Short-Term Dietary Restriction Potentiates an Anti-Inflammatory Circulating Mucosal-Associated Invariant T-Cell Response.

Short-term protein-calorie dietary restriction (StDR) is a promising preoperative strategy for modulating postoperative inflammation. We have previously shown marked gut microbial activity during StDR, but relationships between StDR, the gut microbiome, and systemic immunity remain poorly understood. Mucosal-associated invariant T-cells (MAITs) are enriched on mucosal surfaces and in circulation, bridge innate and adaptive immunity, are sensitive to gut microbial changes, and may mediate systemic responses to StDR. Herein, we characterized the MAIT transcriptomic response to StDR using single-cell RNA sequencing of human PBMCs and evaluated gut microbial species-level changes through sequencing of stool samples. Healthy volunteers underwent 4 days of DR during which blood and stool samples were collected before, during, and after DR. MAITs composed 2.4% of PBMCs. More MAIT genes were differentially downregulated during DR, particularly genes associated with MAIT activation (CD69), regulation of pro-inflammatory signaling (IL1, IL6, IL10, TNFα), and T-cell co-stimulation (CD40/CD40L, CD28), whereas genes associated with anti-inflammatory IL10 signaling were upregulated. Stool analysis showed a decreased abundance of multiple MAIT-stimulating Bacteroides species during DR. The analyses suggest that StDR potentiates an anti-inflammatory MAIT immunophenotype through modulation of TCR-dependent signaling, potentially secondary to gut microbial species-level changes.

OSCAA: A two-dimensional Gaussian mixture model for copy number variation association analysis.

Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome-wide assessment of such variation. In this article, we developed One-Stage CNV-disease Association Analysis (OSCAA), a flexible algorithm to discover disease-associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the PCs from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signals. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.

Migraine and major depression: localizing shared genetic susceptibility in different cell types of the nervous systems.

Background: There is a bidirectional relationship between migraine and major depression disorder (MDD). They likely share important risk genes associated with different cell types in the central nervous system (CNS) and peripheral nervous system (PNS). Profiling the expression of these genes in specific cell types is critical in understanding the pathophysiology of the relationship between migraine and MDD. Methods: Associated genes shared by migraine and MDD were identified by consolidating multiple curations of human disease-gene associations. Subsequently, the expression of overlapping genes was profiled and compared across the different cell types in CNS, PNS and neurovascular cells using eight single cell RNA sequencing datasets, including two human CNS datasets, two mouse CNS datasets, one human PNS dataset and three mouse PNS datasets. Results: 45 shared genes between migraine and MDD were identified. Consistently found in all eight datasets, dopaminergic and serotonergic neurotransmitters were broadly expressed in CNS and PNS cell types. Glutamatergic and endocannabinoid genes were specifically expressed in CNS neurons and astrocytes. Synthesis and/or Release and Binding of Neuropeptides were specifically expressed in PNS peptidergic nociceptor (PEP). Genes related to inflammatory factors and immune responses were specifically expressed in CNS microglia. Among which, IL1B and COMT were highly expressed in CNS microglia cells. Conclusion: Single cell RNA sequencing of the CNS and PNS helps to identify the shared genes between migraine and MDD that are enriched in specific cell types. The findings provide new insight in understanding the underlying mechanism of action for the bidirectional co-morbidity between migraine and MDD.

OSCAA: A Two-Dimensional Gaussian Mixture Model for Copy Number Variation Association Analysis.

Copy number variants (CNVs) are prevalent in the human genome which provide profound effect on genomic organization and human diseases. Discovering disease associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome wide assessment of such variation. In this article, we developed OSCAA, a flexible algorithm to discover disease associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the principal components from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signal. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.

Regional efficacy evaluation in multi-regional clinical trials using a discounting factor weighted Z test.

Multi-regional clinical trial (MRCT) is an efficient design to accelerate drug approval globally. Once the global efficacy of test drug is demonstrated, each local regulatory agency is required to prove effectiveness of test drug in their own population. Meanwhile, the ICH E5/E17 guideline recommends using data from other regions to help evaluate regional drug efficacy. However, one of the most challenges is how to manage to bridge data among multiple regions in an MRCT since various intrinsic and extrinsic factors exist among the participating regions. Furthermore, it is critical for a local agency to determine the proportion of information borrowing from other regions given the ethnic differences between target region and non-target regions. To address these issues, we propose a discounting factor weighted Z statistic to adaptively borrow information from non-target regions. In this weighted Z statistic, the weight is derived from a discounting factor in which the discounting factor denotes the proportion of information borrowing from non-target regions. We consider three ways to construct discounting factors based on the degree of congruency between target and non-target regions either using control group data, or treatment group data, or all data. We use the calibrated power prior to construct discounting factor based on scaled Kolmogorov-Smirnov statistic. Comprehensive simulation studies show that our method has desirable operating characteristics. Two examples are used to illustrate the applications of our proposed approach.

A pilot study that provides evidence of epigenetic changes among mother-child pairs living proximal to mining in the US.

Environmental exposures to chemicals can disrupt gene expression, and the effects could be mediated by methylation. This investigation focused on methylation of genes associated with exposure to metals. Mother-child pairs from three locations in Montana were recruited, and buccal cells were collected for genome-wide methylation assay. Four pairs were from Butte, where there is mining and a Superfund site, four pairs were from Anaconda with a Superfund site, and four pairs were from Missoula with neither a mine nor a Superfund site. Principal component analysis, linear mixed models, hierarchical clustering and heatmap, and gene set enrichment analysis were used to visualize the profiles, identify the top associated methylation loci, and investigate the involved pathways. Distinctly higher or lower methylation in samples from Butte were found at the top differentially methylated loci. The 200 genes harboring the most hypermethylated loci were significantly enriched in genes involved in actin cytoskeleton regulation, ABC transporters, leukocyte transendothelial migration, focal adhesion, and adherens junction, which plays a role in pathogenesis of disease, including autism spectrum disorders. This study lays a foundation for inquiry about genetic changes associated with environmental exposure to metals for people living in proximity to Superfund and open pit mining.

SCANNER: a web platform for annotation, visualization and sharing of single cell RNA-seq data.

In recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality and complexity bring challenges to the data analysis and require extensive collaborations between biologists and bioinformaticians and/or biostatisticians. The communication between these two units demands a platform for easy data sharing and exploration. Here we developed Single-Cell Transcriptomics Annotated Viewer (SCANNER), as a public web resource for the scientific community, for sharing and analyzing scRNA-seq data in a collaborative manner. It is easy-to-use without requiring special software or extensive coding skills. Moreover, it equipped a real-time database for secure data management and enables an efficient investigation of the activation of gene sets on a single-cell basis. Currently, SCANNER hosts a database of 19 types of cancers and COVID-19, as well as healthy samples from lungs of smokers and non-smokers, human brain cells and peripheral blood mononuclear cells (PBMC). The database will be frequently updated with datasets from new studies. Using SCANNER, we identified a larger proportion of cancer-associated fibroblasts cells and more active fibroblast growth-related genes in melanoma tissues in female patients compared to male patients. Moreover, we found ACE2 is mainly expressed in lung pneumocytes, secretory cells and ciliated cells and differentially expressed in lungs of smokers and never smokers.

SCISSOR™: a single-cell inferred site-specific omics resource for tumor microenvironment association study.

Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor-tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes.