Pre-transplant and longitudinal changes in faecal microbiome characteristics are associated with subsequent development of chronic graft-versus-host disease.

The role of the gastrointestinal microbiome in predisposing to chronic graft-versus-host disease (cGVHD), an immune-mediated haematopoietic cell transplant (HCT) complication, is not well defined. We examined the relationship of the host faecal microbiome with subsequent cGVHD development by analysing baseline stool samples as well as post-HCT changes in microbiome composition and metabolite pathway analyses. We analysed pre-transplant baseline samples from 11 patients who subsequently developed cGVHD compared to 13 controls who did not develop acute GVHD or cGVHD at any time. We found a significant differential abundance of multiple taxa at baseline between cGVHD versus controls, including the Actinobacteria phylum and Clostridium genus. A subgroup analysis of longitudinal samples within each patient revealed a greater loss of alpha diversity from baseline to post-engraftment in patients who subsequently developed cGVHD. Metabolic pathways analysis revealed that two pathways associated with short-chain fatty acid metabolism were enriched in cGVHD patient microbiomes: ß-oxidation and acyl-CoA synthesis, and γ-aminobutyrate shunt. In contrast, a tryptophan catabolism pathway was enriched in controls. Our findings show a distinct pattern of baseline microbiome and metabolic capacity that may play a role in modulating alloreactivity in patients developing cGVHD. These findings support the therapeutic potential of microbiome manipulation for cGVHD prevention.

Massive blood transfusion following older adult trauma: The effect of blood ratios on mortality.

BACKGROUND: Massive blood transfusion (MBT) following older adult trauma poses unique challenges. Despite extensive evidence on optimal resuscitative strategies in the younger adult patients, there is limited research in the older adult population. METHODS: We used the Trauma Quality Improvement Program (TQIP) database from 2013 to 2017 to identify all patients over 65 years old who received a MBT. We stratified our population into six fresh-frozen plasma:packed red blood cell (FFP:pRBC) ratio cohorts (1:1, 1:2, 1:3, 1:4, 1:5, 1:6+). Our primary outcomes were 24-h and 30-day mortality. We constructed multivariable regression models with 1:1 group as the baseline and adjusted for confounders to estimate the independent effect of blood ratios on mortality. RESULTS: A total of 3134 patients met our inclusion criteria (median age 73 ± 7.6 years, 65% male). On risk-adjusted multivariable analysis, 1:1 FFP:pRBC ratio was independently associated with lowest 24-h mortality (1:2 odds ratio [OR] 1.60, 95% confidence interval [CI] 1.25-2.06, p < 0.001) and 30-day mortality (1:2 OR 1.44, 95% CI 1.15-1.80, p = 0.002). CONCLUSIONS: Compared to all other ratios, the 1:1 FFP:pRBC ratio had the lowest 24-h and 30-day mortality following older adult trauma consistent with findings in the younger adult population.

Bacterial communities associated with wood rot fungi that use distinct decomposition mechanisms.

Wood decomposer fungi are grouped by how they extract sugars from lignocellulose. Brown rot fungi selectively degrade cellulose and hemicellulose, leaving lignin intact, and white rot fungi degrade all components. Many trees are susceptible to both rot types, giving carbon in Earth's woody biomass, specifically lignin, a flexible fate that is affected not only by the fungal decomposition mechanism but also the associated microbial community. However, little is understood about how rot type may influence the microbial community in decaying wood. In this study, we quantified bacterial communities associated with Fomes fomentarius (white rot) and Fomitopsis betulina (brown rot) found on a shared tree host species, birch (Betula papyrifera). We collected 25 wood samples beneath sporocarps  of F. fomentarius (n = 13) and F. betulina (n = 12) on standing dead trees, and coupled microbial DNA sequencing with chemical signatures of rot type (pH and lignin removal). We found that bacterial communities for both fungi were dominated by Proteobacteria, a commonly reported association. However, rot type exerted significant influence on less abundant taxa in ways that align logically with fungal traits. Amplicon sequence variants (ASVs) were enriched in Firmicutes in white-rotted wood, and were enriched in Alphaproteobacteria, Actinobacteria and Acidobacteria in lower pH brown rot. Our results suggest that wood decomposer strategies may exert significant selection effects on bacteria, or vice versa, among less-abundant taxa that have been overlooked when using abundance as the only measure of influence.

