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Reddy, J. S.

Publications and source records attributed to Reddy, J. S..

13 recordsLinked to original sources

Functional Evaluation of TRPC6 Missense Variants in Cancer Patients via Molecular Docking Analysis Compared with Patch Clamp Electrophysiology

Gain-of-function mutations in the transient receptor potential 6 (TRPC6) channel have recently been recognized as risk factors for both doxorubicin (DOX)-induced cardiomyopathy. Functional evaluation of TRPC6 missense variants is therefore important for cancer patients undergoing anthracycline treatment. However, traditional electrophysiological methods are labor-intensive and time-consuming. In this study, we compared the functional responses of TRPC6 missense variants to 1-oleoyl-2-acetyl-sn-glycerol (OAG), a TRPC6 agonist, using molecular docking and patch clamp recording techniques. For the wild-type (WT) TRPC6 structure (PDB ID: 6UZ8), OAG exhibited a binding energy of -4.49 kcal/mol and a dissociation constant (Kd) of 0.511 mM. Twenty TRPC6 missense variants were identified from cancer patients in the Mayo Clinic database. Of these, fifteen variants had resolvable structures, nine of which displayed increased Kd values and six decreased Kd values compared to WT in molecular docking analysis. Patch clamp recordings revealed that TRPC6 WT and mutant channels were inactive at baseline but were activated upon 50 M OAG stimulation, except two loss-of-function variants. Moreover, a 24-h treatment with 0.5 M DOX significantly enhanced OAG-induced channel activation. All three variants identified in patients with heart failure demonstrated gain-of-function properties in both electrophysiological measurements and in-silico predictions. Importantly, the results obtained from molecular docking and patch clamp recordings were strongly correlated, showing an 82% concordance, higher than the predictions from AlphaMissense. These findings indicate that our computational analysis provides a rapid and reliable method for predicting the functional impact of TRPC6 missense variants, which may aid clinical decision-making in cancer patients receiving chemotherapy.

physiology↗

Integrative Epigenomic Landscape of Alzheimer's Disease Brains Reveals Oligodendrocyte Molecular Perturbations Associated with Tau

Alzheimers disease (AD) brains are characterized by neuropathologic and biochemical changes that are highly variable across individuals. Capturing epigenetic factors that associate with this variability can reveal novel biological insights into AD pathophysiology. We conducted an epigenome-wide association study of DNA methylation (DNAm) in 472 AD brains with neuropathologic measures (Braak stage, Thal phase, and cerebral amyloid angiopathy score) and brain biochemical levels of five proteins (APOE, amyloid-{beta} (A{beta})40, A{beta}42, tau, and p-tau) core to AD pathogenesis. Using a novel regional methylation (rCpGm) approach, we identified 5,478 significant associations, 99.7% of which were with brain tau biochemical measures. Of the tau-associated rCpGms, 93 had concordant associations in external datasets comprising 1,337 brain samples. Integrative transcriptome-methylome analyses uncovered 535 significant gene expression associations for these 93 rCpGms. Genes with concurrent transcriptome-methylome perturbations were enriched in oligodendrocyte marker genes, including known AD risk genes such as BIN1, myelination genes MYRF, MBP and MAG previously implicated in AD, as well as novel genes like LDB3. We further annotated the top oligodendrocyte genes in an additional 6 brain single cell and 2 bulk transcriptome datasets from AD and two other tauopathies, Picks disease and progressive supranuclear palsy (PSP). Our findings support consistent rCpGm and gene expression associations with these tauopathies and tau-related phenotypes in both bulk brain tissue and oligodendrocyte clusters. In summary, we uncover the integrative epigenomic landscape of AD and demonstrate tau-related oligodendrocyte gene perturbations as a common potential pathomechanism across different tauopathies.

genomics↗

Nomination of a novel plasma protein biomarker panel capable of classifying Alzheimer's disease dementia with high accuracy in an African American cohort

IntroductionAfrican Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimers disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD. MethodsUntargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in a cohort of AA clinically diagnosed as AD dementia (n=183) or cognitively unimpaired (CU, n=145). Machine learning approaches were implemented to identify the set of plasma proteins that yields the best classification accuracy. ResultsA plasma protein panel achieved an area under the curve (AUC) of 0.91 to classify AD dementia vs CU. The reproducibility of this finding was observed in the ANMerge plasma and AMP-AD Diversity brain datasets (AUC=0.83; AUC=0.94). DiscussionThis study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning approaches. Our findings also highlight the potential importance of the matrisome and cerebrovascular dysfunction in AD pathophysiology.

neuroscience↗

Large- Scale Deep Proteomic Analysis in Alzheimer's Disease Brain Regions Across Race and Ethnicity

