المؤلفون: Chandrashekar, Pramod Bharadwaj, Alatkar, Sayali, Wang, Jiebiao, Hoffman, Gabriel E., He, Chenfeng, Jin, Ting, Khullar, Saniya, Bendl, Jaroslav, Fullard, John F., Roussos, Panos, Wang, Daifeng

المصدر: Genome Medicine; 10/31/2023, Vol. 15 Issue 1, p1-19, 19p

مصطلحات موضوعية: DEEP learning, ALZHEIMER'S disease, REGULATOR genes, GENETIC regulation, BRAIN diseases, GENE regulatory networks

مستخلص: Background: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. Method: To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype–phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. Results: We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease). Conclusion: We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use. [ABSTRACT FROM AUTHOR]

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المؤلفون: Khullar, Saniya, Wang, Daifeng

المصدر: Human Molecular Genetics; Jun2023, Vol. 32 Issue 11, p1797-1813, 17p

المؤلفون: Liu, Shuang, Won, Hyejung, Clarke, Declan, Matoba, Nana, Khullar, Saniya, Mu, Yudi, Wang, Daifeng, Gerstein, Mark

المصدر: Genome Medicine; 11/24/2022, Vol. 14 Issue 1, p1-15, 15p

مصطلحات موضوعية: LOCUS (Genetics), GENOME-wide association studies, GENETIC variation, ETIOLOGY of diseases, GENE targeting, CIS-regulatory elements (Genetics), PREFRONTAL cortex

مستخلص: Background: Neuropsychiatric disorders afflict a large portion of the global population and constitute a significant source of disability worldwide. Although Genome-wide Association Studies (GWAS) have identified many disorder-associated variants, the underlying regulatory mechanisms linking them to disorders remain elusive, especially those involving distant genomic elements. Expression quantitative trait loci (eQTLs) constitute a powerful means of providing this missing link. However, most eQTL studies in human brains have focused exclusively on cis-eQTLs, which link variants to nearby genes (i.e., those within 1 Mb of a variant). A complete understanding of disease etiology requires a clearer understanding of trans-regulatory mechanisms, which, in turn, entails a detailed analysis of the relationships between variants and expression changes in distant genes. Methods: By leveraging large datasets from the PsychENCODE consortium, we conducted a genome-wide survey of trans-eQTLs in the human dorsolateral prefrontal cortex. We also performed colocalization and mediation analyses to identify mediators in trans-regulation and use trans-eQTLs to link GWAS loci to schizophrenia risk genes. Results: We identified ~80,000 candidate trans-eQTLs (at FDR<0.25) that influence the expression of ~10K target genes (i.e., "trans-eGenes"). We found that many variants associated with these candidate trans-eQTLs overlap with known cis-eQTLs. Moreover, for >60% of these variants (by colocalization), the cis-eQTL's target gene acts as a mediator for the trans-eQTL SNP's effect on the trans-eGene, highlighting examples of cis-mediation as essential for trans-regulation. Furthermore, many of these colocalized variants fall into a discernable pattern wherein cis-eQTL's target is a transcription factor or RNA-binding protein, which, in turn, targets the gene associated with the candidate trans-eQTL. Finally, we show that trans-regulatory mechanisms provide valuable insights into psychiatric disorders: beyond what had been possible using only cis-eQTLs, we link an additional 23 GWAS loci and 90 risk genes (using colocalization between candidate trans-eQTLs and schizophrenia GWAS loci). Conclusions: We demonstrate that the transcriptional architecture of the human brain is orchestrated by both cis- and trans-regulatory variants and found that trans-eQTLs provide insights into brain-disease biology. [ABSTRACT FROM AUTHOR]

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المؤلفون: Gupta, Chirag, Xu, Jielin, Jin, Ting, Khullar, Saniya, Liu, Xiaoyu, Alatkar, Sayali, Cheng, Feixiong, Wang, Daifeng

المصدر: PLoS Computational Biology; 7/18/2022, Vol. 18 Issue 7, p1-31, 31p, 6 Graphs

مصطلحات موضوعية: GENE regulatory networks, ALZHEIMER'S disease, GENETIC regulation, DRUG repositioning, CYTOLOGY, DRUG laws

