Biology subjects

Hu, K.

Publications and source records attributed to Hu, K..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

RNAi-mediated HBV antigen shutdown enhances the antiviral immune effects of PEGIFNalpha via altering T and B cell crosstalk

PEGylated interferon- (PEGIFN) demonstrates promising therapeutic outcomes against chronic hepatitis B (CHB), whereas patient response to PEGIFN therapy remains unsatisfied. Shutdown of hepatitis B virus (HBV) antigens by RNA interference (RNAi) could enhance PEGIFN efficacy in CHB patients, whereas the underlying immunological mechanisms remain obscure. We performed studies by utilizing our newly established extracellular humanized IFNAR (IFNAR-hEC) mice. An in-house constructed small interfering RNAs (GalNac-siHBV) was administrated to mice either alone or in combination with PEGIFN. The phenotypic and functional characteristics of peripheral and organ-specific immune cells were assessed by flow cytometry, ELISpot, RNA sequencing (RNA-seq), and single-cell RNA-seq (scRNA-seq) analysis. Our results demonstrated that combined treatment with PEGIFN and RNAi exerted a synergistic and prolonged inhibition of HBsAg ([~]4log10 IU/mL, vs PBS) and induced a higher incidence of HBsAg seroconversion ([~]30%), comparing with either monotreatment. Mechanistically, combined therapy improved the functionality of global T and B cells, triggered increased anti-HBs producing B cells, and enhanced IFN{gamma}-producing T cells. scRNA-seq analysis revealed that the combined therapy reduced inhibitory B cell-B cell interaction, enhanced MHC-I signaling mediated T cell-T cell communication, and improved T cell-B cell crosstalk, thus improving the functionality of T and B cells. Enhanced MHC-II signaling networks across B cells and hepatocytes/Cd8+ T cells further promoted HBsAg seroconversion in the combined treatment groups. These results together provided scientific rationale and lessons for the combination of the two towards better therapeutic efficacy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/626539v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@84785aorg.highwire.dtl.DTLVardef@f984fborg.highwire.dtl.DTLVardef@189a966org.highwire.dtl.DTLVardef@d081f9_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIShutting down HBsAg through RNA interference augmented the antiviral immune effects of PEGIFN in chronic HBV-carrier IFNAR-hEC mice. C_LIO_LICombined RNAi plus PEGIFN augmented the functionality of T cells, promoted B cell activation and class switch, but also exerted some suppressive effects on B cells. C_LIO_LIReduced inhibitory B cell-B cell interaction, enhanced MHC-I signaling between T cells and T cells, and improved T cell-B cell crosstalk, improved the functionality of T cells and B cells. C_LIO_LIEnhanced MHC-II signaling networks across B cells and hepatocytes/ Cd8+ T cells further promoted HBsAg seroconversion in RNAi plus PEGIFN combined treatment groups. C_LI

microbiology↗

bioRxiv2024

Transcriptome-based identification of long noncoding RNAs (lncRNAs) across the genome of Anopheles gambiae

Anopheles gambiae is a primary malaria vector mosquito in Africa. RNA-seq based transcriptome analysis has been widely used to study gene expression underlying mosquito life traits such as development, reproduction, immunity, metabolism, and behavior. While it is well known that long non-coding RNAs (lncRNAs) are expressed ubiquitously in transcriptomes across metazoans, lncRNAs remain relatively underexplored in mosquitoes including their identity, expression profiles, and biological functions. In this study, publicly available RNA-seq datasets were leveraged to identify lncRNAs across diverse contexts, including whole mosquitoes, mosquito cells or tissues including midguts, salivary glands, and hemocytes, as well as under different physiological conditions including sugar-feeding, blood-feeding, bacterial challenges, and Plasmodium infections. Across this pool of transcriptomes, 2684 unique lncRNA genes, comprising 4082 transcripts, were identified. Following their identification, these lncRNA genes were integrated into the mosquito transcriptome annotation, which was then used as a reference to analyze both mRNAs and lncRNAs for transcriptional dynamics in different conditions. Like mRNAs, lncRNAs exhibited context-dependent expression patterns. Co-expression networks constructed using weighted gene co-expression network analysis (WGCNA) highlighted the interconnections among lncRNAs and mRNAs. Furthermore, we identified polysome-associated lncRNAs within polysome-captured transcripts, suggesting their involvement in translation regulation and coding capacity for micropeptides. A published ChIP-seq dataset was explored to correlate epigenetic signatures with transcriptional activities of lncRNAs. Overall, our analysis demonstrated that lncRNAs are transcribed alongside mRNAs in various biological contexts. Given their prevalence in the transcriptome, incorporating lncRNAs into transcriptome analyses will enhance our understanding of their functions, shedding light on their regulatory roles in An. gambiae biology.

