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.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

High hydrostatic pressure participates in atrial fibrosis through the p300/p53/Smad3 pathway.

As an independent risk factor of atrial fibrillation (AF), hypertension (HTN) can induce atrial fibrosis through cyclic stretch and hydrostatic pressure. The mechanism by which high hydrostatic pressure promotes atrial fibrosis is unclear yet. p300 and p53/Smad3 play important roles in the process of atrial fibrosis. This study investigated whether high hydrostatic pressure promotes atrial fibrosis by activating the p300/p53/Smad3 pathway. Biochemical experiments were used to study the expression of p300/p53/Smad3 pathway in left atrial appendage (LAA) tissues of patients with sinus rhythm (SR), AF, AF + HTN, and C57/BL6 mice, hypertensive C57/BL6 mice and atrial fibroblasts of mice. To investigate the roles of p300 and p53 in the process of atrial fibrosis, p300 and p53 in mice atrial fibroblasts were knocked in or knocked down, respectively. The expression of p300/p53/Smad3 and fibrotic factors was higher in patients with AF and AF + HTN than those with SR only. The expressions of p300/p53/Smad3 and fibrotic factors increased in hypertensive mice. Curcumin (Cur) and knocking down of p300 reversed the expressions of these factors. 40 mmHg hydrostatic pressure/overexpression of p300 upregulated the expressions of p300/p53/Smad3 and fibrotic factors in mice LAA fibroblasts. While Cur or knocking down p300 reversed these changes. Knocking down/overexpression of p53, the expressions of p53/Smad3 and fibrotic factors also decreased/increased, correspondingly. High hydrostatic pressure promotes atrial fibrosis by activating the p300/p53/Smad3 pathway, which further increases the susceptibility to AF.

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.

A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).

Alkbh5 plays indispensable roles in maintaining self-renewal of hematopoietic stem cells.

Alkbh5 is one of the primary demethylases responsible for reversing N6-methyladenosine (m6A) modifications on mRNAs, and it plays a crucial role in many physiological and pathological processes. Previous studies have shown that Alkbh5 is required for maintaining the function of leukemia stem cells but is dispensable for normal hematopoiesis. In this study, we found that Alkbh5 deletion led to a moderate increase in the number of multiple progenitor cell populations while compromising the long-term self-renewal capacity of hematopoietic stem cells (HSCs). Here, we used RNA-seq and m6A-seq strategies to explore the underlying molecular mechanism. At the molecular level, Alkbh5 may regulate hematopoiesis by reducing m6A modification of Cebpa and maintaining gene expression levels. Overall, our study unveiled an essential role for Alkbh5 in regulating HSC homeostasis and provides a reference for future research in this area.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single cell sequencing data.

The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.

Relationship between serum fibroblast growth factor 19 and vascular endothelial growth factor and soluble klotho protein in type 1 diabetic children.

BACKGROUND: Fibroblast growth factor 19 (FGF19) takes part in maintaining the balance of glycolipids and may be involved in complications of type 1 diabetes(T1D) in children. This study aimed at at evaluating the relationship among the levels of serum FGF19 and vascular endothelial growth factor(VEGF)and soluble klotho protein(sklotho) in type 1 diabetic children. METHODS: In a cross-section single center study samples were obtained from 96 subjects: 66 T1D and 30 healthy children.Serum FGF19 and VEGF and sklotho concentrations were measured by ELISA. And 66 type 1 diabetes participants were divided into two groups according to T1D duration or three groups according to HbA1c.Furthermore,we compared the serum levels of FGF19 and VEGF and sklotho in different groups. RESULTS: The concentration of FGF19 was lower in T1D than in the controls(226.52 ± 20.86pg/mu vs.240.08 ± 23.53 pg/L, p = 0.03),while sklotho was also lower in T1D than in the controls (2448.67 ± 791.92pg/mL vs. 3083.55 ± 1113.47pg/mL, p = 0.011). In contrast, VEGF levels were higher in diabetic patients than in controls (227.95 ± 48.65pg/mL vs. 205.92 ± 28.27 pg/mL, p = 0.016). In T1D, FGF19 and VEGF and sklotho was not correlated with the duration of diabetes. FGF19 and VEGF and sklotho were correlated with HbA1c (r=-0.349, p = 0.004 and r = 0.302, p = 0.014 and r=-0.342, p = 0.005, respectively), but not with blood glucose and lipid. Among subjects in the T1D group, concentrations of FGF19,VEGF and sklotho protein were different between different groups according to the degree of HbA1c(P < 0.005).Furthermore, there was a positive correlation between the serum FGF19 concentration and sklotho levels (r = 0.247,p = 0.045), and a negative correlation between the serum FGF19 concentration and VEGF level(r=-0.335,P = 0.006). CONCLUSIONS: The serum FGF19 levels have a close relation with serum VEGF levels and sklotho levels among T1D subjects. FGF19 may be involved in the development of complications in children with type 1 diabetes through interaction with VEGF and sklotho.

