Dissecting the genetic and causal relationship between sleep-related traits and common brain disorders.

BACKGROUND: There is a profound connection between abnormal sleep patterns and brain disorders, suggesting a shared influential association. However, the shared genetic basis and potential causal relationships between sleep-related traits and brain disorders are yet to be fully elucidated. METHODS: Utilizing linkage disequilibrium score regression (LDSC) and bidirectional two-sample univariable Mendelian Randomization (UVMR) analyses with large-scale GWAS datasets, we investigated the genetic correlations and causal associations across six sleep traits and 24 prevalent brain disorders. Additionally, a multivariable Mendelian Randomization (MVMR) analysis evaluated the cumulative effects of various sleep traits on each brain disorder, complemented by genetic loci characterization to pinpoint pertinent genes and pathways. RESULTS: LDSC analysis identified significant genetic correlations in 66 out of 144 (45.8 %) pairs between sleep-related traits and brain disorders, with the most pronounced correlations observed in psychiatric disorders (66 %, 48/72). UVMR analysis identified 29 causal relationships (FDR<0.05) between sleep traits and brain disorders, with 19 associations newly discovered according to our knowledge. Notably, major depression, attention-deficit/hyperactivity disorder, bipolar disorder, cannabis use disorder, and anorexia nervosa showed bidirectional causal relations with sleep traits, especially insomnia's marked influence on major depression (IVW beta 0.468, FDR = 5.24E-09). MVMR analysis revealed a nuanced interplay among various sleep traits and their impact on brain disorders. Genetic loci characterization underscored potential genes, such as HOXB2, while further enrichment analyses illuminated the importance of synaptic processes in these relationships. CONCLUSIONS: This study provides compelling evidence for the causal relationships and shared genetic backgrounds between common sleep-related traits and brain disorders.

Construction of an immune-related prognostic signature and lncRNA-miRNA-mRNA ceRNA network in acute myeloid leukemia.

The progression of acute myeloid leukemia (AML) is influenced by the immune microenvironment in the bone marrow and dysregulated intracellular competing endogenous RNA (ceRNA) networks. Our study utilized data from UCSC Xena, The Cancer Genome Atlas Program, the Gene Expression Omnibus, and the Immunology Database and Analysis Portal. Using Cox regression analysis, we identified an immune-related prognostic signature. Genomic analysis of prognostic messenger RNA (mRNA) was conducted through Gene Set Cancer Analysis (GSCA), and a prognostic ceRNA network was constructed using the Encyclopedia of RNA Interactomes. Correlations between signature mRNAs and immune cell infiltration, checkpoints, and drug sensitivity were assessed using R software, gene expression profiling interactive analysis (GEPIA), and CellMiner, respectively. Adhering to the ceRNA hypothesis, we established a potential long noncoding RNA (lncRNA)/microRNA (miRNA)/mRNA regulatory axis. Our findings pinpointed 9 immune-related prognostic mRNAs (KIR2DL1, CSRP1, APOBEC3G, CKLF, PLXNC1, PNOC, ANGPT1, IL1R2, and IL3RA). GSCA analysis revealed the impact of copy number variations and methylation on AML. The ceRNA network comprised 14 prognostic differentially expressed lncRNAs (DE-lncRNAs), 6 prognostic DE-miRNAs, and 3 prognostic immune-related DE-mRNAs. Correlation analyses linked these mRNAs' expression to 22 immune cell types and 6 immune checkpoints, with potential sensitivity to 27 antitumor drugs. Finally, we identified a potential LINC00963/hsa-miR-431-5p/CSRP1 axis. This study offers innovative insights for AML diagnosis and treatment through a novel immune-related signature and ceRNA axis. Identified novel biomarkers, including 2 mRNAs (CKLF, PNOC), 1 miRNA (hsa-miR-323a-3p), and 10 lncRNAs (SNHG25, LINC01857, AL390728.6, AC127024.5, Z83843.1, AP002884.1, AC007038.1, AC112512, AC020659.1, AC005921.3) present promising candidates as potential targets for precision medicine, contributing to the ongoing advancements in the field.

Chimeric vaccine design against the epidemic Langya Henipavirus using immunoinformatics and validation via immune simulation approaches.

