Biology subjects

Wang, Q.

Publications and source records attributed to Wang, Q..

At least 361 records · Page 20Linked to original sources

bioRxiv2024

Prefrontal cortical dynorphin peptidergic transmission constrains threat-driven behavioral and network states

Prefrontal cortical (PFC) circuits provide top-down control of threat reactivity. This includes ventromedial PFC (vmPFC) circuitry, which plays a role in suppressing fear-related behavioral states. Dynorphin (Dyn) has been implicated in mediating negative affect and mal-adaptive behaviors induced by severe threats and is expressed in limbic circuits, including the vmPFC. However, there is a critical knowledge gap in our understanding of how vmPFC Dyn-expressing neurons and Dyn transmission detect threats and regulate expression of defensive behaviors. Here, we demonstrate that Dyn cells are broadly activated by threats and release Dyn locally in the vmPFC to limit passive defensive behaviors. We further demonstrate that vmPFC Dyn-mediated signaling promotes a switch of vmPFC networks to a fear-related state. In conclusion, we reveal a previously unknown role of vmPFC Dyn neurons and Dyn neuropeptidergic transmission in suppressing defensive behaviors in response to threats via state-driven changes in vmPFC networks. Highlights[bullet] vmPFCDyn neurons are activated by threats and threat-predictive cues [bullet]Characterization of a genetically-encoded kappa-opioid receptor sensor [bullet]vmPFCDyn neurons rapidly release Dyn in response to threats and their predictors [bullet]vmPFCDyn signaling opposes threat-induced passive defensive behaviors [bullet]Dyn signaling promotes threat-evoked state transitions in vmPFC networks

neuroscience↗

bioRxiv2024

A basally active cGAS-STING pathway limits SARS-CoV-2 replication in a subset of ACE2 positive airway cell models

Host factors that define the cellular tropism of SARS-CoV-2 beyond the cognate ACE2 receptor are poorly defined. Here we report that SARS-CoV-2 replication is restricted at a post-entry step in a number of ACE2-positive airway-derived cell lines due to tonic activation of the cGAS-STING pathway mediated by mitochondrial DNA leakage and naturally occurring cGAS and STING variants. Genetic and pharmacological inhibition of the cGAS-STING and type I/III IFN pathways as well as ACE2 overexpression overcome these blocks. SARS-CoV-2 replication in STING knockout cell lines and primary airway cultures induces ISG expression but only in uninfected bystander cells, demonstrating efficient antagonism of the type I/III IFN-pathway in productively infected cells. Pharmacological inhibition of STING in primary airway cells enhances SARS-CoV-2 replication and reduces virus-induced innate immune activation. Together, our study highlights that tonic activation of the cGAS-STING and IFN pathways can impact SARS-CoV-2 cellular tropism in a manner dependent on ACE2 expression levels.

microbiology↗

bioRxiv2024

Cytochrome P450 Enzyme Design by Constraining Catalytic Pocket in Diffusion model

Although cytochrome P450 enzymes are the most versatile biocatalysts in nature, there is insufficient comprehension of the molecular mechanism underlying their functional innovation process. Here, by combining ancestral sequence reconstruction, reverse mutation assay and structure analysis, we identified five founder residues in the catalytic pocket of flavone 6-hydroxylase (F6H) and proposed a "three-point fixation" model to elucidate the functional innovation mechanisms of P450s in nature. According to this design principle of catalytic pocket, we further developed a de novo diffusion model (P450Diffusion) to generate artificial P450s. Ultimately, among the 17 non-natural P450s we generated, ten designs exhibited significant F6H activity and six exhibited a 1.3- to 3.5-fold increase in catalytic capacity compared to the natural CYP706X1. This work not only explores the design principle of catalytic pockets of P450s, but also provides an insight into the artificial design of P450 enzymes with desired functions.

molecular biology↗

bioRxiv2024

CDC5L surveils cellular stress responses and stress granule formation through transcriptional repression

