Biology subjects

Shao, X. M.

Publications and source records attributed to Shao, X. M..

6 recordsLinked to original sources

bioRxiv2023

SpliceMutr enables pan-cancer analysis of splicing-derived neoantigen burden in tumors

Aberrant alternative splicing can generate neoantigens, which can themselves stimulate immune responses and surveillance. Previous methods for quantifying splicing-derived neoantigens are limited by independent references and potential batch effects. Here, we introduce SpliceMutr, a bioinformatics approach and pipeline for identifying splicing derived neoantigens from paired tumor normal data. SpliceMutr facilitates the identification of tumor-specific antigenic splice variants, predicts MHC-binding affinity, and estimates splicing antigenicity scores per gene. By applying this tool to genomic data from The Cancer Genome Atlas (TCGA), we generate splicing-derived neoantigens and neoantigenicity scores per sample and across all cancer types and find numerous correlations between splicing antigenicity and well-established biomarkers of anti-tumor immunity. Notably, carriers of mutations within splicing machinery genes have higher splicing antigenicity, which provides support for our approach. Further analysis of splicing antigenicity in cohorts of melanoma patients treated with mono- or combined immune checkpoint inhibition suggest that the abundance of splicing antigens is reduced post-treatment from baseline in patients who progress, likely because of an immunoediting process. We also observe increased splicing antigenicity in responders to immunotherapy, which may relate to an increased capacity to mount an immune response to splicing-derived antigens. This new computational tool provides novel analytical capabilities for splicing antigenicity and is openly available for further immuno-oncologic analysis.

cancer biology↗

bioRxiv2022

Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity

Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to developing personalized cancer vaccines. Experimental validation of candidate neoepitopes is extremely resource intensive, and the vast majority of candidates are non-immunogenic, making their identification a needle-in-a-haystack problem. To address this challenge, we present computational methods for predicting MHC-I epitopes and identifying immunogenic neoepitopes with improved precision. The BigMHC method comprises an ensemble of seven pan-allelic deep neural networks trained on peptide-MHC eluted ligand data from mass spectrometry assays and transfer learned on data from assays of antigen-specific immune response. Compared with four state-of-the-art classifiers, BigMHC significantly improves the prediction of epitope presentation on a test set of 45,409 MHC ligands among 900,592 random negatives (AUROC=0.9733, AUPRC=0.8779). After transfer learning on immunogenicity data, BigMHC yields significantly higher precision than seven state-of-the-art models in identifying immunogenic neoepitopes, making BigMHC effective in clinical settings. All data and code are freely available at https://github.com/KarchinLab/bigmhc.

bioinformatics↗

bioRxiv2019

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p<2e-16) including CD8+ T cells. Notably, only 0.16% of predicted immunogenic missense mutations were observed in >2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.\n\nSynopsisWe developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

immunology↗

bioRxiv2019

Inspiratory rhythmogenic activity is burst-independent and opioid-sensitive

How mammalian neural circuits generate rhythmic activity in motor behaviors, such as breathing, walking, and chewing, remains elusive. For breathing, rhythm generation can be localized to a brainstem nucleus called the preBotzinger Complex (preBotC). Rhythmic preBotC population activity consists of small amplitude burstlets, which we hypothesize are rhythmogenic, and larger inspiratory bursts, which drive motoneuronal activity. If burstlets are rhythmogenic, opioids, analgesics that can cause profound respiratory depression, should similarly reduce burstlet frequency. In conditions where burstlets were separated from bursts in medullary slices from neonatal mice, the -opioid receptor (OR) agonist DAMGO decreased burstlet frequency. DAMGO-mediated depression was abolished by genetic deletion of ORs in a glutamatergic preBotC subpopulation and was reduced by Substance P, but not blockade of inhibitory synaptic transmission. Our findings suggest that rhythmogenesis need not rely on strong bursts of activity associated with motor output and point to strategies for ameliorating opioid-induced depression of breathing.

