Biology subjects

Gao, J.

Publications and source records attributed to Gao, J..

At least 181 records · Page 10Linked to original sources

bioRxiv2022

Identification of m6A and m5C RNA modifications at single-molecule resolution from Nanopore sequencing

The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUIs capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.

bioinformatics↗

bioRxiv2022

A potential interacting network identified by quantitative proteomics for the sole adenylyl cyclase Cyr1 of Candida albicans

Cyr1, the sole adenylyl cyclase of the fungal pathogen Candida albicans, is a central component of the cAMP/PKA signaling pathway that controls the yeast-to-hyphal morphogenesis. Cyr1 functions as a multivalent senor and integrator of various external and internal signals. To better understand how these signals are relayed to Cyr1 and how Cyr1 activity is regulated, we sought to identify global interacting partners of Cyr1 using stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics. From the proteins co-immunoprecipitated with Myc-tagged Cyr1, 38 were assigned as authentic Cyr1-interacting partners, including two previously characterized Cyr1-binding proteins, Cap1 and Act1. Many of the identified proteins remain uncharacterized, but some can be classified into several functional groups, such as actin regulatory proteins, cell wall components, and mitochondrial proteins. We used biochemical and genetic methods to further characterize the interaction of Cyr1 with Mp65, a cell surface mannoprotein previously reported to have a role in cell wall integrity and possess several virulence-related traits. Taken together, our comprehensive analysis of the Cyr1-interacting proteins provides important information for establishing the Cyr1 interactome and uncovers a potential role for cell wall proteins in Cyr1-mediated cAMP signaling.

microbiology↗

bioRxiv2022

Lnc956-TRIM28-HSP90B1 complex on replication forks promotes CMG helicase retention to ensure stem cell genomic stability and embryogenesis

Replication stress is a major source of endogenous DNA damage. Despite that numerous proteins have been identified on replication forks to modulate fork or replication machinery activities, it remains unexplored whether non-coding RNAs can localize on stalled forks and play critical regulatory roles. Here we identify an uncharacterized lncRNA NONMMUT028956 (Lnc956 for short) predominantly expressed in mouse embryonic stem cells. Lnc956 is recruited to stalled replication forks to prevent fork collapse and preserve genomic stability, and is essential for mouse embryogenesis. Mechanistically, it drives assembly of the Lnc956-TRIM28-HSP90B1 ribonucleoprotein (RNP) complex on stalled forks in an inter-dependent manner downstream of ATR signaling. This RNP complex physically associates with MCM2-7 hexamer via TRIM28 and directly regulates the CMG helicase retention on chromatin. The regulation of RNP on CMG retention is mediated by HSP90B1s chaperoning function. These findings reveal a novel pathway which actively regulates replisome retention to prevent fork collapse.

cell biology↗

bioRxiv2022

Structural insights into the regulation of acetylation-dependent genome condensation

Histone modifications are deposited by chromatin modifying enzymes and read out by proteins that recognize the modified state. BRD4-NUT is an oncogenic fusion protein of the acetyl lysine reader BRD4 that binds to the acetylase p300 and enables formation of long-range intra- and interchromosomal interactions. We here examine how acetylation reading and writing enable formation of such interactions. We show that NUT contains an acidic transcriptional activation domain that binds to the TAZ2 domain of p300. We use NMR to investigate the structure of the complex and found that the TAZ2 domain has an autoinhibitory role for p300. NUT-TAZ2 interaction or mutations found in cancer that interfere with autoinhibition by TAZ2 allosterically activate p300. p300 activation results in a self-organizing, acetylation-dependent feed-forward reaction that enables long-range interactions by bromodomain multivalent acetyl-lysine binding. We discuss the implications for chromatin organisation, gene regulation and dysregulation in disease.

biochemistry↗

bioRxiv2022

Hyperuricemia triggers Renal Tubular Epithelial Pyroptosis by using ROS to activate the NLRP3 inflammasome