Cells have evolved a variety of mechanisms to respond to stress, such as activating the PERK- eIF2 pathway and forming stress granules (SGs). It is important that these mechanisms are inducted only when necessary and exerted at appropriate levels, to prevent spontaneous or excessive activation of stress responses. However, the mechanisms by which cells keep the stress response programs in check are elusive. In this study, we discovered that downregulation of Cell Division Cycle 5 Like (CDC5L) causes spontaneous SG formation in the absence of any stress, which is independent of its known functions in the cell cycle or the PRP19 complex. Instead, we found that CDC5L binds to the PERK promoter through its DNA-binding domains and represses PERK mRNA transcription. As a result, it negatively regulates the abundance of PERK protein and the phosphorylation levels of eIF2, thereby suppressing the PERK-eIF2 signaling pathway and preventing undesirable SG assembly. Further RNA-sequencing (seq) and chromatin immunoprecipitation (ChIP)-seq analyses reveal a dual function of CDC5L in gene transcription: it acts as a transcriptional activator in cell cycle control but as a repressor in cellular stress responses. Finally, we show that the loss of CDC5L decreases cell viability and fly survival under mild stress conditions. Together, our findings demonstrate a previously unknown role and mechanism of CDC5L in the surveillance of cellular stress through transcriptional repression, which serves as a gatekeeper for the stress response programs such as the PERK-eIF2 pathway and SG formation. Significance statementCells need to respond to stress promptly for survival. Meanwhile, it is equally important to prevent spontaneous or excessive activation of stress response programs when no stress or only minor stress is present. Here, we reveal that the DNA/RNA-binding protein CDC5L represses the transcription of a cluster of stress response genes including PERK. In doing so, CDC5L suppresses the PERK-eIF2 pathway and prevents spontaneous SG assembly. Downregulation of CDC5L releases the restraint on these genes, resulting in an exaggerated response to stress and decreased viability in both cell and fly models. Taken together, this study demonstrates the existence of a gatekeeper mechanism that surveils the stress response programs and highlights the crucial role of CDC5L-mediated transcriptional repression in this regulation.

cell biology↗

bioRxiv2023

Interpretable deep learning framework towards understanding molecular changes in human brains with Alzheimer's disease: implication for microglia activation and sex differences in AD

INTRODUCTIONThe objective of this study is to characterize the dysregulation of gene expression in AD affected brain tissues through an interpretable deep learning framework. METHODSWe trained multi-layer perceptron models for the classification of neuropathologically confirmed AD vs. controls using transcriptomic data from three brain regions of ROSMAP study. The disease spectrum was then modeled as a progressive trajectory. SHAP value was derived to explain model predictions and identify significantly implicated genes for subsequent gene co-expression network analysis. RESULTSThe models achieved excellent performance in classification and prediction in two external datasets from Mayo RNA-seq cohort and Mount Sinai Brain Bank cohort. SHAP explainer revealed common and specific transcriptomic signatures from different brain regions. DISCUSSIONWe identified common gene signatures among different brain regions in microglia and sex specific modules in neurons implicated in AD. This work paves the way for utilizing artificial intelligence approaches in studying AD at the molecular level. Research-in-ContextO_LISystematic review: Postmortem brain transcriptomes have been analyzed to study the molecular changes associated with Alzheimers disease, usually by a direct contrast approach such as differential gene expression analysis. Nuanced gene regulatory networks thus cannot be easily pinpointed from convoluted data such as those from bulk-tissue profiling. We applied a novel interpretable deep learning approach to dissect the RNA-seq data collected from three different brain regions of a large clinical cohort and identified significant genes for network analysis implicated for AD. C_LIO_LIInterpretation: Our models successfully predicted neuropathological and clinical traits in both internal and external validations. We corroborated known microglial biology in addition to revealing novel sex chromosome-linked gene contributing to sex dimorphism in AD. C_LIO_LIFuture directions: The framework could have broad utility for interpreting multi-omic data such as those from single-cell profiling, to advance our understanding of molecular mechanisms of complex human disease such as AD. C_LI HighlightsO_LIWe applied novel interpretable deep learning methods to postmortem brain transcriptomes from three different brain regions C_LIO_LIWe interpreted the models to identify genes most strongly implicated in AD C_LIO_LINetwork analysis corroborated known microglial biology and revealed novel sex specific transcriptional factors associated with neuronal loss in AD C_LI

neuroscience↗

bioRxiv2023

A genome-wide association study of neonatal metabolites

The hereditary component significantly influences the concentration of metabolites in adults. Nevertheless, the precise influence of genetic factors on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified a total 17 previous reported associations and 13 novel discovered associations between single nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was calculated to be 76.2%, with a range of 69-78.8%. The aforementioned findings offer valuable insights pertaining to the genetic architecture of neonatal metabolism. In BriefLarge-scale genomes of maternal non-invasive prenatal testing provide insights into the genetic contribution to neonatal metabolism. HighlightsGWAS of 27,785 low-pass genomes revealed 13 novel associations of neonatal metabolic components. Estimated an average of 76.2% heritability of neonatal metabolic components and showed the individual concentration can be accurately predicted from polygenic risk scores. A total of 17 established relationships have been observed, providing evidence that maternal genomes can be utilized in neonatal metabolite GWAS.

