Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Background: Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. Methods: The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. Results: We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. Conclusions: In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

Using proteomics and metabolomics to identify therapeutic targets for senescence mediated cancer: genetic complementarity method.

Background: Senescence have emerged as potential factors of lung cancer risk based on findings from many studies. However, the underlying pathogenesis of lung cancer caused by senescence is not clear. In this study, we try to explain the potential pathogenesis between senescence and lung cancer through proteomics and metabonomics. And try to find new potential therapeutic targets in lung cancer patients through network mendelian randomization (MR). Methods: The genome-wide association data of this study was mainly obtained from a meta-analysis and the Transdisciplinary Research in Cancer of the Lung Consortium (TRICL), respectively.And in this study, we mainly used genetic complementarity methods to explore the susceptibility of aging to lung cancer. Additionally, a mediation analysis was performed to explore the potential mediating role of proteomics and metabonomics, using a network MR design. Results: GNOVA analysis revealed a shared genetic structure between HannumAge and lung cancer with a significant genetic correlation estimated at 0.141 and 0.135, respectively. MR analysis showed a relationship between HannumAge and lung cancer, regardless of smoking status. Furthermore, genetically predicted HannumAge was consistently associated with the proteins C-type lectin domain family 4 member D (CLEC4D) and Retinoic acid receptor responder protein 1 (RARR-1), indicating their potential role as mediators in the causal pathway. Conclusion: HannumAge acceleration may increase the risk of lung cancer, some of which may be mediated by CLEC4D and RARR-1, suggestion that CLEC4D and RARR-1 may serve as potential drug targets for the treatment of lung cancer.

Intestinal metabolites and the risk of autistic spectrum disorder: A two-sample Mendelian randomization study.

Background: Observational studies have reported a strong association between autistic spectrum disorder (ASD) and intestinal metabolites. However, it is unclear whether this correlation is causally or violated by confounding or backward causality. Therefore, this study explored the potential causal relationship between intestinal metabolites and dependent metabolites on ASD. Methods: We used a two-sample Mendelian random analysis and selected variants closely related to intestinal flora-dependent metabolites as instrumental variables. MR-Egger, inverse variance weighted (IVW), MR-PRESSO, maximum likelihood, and weighted median were performed to reveal their causal relationships. Ten metabolites were studied, which included trimethylamine-N-oxide, betaine, carnitine, choline, glutamate, kynurenine, phenylalanine, serotonin, tryptophan, and tyrosine. Sensitivity tests were also performed to evaluate the robustness of the MR study. Results: The IVW method revealed that serotonin may increase the ASD risk (OR 1.060, 95% CI: 1.006-1.118), while choline could decrease the ASD risk (OR 0.925, 95% CI: 0.868-0.988). However, no definite causality was observed between other intestinal metabolites (e.g., trimethylamine-N-oxide, betaine, and carnitine) with ASD. Additionally, neither the funnel plot nor the MR-Egger test showed horizontal pleiotropy, and the MR-PRESSO test found no outliers. Cochran's Q test showed no significant heterogeneity among the studies, suggesting the robustness of the study. Conclusion: Our study found potential causality from intestinal metabolites on ASD. Clinicians are encouraged to offer preventive measures to such populations.

Efficient removal of perfluorinated compounds from water using a regenerable magnetic activated carbon.

Adsorption by powder activated carbon (PAC) is recognized as an efficient method for the removal of perfluorinated compounds (PFCs) in water, while the poor separation of spent PAC makes it difficult for further regeneration, increasing the treatment cost significantly. In this study, an ultrafine magnetic activated carbon (MAC) consisting of Fe3O4 and PAC was prepared by ball milling to remove PFCs from water efficiently. Increasing the percentage of Fe3O4 and balling milling time decreased its adsorption capacity for perfluoroctane sulfonate (PFOS), whereas increased the magnetic separation property to some degree. The optimized MAC was prepared with a Fe3O4 to PAC mass ratio of 1:3 after ball milling for 2 h, and the adsorption equilibriums of all the four PFCs on the optimal MAC were reached within less than 2 h, with the adsorption capacities of 1.63, 0.90, 0.33 and 0.21 mmol/g for PFOS, perfluorooctanoic acid (PFOA), perfluorohexane sulfonate (PFHxS) and perfluorobutane sulfonate (PFBS), respectively. Increasing the solution pH hindered the adsorption of PFOS significantly when the pH was less than the zero potential point (around 6) of the MAC, due to the decreased electrostatic attraction. The spent MAC could be easily separated with a magnet and regenerated by a small volume of methanol, and the regenerated MAC could be reused for more than 5 time and remain stable adsorption capacity for PFOS after 3 cycles. This study provides useful insights into the removal of PFCs by separable magnetic PAC in wastewater.

TLR3 regulates PD-L1 expression in human cytomegalovirus infected glioblastoma.

Glioblastoma, the most common primary brain tumor of adults, is characterized by poor survival rates. Programmed death ligand 1 (PD-L1, CD274) has been implicated in the immune escape of glioblastoma. The presence of human cytomegalovirus (HCMV) in glioblastoma multiforme (GBM) has sparked considerable interest and controversy. The exposure of toll-like receptor 3 (TLR3) to pathogens induces an antiviral state in cells or in animals. In the current study, the expression of PD-L1 and TLR3 in HCMV-infected glioma specimens was observed to be higher compared to the control. We therefore investigated if PD-L1 expression in glioblastoma is mediated by TLR3 triggering in HCMV infected glioblastoma. TLR3 siRNA transfections were utilized to identify the induction of PD-L1 via TLR3 triggering in HCMV infected cell lines. Also, IL-8 and TGF-ß were detected by ELISA for the antitumor role of TLR3. Thus, we propose a novel immune treatment using a combination of PD-L1 blockade with TLR3 triggering against HCMV infected glioblastoma.