Biology subjects

Choi, P.

Publications and source records attributed to Choi, P..

7 recordsLinked to original sources

bioRxiv2026

Central Glucagon-Like Peptide-1 Agonism Ameliorates Colonic Inflammation in a Murine Model of Inflammatory Bowel Disease

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have revolutionized the treatment of obesity and type 2 diabetes (T2D). These drugs also have anti-inflammatory effects that have recently been extended to the treatment of inflammatory bowel disease (IBD). Whether the mechanism underlying this benefit involves a direct action on the intestine or instead is mediated via GLP-1Rs in the brain, however, is unknown. Based on evidence implicating the brain as the target for the anti-obesity and anti-inflammatory effects of GLP-1RAs, we sought to determine if central GLP-1R signaling is sufficient to explain its intestinal anti-inflammatory effects. To this end, we compared the efficacy of peripheral vs central administration of the GLP-1RA, semaglutide (SEMA), in diet-induced obese (DIO) mice with dextran sodium sulfate (DSS)-induced colitis. We report that compared to vehicle, both colitis severity and histopathological injury following DSS administration are reduced by peripheral administration of SEMA. We also report that this benefit is largely recapitulated by intracerebroventricular administration of SEMA at a low dose that, when administered peripherally, is below that needed to affect food intake and body weight. We conclude that in a mouse model of colitis, activation of GLP-1Rs in the brain is sufficient to explain the intestinal anti-inflammatory effect conferred by GLP-1R agonism.

neuroscience↗

bioRxiv2026

Detection of a sequence feature for recursive splicing

RNA splicing is directed by cis-acting sequence signals interacting with trans-acting factors to remove introns from newly transcribed pre-mRNA, joining exons to generate mature mRNA. Splicing happens far more often than observed exon-exon junctions in mRNA. As one contributing process, the spliceosome progressively removes large introns in small segments by recursive splicing, instead of splicing the whole intron at one time. However, the cis-acting sequences associated with recursive splicing have not been identified. Using probabilistic mixture models, we found that recursive splicing occurs more frequently in first introns, which are typically longer, and exhibit a distinct CG-rich sequence feature in the sequences flanking the upstream 5SS, and depletion of CGs in the downstream polypyrimidine tract. Remarkably, recursive splicing is also more frequent in downstream introns of genes containing first introns with these properties. Mechanistically, these data suggest that early events in RNA synthesis and processing influence the prevalence of recursive splicing for the rest of the transcript. Finally, we developed a sequence-dependent classifier for recursive splicing, which we tested with a novel medium-throughput primer extension assay. In summary, the usage of recursive splicing sites is established at the beginning of RNA synthesis through newly-identified sequence motifs flanking both ends of the first intron.

genomics↗

bioRxiv2025

Sources of non-uniform coverage in short-read RNA-Seq data

The origin of several normal cellular functions and related abnormalities can be traced back to RNA splicing. As such, RNA splicing is currently the focus of a vast array of studies. To quantify the transcriptome, short-read RNA-Seq remains the standard assay. The primary technical artifact of RNA-Seq library prep, which severely interferes with analysis, is extreme non-uniformity in coverage across transcripts. This non-uniformity is present in both bulk and single-cell RNA-Seq and is observed even when the sample contains only full-length transcripts. This issue dramatically affects the accuracy of isoform-level quantification of multi-isoform genes. Understanding the sources of this non-uniformity is critical to developing improved protocols and analysis methods. Here, we explore eight potential sources of non-uniformity. We demonstrate that it cannot be explained by one factor alone. We performed targeted experiments to investigate the effect of fragment length, PCR ramp rate, and ribosomal depletion. We assessed existing data sets with varying sample quality, PCR cycle number, reverse transcriptase, and technical or biological replicates. We found evidence that interference of reverse transcription by secondary structure is unlikely to be the major contributing factor, that rRNA pull-down methods do not cause non-uniformity, that PCR ramp rate does not substantially impact non-uniformity, and that shorter fragments do not reduce non-uniformity. All these findings contradict prior publications or recommendations.