الشركة/الكيان: UNITED States. Food & Drug Administration

مستخلص: Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD. Author summary: Alzheimer's Disease (AD) is the leading cause of dementia. It affects parts of the brain that control language, behavior, and memory. The human brain is comprised of tens of billions of cells, such as neuronal cells that transmit information via electrical and chemical signals, and glial cells that maintain the brain's immune system. Researchers have found that AD causes changes in the expression of genes within the brain cells. Gene expression is a tightly regulated process involving interconnected networks of multiple genes. Understanding how these gene networks change in AD is critical to identifying genetic biomarkers and potential drug targets. Using genomic data of post-mortem brains diagnosed with AD and healthy individuals, we identified gene networks that play a crucial role in regulating biological processes within neuronal and glial cells. We utilized these gene networks to make predictions on existing FDA approved drugs that could potentially be repurposed for AD. Furthermore, we used a machine learning strategy to identify novel genes that are more likely to be involved in AD pathology. The systems-level approach lends itself to analysis of single-cell genomics data of other human diseases. [ABSTRACT FROM AUTHOR]

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المؤلفون: Gupta, Chirag, Chandrashekar, Pramod, Jin, Ting, He, Chenfeng, Khullar, Saniya, Chang, Qiang, Wang, Daifeng

المصدر: Journal of Neurodevelopmental Disorders; 5/2/2022, Vol. 14 Issue 1, p1-22, 22p

مصطلحات موضوعية: DEVELOPMENTAL disabilities, MACHINE learning, NEUROLOGICAL disorders, INTELLECTUAL disabilities, FRAGILE X syndrome, NUCLEOTIDE sequencing

مستخلص: Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions. [ABSTRACT FROM AUTHOR]

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المؤلفون: Heffron, Anna S., McIlwain, Sean J., Amjadi, Maya F., Baker, David A., Khullar, Saniya, Armbrust, Tammy, Halfmann, Peter J., Kawaoka, Yoshihiro, Sethi, Ajay K., Palmenberg, Ann C., Shelef, Miriam A., O'Connor, David H., Ong, Irene M.

المصدر: PLoS Biology; 6/18/2021, Vol. 19 Issue 6, p1-25, 25p, 1 Color Photograph, 1 Black and White Photograph, 2 Charts, 5 Graphs

مصطلحات موضوعية: COVID-19, SARS-CoV-2, CONVALESCENT plasma, ENZYME-linked immunosorbent assay, PEPTIDE mass fingerprinting, AMINO acid sequence

مستخلص: The search for potential antibody-based diagnostics, vaccines, and therapeutics for pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has focused almost exclusively on the spike (S) and nucleocapsid (N) proteins. Coronavirus membrane (M), ORF3a, and ORF8 proteins are humoral immunogens in other coronaviruses (CoVs) but remain largely uninvestigated for SARS-CoV-2. Here, we use ultradense peptide microarray mapping to show that SARS-CoV-2 infection induces robust antibody responses to epitopes throughout the SARS-CoV-2 proteome, particularly in M, in which 1 epitope achieved excellent diagnostic accuracy. We map 79 B cell epitopes throughout the SARS-CoV-2 proteome and demonstrate that antibodies that develop in response to SARS-CoV-2 infection bind homologous peptide sequences in the 6 other known human CoVs. We also confirm reactivity against 4 of our top-ranking epitopes by enzyme-linked immunosorbent assay (ELISA). Illness severity correlated with increased reactivity to 9 SARS-CoV-2 epitopes in S, M, N, and ORF3a in our population. Our results demonstrate previously unknown, highly reactive B cell epitopes throughout the full proteome of SARS-CoV-2 and other CoV proteins. Profiling of antibody binding from naïve and COVID-19 convalescent human sera to the entire proteome of SARS-CoV-2 and other human, bat and pangolin coronaviruses identifies 79 B cell epitopes throughout the SARS-CoV-2 proteome, finding that the most sensitive and specific binding occurred in the membrane (M) protein, and revealing cross-reactivity patterns. [ABSTRACT FROM AUTHOR]

: Copyright of PLoS Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Khullar, Saniya, Wang, Daifeng

المصدر: Alzheimer's & Dementia: The Journal of the Alzheimer's Association; Dec2022 Supplement 4, Vol. 18 Issue 4, p1-2, 2p