Renal injury resulting from hyperuricemia has gained a lot of interest. Pyroptosis refers to inflammatory cell death. The activated caspase-1 cleavage, and the pivotal protein - GSDMD could have an association with the hyperuricemic kidney lesion pathogenesis. ROS is a vital NLRP3 inflammasome antagonist in various cells. We investigated the mechanism through which ROS stimulates NLRP3 to modulate pyroptosis in renal tubular epithelial cells as well as hyperuricemic rat kidneys. MethodsIn vitro cultured renal tubular epithelial cells (NRK-52E cells) were incubated with a gradient concentration of uric acid for 24 hr to investigate the pyroptosis through flow cytometry. Next, we used the inhibitors of ROS, mitochondrial ROS, NLRP3 and Caspase-1 respectively to intervene in uric acid treated cells to analyse pyproptosis and activation of ROS- NLRP3 inflammasome signal pathway. Finally, we evaluated the mechanism of hyperuricemia triggering renal tubular epithelial pyroptosis in rat kidney tissues. ResultsThe levels of ROS and mitochondrial ROS, the mRNA and protein expression of pyroptosis-associated factors Caspase-1 (p45, p20/10), NLRP3, and GSDMD were upregulated in uric acid, the induced NRK-52E cells as well as hyperuricemic model kidneys. The inhibition of ROS, mitochondrial ROS, NLRP3, or caspase-1 in the uric acid-induced NRK-52E cells may help in controlling pyroptosis. The expression of mRNA and protein by the cytokines IL-18 and IL-1{beta} also increased. ConclusionsGenerally, hyperuricemia triggered renal tubular epithelial pyroptosis via excessive ROS to modulate NLRP3 inflammasome activation in uric acid stimulated renal tubular epithelial cells as well as the oxonic acid potassium induced hyperuricemia.

pathology↗

bioRxiv2022

Ribosome profiling reveals the translational landscape and allele-specific translation efficiency in rice

Translational regulation is a critical step in the process of gene expression and governs the synthesis of proteins from mRNAs. Many studies have revealed the translational regulation in plants in response to various environmental stimuli. However, there has been no comprehensive landscape of translational regulation and allele-specific translation efficiency in multiple tissues of plants, especially in rice, a main staple crop feeding nearly half of the world population. Here, we used RNA-seq and Ribo-seq data to analyze the transcriptome and translatome of an elite hybrid rice SY63 and its parental varieties ZS97 and MH63. The results revealed that gene expression patterns varied more significantly between tissues than between varieties at both transcriptional and translational levels. Besides, we identified 3,392 upstream open reading frames (uORFs), and most of the uORF-containing genes were enriched for transcription factors. Only 668 long non-coding RNAs could be translated into peptides. Finally, we discovered numerous genes with allele-specific translation efficiency in SY63, and further demonstrated that some cis-regulatory elements (secondary structures of mRNAs and the binding of miRNAs) may contribute to allelic divergence in translation efficiency. Overall, our findings may improve the understanding of translational regulation in rice and provide information for the molecular basis of breading research.

bioinformatics↗

bioRxiv2022

Comprehensive dissection of immune microenvironment in the progression of early gastric cancer at spatial and single-cell resolution

While the changes of tumor immune microenvironment (TME) have critical implications for most tumor progression, works that could reveal the compositions and immunity features of TME are needed. Profiling gastric malignant cells at single-cells resolution has shown the transcriptional heterogeneity is represented at different states of gastric cancer, implying that diverse cell states may exist, including immune cells, and all components in TME make some balances in early gastric cancer (EGC) progression. However, it remains unclear how immune cells contributing malignancy of gastritis, constituting general characteristics of gastric TME. Furthermore, the role of specific interactions among cells in gastric TME remains to be illustrated. Here, we performed spatial transcriptomes and single-cell RNA-seq analysis across 18 gastric samples, identifying 17 celltypes and reconstructing their location information. We found that immune cells represented different degree of dysregulations during the progression from non-atrophic gastritis (NAG), atrophic gastritis (AG) to EGC, including imbalance of cytotoxic and inhibitory effects in T cells, maturation inhibition in B cells and malignant genes up-regulated obviously in myeloid cells. Besides, pathway activities showed that hypoxia, reactive oxygen species and fatty metabolism signaling were activated from AG stage, which may accelerate progression of EGC. Moreover, cellular interactions further identified the roles of hypoxia in gastric TME. Overall, the multi-omics data presented in this study offer a comprehensive view of immune cell types, states changes and locations within the gastric tissues during the progression from NAG, AG to EGC, advancing our understanding of the composition and immunity of different gastric states, offering diagnostic and preventive thoughts for EGC.

