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1. Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer’s disease

   https://doi.org/10.1093/bib/bbab121  

   Xie, Linhui ; He, Bing ; Varathan, Pradeep ; Nho, Kwangsik ; Risacher, Shannon L ; Saykin, Andrew J ; Salama, Paul ; Yan, Jingwen ( November 2021 , Briefings in Bioinformatics)

   Abstract A large number of genetic variations have been identified to be associated with Alzheimer’s disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of ‘vision’, a brain function partly controlled by frontalmore »cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.« less
2. BIN1 is a key regulator of proinflammatory and neurodegeneration-related activation in microglia

   https://doi.org/10.1186/s13024-022-00535-x  

   Sudwarts, Ari ; Ramesha, Supriya ; Gao, Tianwen ; Ponnusamy, Moorthi ; Wang, Shuai ; Hansen, Mitchell ; Kozlova, Alena ; Bitarafan, Sara ; Kumar, Prateek ; Beaulieu-Abdelahad, David ; et al ( December 2022 , Molecular Neurodegeneration)

   Abstract Background The BIN1 locus contains the second-most significant genetic risk factor for late-onset Alzheimer’s disease. BIN1 undergoes alternate splicing to generate tissue- and cell-type-specific BIN1 isoforms, which regulate membrane dynamics in a range of crucial cellular processes. Whilst the expression of BIN1 in the brain has been characterized in neurons and oligodendrocytes in detail, information regarding microglial BIN1 expression is mainly limited to large-scale transcriptomic and proteomic data. Notably, BIN1 protein expression and its functional roles in microglia, a cell type most relevant to Alzheimer’s disease, have not been examined in depth. Methods Microglial BIN1 expression was analyzed by immunostaining mouse and human brain, as well as by immunoblot and RT-PCR assays of isolated microglia or human iPSC-derived microglial cells. Bin1 expression was ablated by siRNA knockdown in primary microglial cultures in vitro and Cre-lox mediated conditional deletion in adult mouse brain microglia in vivo. Regulation of neuroinflammatory microglial signatures by BIN1 in vitro and in vivo was characterized using NanoString gene panels and flow cytometry methods. The transcriptome data was explored by in silico pathway analysis and validated by complementary molecular approaches. Results Here, we characterized microglial BIN1 expression in vitro and in vivo and ascertained microglia expressedmore »BIN1 isoforms. By silencing Bin1 expression in primary microglial cultures, we demonstrate that BIN1 regulates the activation of proinflammatory and disease-associated responses in microglia as measured by gene expression and cytokine production. Our transcriptomic profiling revealed key homeostatic and lipopolysaccharide (LPS)-induced inflammatory response pathways, as well as transcription factors PU.1 and IRF1 that are regulated by BIN1. Microglia-specific Bin1 conditional knockout in vivo revealed novel roles of BIN1 in regulating the expression of disease-associated genes while counteracting CX3CR1 signaling. The consensus from in vitro and in vivo findings showed that loss of Bin1 impaired the ability of microglia to mount type 1 interferon responses to proinflammatory challenge, particularly the upregulation of a critical type 1 immune response gene, Ifitm3 . Conclusions Our convergent findings provide novel insights into microglial BIN1 function and demonstrate an essential role of microglial BIN1 in regulating brain inflammatory response and microglial phenotypic changes. Moreover, for the first time, our study shows a regulatory relationship between Bin1 and Ifitm3 , two Alzheimer’s disease-related genes in microglia. The requirement for BIN1 to regulate Ifitm3 upregulation during inflammation has important implications for inflammatory responses during the pathogenesis and progression of many neurodegenerative diseases. Graphical Abstract« less
3. Gene co-expression changes underlying the functional connectomic alterations in Alzheimer’s disease

   https://doi.org/10.1186/s12920-022-01244-6  

   He, Bing ; Gorijala, Priyanka ; Xie, Linhui ; Cao, Sha ; Yan, Jingwen ( December 2022 , BMC Medical Genomics)

   Abstract Background There is growing evidence indicating that a number of functional connectivity networks are disrupted at each stage of the full clinical Alzheimer’s disease spectrum. Such differences are also detectable in cognitive normal (CN) carrying mutations of AD risk genes, suggesting a substantial relationship between genetics and AD-altered functional brain networks. However, direct genetic effect on functional connectivity networks has not been measured. Methods Leveraging existing AD functional connectivity studies collected in NeuroSynth, we performed a meta-analysis to identify two sets of brain regions: ones with altered functional connectivity in resting state network and ones without. Then with the brain-wide gene expression data in the Allen Human Brain Atlas, we applied a new biclustering method to identify a set of genes with differential co-expression patterns between these two set of brain regions. Results Differential co-expression analysis using biclustering method led to a subset of 38 genes which showed distinctive co-expression patterns between AD-related and non AD-related brain regions in default mode network. More specifically, we observed 4 sub-clusters with noticeable co-expression difference, where the difference in correlations is above 0.5 on average. Conclusions This work applies a new biclustering method to search for a subset of genes with alteredmore »co-expression patterns in AD-related default mode network regions. Compared with traditional differential expression analysis, differential co-expression analysis yielded many more significant hits with extra insights into the wiring mechanism between genes. Particularly, the differential co-expression pattern was observed between two sets of genes, suggesting potential upstream genetic regulators in AD development.« less
4. Evaluation of the Common Molecular Basis in Alzheimer’s and Parkinson’s Diseases

   https://doi.org/10.3390/ijms20153730  

   Rana, Pratip ; Franco, Edian F. ; Rao, Yug ; Syed, Khajamoinuddin ; Barh, Debmalya ; Azevedo, Vasco ; Ramos, Rommel T. ; Ghosh, Preetam ( August 2019 , International Journal of Molecular Sciences)

   Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between them is still unknown. In this paper, we analyzed how these two diseases relate to each other from the genomic, epigenomic, and transcriptomic viewpoints. Using an extensive literature mining, we first accumulated the list of genes from major genome-wide association (GWAS) studies. Based on these GWAS studies, we observed that only one gene (HLA-DRB5) was shared between AD and PD. A subsequent literature search identified a few other genes involved in these two diseases, among which SIRT1 seemed to be the most prominent one. While we listed all the miRNAs that have been previously reported for AD and PD separately, we found only 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene co-expression network for both AD and PD using network analysis algorithms applied to two GEO datasets. The network analysis revealed six clusters of genes related to AD and four clusters of genes related to PD; however, there was very low functional similarity between these clusters, pointing to insignificantmore »similarity between AD and PD even at the level of affected biological processes. Finally, we postulated the putative epigenetic regulator modules that are common to AD and PD.« less
5. Mining Regional Imaging Genetic Associations via Voxel-wise Enrichment Analysis

   https://doi.org/10.1109/BHI.2019.8834450  

   Yao, Xiaohui ; Cong, Shan ; Yan, Jingwen ; Risacher, Shannon L. ; Saykin, Andrew J. ; Moore, Jason H. ; Shen, Li ( May 2019 , 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI))

   Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful annotation and modest dimensionality compared with voxel-wise approaches. Typical ROI-level measures used in these studies are summary statistics from voxel-wise measures in the region, without making full use of individual voxel signals. In this paper, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxel-wise enrichment analysis, which embraces the collective effect of weak voxel-level signals within an ROI. We demonstrate our method on an imaging genetic analysis using data from the Alzheimers Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxel-wise FDG-PET measures between 116 ROIs and 19 AD candidate SNPs. Compared with traditional ROI-wise and voxel-wise approaches, our method identified 102 additional significant associations, some of which were further supported by evidences in brain tissue-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain.