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Background: Many important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are 'novel' compared to the others in the same dataset, and low weights to sequences that are over-represented. Results: We formalise this principle by rigorously defining the evolutionary 'novelty' of a sequence within an alignment. This results in new sequence weights that we call 'phylogenetic novelty scores'. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column—important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they are versatile and can improve the accuracy of character frequency estimation compared to existing sequence weighting schemes. Conclusions: Our phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy. [ABSTRACT FROM AUTHOR]

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data. [ABSTRACT FROM AUTHOR]

The diversity of microbiota is best explored by understanding the phylogenetic structure of the microbial communities. Traditionally, sequence alignment has been used for phylogenetic inference. However, alignment-based approaches come with significant challenges and limitations when massive amounts of data are analyzed. In the recent decade, alignment-free approaches have enabled genome-scale phylogenetic inference. Here we evaluate three alignment-free methods: ACS, CVTree, and Kr for phylogenetic inference with 16s rRNA gene data. We use a taxonomic gold standard to compare the accuracy of alignment-free phylogenetic inference with that of common microbiome-wide phylogenetic inference pipelines based on PyNAST and MUSCLE alignments with FastTree and RAxML. We re-simulate fecal communities from Human Microbiome Project data to evaluate the performance of the methods on datasets with properties of real data. Our comparisons show that alignment-free methods are not inferior to alignment-based methods in giving accurate and robust phylogenic trees. Moreover, consensus ensembles of alignment-free phylogenies are superior to those built from alignment-based methods in their ability to highlight community differences in low power settings. In addition, the overall running times of alignment-based and alignment-free phylogenetic inference are comparable. Taken together our empirical results suggest that alignment-free methods provide a viable approach for microbiome-wide phylogenetic inference. [ABSTRACT FROM AUTHOR]

Motivation: Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method, however, suffers from loss of power and type I error inflation in the presence of heteroscedasticity and sample size imbalances. Results: We develop a solution in the form of a distance-based Welch t-test, T2W, for two sample potentially unbalanced and heteroscedastic data. We demonstrate empirically the desirable type I error and power characteristics of the new test. We compare the performance of PERMANOVA and T2W in reanalysis of two existing microbiome datasets, where the methodology has originated. [ABSTRACT FROM AUTHOR]

Glucosyltransferases (Gtfs) catalyze the synthesis of glucans from sucrose and are produced by several species of lactic-acid bacteria. The oral bacterium Streptococcus mutans produces large amounts of glucans through the action of three Gtfs. GtfD produces water-soluble glucan (WSG), GtfB synthesizes water-insoluble glucans (WIG) and GtfC produces mainly WIG but also WSG. These enzymes, especially those synthesizing WIG, are of particular interest because of their role in the formation of dental plaque, an environment where S. mutans can thrive and produce lactic acid, promoting the formation of dental caries. We sequenced the gtfB, gtfC and gtfD genes from several mutans streptococcal strains isolated from the oral cavity of humans and searched for their homologues in strains isolated from chimpanzees and macaque monkeys. The sequence data were analyzed in conjunction with the available Gtf sequences from other bacteria in the genera Streptococcus, Lactobacillus and Leuconostoc to gain insights into the evolutionary history of this family of enzymes, with a particular emphasis on S. mutans Gtfs. Our analyses indicate that streptococcal Gtfs arose from a common ancestral progenitor gene, and that they expanded to form two clades according to the type of glucan they synthesize. We also show that the clade of streptococcal Gtfs synthesizing WIG appeared shortly after the divergence of viviparous, dentate mammals, which potentially contributed to the formation of dental plaque and the establishment of several streptococci in the oral cavity. The two S. mutans Gtfs capable of WIG synthesis, GtfB and GtfC, are likely the product of a gene duplication event. We dated this event to coincide with the divergence of the genomes of ancestral early primates. Thus, the acquisition and diversification of S. mutans Gtfs predates modern humans and is unrelated to the increase in dietary sucrose consumption. [ABSTRACT FROM AUTHOR]

New contributions toward generalizing evolutionary models expand greatly our ability to analyze complex evolutionary characters and advance phylogeny reconstruction. In this article, we extend the binary stochastic Dollo model to allow for multi-state characters. In doing so, we align previously incompatible Wagner and Dollo parsimony principles under a common probabilistic framework by embedding arbitrary continuous-time Markov chains into the binary stochastic Dollo model. This approach enables us to analyze character traits that exhibit both Dollo and Wagner characteristics throughout their evolutionary histories. Utilizing Bayesian inference, we apply our novel model to analyze intron conservation patterns and the evolution of alternatively spliced exons. The generalized framework we develop demonstrates potential in distinguishing between phylogenetic hypotheses and providing robust estimates of evolutionary rates. Moreover, for the two applications analyzed here, our framework is the first to provide an adequate stochastic process for the data. We discuss possible extensions to the framework from both theoretical and applied perspectives. [ABSTRACT FROM AUTHOR]