AbstractO_ST_ABSIntroductionC_ST_ABSAlzheimers disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within the non-Hispanic White (NHW) population. Here we aimed to provide comprehensive insights into the proteomic landscape of AD across diverse racial and ethnic groups. MethodsDorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) brain tissues were donated from multiple centers (Mayo Clinic, Emory University, Rush University, Mt. Sinai School of Medicine) and were harmonized through neuropathological evaluation, specifically adhering to the Braak staging and CERAD criteria. Among 1105 DLPFC tissue samples (998 unique individuals), 333 were from African American donors, 223 from Latino Americans, 529 from NHW donors, and the rest were from a mixed or unknown racial background. Among 280 STG tissue samples (244 unique individuals), 86 were African American, 76 Latino American, 116 NHW and the rest were mixed or unknown ethnicity. All tissues were uniformly homogenized and analyzed by tandem mass tag mass spectrometry (TMT-MS). ResultsAs a Quality control (QC) measure, proteins with more than 50% missing values were removed and iterative principal component analysis was conducted to remove outliers within brain regions. After QC, 9,180 and 9,734 proteins remained in the DLPC and STG proteome, respectively, of which approximately 9,000 proteins were shared between regions. Protein levels of microtubule-associated protein tau (MAPT) and amyloid-precursor protein (APP) demonstrated AD-related elevations in DLPFC tissues with a strong association with CERAD and Braak across racial groups. APOE4 protein levels in brain were highly concordant with APOE genotype of the individuals. DiscussionThis comprehensive region resolved large-scale proteomic dataset provides a resource for the understanding of ethnoracial-specific protein differences in AD brain.

neuroscience↗

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer's Disease and Older Controls in Multi-Ethnic Populations

INTRODUCTIONMulti-omics studies in Alzheimers disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked African Americans (AA) and Latin Americans (LA), who are disproportionately affected by AD. METHODSTo bridge this gap, Accelerating Medicines Partnership in AD (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors. RESULTSWe generated multi-omics data and curated and harmonized phenotypic data from AA (n=306), LA (n=326), or AA and LA (n=4) brain donors plus Non-Hispanic White (n=252) and other (n=20) ethnic groups, to establish a foundational dataset enriched for AA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSIONInclusion of traditionally underrepresented groups in multi-omics studies is essential to discover the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.

neuroscience↗

Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's Disease related proteins

Alzheimers disease (AD) is neuropathologically characterized by amyloid-beta (A{beta}) plaques and neurofibrillary tangles. Main protein components of these hallmarks include A{beta}40, A{beta}42, tau, phospho-tau and APOE. With the exception of the APOE-{varepsilon}4 variant, genetic risk factors associated with brain biochemical measures of these proteins have yet to be characterized. We performed a genome-wide association study in brains of 441 AD patients for quantitative levels of these proteins collected from three distinct fractions reflecting soluble, membrane-bound and insoluble biochemical states. We identified 123 genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD- related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways. Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.

genetics↗

Single Nuclei Transcriptome Reveals Perturbed Brain Vascular Molecules in Alzheimer's Disease

Blood-brain barrier (BBB) dysfunction is well-known in Alzheimers disease (AD), but the precise molecular changes contributing to its pathophysiology are unclear. To understand the transcriptional changes in brain vascular cells, we performed single nucleus RNA sequencing (snRNAseq) of temporal cortex tissue in 24 AD and control brains resulting in 79,751 nuclei, 4,604 of which formed three distinct vascular clusters characterized as activated pericytes, endothelia and resting pericytes. We identified differentially expressed genes (DEGs) and their enriched pathways in these clusters and detected the most transcriptional changes within activated pericytes. Using our data and a knowledge-based predictive algorithm, we discovered and prioritized molecular interactions between vascular and astrocyte clusters, the main cell types of the gliovascular unit (GVU) of the BBB. Vascular targets predicted to interact with astrocytic ligands have biological functions in signalling, angiogenesis, amyloid {beta} metabolism and cytoskeletal structure. Top astrocytic and vascular interacting molecules include both novel and known AD risk genes such as APOE, APP and ECE1. Our findings provide information on transcriptional changes in predicted vascular-astrocytic partners at the GVU, bringing insights to the molecular mechanisms of BBB breakdown in AD. Graphical AbstractPericytes (yellow), endothelia (salmon) and astrocytes (purple) that form the gliovascular unit (GVU) at the blood brain barrier (BBB) were interrogated for their differentially expressed genes (DEG) and vascular cell (pericyte or endothelia) to astrocyte interactions using single nucleus RNA sequencing (RNAseq) transcriptome obtained from brains of Alzheimers disease (AD) patients and controls. We identified many upregulated (red) or downregulated (blue) DEGs in AD brains in these cell types. These genes have known biological functions in amyloid {beta} (A{beta}) clearance, immune modulation, astrogliosis and neuronal death. Novel predicted interactions were identified between vascular cells and astrocytic DEGs. Collectively, our findings highlight the vast transcriptome changes that occur at the GVU and provide mechanistic insights into BBB dysfunction in AD. This figure was created with Biorender.com. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/474255v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@f48748org.highwire.dtl.DTLVardef@1ddc3f4org.highwire.dtl.DTLVardef@6bfa5eorg.highwire.dtl.DTLVardef@1024506_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Conserved Architecture of Brain Transcriptome Changes between Alzheimer's Disease and Progressive Supranuclear Palsy in Pathologically Affected and Unaffected Regions

