Law, K. Y. T. | bioRxiv Science

bioRxiv2026

Acute Ethanol Exposure Induces Stage-Specific Bioenergetic, Mitochondrial and Neurodevelopmental Transcriptional Remodeling in Human Forebrain Progenitors

Prenatal alcohol exposure (PAE) is associated with long-term neurodevelopmental risk, yet how neuronal progenitor metabolism responds to alcohol across developmental stages remains unclear. Here, we integrated brain-isoform-focused analysis of public datasets with targeted transcriptional, translational, and functional mitochondrial assessments in human iPSC-derived cortical progenitor models representing distinct bioenergetic states. Re-analysis of prior PAE studies revealed broad metabolic gene downregulation in embryonic systems, whereas neurodevelopmental models showed limited and non-coherent transcriptional signatures. In vitro, acute ethanol exposure (AEE) induced stage-dependent transcriptional remodeling in iPSC-derived neuronal progenitor cells. Early progenitors exhibited selective upregulation of mitochondrial-associated transcripts alongside increased neuronal lineage markers. In contrast, late progenitors showed broader increases in glycolytic, lipid, and mitochondrial gene expression. Despite these transcriptional changes, mitochondrial ATP production and mitochondrial protein abundance remained unchanged in both models with altered dynamics being restricted to late progenitors. These findings indicate that ethanol exposure is associated with developmental stage-dependent neuronal remodeling of bioenergetic genes. Measurements of mitochondrial-neural frameworks after AEE showed no major alterations in either model. Overall, our results reveal a dissociation between transcriptional, translational, and functional bioenergetic outputs in models of early human neurodevelopment and highlight that transcriptional alterations of mitochondria and cellular bioenergetics under AEE should be interpreted in the context of developmental stage rather than in isolation as evidence of energetic dysfunction.

neuroscience↗

bioRxiv2020

Differential BUM-HMM: a robust statistical modelling approach for detecting RNA flexibility changes in high-throughput structure probing data

Combining RNA structure probing with high-throughput sequencing technologies has greatly enhanced our ability to analyze RNA structure at transcriptome scale. However, the high level of noise and variability encountered in these data call for the development of computational tools that robustly extract RNA structural information. Here we present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. DiffBUM-HMM is compatible with a wide variety of high-throughput RNA structure probing data, taking into consideration biological variation, sequence coverage and sequence representation biases. We demonstrate that, compared to the existing approaches, diffBUM-HMM displays higher sensitivity while calling virtually no false positives. DiffBUM-HMM analysis of ex vivo and in vivo Xist SHAPE-MaP data detected many more RNA structural differences, involving mostly single-stranded nucleotides located at or near protein-binding sites. Collectively, our analyses demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and reinforce the notion that RNA structure probing is a very powerful tool for identifying protein-binding sites.

systems biology↗