Saliva Testing Is Accurate for Early-Stage and Presymptomatic COVID-19.

Although nasopharyngeal samples have been considered the gold standard for COVID-19 testing, variability in viral load across different anatomical sites could cause nasopharyngeal samples to be less sensitive than saliva or nasal samples in certain cases. Self-collected samples have logistical advantages over nasopharyngeal samples, making them amenable to population-scale screening. To evaluate sampling alternatives for population screening, we collected nasopharyngeal, saliva, and nasal samples from two cohorts with varied levels and types of symptoms. In a mixed cohort of 60 symptomatic and asymptomatic participants, we found that saliva had 88% concordance with nasopharyngeal samples when tested in the same testing lab (n = 41) and 68% concordance when tested in different testing labs (n = 19). In a second cohort of 20 participants hospitalized for COVID-19, saliva had 74% concordance with nasopharyngeal samples tested in the same testing lab but detected virus in two participants that tested negative with nasopharyngeal samples on the same day. Medical record review showed that the saliva-based testing sensitivity was related to the timing of symptom onset and disease stage. We find that no sample site will be perfectly sensitive for COVID-19 testing in all situations, and the significance of negative results will always need to be determined in the context of clinical signs and symptoms. Saliva retained high clinical sensitivity for early-stage and presymptomatic COVID-19 while allowing easier collection, minimizing the exposure of health care workers, and need for personal protective equipment and making it a viable option for population-scale testing. IMPORTANCE Methods for COVID-19 detection are necessary for public health efforts to monitor the spread of disease. Nasopharyngeal samples have been considered the best approach for COVID-19 testing. However, alternative samples like self-collected saliva offer advantages for population-scale screening. Meta-analyses of recent studies suggest that saliva is useful for detecting SARS-CoV-2; however, differences in disease prevalence, sample collection, and analysis methods still confound strong conclusions on the utility of saliva compared to nasopharyngeal samples. Here, we find that the sensitivity of saliva testing is related to both the timing of the sample collection relative to symptom onset and the disease stage. Importantly, several clinical vignettes in our cohorts highlight the challenges of medical decision making with limited knowledge of the associations between laboratory test data and the natural biology of infection.

Gut microbiome signatures of nursing home residents carrying Enterobacteria producing extended-spectrum ß-lactamases.

BACKGROUND: The prevalence of extended beta-lactamase producing Enterobacteriaceae (ESBL-E) has been constantly increasing over the last few decades. These microorganisms that have acquired broad antibiotic resistance are now common human pathogens. Changes in the gut microbiome, induced by antibiotics or other drugs, enable expansion of these microorganisms, but the mechanisms are not yet fully understood. OBJECTIVES: The main objective was to identify specific bacteria and functional pathways and genes characterizing the gut microbiome of nursing home residents carrying ESBL-E, using metagenomics. SUBJECTS AND METHODS: We included 144 residents living in two different nursing homes. All fecal samples were screened for ESBL-E and gut microbiome was characterized using shallow shotgun metagenomic DNA sequencing. RESULTS: Ten nursing home residents were colonized by ESBL-E, namely Escherichia coli, Klebsiella pneumoniae and Enterobacter cloacae species, and were compared to non-carriers. We found that ESBL-E carriers had an alteration in within-sample diversity. Using a bootstrap algorithm, we found that the gut microbiome of ESBL-E carriers was depleted in butyrate-producing species, enriched in succinate-producing species and enriched in pathways involved in intracellular pH homeostasis compared to non-carriers individuals. Several energy metabolism pathways were overrepresented in ESBL-E carriers suggesting a greater ability to metabolize multiple microbiota and mucus layer-derived nutrients. CONCLUSIONS: The gut microbiome of ESBL-E carriers in nursing homes harbors specific taxonomic and functional characteristics, conferring an environment that enables Enterobacteriaceae expansion. Here we describe new functional features associated with ESBL-E carriage that could help us to elucidate the complex interactions leading to colonization persistence in the human gut microbiota.