genetics↗

bioRxiv2023

XBB.1.5 monovalent mRNA vaccine booster elicits robust neutralizing antibodies against emerging SARS-CoV-2 variants

COVID-19 vaccines have recently been updated with the spike protein of SARS-CoV-2 XBB.1.5 subvariant alone, but their immunogenicity in humans has yet to be fully evaluated and reported, particularly against emergent viruses that are rapidly expanding. We now report that administration of an updated monovalent mRNA vaccine (XBB.1.5 MV) to uninfected individuals boosted serum virus-neutralization antibodies significantly against not only XBB.1.5 (27.0-fold) and the currently dominant EG.5.1 (27.6-fold) but also key emergent viruses like HV.1, HK.3, JD.1.1, and JN.1 (13.3-to-27.4-fold). In individuals previously infected by an Omicron subvariant, serum neutralizing titers were boosted to highest levels (1,504-to-22,978) against all viral variants tested. While immunological imprinting was still evident with the updated vaccines, it was not nearly as severe as the previously authorized bivalent BA.5 vaccine. Our findings strongly support the official recommendation to widely apply the updated COVID-19 vaccines to further protect the public.

immunology↗

bioRxiv2023

USP7 Inhibition Promotes Mesenchymal Stem cell-Macrophage Crosstalk in Age-related Osteoporosis via Efferocytosis and Senolysis

Although elderly osteoporotic patients have similar implant survival rates compared to those of normal individuals, they require longer healing periods to achieve proper osseointegration. This may be related to chronic inflammatory responses and impaired stem cell repair functions in the osteoporotic bone microenvironment. Recently, the deubiquitinating enzyme, ubiquitin-specific peptidase 7 (USP7), was found to regulate macrophage immune response and modulate stem cell osteogenic differentiation. The selective inhibitor of USP7, P5091, has also been found to promote bone repair and homeostasis in osteoporotic conditions. However, the roles of USP7 and P5091 in osteoimmunology and dental implant osseointegration under senile osteoporotic conditions remain unclear. In this study, USP7 depletion and P5091 were showed to inhibit inflammation in senescent bone marrow derived macrophages (BMDMs) and promote osteogenic differentiation in aged BMSCs. Furthermore, mRNA-Seq revealed that USP7 depletion could enhance efferocytosis in senescent BMDMs through the EPSIN1/ low-density lipoprotein receptor-related protein 1 (LRP1) pathway and selectively induce apoptosis (senolysis) in aged BMSCs. In senile osteoporotic mice, we found that the osseointegration period was prolonged compared to young mice, and P5091 promoted the early stage of osseointegration, which may be related to macrophage efferocytosis around the implant. Collectively, this study suggests that USP7 inhibition may accelerate the osseointegration process in senile osteoporotic conditions by promoting macrophage efferocytosis and aged BMSCs apoptosis. This has implications for understanding the cellular interactions and signaling mechanisms in the peri-implant bone microenvironment under osteoporotic conditions. It may also provide clinical significance in developing new therapies to enhance osseointegration quality and shorten the edentulous period in elderly osteoporotic patients.

cell biology↗

bioRxiv2023

Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning

Predicting neuromarkers for cognitive abilities using fMRI has been a major focus of research in the past few years. However, it has recently been reported that many thousands of participants are required to obtain reproducible results (Marek et al (2022)). This appears to be a major impediment to obtaining neuromarkers from fMRI because large sample sizes are typically not available in neuroimaging studies. Here we show that the out-of-sample prediction accuracy can be dramatically improved by supplementing fMRI with readily available non-imaging information so that reliable predictive modeling becomes feasible even for small sample sizes. Specifically, we introduce a novel machine learning method that predicts intelligence from resting-state fMRI data, leveraging educational level as supplementary information. We refer to our approach as "semi-blind machine learning (SML)" because it operates under the assumption that supplementary information, such as educational level, is available for subjects in both the training and test sets. This setup closely mirrors real-world scenarios, especially in clinical contexts, where patient background information typically exists and can be utilized to boost prediction accuracy. However, guarding against bias is crucial. Subjects should not be categorized as more intelligent simply based on their higher education levels. Therefore, our approach contains a component explicitly designed for bias control. We have applied our method to three different data collections and observed marked improvements in prediction accuracies across a wide range of sample sizes. We anticipate that semi-blind machine learning provides a promising approach to fMRI-based predictive modelling with the potential for a wide range of future applications.

neuroscience↗

bioRxiv2023

Decoding Heterogenous Single-cell Perturbation Responses

Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets. One sentence summaryWe present a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.

