Biology subjects

Liu, P.

Publications and source records attributed to Liu, P..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

A single-cell map of hypertension

Hypertension is a leading risk factor for disease burden and death worldwide. Several organ systems are involved in the development of hypertension, which contributes to stroke, heart disease, and kidney disease. Despite the broad health relevance, our understanding of the molecular landscape in hypertension is limited and lags other major diseases. Here we report an extensive analysis of the molecular landscape in hypertension and its end-organ damage and uncover novel mechanisms linking human genetic variants to the development of these diseases. We obtained single-nucleus RNA-seq (612,984 nuclei), single-nucleus ATAC-seq (179,637 nuclei), or spatial transcriptome data from five organs (hypothalamus, kidney, heart, 3rd order mesenteric artery, middle cerebral artery) in three mouse and rat models under twelve experimental conditions. More than one third of all hypertension research in animal models involves these three models. We identified both model-specific and convergent responses in cell types, genes, and pathways. By integrating our data with human genomic data, we partitioned the blood pressure and end-organ damage traits into cell type-specific transcriptional contributions and cell types common across multiple traits. Using genomic editing in animal models and human induced pluripotent stem cells, we extended key findings and identified new mechanisms linking human genetic variants to the development of hypertension and related renal injury. We anticipate that our rich data sets and findings will broadly drive forward the research of hypertension and hypertensive end-organ damage. Our approach of integrating multi-model and multi-tissue single-cell analysis with human genetic data and in vivo and in vitro genome editing can be applied to investigate other complex traits.

physiology↗

bioRxiv2024

DDA-BERT: leveraging transformer architecture pre-training for data-dependent acquisition mass spectrometry-based proteomics

In data-dependent acquisition mass spectrometry (DDA-MS)-based proteomics, machine learning based rescoring methods are often employed to integrate multiple scores measuring quality of peptide-spectrum matches (PSMs) from different aspects. Existing rescoring tools face limitations incurred by manual feature extraction, shallow machine learning models, and limited training data. Here, we introduce DDA-BERT, a transformer-based end-to-end deep learning model trained with 95 million human spectra for PSM rescoring. DDA-BERT demonstrates superior performance across various sample types, mass spectrometer platforms, trace sample proteomics, and multiple species proteome data. It consistently outperforms state-of-the-art methods of its kind for DDA-MS spectra analysis with up to 103.5% increased protein group identifications. For single cell proteomics, DDA-BERT identifies up to 85.6% more protein groups compared to existing tools. In addition, DDA-BERT can be effectively extended to proteome of non-human species. DDA-BERT offers a robust and scalable solution for enhancing PSM rescoring in proteomics.

bioinformatics↗

bioRxiv2024

Exploring methanogenic archaea and their thermal responses in the glacier-fed stream sediments of Rongbuk River Basin, Mt. Everest

Glacier-fed streams (GFS) are emergent sources of greenhouse gas methane, and methanogenic archaea in sediments contribute largely to stream methane emissions. However, little is known about the methanogenic communities in GFS sediments and their key environmental driving factors. This study analyzed stream sediments from the Rongbuk River basin on Mount Everest for methanogenic communities and their temperature responses through anaerobic microcosm incubations at 5{degrees}C and 15{degrees}C. Diverse methanogens were identified, including acetoclastic, hydrogenotrophic, and methylotrophic types. Substantial methane and CO2 production were detected across altitudes and increased significantly at 15{degrees}C, with both methane and CO2 production rate negatively correlated with altitude. While temperature sensitivity of CO2 production but not CH4 showed a negative altitude correlation. Methanogens increased substantially over long-term incubation, dominating the archaeal community. At 15{degrees}C, the relative abundance of several methanogenic groups was strongly correlated with altitude, with positive correlations observed for Methanomassiliicoccaceae and Methanoregulaceae, and negative correlations for Methanocellaceae and Methanotrichaceae, respectively. Besides altitude, factors like phosphorus, C-to-N ratio, and pH also affected methanogenic structure, methane and CO2 production, and temperature sensitivities. This study offers new insights into methanogens and methane production in GFS sediments, improving our understanding of GFS carbon cycling and its potential responses to climate change.