Background: Adolescence is a sensitive stage of oral microbial development that often coincides with the initiation and escalation of alcohol use. Thus, adolescents may be particularly susceptible to alcohol‐induced alterations in the oral microbiome, though minimal research has been done in this area. Understanding the connection between the oral microbiome and alcohol use during adolescence is important to understand fully the biological consequences of alcohol use to mitigate potential adverse outcomes. Methods: Saliva samples were collected from adolescents aged 17–19 who used alcohol heavily (n = 21, 52.4% female) and those who did not use alcohol or any other substances (n = 18, 44.4% female). We utilized 16S rRNA sequencing to examine differences in microbial diversity and composition between the groups. Results: For alpha diversity, evenness was significantly lower in the drinking group than the control group as indicated by Pielou's evenness, Shannon, and Simpson indices. There were no statistically significant findings for beta diversity. Differential abundance analyses revealed higher abundances of Rothia and Corynebacterium in the alcohol‐using group using both centered‐log‐ratio and relative abundance normalization. These genera are known for their high capacity to convert alcohol into acetaldehyde, a toxic metabolite reported to play a role in the neurobiological effects of alcohol. An unclassified Clostridia UCG‐014, Streptobacillus, Comamonas, unclassified Lachnospiraceae, and Parvimonas were also identified as significantly different between groups when using only one of the normalization techniques. Conclusions: This is the first study designed specifically to compare the oral microbiome of adolescents who use alcohol with that of control participants. Our findings reveal distinct alcohol‐related differences in microbial composition and taxon abundance, emphasizing the importance of understanding the impact on the oral microbiome of alcohol use during adolescence. Because the oral microbiome is malleable, this study provides foundational work for future prevention and intervention studies. [ABSTRACT FROM AUTHOR]

Aims The microbiome is a critical factor in health throughout human development. The aims of this scoping review are to (i) elucidate the differences between the youth (post-natal day 21–65 for rodents, 2–7 years for non-human primates, and 10–25 years for humans) microbiome with other life stages and (ii) identify youth-specific microbial changes associated with substance use. Methods Peer-reviewed studies published up to May 2023 were identified in PubMed and SCOPUS and included gut and oral microbiome studies from rodents, non-human primates, and humans (N  = 1733). Twenty-six articles were determined eligible based on inclusion criteria (aim 1: n  = 19, aim 2: n  = 7). Results The adolescent and young adult oral and gut microbiomes are distinct compared to other life stages, within both non-human and human models. While there is limited research in this area, the microbiome appears to be vulnerable to substance use exposure earlier in life, including substances commonly initiated and escalated during adolescence and young adulthood (i.e. alcohol, cannabis, and tobacco). Conclusions Studies across the lifespan indicate that adolescence and young adulthood are distinct periods of development, where the microbiome is sensitive to exposures, including substance use. There is a need for more studies focused on the adolescent and young adult microbiome and substance use, as well as focused on the oral microbiome during this developmental period. Understanding the gut and oral microbiome during adolescence and young adulthood may provide insight into the pathophysiology of substance use disorders. [ABSTRACT FROM AUTHOR]

Aim: Antimicrobial‐induced shifts in commensal oral microbiota can dysregulate helper T‐cell oral immunity to affect osteoclast–osteoblast actions in alveolar bone. Antibiotic prophylaxis is commonly performed with dental implant placement surgery to prevent post‐surgical complications. However, antibiotic prophylaxis effects on osteoimmune processes supporting dental implant osseointegration are unknown. The aim of the study was to discern the impact of antibiotic prophylaxis on dental implant placement surgery‐induced osteoimmune wound healing and osseointegration. Materials and Methods: We performed SHAM or dental implant placement surgery in mice. Groups were administered prophylactic antibiotics (amoxicillin or clindamycin) or vehicle. Gingival bacteriome was assessed via 16S sequencing. Helper T‐cell oral immunity was evaluated by flow cytometry. Osteoclasts and osteoblasts were assessed via histomorphometry. Implant osseointegration was evaluated by micro‐computed tomography. Results: Dental implant placement surgery up‐regulated TH1, TH2 and TREG cells in cervical lymph nodes (CLNs), which infers helper T‐cell oral immunity contributes to dental implant placement osseous wound healing. Prophylactic antibiotics with dental implant placement surgery caused a bacterial dysbiosis, suppressed TH1, TH2 and TREG cells in CLNs, reduced osteoclasts and osteoblasts lining peri‐implant alveolar bone, and attenuated the alveolar bone–implant interface. Conclusions: Antibiotic prophylaxis dysregulates dental implant placement surgery‐induced osteoimmune wound healing and attenuates the alveolar bone–implant interface in mice. [ABSTRACT FROM AUTHOR]