BISC: accurate inference of transcriptional bursting kinetics from single-cell transcriptomic data.

Gene expression in mammalian cells is inherently stochastic and mRNAs are synthesized in discrete bursts. Single-cell transcriptomics provides an unprecedented opportunity to explore the transcriptome-wide kinetics of transcriptional bursting. However, current analysis methods provide limited accuracy in bursting inference due to substantial noise inherent to single-cell transcriptomic data. In this study, we developed BISC, a Bayesian method for inferring bursting parameters from single cell transcriptomic data. Based on a beta-gamma-Poisson model, BISC modeled the mean-variance dependency to achieve accurate estimation of bursting parameters from noisy data. Evaluation based on both simulation and real intron sequential RNA fluorescence in situ hybridization data showed improved accuracy and reliability of BISC over existing methods, especially for genes with low expression values. Further application of BISC found bursting frequency but not bursting size was strongly associated with gene expression regulation. Moreover, our analysis provided new mechanistic insights into the functional role of enhancer and superenhancer by modulating both bursting frequency and size. BISC also formulated a downstream framework to identify differential bursting (in frequency and size separately) genes in samples under different conditions. Applying to multiple datasets (a mouse embryonic cell and fibroblast dataset, a human immune cell dataset and a human pancreatic cell dataset), BISC identified known cell-type signature genes that were missed by differential expression analysis, providing additional insights in understanding the cell-specific stochastic gene transcription. Applying to datasets of human lung and colon cancers, BISC successfully detected tumor signature genes based on alterations in bursting kinetics, which illustrates its value in understanding disease development regarding transcriptional bursting. Collectively, BISC provides a new tool for accurately inferring bursting kinetics and detecting differential bursting genes. This study also produced new insights in the role of transcriptional bursting in regulating gene expression, cell identity and tumor progression.

BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variants.

Whole-exome sequencing (WES) enables the detection of copy number variants (CNVs) with high resolution in protein-coding regions. However, variants in the intergenic or intragenic regions are excluded from studies. Fortunately, many of these samples have been previously sequenced by other genotyping platforms which are sparse but cover a wide range of genomic regions, such as SNP array. Moreover, conventional single sample-based methods suffer from a high false discovery rate due to prominent data noise. Therefore, methods for integrating multiple genotyping platforms and multiple samples are highly demanded for improved copy number variant detection. We developed BMI-CNV, a Bayesian Multisample and Integrative CNV (BMI-CNV) profiling method with data sequenced by both whole-exome sequencing and microarray. For the multisample integration, we identify the shared copy number variants regions across samples using a Bayesian probit stick-breaking process model coupled with a Gaussian Mixture model estimation. With extensive simulations, BMI-copy number variant outperformed existing methods with improved accuracy. In the matched data from the 1000 Genomes Project and HapMap project data, BMI-CNV also accurately detected common variants and significantly enlarged the detection spectrum of whole-exome sequencing. Further application to the data from The Research of International Cancer of Lung consortium (TRICL) identified lung cancer risk variant candidates in 17q11.2, 1p36.12, 8q23.1, and 5q22.2 regions.

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.

SCRIP: an accurate simulator for single-cell RNA sequencing data.

MOTIVATION: Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled time-efficient transcriptome profiling in individual cells. To optimize sequencing protocols and develop reliable analysis methods for various application scenarios, solid simulation methods for scRNA-seq data are required. However, due to the noisy nature of scRNA-seq data, currently available simulation methods cannot sufficiently capture and simulate important properties of real data, especially the biological variation. In this study, we developed scRNA-seq information producer (SCRIP), a novel simulator for scRNA-seq that is accurate and enables simulation of bursting kinetics. RESULTS: Compared to existing simulators, SCRIP showed a significantly higher accuracy of stimulating key data features, including mean-variance dependency in all experiments. SCRIP also outperformed other methods in recovering cell-cell distances. The application of SCRIP in evaluating differential expression analysis methods showed that edgeR outperformed other examined methods in differential expression analyses, and ZINB-WaVE improved the AUC at high dropout rates. Collectively, this study provides the research community with a rigorous tool for scRNA-seq data simulation. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=SCRIP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SARS-CoV-2 Impairs Dendritic Cells and Regulates DC-SIGN Gene Expression in Tissues.

