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Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations.

Genome-wide association studies (GWASs) have been widely applied in the neuroimaging field to discover genetic variants associated with brain-related traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on univariate quantitative features summarized from brain images. On the other hand, powerful deep learning technologies have dramatically improved our ability to classify images. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying genetic variants that lead to detectable nuances on Magnetic Resonance Images (MRI). For a specific single nucleotide polymorphism (SNP), if MRI images labeled by genotypes of this SNP can be reliably distinguished using machine learning, we then hypothesized that this SNP is likely to be associated with brain anatomy or function which is manifested in MRI brain images. We applied this strategy to a catalog of MRI image and genotype data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI) consortium. From the results, we identified novel variants that show strong association to brain phenotypes. Author summary: Genome-wide association study (GWAS) is a powerful method to identify associations between genetic variants and traits such as height, weight and disease status. When applying to Magnetic resonance imaging (MRI) data, traditional GWAS methods often rely on simplified summaries of brain imaging data, potentially missing subtle but significant global patterns. We proposed and implemented a different strategy: training a machine learning model to distinguish MR images based on genetic variants. If MRI images labeled by the mutation status of a variant can be reliably distinguished using machine learning, we then hypothesized that this variant is likely to be associated with brain anatomy or function which is manifested in MRI brain images. By applying this method to data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we found new genetic variants highly likely to affect brain phenotypes. This innovative approach not only handles high-dimensional imaging data more effectively but also captures complex, non-linear relationships between genetic variants and various brain traits, offering a fresh perspective on neuroimaging genetics. [ABSTRACT FROM AUTHOR]

Sequencing bacterial genomes from DNA isolated directly from clinical samples offers the promise of rapid and precise acquisition of informative genetic information. In the case of Chlamydia trachomatis, direct sequencing is particularly desirable because it obviates the requirement for culture in mammalian cells, saving time, cost and the possibility of missing low abundance strains. In this proof of concept study, we developed methodology that would allow genome-scale direct sequencing, using a multiplexed microdroplet PCR enrichment technology to amplify a 100 kb region of the C. trachomatis genome with 500 1.1-1.3 kb overlapping amplicons (5-fold amplicon redundancy). We integrated comparative genomic data into a pipeline to preferentially select conserved sites for amplicon design. The 100 kb target region could be amplified from clinical samples, including remnants from diagnostics tests, originating from the cervix, urethra and urine, For rapid analysis of these data, we developed a framework for whole-genome based genotyping called binstrain. We used binstrain to estimate the proportion of SNPs originating from 14 C. trachomatis reference serotype genomes in each sample. Direct DNA sequencing methods such as the one described here may have an important role in understanding the biology of C. trachomatis mixed infections and the natural genetic variation of the species within clinically relevant ecological niches.

Identifying biomarkers for Alzheimer's disease with a goal of early detection is a fundamental problem in clinical research. Both medical imaging and genetics have contributed informative biomarkers in literature. To further improve the performance, recently, there is an increasing interest in developing analytic approaches that combine data across modalities such as imaging and genetics. However, there are limited methods in literature that are able to systematically combine high-dimensional voxel-level imaging and genetic data for accurate prediction of clinical outcomes of interest. Existing prediction models that integrate imaging and genetic features often use region level imaging summaries, and they typically do not consider the spatial configurations of the voxels in the image or incorporate the dependence between genes that may compromise prediction ability. We propose a novel integrative Bayesian scalar-on-image regression model for predicting cognitive outcomes based on high-dimensional spatially distributed voxel-level imaging data, along with correlated transcriptomic features. We account for the spatial dependencies in the imaging voxels via a tensor approach that also enables massive dimension reduction to address the curse of dimensionality, and models the dependencies between the transcriptomic features via a Graph-Laplacian prior. We implement this approach via an efficient Markov chain Monte Carlo (MCMC) computation strategy. We apply the proposed method to the analysis of longitudinal ADNI data for predicting cognitive scores at different visits by integrating voxel-level cortical thickness measurements derived from T1w-MRI scans and transcriptomics data. We illustrate that the proposed imaging transcriptomics approach has significant improvements in prediction compared to prediction using a subset of features from only one modality (imaging or genetics), as well as when using imaging and transcriptomics features but ignoring the inherent dependencies between the features. Our analysis is one of the first to conclusively demonstrate the advantages of prediction based on combining voxel-level cortical thickness measurements along with transcriptomics features, while accounting for inherent structural information. [ABSTRACT FROM AUTHOR]