SHOGUN: a modular, accurate and scalable framework for microbiome quantification.

SUMMARY: The software pipeline SHOGUN profiles known taxonomic and gene abundances of short-read shotgun metagenomics sequencing data. The pipeline is scalable, modular and flexible. Data analysis and transformation steps can be run individually or together in an automated workflow. Users can easily create new reference databases and can select one of three DNA alignment tools, ranging from ultra-fast low-RAM k-mer-based database search to fully exhaustive gapped DNA alignment, to best fit their analysis needs and computational resources. The pipeline includes an implementation of a published method for taxonomy assignment disambiguation with empirical Bayesian redistribution. The software is installable via the conda resource management framework, has plugins for the QIIME2 and QIITA packages and produces both taxonomy and gene abundance profile tables with a single command, thus promoting convenient and reproducible metagenomics research. AVAILABILITY AND IMPLEMENTATION: https://github.com/knights-lab/SHOGUN.

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Daily Sampling Reveals Personalized Diet-Microbiome Associations in Humans.

Diet is a key determinant of human gut microbiome variation. However, the fine-scale relationships between daily food choices and human gut microbiome composition remain unexplored. Here, we used multivariate methods to integrate 24-h food records and fecal shotgun metagenomes from 34 healthy human subjects collected daily over 17 days. Microbiome composition depended on multiple days of dietary history and was more strongly associated with food choices than with conventional nutrient profiles, and daily microbial responses to diet were highly personalized. Data from two subjects consuming only meal replacement beverages suggest that a monotonous diet does not induce microbiome stability in humans, and instead, overall dietary diversity associates with microbiome stability. Our work provides key methodological insights for future diet-microbiome studies and suggests that food-based interventions seeking to modulate the gut microbiota may need to be tailored to the individual microbiome. Trial Registration: ClinicalTrials.gov: NCT03610477.

Microbiome Learning Repo (ML Repo): A public repository of microbiome regression and classification tasks.

The use of machine learning in high-dimensional biological applications, such as the human microbiome, has grown exponentially in recent years, but algorithm developers often lack the domain expertise required for interpretation and curation of the heterogeneous microbiome datasets. We present Microbiome Learning Repo (ML Repo, available at https://knights-lab.github.io/MLRepo/), a public, web-based repository of 33 curated classification and regression tasks from 15 published human microbiome datasets. We highlight the use of ML Repo in several use cases to demonstrate its wide application, and we expect it to be an important resource for algorithm developers.

An Increased Abundance of Clostridiaceae Characterizes Arthritis in Inflammatory Bowel Disease and Rheumatoid Arthritis: A Cross-sectional Study.

