Biology subjects

Cherupally, V. K.

Publications and source records attributed to Cherupally, V. K..

2 recordsLinked to original sources

bioRxiv2025

Combining Machine Learning and Multiplexed, In Situ Profiling to Engineer Cell Type and Behavioral Specificity

A promising strategy for the precise control of neural circuits is to use cis-regulatory enhancers to drive transgene expression in specific cells. However, enhancer discovery faces key challenges: low in vivo success rates, species-specific differences in activity, challenges with multiplexing adeno-associated viruses (AAVs), and the lack of spatial detail from single-cell sequencing. In order to accelerate enhancer discovery for the dorsal spinal cord--a region critical for pain and itch processing--we developed an end-to-end platform, ESCargoT (Engineered Specificity of Cargo Transcription), combining machine learning (ML)-guided enhancer prioritization, modular AAV assembly, and multiplexed, in situ screening. Using cross-species chromatin accessibility data, we trained ML models to predict enhancer activity in oligodendrocytes and in 15 dorsal horn neuronal subtypes. We first demonstrated that an initial enhancer, Excit-1, targeted excitatory dorsal horn neurons and drove reversal of mechanical allodynia in an inflammatory pain model. To enable parallel profiling of a 27-enhancer-AAV library delivered intraspinally in mice, we developed a Spatial Parallel Reporter Assay (SPRA) by integrating a novel Golden-Gate assembly pipeline with multiplexed, in situ screening. Regression adjustment for spatial confounding enabled specificity comparisons between enhancers, demonstrating the ability to screen enhancers targeting diverse cell types (oligodendrocytes, motoneurons, dorsal neuron subtypes) in one experiment. We then validated two candidates, targeting Exc-LMO3 and Exc-SKOR2 neurons, respectively. In a companion paper by Noh et al, our colleagues show that the functional specificity of the Exc-SKOR2-targeting enhancer, unlike Excit-1, is capable of blocking the sensation of chemical itch in mice. These enhancers were derived from the macaque genome but displayed functional sensitivity in mice. This platform enables spatially resolved, multiplexed in vivo enhancer profiling to accelerate discovery of cell-targeting tools and gene therapy development.

neuroscience↗

bioRxiv2024

Ancestral Sequence Reconstruction as a tool to detect and study de novo gene emergence

New protein-coding genes can evolve from previously non-coding genomic regions through a process known as de novo gene emergence. Evidence suggests that this process has likely occurred throughout evolution and across the tree of life. Yet, confidently identifying de novo emerged genes remains challenging. Ancestral Sequence Reconstruction (ASR) is a promising approach for inferring whether a gene has emerged de novo or not, as it can enable us to inspect whether a given genomic locus ancestrally harbored protein-coding capacity. However, the use of ASR in the context of de novo emergence is still in its infancy and its capabilities, limitations, and overall potential are largely unknown. Notably, it is difficult to formally evaluate the protein-coding capacity of ancestral sequences, particularly when new gene candidates are short. How well-suited is ASR as a tool for the detection and study of de novo genes? Here, we address this question by designing an ASR workflow incorporating different tools and sets of parameters and by introducing a formal criterion that allows to estimate, within a desired level of confidence, when protein-coding capacity originated at a particular locus. Applying this workflow on [~]2,600 short, annotated budding yeast genes (<1,000 nucleotides), we found that ASR robustly predicts an ancient origin for most widely conserved genes, which constitute "easy" cases. For less robust cases, we calculated a randomization-based empirical P-value estimating whether the observed conservation between the extant and ancestral reading frame could be attributed to chance. This formal criterion allowed us to pinpoint a branch of origin for most of the less robust cases, identifying 33 genes that can unequivocally be considered de novo originated since the split of the Saccharomyces genus, including 20 S. cerevisiae-specific genes. We find that the remaining, equivocal cases, may be explained by different evolutionary scenarios including rapid evolution and multiple losses, as well as a very recent de novo origin. Overall, our findings suggest that ASR is a valuable tool to study de novo gene emergence but should be applied with caution and awareness of its limitations.

evolutionary biology↗