• Murine systems have offered paradigms by which imbalances and translocations of gut pathobionts contribute to autoimmune pathogenesis. • Commensal microbes express orthologues of SSA/Ro and β2 glycoprotein I that prime for the autoantigen-specific immune responses in predisposed individuals. • In several independent reports, characteristic patterns of gut dysbiosis have been identified in clinical Lupus cohorts. • Candidate pathobionts include Ruminococcus gnavus , an obligate anaerobe implicated in Lupus nephritis as well as in several other diseases. • R. gnavus strains may produce a B cell superantigen postulated to contribute to immune pathogenesis. Throughout our lives we are immersed in, and colonized by, immense and complex microbial communities. These microbiota serve as activators and early sparring partners for the progressive construction of the layers within our immune defenses and are essential to immune homeostasis. Yet, at times imbalances within the microbiota may contribute to metabolic and immune regulatory abnormalities that underlie the development of inflammatory and autoimmune diseases. Here, we review recent progress in investigations of the microbiome, with emphasis on the gut microbiota associated with systemic autoimmunity. In particular, these studies are beginning to illuminate aspects of the pathogenesis of Systemic Lupus Erythematosus, and may suggest that interconnections with specific disease-associated patterns of dysbiosis within gut communities are bidirectional and mutually reinforcing. [ABSTRACT FROM AUTHOR]

The metabolic consequences of mitophagy alterations due to age‐related stress in healthy aging brains versus neurodegeneration remain unknown. Here, we demonstrate that ceramide synthase 1 (CerS1) is transported to the outer mitochondrial membrane by the p17/PERMIT transporter that recognizes mislocalized mitochondrial ribosomes (mitoribosomes) via 39‐FLRN‐42 residues, inducing ceramide‐mediated mitophagy. P17/PERMIT‐CerS1‐mediated mitophagy attenuated the argininosuccinate/fumarate/malate axis and induced d‐glucose and fructose accumulation in neurons in culture and brain tissues (primarily in the cerebellum) of wild‐type mice in vivo. These metabolic changes in response to sodium‐selenite were nullified in the cerebellum of CerS1to/to (catalytically inactive for C18‐ceramide production CerS1 mutant), PARKIN−/− or p17/PERMIT−/− mice that have dysfunctional mitophagy. Whereas sodium selenite induced mitophagy in the cerebellum and improved motor‐neuron deficits in aged wild‐type mice, exogenous fumarate or malate prevented mitophagy. Attenuating ceramide‐mediated mitophagy enhanced damaged mitochondria accumulation and age‐dependent sensorimotor abnormalities in p17/PERMIT−/− mice. Reinstituting mitophagy using a ceramide analog drug with selenium conjugate, LCL768, restored mitophagy and reduced malate/fumarate metabolism, improving sensorimotor deficits in old p17/PERMIT−/− mice. Thus, these data describe the metabolic consequences of alterations to p17/PERMIT/ceramide‐mediated mitophagy associated with the loss of mitochondrial quality control in neurons and provide therapeutic options to overcome age‐dependent sensorimotor deficits and related disorders like amyotrophic lateral sclerosis (ALS). [ABSTRACT FROM AUTHOR]

Case-control Genome-Wide Association Studies (GWAS) provide a rich resource for studying the genetic architecture of complex diseases. A key is to elucidate how the genetic effects vary by the environment, what is traditionally defined by Gene-Environment interactions (GxE). The overlooked complication is that multiple, distinct pathophysiologic mechanisms may lead to the same clinical diagnosis and often these mechanisms have distinct genetic bases. In this paper, we first show that using the clinically diagnosed status can lead to severely biased estimates of GxE interactions in situations when the frequency of the pathologic diagnosis of interest, as compared to other diagnoses, depends on the environment. We then propose a pseudo-likelihood solution to correct the bias. Finally, we demonstrate our method in extensive simulations and in a GWAS of Alzheimer’s disease. [ABSTRACT FROM AUTHOR]

Motivation: Association analysis of microbiome composition with disease-related outcomes provides invaluable knowledge towards understanding the roles of microbes in the underlying disease mechanisms. Proper analysis of sparse compositional microbiome data is challenging. Existing methods rely on strong assumptions on the data structure and fail to pinpoint the associated microbial communities. Results: We develop a general framework to: (i) perform robust association tests for the microbial community that exhibits arbitrary inter-taxa dependencies; (ii) localize lineages on the taxonomic tree that are associated with covariates (e.g. disease status); and (iii) assess the overall association of the whole microbial community with the covariates. Unlike existing methods for microbiome association analysis, our framework does not make any distributional assumptions on the microbiome data; it allows for the adjustment of confounding variables and accommodates excessive zero observations; and it incorporates taxonomic information. We perform extensive simulation studies under a wide-range of scenarios to evaluate the new methods and demonstrate substantial power gain over existing methods. The advantages of the proposed framework are further demonstrated with real datasets from two microbiome studies. The relevant R package miLineage is publicly available. [ABSTRACT FROM AUTHOR]

Motivation: Recent advances in sequencing technology have made it possible to obtain heighthroughput data on the composition of microbial communities and to study the effects of dysbiosis on the human host. Analysis of pairwise intersample distances quantifies the association between the microbiome diversity and covariates of interest (e.g. environmental factors, clinical outcomes, treatment groups). In the design of these analyses, multiple choices for distance metrics are available. Most distance-based methods, however, use a single distance and are underpowered if the distance is poorly chosen. In addition, distance-based tests cannot flexibly handle confounding variables, which can result in excessive false-positive findings. Results: We derive presence-weighted UniFrac to complement the existing UniFrac distances for more powerful detection of the variation in species richness. We develop PERMANOVA-S, a new distance-based method that tests the association of microbiome composition with any covariates of interest. PERMANOVA-S improves the commonly-used Permutation Multivariate Analysis of Variance (PERMANOVA) test by allowing flexible confounder adjustments and ensembling multiple distances. We conducted extensive simulation studies to evaluate the performance of different distances under various patterns of association. Our simulation studies demonstrate that the power of the test relies on how well the selected distance captures the nature of the association. The PERMANOVA-S unified test combines multiple distances and achieves good power regardless of the patterns of the underlying association. We demonstrate the usefulness of our approach by reanalyzing several real microbiome datasets. [ABSTRACT FROM AUTHOR]

