Biology subjects

Holmes, S.

Publications and source records attributed to Holmes, S..

26 records · Page 2Linked to original sources

bioRxiv2019

Specific Gut Microbiome Members are Associated with Distinct Immune Markers in Allogeneic Hematopoietic Stem Cell Transplantation

BackgroundIncreasing evidence reveals the importance of the microbiome in health and disease and inseparable host-microbial dependencies. Host-microbe interactions are highly relevant in patients receiving allogeneic hematopoietic stem cell transplantation, (HSCT), i.e. a replacement of the cellular components of the patients immune system with that of a foreign donor. HSCT is employed as curative immunotherapy for a number of non-malignant and malignant hematologic conditions, including acute lymphoblastic leukemia. The procedure can be accompanied by severe side effects such as infections, acute graft-versus-host disease (aGvHD), and death. Here, we performed a longitudinal analysis of immunological markers, immune reconstitution and gut microbiota composition in relation to clinical outcomes in children undergoing HSCT. Such an analysis could reveal biomarkers, e.g. at the time point prior to HSCT, that in the future could be used to predict which patients are of high risk in relation to side effects and clinical outcomes and guide treatment strategies accordingly.\n\nResultsIn two multivariate analyses (sparse partial least squares regression and canonical correspondence analysis), we identified three consistent clusters: (1) High concentrations of the antimicrobial peptide human beta-defensin 2 (hBD2) prior to the transplantation in patients with high abundances of Lactobacillaceae, who later developed moderate or severe aGvHD and exhibited high mortality. (2) Rapid reconstitution of NK and B cells in patients with high abundances of obligate anaerobes such as Ruminococcaceae, who developed no or mild aGvHD and exhibited low mortality. (3) High inflammation, indicated by high levels of C-reactive protein, in patients with high abundances of facultative anaerobic bacteria such as Enterobacteriaceae. Furthermore, we observed that antibiotic treatment influenced the bacterial community state.\n\nConclusionsWe identify multivariate associations between specific microbial taxa, host immune markers, immune cell reconstitution and clinical outcomes in relation to HSCT. Our findings encourage further investigations into establishing longitudinal surveillance of the intestinal microbiome and relevant immune markers, such as hBD2, in HSCT patients. Profiling of the microbiome may prove useful as a prognostic tool that could help identify patients at risk of poor immune reconstitution and adverse outcomes, such as aGvHD and death, upon HSCT, providing actionable information in guiding precision medicine.

microbiology↗

bioRxiv2019

Strategies for understanding dynamic, personalized profiles of host-derived proteins and microbes from human stool

Measuring host proteins through noninvasive stool-based assays opens new avenues for characterizing states of gastrointestinal health. However, the extent to which these proteins vary over time and between healthy subjects is poorly characterized. Here, we characterize technical and biological sources of variability in mass spectrometry-based measurements of host proteins in stool. We identify the proteins that most vary over time within an individual, and among different individuals. Finally, we examine and compare temporal and inter-individual variation in host protein and bacterial taxonomic profiles of the same fecal specimens. To address these issues, five self-reported healthy individuals were each sampled eight times over four weeks. First, we demonstrate that mass spectrometry-based identification and label-free quantification of stool proteins exhibit non-significant variability (p>0.05) between both technical and preparative replicates for a subset of 78 proteins, supporting the utility of this method for biomarker measurement. Second, although 13 human stool proteins varied significantly in relative abundance over time within individuals, 58 proteins varied significantly (at least four-fold) between subjects. The average pair-wise difference between individuals was greater than the average within-subject difference for both the proteome and microbiome datasets (p<0.0001). Fecal host proteins, like the traditional fecal protein marker, calprotectin, unambiguously pointed to innate and adaptive immune responses. For example, one subjects fecal protein profile suggested a sub-clinical inflammatory state. From these data, we conclude that host-centric protein measurements in stool reveal a wide range of variation during states of apparent health, and add a valuable complementary insight into host-microbiota relationships. IMPORTANCEHuman proteins in stool hold untapped potential for characterizing gastrointestinal health. To fully harness this potential and create a baseline of healthy stool protein abundances and identifications, it will be important to establish the extent to which these proteins might vary in the absence of disease. This study quantifies the major sources of variation in stool protein abundance data. We assessed technical, preparative, temporal, and inter-subject variability of human protein abundances in stool and found that among these sources, differences between subjects accounted for the greatest amount of variation, followed by temporal differences, and then technical factors. Our paired microbiome analysis found matching patterns of temporal and inter-subject variability. By characterizing multiple variance parameters in host stool protein abundances, our analysis helps to contextualize a wide range of future disease-focused stool studies as well as elucidate host-microbe interactions.