Simple Summary: There is a need to identify new translational prognostic biomarkers in head and neck cancer. Metabolomics, the study of small molecules resulting from cellular metabolism, is an emerging and promising field regarding head and neck cancer. We performed metabolomics on patients' blood prior to treatment and found that it can divide patients into high-risk and low-risk groups based on their cancer progression and survival. We believe our study provides compelling results to consider metabolomics as a translational prognostic biomarker and that it may offer novel information for patient risk stratification. With continued research, we hope to gain a fuller understanding of how metabolomics may aid in the early detection, prognosis, treatment monitoring, and targeted therapies of head and neck cancer. There is growing evidence that the metabolism is deeply intertwined with head and neck squamous cell carcinoma (HNSCC) progression and survival but little is known about circulating metabolite patterns and their clinical potential. We performed unsupervised hierarchical clustering of 209 HNSCC patients via pre-treatment plasma metabolomics to identify metabolic subtypes. We annotated the subtypes via pathway enrichment analysis and investigated their association with overall and progression-free survival. We stratified the survival analyses by smoking history. High-resolution metabolomics extracted 186 laboratory-confirmed metabolites. The optimal model created two patient clusters, of subtypes A and B, corresponding to 41% and 59% of the study population, respectively. Fatty acid biosynthesis, acetyl-CoA transport, arginine and proline, as well as the galactose metabolism pathways differentiated the subtypes. Relative to subtype B, subtype A patients experienced significantly worse overall and progression-free survival but only among ever-smokers. The estimated three-year overall survival was 61% for subtype A and 86% for subtype B; log-rank p = 0.001. The association with survival was independent of HPV status and other HNSCC risk factors (adjusted hazard ratio = 3.58, 95% CI: 1.46, 8.78). Our findings suggest that a non-invasive metabolomic biomarker would add crucial information to clinical risk stratification and raise translational research questions about testing such a biomarker in clinical trials. [ABSTRACT FROM AUTHOR]

Fig 6 is incorrect. The authors have provided a corrected version here.Graph: Fig 6 Kaplan-Meier curves of the molecular subtypes of HNSCC cohort based on p16 status.By Nabil F. Saba; Ashok R. Dinasarapu; Kelly R. Magliocca; Bhakti Dwivedi; Sandra Seby; Zhaohui S. Qin; Mihir Patel; Christopher C. Griffith; Xu Wang; Mark El-Deiry; Conor Ernst Steuer; Jeanne Kowalski; Dong Moon Shin; Michael E. Zwick and Zhuo Georgia ChenReported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author [Extracted from the article]

Men who develop metastatic castration-resistant prostate cancer (CRPC) invariably succumb to the disease. Progression to CRPC after androgen ablation therapy is predominantly driven by deregulated androgen receptor (AR) signalling (1-3). [...]

Donor T cells that respond to host alloantigens following allogeneic bone marrow transplantation (BMT) induce graft-versus-host (GVH) responses, but their molecular landscape is not well understood. Micro-RNAs (miRNAs) regulate gene (mRNA) expression and fine-tune the molecular responses of T cells. We stimulated naive T cells with either allogeneic or nonspecific stimuli and used argonaute cross-linked immunoprecipitation (CLIP) with subsequent ChIP microarray analyses to profile miR responses and their direct mRNA targets. We identified a unique expression pattern of miRs and mRNAs following the allostimulation of T cells and a high correlation between the expression of the identified miRs and a reduction of their mRNA targets. miRs and mRNAs that were predicted to be differentially regulated in allogeneic T cells com-pared with nonspecifically stimulated T cells were validated in vitro. These analyses identified wings apart-like homolog (Wapal) and synaptojanin 1 (Synj1) as potential regulators of allogeneic T cell responses. The expression of these molecular targets in vivo was confirmed in MHC-mismatched experimental BMT. Targeted silencing of either Wapal or Synj1 prevented the development of GVH response, confirming a role for these regulators in allogeneic T cell responses. Thus, this genome-wide analysis of miRNA-mRNA interactions identifies previously unrecognized molecular regulators of T cell responses., Introduction The molecular landscape of T cell responses to specific antigens is not well understood. The functional responses of cells rely upon the genes that are expressed and the fine-tuning [...]

