Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

Abstract

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

Keywords: Alzheimer’s disease; complex diseases; edgetic perturbations in disease; epichaperome; epichaperomics; interactome network dysfunctions; precision medicine; protein connectivity dysfunctions; protein-protein interactions; tissue-specific interactome.

Fig. 1.

(A) Changes in protein connectivity are independent of protein expression changes, and changes in expression levels do not necessarily equate to changes in connectivity. Epichaperomics and proteomics detected proteins in AD (postmortem AD brain samples {Females (F, n = 8) vs Males (M, n = 6)}). For epichaperomic analysis (left panel), MS-derived files were subjected to Label-Free Quantification analysis using the MaxQuant proteomic data analysis workflow [72]. For proteomic analysis (right panel), quantitative bottom-up proteomics was performed using isobaric mass tag TMT10plex labeling reagents [142]. The calculated differences (t-values) were reordered based on hierarchical clustering. Results indicate connectivity, and expression level changes are independent. (B) Schematic showing the influence of identified proteins in a dataset on the ability to make tissue-specific functional predictions. A single protein may carry out different functions with different partners in different biological contexts. Determining if a differentially expressed protein (for proteomics) or differentially connected protein (for epichaperomics) is associated with a certain biological process or molecular function depends on the enrichment of its partners in the specific dataset. Gene Ontology (GO), which contains standardized annotation of proteins, is commonly used for this purpose. It works by comparing the frequency of individual annotations in the protein list with a reference list. Enrichment of biological pathways supplied by Reactome, WikiPathways, KEGG (Kyoto Encyclopedia of Genes and Genomes), or other pathway analysis resources can be performed in a similar manner.

Fig. 2.

(A) The chaperome is an assembly of chaperones and cochaperones (left panel). Their effects are executed through short-lived chaperome complexes and in a one-to-one, dynamic cyclic fashion, aiding protein folding, degradation, or disaggregation [81]. Stressors associated with disease increase connectivity among chaperome proteins to form stable hetero-oligomeric complexes termed “epichaperomes” (right panel) [72,74,81]. These chaperome pools do not act in protein folding and degradation, but rather as multimolecular scaffolding platforms that pathologically remodel cellular processes [81]. (B) Epichaperomics is an affinity purification technique. Chemical probes that bind key epichaperome components and trap individual epichaperomes bound to their interacting proteins are used to capture and isolate these complexes thus retaining interactions through subsequent isolation steps and enabling their unbiased identification by MS. A bioinformatics pipeline was developed to derive the context-specific interactome maps from the resulting MS datasets [72]. See also Figs 3 and 4. HSP90, heat-shock protein 90 (HSP90α and HSP90β isoforms, encoded by the HSP90AA1 and HSP90AB1 genes, respectively). HSC70, heat-shock cognate 70 protein encoded by the HSPA8 gene.

Fig. 3.

Interactome changes as identified by epichaperomics following individual AD- and AD-relevant model stressor conditions via Venn diagram and Reactome pathway enrichment analyses. In the Venn diagram (center), each circle represents the number of proteins whose connectivity is significantly affected by each stressor. In the Reactome maps, generated in Cytoscape, each circle represents a function (i.e., a protein pathway). The center Reactome map is a merged map depiction of all four individual stressor conditions (left- and right-side panels). If the circle is divided into blue, yellow, red and green segments, it means all four stressors (or stressors characteristic of each condition) mediate imbalances in the select pathway. An exclusively red circle indicates the pathway alteration is AD-specific. Key: human brains (sporadic late-onset AD vs NCI), iPSC-derived neurons (APP duplication vs WT), transgenic mouse brains (PS19 vs WT), and cellular models of human tau toxicity (N2a cells overexpressing human tau vs N2a cells with vector only). Figure adapted from [72].

Fig. 4.

(A) AD stressors induce proteome-wide interactome network dysfunctions through maladaptive epichaperomes. Epichaperomics is positioned to be a new approach to track and study interactome networks across the AD spectrum and provide unprecedented information on molecular mechanisms by which stressors influence phenotypes. (B) Investigating the trajectory of epichaperome-mediated interactome dysfunctions may reveal not only defects within intrinsic neuronal proteins and protein pathways but also connectome disruptions in the intrinsic network connectivity of cells and of brain circuits. By applying epichaperomics to well-characterized brains with available patient-specific genetic, clinical, and pathologic measures, we expect to find clues on specific dysfunctions impacted by these stressors that will lead to novel insights into stressor–phenotype relationships. To make epichaperomics datasets available to and readable by the scientific community, in addition to depositing raw data and analytics into free-access portals such as MassIVE and AD Knowledge Portal Synapse (https://adknowledgeportal.synapse.org/), a web-based user-interface “Epichaperomics shiny app” is envisioned. Its role would be to facilitate data processing and visualization by scientists with or without a bioinformatics background and expertise. We posit a whole new treatment paradigm may open and provide a previously unavailable precision medicine approach to AD by understanding and targeting the interactome.