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Additional file 1 Figure on histograms of some simulated abundances vs. true abundances.

Additional file 1. A PDF file that includes additional methods and results (Section S1-S3), supplemental figures (Fig. S1-S7) and short tables (Table S1-S2).

Additional file 7 Section S1. In this section, we conducted association analysis at the ASV level (i.e., 0% dissimilarity) using the same analysis strategies as described in the main texts for the bacterial vaginosis (BV) and the preterm birth (PTB) association studies.

Additional file 1: Supplementary material, including a detailed example of the TCRhom calculation, the proof of independence of score statistics and more simulation studies.

Additional file 6 Table S2. OTUs significantly associated with preterm birth at FDR level of 0.05. TF: TreeFDR; SO: Single-OTU test implemented by POST with c=0; DE: DESeq2; WR-P: Wilcoxon rank-sum test using proportional data; WR-R: Wilcoxon rank-sum test using CLR transformed data.

Additional file 8 Table S3. The AUC using different pseudo-counts in Simulation A. The AUCs based on pseudo-count 0.5 are very close to the AUCs based on pseudo-count 1 across different scenarios and effect sizes.

Successful prediction of clinical outcomes facilitates tailored diagnosis and treatment. The microbiome has been shown to be an important biomarker to predict host clinical outcomes. Further, the incorporation of microbial phylogeny, the evolutionary relationship among microbes, has been demonstrated to improve prediction accuracy. We propose a phylogeny-driven deep neural network (PhyNN) and develop an ensemble method, DeepEn-Phy, for host clinical outcome prediction. The method is designed to optimally extract features from phylogeny, thereby take full advantage of the information in phylogeny while harnessing the core principles of phylogeny (in contrast to taxonomy). We apply DeepEn-Phy to a real large microbiome data set to predict both categorical and continuous clinical outcomes. DeepEn-Phy demonstrates superior prediction performance to existing machine learning and deep learning approaches. Overall, DeepEn-Phy provides a new strategy for designing deep neural network architectures within the context of phylogeny-constrained microbiome data.

Additional file 1 PDF file includes supplemental tables (Tables S1–S18).

Animal models support a role for the gut microbiota in the development of hypertension. There has been a lack of epidemiologic cohort studies to confirm these findings in human populations. We examined cross-sectional associations between measures of gut microbial diversity and taxonomic composition and blood pressure in 529 participants of the biracial (African- and European-American) Coronary Artery Risk Development in Young Adults (CARDIA) study. We sequenced V3-V4 regions of the 16S ribosomal RNA marker gene using DNA extracted from stool samples collected at CARDIA’s Year 30 follow-up examination (2015-16; aged 48-60 years). We quantified associations between blood pressure [hypertension (defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg and/or antihypertension medication use) and SBP] and with- and between-person diversity measures. We conducted genera-specific multivariable-adjusted regression analysis, accounting for multiple comparisons using the false discovery rate. Hypertension and SBP were inversely associated with measures of alpha-diversity, including richness and the Shannon Diversity Index, and were distinguished with respect to principal coordinates based on a similarity matrix of genera abundance. Several specific genera were significantly associated with hypertension and SBP, though results were attenuated with adjustment for body mass index. Our findings support associations between within-person and between-person gut microbial community diversity and taxonomic composition and blood pressure in a diverse population-based cohort of middle-aged adults. Future work is needed to define functional pathways that underlie observed associations and identify specific microbial targets for intervention.

BackgroundCannabis is a widely used drug in the United States, and the frequency of cannabis use in the human immunodeficiency virus (HIV)-infected population is disproportionately high. Previous human and macaque studies suggest that cannabis may have an impact on plasma viral load; however, the relationship between cannabis use and HIV-associated systemic inflammation and immune activation has not been well defined.MethodsThe impact of cannabis use on peripheral immune cell frequency, activation, and function was assessed in 198 HIV-infected, antiretroviral-treated individuals by flow cytometry. Individuals were categorized into heavy, medium, or occasional cannabis users or noncannabis users based on the amount of the cannabis metabolite 11-nor-carboxy-tetrahydrocannabinol (THC-COOH) detected in plasma by mass spectrometry.ResultsHeavy cannabis users had decreased frequencies of human leukocyte antigen (HLA)-DR+CD38+CD4+ and CD8+ T-cell frequencies, compared to frequencies of these cells in non-cannabis-using individuals. Heavy cannabis users had decreased frequencies of intermediate and nonclassical monocyte subsets, as well as decreased frequencies of interleukin 23- and tumor necrosis factor-α-producing antigen-presenting cells.ConclusionsWhile the clinical implications are unclear, our findings suggest that cannabis use is associated with a potentially beneficial reduction in systemic inflammation and immune activation in the context of antiretroviral-treated HIV infection.

A number of isomer structures can be formed in hydrogen-bonded clusters, reflecting the essential variety of structural motifs of hydrogen bond networks. Control of isomer distribution of a cluster is important not only in practical use for isomer-specific spectroscopy but also in understanding of isomerization processes of hydrogen bond networks. Protonated methanol clusters have relatively simple networks and they are model systems suitable to investigate isomer distribution changes. In this paper, isomer distribution of H(+)(CH(3)OH)(7) is studied by size-selective infrared spectroscopy in the OH and CH stretching vibrational region and density functional theory calculations. While the clusters produced by a supersonic jet expansion combined with electron ionization were predominantly isomers having open hydrogen bond networks such as a linear chain, the Ar or Ne attachment (so-called rare gas tagging) entirely switches the isomer structures to compactly folded ones, which are composed only of closed multiple rings. The origin of the isomer switching is discussed in terms of thermal effects and specific isomer preference.

Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2× 10(-16)). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure.