Chen, Jun

AbstractMotivationGenomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction.ResultsThis study shows that the traditional approach is suboptimal and proposes a new two-dimensional false discovery rate control framework (2DFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2DFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2DFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. Promising finite sample performance is demonstrated via extensive simulations and real data applications.Availability and implementationR codes and vignettes are available at https://github.com/asmita112358/tdfdr.np.

Although the etiology of rheumatoid arthritis (RA) is unknown, a strong genetic predisposition and the presence of preclinical antibodies before the onset of symptoms is documented. An expansion of Eggerthella lenta is associated with severe disease in RA. Here, using a humanized mouse model of collagen-induced arthritis, we determined the impact of E. lenta abundance on RA severity. Naïve mice gavaged with E. lenta produce preclinical rheumatoid factor and, when induced for arthritis, develop severe disease. The augmented antibody response was much higher in female mice, and among patients with RA, women had higher average load of E. lenta . Expansion of E. lenta increased CXCL5 and CD4 T cells, and both interleukin-17– and interferon-γ–producing B cells. Further, E. lenta gavage caused gut dysbiosis and decline in amino acids and nicotinamide adenine dinucleotide with an increase in microbe-dependent bile acids and succinyl carnitine causing systemic senescent-like inflammation.

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

Abstract Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.

Abstract Summary Due to the sparsity and high dimensionality, microbiome data are routinely summarized into pairwise distances capturing the compositional differences. Many biological insights can be gained by analyzing the distance matrix in relation to some covariates. A microbiome sampling method that characterizes the inter-sample relationship more reproducibly is expected to yield higher statistical power. Traditionally, the intraclass correlation coefficient (ICC) has been used to quantify the degree of reproducibility for a univariate measurement using technical replicates. In this work, we extend the traditional ICC to distance measures and propose a distance-based ICC (dICC). We derive the asymptotic distribution of the sample-based dICC to facilitate statistical inference. We illustrate dICC using a real dataset from a metagenomic reproducibility study. Availability and implementation dICC is implemented in the R CRAN package GUniFrac. Supplementary information Supplementary data are available at Bioinformatics online.

AbstractBackgroundA third dose of measles-mumps-rubella vaccine (MMR3) is recommended in mumps outbreak scenarios, but the immune response and the need for widespread use of MMR3 remain uncertain. Herein, we characterized measles-specific immune responses to MMR3 in a cohort of 232 healthy subjects.MethodsSerum and peripheral blood mononuclear cells (PBMCs) were sampled at day 0 and day 28 after MMR3. Measles-specific binding and neutralizing antibodies were quantified in sera by enzyme-linked immunosorbent assay and a microneutralization assay, respectively. PBMCs were stimulated with inactivated measles virus, and the release of cytokines/chemokines was assessed by a multiplex assay. Demographic variables of subjects were examined for potential correlations with immune outcomes.ResultsOf the study participants, 95.69% and 100% were seropositive at day 0 and day 28, respectively. Antibody avidity significantly increased from 38.08% at day 0 to 42.8% at day 28 (P = .00026). Neutralizing antibodies were significantly enhanced, from 928.7 at day 0 to 1289.64 mIU/mL at day 28 (P = .0001). Meanwhile, cytokine/chemokine responses remained largely unchanged. Body mass index was significantly correlated with the levels of inflammatory cytokines/chemokines.ConclusionsMeasles-specific humoral immune responses, but not cellular responses, were enhanced after MMR3 receipt, extending current understanding of immune responses to MMR3 and supporting MMR3 administration to seronegative or high-risk individuals.