neuroscience↗

bioRxiv2017

Inhaled nicotine equivalent to cigarette smoking disrupts systemic and uterine hemodynamics and induces cardiac arrhythmia in pregnant rats

Maternal smoking with obligatory nicotine inhalation is associated with preterm delivery, low birth weight, fetal growth retardation and developmental defects. We tested the hypothesis that cigarette smoking-relevant nicotine inhalation during pregnancy impairs cardiovascular function and uterine hemodynamics with consequential fetal ischemia. Pregnant rats exposed to episodic inhaled nicotine via a novel lung alveolar region-targeted aerosol method produced nicotine pharmacokinetics resembling cigarette smoking in humans. This clinically relevant nicotine aerosol inhalation (NAI) induced transient reduction and irregular fluctuations in uterine artery blood flow associated with cardiac arrhythmia and high magnitude irregular fluctuations of systemic blood pressure. The arrhythmia included sinoatrial (SA) block, sinus arrest, 2o and 3o atrioventricular (A-V) block and supraventricular escape rhythm. These effects were blocked by the nicotinic receptor (nAChR) antagonist mecamylamine. Resection of the ovarian nerve, which innervates uterine blood vessels, counteracted the NAI-induced reduction in uterine blood flow. We suggest that the rapid rise pattern of arterial blood nicotine concentration stimulates and then desensitizes autonomic nAChRs leading to disruptions of cardiac function as well as systemic and uterine hemodynamics that reduces uteroplacental blood flow, a mechanism underlying maternal smoking-associated pregnancy complications and developmental disorders. These findings challenge the safety of pure nicotine inhalation, i.e., E-cigarettes.

pharmacology and toxicology↗

bioRxiv2017

Chronic intermittent nicotine delivery with lung alveolar region-targeted aerosol technology produces circadian blood pharmacokinetics in rats resembling human smokers

IntroductionCigarette smoke is an aerosol containing microparticles that carry nicotine into lung alveolar region where nicotine is rapidly absorbed into circulation. Nicotine exposure in smokers is a chronic intermittent process, with intake during wakefulness and abstinence during sleep resulting in circadian fluctuation of blood nicotine levels. Here we present a smoking-relevant nicotine exposure device and rodent model.\n\nMethodsWe developed a computer controlled integrated platform where freely moving rodents can be exposed to episodic nicotine aerosol on an investigator-designed schedule. Rats were exposed to nicotine aerosol once every half hr in the dark phase of 12/12-hr dark/light cycles for 10 days. Plasma nicotine and its metabolite cotinine levels were determined with a LC-MS/MS method.\n\nResultsWe characterized the aerosol in the breathing zone of the rodent exposure chamber. The droplet size distribution was within the respirable diameter range. The system can generate a wide range of nicotine concentrations in air that meet a variety of experimental needs. We optimized the parameters of aerosol generation and exposure: plasma nicotine and cotinine concentrations reached 30-35 ng/ml and 190-240 ng/ml, respectively. The nicotine levels and circadian patterns resembled the pharmacokinetic pattern of human smokers.\n\nConclusionsWe developed an aerosol system that can produce chronic intermittent nicotine exposure in unanesthetized and unrestrained rodents with route of administration and circadian blood pharmacokinetics resembling human smokers. This methodology is a novel tool for studies of behavior, pharmacology and toxicology of chronic nicotine exposure, nicotine addiction, tobacco-related diseases, teratogenicity, and for discovery of therapeutics.\n\nImplicationsWe developed a method and an alveolar region-targeted aerosol system that provides chronic intermittent nicotine exposure in rodents. The method produces clinically relevant animal models with the route of administration and circadian pharmacokinetics resembling human smokers. This method is a novel tool for understanding the health effects of chronic nicotine exposures such as with tobacco cigarettes, E-cigarettes and other tobacco products, for studies of pharmacology, toxicology, nicotine addiction, tobacco-related diseases, and for discovery of medications.

pharmacology and toxicology↗