Multiomics Analyses Reveal Microbiome-Gut-Brain Crosstalk Centered on Aberrant Gamma-Aminobutyric Acid and Tryptophan Metabolism in Drug-Naïve Patients with First-Episode Schizophrenia.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SCZ) is associated with complex crosstalk between the gut microbiota and host metabolism, but the underlying mechanism remains elusive. Investigating the aberrant neurotransmitter processes reflected by alterations identified using multiomics analysis is valuable to fully explain the pathogenesis of SCZ. STUDY DESIGN: We conducted an integrative analysis of multiomics data, including the serum metabolome, fecal metagenome, single nucleotide polymorphism data, and neuroimaging data obtained from a cohort of 127 drug-naïve, first-episode SCZ patients and 92 healthy controls to characterize the microbiome-gut-brain axis in SCZ patients. We used pathway-based polygenic risk score (PRS) analyses to determine the biological pathways contributing to genetic risk and mediation effect analyses to determine the important neuroimaging features. Additionally, a random forest model was generated for effective SCZ diagnosis. STUDY RESULTS: We found that the altered metabolome and dysregulated microbiome were associated with neuroactive metabolites, including gamma-aminobutyric acid (GABA), tryptophan, and short-chain fatty acids. Further structural and functional magnetic resonance imaging analyses highlighted that gray matter volume and functional connectivity disturbances mediate the relationships between Ruminococcus_torgues and Collinsella_aerofaciens and symptom severity and the relationships between species Lactobacillus_ruminis and differential metabolites l-2,4-diaminobutyric acid and N-acetylserotonin and cognitive function. Moreover, analyses of the Polygenic Risk Score (PRS) support that alterations in GABA and tryptophan neurotransmitter pathways are associated with SCZ risk, and GABA might be a more dominant contributor. CONCLUSIONS: This study provides new insights into systematic relationships among genes, metabolism, and the gut microbiota that affect brain functional connectivity, thereby affecting SCZ pathogenesis.

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.

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.