Introduction: Primary care providers (PCPs) and oncologists lack time and training to appropriately identify patients at increased risk for hereditary cancer using family health history (FHx) and clinical practice guideline (CPG) criteria. We built a tool, "ItRunsInMyFamily" (ItRuns) that automates FHx collection and risk assessment using CPGs. The purpose of this study was to evaluate ItRuns by measuring the level of concordance in referral patterns for genetic counseling/testing (GC/GT) between the CPGs as applied by the tool and genetic counselors (GCs), in comparison to oncologists and PCPs. The extent to which non-GCs are discordant with CPGs is a gap that health information technology, such as ItRuns, can help close to facilitate the identification of individuals at risk for hereditary cancer. Methods: We curated 18 FHx cases and surveyed GCs and non-GCs (oncologists and PCPs) to assess concordance with ItRuns CPG criteria for referring patients for GC/GT. Percent agreement was used to describe concordance, and logistic regression to compare providers and the tool's concordance with CPG criteria. Results: GCs had the best overall concordance with the CPGs used in ItRuns at 82.2%, followed by oncologists with 66.0% and PCPs with 60.6%. GCs were significantly more likely to concur with CPGs (OR = 4.04, 95% CI = 3.35–4.89) than non-GCs. All providers had higher concordance with CPGs for FHx cases that met the criteria for genetic counseling/testing than for cases that did not. Discussion/Conclusion: The risk assessment provided by ItRuns was highly concordant with that of GC's, particularly for at-risk individuals. The use of such technology-based tools improves efficiency and can lead to greater numbers of at-risk individuals accessing genetic counseling, testing, and mutation-based interventions to improve health. [ABSTRACT FROM AUTHOR]

Periodontitis‐mediated alveolar bone loss is caused by dysbiotic shifts in the commensal oral microbiota that upregulate proinflammatory osteoimmune responses. The study purpose was to determine whether antimicrobial‐induced disruption of the commensal microbiota has deleterious effects on alveolar bone. We administered an antibiotic cocktail, minocycline, or vehicle‐control to sex‐matched C57BL/6T mice from age 6‐ to 12 weeks. Antibiotic cocktail and minocycline had catabolic effects on alveolar bone in specific‐pathogen‐free (SPF) mice. We then administered minocycline or vehicle‐control to male mice reared under SPF and germ‐free conditions, and we subjected minocycline‐treated SPF mice to chlorhexidine oral antiseptic rinses. Alveolar bone loss was greater in vehicle‐treated SPF versus germ‐free mice, demonstrating that the commensal microbiota drives naturally occurring alveolar bone loss. Minocycline‐ versus vehicle‐treated germ‐free mice had similar alveolar bone loss outcomes, implying that antimicrobial‐driven alveolar bone loss is microbiota dependent. Minocycline induced phylum‐level shifts in the oral bacteriome and exacerbated naturally occurring alveolar bone loss in SPF mice. Chlorhexidine further disrupted the oral bacteriome and worsened alveolar bone loss in minocycline‐treated SPF mice, validating that antimicrobial‐induced oral dysbiosis has deleterious effects on alveolar bone. Minocycline enhanced osteoclast size and interface with alveolar bone in SPF mice. Neutrophils and plasmacytoid dendritic cells were upregulated in cervical lymph nodes of minocycline‐treated SPF mice. Paralleling the upregulated proinflammatory innate immune cells, minocycline therapy increased TH1 and TH17 cells that have known pro‐osteoclastic actions in the alveolar bone. This report reveals that antimicrobial perturbation of the commensal microbiota induces a proinflammatory oral dysbiotic state that exacerbates naturally occurring alveolar bone loss. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the differences in the colon microbiota composition of Hirschsprung's disease (HSCR) patients with and without a history of postoperative Hirschsprung's associated enterocolitis (HAEC). Methods: Colon tissue microbiota was characterized by bacterial deoxyribonucleic acid (DNA) extraction and 16S rDNA sequencing for taxonomic classification and comparison. Results: The sequence diversity richness within samples was significantly higher in samples from patients with a history of postoperative HAEC. We observed an increased relative abundance of the phyla Bacteroidetes, Firmicutes and Cyanobacteria in HAEC patients and Fusobacteria, Actinobacteria and Proteobacteria in HSCR patients and, an increased relative abundance of the genera Dolosigranulum, Roseouria and Streptococcus in HAEC patients and Propionibacterium and Delftia in HSCR patients. Conclusion: Our findings provide evidence that the colon tissue microbiota composition is different in HSCR patients with and without postoperative HAEC. [ABSTRACT FROM AUTHOR]