The rising global HIV-1 burden urgently requires vaccines capable of providing heterologous protection. Here, we developed a clade C HIV-1 vaccine consisting of priming with modified vaccinia Ankara (MVA) and boosting with cyclically permuted trimeric gp120 (CycP-gp120) protein, delivered either orally using a needle-free injector or through parenteral injection. We tested protective efficacy of the vaccine against intrarectal challenges with a pathogenic heterologous clade C SHIV infection in rhesus macaques. Both routes of vaccination induced a strong envelope-specific IgG in serum and rectal secretions directed against V1V2 scaffolds from a global panel of viruses with polyfunctional activities. Envelope-specific IgG showed lower fucosylation compared with total IgG at baseline, and most of the vaccine-induced proliferating blood CD4+ T cells did not express CCR5 and α4β7, markers associated with HIV target cells. After SHIV challenge, both routes of vaccination conferred significant and equivalent protection, with 40% of animals remaining uninfected at the end of six weekly repeated challenges with an estimated efficacy of 68% per exposure. Induction of envelope-specific IgG correlated positively with G1FB glycosylation, and G2S2F glycosylation correlated negatively with protection. Vaccine-induced TNF-α+ IFN-γ+ CD8+ T cells and TNF-α+ CD4+ T cells expressing low levels of CCR5 in the rectum at prechallenge were associated with decreased risk of SHIV acquisition. These results demonstrate that the clade C MVA/CycP-gp120 vaccine provides heterologous protection against a tier2 SHIV rectal challenge by inducing a polyfunctional antibody response with distinct Fc glycosylation profile, as well as cytotoxic CD8 T cell response and CCR5-negative T helper response in the rectum. A clade C HIV-1 vaccine: Clade C HIV-1 infections are responsible for a large proportion of global HIV-1 burden, and a vaccine is urgently required. Sahoo et al. created a clade C HIV-1 vaccination regimen that they tested in rhesus macaques, consisting of priming with modified vaccinia Ankara (MVA) and boosting with cyclically permuted trimeric gp120 (CycP-gp120). To assess delivery methods, vaccines were administered by parenteral injection or oral administration using a needle-free injector. Both routes of MVA/CycP-gp120 administration provided equivalent protection against mucosal SHIV infection. Reduced risk of infection correlated with distinct env-specific IgG Fc glycosylation profiles, as well as with vaccine-induced TNF-α+ IFN-γ+ CD8+ T cells and TNF-α+ CCR5lo CD4+ T cells. These findings demonstrate that the MVA/CycP-gp120 immunization regimen provides significant protection against heterologous SHIV infection. [ABSTRACT FROM AUTHOR]

Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell , a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods. [ABSTRACT FROM AUTHOR]