cancer biology↗

bioRxiv2022

Diverse innate immune factors protect yeast from lethal viral pathogenesis

In recent years, newly characterized anti-viral systems have proven to be remarkably conserved from bacteria to mammals, demonstrating that unique insights into these systems can be gained by studying microbial organisms. Despite the enthusiasm generated by these findings, the key microbial model organism Saccharomyces cerevisiae (budding yeast) has been minimally exploited for studies of viral defense, primarily because it is not infected with exogenously transmitted viruses. However, most yeast strains are infected with an endogenous double stranded RNA (dsRNA) virus called L-A, and previous studies identified conserved antiviral systems that attenuate L-A replication. Although these systems do not completely eradicate L-A, we show here that they do prevent proteostatic stress and lethality caused by L-A over-proliferation. Exploiting this new finding, we demonstrate that the genetic screening methods available in yeast can be used to identify additional conserved antiviral systems. Using these approaches, we discovered antiviral functions for the yeast homologs of polyA-binding protein (PABPC1) and the La-domain containing protein Larp1, which are both involved in viral innate immunity in humans. We also identified new antiviral functions for the RNA exonucleases REX2 and MYG1, both of which have distinct but poorly characterized human and bacterial homologs. These findings highlight the potential of yeast as a powerful model system for the discovery and characterization of conserved antiviral systems. Significance StatementThe budding yeast Saccharomyces cerevisiae has been minimally exploited for investigation of host-virus interactions despite its chronic infection with a double-stranded RNA virus called L-A. Controverting its presumed harmless nature, we show here that L-A causes pathogenesis in cells lacking parallel-acting viral attenuation pathways. Taking advantage of the genetic tools available in budding yeast, we identify several highly conserved proteins to play a role in antiviral defense. Some of these have been recently identified in humans to be involved in viral innate immunity, thus highlighting the potential of budding yeast as a model organism to identify and investigate new antiviral systems.

microbiology↗

bioRxiv2022

Lineage-mosaic and mutation-patched spike proteins for broad-spectrum COVID-19 vaccine

The widespread SARS-CoV-2 in humans results in the continuous emergence of new variants. Recently emerged Omicron variant with multiple spike mutations sharply increases the risk of breakthrough infection or reinfection, highlighting the urgent need for new vaccines with broad-spectrum antigenic coverage. Using inter-lineage chimera and mutation patch strategies, we engineered a recombinant monomeric spike variant (STFK1628x), which showed high immunogenicity and mutually complementary antigenicity to its prototypic form (STFK). In hamsters, a bivalent vaccine comprised of STFK and STFK1628x elicited high titers of broad-spectrum antibodies to neutralize all 14 circulating SARS-CoV-2 variants, including Omicron; and fully protected vaccinees from intranasal SARS-CoV-2 challenges of either the ancestral strain or immune-evasive Beta variant. Strikingly, the vaccination of hamsters with the bivalent vaccine completely blocked the within-cage virus transmission to unvaccinated sentinels, for either the ancestral SARS-CoV-2 or Beta variant. Thus, our study provides new insights and antigen candidates for developing next-generation COVID-19 vaccines.

microbiology↗

bioRxiv2022

A genome-wide atlas of antibiotic susceptibility targets and pathways to tolerance.

Detailed knowledge on how bacteria evade antibiotics and eventually develop resistance could open avenues for novel therapeutics and diagnostics. It is thereby key to develop a comprehensive genome-wide understanding of how bacteria process antibiotic stress, and how modulation of the involved processes affects their ability to overcome said stress. Here we undertake a comprehensive genetic analysis of how the major human pathogen Streptococcus pneumoniae responds to 20 antibiotics. We built a genome-wide atlas of drug susceptibility determinants and generate a genetic interaction network that connects cellular processes and genes of unknown function, which we show can be used as therapeutic targets. Pathway analysis reveals a genome-wide "tolerome", defined by cellular processes that can make a bacterium less susceptible, and often tolerant, in an antibiotic specific manner. Importantly, modulation of these processes confers fitness benefits during active infections under antibiotic selection. Moreover, screening of sequenced clinical isolates demonstrates that mutations in tolerome genes readily evolve and are frequently associated with resistant strains, indicating such mutations may be an important harbinger for the emergence of antibiotic resistance.