There is an increasing awareness that the gut microbiome plays a critical role in human health and disease, but mechanistic insights are often lacking. In June 2018, the Health and Environmental Sciences Institute (HESI) held a workshop, "The Gut Microbiome: Markers of Human Health, Drug Efficacy and Xenobiotic Toxicity" (https://hesiglobal.org/event/the-gut-microbiome-workshop) to identify data gaps in determining how gut microbiome alterations may affect human health. Speakers and stakeholders from academia, government, and industry addressed multiple topics including the current science on the gut microbiome, endogenous and exogenous metabolites, biomarkers, and model systems. The workshop presentations and breakout group discussions formed the basis for identifying data gaps and research needs. Two critical issues that emerged were defining the microbial composition and function related to health and developing standards for models, methods and analysis in order to increase the ability to compare and replicate studies. A series of key recommendations were formulated to focus efforts to further understand host-microbiome interactions and the consequences of exposure to xenobiotics as well as identifying biomarkers of microbiome-associated disease and toxicity. [ABSTRACT FROM AUTHOR]

Background: Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases. Extensive HMI studies have generated large amounts of data. We propose that the logical representation of the knowledge derived from these data and the standardized representation of experimental variables and processes can foster integration of data and reproducibility of experiments and thereby further HMI knowledge discovery. Methods: Through a multi-institutional collaboration, a community-based Ontology of Host-Microbiome Interactions (OHMI) was developed following the Open Biological/Biomedical Ontologies (OBO) Foundry principles. As an OBO library ontology, OHMI leverages established ontologies to create logically structured representations of (1) microbiomes, microbial taxonomy, host species, host anatomical entities, and HMIs under different conditions and (2) associated study protocols and types of data analysis and experimental results. Results: Aligned with the Basic Formal Ontology, OHMI comprises over 1000 terms, including terms imported from more than 10 existing ontologies together with some 500 OHMI-specific terms. A specific OHMI design pattern was generated to represent typical host-microbiome interaction studies. As one major OHMI use case, drawing on data from over 50 peer-reviewed publications, we identified over 100 bacteria and fungi from the gut, oral cavity, skin, and airway that are associated with six rheumatic diseases including rheumatoid arthritis. Our ontological study identified new high-level microbiota taxonomical structures. Two microbiome-related competency questions were also designed and addressed. We were also able to use OHMI to represent statistically significant results identified from a large existing microbiome database data analysis. Conclusion: OHMI represents entities and relations in the domain of HMIs. It supports shared knowledge representation, data and metadata standardization and integration, and can be used in formulation of advanced queries for purposes of data analysis. [ABSTRACT FROM AUTHOR]

Objective: Systemic lupus erythematosus (SLE) is characterized by the production of antibodies against self antigens. However, the events underlying autoantibody formation in SLE remain unclear. This study was undertaken to investigate the role of plasma autoantibody levels, microbial translocation, and the microbiome in SLE. Methods: Plasma samples from 2 cohorts, one with 18 unrelated healthy controls and 18 first‐degree relatives and the other with 19 healthy controls and 21 SLE patients, were assessed for autoantibody levels by autoantigen microarray analysis, measurement of lipopolysaccharide (LPS) levels by Limulus amebocyte assay, and determination of microbiome composition by microbial 16S ribosomal DNA sequencing. Results: First‐degree relatives and SLE patients exhibited increased plasma autoantibody levels compared to their control groups. Parents and children of lupus patients exhibited elevated plasma LPS levels compared to controls (P = 0.02). Plasma LPS levels positively correlated with plasma anti–double‐stranded DNA IgG levels in first‐degree relatives (r = 0.51, P = 0.03), but not in SLE patients. Circulating microbiome analysis revealed that first‐degree relatives had significantly reduced microbiome diversity compared to their controls (observed species, P = 0.004; Chao1 index, P = 0.005), but this reduction was not observed in SLE patients. The majority of bacteria that were differentially abundant between unrelated healthy controls and first‐degree relatives were in the Firmicutes phylum, while differences in bacteria from several phyla were identified between healthy controls and SLE patients. Bacteria in the Paenibacillus genus were the only overlapping differentially abundant bacteria in both cohorts, and were reduced in first‐degree relatives (adjusted P [Padj] = 2.13 × 10−12) and SLE patients (Padj = 0.008) but elevated in controls. Conclusions: These results indicate a possible role of plasma microbial translocation and microbiome composition in influencing autoantibody development in SLE. [ABSTRACT FROM AUTHOR]

Many important exposure–response relationships, such as diet and weight, can be influenced by intermediates, such as the gut microbiome. Understanding the role of these intermediates, the mediators, is important in refining cause–effect theories and discovering additional medical interventions (e.g., probiotics, prebiotics). Mediation analysis has been at the heart of behavioral health research, rapidly gaining popularity with the biomedical sciences in the last decade. A specific analytic challenge is being able to incorporate an entire 'omics assay as a mediator. To address this challenge, we propose a hypothesis testing framework for multivariate omnibus distance mediation analysis (MODIMA). We use the power of energy statistics, such as partial distance correlation, to allow for analysis of multivariate exposure–mediator–response triples. Our simulation results demonstrate the favorable statistical properties of our approach relative to the available alternatives. Finally, we demonstrate the application of the proposed methods in two previously published microbiome datasets. Our framework adds a new tool to the toolbox of approaches to the integration of 'omics big data. [ABSTRACT FROM AUTHOR]