molecular biology↗

bioRxiv2019

Microbial biogeography and ecology of the mouth and implications for periodontal diseases

Human-associated microbial communities differ in composition among body sites and between habitats within a site. Patterns of variation in the distribution of organisms across time and space is referred to as biogeography. The human oral cavity is a critical observatory for exploring microbial biogeography because it is spatially structured, easily accessible, and its microbiota has been linked to the promotion of both health and disease. The biogeographic features of microbial communities residing in spatially distinct but ecologically similar environments on the human body, including the subgingival crevice, have not yet been adequately explored. The purpose of this paper is twofold. First, we seek to provide the dental community with a primer on biogeographic theory, highlighting its relevance to the study of the human oral cavity. For this reason, we summarize what is known about the biogeographic variation of dental caries and periodontitis and postulate as to how this may be driven by spatial patterning in oral microbial community composition and structure. Second, we present a number of methods that investigators can use to test specific hypotheses using biogeographic theory. To anchor our discussion, we apply each method to a case study and examine the spatial variation of the human subgingival microbiota of 2 individuals. Our case study suggests that subgingival communities in the aggregate may conform to an anterior-to-posterior gradient in community composition. The gradient appears to be structured both by deterministic and non-deterministic processes, though additional work is needed to test and confirm specific hypotheses. A better understanding of biogeographic patterns and processes will advance our understanding of ways to optimize the efficacy of dental interventions targeting the oral microbiota.

ecology↗

bioRxiv2018

Treatment-Specific Composition of Gut Microbiota Is Associated with Disease Remission in a Pediatric Crohn’s Disease Cohort

BackgroundThe beneficial effects of antibiotics in Crohns disease (CD) depend in part on the gut microbiota but are inadequately understood. We investigated the impact of metronidazole (MET) and metronidazole plus azithromycin (MET+AZ) on the microbiota in pediatric CD, and the use of microbiota features as classifiers or predictors of disease remission.\n\nMethods16S rRNA-based microbiota profiling was performed on stool samples from 67 patients in a multinational, randomized, controlled, longitudinal, 12-week trial of MET vs. MET+AZ in children with mild to moderate CD. Profiles were analyzed together with disease activity, and then used to construct Random Forest models to classify remission or predict treatment response.\n\nResultsBoth MET and MET+AZ significantly decreased diversity of the microbiota and caused large treatment-specific shifts in microbiota structure at week 4. Disease remission was associated with a treatment-specific microbiota configuration. Random Forest models constructed from microbiota profiles pre- and during antibiotic treatment with metronidazole accurately classified disease remission in this treatment group (AUC of 0.879, 95% CI 0.683, 0.9877; sensitivity 0.7778; specificity 1.000, P < 0.001). A Random Forest model trained on preantibiotic microbiota profiles predicted disease remission at week 4 with modest accuracy (AUC of 0.8, P = 0.24).\n\nConclusionsMET and MET+AZ antibiotic regimens in pediatric CD lead to distinct gut microbiota structures at remission. It may be possible to classify and predict remission based in part on microbiota profiles, but larger cohorts will be needed to realize this goal.\n\nSummaryWe investigated the impact of metronidazole and metronidazole plus azithromycin on the gut microbiota in pediatric Crohns disease. Disease remission was associated with a treatment-specific microbiota configuration, and could be predicted based on pre-antibiotic microbiota profiles.

microbiology↗

bioRxiv2018

CD38 is a key regulator of enhanced NK cell immune responses during pregnancy through its role in immune synapse formation