microbiology↗

bioRxiv2022

Harringtonine has the effects of double blocking SARS-CoV-2 membrane fusion

Fusion with host cell membrane is the main mechanism of infection of SARS-CoV-2. Here, we propose a new strategy to double block SARS-CoV-2 membrane fusion by using Harringtonine (HT), a small-molecule antagonist. By using cell membrane chromatography (CMC), we found that HT specifically targeted the SARS-CoV-2 S protein and host cell TMPRSS2, and then confirmed that HT can inhibit pseudotyped virus membrane fusion. Furthermore, HT successfully blocked SARS-CoV-2 infection, especially in the delta and Omicron mutant. Since HT is a small-molecule antagonist, it is minimally affected by the continuous variation of SARS-CoV-2. Our findings show that HT is a potential small-molecule antagonist with a new mechanism of action against SARS-CoV-2 infection, and thus HT mainly targets the S protein, and thus, greatly reduces the damage of the S proteins autotoxicity to the organ system, has promising advantages in the clinical treatment of COVID-19.

pharmacology and toxicology↗

bioRxiv2021

DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA

Models developed using Nanopore direct RNA sequencing data from in vitro synthetic RNA with all adenosine replaced by N6-methyladenosine (m6A), are likely distorted due to superimposed signals from saturated m6A residues. Here, we develop a neural network, DENA, for m6A quantification using the sequencing data of in vivo transcripts from Arabidopsis. DENA identifies 90% of miCLIP-detected m6A sites in Arabidopsis, and obtains modification rates in human consistent to those found by SCARLET, demonstrating its robustness across species. We sequence the transcriptome of two additional m6A-deficient Arabidopsis, mtb and fip37-4, using Nanopore and evaluate their single-nucleotide m6A profiles using DENA.

bioinformatics↗

bioRxiv2021

Fast Medical Image Auto-segmentation for Bleeding Gastric Tissue Detection based on Deep DuS-KFCM Clustering

BackgroundDetection and classification of gastric bleeding tissues are one of the challenging tasks in endoscopy image analysis. Lesion detection plays an important role in gastric cancer (GC) diagnosis and follow-up. Manual segmentation of endoscopy images is a very time-consuming task and subject to intra- and interrater variability. Accurate GB segmentation in abdominal sequences is an essential and crucial task for surgical planning and navigation in gastric lesion ablation. However, GB segmentation in endoscope is a substantially challenging work because the intensity values of gastric blood are similar to those of adjacent structures. ObjectiveIn this paper the idea is to combine two parts: Neural Network and Fuzzy Logic--Hybrid Neuro-Fuzzy system. The objective of this manuscript is to provide an efficient way to segment the gastric bleeding lesion area. This work focuses on design and development of an automated diagnostic system using gastric bleeding cancer endoscopy images. MethodsIn this paper, a coarse-to-fine method was applied to segment gastric bleeding lesion from endoscopy images, which consists of two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm combined with spatial gray level co-occurrence matrix (GLCM) and the refined segmentation is implemented with deeplabv3+ (backbone with resnet50) algorithm to improve the overall accuracy. ResultsExperimental results for gastric bleeding segmentation show that the method provides an accuracy of 87.9476% with specificity of 96.3343% and performs better than other related methods. onclusionsThe performance of the method was evaluated using two benchmark datasets: The GB Segmentation and the healthy datasets. Then use the gastric red spots (GRS) dataset to do the final test to verify weak bleeding symptoms. Our method achieves high accuracy in gastric bleeding lesion segmentation. The work describes an innovative way of using GLCM based textural features to extract underlying information in gastric bleeding cancer imagery. Modified deep DuS-KFCM endoscopy image segmentation method based on GLCM feature, The experimental results shown to be effective in image segmentation and has good performance of resisting noise, segmentation effect more ideal.

bioinformatics↗

bioRxiv2021

A Deep Learning Approach for NeoAG-Specific Prediction Considering Both HLA-Peptide Binding and Immunogenicity: Finding Neoantigens to Making T-Cell Products More Personal