BACKGROUND: Inflammatory bowel diseases (IBDs) are a group of heterogeneous inflammatory conditions affecting the gastrointestinal tract. Although there is considerable evidence linking the gut microbiota to intestinal inflammation, there is limited knowledge on its potential role in the development of extraintestinal manifestations of IBD. METHODS: Four groups of patients were included: IBD-associated arthropathy (IBD-A); IBD without arthropathy (IBD-N); rheumatoid arthritis (RA); and non-IBD, nonarthritis controls. DNA from stool samples was isolated and sequenced using the Illumina platform. Paired-end reads were quality-controlled using SHI7 and processed with SHOGUN. Abundance and diversity analyses were performed using QIIME, and compositional biomarker identification was performed using LEfSe. RESULTS: One hundred eighty patients were included in the analysis. IBD-A was associated with an increased abundance of microbial tyrosine degradation pathways when compared with IBD-N (P = 0.02), whereas IBD-A and RA patients both shared an increased abundance of Clostridiaceae when compared with controls (P = 0.045). We found that history of bowel surgery was a significant source of variability (P = 0.001) among all IBD patients and was associated with decreased alpha diversity and increased abundance of Enterobacteriaceae (P = 0.004). CONCLUSIONS: An increased abundance of gut microbial tyrosine degradation pathways was associated with IBD-A. An increased abundance of Clostridiaceae was shared by both IBD-A and RA patients and suggests a potentially common microbial link for inflammatory arthritis. The increased abundance of Enterobacteriaceae, previously reported in IBD, may be due to the effects of previous bowel surgery and highlights the importance of controlling for this variable in future studies.

US Immigration Westernizes the Human Gut Microbiome.

Many US immigrant populations develop metabolic diseases post immigration, but the causes are not well understood. Although the microbiome plays a role in metabolic disease, there have been no studies measuring the effects of US immigration on the gut microbiome. We collected stool, dietary recalls, and anthropometrics from 514 Hmong and Karen individuals living in Thailand and the United States, including first- and second-generation immigrants and 19 Karen individuals sampled before and after immigration, as well as from 36 US-born European American individuals. Using 16S and deep shotgun metagenomic DNA sequencing, we found that migration from a non-Western country to the United States is associated with immediate loss of gut microbiome diversity and function in which US-associated strains and functions displace native strains and functions. These effects increase with duration of US residence and are compounded by obesity and across generations.

Evaluating the Information Content of Shallow Shotgun Metagenomics.

Although microbial communities are associated with human, environmental, plant, and animal health, there exists no cost-effective method for precisely characterizing species and genes in such communities. While deep whole-metagenome shotgun (WMS) sequencing provides high taxonomic and functional resolution, it is often prohibitively expensive for large-scale studies. The prevailing alternative, 16S rRNA gene amplicon (16S) sequencing, often does not resolve taxonomy past the genus level and provides only moderately accurate predictions of the functional profile; thus, there is currently no widely accepted approach to affordable, high-resolution, taxonomic, and functional microbiome analysis. To address this technology gap, we evaluated the information content of shallow shotgun sequencing with as low as 0.5 million sequences per sample as an alternative to 16S sequencing for large human microbiome studies. We describe a library preparation protocol enabling shallow shotgun sequencing at approximately the same per-sample cost as 16S sequencing. We analyzed multiple real and simulated biological data sets, including two novel human stool samples with ultradeep sequencing of 2.5 billion sequences per sample, and found that shallow shotgun sequencing recovers more-accurate species-level taxonomic and functional profiles of the human microbiome than 16S sequencing. We discuss the inherent limitations of shallow shotgun sequencing and note that 16S sequencing remains a valuable and important method for taxonomic profiling of novel environments. Although deep WMS sequencing remains the gold standard for high-resolution microbiome analysis, we recommend that researchers consider shallow shotgun sequencing as a useful alternative to 16S sequencing for large-scale human microbiome research studies where WMS sequencing may be cost-prohibitive. IMPORTANCE A common refrain in recent microbiome-related academic meetings is that the field needs to move away from broad taxonomic surveys using 16S sequencing and toward more powerful longitudinal studies using shotgun sequencing. However, performing deep shotgun sequencing in large longitudinal studies remains prohibitively expensive for all but the most well-funded research labs and consortia, which leads many researchers to choose 16S sequencing for large studies, followed by deep shotgun sequencing on a subset of targeted samples. Here, we show that shallow- or moderate-depth shotgun sequencing may be used by researchers to obtain species-level taxonomic and functional data at approximately the same cost as amplicon sequencing. While shallow shotgun sequencing is not intended to replace deep shotgun sequencing for strain-level characterization, we recommend that microbiome scientists consider using shallow shotgun sequencing instead of 16S sequencing for large-scale human microbiome studies.

