Investigators have long suspected that pathogenic microbes might contribute to the onset and progression of Alzheimer’s disease (AD) although definitive evidence has not been presented. Whether such ...findings represent a causal contribution, or reflect opportunistic passengers of neurodegeneration, is also difficult to resolve. We constructed multiscale networks of the late-onset AD-associated virome, integrating genomic, transcriptomic, proteomic, and histopathological data across four brain regions from human post-mortem tissue. We observed increased human herpesvirus 6A (HHV-6A) and human herpesvirus 7 (HHV-7) from subjects with AD compared with controls. These results were replicated in two additional, independent and geographically dispersed cohorts. We observed regulatory relationships linking viral abundance and modulators of APP metabolism, including induction of APBB2, APPBP2, BIN1, BACE1, CLU, PICALM, and PSEN1 by HHV-6A. This study elucidates networks linking molecular, clinical, and neuropathological features with viral activity and is consistent with viral activity constituting a general feature of AD.
•Common viral species frequently detected in normal, aging brain•Increased HHV-6A and HHV-7 in brains of subjects with Alzheimer’s disease (AD)•Findings were replicated in two additional, independent cohorts•Multiscale networks reveal viral regulation of AD risk, and APP processing genes
Readhead et al. construct multiscale networks of the late-onset Alzheimer’s disease (AD)-associated virome and observe pathogenic regulation of molecular, clinical, and neuropathological networks by several common viruses, particularly human herpesvirus 6A and human herpesvirus 7.
Several studies have revealed that the hyper-inflammatory response induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major cause of disease severity and death. However, ...predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α and IL-1β in hospitalized patients with coronavirus disease 2019 (COVID-19) upon admission to the Mount Sinai Health System in New York. Patients (n = 1,484) were followed up to 41 d after admission (median, 8 d), and clinical information, laboratory test results and patient outcomes were collected. We found that high serum IL-6, IL-8 and TNF-α levels at the time of hospitalization were strong and independent predictors of patient survival (P < 0.0001, P = 0.0205 and P = 0.0140, respectively). Notably, when adjusting for disease severity, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF-α serum levels remained independent and significant predictors of disease severity and death. These findings were validated in a second cohort of patients (n = 231). We propose that serum IL-6 and TNF-α levels should be considered in the management and treatment of patients with COVID-19 to stratify prospective clinical trials, guide resource allocation and inform therapeutic options.
Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the ...sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.
Display omitted
•Gene expression analysis characterizes 317 human iPSC lines from 101 individuals•eQTLs contribute significantly to a cross individual variation in iPSC lines•Polycomb target genes are a significant source of non-genetic variation•Predictive networks highlight candidate key drivers of differentiation efficiency
Using large-scale analyses of over 300 iPSC lines, Chang, Quertermous, Lemischka, and colleagues of the NHLBI NextGen consortium examine sources of gene expression variation between lines and illustrate how this approach can identify genetic and non-genetic drivers relevant to line variation with implications for iPSC characterization and disease modeling.
Alzheimer's disease (AD) affects half the US population over the age of 85 and is universally fatal following an average course of 10 years of progressive cognitive disability. Genetic and ...genome-wide association studies (GWAS) have identified about 33 risk factor genes for common, late-onset AD (LOAD), but these risk loci fail to account for the majority of affected cases and can neither provide clinically meaningful prediction of development of AD nor offer actionable mechanisms. This cohort study generated large-scale matched multi-Omics data in AD and control brains for exploring novel molecular underpinnings of AD. Specifically, we generated whole genome sequencing, whole exome sequencing, transcriptome sequencing and proteome profiling data from multiple regions of 364 postmortem control, mild cognitive impaired (MCI) and AD brains with rich clinical and pathophysiological data. All the data went through rigorous quality control. Both the raw and processed data are publicly available through the Synapse software platform.
Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute ...respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid. Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte-platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.
