Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer’s disease phenotypes

  1. Saniya Khullar
  2. Daifeng Wang

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Abstract

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 risk variants 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 the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt 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 gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes.

Results

Our analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.

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  1. Version published to 10.1101/2021.06.21.449165v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.06.21.449165: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Identification of AD-Covid genes and regulatory networks: We compared the KEGG pathways50 for AD (hsa05010) and Covid-19 (hsa05171).
    Covid-19 ( hsa05171
    suggested: None
    Software and Algorithms
    SentencesResources
    Association of genes and modules with AD phenotypes: We further associated genes and modules with these key AD developmental phenotypes: AD Stages and Progression (Moderate Stage, Severe Stage, and AD Progression), Healthy/Resilient (Control Stage or other resilient individuals with better cognitive abilities despite AD pathology), APOE genotype (APOE E4/E4 is a huge AD risk factor38), Braak Staging, neuritic plaque accumulation (measured by CERAD Score), and cognitive impairment level (based on the MMSE Score).
    CERAD
    suggested: None
    First, we identified REs including enhancers and promoters that potentially interact using recent chromatin interaction data (Hi-C) and the scGRNom pipeline39.
    scGRNom
    suggested: None
    Besides, we used GENIE3 to predict additional GRNs via Random Forest regression, predicting each gene’s expression pattern from the expression patterns of all TFs (TF-TG pairs with weights greater than 0.0025 were retained).
    GENIE3
    suggested: (GENIE3, RRID:SCR_000217)
    In particular, the permutation analysis with 1,000 permutations was applied bootstrapping and the ARACNe algorithm47 was used to select most meaningful network edges.
    ARACNe
    suggested: (ARACNE, RRID:SCR_002180)
    Then, we identified the variants that interrupt the TFBSs on the regulatory elements by motifbreakR49 (using ENCODE-motif, FactorBook, HOCOMOCO, and HOMER data sources and default methodology, with a threshold of 0.001), and further linked them to the genes from the regulatory elements with interrupted TFBSs.
    HOCOMOCO
    suggested: (HOCOMOCO, RRID:SCR_005409)
    HOMER
    suggested: (HOMER, RRID:SCR_010881)
    Identification of AD-Covid genes and regulatory networks: We compared the KEGG pathways50 for AD (hsa05010) and Covid-19 (hsa05171).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    The Python package, Scikit-Learn57, was used for our machine learning analysis.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.06.21.449165v1 on bioRxiv