SALVE: prediction of interorgan communication with transcriptome latent space representation.

Massive transcriptomics data allow gene relationships to be discovered from their correlated expression. We describe secretome association with latent variables between tissues (SALVE), a method to infer the associations between secretome-encoding transcripts and gene modules in a distal organ from RNA sequencing data. This method builds upon similar bioinformatics approaches by introducing transcriptome latent space representations and transfer learning to simultaneously increase discovery power and predict downstream functional associations. Applied to Genotype-Tissue Expression v8 data, we show that the method readily recapitulates canonical endocrine relationships, including insulin and adiponectin signaling while inferring new candidate organokines and their signaling modality. We also explore its utility for generating new hypotheses on cardiokine candidates and finding distal factors that may affect cardiac protein synthesis and metabolism. The predictions suggest a potential role of circulating galectin-3 (LGALS3) in regulating cardiac protein synthesis and homeostasis, which can be recapitulated in part in human-induced pluripotent stem cell-derived cardiomyocytes. This method may aid in ongoing efforts to delineate interorgan communications and endocrine networks in various areas of study.