BackgroundAn emerging type of cancer treatment, known as cell immunotherapy, is gaining popularity over chemotherapy or other radiation therapy that causes mass destruction to our body. One favourable approach in cell immunotherapy is the use of neoantigens as targets that help our body immune system identify the cancer cells from healthy cells. Neoantigens, which are non-autologous proteins with individual specificity, are generated by non-synonymous mutations in the tumor cell genome. Owing to its strong immunogenicity and lack of expression in normal tissues, it is now an important target for tumor immunotherapy. Neoantigens are some form of special protein fragments excreted as a by-product on the surface of cancer cells during the DNA mutation at the tumour. In cancer immunotherapies, certain neoantigens which exist only on cancer cells elicit our white blood cells (bodys defender, anti-cancer T-cell) responses that fight the cancer cells while leaving healthy cells alone. Personalized cancer vaccines therefore can be designed de novo for each individual patient, when the specific neoantigens are found to be relevant to his/her tumour. The vaccine which is usually coded in synthetic long peptides, RNA or DNA representing the neoantigens trigger an immune response in the body to destroy the cancer cells (tumour). The specific neoantigens can be found by a complex process of biopsy and genome sequencing. Alternatively, modern technologies nowadays tap on AI to predict the right neoantigen candidates using algorithms. However, determining the binding and non-binding of neoantigens on T-cell receptors (TCR) is a challenging computational task due to its very large search space. ObjectiveTo enhance the efficiency and accuracy of traditional deep learning tools, for serving the same purpose of finding potential responsiveness to immunotherapy through correctly predicted neoantigens. It is known that deep learning is possible to explore which novel neoantigens bind to T-cell receptors and which ones dont. The exploration may be technically expensive and time-consuming since deep learning is an inherently computational method. one can use putative neoantigen peptide sequences to guide personalized cancer vaccines design. MethodsThese models all proceed through complex feature engineering, including feature extraction, dimension reduction and so on. In this study, we derived 4 features to facilitate prediction and classification of 4 HLA-peptide binding namely AAC and DC from the global sequence, and the LAAC and LDC from the local sequence information. Based on the patterns of sequence formation, a nested structure of bidirectional long-short term memory neural network called local information module is used to extract context-based features around every residue. Another bilstm network layer called global information module is introduced above local information module layer to integrate context-based features of all residues in the same HLA-peptide binding chain, thereby involving inter-residue relationships in the training process. introduced ResultsFinally, a more effective model is obtained by fusing the above two modules and 4 features matric, the method performs significantly better than previous prediction schemes, whose overall r-square increased to 0.0125 and 0.1064 on train and increased to 0.0782 and 0.2926 on test datasets. The RMSE for our proposed models trained decreased to approximately 0.0745 and 1.1034, respectively, and decreased to 0.6712 and 1.6506 on test dataset. ConclusionOur work has been actively refining a machine-learning model to improve neoantigen identification and predictions with the determinants for Neoantigen identification. The final experimental results show that our method is more effective than existing methods for predicting peptide types, which can help laboratory researchers to identify the type of novel HLA-peptide binding.

bioinformatics↗

bioRxiv2021

A deep learning-based multisite neuroimage harmonization framework established with traveling-subject dataset

The accumulation of multisite large-sample MRI datasets collected by large brain research projects in the last decade has provided a critical resource for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects, observed in the imaging data and their derived structural and functional features, has prevented the derivation of consistent findings across different studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we proposed a deep learning-based framework to harmonize imaging data from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling-subject dataset from SRPBS and harmonized the gray matter volume maps from eight source sites to a target site. The proposed framework significantly eliminated inter-site differences in gray matter volume. The embedded encoders successfully captured both the abstract texture of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in site effect removal, data distribution homogenization, and intra-subject similarity improvement. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that could enhance reliability and reproducibility in multisite studies for brain development and brain disorders.

neuroscience↗

bioRxiv2021

Identification and Characterization of Long Non-Coding RNA in Tomato Roots under Salt Stress