molecular biology↗

bioRxiv2024

Non-Optical, Label-free Electrical Capacitance Imaging of Microorganisms

Many fundamental insights into microbiology have come from imaging, which is typically synonymous with optical techniques. However, the sample preparation needed for many optical microscopy methods such as labeling, fixing, or genetic modification, limits the range of species and environments we can investigate. Here we demonstrate the use of electrical capacitance measurements as a non-optical method for imaging live microbial samples. In electrical capacitance imaging (ECI), samples are positioned in contact with a semiconductor sensor array, and localized capacitance measurements are made across the array. From these measurements, we generate textured images of a variety of microbial colonies. We determine that capacitance is correlated with local sample thickness by comparing ECI data to 3D confocal scans. We further illustrate with ECI that a difference in capacitance signal allows microbial species to be spatially distinguished in co-culture conditions. In order to highlight the versatility of our system, we capture the cross-sectional development of floating pellicle biofilms in a liquid culture at millimeter length scales during weeks-long time-lapse experiments. These novel results establish a new low-cost and portable platform which can be used for spatially and temporally resolved experiments in diverse environments with a wide variety of microbial species. IMPORTANCEMicrobes live in diverse environments, and occupy biological roles across many timescales. Investigating the full scope of microbial activity requires imaging systems appropriate to each context. Though optical microscopy is powerful, the use of light, lenses, and other hardware limits where it can be applied. At the same time, existing non-optical imaging methods are frequently destructive to samples and require extensive equipment. In this paper we present a non-optical imaging system that is small, cheap, requires no sample labeling, and is compatible with a variety of microbial species. Our system uses semiconductor chips to measure the inherent material properties of a sample with spatial sensitivity, producing images of microbes contrasted against their environment and each other. Our technique captures label-free, micrometer-resolution images with a pocket-sized device, enabling microbiological imaging experiments in new environments with new species.

microbiology↗

bioRxiv2024

Cell-type-specific splicing of transcription regulators and Ptbp1 by Rbfox1/2/3 in the developing neocortex

How master splicing regulators crosstalk with each other and to what extent transcription regulators are differentially spliced remain unclear in the developing brain. Here, cell-type-specific RNA-Seq of the developing neocortex uncover that transcription regulators are enriched for differential splicing, altering protein isoforms or inducing nonsense-mediated mRNA decay. Transient expression of Rbfox proteins in radial glia progenitors induces neuronal splicing events preferentially in transcription regulators such as Meis2 and Tead1. Surprisingly, Rbfox proteins promote the inclusion of a mammal-specific alternative exon and a previously undescribed poison exon in Ptbp1. Simultaneous ablation of Rbfox1/2/3 in the neocortex downregulates neuronal isoforms and disrupts radial neuronal migration. Furthermore, the progenitor isoform of Meis2 promotes Tgfb3 transcription, while the Meis2 neuron isoform promotes neuronal differentiation. These observations indicate that transcription regulators are differentially spliced between cell types in the developing neocortex.

developmental biology↗

bioRxiv2024

Attentional priority and limbic activity favor gains over losses

Prospect theory has suggested that decisions reflect a bias toward avoidance of loss compared to equivalent gains. In our study we examine whether a similar bias is found in decisions regarding whether a loss associated stimulus or a gain associated stimulus is given priority in perception. We find that for most people, gains are given priority over loss in the decision of which of the two stimuli occurs first. We also ask whether gains are reflected in greater activity in limbic systems related to emotion. In an fMRI study, we find that most people show greater emotional response to gains, not losses. We consider how these findings might be related to risk aversion in studies of decision making.

