Speaker
Description
Mitochondria harbor a largely unexplored layer of genetic information beyond the 37 canonical genes currently represented in standard annotations. Recent studies have revealed that an increasing number of non-canonical mitochondrial transcripts and mitochondrial-derived microproteins (MDPs) can act as biologically active molecules involved in cellular stress responses, metabolic regulation, and resilience. However, these elements remain largely invisible to conventional transcriptomic and proteomic analyses, limiting our understanding of their contribution to human health.
Here, we establish an annotation-extended multi-omics framework to systematically uncover hidden mitochondrial transcripts and MDPs within existing large-scale omics datasets. Rather than generating new data, we leverage publicly available and in-house resources by expanding standard genome and protein references with custom-curated sequences representing known and predicted non-canonical mitochondrial elements. This enables their detection within established computational workflows using nf-core pipelines.
We apply this approach to stress-related psychiatric disorders, including post-traumatic stress disorder (PTSD), major depressive disorder (MDD), and related conditions, where mitochondrial dysfunction has emerged as an important contributor to altered cellular adaptation and stress resilience. Preliminary analyses from two distict PTSD studies demonstrate that extending conventional annotations enables the identification of additional mitochondrial transcripts and candidate MDPs that would remain undetected using standard workflows.
A major remaining challenge is the validation and genomic localization of candidate MDPs, as conventional annotation approaches do not reliably resolve non-canonical open reading frames within the mitochondrial genome. To address this, we will integrate publicly available ribosome profiling (Ribo-seq) datasets to provide orthogonal evidence of active translation and improve the assignment of mitochondrial-derived coding regions.
Together, this project establishes a scalable computational framework for exploring the hidden mitochondrial transcriptome and microproteome across large-scale omics resources. By integrating transcriptomics, proteomics, and future Ribo-seq analyses, we aim to generate a more complete understanding of mitochondrial contributions to stress biology. The presented workflow provides a reusable strategy for uncovering non-canonical mitochondrial elements across diverse disease contexts without requiring additional data generation.