We identify a striking correlation in the directionality and magnitude of gene expression changes in brain transcriptomes between Alzheimers disease (AD) and Progressive Supranuclear Palsy (PSP). Further, the transcriptome architecture in AD and PSP is highly conserved between the temporal and cerebellar cortices, indicating highly similar transcriptional changes occur in pathologically affected and "unaffected" areas of the brain. These data have broad implications for interpreting transcriptomic data in neurodegenerative disorders.

neuroscience↗

Identification of novel Alzheimer's disease genes co-expressed with TREM2

By analyzing whole-exome data from the Alzheimers disease sequencing project (ADSP), we identify a set of 4 genes that show highly significant association with Alzheimers disease (AD). These genes were identified within a human TREM2 co-expression network using a novel approach wherein prioritized polygenic score analyses were performed sequentially to identify significant polygenic components. Two of the 4 genes (TREM2, RIN3) have previously been linked to AD and two (ATP8B4, IL17RA) are novel. Like TREM2, the 2 novel AD genes are selectively expressed in human microglial cells. The most significant variants in ATP8B4 and IL17RA are non-synonymous variants with strong effects comparable to the APOE {varepsilon}4 and {varepsilon}2 alleles. These protein-altering variants will provide unique opportunities to further explore the biological role of microglial cells in AD and help inform future immune modulatory therapeutic development for AD.

genetics↗

Single Cell-type Integrative Network Modeling IdentifiedNovel Microglial-specific Targets for the Phagosome inAlzheimer's disease

SummaryLate-Onset Alzheimer’s Disease (LOAD) results from a complex pathological process influenced by genetic variation, aging and environment factors. Genetic susceptibility factors indicate that myeloid cells such as microglia play a significant role in the onset of LOAD. Here, we developed a computational systems biology approach to construct probabilistic causal and predictive network models of genetic regulatory programs of microglial cells under LOAD diagnosis by integrating two independent brain transcriptome and genome-wide genotype datasets from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MAYO) studies in the AMP-AD consortium. From this network model, we identified and replicated novel microglial-specific master regulators predicted to modulate network states associated with LOAD. We experimentally validated three microglial master regulators (FCER1G, HCK and LAPTM5) in primary human microglia-like cells (MDMi) by demonstrating the molecular impact these master regulators have on modulating downstream genomic targets identified by our top-down/bottom-up method and the causal relations among the three key drivers. These master regulators are involved in phagocytosis, a process associated with LOAD. Thus, we propose three new master regulator (key driver) genes that emerged from our network analyses as robust candidates for further evaluation in LOAD therapeutic development efforts.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

Deciphering cellular transcriptional alterations in Alzheimer's disease brains

Large-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimers disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.

neuroscience↗

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimers Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes), and provide the results as a community resource. We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. In addition, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a common resource for use across the community in research programs. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

genetics↗

Systematic analysis of dark and camouflaged genes: disease-relevant genes hiding in plain sight

BackgroundThe human genome contains dark gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions that are dark by depth (few mappable reads) and others that are camouflaged (ambiguous alignment), and we assess how well long-read technologies resolve these regions. We further present an algorithm to resolve most camouflaged regions (including in short-read data) and apply it to the Alzheimers Disease Sequencing Project (ADSP; 13142 samples), as a proof of principle.\n\nResultsBased on standard whole-genome lllumina sequencing data, we identified 37873 dark regions in 5857 gene bodies (3635 protein-coding) from pathways important to human health, development, and reproduction. Of the 5857 gene bodies, 494 (8.4%) were 100% dark (142 protein-coding) and 2046 (34.9%) were [≥]5% dark (628 protein-coding). Exactly 2757 dark regions were in protein-coding exons (CDS) across 744 genes. Long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduced dark CDS regions to approximately 45.1%, 33.3%, and 18.2% respectively. Applying our algorithm to the ADSP, we rescued 4622 exonic variants from 501 camouflaged genes, including a rare, ten-nucleotide frameshift deletion in CR1, a top Alzheimers disease gene, found in only five ADSP cases and zero controls.\n\nConclusionsWhile we could not formally assess the CR1 frameshift mutation in Alzheimers disease (insufficient sample-size), we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.

genomics↗