مستخلص: Background: Genome‐wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease SNPs to various phenotypes is still limited. To address these problems, we performed an integrative multi‐omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes. Method: First, given population gene expression data of a cohort, we construct and cluster its gene co‐expression network to identify modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co‐expressed genes and SNPs interrupting TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and modules for AD phenotypes. This network provides systematic insights into gene regulatory mechanisms from SNPs to phenotypes. We looked at our GRNs relating to genes from shared AD‐Covid pathways (e.g. NFKB Pathway) and used machine learning to prioritize those genes for predicting Covid‐19 severity Result: Our analysis predicted cross‐region‐conserved and region‐specific GRNs in 3 regions Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of INPP5D in Hippocampus and LTL. While rs4802200 disrupts E2F7 regulation of KCNN4 (belongs to AD LTL module), rs117863556 interrupts REST regulation of GAB2 in DLPFC. Further, we used Covid‐19 as a proxy for immune dysregulation to identify possible regulatory mechanisms for AD neuroimmunology. Decision Curve Analysis suggest our AD‐Covid genes along with linked SNPs (that outperform known genes) can be potential novel biomarkers for neuroimmunology. Finally, our results are open‐source available as a comprehensive AD functional genomic map, providing deeper mechanistic understanding of the interplay among multi‐omics, regions, gene functions, phenotypes. Conclusion: Our pipeline predicts how non‐coding risk SNPs may be associated with changes in regulation and subsequent expression of genes associated with different phenotypes and pathways in AD. Moreover, we flagged 51 potential AD‐neuroinflammatory risk genes, which may be early biomarkers as neuroinflammation may begin decades before clinical onset. [ABSTRACT FROM AUTHOR]

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المؤلفون: Shen, Minjie^1,2 (AUTHOR), Sirois, Carissa L.^1,2 (AUTHOR), Guo, Yu^1,2 (AUTHOR), Li, Meng^1,2 (AUTHOR), Dong, Qiping¹ (AUTHOR), Méndez-Albelo, Natasha M.^1,2,3 (AUTHOR), Gao, Yu^1,2 (AUTHOR), Khullar, Saniya^1,4 (AUTHOR), Kissel, Lee⁵ (AUTHOR), Sandoval, Soraya O.^1,2,5 (AUTHOR), Wolkoff, Natalie E.^1,2 (AUTHOR), Huang, Sabrina X.^1,2 (AUTHOR), Xu, Zhiyan^1,2,6 (AUTHOR), Bryan, Jonathan E.^1,4 (AUTHOR), Contractor, Amaya M.^1,2 (AUTHOR), Korabelnikov, Tomer^1,2 (AUTHOR), Glass, Ian A.⁷ (AUTHOR), Doherty, Dan⁷ (AUTHOR), Levine, Jon E.^2,8 (AUTHOR), Sousa, André M.M.^1,2 (AUTHOR)

المصدر: Neuron. Dec2023, Vol. 111 Issue 24, p3988-3988. 1p.

مصطلحات موضوعية: *FETAL development, *FRAGILE X syndrome, *AUTISM spectrum disorders, *NEURAL development, *FETAL brain

مستخلص: Fragile X messenger ribonucleoprotein 1 protein (FMRP) deficiency leads to fragile X syndrome (FXS), an autism spectrum disorder. The role of FMRP in prenatal human brain development remains unclear. Here, we show that FMRP is important for human and macaque prenatal brain development. Both FMRP-deficient neurons in human fetal cortical slices and FXS patient stem cell-derived neurons exhibit mitochondrial dysfunctions and hyperexcitability. Using multiomics analyses, we have identified both FMRP-bound mRNAs and FMRP-interacting proteins in human neurons and unveiled a previously unknown role of FMRP in regulating essential genes during human prenatal development. We demonstrate that FMRP interaction with CNOT1 maintains the levels of receptor for activated C kinase 1 (RACK1), a species-specific FMRP target. Genetic reduction of RACK1 leads to both mitochondrial dysfunctions and hyperexcitability, resembling FXS neurons. Finally, enhancing mitochondrial functions rescues deficits of FMRP-deficient cortical neurons during prenatal development, demonstrating targeting mitochondrial dysfunction as a potential treatment. [Display omitted] • FMRP is critical for mitochondrial functions in developing human cortical neurons • FMRP interacts with and regulates essential genes during human prenatal development • FMRP interacts with CNOT1 to regulate RACK1, a species-specific FMRP target • Enhancing mitochondrial functions rescues hyperexcitability of FXS neurons Shen et al. demonstrate that FMRP is critical for prenatal human brain development through regulating mitochondrial functions. They have discovered that FMRP interacts with other proteins to regulate genes important for neuronal development. They show that enhancing mitochondrial functions rescues the hyperexcitability of human fragile X syndrome patient-derived neurons. [ABSTRACT FROM AUTHOR]