In July 2022, a new virus called Langya virus (LayV) was discovered in China in patients who had a fever. This virus is a type of Henipavirus (HNV) and is considered a potential threat as it could spread from animals to humans. It causes respiratory disease with symptoms including fever, coughing, and fatigue and is closely linked to two other henipaviruses that are known to infect humans, namely Hendra and Nipah viruses. These viruses may cause fatal respiratory illnesses. Investigators believe that the LayV is spread by shrews, and may have infected humans directly or via an intermediary species. Thus, the use of vaccines or immunizations against LayV is an alternate strategy for disease prevention. In this study, we employed various immunoinformatics methods to predict B cell, HTL and T cell epitopes from the LayV proteome in order to find the most promising candidate for a LayV vaccine. The most potent epitopes that are immunogenic and non-allergenic were joined with each other through suitable linkers. Human ß-defensin 2 was employed as an adjuvant to increase the immunogenicity of the vaccine construct. The final sequence of a multi-epitope vaccine construct was modelled for docking with TLRs. Concisely, our results suggest that the docked complexes of vaccine-TLRs seemed to be stable. Additionally, in silico cloning was done using E. coli as the host in order to validate the expression of our designed vaccine construct. The GC content of 54.39% and CAI value of 0.94 revealed that the vaccine component expresses efficiently in the host. This study presents the novel vaccine construct for LayV which will be essential for further experimental validations to confirm the immunogenicity and safety of the proposed vaccine structure, and eventually to treat HNV-related diseases.

Identification of Distinct Genetic Profiles of Palindromic Rheumatism Using Whole-Exome Sequencing.

OBJECTIVE: Previous studies have underlined the genetic susceptibility in the pathogenesis of palindromic rheumatism (PR), but the known PR loci only partially explain the disease's genetic background. We aimed to genetically identify PR by whole-exome sequencing (WES). METHODS: This multicenter prospective study was conducted in 10 Chinese specialized rheumatology centers between September 2015 and January 2020. WES was performed in 185 patients with PR and in 272 healthy controls. PR patients were divided into PR subgroups who were negative for anti-citrullinated protein antibody (ACPA-) and positive for ACPA (ACPA+) according to ACPA titer (cutoff value 20 IU/liter). We conducted whole-exome association analysis for the WES data. We used HLA imputation to type HLA genes. In addition, we used the polygenic risk score to measure the genetic correlations between PR and rheumatoid arthritis (RA) and the genetic correlations between ACPA- PR and ACPA+ PR. RESULTS: Among 185 patients with PR enrolled in our study, 50 patients (27.02%) were ACPA+ and 135 PR patients (72.98%) were ACPA-. We identified 8 novel loci (in the ACPA- PR group: ZNF503, RPS6KL1, HOMER3, HLA-DRA; in the ACPA+ PR group: RPS6KL1, TNPO2, WASH2P, FANK1) and 3 HLA alleles (in the ACPA- PR group: HLA-DRB1*0803 and HLA-DQB1; in the ACPA+ PR group: HLA-DPA1*0401) that were associated with PR and that surpassed genome-wide significance (P < 5 × 10-8 ). Furthermore, polygenic risk score analysis showed that PR and RA were not similar (R2 < 0.025), whereas ACPA+ PR and ACPA- PR showed a moderate genetic correlation (0.38 < R2 < 0.8). CONCLUSION: This study demonstrated the distinct genetic background between ACPA- and ACPA+ PR patients. Additionally, our findings strengthened that PR and RA were not genetically similar.

Integrative Genomic Analysis of m6a-SNPs Identifies Potential Functional Variants Associated with Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that affects 35 million people worldwide. However, no potential therapeutics currently are available for AD because of the multiple factors involved in it, such as regulatory factors with their candidate genes, factors associated with the expression levels of its corresponding genes, and many others. To date, 29 novel loci from GWAS have been reported for AD by the Psychiatric Genomics Consortium (PGC2). Nevertheless, the main challenge of the post-GWAS era, namely to detect significant variants of the target disease, has not been conducted for AD. N6-methyladenosine (m6a) is reported as the most prevalent mRNA modification that exists in eukaryotes and that influences mRNA nuclear export, translation, splicing, and the stability of mRNA. Furthermore, studies have also reported m6a's association with neurogenesis and brain development. We carried out an integrative genomic analysis of AD variants from GWAS and m6a-SNPs from m6AVAR to identify the effects of m6a-SNPs on AD and identified the significant variants using the statistically significance value (p-value <0.05). The cis-regularity variants with their corresponding genes and their influence on gene expression in the gene expression profiles of AD patients were determined, and showed 1458 potential m6a-SNPs (based on p-value <0.05) associated with AD. eQTL analysis showed that 258 m6a-SNPs had cis-eQTL signals that overlapped with six significant differentially expressed genes based on p-value <0.05 in two datasets of AD gene expression profiles. A follow-up study to elucidate the impact of our identified m6a-SNPs in the experimental study would validate our findings for AD, which would contribute to the etiology of AD.