Microbial signals have been linked to autoantibody induction. Recently, we found that purified anti-CD4 autoantibodies from the plasma of chronic HIV-1-infected patients under viral-suppressed antiretroviral therapy (ART) play a pathologic role in poor CD4+ T cell recovery. The purpose of the study was to investigate the association of systemic microbiome and anti-CD4 autoantibody production in HIV. Plasma microbiome from 12 healthy controls and 22 HIV-infected subjects under viral-suppressed ART were analyzed by MiSeq sequencing. Plasma level of autoantibodies and microbial translocation (LPS, total bacterial 16S rDNA, soluble CD14, and LPS binding protein) were analyzed by ELISA, limulus amebocyte assay, and qPCR. We found that plasma level of anti-CD4 IgGs but not anti-CD8 IgGs was increased in HIV+ subjects compared to healthy controls. HIV+ subjects with plasma anti-CD4 IgG > 50 ng/mL (high) had reduced microbial diversity compared to HIV+ subjects with anti-CD4 IgG ≤ 50 ng/mL (low). Moreover, plasma anti-CD4 IgG level was associated with elevated microbial translocation and reduced microbial diversity in HIV+ subjects. The Alphaproteobacteria class was significantly enriched in HIV+ subjects with low anti-CD4 IgG compared to patients with high anti-CD4 IgG even after controlling for false discovery rate (FDR). The microbial components were different from the phylum to genus level in HIV+ subjects with high anti-CD4 IgGs compared to the other two groups, but these differences were not significant after controlling for FDR. These results suggest that systemic microbial translocation and microbiome may associate with anti-CD4 autoantibody production in ART-treated HIV disease. [ABSTRACT FROM AUTHOR]

Gut microorganisms contribute to the metabolism of environmental toxicants, including methylmercury (MeHg). Our main objective was to investigate whether associations between biomarkers for prenatal MeHg exposure and maternal gut microbiota differed between early and late gestation. Maternal blood and stool samples were collected during early (8.3–17 weeks, n=28) and late (27–36 weeks, n=24) gestation. Total mercury and MeHg concentrations were quantified in biomarkers, and inorganic mercury was estimated by subtraction. The diversity and structure of the gut microbiota were investigated using 16S rRNA gene profiling (n = 52). Biomarkers were dichotomized, and diversity patterns were compared between high/low mercury concentrations. Spearman's correlation was used to assess bivariate associations between MeHg biomarkers (stool, blood, and meconium), and 23 gut microbial taxa (genus or family level, >1% average relative abundance). Within-person and between-person diversity patterns in gut microbiota differed between early/late gestation. The overall composition of the microbiome differed between high/low MeHg concentrations (in blood and stool) during early gestation, but not late gestation. Ten (of 23) taxa were significantly correlated with MeHg biomarkers (increasing or decreasing); however, associations differed, depending on whether the sample was collected during early or late gestation. A total of 43% of associations (69/161) reversed the direction of correlation between early/late gestation. The time point at which a maternal fecal sample is collected may yield different associations between gut microorganisms and MeHg biomarkers, which may be due in part to remodeling of maternal microbiota during pregnancy. Our results suggest the effectiveness of dietary interventions to reduce prenatal MeHg exposure may differ between early and late gestation. • Pregnant mothers donated blood and stool samples during early/late gestation. • Total mercury and methylmercury (MeHg) were determined in both biomarkers. • Gut microbiota community structure was determined using 16S rRNA gene profiling. • Within- and between-person diversity patterns differed between early/late gestation. • Associations between gut microbiota and MeHg biomarkers differed longitudinally. [ABSTRACT FROM AUTHOR]

Highlights • This work highlights BioSimulator.jl, an open-source master equation simulation engine implemented in the Julia language. • We highlight the comparative strengths of the exact and approximate stochastic simulation algorithms provided by our software. • Stochastic simulation finds applications across disciplines, including ecology, chemistry, and genetics. We illustrate BioSimulator.jl's workflow and utility with examples. • Our benchmarks favor a Julia implementation: BioSimulator.jl provides code readability and ease-of-use without sacrificing performance. Abstract Background and Objectives : Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl , for implementing the Gillespie algorithm, τ -leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. Methods : We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. Results : BioSimulator.jl 's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. Conclusion : The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification. [ABSTRACT FROM AUTHOR]

