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. 2018 Jul 11;99(1):64-82.e7.
doi: 10.1016/j.neuron.2018.05.023. Epub 2018 Jun 21.

Multiscale Analysis of Independent Alzheimer's Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus

Affiliations

Multiscale Analysis of Independent Alzheimer's Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus

Ben Readhead et al. Neuron. .

Abstract

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.

Keywords: Alzheimer's disease; HHV-6A; HHV-6B; HHV-7; Roseolovirus; human herpesvirus; integrative genomics; multiscale networks; network biology; systems biology.

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Conflict of interest statement

Declarations of Interests

The authors declare that they have no competing financial interests in relation to the work described.

Figures

Figure 1:
Figure 1:. Multiomic evaluation of Alzheimer’s Disease associated virome
(a) This study was performed in two phases, an initial exploration into the earliest network changes associated with preclinical AD, which identified multiple shifts in network biology consistent with viral perturbations and prompted a systematic, multiomic evaluation of viral biology on larger data sets focused on clinical AD. (b) Regulatory networks built across EC and HIP samples showed differences in gene drivers for preclinical AD vs. control networks (c) including many AD associated genes, (d) although drivers exclusive to each network shared functional characteristics, such as association with viral biology, and (e) promoter enrichments for the same C2H2 zinc finger transcription factors binding motifs. (f) G-quadruplex sequence motifs were strongly enriched in the promoters of the drivers “lost in preclinical AD”, and those “gained in preclinical AD”, however (g) drivers “lost in preclinical AD” had much higher G-Quadruplex density in introns, exons and 3’-UTR. (h) Genes with high G-Quadruplex density in these locations were uniformly down regulated in the EC of preclinical AD and AD samples, suggesting some significant alteration of G-Quadruplex regulation. (i) To investigate the possibility of an AD-associated virome, we incorporated clinical, neuropathological, RNA, DNA and proteomic data from individuals with clinical AD (and controls) across four brain regions to (j-k) identify and characterize viral activity associated with AD biology, (l) highlighting Roseoloviruses HHV-6A and HHV7. (Region abbreviations: EC: entorhinal cortex, HIP: hippocampus, MTG: medial temporal gyrus, PC: posterior cingulate cortex, SFG: superior frontal gyrus. VCX: visual cortex)
Figure 2:
Figure 2:. Differential abundance of viral RNA and DNA in AD
Multiregional comparison between AD vs. controls of viral RNA and DNA, summarized to the level of (a,c) full viral sequences, and (b,d) viral genomic features. Meta-analysis of differential abundance of viral RNA in AD (e-f), incorporating post-mortem brain RNA-seq data from the MSBB, ROS, MAP and MAYO TCX consortium studies also revealed increased HHV-6A and HHV-7 in AD. (P-values shown in cells with FDR < 0.1, Features with a meta-analysis FDR < 0.1 shown in e-f)
Figure 3:
Figure 3:. Viral abundance associates with AD clinical and neuropathology traits
Multiregional associations between abundance of viral genomic feature RNA and clinical and neuropathological traits. Viral features with significant (FDR < 0.1) associations across multiple traits or brain regions are shown. (P-values shown in cells with FDR < 0.1).
Figure 4:
Figure 4:. Viral QTL detection and association with AD genetics
(a) Host DNA markers significantly associated (FDR < 0.25) with viral abundance were classified as vQTL for that feature-tissue combination. (b) Top multi-viral vQTL associations, with overlapping gene symbols and associated viruses. (c) Top vQTL associations where vQTL is also a cis-eQTL for at least one host gene. (d) Sequence kernel association test to evaluate whether vQTL markers are also associated with AD traits. (e) Virus level vQTL sets for HHV-6A were highlighted most strongly, (f) whereas viral feature level associations implicated features from HHV-6A and HSV-1. (Panels e-f show associations for any viruses/viral features that were implicated in viral differential RNA abundance, P-values < 0.05 shown in cells)
Figure 5:
Figure 5:. Viral regulation of AD associated host networks
(a) Integration of vQTL with viral abundance, and host gene expression was used to infer directed virus / host subnetworks. (b) Virus / host gene network sizes for viruses with interactions detected in multiple tissues (c) Host genes that are most frequently perturbed by viruses (d) Host genes upregulated by HHV-6A are enriched for a heterogeneous set of AD risk and biomarker associated genes. Shown here is the HHV-6A / AD-associated gene subnetwork, and detected interactions with additional viruses. (e-g) Brain cis-eQTLs associated with expression of specific virus/host networks are enriched for AD GWAS risk loci. (P-values shown in cells with FDR < 0.1)
Figure 6:
Figure 6:. Viral mediation of neuronal loss in AD
(a) Cell fraction estimates in the MSBB RNA-seq data, linking cell fractions with (b) AD traits, and (c) viral RNA abundance. (d) HHV-6A was negatively correlated with neuronal fraction, while also at increased abundance in the STG. (e-f) Causal inference testing identified HHV-6A vQTL associated with neuronal fraction, which also have their effect mediated by HHV-6A sequences. (g) Neuronal loss network of host genes regulated by HHV-6A, which also exert an effect on neuronal fraction, includes MIR155HG. Gene label size is inversely proportional to the smallest AD risk P-value for its associated cis-eQTL. (h-i) We initially prioritized miR-155 during our analysis of the preclinical AD networks, due to strong associations between known miR155 gene targets, and a variety of multiregional AD and preclinical AD transcriptomic changes, as well as differential drivers of the preclinical AD network. (j-l) Four month old miR-155-KOxAPP/PS1 produce increased cortical β-amyloid plaques and oligomers compared with APP/PS1 mice. (m) We hypothesized that HHV-6A inhibition of miR-155 might cause a disinhibition of miR-155 targets. Cortical RNA-seq of miR-155KO vs. WT mice demonstrated significant overlap between genes upregulated (FDR < 0.1) in the absence of miR-155, and host genes detected as upregulated by HHV-6A in the virus/host networks. (n) Gene set enrichments for miR-155-KO vs. WT differentially expressed genes identified multiple enrichments (FDR < 0.1) consistent with a role for miR-155 in neuronal loss.
Figure 7:
Figure 7:. Viral perturbation of transcription factor regulatory networks
(a) Comparison of virus/host networks with diverse TF-Target networks built from multiple independent AD data sets, to identify virus / TF network enrichments. (b) Examination of the concordance of effect exerted by associated virus and TF upon target genes, and (c) summarization of results across all TF-target networks (d) to identify unanimous virus/TF associations. (e) Kinase enrichment analysis of the most strongly implicated TF identified several kinases that we also detected as regulated by HHV-6A, with known associations to AD and HHV-6A, indicating a potential mechanism for viral co-option of TF networks in AD.
Figure 8:
Figure 8:. Multiomic evaluation of AD associated virome implicates Roseoloviruses HHV-6 and HHV-7
(a) Summarized associations for each virus to diverse aspects of AD biology. Multiple viruses appear to have significant impacts on AD associated biology, particularly HHV-6A, HHV-7 and HHV-6B. (b) Findings from this study indicate complex relationships between viral and host factors that are likely to be relevant across a range of time scales and organ systems. Key biological processes that have been highlighted are shown, along with top candidate molecular mediators.

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