neuroscience↗

bioRxiv2024

Longevity or Well-being? A Dual-Dimension Structure of Neuroticism

The development of personality traits is often viewed as evolutionarily adaptive. Current neuroticism research, however, predominantly highlights its negative health impacts, neglecting its potential evolutionary advantages. We propose that neuroticisms inter-individual variability can be structured into two distinct geometric dimensions. One, named the Emotional Reactivity-Instability/Distress Spectrum (ERIS), correlates strongly with longevity and is associated with chronic diseases and risk-averse lifestyle. This dimension is underpinned by evolutionarily conserved subcortical brain regions and genes. The other, resembling the overall neuroticism score, is primarily linked to mental and stress-related disorders, as well as life satisfaction. It involves higher-order emotional brain regions and is genetically enriched in human-accelerated regions. Collectively, these dimensions represent a dual-strategy personality framework that optimizes survival and well-being, with the former being evolutionarily conservative and the latter potentially a unique human adaptation.

neuroscience↗

bioRxiv2024

Discovery and Engineering of Retrons for Precise GenomeEditing

Retrons are promising gene editing tools because they can produce multi-copy single-stranded DNA in cells via self-primed reverse transcription. However, their potential for inserting genetic cargos in eukaryotes remains largely unexplored. Here we report the discovery and engineering of highly efficient retron-based gene editors for mammalian cells and vertebrates. Through bioinformatic analysis of metagenomic data and functional screening, we identified novel retron reverse transcriptases (RTs) that are highly active in mammalian cells. Rational design further improved the editing efficiency to levels comparable with conventional single-stranded oligodeoxynucleotide donors but from a genetically encoded cassette. Small molecule inhibitors of non-homologous end joining factors and Cas9-DNA repair protein fusions further increase homology-directed repair. Retron editors also exhibited robust activity with Cas12a nuclease and Cas9 nickase, expanding the genomic target scope and bypassing the need for a DNA double-stranded break. Using a rationally engineered retron editor, we incorporate a split GFP epitope tag for live cell imaging. Finally, we develop an all-RNA delivery strategy to enable DNA-free gene editing in cells and vertebrate embryos. This work establishes retron editors as a versatile and efficient tool for precise gene editing, offering new opportunities for biotechnology and biomedical research.

molecular biology↗

bioRxiv2024

The initiation and early development of apical-basal polarity in Toxoplasma gondii

The human parasite Toxoplasma gondii has a distinctive body plan with a well-defined polarity. In the apical complex, the minus ends of the 22 cortical microtubules are anchored to the apical polar ring, a putative microtubule-organizing center. The basal complex caps and constricts the parasite posterior end, and is critical for cytokinesis. How this apical-basal polarity axis is initiated was unknown. Here we examined the development of the apical polar ring and the basal complex in nascent daughters using expansion microscopy. We found that different substructures in the apical polar ring have different sensitivity to stress. In addition, apical-basal differentiation is already established upon nucleation of the cortical microtubule array: arc forms of the apical polar ring and basal complex associate with opposite ends of the microtubules. As the construction of the daughter framework progresses towards the centrioles, the apical and the basal arcs co-develop in striking synchrony ahead of the microtubule array, and close into a ring-form before all the microtubules are nucleated. We also found that two apical polar ring components, APR2 and KinesinA, act synergistically. The removal of each protein individually has modest to no impact on the lytic cycle. However, the loss of both results in abnormalities in the microtubule array and highly reduced plaquing and invasion efficiency.

cell biology↗

bioRxiv2024

Beyond Static Models: The Dynamic Interplay of Facial Emotions and Attentional Scope

The interplay between emotion and attention has long been intensely scrutinized, with competing theories proposing divergent mechanisms. Building on our previous work, here we present evidence that refines these perspectives, revealing a nuanced, temporally dynamic relationship between emotional stimuli and attentional focus. Using a modified Flanker task with facial emotion cues, we demonstrate that the effects of emotional stimuli on attention evolve over time, contrary to traditional fixed-effect assumptions. Our results show distinct temporal patterns: Neutral faces elicited typical flanker effects initially, but only interference persisted later. Early-stage happy faces amplified flanker facilitation but not interference, while threat faces augmented flanker interference but not facilitation. In the late stage, flanker facilitation disappeared across all emotion conditions, and interference patterns converged, mirroring the neutral face condition. These findings indicate emotions influence on attention is more complex and dynamic than previously recognized, potentially reflecting learning or habituation processes. We propose a new framework for understanding emotion-attention interactions that transcends traditional dichotomies of attention focus and approach-avoidance, offering a more nuanced perspective on this critical cognitive interface.