To identify the molecular mechanisms and novel therapeutic targets of late-onset Alzheimer’s Disease (LOAD), we performed an integrative network analysis of multi-omics profiling of four cortical ...areas across 364 donors with varying cognitive and neuropathological phenotypes. Our analyses revealed thousands of molecular changes and uncovered neuronal gene subnetworks as the most dysregulated in LOAD. ATP6V1A was identified as a key regulator of a top-ranked neuronal subnetwork, and its role in disease-related processes was evaluated through CRISPR-based manipulation in human induced pluripotent stem cell-derived neurons and RNAi-based knockdown in Drosophila models. Neuronal impairment and neurodegeneration caused by ATP6V1A deficit were improved by a repositioned compound, NCH-51. This study provides not only a global landscape but also detailed signaling circuits of complex molecular interactions in key brain regions affected by LOAD, and the resulting network models will serve as a blueprint for developing next-generation therapeutic agents against LOAD.
Display omitted
•Development of gene network models of four cortical areas affected by LOAD•Identification of region-specific molecular changes and gene subnetworks in LOAD•ATP6V1A is a top key regulator of a neuronal subnetwork most disrupted in LOAD•NCH-51 normalizes neuronal impairment and neurodegeneration caused by ATP6V1A deficit
Employing an integrative network biology approach, Wang et al. identify critical gene subnetworks associated with late-onset Alzheimer’s disease (LOAD) and predict ATP6V1A as a key regulator of a neuron-specific subnetwork most affected by LOAD. ATP6V1A deficit causes neuronal impairment and neurodegeneration, which are normalized by a predicted compound, NCH-51.
Plasma liver-enzyme tests are widely used in the clinic for the diagnosis of liver diseases and for monitoring the response to drug treatment. There is considerable evidence that human genetic ...variation influences plasma levels of liver enzymes. However, such genetic variation has not been systematically assessed. In the present study, we performed a genome-wide association study of plasma liver-enzyme levels in three populations (total n = 7715) with replication in three additional cohorts (total n = 4704). We identified two loci influencing plasma levels of alanine-aminotransferase (ALT) (
CPN1-ERLIN1-CHUK on chromosome 10 and
PNPLA3-SAMM50 on chromosome 22), one locus influencing gamma-glutamyl transferase (GGT) levels (
HNF1A on chromosome 12), and three loci for alkaline phosphatase (ALP) levels (
ALPL on chromosome 1,
GPLD1 on chromosome 6, and
JMJD1C-REEP3 on chromosome 10). In addition, we confirmed the associations between the
GGT1 locus and GGT levels and between the
ABO locus and ALP levels. None of the ALP-associated SNPs were associated with other liver tests, suggesting intestine and/or bone specificity. The mechanisms underlying the associations may involve
cis- or
trans-transcriptional effects (some of the identified variants were associated with mRNA transcription in human liver or lymphoblastoid cells), dysfunction of the encoded proteins (caused by missense variations at the functional domains), or other unknown pathways. These findings may help in the interpretation of liver-enzyme tests and provide candidate genes for liver diseases of viral, metabolic, autoimmune, or toxic origin. The specific associations with ALP levels may point to genes for bone or intestinal diseases.
Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these ...population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.
Display omitted
•Genomic data linked to health records capture demography in health systems•Genetic networks reveal recent common ancestry in diverse populations•Evidence of many founder populations in New York City•Fine-scale population structure impacts genetic risk predictions
Taking a quantitative approach to genetic ancestry in health systems furthers understanding of disease burdens specific to fine-scale populations and the environmental and demographic ties that can impact disease.
Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated ...with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.
Though discovered over 100 years ago, the molecular foundation of sporadic Alzheimer's disease (AD) remains elusive. To better characterize the complex nature of AD, we constructed multiscale causal ...networks on a large human AD multi-omics dataset, integrating clinical features of AD, DNA variation, and gene- and protein-expression. These probabilistic causal models enabled detection, prioritization and replication of high-confidence master regulators of AD-associated networks, including the top predicted regulator, VGF. Overexpression of neuropeptide precursor VGF in 5xFAD mice partially rescued beta-amyloid-mediated memory impairment and neuropathology. Molecular validation of network predictions downstream of VGF was also achieved in this AD model, with significant enrichment for homologous genes identified as differentially expressed in 5xFAD brains overexpressing VGF. Our findings support a causal role for VGF in protecting against AD pathogenesis and progression.
PDF