bioinformatics↗

bioRxiv2023

A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease

The emergence of technologies that can support high-throughput profiling of single cell transcriptomes offers to revolutionize the study of brain tissue from persons with and without Alzheimers disease (AD). Integration of these data with additional complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link observed cell subpopulations and molecular network features within a broader disease-relevant context. We report here single nucleus RNA sequencing (snRNA-seq) profiles generated from superior frontal gyrus cortical tissue samples from 101 exceptionally well characterized, aged subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in peripheral hematological lab parameters, with these observations replicated in an independent, prospective cohort study of ageing and dementia. We also observed an AD-associated CD83(+) microglial subtype with unique molecular networks that encompass many known regulators of AD-relevant microglial biology, and which are associated with immunoglobulin IgG4 production in the transverse colon. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal novel disease biology. The transcriptomic, genetic, phenotypic, and network data resources described within this study are available for access and utilization by the scientific community.

genomics↗

bioRxiv2023

A bioactive peptide from the pearl has dual roles in resisting SARS-CoV-2 infection and its complications

Angiotensin-converting enzyme 2 (ACE2) is a critical receptor for the entry of the SARS-CoV-2 virus into cells. Moreover, a decrease in ACE2 level and its activity due to SARS-CoV-2 infection is considered a crucial reason for the development of Covid-19-associated complications. Here, we report a bioactive peptide derived from the seawater pearl oyster Pinctada fucata, named SCOL polypeptide, which binds strongly to ACE2 and effectively inhibits 65% of the binding of the SARS-CoV-2 S protein to ACE2; thus, this peptide can be used as a blocker to enable cells to resist SARS-CoV-2 infection. The SCOL polypeptide also increases ACE2 enzyme activity by 3.76 times. Previous studies have shown that ACE2 deficiency is associated with inflammation, pain, cardiovascular diseases, insulin resistance, and nervous system injury. Therefore, the SCOL polypeptide can be used to treat or alleviate complications such as lung inflammation, pain, diabetes, cardiovascular diseases, and loss of taste or smell caused by SARS-CoV-2 infection. Thus, the SCOL polypeptide can play a dual role in resisting SARS-CoV-2 infection.

biochemistry↗

bioRxiv2023

OPUS-Rota5: A Highly Accurate Protein Side-chain Modeling Method with 3D-Unet and RotaFormer

Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. A protein structure with large errors in side chains has limited usage such as in drug design. Previous research on AlphaFold2 (AF2) predictions of GPCR targets indicates that the docking of natural ligands back on AF2-predicted structures has limited successful rate presumably due to large errors in side chains. Here, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features including ligand information of each residue, and then employs RotaFormer module to aggregate various types of feature. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, reveals that side chains modeled by OPUS-Rota5 are significantly more accurate than those predicted by other methods. We also employ OPUS-Rota5 to refine the side chains of 25 GPCR targets predicted by AF2 and then performed docking of their natural ligands back with a significantly improved successful rate. Such results suggest that OPUS-Rota5 could be a valuable tool for molecular docking, particularly for targets with relatively accurate predicted backbones, but not side chains.

bioinformatics↗

bioRxiv2023

TFEB degradation is regulated by an IKK/β-TrCP2 phosphorylation-ubiquitination cascade

Transcription factor EB (TFEB) is a master regulator of lysosomal biogenesis and autophagy that plays a key role in the regulation of cellular clearance pathways. TFEB is regulated via a complex array of post-translational modifications (PTMs), but the exact molecular mechanism that regulates TFEB stability has remained elusive. Here, we show that TFEB levels are critically regulated by a defined phosphorylation-ubiquitination cascade. A human kinome screen identifies IKK (inhibitor of {kappa}B kinase) as a TFEB modifier, and a combination of phosphorylation assays, mass spectrometry analyses, and site-specific mutagenesis unveils a previously unrecognized TFEB phospho-degron (423SPFPSLS429) as the target of IKK. We show that IKK-mediated phosphorylation of TFEB triggers ubiquitination of adjacent lysine residues (K430 and K431) by the E3 ligase {beta}-TrCP2 ({beta}-Transducin repeat-containing protein 2), thereby tagging TFEB for degradation. Modified TFEB constructs that abolish these PTMs show much increased stability and expression levels but remain equally sensitive to autophagy- or stress- related stimuli while maintaining the capability to promote the expression of TFEB target genes and the clearance of Alzheimers associated tau in a cellular model of disease. Our results therefore uncover an IKK/{beta}-TrCP2 phosphorylation-ubiquitination cascade as a major mechanism that governs TFEB stability independently of other TFEB regulators.

cell biology↗

bioRxiv2023

A peptide-centric local stability assay to unveil protein targets of diverse ligands

