Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies

Racial Differences in the Association Between Luminal Master Regulator Gene Expression Levels and Breast Cancer Survival

Compared with their European American (EA) counterparts, African American (AA) women are more likely to die from breast cancer in the United States. This disparity is greatest in hormone receptor-positive subtypes. Here we uncover biological factors underlying this disparity by comparing functional expression and prognostic significance of master transcriptional regulators of luminal differentiation.Fil: Byun, Jung S.. National Institutes of Health; Estados UnidosFil: Singhal, Sandeep K.. Columbia University; Estados UnidosFil: Park, Samson. National Institutes of Health; Estados UnidosFil: Yi, Dae Ik. National Institutes of Health; Estados UnidosFil: Yan, Tingfen. National Institutes of Health; Estados UnidosFil: Caban, Ambar. Columbia University Medical Center; Estados UnidosFil: Jones, Alana. National Institutes of Health; Estados UnidosFil: Mukhopadhyay, Partha. Columbia University Medical Center; Estados UnidosFil: Gille, Sarah. National Institutes of Health; Estados UnidosFil: Hewitt, Stephen M.. No especifíca;Fil: Newman, Lisa. No especifíca;Fil: Davis, Melissa B.. Henry Ford Health System; Estados UnidosFil: Jenkins, Brittany D.. Henry Ford Health System; Estados UnidosFil: Sepulveda, Jorge L.. Columbia University Medical Center; Estados UnidosFil: de Siervi, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Nápoles, Anna María. National Institute On Minority Health And Health Disparities; Estados UnidosFil: Vohra, Nasreen A.. East Carolina University; Estados UnidosFil: Gardner, Kevin. Columbia University Medical Center; Estados Unido

Protein expression of the gp78 E3 ligase predicts poor breast cancer outcome based on race

Women of African ancestry suffer higher rates of breast cancer mortality compared with all other groups in the United States. Though the precise reasons for these disparities remain unclear, many recent studies have implicated a role for differences in tumor biology. Using an epitope-validated antibody against the endoplasmic reticulum-associated E3 ligase, gp78, we show that elevated levels of gp78 in patient breast cancer cells predict poor survival. Moreover, high levels of gp78 are associated with poor outcomes in both ER+ and ER- tumors, and breast cancers expressing elevated amounts of gp78 protein are enriched in gene expression pathways that influence cell cycle, metabolism, receptor-mediated signaling, and cell stress response pathways. In multivariate analysis adjusted for subtype and grade, gp78 protein is an independent predictor of poor outcomes in women of African ancestry. Furthermore, gene expression signatures, derived from patients stratified by gp78 protein expression, are strong predictors of recurrence and pathological complete response in retrospective clinical trial data and share many common features with gene sets previously identified to be overrepresented in breast cancers based on race. These findings implicate a prominent role for gp78 in tumor progression and offer insights into our understanding of racial differences in breast cancer outcomes.Fil: Singhal, Sandeep K.. No especifíca;Fil: Byun, Jung S.. National Institutes of Health; Estados UnidosFil: Yan, Tingfen. National Institutes of Health; Estados UnidosFil: Yancey, Ryan. Columbia University; Estados UnidosFil: Caban, Ambar. Columbia University; Estados UnidosFil: Hernandez, Sara Gil. National Institutes of Health; Estados UnidosFil: Bufford, Sediqua. No especifíca;Fil: Hewitt, Stephen M.. No especifíca;Fil: Winfield, Joy. Columbia University; Estados UnidosFil: Pradhan, Jaya. Columbia University; Estados UnidosFil: Mustkov, Vesco. Columbia University; Estados UnidosFil: McDonald, Jasmine A.. No especifíca;Fil: Pérez Stable, Eliseo J.. National Institutes of Health; Estados UnidosFil: Nápoles, Anna María. National Institutes of Health; Estados UnidosFil: Vohra, Nasreen. No especifíca;Fil: de Siervi, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Yates, Clayton. No especifíca;Fil: Davis, Melissa B.. No especifíca;Fil: Yang, Mei. No especifíca;Fil: Tsai, Yien Che. No especifíca;Fil: Weissman, Allan M.. No especifíca;Fil: Gardner, Kevin. Columbia University; Estados Unido

Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival

The use of digital pathology for the histomorphologic profiling of pathological specimens is expanding the precision and specificity of quantitative tissue analysis at an unprecedented scale; thus, enabling the discovery of new and functionally relevant histological features of both predictive and prognostic significance. In this study, we apply quantitative automated image processing and computational methods to profile the subcellular distribution of the multi-functional transcriptional regulator, Kaiso (ZBTB33), in the tumors of a large racially diverse breast cancer cohort from a designated health disparities region in the United States. Multiplex multivariate analysis of the association of Kaiso’s subcellular distribution with other breast cancer biomarkers reveals novel functional and predictive linkages between Kaiso and the autophagy-related proteins, LC3A/B, that are associated with features of the tumor immune microenvironment, survival, and race. These findings identify effective modalities of Kaiso biomarker assessment and uncover unanticipated insights into Kaiso’s role in breast cancer progression.Fil: Singhal, Sandeep K.. North Dakota State University; Estados UnidosFil: Byun, Jung S.. National Institutes of Health; Estados UnidosFil: Park, Samson. National Institutes of Health; Estados UnidosFil: Yan, Tingfen. National Institutes of Health; Estados UnidosFil: Yancey, Ryan. Columbia University; Estados UnidosFil: Caban, Ambar. Columbia University; Estados UnidosFil: Hernandez, Sara Gil. National Institutes of Health; Estados UnidosFil: Hewitt, Stephen M.. U.S. Department of Health & Human Services. National Institute of Health. National Cancer Institute; Estados UnidosFil: Boisvert, Heike. Ultivue, Inc; Reino UnidoFil: Hennek, Stephanie. Ultivue Inc.; Reino UnidoFil: Bobrow, Mark. Ultivue Inc.; Reino UnidoFil: Ahmed, Md Shakir Uddin. Tuskegee University; Estados UnidosFil: White, Jason. Tuskegee University; Estados UnidosFil: Yates, Clayton. Tuskegee University; Estados UnidosFil: Aukerman, Andrew. Columbia University; Estados UnidosFil: Vanguri, Rami. Columbia University; Estados UnidosFil: Bareja, Rohan. Columbia University; Estados UnidosFil: Lenci, Romina. Columbia University; Estados UnidosFil: Farré, Paula Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: de Siervi, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Nápoles, Anna María. National Institutes of Health; Estados UnidosFil: Vohra, Nasreen. East Carolina University; Estados UnidosFil: Gardner, Kevin. Columbia University; Estados Unido

ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19

Background COVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain. Subjects/methods In a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry. Results Lower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 x 10(-6)), obesity status (P = 4.81 x 10(-5)), higher serum fasting insulin (P = 5.32 x 10(-4)), BMI (P = 3.94 x 10(-4)), and lower serum HDL levels (P = 1.92 x 10(-7)). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 x 10(-4)) and higher proportion of macrophages (P = 2.74 x 10(-5)). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression. Conclusions Our results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19.Peer reviewe

Generation and Validation of a Shewanella oneidensis MR-1 Clone Set for Protein Expression and Phage Display

A comprehensive gene collection for S. oneidensis was constructed using the lambda recombinase (Gateway) cloning system. A total of 3584 individual ORFs (85%) have been successfully cloned into the entry plasmids. To validate the use of the clone set, three sets of ORFs were examined within three different destination vectors constructed in this study. Success rates for heterologous protein expression of S. oneidensis His- or His/GST- tagged proteins in E. coli were approximately 70%. The ArcA and NarP transcription factor proteins were tested in an in vitro binding assay to demonstrate that functional proteins can be successfully produced using the clone set. Further functional validation of the clone set was obtained from phage display experiments in which a phage encoding thioredoxin was successfully isolated from a pool of 80 different clones after three rounds of biopanning using immobilized anti-thioredoxin antibody as a target. This clone set complements existing genomic (e.g., whole-genome microarray) and other proteomic tools (e.g., mass spectrometry-based proteomic analysis), and facilitates a wide variety of integrated studies, including protein expression, purification, and functional analyses of proteins both in vivo and in vitro

Leptospire Genomic Diversity Revealed by Microarray-Based Comparative Genomic Hybridization

Comparative genomic hybridization was used to compare genetic diversity of five strains of Leptospira (Leptospira interrogans serovars Bratislava, Canicola, and Hebdomadis and Leptospira kirschneri serovars Cynopteri and Grippotyphosa). The array was designed based on two available sequenced Leptospira reference genomes, those of L. interrogans serovar Copenhageni and L. interrogans serovar Lai. A comparison of genetic contents showed that L. interrogans serovar Bratislava was closest to the reference genomes while L. kirschneri serovar Grippotyphosa had the least similarity to the reference genomes. Cluster analysis indicated that L. interrogans serovars Bratislava and Hebdomadis clustered together first, followed by L. interrogans serovar Canicola, before the two L. kirschneri strains. Confirmed/potential virulence factors identified in previous research were also detected in the tested strains

Transcriptome of a Nitrosomonas europaea Mutant with a Disrupted Nitrite Reductase Gene (nirK)

Global gene expression was compared between the Nitrosomonas europaea wild type and a nitrite reductase-deficient mutant using a genomic microarray. Forty-one genes were differentially regulated between the wild type and the nirK mutant, including the nirK operon, genes for cytochrome c oxidase, and seven iron uptake genes. Relationships of differentially regulated genes to the nirK mutant phenotype are discussed