Background: More than 50 years after the discovery of antibiotics, bacterial infections have decreased substantially; however, antibiotics also may have negative effects such as increasing susceptibility to pathogens. An intact microbiome is an important line of defense against pathogens. We sought to determine the effect of orally administered antibiotics both on susceptibility to pathogens and on impact to the microbiome. We studied Campylobacter jejuni, one of the most common causes of human diarrhea, and Acinetobacter baumannii, which causes wound infections. We examined the effects of antibiotic treatment on the susceptibility of mice to those pathogens as well as their influence on the mouse gut microbiome. Results: In C57/BL6 mice models, we explored the effects of pathogen challenge, and antibiotic treatment on the intestinal microbiota. Mice were treated with either ciprofloxacin, penicillin, or water (control) for a 5-day period followed by a 5-day washout period prior to oral challenge with C. jejuni or A. baumannii to assess antibiotic effects on colonization susceptibility. Mice were successfully colonized with C. jejuni more than 118 days, but only transiently with A. baumannii. These challenges did not lead to any major effects on the composition of the gut microbiota. Although antibiotic pre-treatment did not modify pathogen colonization, it affected richness and community structure of the gut microbiome. However, the antibiotic dysbiosis was significantly reduced by pathogen challenge. Conclusions: We conclude that despite gut microbiota disturbance, susceptibility to gut colonization by these pathogens was unchanged. The major gut microbiome disturbance produced by antibiotic treatment may be reduced by colonization with specific microbial taxa. [ABSTRACT FROM AUTHOR]

Summary Acquisition of the intestinal microbiota begins at birth, and a stable microbial community develops from a succession of key organisms. Disruption of the microbiota during maturation by low-dose antibiotic exposure can alter host metabolism and adiposity. We now show that low-dose penicillin (LDP), delivered from birth, induces metabolic alterations and affects ileal expression of genes involved in immunity. LDP that is limited to early life transiently perturbs the microbiota, which is sufficient to induce sustained effects on body composition, indicating that microbiota interactions in infancy may be critical determinants of long-term host metabolic effects. In addition, LDP enhances the effect of high-fat diet induced obesity. The growth promotion phenotype is transferrable to germ-free hosts by LDP-selected microbiota, showing that the altered microbiota, not antibiotics per se, play a causal role. These studies characterize important variables in early-life microbe-host metabolic interaction and identify several taxa consistently linked with metabolic alterations. PaperClip [ABSTRACT FROM AUTHOR]

Highlights: [•] Development of an in silico genome subtraction method and the MGC tool software. [•] Application of Simrank to the estimation of genome sequence similarity. [•] Comparative genomics of whole genome sequences of Streptococcus mutans. [•] No S. mutans loci found associated with severe early childhood caries. [ABSTRACT FROM AUTHOR]

Background. Diabetic foot infections are a leading cause of lower extremity amputations. Our study examines the microbiota of diabetic skin prior to ulcer development or infection.Methods. In a case-control study, outpatient males were recruited at a veterans hospital. Subjects were swabbed at 4 cutaneous sites, 1 on the forearm and 3 on the foot. Quantitative polymerase chain reaction (qPCR) with primers and probes specific for bacteria, Staphylococcus species, Staphylococcus aureus, and fungi were performed on all samples. High-throughput 16S ribosomal RNA (rRNA) sequencing was performed on samples from the forearm and the plantar aspect of the foot.Results. qPCR analysis of swab specimens from 30 diabetic subjects and 30 control subjects showed no differences in total numbers of bacteria or fungi at any sampled site. Increased log10 concentrations of Staphylococcus aureus, quantified by the number of nuc gene copies, were present in diabetic men on the plantar aspect of the foot. High-throughput 16S rRNA sequencing found that, on the foot, the microbiota in controls (n = 24) was dominated by Staphylococcus species, whereas the microbiota in diabetics (n = 23) was more diverse at the genus level. The forearm microbiota had similar diversity in diabetic and control groups.Conclusions. The feet of diabetic men had decreased populations of Staphylococcus species, increased populations of S. aureus, and increased bacterial diversity, compared with the feet of controls. These ecologic changes may affect the risk for wound infections. [ABSTRACT FROM PUBLISHER]

Diet influences host metabolism and intestinal microbiota; however, detailed understanding of this tripartite interaction is limited. To determine whether the nonfermentable fiber hydroxypropyl methylcellulose (HPMC) could alter the intestinal microbiota and whether such changes correlated with metabolic improvements, C57B/L6 mice were normalized to a high-fat diet (HFD), then either maintained on HFD (control), or switched to HFD supplemented with 10% HPMC, or a low-fat diet (LFD). Compared to control treatment, both LFD and HPMC reduced weight gain (11.8 and 5.7 g, respectively), plasma cholesterol (23.1 and 19.6%), and liver triglycerides (73.1 and 44.6%), and, as revealed by 454-pyrosequencing of the microbial 16S rRNA gene, decreased microbial α-diversity and differentially altered intestinal microbiota. Both LFD and HPMC increased intestinal Erysipelotrichaceae (7.3- and 12.4-fold) and decreased Lachnospiraceae (2.0- and 2.7-fold), while only HPMC increased Peptostreptococcaceae (3.4-fold) and decreased Ruminococcaceae (2.7-fold). Specific microorganisms were directly linked with weight change and metabolic parameters in HPMC and HFD mice, but not in LFD mice, indicating that the intestinal microbiota may play differing roles during the two dietary modulations. This work indicates that HPMC is a potential prebiotic fiber that influences intestinal microbiota and improves host metabolism. [ABSTRACT FROM AUTHOR]