Natural killer (NK) cells use a diverse array of activating and inhibitory surface receptors to detect threats and provide an early line of defense against viral infections and cancer. Here, we demonstrate that the cell surface protein CD38 is a key human NK cell functional receptor through a role in immune synapse formation. CD38 expression marks a mature subset of human NK cells with a high functional capacity. NK cells expressing high levels of CD38 display enhanced killing and IFN-{gamma} secretion in response to influenza virus-infected and tumor cells. Inhibition of CD38 enzymatic activity does not influence NK cell function, but blockade of CD38 and its ligand CD31 abrogates killing and IFN-{gamma} expression in response to influenza-infected cells. Blockade of CD38 on NK cells similarly inhibits killing of tumor cells. CD38 localizes and accumulates at the immune synapse between NK cells and their targets, and blocking CD38 severely abrogates the ability of NK cells to form conjugates and immune synapses with target cells. Thus, CD38 plays a critical role in NK cell immune synapse formation. These findings open new avenues in immunotherapeutic development for cancer and infection by revealing a critical role for CD38 in NK cell function.

immunology↗

bioRxiv2018

Gut microbiome transition across a lifestyle gradient in Himalaya

The composition of the gut microbiome in industrialized populations differs from those living traditional lifestyles. However, it has been difficult to separate the contributions of human genetic and geographic factors from lifestyle/modernization. Here, we characterize the stool bacterial composition of four Himalayan populations to investigate how the gut community changes in response to shifts in human lifestyles. These groups led seminomadic hunting-gathering lifestyles until transitioning to varying dependence upon farming. The Tharu began farming 250-300 years ago, the Raute and Raji transitioned 30-40 years ago, and the Chepang retain many aspects of a foraging lifestyle. We assess the contributions of dietary and environmental factors on their gut microbiota and find that the gut microbiome composition is significantly associated with lifestyle. The Chepang foragers harbor elevated abundance of taxa associated with foragers around the world. Conversely, the gut microbiomes of populations that have transitioned to farming are more similar to those of Americans, with agricultural dependence and several associated lifestyle and environmental factors correlating with the extent of microbiome divergence from the foraging population. For example, our results show that drinking water source and solid cooking fuel are significantly associated with the gut microbiome. Despite the pronounced differences in gut bacterial composition across populations, we found little differences in alpha diversity across populations. These findings in genetically similar populations living in the same geographical region establish the key role of lifestyle in determining human gut microbiome composition and point to the next challenging steps of isolating dietary effects from other factors that change during modernization.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC=\"FIGDIR/small/253450_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (108K):\norg.highwire.dtl.DTLVardef@68f595org.highwire.dtl.DTLVardef@d33792org.highwire.dtl.DTLVardef@e56314org.highwire.dtl.DTLVardef@1cbbba2_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2017

Bayesian Unidimensional Scaling for visualizing uncertainty in high dimensional datasets with latent ordering of observations

BackgroundDetecting patterns in high-dimensional multivariate datasets is non-trivial. Clustering and dimensionality reduction techniques often help in discerning inherent structures. In biological datasets such as microbial community composition or gene expression data, observations can be generated from a continuous process, often unknown. Estimating data points natural ordering and their corresponding uncertainties can help researchers draw insights about the mechanisms involved.\n\nResultsWe introduce a Bayesian Unidimensional Scaling (BUDS) technique which extracts dominant sources of variation in high dimensional datasets and produces their visual data summaries, facilitating the exploration of a hidden continuum. The method maps multivariate data points to latent one-dimensional coordinates along their underlying trajectory, and provides estimated uncertainty bounds. By statistically modeling dissimilarities and applying a DiSTATIS registration method to their posterior samples, we are able to incorporate visualizations of uncertainties in the estimated data trajectory across different regions using confidence contours for individual data points. We also illustrate the estimated overall data density across different areas by including density clouds. One-dimensional coordinates recovered by BUDS help researchers discover sample attributes or covariates that are factors driving the main variability in a dataset. We demonstrated usefulness and accuracy of BUDS on a set of published microbiome 16S and RNA-seq and roll call data.\n\nConclusionsOur method effectively recovers and visualizes natural orderings present in datasets. Automatic visualization tools for data exploration and analysis are available at: https://nlhuong.shinyapps.io/visTrajectory/.

bioinformatics↗

bioRxiv2017

Multivariate Heteroscedasticity Models for Functional Brain Connectivity

Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI). We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP) comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.

neuroscience↗