CLOUD: a non-parametric detection test for microbiome outliers.

BACKGROUND: Dysbiosis of the human gut microbiome is defined as a maladaptive or clinically relevant deviation of the community profile from the healthy or normal state. Dysbiosis has been implicated in an extensive set of metabolic, auto-immune, and infectious diseases, and yet there is substantial inter-individual variation in microbiome composition even within body sites of healthy humans. An individual's microbiome varies over time in a high-dimensional space to form their personal microbiome cloud. This cloud may or may not be similar to that of other people, both in terms of the average microbiome profile (conformity) and the diameter of the cloud (stability). However, there is currently no robust non-parametric test that determines whether a patient's microbiome cloud is an outlier with respect to a reference group of healthy individuals with widely varying microbiome profiles. METHODS: Here, we propose a test for outliers' detection in the human gut microbiome that accounts for the wide range of microbiome phenotypes observed in a typical set of healthy individuals and for intra-individual temporal variation. Our robust nonparametric outlier detection test, the CLOUD test, performs two assessments of a patient's microbiome health: conformity, the extent to which the patient's microbiome cloud is ecologically similar to a subset of healthy subjects; and stability, which compares the cloud diameter of a patient to those of healthy subjects. The CLOUD test is based on locally linear embedded ecological distances, allowing it to account for widely varying microbiome compositions among reference individuals. It also leverages temporal variability within patients and reference individuals to increase the robustness of the test. RESULTS: We describe the CLOUD test, and we apply it to one novel and two previously published cohorts of patients receiving fecal microbiota transplantation for recurrent Clostridium difficile colitis, as well as to two known healthy cohorts, demonstrating high concordance of the CLOUD conformity and stability indices with clinical outcomes. CONCLUSIONS: Although the CLOUD test is not, on its own, a test for clinical dysbiosis, it nonetheless provides a framework for outlier testing that could be incorporated into evaluation of suspected dysbiosis, which may play a role in diagnosis and prognosis of numerous pediatric and adult diseases.

SHI7 Is a Self-Learning Pipeline for Multipurpose Short-Read DNA Quality Control.

Next-generation sequencing technology is of great importance for many biological disciplines; however, due to technical and biological limitations, the short DNA sequences produced by modern sequencers require numerous quality control (QC) measures to reduce errors, remove technical contaminants, or merge paired-end reads together into longer or higher-quality contigs. Many tools for each step exist, but choosing the appropriate methods and usage parameters can be challenging because the parameterization of each step depends on the particularities of the sequencing technology used, the type of samples being analyzed, and the stochasticity of the instrumentation and sample preparation. Furthermore, end users may not know all of the relevant information about how their data were generated, such as the expected overlap for paired-end sequences or type of adaptors used to make informed choices. This increasing complexity and nuance demand a pipeline that combines existing steps together in a user-friendly way and, when possible, learns reasonable quality parameters from the data automatically. We propose a user-friendly quality control pipeline called SHI7 (canonically pronounced "shizen"), which aims to simplify quality control of short-read data for the end user by predicting presence and/or type of common sequencing adaptors, what quality scores to trim, whether the data set is shotgun or amplicon sequencing, whether reads are paired end or single end, and whether pairs are stitchable, including the expected amount of pair overlap. We hope that SHI7 will make it easier for all researchers, expert and novice alike, to follow reasonable practices for short-read data quality control. IMPORTANCE Quality control of high-throughput DNA sequencing data is an important but sometimes laborious task requiring background knowledge of the sequencing protocol used (such as adaptor type, sequencing technology, insert size/stitchability, paired-endedness, etc.). Quality control protocols typically require applying this background knowledge to selecting and executing numerous quality control steps with the appropriate parameters, which is especially difficult when working with public data or data from collaborators who use different protocols. We have created a streamlined quality control pipeline intended to substantially simplify the process of DNA quality control from raw machine output files to actionable sequence data. In contrast to other methods, our proposed pipeline is easy to install and use and attempts to learn the necessary parameters from the data automatically with a single command.