The current spreading coronavirus SARS-CoV-2 is highly infectious and pathogenic. In this study, we screened the gene expression of three host receptors (ACE2, DC-SIGN and L-SIGN) of SARS coronaviruses and dendritic cells (DCs) status in bulk and single cell transcriptomic datasets of upper airway, lung or blood of COVID-19 patients and healthy controls. In COVID-19 patients, DC-SIGN gene expression was interestingly decreased in lung DCs but increased in blood DCs. Within DCs, conventional DCs (cDCs) were depleted while plasmacytoid DCs (pDCs) were augmented in the lungs of mild COVID-19. In severe cases, we identified augmented types of immature DCs (CD22+ or ANXA1+ DCs) with MHCII downregulation. In this study, our observation indicates that DCs in severe cases stimulate innate immune responses but fail to specifically present SARS-CoV-2. It provides insights into the profound modulation of DC function in severe COVID-19.

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.

Shall genomic correlation structure be considered in copy number variants detection?

Copy number variation has been identified as a major source of genomic variation associated with disease susceptibility. With the advent of whole-exome sequencing (WES) technology, massive WES data have been generated, allowing for the identification of copy number variants (CNVs) in the protein-coding regions with direct functional interpretation. We have previously shown evidence of the genomic correlation structure in array data and developed a novel chromosomal breakpoint detection algorithm, LDcnv, which showed significantly improved detection power through integrating the correlation structure in a systematic modeling manner. However, it remains unexplored whether the genomic correlation exists in WES data and how such correlation structure integration can improve the CNV detection accuracy. In this study, we first explored the correlation structure of the WES data using the 1000 Genomes Project data. Both real raw read depth and median-normalized data showed strong evidence of the correlation structure. Motivated by this fact, we proposed a correlation-based method, CORRseq, as a novel release of the LDcnv algorithm in profiling WES data. The performance of CORRseq was evaluated in extensive simulation studies and real data analysis from the 1000 Genomes Project. CORRseq outperformed the existing methods in detecting medium and large CNVs. In conclusion, it would be more advantageous to model genomic correlation structure in detecting relatively long CNVs. This study provides great insights for methodology development of CNV detection with NGS data.

A Systematic Review and Meta-Analysis on the Efficacy of Repeated Transcranial Direct Current Stimulation for Migraine.

PURPOSE: Transcranial direct current stimulation (tDCS) may have therapeutic potential in the management of migraine. However, studies to date have yielded conflicting results. We reviewed studies using repeated tDCS for longer than 4 weeks in migraine treatment, and performed meta-analysis on the efficacy of tDCS in migraine. METHODS: In this meta-analysis, we included the common outcome measurements reported across randomized controlled trials (RCTs). Subgroup analysis was performed at different post-treatment endpoints, and with different stimulation intensities and polarities. RESULTS: Five RCTs were included in the quantitative meta-analysis with a total of 104 migraine patients. We found a significant reduction of migraine pain intensity (MD: -1.44; CI: [-2.13, -0.76]) in active vs sham tDCS treated patients. Within active treatment groups, pain intensity and duration were significantly improved from baseline after tDCS treatment (intensity MD: -1.86; CI: [-3.30, -0.43]; duration MD: -4.42; CI: [-8.11, -0.74]) and during a follow-up period (intensity MD: -1.52; CI: [-1.84, -1.20]; duration MD: -1.94; CI: [-3.10, -0.77]). There was a significant reduction of pain intensity by both anodal (MD: -1.74; CI: [-2.80, -0.68]) and cathodal (MD: -1.49; CI: [-1.89, -1.09]) stimulation conditions. CONCLUSION: tDCS treatment repeated over days for a period of 4 weeks or more is effective in reducing migraine pain intensity and duration of migraine episode. The benefit of tDCS can persist for at least 4 weeks after the completion of last tDCS session. Both anodal and cathodal stimulation are effective for reducing migraine pain intensity.