neuroscience↗

bioRxiv2024

Specialized signaling centers direct cell fate and spatial organization in a limb organoid model

Specialized signaling centers orchestrate robust development and regeneration. Limb morphogenesis, for instance, requires interactions between the mesoderm and the signaling center apical-ectodermal ridge (AER), whose properties and role in cell fate decisions have remained challenging to dissect. To tackle this, we developed mouse embryonic stem cells (mESCs)-based heterogeneous cultures and a limb organoid model, termed budoids, comprising cells with AER, surface ectoderm, and mesoderm properties. mESCs were first induced into heterogeneous cultures that self-organized into domes in 2D. Aggregating these cultures resulted in formation of limb bud-like structures in 3D, exhibiting chondrogenesis-based symmetry breaking and elongation. Using our organoids and quantitative in situ expression profiling, we uncovered that AER-like cells support nearby limb mesoderm and fibroblast identities while enhancing tissue polarization that permits distant cartilage formation. Together, our findings provide a powerful model to study aspects of limb morphogenesis, and reveal the ability of signaling center AER cells to concurrently modulate cell fate and spatial organization.

developmental biology↗

bioRxiv2024

Mapping Individual Differences in the Topological Landscape of Naturalistic Brain Dynamics

Psychiatry seeks to unravel brain dysfunction and individual differences in real-world contexts. Naturalistic stimuli, like movie watching, are increasingly recognized for eliciting complex, context-dependent neural activity with high ecological validity. Yet, current methods often rely on standard paradigms that average data across time, limiting the full potential of such stimuli. Here, we present STIM, a Topological Data Analysis-based framework designed to dynamically track how individuals integrate complex contexts in real time. Applied to large-sample fMRI data from movie watching, STIM constructs a robust low-dimensional dynamical landscape that reflects group consensus while probing individual variations at both global (spanning narratives) and local (within specific narratives) levels. At the global level, individual differences emerge along a center-periphery gradient in the dynamical landscape, which significantly predicts fluid intelligence, underscoring the importance of neural adaptability and diversity. At finer scales, local geometric features correlate with context-specific psychological traits beyond cognition. STIM also captures developmental changes in the dynamical landscape and reveals abnormalities in conditions such as autism. These findings demonstrate that STIM leverages the rich information from movie stimuli and fMRI recordings as neural probes to assess individual differences in cognition and mental health.

neuroscience↗

bioRxiv2024

Assessing drug uptake and response differences in 2D and 3D cellular environments using stimulated Raman scattering microscopy

The architecture of cell culture--two-dimensional (2D) versus three-dimensional (3D)--significantly impacts various cellular factors, including cell-cell interactions, nutrient and oxygen gradients, metabolic activity, and gene expression profiles. This can result in different cellular responses during cancer drug treatment, with 3D-cultured cells often exhibiting higher resistance to chemotherapeutic drugs. While various genetic and proteomic analyses have been employed to investigate the underlying mechanisms of this increased resistance, complementary techniques that provide experimental evidence of spatial molecular profiling data are limited. Stimulated Raman scattering (SRS) microscopy has demonstrated its capability to measure both intracellular drug uptake and growth inhibition. In this work, we applied three-band SRS imaging to 2D and 3D cell cultures and provided a comparative analysis of drug uptake and response with the goal of understanding whether the difference in drug uptake explains the drug resistance in 3D culture compared to 2D. Our investigations revealed that despite similar intracellular drug levels in 2D and 3D A549 cells during lapatinib treatment, the growth of 3D spheroids is less impacted, supporting an enhanced drug tolerance in the 3D microenvironment. We further elucidated drug penetration patterns and the resulting heterogeneous cellular responses across different spheroid layers. Additionally, we investigated the role of the extracellular matrix in modulating drug delivery and cell response, and we discovered that limited drug penetration in 3D could also contribute to lower drug response. Our study provides valuable insights into the intricate mechanisms of increased drug resistance in 3D tumor models during cancer drug treatments.