We examined intragenomic variation of paralogous 5 S r RNA genes to evaluate the concept of ribosomal constraints. In a dataset containing 1161 genomes from 779 unique species, 96 species exhibited > 3% diversity. Twenty-seven species with > 10% diversity contained a total of 421 mismatches between all pairs of the most dissimilar copies of 5 S r RNA genes. The large majority (401 of 421) of the diversified positions were conserved at the secondary structure level. The high diversity was associated with partial r RNA operon, split operon, or spacer length-related divergence. In total, these findings indicated that there are tight ribosomal constraints on paralogous 5 S r RNA genes in a genome despite of the high degree of diversity at the primary structure level. [ABSTRACT FROM AUTHOR]

Antibiotics administered in low doses have been widely used as growth promoters in the agricultural industry since the 1950s, yet the mechanisms for this effect are unclear. Because antimicrobial agents of different classes and varying activity are effective across several vertebrate species, we proposed that such subtherapeutic administration alters the population structure of the gut microbiome as well as its metabolic capabilities. We generated a model of adiposity by giving subtherapeutic antibiotic therapy to young mice and evaluated changes in the composition and capabilities of the gut microbiome. Administration of subtherapeutic antibiotic therapy increased adiposity in young mice and increased hormone levels related to metabolism. We observed substantial taxonomic changes in the microbiome, changes in copies of key genes involved in the metabolism of carbohydrates to short-chain fatty acids, increases in colonic short-chain fatty acid levels, and alterations in the regulation of hepatic metabolism of lipids and cholesterol. In this model, we demonstrate the alteration of early-life murine metabolic homeostasis through antibiotic manipulation. [ABSTRACT FROM AUTHOR]

There are two competing hypotheses for the origin of the Indo-European language family. The conventional view places the homeland in the Pontic steppes about 6000 years ago. An alternative hypothesis claims that the languages spread from Anatolia with the expansion of farming 8000 to 9500 years ago. We used Bayesian phylogeographic approaches, together with basic vocabulary data from 103 ancient and contemporary Indo-European languages, to explicitly model the expansion of the family and test these hypotheses. We found decisive support for an Anatolian origin over a steppe origin. Both the inferred timing and root location of the Indo-European language trees fit with an agricultural expansion from Anatolia beginning 8000 to 9500 years ago. These results highlight the critical role that phylogeographic inference can play in resolving debates about human prehistory. [ABSTRACT FROM AUTHOR]

Misrepair of DNA double-strand breaks produced by the V(D)J recombinase (the RAG1/RAG2 proteins) at immunoglobulin (Ig) and T cell receptor (Tcr) loci has been implicated in pathogenesis of lymphoid malignancies in humans and in mice. Defects in DNA damage response factors such as ataxia telangiectasia mutated (ATM) protein and combined deficiencies in classical non-homologous end joining and p53 predispose to RAG-initiated genomic rearrangements and lymphomagenesis. Although we showed previously that RAG1/RAG2 shepherd the broken DNA ends to classical non-homologous end joining for proper repair, roles for the RAG proteins in preserving genomic stability remain poorly defined. Here we show that the RAG2 carboxy (C) terminus, although dispensable for recombination, is critical for maintaining genomic stability. Thymocytes from 'core' Rag2 homozygotes (Rag2c/c mice) show dramatic disruption of Tcrα/δ locus integrity. Furthermore, all Rag2c/c p53−/− mice, unlike Rag1c/c p53−/− and p53−/− animals, rapidly develop thymic lymphomas bearing complex chromosomal translocations, amplifications and deletions involving the Tcrα/δ and Igh loci. We also find these features in lymphomas from Atm−/− mice. We show that, like ATM-deficiency, core RAG2 severely destabilizes the RAG post-cleavage complex. These results reveal a novel genome guardian role for RAG2 and suggest that similar 'end release/end persistence' mechanisms underlie genomic instability and lymphomagenesis in Rag2c/c p53−/− and Atm−/− mice. [ABSTRACT FROM AUTHOR]

There is currently no criterion to select appropriate bioinformatics tools and reference databases for analysis of 16S rRNA amplicon data in the human oral microbiome. Our study aims to determine the influence of multiple tools and reference databases on α-diversity measurements and β-diversity comparisons analyzing the human oral microbiome. We compared the results of taxonomical classification by Greengenes, the Human Oral Microbiome Database (HOMD), National Center for Biotechnology Information (NCBI) 16S, SILVA, and the Ribosomal Database Project (RDP) using Quantitative Insights Into Microbial Ecology (QIIME) and the Divisive Amplicon Denoising Algorithm (DADA2). There were 15 phyla present in all of the analyses, four phyla exclusive to certain databases, and different numbers of genera were identified in each database. Common genera found in the oral microbiome, such as Veillonella, Rothia, and Prevotella, are annotated by all databases; however, less common genera, such as Bulleidia and Paludibacter, are only annotated by large databases, such as Greengenes. Our results indicate that using different reference databases in 16S rRNA amplicon data analysis could lead to different taxonomic compositions, especially at genus level. There are a variety of databases available, but there are no defined criteria for data curation and validation of annotations, which can affect the accuracy and reproducibility of results, making it difficult to compare data across studies. [ABSTRACT FROM AUTHOR]