SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.

The pigtail macaque (Macaca nemestrina) model of COVID-19 reproduces diverse clinical outcomes and reveals new and complex signatures of disease.

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 disease, has killed over five million people worldwide as of December 2021 with infections rising again due to the emergence of highly transmissible variants. Animal models that faithfully recapitulate human disease are critical for assessing SARS-CoV-2 viral and immune dynamics, for understanding mechanisms of disease, and for testing vaccines and therapeutics. Pigtail macaques (PTM, Macaca nemestrina) demonstrate a rapid and severe disease course when infected with simian immunodeficiency virus (SIV), including the development of severe cardiovascular symptoms that are pertinent to COVID-19 manifestations in humans. We thus proposed this species may likewise exhibit severe COVID-19 disease upon infection with SARS-CoV-2. Here, we extensively studied a cohort of SARS-CoV-2-infected PTM euthanized either 6- or 21-days after respiratory viral challenge. We show that PTM demonstrate largely mild-to-moderate COVID-19 disease. Pulmonary infiltrates were dominated by T cells, including CD4+ T cells that upregulate CD8 and express cytotoxic molecules, as well as virus-targeting T cells that were predominantly CD4+. We also noted increases in inflammatory and coagulation markers in blood, pulmonary pathologic lesions, and the development of neutralizing antibodies. Together, our data demonstrate that SARS-CoV-2 infection of PTM recapitulates important features of COVID-19 and reveals new immune and viral dynamics and thus may serve as a useful animal model for studying pathogenesis and testing vaccines and therapeutics.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

The Impact of SIV-Induced Immunodeficiency on SARS-CoV-2 Disease, Viral Dynamics, and Antiviral Immune Response in a Nonhuman Primate Model of Coinfection.

The effects of immunodeficiency associated with chronic HIV infection on COVID-19 disease and viral persistence have not been directly addressed in a controlled setting. In this pilot study, we exposed two pigtail macaques (PTMs) chronically infected with SIVmac239, exhibiting from very low to no CD4 T cells across all compartments, to SARS-CoV-2. We monitored the disease progression, viral replication, and evolution, and compared these outcomes with SIV-naïve PTMs infected with SARS-CoV-2. No overt signs of COVID-19 disease were observed in either animal, and the SARS-CoV-2 viral kinetics and evolution in the SIVmac239 PTMs were indistinguishable from those in the SIV-naïve PTMs in all sampled mucosal sites. However, the single-cell RNA sequencing of bronchoalveolar lavage cells revealed an infiltration of functionally inert monocytes after SARS-CoV-2 infection. Critically, neither of the SIV-infected PTMs mounted detectable anti-SARS-CoV-2 T-cell responses nor anti-SARS-CoV-2 binding or neutralizing antibodies. Thus, HIV-induced immunodeficiency alone may not be sufficient to drive the emergence of novel viral variants but may remove the ability of infected individuals to mount adaptive immune responses against SARS-CoV-2.

SUMMIT-FA: A new resource for improved transcriptome imputation using functional annotations.

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), that improves the accuracy of gene expression prediction by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models using SUMMIT-FA with a comprehensive functional database MACIE and the eQTL summary-level data from the eQTLGen consortium. By applying the resulting models to GWASs for 24 complex traits and exploring it through a simulation study, we show that SUMMIT-FA improves the accuracy of gene expression prediction models in whole blood, identifies significantly more gene-trait associations, and improves predictive power for identifying "silver standard" genes compared to several benchmark methods.

African American patients with Multiple Sclerosis (MS) have higher proportions of CD19+ and CD20+ B-cell lineage cells in their cerebrospinal fluid than White MS patients.

OBJECTIVES: To compare proportions of B-cell lineage CD19+ and CD20+ cells in CSF of African-American (AA) and White (W) patients with MS. BACKGROUND: AA MS patients are more likely to have oligoclonal bands in CSF, higher IgG index in CSF, and higher circulating plasmablasts in blood than W MS patients. It is unknown whether the proportion of B-cells in CSF differs between AA and W patients in MS. METHODS: Demographics, disease-related information, treatment history were retrospectively collected on patients with MS who self-identified as AA or W and underwent flow cytometry of CSF during diagnostic work-up. Proportion of B-lymphocytes, T-lymphocytes, NK cells, monocytes, and plasma cells were analyzed with flow cytometry. RESULTS: 20 AA and 56 W MS patients fulfilled our inclusion criteria. The groups had similar demographics, CSF cell counts, protein and glucose CSF concentrations, and oligoclonal band number. IgG index was higher in AA compared to W (1.08 vs. 0.85, p = 0.031). AA had higher proportions of CD19+ (5.46 % AA vs. 2.26 % W, p = 0.006) and CD20+ (4.64 % AA vs. 1.91 % W, p = 0.004) cells but did not significantly differ in proportion of CD4+, CD8+, CD38+ bright B-cells, NK cells and monocytes. CONCLUSIONS: B-cells are overrepresented in the CSF of African American patients with MS relative to Whites.

MIMOSA: A resource consisting of improved methylome imputation models increases power to identify DNA methylation-phenotype associations.

Although DNA methylation has been implicated in the pathogenesis of numerous complex diseases, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified.However, current MWAS models are primarily trained by using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large, summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). With the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study in high cholesterol, pinpointing 146 putatively causal CpG sites.

The Impact of SIV-Induced Immunodeficiency on Clinical Manifestation, Immune Response, and Viral Dynamics in SARS-CoV-2 Coinfection.

Persistent and uncontrolled SARS-CoV-2 replication in immunocompromised individuals has been observed and may be a contributing source of novel viral variants that continue to drive the pandemic. Importantly, the effects of immunodeficiency associated with chronic HIV infection on COVID-19 disease and viral persistence have not been directly addressed in a controlled setting. Here we conducted a pilot study wherein two pigtail macaques (PTM) chronically infected with SIVmac239 were exposed to SARS-CoV-2 and monitored for six weeks for clinical disease, viral replication, and viral evolution, and compared to our previously published cohort of SIV-naïve PTM infected with SARS-CoV-2. At the time of SARS-CoV-2 infection, one PTM had minimal to no detectable CD4+ T cells in gut, blood, or bronchoalveolar lavage (BAL), while the other PTM harbored a small population of CD4+ T cells in all compartments. Clinical signs were not observed in either PTM; however, the more immunocompromised PTM exhibited a progressive increase in pulmonary infiltrating monocytes throughout SARS-CoV-2 infection. Single-cell RNA sequencing (scRNAseq) of the infiltrating monocytes revealed a less activated/inert phenotype. Neither SIV-infected PTM mounted detectable anti-SARS-CoV-2 T cell responses in blood or BAL, nor anti-SARS-CoV-2 neutralizing antibodies. Interestingly, despite the diminished cellular and humoral immune responses, SARS-CoV-2 viral kinetics and evolution were indistinguishable from SIV-naïve PTM in all sampled mucosal sites (nasal, oral, and rectal), with clearance of virus by 3-4 weeks post infection. SIV-induced immunodeficiency significantly impacted immune responses to SARS-CoV-2 but did not alter disease progression, viral kinetics or evolution in the PTM model. SIV-induced immunodeficiency alone may not be sufficient to drive the emergence of novel viral variants.