Computational Interspecies Translation Between Alzheimer’s Disease Mouse Models and Human Subjects Identifies Innate Immune Complement, TYROBP, and TAM Receptor Agonist Signatures, Distinct From Influences of Aging

Abstract

Mouse models are vital for preclinical research on Alzheimer’s disease (AD) pathobiology. Many traditional models are driven by autosomal dominant mutations identified from early onset AD genetics whereas late onset and sporadic forms of the disease are predominant among human patients. Alongside ongoing experimental efforts to improve fidelity of mouse model representation of late onset AD, a computational framework termed Translatable Components Regression (TransComp-R) offers a complementary approach to leverage human and mouse datasets concurrently to enhance translation capabilities. We employ TransComp-R to integratively analyze transcriptomic data from human postmortem and traditional amyloid mouse model hippocampi to identify pathway-level signatures present in human patient samples yet predictive of mouse model disease status. This method allows concomitant evaluation of datasets across different species beyond observational seeking of direct commonalities between the species. Additional linear modeling focuses on decoupling disease signatures from effects of aging. Our results elucidated mouse-to-human translatable signatures associated with disease: excitatory synapses, inflammatory cytokine signaling, and complement cascade- and TYROBP-based innate immune activity; these signatures all find validation in previous literature. Additionally, we identified agonists of the Tyro3 / Axl / MerTK (TAM) receptor family as significant contributors to the cross-species innate immune signature; the mechanistic roles of the TAM receptor family in AD merit further dedicated study. We have demonstrated that TransComp-R can enhance translational understanding of relationships between AD mouse model data and human data, thus aiding generation of biological hypotheses concerning AD progression and holding promise for improved preclinical evaluation of therapies.

Cross species modeling reveals a role for the unfolded protein response in shaping the transcriptional reaction toMycobacterium tuberculosisinfection

“…Our lab has developed a crossspecies modeling framework, TransComp-R, to overcome animal model limitations by identifying axes of molecular variation in preclinical models that correlate with the observed human disease pathologies, while acknowledging that each individual pathway may be variably important between individuals or species (22). The application of this translational modeling framework in other disease contexts identified novel disease-relevant biological pathways, which were not apparent when only the commonly dominant features across both species were considered (22)(23)(24). Here we apply TransComp-R to TB in efforts to untangle the heterogeneity underlying human TB disease presentation, and similarly identify multiple diverse disease-associated biological axes that are shared by humans and mice.…”

“…Applying this framework in the context of inflammatory bowel disease (IBD), the study demonstrated that mouse proteomic features could provide insights relating to human transcriptomic expression, allowing for better classification of patient responses to IBD treatment and identification of novel pathways implicated in therapy resistance. TransComp-R has since been applied, and extended to translate gene pathway analyses, with success in the realm of neuropathologies but has yet to be applied in the context of infectious disease (23, 24).…”

Involvement of microglia‐expressed MS4A6A in the onset of glioblastoma

“…The whole family of the MS4 gene cluster is considered to have an immune‐mediated function. Also, studies have shown that the MS4A family is closely related to the occurrence and development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases (Karch & Goate, 2015; Lee et al, 2021; Martiskainen et al, 2015). The increasing MS4A6A gene expression has previously been validated to be associated with the increased risks of Alzheimer's disease (Proitsi et al, 2014).…”

Autoencoder Model for Translating Omics Signatures

“…The framework builds upon ideas of the CPA approach 32 and other species and cellular translation methods 13,15,18,31 , together with ideas from language translation models 33 . The explicit goal is to align omics signatures between systems, rather than identifying what information inherent in the signature of one system is most germane for understanding phenotype characteristics in the other, which has been the objective in many previous studies [16][17][18][19] . The framework performs as well as (or even better than) other state-of-the-art translation techniques, when using homolog features between systems, and performs similarly also without a 1-1 mapping between features.…”