cell biology↗

bioRxiv2024

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno, and Aytan-Aktug), an ML baseline, and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amox-clav, cefoxitin, ceftazidime, and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, C. jejuni and E. faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae, and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

bioinformatics↗

bioRxiv2024

NeuralTE: an accurate approach for Transposable Element superfamily classification with multi-feature fusion

MotivationClassifying Transposable Elements (TEs) at the superfamily level offers deeper insights into species variation and evolution. Recent advancements in third-generation sequencing technologies have made a large number of genomes from non-model species becoming available. However, existing TE classification methods suffer from several limitations, including the necessity to train multiple hierarchical classification models, the incapacity to perform classification at the superfamily level, and deficiencies in both accuracy and robustness. Therefore, there is an urgent need for an accurate TE classification method to improve genome annotation. ResultsIn this study, we develop NeuralTE, a deep learning method designed to classify transposons at the superfamily level. To achieve accurate TE classification, we identify various structural features of transposons, and use different combinations of k-mers for terminal repeats and internal sequences to uncover distinct patterns. Evaluation on all transposons from Repbase shows that NeuralTE outperforms existing deep learning, machine learning, and homology-based methods in classifying TEs. Testing on the transposons from novel species highlights the superior performance of NeuralTE compared to existing methods. We also conduct TE annotation experiments on rice using different classification tools, and the results show that NeuralTE achieves annotations nearly identical to the gold standard, highlighting its robustness and accuracy in classifying transposons. AvailabilityNeuralTE is publicly available at https://github.com/CSU-KangHu/NeuralTE.

bioinformatics↗

bioRxiv2024

An intracranial dissection of human escape circuits

Predators attack at different spatiotemporal scales, spurring prey to elicit escape responses that range from simple motor reactions and strategic planning that involve more complex cognitive processes. Recent work in humans suggests that escape relies on two distinct circuits: the reactive and cognitive fear circuits. However, the specific involvement of these two circuits in different stages of human escaping remains poorly characterized. In this study, we recorded intracranial electroencephalography (iEEG) from epilepsy patients while they performed a modified flight initiation distance (FID) task. We found brain regions in the cognitive fear circuit, including the ventromedial prefrontal cortex and hippocampus, encoded the threat level during the information processing stage. The actual escaping stage, especially under rapid attack, prominently activated areas within the reactive fear circuit, including the midcingulate cortex and amygdala. Furthermore, we observed a negative correlation between the high gamma activity (HGA) of the amygdala and the HGA of the vmPFC and HPC under rapid attacks. This indicates that the amygdala may suppress the activity of the cognitive fear circuit under rapid attacks, enabling the organism to react quickly to ensure survival under the imminent threat. These findings highlight the distinct roles of the reactive and cognitive fear circuits in human escaping and provide accounts for the importance of fear in human survival decisions.

neuroscience↗

bioRxiv2023

Overlapping and Distinct Mechanisms of Effective Neoantigen Cancer Vaccines and Immune Checkpoint Therapy