ecology↗

bioRxiv2024

DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis

Data-independent acquisition mass spectrometry (DIA-MS) plays an increasingly important role in quantitative proteomics. Here, we introduce DIA-BERT, a software tool that leverages a transformer-based pre-trained artificial intelligence (AI) model for the analysis of DIA proteomics data. Over 276 million of high-quality peptide precursors extracted from existing DIA-MS files were used for training the identification model, while 34 million peptide precursors from synthetic DIA-MS files were used for training the quantification model. Compared to DIA-NN, DIA-BERT led to on average 54% more protein identifications and 37% more peptide precursors in five different human cancer (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma) sample sets with a high degree of quantitative accuracy. This study highlights the potential of utilizing pre-trained models and synthetic datasets to advance DIA proteomics analysis.

systems biology↗

bioRxiv2024

Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygote with few-shot learning

The CRISPR-Cas system has revolutionized gene editing, while its outcome prediction remains unsatisfactory, especially in new cell states due to their distinct DNA repair preferences. In this study, we introduce inDecay, a flexible system for predicting CRISPR editing outcomes from target sequence, returning probabilities of nearly the full spectrum of indel events. Uniquely, inDecay utilizes informative and parameter-efficient features for each indel event and incorporates cell-type-specific repair preferences through a multi-stage design. While both inDecay and existing methods achieve accurate results for prediction within cell lines, only inDecay with transfer learning can retain the high performance for cross-cell line prediction. We then applied inDecay to mouse embryo editing using our newly generated data and observed remarkable accuracy by including as few as 30 fine-tuning embryonic samples. Notably, inDecay is the first software to predict embryonic editing. Therefore, our few-shot learning-supported system may accelerate guide RNA prioritization in mouse model generation, mammal embryonic gene editing, and cellular therapeutics.

bioinformatics↗

bioRxiv2024

mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis

In single-cell data analysis, addressing sparsity often involves aggregating the profiles of homogeneous single cells into metacells. However, existing metacell partitioning methods lack checks on the homogeneity assumption and may aggregate heterogeneous single cells, potentially biasing downstream analysis and leading to spurious discoveries. To fill this gap, we introduce mcRigor, a statistical method to detect dubious metacells, which are composed of heterogeneous single cells, and optimize the hyperparameter of a metacell partitioning method. The core of mcRigor is a feature-correlation-based statistic that measures the heterogeneity of a metacell, with its null distribution derived from a double permutation scheme. As an optimizer for existing metacell partitioning methods, mcRigor has been shown to improve the reliability of discoveries in single-cell RNA-seq and multiome (RNA+ATAC) data analyses, such as uncovering differential gene co-expression modules, enhancer-gene associations, and gene temporal expression. Moreover, mcRigor enables benchmarking and selection of the most suitable metacell partitioning method with optimized hyperparameters tailored to specific datasets, ensuring reliable downstream analysis. Our results indicate that among existing metacell partitioning methods, MetaCell and SEACells consistently outperform MetaCell2 and SuperCell, albeit with the trade-off of longer runtimes.

bioinformatics↗

bioRxiv2024

ConvNTC: Convolutional neural tensor completion for predicting the disease-related miRNA pairs and cell-related drug pairs

Systematic investigation of high-order molecular interactions can deepen our understanding of the mechanisms underlying biological systems. However, effectively capturing both multilinear and nonlinear relationships to accurately identify the complex triplet relationships remains a challenge. In this paper, we present a novel Convolutinal Neural Tensor Completion (ConvNTC) to model triplet network connections. ConvNTC consists of a multilinear module and nonlinear module. The former is a tensor decomposition approach that integrates multiple constraints to learn the tensor factor embeddings. The latter contains three components: an embedding generator to produce position-specific index embeddings for each tensor entry in addition to the factor embeddings, a convolutional encoder to perform nonlinear feature mapping while preserving the tensors rank-one property, and a Kolmogorov-Arnold Network (KAN) based predictor to effectively capture high-dimensional relationships aligned with the intrinsic structure of real-world data. We demonstrate ConvNTC on two triplet prediction tasks of the disease-related miRNA pairs and cell-related drug pairs, respectively. Comprehensive experiments against ten state-of-the-art methods demonstrate the superiority of ConvNTC in terms of triplet imputation. ConvNTC reveals promising prognostic values of the miRNA-miRNA interactions on breast cancer and detects synergistic drug combinations in cancer cell lines. The source code is available at https://github.com/Liangyushi/ConvNTC.