While tremendous progress has been made in chemical proteomics for identifying protein-ligand interactions, it remains challenging for proteome-wide identification of ligand-binding regions without modifying the ligands. Here, we discovered that "disruptive trypsinization" amplifies the readout of ligand-induced protein local stability shifts, and explored this notion in developing "peptide-centric local stability assay" (PELSA), a modification-free approach which achieves unprecedented sensitivity in proteome-wide target identification and binding-region determination. We demonstrate the versatility of PELSA by investigating the interactions across various biological contexts including drug-target interactions, metabolism, epitope mapping, metal proteomics, and post-translational modification recognition. A PELSA study of the oncometabolite R2HG revealed functional insights about its targets and pathogenic processes in both cancer and immune cells. Thus, beyond offering users unprecedented sensitivity for characterizing diverse target-ligand interactions, PELSA supports informative screening and hypothesis generation studies throughout life science.

biochemistry↗

bioRxiv2023

Unveiling the A-to-I mRNA editing machinery and its regulation and evolution in fungi

A-to-I mRNA editing occurs during fungal sexual reproduction with an unknown mechanism. Here, we demonstrated that the eukaryotic tRNA-specific heterodimeric deaminase FgTad2-FgTad3, not typically associated with mRNA editing, is responsible for A-to-I mRNA editing in Fusarium graminearum. This editing capacity relies on the interaction between FgTad3 and a sexual stage-specific protein called Ame1. The interaction emerged in Sordariomycetes. Key residues involved in the interaction have been identified. Expression and activity of FgTad2-FgTad3 are regulated through alternative promoters, alternative translation initiation, and post-translational modifications. FgTad2-FgTad3-Ame1 efficiently edits target mRNAs in yeasts, bacteria, and human cells, with significant implications for developing base editors in therapy and agriculture. This study reveals mechanisms, regulation, and evolution of RNA editing in fungi, emphasizing protein-protein interactions in controlling enzyme function.

molecular biology↗

bioRxiv2023

Enhanced K Na 1.1 Channel Underlies Cortical Hyperexcitability and Seizure Susceptibility after Traumatic Brain Injury

Pathogenic variants of the sodium-activated potassium channel KNa1.1, have been reported in multiple epileptic disorders. However, whether and how KNa1.1 channel is involved in epileptogenesis after traumatic brain injury (TBI) remains unknown. Firstly, we used behavioral monitoring and EEG recording to examine physiological property, spontaneous seizure activity, and seizure susceptibility after TBI. We explored the changes of KNa1.1 channel following TBI, including changes of subcellular distribution and expression pattern. Meanwhile, we performed patch-clamp recording to detect the neuronal excitability. Furthermore, we built TBI model using kcnt1-/- mice and compared seizure activity with those on wild- type mice. We found severity-dependent seizure susceptibility in different degree of injured mice. Meanwhile, increased neuronal expression of KNa1.1 channel, especially in inhibitory neurons, around the lesion was also observed following TBI with increased neuronal excitability including reduced firing rate of interneurons and imbalanced excitation and inhibition (E/I). Although the maximum frequency of action potential of kcnt1-/- neurons was increased, kcnt1-/- mice displayed decreased seizure susceptibility to the pentylenetetrazole (PTZ) after TBI. Taken together, this study suggests that pathologically enhanced expression and abnormally distributed KNa1.1 channel after TBI contribute to disputed E/I and seizure susceptibility, which might provide a potential therapeutic target on the epileptogenesis after TBI.

neuroscience↗

bioRxiv2023

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of complex traits in pig.

Understanding the molecular and cellular mechanisms that underlie complex traits in pigs is crucial for enhancing their genetic improvement program and unleashing their substantial potentials in human biomedicine research. Here, we conducted a meta-GWAS analysis for 232 complex traits with 28.3 million imputed whole-genome sequence variants in 70,328 individuals from 14 pig breeds. We identified a total of 6,878 genomic regions associated with 139 complex traits. By integrating with the Pig Genotype-Tissue Expression (PigGTEx) resource, we systemically explored the biological context and regulatory circuits through which these trait-associated variants act and finally prioritized 16,664 variant-gene-tissue-trait circuits. For instance, rs344053754 regulates the expression of UGT2B31 in the liver by affecting the activity of regulatory elements and ultimately influences litter weight at weaning. Furthermore, we investigated the conservation of genetic and regulatory mechanisms underlying 136 human traits and 232 pig traits. Overall, our multi-breed meta-GWAS in pigs provides invaluable resources and novel insights for understanding the regulatory and evolutionary mechanisms of complex traits in both pigs and humans.

genetics↗