Fecal microbiota transplantation reverses antibiotic and chemotherapy-induced gut dysbiosis in mice.

Fecal microbiota transplantation (FMT) is now widely used to treat recurrent Clostridium difficile infection, but has been less studied as a means to restore microbiome diversity and composition following antibiotic or chemotherapy treatments. The purpose of our study was to assess the efficacy of FMT to reverse antibiotic- and chemotherapy-induced gut dysbiosis in a mouse model. C57BL/6J mice were treated with ampicillin for 1 week and/or received a single intraperitoneal injection of 5-Fluorouracil. Fresh stool was collected and analyzed using shotgun metagenomics and the Illumina sequencing platform. Ampicillin caused a significant and immediate decrease in bacterial species richness and diversity that persisted for one week. In mice that received FMT, disruption of the intestinal microbiota was reversed immediately. Antibiotic and chemotherapy administration caused significant alteration in species distribution, including a decrease in the relative proportions of Clostridium scindens and Faecalibacterium prausnitzii, and an increase in known pathogenic species. In mice receiving FMT, we observed a significant increase in species known to exhibit anti-inflammatory properties. Moreover, chemotherapy led to a critical decrease in key 'health-promoting' species and to an altered functional profile, especially when chemotherapy was administered in tandem with antibiotics, and that FMT can ameliorate these effects.

Stable Engraftment of Bifidobacterium longum AH1206 in the Human Gut Depends on Individualized Features of the Resident Microbiome.

Live bacteria (such as probiotics) have long been used to modulate gut microbiota and human physiology, but their colonization is mostly transient. Conceptual understanding of the ecological principles as they apply to exogenously introduced microbes in gut ecosystems is lacking. We find that, when orally administered to humans, Bifidobacterium longum AH1206 stably persists in the gut of 30% of individuals for at least 6 months without causing gastrointestinal symptoms or impacting the composition of the resident gut microbiota. AH1206 engraftment was associated with low abundance of resident B. longum and underrepresentation of specific carbohydrate utilization genes in the pre-treatment microbiome. Thus, phylogenetic limiting and resource availability are two factors that control the niche opportunity for AH1206 colonization. These findings suggest that bacterial species and functional genes absent in the gut microbiome of individual humans can be reestablished, providing opportunities for precise and personalized microbiome reconstitution.

Captivity humanizes the primate microbiome.

The primate gastrointestinal tract is home to trillions of bacteria, whose composition is associated with numerous metabolic, autoimmune, and infectious human diseases. Although there is increasing evidence that modern and Westernized societies are associated with dramatic loss of natural human gut microbiome diversity, the causes and consequences of such loss are challenging to study. Here we use nonhuman primates (NHPs) as a model system for studying the effects of emigration and lifestyle disruption on the human gut microbiome. Using 16S rRNA gene sequencing in two model NHP species, we show that although different primate species have distinctive signature microbiota in the wild, in captivity they lose their native microbes and become colonized with Prevotella and Bacteroides, the dominant genera in the modern human gut microbiome. We confirm that captive individuals from eight other NHP species in a different zoo show the same pattern of convergence, and that semicaptive primates housed in a sanctuary represent an intermediate microbiome state between wild and captive. Using deep shotgun sequencing, chemical dietary analysis, and chloroplast relative abundance, we show that decreasing dietary fiber and plant content are associated with the captive primate microbiome. Finally, in a meta-analysis including published human data, we show that captivity has a parallel effect on the NHP gut microbiome to that of Westernization in humans. These results demonstrate that captivity and lifestyle disruption cause primates to lose native microbiota and converge along an axis toward the modern human microbiome.