Introduction: Integrating brain imaging with large scale omics data may identify novel mechanisms of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). We integrated and analyzed brain magnetic resonance imaging (MRI) with cerebrospinal fluid (CSF) metabolomics to elucidate metabolic mechanisms and create a "metabolic map" of the brain in prodromal AD. Methods: In 145 subjects (85 cognitively normal controls and 60 with MCI), we derived voxel-wise gray matter volume via whole-brain structural MRI and conducted high-resolution untargeted metabolomics on CSF. Using a data-driven approach consisting of partial least squares discriminant analysis, a multiomics network clustering algorithm, and metabolic pathway analysis, we described dysregulated metabolic pathways in CSF mapped to brain regions associated with MCI in our cohort. Results: The multiomics network algorithm clustered metabolites with contiguous imaging voxels into seven distinct communities corresponding to the following brain regions: hippocampus/parahippocampal gyrus (three distinct clusters), thalamus, posterior thalamus, parietal cortex, and occipital lobe. Metabolic pathway analysis indicated dysregulated metabolic activity in the urea cycle, and many amino acids (arginine, histidine, lysine, glycine, tryptophan, methionine, valine, glutamate, beta-alanine, and purine) was significantly associated with those regions (P < 0.05). Conclusion: By integrating CSF metabolomics data with structural MRI data, we linked specific AD-susceptible brain regions to disrupted metabolic pathways involving nitrogen excretion and amino acid metabolism critical for cognitive function. Our findings and analytical approach may extend drug and biomarker research toward more multiomics approaches. [ABSTRACT FROM AUTHOR]

Understanding the impact of non-coding sequence variants on complex diseases is an essential problem. We present a novel ensemble learning framework—CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores. [ABSTRACT FROM AUTHOR]

Background: Metabolic differences between human papillomavirus (HPV)-associated head and neck squamous cell carcinoma (HNSCC) and smoking-associated HNSCC may partially explain differences in prognosis. The former relies on mitochondrial oxidative phosphorylation (OXPHOS) while the latter relies on glycolysis. These differences have not been studied in blood. Methods: We extracted metabolites using untargeted liquid chromatography high-resolution mass spectrometry from pretreatment plasma in a cohort of 55 HPV-associated and 82 smoking-associated HNSCC subjects. Metabolic pathway enrichment analysis of differentially expressed metabolites produced pathway-based signatures. Significant pathways (P < 0.05) were reduced via principal component analysis and assessed with overall survival via Cox models. We classified each subject as glycolytic or OXPHOS phenotype and assessed it with survival. Results: Of 2,410 analyzed metabolites, 191 were differentially expressed. Relative to smoking-associated HNSCC, bile acid biosynthesis (P < 0.0001) and octadecatrienoic acid beta-oxidation (P = 0.01), were upregulated in HPV-associated HNSCC, while galactose metabolism (P = 0.001) and vitamin B6 metabolism (P = 0.01) were downregulated; the first two suggest an OXPHOS phenotype while the latter two suggest glycolytic. First principal components of bile acid biosynthesis [HR = 0.52 per SD; 95% confidence interval (CI), 0.38-0.72; P < 0.001] and octadecatrienoic acid beta-oxidation (HR = 0.54 per SD; 95% CI, 0.38-0.78; P < 0.001) were significantly associated with overall survival independent of HPV and smoking. The glycolytic versus OXPHOS phenotype was also independently associated with survival (HR = 3.17; 95% CI, 1.07-9.35; P = 0.04). Conclusions: Plasma metabolites related to glycolysis and mitochondrial OXPHOS may be biomarkers of HNSCC patient prognosis independent of HPV or smoking. Future investigations should determine whether they predict treatment efficacy. [ABSTRACT FROM AUTHOR]

The majority of the single nucleotide variants (SNVs) identified by genome-wide association studies (GWAS) fall outside of the protein-coding regions. Elucidating the functional implications of these variants has been a major challenge. A possible mechanism for functional non-coding variants is that they disrupted the canonical transcription factor (TF) binding sites that affect the in vivo binding of the TF. However, their impact varies since many positions within a TF binding motif are not well conserved. Therefore, simply annotating all variants located in putative TF binding sites may overestimate the functional impact of these SNVs. We conducted a comprehensive survey to study the effect of SNVs on the TF binding affinity. A sequence-based machine learning method was used to estimate the change in binding affinity for each SNV located inside a putative motif site. From the results obtained on 18 TF binding motifs, we found that there is a substantial variation in terms of a SNV's impact on TF binding affinity. We found that only about 20% of SNVs located inside putative TF binding sites would likely to have significant impact on the TF-DNA binding. [ABSTRACT FROM AUTHOR]

Abstract Background Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, (ChIP-Seq). This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method. Results Here we introduce HPeak, a Hidden Markov model (HMM)-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage. Conclusions Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.