bioinformatics↗

bioRxiv2024

Functional and evolutionary characterization of potential auxiliary metabolic genes (AMGs) of the global RNA virome

RNA viruses play an important role in regulating their host and global biogeochemical cycling, and auxiliary metabolic genes (AMGs) are one of the main pathways by which viruses regulate hosts metabolism. However, the AMGs encoded by global RNA viruses are largely unexplored. Here, we annotated RNA viral genomes and identified AMGs from recently published global RNA virome datasets. We found 274 AMGs encoded by 243 RNA viral genomes, which accounts for only 0.008% of all 3,216,257 genomes investigated. These AMGs span 25 functional categories, the most prevalent being translation, energy metabolism, membrane transport, transcription, replication and repair, and signal transduction. Unlike DNA viruses, RNA viruses tend to encode AMGs influencing the genetic and environments information processing of their hosts, with fewer AMGs linked to nutrient cycling, such as carbon and nitrogen metabolism. In addition, RNA viruses encoding these AMGs were taxonomically diverse, spanning five established phyla, the newly proposed phylum "Taraviricota" and fourteen LucaProt supergroups. Host prediction indicated that most of these RNA viruses encoding AMGs infect eukaryotes, while only a few of them, including Leviviricetes and Vidaverviricetes, infect prokaryotes. Interestingly, the potential host phylum for most of these RNA viruses were different from the likely source phylum of their AMGs, suggesting the possibility that these RNA viruses acquired AMGs from organisms outside their predicted host range. This study provides the first comprehensive analysis of AMGs from global RNA virome, which greatly expands our understanding of their function roles, evolutionary history and virus-host interactions.

ecology↗

bioRxiv2024

Chromatin interaction maps of human arterioles reveal new mechanisms for the genetic regulation of blood pressure

Arterioles are small blood vessels located just upstream of capillaries in nearly all tissues. The constriction and dilation of arterioles regulate tissue perfusion and are primary determinants of systemic blood pressure (BP). Abnormalities in arterioles are central to the development of major diseases such as hypertension, stroke, and microvascular complications of diabetes. Despite the broad and essential role of arterioles in physiology and disease, current knowledge of the functional genomics of arterioles is largely absent, partly because it is challenging to obtain and analyze human arteriole samples. Here, we report extensive maps of chromatin interactions, single-cell expression, and other molecular features in human arterioles and uncover new mechanisms linking human genetic variants to gene expression in vascular cells and the development of hypertension. Compared to large arteries, arterioles exhibited a higher proportion of pericytes which were strongly associated with BP traits. BP-associated single nucleotide polymorphisms (SNPs) were enriched in chromatin interaction regions in arterioles, particularly through enhancer SNP-promoter interactions, which were further linked to gene expression specificity across tissue components and cell types. Using genomic editing in animal models and human induced pluripotent stem cells, we discovered novel mechanisms linking BP-associated noncoding SNP rs1882961 to gene expression through long-range chromatin contacts and revealed remarkable effects of a 4-bp noncoding genomic segment on hypertension in vivo. We anticipate that our rich data and findings will advance the study of the numerous diseases involving arterioles. Moreover, our approach of integrating chromatin interaction mapping in trait-relevant tissues with SNP analysis and in vivo and in vitro genome editing can be applied broadly to bridge the critical gap between genetic discoveries and physiological understanding.

genomics↗

bioRxiv2024

Sex-specific effects of intensity and dose of physical activity on BOLD-fMRI cerebrovascular reactivity and cerebral pulsatility