DROEG: a method for cancer drug response prediction based on omics and essential genes integration.

Predicting therapeutic responses in cancer patients is a major challenge in the field of precision medicine due to high inter- and intra-tumor heterogeneity. Most drug response models need to be improved in terms of accuracy, and there is limited research to assess therapeutic responses of particular tumor types. Here, we developed a novel method DROEG (Drug Response based on Omics and Essential Genes) for prediction of drug response in tumor cell lines by integrating genomic, transcriptomic and methylomic data along with CRISPR essential genes, and revealed that the incorporation of tumor proliferation essential genes can improve drug sensitivity prediction. Concisely, DROEG integrates literature-based and statistics-based methods to select features and uses Support Vector Regression for model construction. We demonstrate that DROEG outperforms most state-of-the-art algorithms by both qualitative (prediction accuracy for drug-sensitive/resistant) and quantitative (Pearson correlation coefficient between the predicted and actual IC50) evaluation in Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia datasets. In addition, DROEG is further applied to the pan-gastrointestinal tumor with high prevalence and mortality as a case study at both cell line and clinical levels to evaluate the model efficacy and discover potential prognostic biomarkers in Cisplatin and Epirubicin treatment. Interestingly, the CRISPR essential gene information is found to be the most important contributor to enhance the accuracy of the DROEG model. To our knowledge, this is the first study to integrate essential genes with multi-omics data to improve cancer drug response prediction and provide insights into personalized precision treatment.

The Relationship between Alcohol Consumption and Gout: A Mendelian Randomization Study.

Gout is a disease that manifests itself after decades of following a high-purine diet, with excessive alcohol consumption assumed to be one of the main contributors to the development of the disease. This study performs a Mendelian randomization (MR) analysis to determine whether alcohol consumption causally affects the risk of developing both hyperuricemia and gout. The results indicate that genetically predicted drinks consumed per week have no causal effect on neither the risk of gout (p = 0.35), nor serum uric acid levels (p = 0.73). For MR analysis in the other direction, genetic risk of gout was significantly associated with drinks per week (p = 0.03). Furthermore, the results of the MR analysis were verified in a cohort of individuals diagnosed with hyperuricemia and gout, comprising of alcohol-consuming and alcohol-abstaining subgroups. When split by alcohol status, the serum uric acid levels failed to show a significant difference in both gout (p = 0.92) and hyperuricemia (p = 0.23) subgroups. Overall, the results suggest that increased alcohol consumption does not play a causal role in the development of gout.

Structural Comparison and Drug Screening of Spike Proteins of Ten SARS-CoV-2 Variants.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has evolved many variants with stronger infectivity and immune evasion than the original strain, including Alpha, Beta, Gamma, Delta, Epsilon, Kappa, Iota, Lambda, and 21H strains. Amino acid mutations are enriched in the spike protein of SARS-CoV-2, which plays a crucial role in cell infection. However, the impact of these mutations on protein structure and function is unclear. Understanding the pathophysiology and pandemic features of these SARS-CoV-2 variants requires knowledge of the spike protein structures. Here, we obtained the spike protein structures of 10 main globally endemic SARS-CoV-2 strains using AlphaFold2. The clustering analysis based on structural similarity revealed the unique features of the mainly pandemic SARS-CoV-2 Delta variants, indicating that structural clusters can reflect the current characteristics of the epidemic more accurately than those based on the protein sequence. The analysis of the binding affinities of ACE2-RBD, antibody-NTD, and antibody-RBD complexes in the different variants revealed that the recognition of antibodies against S1 NTD and RBD was decreased in the variants, especially the Delta variant compared with the original strain, which may induce the immune evasion of SARS-CoV-2 variants. Furthermore, by virtual screening the ZINC database against a high-accuracy predicted structure of Delta spike protein and experimental validation, we identified multiple compounds that target S1 NTD and RBD, which might contribute towards the development of clinical anti-SARS-CoV-2 medicines. Our findings provided a basic foundation for future in vitro and in vivo investigations that might speed up the development of potential therapies for the SARS-CoV-2 variants.

