EARLY-LIFE STRESS IN A ERA OF BEHAVIOROMICS

​Exposure to early life stress (ELS) results in complex effects on brain function and behavior that traditional reductionist approaches fail to capture in depth. Understanding this complexity requires sequencing in depth at both behavioral and molecular levels. We exposed mice to ELS via limited bedding and nesting paradigm and used complementary high-resolution technologies. (1) Behavioral sequencing combined deep-learning pose estimation (DeepLabCut) and unsupervised machine learning motif discovery (keypoint MoSeq) to identify latent structure within behavioral repertoires during auditory fear conditioning. (2) Omics sequencing involved hippocampal bulk RNA-sequencing across independent cohorts, examining individual gene expression regulation and global gene-length-dependent transcriptional patterns. Behavioral sequencing revealed that ELS-exposed animals, upon a secondary challenge, showed reduced behavioral diversity with less flexible, predictable and stereotypical, active responses. Remarkably, using multidimensional scaling, we identified ELS-exposed animals displaying control-like behavioral profiles: could those be resilient animals? RNA-sequencing showed poor reproducibility of individual gene expression across cohorts, yet revealed a consistent gene-length-dependent transcriptional decline pattern in which shorter genes are more expressed in detriment of longer ones. Overall, new technologies allow us to understand the underlying structure behind ELS exposure, moving beyond reductionist single-gene or single-behavior approaches toward understanding how brain changes shape cognitive and behavioral outcomes following early adversity, the so-called behavioralomics. By combining both large datasets, we aim to identify molecular signatures driving vulnerability and resilience after ELS, ultimately understanding what in our brain allows us to cope better or worse after trauma.