Cerebrovascular reactivity (CVR) and cerebral pulsatility (CP) are important indicators of cerebrovascular health and have been shown to be associated with physical activity (PA). Sex differences have been shown to influence the impact of PA on cerebrovascular health. However, the sex-specific effects of PA on CP and CVR, particularly in relation to intensity and dosage of PA, remains unknown. Thus, this cross-sectional study aimed to evaluate the sex-specific effects of different intensities and doses of PA on CVR and CP. The Human Connectome - Aging dataset was used, including 626 participants (350 females, 276 males) aged 36-85 (mean age: 58.8 {+/-} 14.1 years). Females were stratified into premenopausal and postmenopausal groups to assess the potential influence of menopausal status. Novel tools based solely on resting state fMRI data were used to estimate both CVR and CP. The International Physical Activity Questionnaire was used to quantify weekly self-reported PA as metabolic equivalent of task. Results indicated that both sexes and menopausal subgroups revealed negative linear relationships between relative CVR and PA. Furthermore, females presented a unique non-linear relationship between relative CVR and total PA in the cerebral cortex. In females, there were also relationships with total and walking PA in occipital and cingulate regions. In males, we observed relationships between total or vigorous PA and CVR in parietal and cingulate regions. Sex-specific effects were also observed with CP, whereby females benefited across a greater number of regions and intensities than males, especially in the postmenopause group. Overall, males and females appear to benefit from different amounts and intensities of PA, with menopause status significantly influencing the effect of PA on cerebrovascular outcomes, underscoring the need for sex-specific recommendations in promoting cerebrovascular health.

neuroscience↗

bioRxiv2024

Mid1 deletion leads to cognitive dysfunction in Opitz syndrome by regulates neural rhythms through the inhibition of p-Creb by PP2Ac

MID1 is an E3 ubiquitin ligase of the tripartite motif (TRIM) subfamily of RING-containing protein1. MID1 is involved in many basic biological processes, especially during embryonic development. Mutation, truncation or complete deletion of MID1 gene is the cause of Opitz G/BBB syndrome (OS). OS is a rare genetic disease of nervous system, which is characterized by midline structural development defects during embryogenesis, including structural brain abnormalities, developmental retardation and mental retardation2. Although the function of MID1 has been studied for many years, the effect and mechanism of complete deletion of MID1 gene on OS nervous system still need to be further explored. Here we find that Mid1 gene is necessary for the normal development of hippocampus (HPC), and Mid1 gene knockout (Mid1-/y) mice showed a significant decrease in rhythm in HPC and abnormal synchronization of {gamma} rhythm in prefrontal cortex and hippocampus (PFC-HPC), showing decreased synaptic plasticity and learning and memory dysfunction.

neuroscience↗

bioRxiv2024

Aβ*56 is present in brain extracts enriched for extracellular proteins

Amyloid-{beta} (A{beta}) oligomers are believed to be important in the pathogenesis of Alzheimers disease (AD). A{beta}*56 is an A{beta} oligomer that has been reported in several lines of transgenic mice modeling AD, including Tg2576, hAPP-J20, 3xTgAD, 5xFAD, and APPTTA. In Tg2576 mice, A{beta}*56 appears several months before neuritic plaques and impairs memory when injected into the brains of healthy rodents. A{beta}*56 has several distinctive biochemical features: 1) it is soluble in aqueous buffers, 2) it is stable in the ionic detergent sodium dodecyl sulfate (SDS), 3) it has an apparent mass of 56 kDa whether measured by denaturing SDS polyacrylamide gel electrophoresis or non-denaturing size-exclusion chromatography, 4) it binds to A11 conformational antibodies that recognize non-fibrillar oligomeric assemblies, and 5) it contains canonical A{beta}(1-40) and/or A{beta}(1-42). Here, we show its presence in Tg2576 brain extracts enriched for extracellular proteins and conclude that A{beta}*56 is present in the extracellular space.

biochemistry↗

bioRxiv2024

Temporal High-Resolution Atlas of Human Blood Leukocyte Composition in Response to Respiratory Virus Inoculation