As one of the most important vegetable crops in the world, the production of tomatoes was restricted by salt stress. Therefore, it is of great interest to analyze the salt stress tolerance genes. As the non-coding RNAs (ncRNAs) with a length of more than 200 nucleotides, long non-coding RNAs (lncRNAs) lack the ability of protein-coding, but they can play crucial roles in plant development and response to abiotic stresses by regulating gene expression. Nevertheless, there are few studies on the roles of salt-induced lncRNAs in tomatoes. Therefore, we selected wild tomato Solanum pennellii (S. pennellii) and cultivated tomato M82 to be materials. By high-throughput sequencing, 1044 putative lncRNAs were identified here. Among them, 154 and 137 lncRNAs were differentially expressed in M82 and S. pennellii, respectively. Through functional analysis of target genes of differentially expressed lncRNAs (DE-lncRNAs), some genes were found to respond positively to salt stress by participating in Abscisic Acid (ABA) signaling pathway, brassinosteroid (BR) signaling pathway, ethylene (ETH) signaling pathway and anti-oxidation process. We also construct a salt-induced lncRNA-mRNA co-expression network to dissect the putative mechanisms of high salt tolerance in S. pennellii. We analyze the function of salt-induced lncRNAs in tomato roots at the genome-wide levels for the first time. These results will contribute to understanding the molecular mechanisms of salt tolerance in tomatoes from the perspective of lncRNAs.

bioinformatics↗

bioRxiv2021

SCARECROW maintains the stem cell niche in Arabidopsis root by ensuring telomere integrity

Stem cells are the ultimate source of the cells in various tissues and organs, and thus are essential to postembryonic plant growth and development. SCARECROW (SCR) is a plant-specific transcription regulator well known for its role in stem-cell renewal in plant roots, but the mechanism by which SCR exerts this function is still unclear. To address this question, we carried out a genetic screen for mutants that no longer express SCR in the stem-cell niche in the Arabidopsis root, and one of the mutants is characterized herein. Using marker-assisted mapping, whole genome sequencing, and complementation tests, we pinpointed the causal mutation in this mutant at TEN1, which encodes telomere-end protecting factor. By sequence alignment of TEN1 homologs in a wide range of eukaryotes, we identified two novel motifs. The importance of these motifs was examined through site-directed mutagenesis of a conserved amino acid, and through complementation tests, which showed that G100 is required for TEN1 stability. Interestingly, we found that TEN1 expression was dramatically reduced in the scr mutant. Two components in the same protein complex as TEN1, STN1 and CTC1, were found to be also dramatically downregulated in scr, as well as telomerase. Further studies showed that loss of STN1, CTC1 and telomerase also caused defects in the root stem cells. In line with these findings, the scr mutant was hypersensitive to DNA damage reagents such as Zeocin. These results together suggest that SCR maintains root stem cells by promoting expression of genes that ensures genome integrity. One sentence summaryThis study reveals a role for the root development regulator SCARECROW in maintaining the expression of telomere-end protecting factors and a connection between genome integrity and stem-cell maintenance in Arabidopsis root.

plant biology↗

bioRxiv2021

Genome-wide association study of open fieldbehavior in outbred heterogeneous stock ratsidentifies multiple loci implicated inpsychiatric disorders

Many personality traits are influenced by genetic factors. Rodents models provide an efficient system for analyzing genetic contribution to these traits. Using 1,246 adolescent heterogeneous stock (HS) male and female rats, we conducted a genome-wide association study (GWAS) of behaviors measured in an open field, including locomotion, novel object interaction, and social interaction. We identified 30 genome-wide significant quantitative trait loci (QTL). Using multiple criteria, including the presence of high impact genomic variants and co-localization of cis-eQTL, we identified 13 candidate genes (Adarb2, Ankrd26, Cacna1c, Clock, Crhr1, Ctu2, Cyp26b1, Eva1a, Fam114a1, Kcnj9, Mlf2, Rab27b, Sec11a) for these traits. Most of these genes have been implicated by human GWAS of various psychiatric traits. For example, Cacna1c, a gene known to be critical for social behavior in rodents and implicated in human schizophrenia and bipolar disorder, is a candidate gene for distance to the social zone. In addition, the QTL region for total distance to the novel object zone, on Chr1 at 144 Mb, is syntenic to a hotspot on human Chr15 (82.5-90.8 Mb) that contains 14 genes associated with psychiatric or substance abuse traits. Although some of the genes identified by this study appear to replicate findings from prior human GWAS, others likely represent novel findings that can be the catalyst for future molecular and genetic insights into human psychiatric diseases. Together, these findings provide strong support for the use of the HS population to study psychiatric disorders.

genetics↗