bioinformatics↗

bioRxiv2025

Generative modeling for RNA splicing predictions and design

Alternative splicing (AS) of pre-mRNA plays a crucial role in tissue-specific gene regulation, with disease implications due to splicing defects. Predicting and manipulating AS can therefore uncover new regulatory mechanisms and aid in therapeutics design. We introduce TrASPr+BOS, a generative AI model with Bayesian Optimization for predicting and designing RNA for tissue-specific splicing outcomes. TrASPr is a multi-transformer model that can handle different types of AS events and generalize to unseen cellular conditions. It then serves as an oracle, generating labeled data to train a Bayesian Optimization for Splicing (BOS) algorithm to design RNA for condition-specific splicing outcomes. We show TrASPr+BOS outperforms existing methods, enhancing tissue-specific AUPRC by up to 1.8 fold and capturing tissue-specific regulatory elements. We validate hundreds of predicted novel tissue-specific splicing variations and confirm new regulatory elements using dCas13. We envision TrASPr+BOS as a light yet accurate method researchers can probe or adopt for specific tasks.

bioinformatics↗

bioRxiv2024

A Deep Dive into Statistical Modeling of RNA Splicing QTLs Reveals New Variants that Explain Neurodegenerative Disease

Genome-wide association studies (GWAS) have identified thousands of putative disease causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a new pipeline for sQTL discovery. MAJIQTL includes two new statistical methods: a weighted multiple testing approach for sGene discovery and a model for sQTL effect size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the novel variant rs582283 in Alzheimers disease. Using antisense oligonucleotides, we validate this variants effect by blocking the implicated YBX3 binding site, leading to exon skipping in the gene MS4A3.

genomics↗

bioRxiv2024

PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation Model

Pathogen identification is pivotal in diagnosing, treating, and preventing diseases, crucial for controlling infections and safeguarding public health. Traditional alignment-based methods, though widely used, are computationally intense and reliant on extensive reference databases, often failing to detect novel pathogens due to their low sensitivity and specificity. Similarly, conventional machine learning techniques, while promising, require large annotated datasets and extensive feature engineering and are prone to overfitting. Addressing these challenges, we introduce PathoLM, a cutting-edge pathogen language model optimized for the identification of pathogenicity in bacterial and viral sequences. Leveraging the strengths of pre-trained DNA models such as the Nucleotide Transformer, PathoLM requires minimal data for fine-tuning, thereby enhancing pathogen detection capabilities. It effectively captures a broader genomic context, significantly improving the identification of novel and divergent pathogens. We developed a comprehensive data set comprising approximately 30 species of viruses and bacteria, including ESKAPEE pathogens, seven notably virulent bacterial strains resistant to antibiotics. Additionally, we curated a species classification dataset centered specifically on the ESKAPEE group. In comparative assessments, PathoLM dramatically outperforms existing models like DciPatho, demonstrating robust zero-shot and few-shot capabilities. Furthermore, we expanded PathoLM-Sp for ESKAPEE species classification, where it showed superior performance compared to other advanced deep learning methods, despite the complexities of the task.

bioinformatics↗

bioRxiv2022

ATG9 vesicles comprise the seed membrane of mammalian autophagosomes

During autophagosome biogenesis, the incorporation of transmembrane proteins into the expanding phagophore is not readily observed. In addition, the membrane surface area of the organelle expands rapidly, while the volume of the autophagosome is kept low. Several recent studies have suggested a model of membrane expansion that explains how these attributes are maintained. The autophagosome expands predominantly through the direct protein-mediated transfer of lipids through the lipid transfer protein ATG2. As these lipids are only introduced into the cytoplasmic-facing leaflet of the expanding phagophore, full membrane growth also requires lipid scramblase activity. ATG9 has been demonstrated to harbor scramblase activity and is essential to autophagosome formation, however if and when it is integrated into mammalian autophagosomes remains unclear. Here we show that in the absence of lipid transport, ATG9 vesicles are already fully competent to collect proteins normally found on mature autophagosomes, including LC3-II. Further, through the novel use of styrene-maleic acid lipid particles as a nanoscale interrogation of protein organization on intact membranes, we show that ATG9 is fully integrated in the same membranes as LC3-II, even on maturing autophagosomes. The ratios of these two proteins at different stages of maturation demonstrate that ATG9 proteins are not continuously integrated, but rather are present on the seed vesicles only and become diluted in the rapidly expanding autophagosome membrane. Thus, ATG9 vesicles are the seed membrane from which mammalian autophagosomes form.

cell biology↗