BackgroundRespiratory viral infections produce highly variable clinical outcomes, even after controlled exposure. Human viral challenge studies are well suited to study this variation because they capture immune profiles before inoculation and across defined post-challenge intervals. However, how circulating immune-cell populations change over time, and which responses are shared or virus-specific across respiratory viruses, remains incompletely defined. MethodsWe leveraged HR-VILAGE-3K3M, a curated human respiratory viral challenge transcriptomic resource, to analyze longitudinal whole-blood profiles from H1N1, H3N2, RSV, and HRV challenge studies. Using statistical deconvolution and xCell-based digital sorting, we estimated changes in major leukocyte populations and 28 immune-cell subtypes in symptomatic infected (Sx/Inf) and asymptomatic uninfected (Asx/Uninf) participants. Across viruses, symptomatic infection was marked by a broad shared pattern of innate immune activation together with reduced circulating lymphocyte enrichment. Activated dendritic cells, monocytes, and neutrophils increased following challenge, whereas strong pDC activation was observed primarily in influenza studies. Most T-cell and B-cell populations decreased after challenge. Plasma-cell enrichment increased progressively over time. Despite this shared architecture, response timing differed by virus: H3N2 showed the earliest changes, followed by H1N1, whereas RSV responses were delayed and HRV showed more distinct immune-cell trajectories. Migration-associated transcriptional programs were detected across both innate and adaptive immune-cell populations, supporting coordinated immune-cell trafficking during infection. Baseline immune composition also differed between outcome groups, suggesting that pre-existing immune-state differences may influence infection outcomes. ConclusionsThese findings define a cellular atlas of human respiratory viral challenge responses and show that respiratory viruses induce a broadly conserved immune-response program with distinct virus-specific kinetics. This framework may help clarify immune features associated with natural resistance, symptomatic infection, and vaccine-relevant antiviral immunity.

genomics↗

bioRxiv2024

Potent and Selective SETDB1 Covalent Negative Allosteric Modulator Reduces Methyltransferase Activity in Cells

A promising drug target, SETDB1, is a dual Kme reader and methyltransferase, which has been implicated in cancer and neurodegenerative disease progression. To help understand the role of the triple Tudor domain (3TD) of SETDB1, its Kme reader, we first identified a low micromolar small molecule ligand, UNC6535, which occupies simultaneously both the TD2 and TD3 reader binding sites. Further optimization led to the discovery of UNC10013, the first covalent 3TD ligand targeting Cys385 of SETDB1. UNC10013 is potent with a kinact/KI of 1.0 x 106 M-1s-1 and demonstrated proteome-wide selectivity. In cells, negative allosteric modulation of SETDB1-mediated Akt methylation was observed after treatment with UNC10013. Therefore, UNC10013 is a potent, selective and cell-active covalent ligand for the 3TD of SETDB1, demonstrating negative allosteric modulator properties and making it a promising tool to study the biological role of SETDB1 in disease progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/615363v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@6db8e2org.highwire.dtl.DTLVardef@1852ee3org.highwire.dtl.DTLVardef@c95bb6org.highwire.dtl.DTLVardef@1a43951_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination

Our understanding of the human immune systems response to viral respiratory tract infections (VRTIs) and vaccines, including the molecular mechanisms and correlates of protection, remains incomplete. Extensive transcriptomic data from inoculation and vaccination studies have been deposited in publicly available databases. However, these studies are often separate and difficult to locate. We provide a curated compendium of public gene expression data repositories for researchers to reanalyze transcriptomes from human whole blood, peripheral blood mononuclear cells (PBMCs), and nasal swab samples. This enables the study of transcriptional responses to viral inoculation or vaccination. This collection includes 18 datasets from inoculation studies and 37 datasets from vaccination studies, sourced from the NCBI Gene Expression Omnibus (GEO), ImmPort Shared Data, and ArrayExpress.

genomics↗

bioRxiv2024

Novel regulator Fpr2 controls immune homeostasis via CCL4-dependent neutrophils

Polymorphonuclear leukocytes (PMNs) in Fpr2-deficient mice have significantly impaired bactericidal activity, yet the precise mechanism remains elusive. Our research unveiled the cellular heterogeneity of peripheral blood PMNs through single-cell RNA-sequencing (scRNA-seq) and identified two new neutrophil clusters related to bacterial infection, including a sterilization cluster named Neu_CSTA and a regulation cluster named Neu_CCL4. The lack of Fpr2 leads to an immune regulation imbalance of Neu_CCL4 on other clusters in uninfected conditions, resulting in persistent instability of PMNs in peripheral blood, elevated inflammatory factors, and increased NOX2 expression. Furthermore, our findings corroborate that the regulatory function of Neu_CCL4 on PMNs immune balance is closely tied to its high expression of the anti-inflammatory ligand Anxa1. Regulon analysis inferred by scRNA-seq and ChIP-seq data suggested that a specific transcription factor Elf4, could modulate the expression of Anxa1, thereby facilitating its regulatory impact on other clusters. In the infection state, there was a noted increase in intercellular interactions across different clusters, leading to a general improvement in the bactericidal activity of PMNs. In summary, our research revealed distinct functional cellular sub-populations of PMNs, discovered the potential regulatory mechanisms, and elucidated the essential roles of Fpr2 in their ability to resist infection.

