstrated the effectiveness of CODY using cross-sectional data from two longitudinal metagenomics studies—the microbiota development during early infancy and during short-term diet intervention of obese adults. For each cohort, CODY correctly predicts the microbial variations in response to diet intervention, as validated by available metagenomics and metabolomics data. Model simulations provide insight into the biogeographical heterogeneity among lumen, mucus, and feces, which provides insight into how host physical forces and spatial structure are shaping microbial structure and functionality.

Mining microbes for mental health: Determining the role of microbial metabolic pathways in human brain health and disease

There is increasing knowledge regarding the role of the microbiome in modulating the brain and behaviour. Indeed, the actions of microbial metabolites are key for appropriate gut-brain communication in humans. Among these metabolites, short-chain fatty acids, tryptophan, and bile acid metabolites/pathways show strong preclinical evidence for involvement in various aspects of brain function and behaviour. With the identification of neuroactive gut-brain modules, new predictive tools can be applied to existing datasets. We identified 278 studies relating to the human microbiota-gut-brain axis which included sequencing data. This spanned across psychiatric and neurological disorders with a small number also focused on normal behavioural development. With a consistent bioinformatics pipeline, thirty-five of these datasets were reanalysed from publicly available raw sequencing files and the remainder summarised and collated. Among the reanalysed studies, we uncovered evidence of disease-related alterations in microbial metabolic pathways in Alzheimer's Disease, schizophrenia, anxiety and depression. Amongst studies that could not be reanalysed, many sequencing and technical limitations hindered the discovery of specific biomarkers of microbes or metabolites conserved across studies. Future studies are warranted to confirm our findings. We also propose guidelines for future human microbiome analysis to increase reproducibility and consistency within the field.

Robust direct digital-to-biological data storage in living cells

DNA has been the predominant information storage medium for biology and holds great promise as a next-generation high-density data medium in the digital era. Currently, the vast majority of DNA-based data storage approaches rely on in vitro DNA synthesis. As such, there are limited methods to encode digital data into the chromosomes of living cells in a single step. Here, we describe a new electrogenetic framework for direct storage of digital data in living cells. Using an engineered redox-responsive CRISPR adaptation system, we encoded binary data in 3-bit units into CRISPR arrays of bacterial cells by electrical stimulation. We demonstrate multiplex data encoding into barcoded cell populations to yield meaningful information storage and capacity up to 72 bits, which can be maintained over many generations in natural open environments. This work establishes a direct digital-to-biological data storage framework and advances our capacity for information exchange between silicon- and carbon-based entities.

Gradient Internal Standard Method for Absolute Quantification of Microbial Amplicon Sequencing Data

High-throughput amplicon sequencing is a critical tool for studying microbiota; however, it results only in relative abundance data. Thus, changes in absolute abundance of microbiota cannot be determined, which hinders further microbiology research. We have therefore established a gradient internal standard absolute quantification (GIS-AQ) method to overcome this issue, which can simultaneously obtain the absolute abundances of bacteria and fungi. Deviations from the quantitative equations of microbes and internal standards were eliminated through calibration. Compared with traditional quantitative real-time PCR and microscopy quantifications, this method is reliable (R 2 average = 0.998; P < 0.001) and accurate (P internals versus microscopy > 0.05). The GIS-AQ method can be adapted to any amplicon primer choice (e.g., 336F/806R and ITS3/ITS4), rendering it applicable to ecosystem studies including food, soil, and water samples. Crucially, when using solid-state fermentation samples from various temporal dimensions, the results obtained from the relative and absolute abundance are different. The absolute abundance can be used to study the difference in communities between different samples, and the GIS-AQ method allows this to be done rapidly. Therefore, combining the absolute abundance with relative abundance can accurately reflect the microbiota composition.IMPORTANCE To solve the problem of amplicon sequencing cannot discern the microbiota absolute abundance, we proposed a gradient internal standard absolute quantification method. We used Chinese liquor fermentation as a model system to demonstrate the reliability and accuracy of the method. By comparing the relative and absolute abundances of microbiota in various temporal dimensions, we found dynamic changes in the absolute abundance of communities under various temporal dimensions from the relative abundance. Based on its design principle, this method can be widely applied to different ecosystems. Therefore, we believe that the GIS-AQ method can play an immeasurably useful role in microbiological research.

The quest for absolute abundance: The use of internal standards for DNA-based community ecology

To characterize microbiomes and other ecological assemblages, ecologists routinely sequence and compare loci that differ among focal taxa. Counts of these sequences convey information regarding the occurrence and relative abundances of taxa, but provide no direct measure of their absolute abundances, due to the technical limitations of the sequencing process. The relative abundances in compositional data are inherently constrained and difficult to interpret. The incorporation of internal standards (ISDs; colloquially referred to as 'spike-ins') into DNA pools can ameliorate the problems posed by relative abundance data and allow absolute abundances to be approximated. Unfortunately, many laboratory and sampling biases cause ISDs to underperform or fail. Here, we discuss how careful deployment of ISDs can avoid these complications and be an integral component of well-designed studies seeking to characterize ecological assemblages via sequencing of DNA.