Integrative omics analysis reveals effective stratification and potential prognosis markers of pan-gastrointestinal cancers.

Gastrointestinal (GI) tract cancers are the most common malignant cancers with high mortality rate. Pan-cancer multi-omics data fusion provides a powerful strategy to examine commonalities and differences among various cancer types and benefits for the identification of pan-cancer drug targets. Herein, we conducted an integrative omics analysis on The Cancer Genome Atlas pan-GI samples including six carcinomas and stratified into 9 clusters, i.e. 5 single-type-dominant clusters and 4 mixed clusters, the clustering reveals the molecular features of different subtypes, other than the organ and cell-of-origin classifications. Especially the mixed clusters revealed the homogeneity of pan-GI cancers. We demonstrated that the prognosis differences among pan-GI subtypes based on multi-omics integration are more significant than clustering by single-omics. The potential prognostic markers for pan-GI stratification were identified by proportional hazards model, such as PSCA (for colorectal and stomach cancer) and PPP1CB (for liver and pancreatic cancer), which have prominent prognostic power supported by high concordance index.

Systematic comparative study of computational methods for HLA typing from next-generation sequencing.

The human leukocyte antigen (HLA) system plays an important role in hematopoietic stem cell transplantation (HSCT) and organ transplantations, immune disorders as well as oncological immunotherapy. However, HLA typing remains a challenging task due to the high level of polymorphism and homology among HLA genes. Based on the high-throughput next-generation sequencing data, new HLA typing algorithms and software tools were developed. But there is still a deficit of systematic comparative studies to assist in the selection of the optimal analytical approaches under different conditions. Here, we present a detailed comparison of eight software tools for HLA typing on different real datasets (whole-genome sequencing, whole-exome sequencing and transcriptomic sequencing data) and in-silico samples with different sequencing lengths, depths, and error rates. We figure out the algorithms with the best efficiency in different scenarios, and demonstrate the effect of different raw reads on analytical performances. Our results provide a comprehensive picture of specifications and performances of the eight existing HLA genotyping algorithms, which could assist researchers in selecting the most appropriate tool for specific raw datasets.

Structural Analysis of the SARS-CoV-2 Omicron Variant Proteins.

The spread of the latest SARS-CoV-2 variant Omicron is particularly concerning because of the large number of mutations present in its genome and lack of knowledge about how these mutations would affect the current SARS-CoV-2 vaccines and treatments. Here, by performing phylogenetic analysis using the Omicron spike (S) protein sequence, we found that the Omicron S protein presented the longest evolutionary distance in relation to the other SARS-CoV-2 variants. We predicted the structures of S, M, and N proteins of the Omicron variant using AlphaFold2 and investigated how the mutations have affected the S protein and its parts, S1 NTD and RBD, in detail. We found many amino acids on RBD were mutated, which may influence the interactions between the RBD and ACE2, while also showing the S309 antibody could still be capable of neutralizing Omicron RBD. The Omicron S1 NTD structures display significant differences from the original strain, which could lead to reduced recognition by antibodies resulting in potential immune escape and decreased effectiveness of the existing vaccines. However, this study of the Omicron variant was mainly limited to structural predictions, and these findings should be explored and verified by subsequent experiments. This study provided basic data of the Omicron protein structures that lay the groundwork for future studies related to the SARS-CoV-2 Omicron variant.

Common variants in FAN1, located in 15q13.3, confer risk for schizophrenia and bipolar disorder in Han Chinese.