Abstract Purpose: Regulatory T-cells (Treg) are essential to Tregs homeostasis and modulate the antitumor immune response in patients with lymphoma. However, the biology and prognostic impact of Tregs in splenic marginal zone lymphoma (SMZL) have not been studied. Experimental Design: Biopsy specimens from 24 patients with SMZL and 12 reactive spleens (rSP) from individuals without lymphoma were analyzed by using CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing), CyTOF (mass cytometry) analysis, and flow cytometry to explore the phenotype, transcriptomic profile, and clinical significance of intratumoral Tregs and their subsets. The biological characteristics and cell signaling pathways of intratumoral Treg subsets were confirmed by in vitro functional assays. Results: We found that Tregs are more abundant in SMZL patients' spleens than rSP, and Tregs from patients with SMZL and rSP can be separated into CD161+Treg and CD26+Treg subsets. CD161+Tregs are increased in SMZL but have dysregulated immune function. We found that CD161+Treg and CD26+Tregs have unique gene expression and phenotypic profiles and are differentially correlated with patient outcomes. Specifically, increased CD161+Tregs are significantly associated with a favorable prognosis in patients with SMZL, whereas CD26+Tregs are associated with a poor prognosis. Furthermore, activation of the IL2/STAT5 pathway contributes to the induction of CD26+Tregs and can be reversed by STAT5 inhibition. Conclusions: IL2/STAT5-mediated expansion of CD26+Tregs contributes to a poor clinical outcome in SMZL and may represent a therapeutic opportunity in this disease.

INTRODUCTION: Intragastric balloons (IGBs) are a safe and effective treatment for obesity. However, limited knowledge exists on the underlying biological changes with IGB placement. METHODS: This single-institution study was part of an adjustable IGB randomized controlled trial. Subjects with obesity were randomized in a 2 is to 1 ratio to 32 weeks of IGB with diet/exercise counseling (n = 8) vs counseling alone (controls, n = 4). Diet/exercise counseling was continued for 24 weeks post-IGB removal to assess weight maintenance. We used mass spectrometry for nontargeted plasma lipidomics analysis and 16S rRNA sequencing to profile the fecal microbiome. RESULTS: Subjects with IGBs lost 15.5% of their body weight at 32 weeks vs 2.59% for controls ( P < 0.05). Maintenance of a 10.5% weight loss occurred post-IGB explant. IGB placement, followed by weight maintenance, led to a −378.9 μM/L reduction in serum free fatty acids compared with pre-IGB (95% confidence interval: 612.9, −145.0). This reduction was mainly in saturated, mono, and omega-6 fatty acids when compared with pre-IGB. Polyunsaturated phosphatidylcholines also increased after IGB placement (difference of 27 μM/L; 95% confidence interval: 1.1, 52.8). Compared with controls, saturated and omega-6 free fatty acids (linoleic and arachidonic acids) were reduced after IGB placement. The fecal microbiota changed post-IGB placement and weight maintenance vs pre-IGB ( P < 0.05). Further analysis showed a possible trend toward reduced Firmicutes and increased Bacteroidetes post-IGB and counseling, a change that was not conclusively different from counseling alone. DISCUSSION: IGB treatment is associated with an altered fecal microbiome profile and may have a better effect on obesity-related lipidome than counseling alone.

Background: Primary sclerosing cholangitis (PSC) is a major risk factor for cholangiocarcinoma (CCA). We investigated biliary and fecal microbiota to determine whether specific microbes in the bile or stool are associated with PSC or CCA. Methods: Bile was obtained from 32 patients with PSC, 23 with CCA with PSC, 26 with CCA without PSC, and 17 controls. Over 90% of bile samples were from patients with perihilar CCA. Stool was obtained from 31 patients with PSC (11 were matched to bile), 16 with CCA with PSC (10 matched to bile), and 11 with CCA without PSC (6 matched to bile). Microbiota composition was assessed using 16SrRNA-marker-based sequencing and was compared between groups. Results: Bile has a unique microbiota distinguished from negative DNA controls and stool. Increased species richness and abundance of Fusobacteria correlated with duration of PSC and characterized the biliary microbiota in CCA. Stool microbiota composition showed no significant differences between groups. Conclusions: We identified a unique microbial signature in the bile of patients with increased duration of PSC or with CCA, suggesting a role for microbiota-driven inflammation in the pathogenesis and or progression to perihilar CCA. Further studies are needed to test this hypothesis.