The goal of therapeutic cancer vaccines and immune checkpoint therapy (ICT) is to eliminate cancer by expanding and/or sustaining T cells with anti-tumor capabilities. However, whether cancer vaccines and ICT enhance anti-tumor immunity by distinct or overlapping mechanisms remains unclear. Here, we compared effective therapeutic tumor-specific mutant neoantigen (NeoAg) cancer vaccines with anti-CTLA-4 and/or anti-PD-1 ICT in preclinical models. Both NeoAg vaccines and ICT induce expansion of intratumoral NeoAg-specific CD8 T cells, though the degree of expansion and acquisition of effector activity was much more substantial following NeoAg vaccination. Further, we found that NeoAg vaccines are particularly adept at inducing proliferating and stem-like NeoAg-specific CD8 T cells. Single cell T cell receptor (TCR) sequencing revealed that TCR clonotype expansion and diversity of NeoAg-specific CD8 T cells relates to their phenotype and functional state associated with specific immunotherapies employed. Effective NeoAg vaccines and ICT required both CD8 and CD4 T cells. While NeoAg vaccines and anti-PD-1 affected the CD4 T cell compartment, it was to less of an extent than observed with anti-CTLA-4, which notably induced ICOS+Bhlhe40+ Th1-like CD4 T cells and, when combined with anti-PD-1, a small subset of Th2-like CD4 T cells. Although effective NeoAg vaccines or ICT expanded intratumoral M1-like iNOS+ macrophages, NeoAg vaccines expanded rather than suppressed (as observed with ICT) M2-like CX3CR1+CD206+ macrophages, associated with the vaccine adjuvant. Further, combining NeoAg vaccination with ICT induced superior efficacy compared to either therapy in isolation, highlighting the utility of combining these modalities to eliminate cancer. HighlightsO_LINeoAg cancer vaccines utilize distinct mechanisms from CTLA-4 or PD-1 ICT C_LIO_LINeoAg vaccines induce TCF1+ stem-like and proliferating NeoAg-specific CD8 T cells C_LIO_LICD8 TCR clonotype expansion relates to phenotype and functional state associated with immunotherapy C_LIO_LINeoAg vaccines induce partially distinct macrophage remodeling from ICT C_LIO_LINeoAg vaccines synergize with ICT, exceeding combination CTLA-4/PD-1 ICT efficacy C_LI

immunology↗

bioRxiv2023

Predicting the Trend of SARS-CoV-2 Mutation Frequencies Using Historical Data

As the SARS-CoV-2 virus rapidly evolves, predicting the trajectory of viral variations has become a critical yet complex task. A deep understanding of future mutation patterns, in particular the mutations that will prevail in the near future, is vital in steering diagnostics, therapeutics, and vaccine strategies in the coming months. In this study, we developed a model to forecast future SARS-CoV-2 mutation surges in real-time, using historical mutation frequency data from the USA. To improve upon the accuracy of traditional time-series models, we transformed the prediction problem into a supervised learning framework using a sliding window approach. This involved breaking the time series of mutation frequencies into very short segments. Considering the time-dependent nature of the data, we focused on modeling the first-order derivative of the mutation frequency. We predicted the final derivative in each segment based on the preceding derivatives, employing various machine learning methods, including random forest, XGBoost, support vector machine, and neural network models, in this supervised learning setting. Empowered by the novel transformation strategy and the high capacity of machine learning models, we witnessed low prediction error that is confined within 0.1% and 1% when making predictions for future 30 and 80 days respectively. In addition, the method also led to a notable increase in prediction accuracy compared to traditional time-series models, as evidenced by lower MAE, and MSE for predictions made within different time horizons. To further assess the methods effectiveness and robustness in predicting mutation patterns for unforeseen mutations, we categorized all mutations into three major patterns. The model demonstrated its robustness by accurately predicting unseen mutation patterns when training on data from two pattern categories while testing on the third pattern category, showcasing its potential in forecasting a variety of mutation trajectories. To enhance accessibility and utility, we built our methodology into an R-shiny app (https://swdatpredicts.shinyapps.io/rshiny_predict/), a tool with potential applicability in studying other infectious diseases, thus extending its relevance beyond the current pandemic.

bioinformatics↗

bioRxiv2023

RPL22 is a tumor suppressor in MSI-high cancers and a key splicing regulator of MDM4

Microsatellite instability high (MSI-H) tumors are malignant tumors that, despite harboring a high mutational burden, often have intact TP53. One of the most frequent mutations in MSI-H tumors is a frameshift mutation in RPL22, a ribosomal protein. Here, we identified RPL22 as a modulator of MDM4 splicing through an alternative splicing switch in exon 6. RPL22 loss increases MDM4 exon 6 inclusion, cell proliferation, and augments resistance to the MDM inhibitor Nutlin-3a. RPL22 represses expression of its paralog, RPL22L1, by mediating the splicing of a cryptic exon corresponding to a truncated transcript. Therefore, damaging mutations in RPL22 drive oncogenic MDM4 induction and reveal a common splicing circuit in MSI-H tumors that may inform therapeutic targeting of the MDM4-p53 axis and oncogenic RPL22L1 induction.

cancer biology↗