Multiple genetic risk factors have been associated with psychiatric disorders which provides the genetic insight to these disorders; however, the etiology of these disorders is still elusive. 15q13.3 was previously associated with schizophrenia, bipolar and other neurodevelopmental disorders. Whereas, the FAN1 which encodes the Fanconi anemia associated nuclease 1 was suggested to be causal gene for 15q13.3 related psychiatric disorders. This study aimed to investigate the association of FAN1 with three major psychiatric disorders. Herein, we conducted a case-control study with the Chinese Han population. Three single nucleotide polymorphisms (SNPs) of FAN1 were genotyped in 1248 schizophrenia cases, 1344 bipolar disorder cases, 1056 major depressive disorder cases and 1248 normal controls. We found that SNPs rs7171212 was associated with bipolar (pallele = 0.023, pgenotype = 0.022, OR = 0.658) and schizophrenia (pallele = 0.021, pgenotype = 0.019, OR = 0.645). Whereas, rs4779796 was associated with schizophrenia (pgenotype = 0.001, adjusted pgenotype = 0.003, OR = 1.089). In addition, rs7171212 (adjusted pallele = 0.018, adjusted pgenotype = 0.018, OR = 0.652) and rs4779796 (adjusted pgenotype = 0.024, OR = 1.12) showed significantly associated with combined cases of schizophrenia and bipolar disorder. Further, meta-analysis was performed with the case-control data and dataset extracted from previously reported genome-wide association study to validate the promising SNPs. Our results provide the new evidence that FAN1 may be a common susceptibility gene for schizophrenia and bipolar disorder in Han Chinese. These novel findings need further validation with larger sample size and functional characterization to understand the underlying pathogenic mechanism behind FAN1 in the prevalence of schizophrenia and bipolar disorders.

Prediction of causal genes and gene expression analysis of attention-deficit hyperactivity disorder in the different brain region, a comprehensive integrative analysis of ADHD.

Recent genome-wide association study (GWAS) identified 12 independent loci for Attention-deficit hyperactivity disorder (ADHD). However, the causal genes expression and pathways of ADHD is still vague. We integrated GWAS, eQTL and genes expression data to find the causal genes, genes expression, and genes prioritization in the different brain tissues and whole blood cells. Overall 47 genes were prioritized, the most promising genes were LSG1, HYAL3, PIDD, PNPLA2, BLOC1S2, PLK1S1, CALN1, KAT2B, CTNNB1 and WDR11. Whereas, the CALN1, KAT2B, and WDR11 were previously associated with schizophrenia (SZ), bipolar (BP) and drug abuse. Gene ontology analysis shows that the glutamate receptor signaling pathway (P = 8.009E-07, with false discovery rate (FDR) < 5%), GRIK5 sub network (P = 2.887E-06, FDR < 5%), abnormal gait (P = 3.657E-06, FDR < 5%), REACTOME_SIGNALING_BY_ERBB2 (P = 5.161E-06, FDR < 5%), and abnormal nervous system physiology (P = 5.239E-06, FDR < 5%) were associated with ADHD. These causal genes were highly expressed in Fetal Astrocytes, Neurons, and Microglia/Macrophage. This study illustrates the comprehensive GWAS integrative approach of ADHD. However, further genetic and functional studies are required to validate the role of these genes in the etiology of ADHD, which should provide novel insights into the understanding of this disease.

Identification of recurrent USP48 and BRAF mutations in Cushing's disease.

Cushing's disease results from corticotroph adenomas of the pituitary that hypersecrete adrenocorticotropin (ACTH), leading to excess glucocorticoid and hypercortisolism. Mutations of the deubiquitinase gene USP8 occur in 35-62% of corticotroph adenomas. However, the major driver mutations in USP8 wild-type tumors remain elusive. Here, we report recurrent mutations in the deubiquitinase gene USP48 (predominantly encoding p.M415I or p.M415V; 21/91 subjects) and BRAF (encoding p.V600E; 15/91 subjects) in corticotroph adenomas with wild-type USP8. Similar to USP8 mutants, both USP48 and BRAF mutants enhance the promoter activity and transcription of the gene encoding proopiomelanocortin (POMC), which is the precursor of ACTH, providing a potential mechanism for ACTH overproduction in corticotroph adenomas. Moreover, primary corticotroph tumor cells harboring BRAF V600E are sensitive to the BRAF inhibitor vemurafenib. Our study thus contributes to the understanding of the molecular mechanism of the pathogenesis of corticotroph adenoma and informs therapeutic targets for this disease.