immunology↗

bioRxiv2024

Enzymatic Degrading Chlorophenol Wastewater by Mixed Strains of Immobilized White Rot Fungi

To address chlorophenol wastewater pollution, immobilized mixed white rot fungi (WRF) strain microsphere was designed as a solid degradation agent, using lignin peroxidase (LiP), manganese peroxidase (MnP), and laccase (Lac) to degradating the wastewater. Considering the diverse physical and chemical properties of the fungal sphere, the immobilization agent formula is optimized and comprehensive environmental factor design response surface analysis are implemented to determine the delivery conditions. Consequently, the 2,4-DCP treatment rate and extracellular enzyme activity for a 1:1 encapsulation of T. versicolor and P. sajor-caju significantly outperform those of individual strains. Using polyvinyl alcohol (PVA), sodium alginate (SA), and biochar as carriers, with sodium dihydrogen phosphate solution as crosslinker and SiO2/zeolite as additives, immobilizing mixed bacteria yielded a high-quality solid agent. This achieved a 99.33% 2,4-DCP degradation rate over 96 hours, with optimal dosage, pH, and initial 2,4-DCP concentration at 11.5 g/L, 5.5, and 40 mg/L. The degradation of 2,4-DCP by WRF selectively removes adjacent chlorine atoms to produce 4-CP, enhancing the dechlorination efficiency. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/611973v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@1653067org.highwire.dtl.DTLVardef@4cc31dorg.highwire.dtl.DTLVardef@19daa33org.highwire.dtl.DTLVardef@1115c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

CRISPR editing of candidate host factors that impact influenza A virus infection

Influenza A virus (IAV) is a respiratory pathogen with a segmented negative-sense RNA genome that can cause epidemics and pandemics. The host factors required for the complete IAV infectious cycle have not been fully identified. Here, we examined select host factors that were identified by independent CRISPR screens as candidate contributors to IAV infectivity. We performed CRISPR-mediated knockout of cytidine monophosphate N-acetylneuraminic acid synthetase (CMAS) as well as CRISPR-mediated overexpression of beta-1,4 N-acetylgalactosaminyltransferase 2 (B4GALNT2) and adenosine deaminase acting on RNA 1 (ADAR1) in the human bronchial epithelial A549 cell line and evaluated IAV infectivity. We confirmed that the knockout of CMAS or overexpression of B4GALNT2 restricts IAV infection by diminishing binding to the cell surface but has no effect on vesicular stomatitis virus infection. While ADAR1 overexpression does not significantly inhibit IAV replication, it has a pro-viral effect with coxsackie B virus (CVB) infection. This pro-viral effect is not likely secondary to reduced type I interferon (IFN) production, as the induction of the IFN-stimulated genes ISG15 and CXCL10 is negligible in both parent and ADAR1-overexpressing A549 cells following CVB challenge. In contrast, ISG15 and CXCL10 production is robust and equal for parent and ADAR1-overexpressing A549 cells challenged with IAV. Taken together, these data provide insight into how host factors identified in CRISPR screens can be further explored to understand the dynamics of pro- and anti-viral factors. ImportanceInfluenza A virus (IAV) remains a global threat due to its ability to cause pandemics, making the identification of host factors essential for developing new antiviral strategies. In this study, we utilized CRISPR-based techniques to investigate host factors identified in screens as reducing IAV infectivity. Knockout of CMAS, a key enzyme in sialic acid biosynthesis, significantly reduced IAV binding and infection by disrupting sialic acid production on the cell surface. Overexpression of B4GALNT2 had similar effects, conferring resistance to IAV infection through diminished cell-surface binding. While overexpression of ADAR1, known for its role in RNA editing and immune regulation, slightly reduced IAV replication, it increased coxsackie B virus replication. Such findings reveal the diverse roles of host factors in viral infection, offering insights for targeted therapeutic development against IAV and other pathogens.

immunology↗