How to Count Our Microbes? The Effect of Different Quantitative Microbiome Profiling Approaches

Next-generation sequencing (NGS) has instigated the research on the role of the microbiome in health and disease. The compositional nature of such microbiome datasets makes it however challenging to identify those microbial taxa that are truly associated with an intervention or health outcome. Quantitative microbiome profiling overcomes the compositional structure of microbiome sequencing data by integrating absolute quantification of microbial abundances into the NGS data. Both cell-based methods (e.g., flow cytometry) and molecular methods (qPCR) have been used to determine the absolute microbial abundances, but to what extent different quantification methods generate similar quantitative microbiome profiles has so far not been explored. Here we compared relative microbiome profiling (without incorporation of microbial quantification) to three variations of quantitative microbiome profiling: (1) microbial cell counting using flow cytometry (QMP), (2) counting of microbial cells using flow cytometry combined with Propidium Monoazide pre-treatment of fecal samples before metagenomics DNA isolation in order to only profile the microbial composition of intact cells (QMP-PMA), and (3) molecular based quantification of the microbial load using qPCR targeting the 16S rRNA gene. Although qPCR and flow cytometry both resulted in accurate and strongly correlated results when quantifying the bacterial abundance of a mock community of bacterial cells, the two methods resulted in highly divergent quantitative microbial profiles when analyzing the microbial composition of fecal samples from 16 healthy volunteers. These differences could not be attributed to the presence of free extracellular prokaryotic DNA in the fecal samples as sample pre-treatment with Propidium Monoazide did not improve the concordance between qPCR-based and flow cytometry-based QMP. Also lack of precision of qPCR was ruled out as a major cause of the disconcordant findings, since quantification of the fecal microbial load by the highly sensitive digital droplet PCR correlated strongly with qPCR. In conclusion, quantitative microbiome profiling is an elegant approach to bypass the compositional nature of microbiome NGS data, however it is important to realize that technical sources of variability may introduce substantial additional bias depending on the quantification method being used.

A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities

A fundamental goal in microbiome studies is determining which microbes affect host physiology. Standard methods for determining changes in microbial taxa measure relative, rather than absolute abundances. Moreover, studies often analyze only stool, despite microbial diversity differing substantially among gastrointestinal (GI) locations. Here, we develop a quantitative framework to measure absolute abundances of individual bacterial taxa by combining the precision of digital PCR with the high-throughput nature of 16S rRNA gene amplicon sequencing. In a murine ketogenic-diet study, we compare microbial loads in lumenal and mucosal samples along the GI tract. Quantitative measurements of absolute (but not relative) abundances reveal decreases in total microbial loads on the ketogenic diet and enable us to determine the differential effects of diet on each taxon in stool and small-intestine mucosa samples. This rigorous quantitative microbial analysis framework, appropriate for diverse GI locations enables mapping microbial biogeography of the mammalian GI tract and more accurate analyses of changes in microbial taxa in microbiome studies.

Scalable and cost-effective ribonuclease-based rRNA depletion for transcriptomics

Bacterial RNA sequencing (RNA-seq) is a powerful approach for quantitatively delineating the global transcriptional profiles of microbes in order to gain deeper understanding of their physiology and function. Cost-effective bacterial RNA-seq requires efficient physical removal of ribosomal RNA (rRNA), which otherwise dominates transcriptomic reads. However, current methods to effectively deplete rRNA of diverse non-model bacterial species are lacking. Here, we describe a probe and ribonuclease based strategy for bacterial rRNA removal. We implemented the method using either chemically synthesized oligonucleotides or amplicon-based single-stranded DNA probes and validated the technique on three novel gut microbiota isolates from three distinct phyla. We further showed that different probe sets can be used on closely related species. We provide a detailed methods protocol, probe sets for >5000 common microbes from RefSeq, and an online tool to generate custom probe libraries. This approach lays the groundwork for large-scale and cost-effective bacterial transcriptomics studies.

Recording mobile DNA in the gut microbiota using an Escherichia coli CRISPR-Cas spacer acquisition platform

The flow of genetic material between bacteria is central to the adaptation and evolution of bacterial genomes. However, our knowledge about DNA transfer within complex microbiomes is lacking, with most studies of horizontal gene transfer (HGT) relying on bioinformatic analyses of genetic elements maintained on evolutionary timescales or experimental measurements of phenotypically trackable markers. Here, we utilize the CRISPR-Cas spacer acquisition process to detect DNA acquisition events from complex microbiota in real-time and at nucleotide resolution. In this system, an E. coli recording strain is exposed to a microbial sample and spacers are acquired from transferred plasmids and permanently stored in genomic CRISPR arrays. Sequencing and analysis of acquired spacers enables identification of the transferred plasmids. This approach allowed us to identify individual mobile elements without relying on phenotypic markers or post-transfer replication. We found that HGT into the recording strain in human clinical fecal samples can be extensive and is driven by different plasmid types, with the IncX type being the most actively transferred.

Spatial metagenomic characterization of microbial biogeography in the gut

Spatial structuring is important for the maintenance of natural ecological systems^1,2. Many microbial communities, including the gut microbiome, display intricate spatial organization^3–9. Mapping the biogeography of bacteria can shed light on interactions that underlie community functions^10–12, but existing methods cannot accommodate hundreds of species found in natural microbiomes^13–17. Here we describe metagenomic plot-sampling by sequencing (MaP-Seq), a culture-independent method to characterize the spatial organization of a microbiome at micron-scale resolution. Intact microbiome samples are immobilized in a gel matrix and cryo-fractured into particles. Neighboring microbial taxa in the particles are then identified by droplet-based encapsulation, barcoded 16S rRNA amplification and deep sequencing. Analysis of three regions of the mouse intestine revealed heterogeneous microbial distributions with positive and negative co-associations between specific taxa. We identified robust associations between Bacteroidales taxa in all gut compartments and showed that phylogenetically clustered local regions of bacteria were associated with a dietary perturbation. Spatial metagenomics could be used to study microbial biogeography in complex habitats.