Olink proteomics profiling platform reveals non-invasive inflammatory related protein biomarkers in autism spectrum disorder

Interactions between circulating inflammatory factors and autism spectrum disorder: a bidirectional Mendelian randomization study in European population

Background

Extensive observational studies have reported an association between inflammatory factors and autism spectrum disorder (ASD), but their causal relationships remain unclear. This study aims to offer deeper insight into causal relationships between circulating inflammatory factors and ASD.

Methods

Two-sample bidirectional Mendelian randomization (MR) analysis method was used in this study. The genetic variation of 91 circulating inflammatory factors was obtained from the genome-wide association study (GWAS) database of European ancestry. The germline GWAS summary data for ASD were also obtained (18,381 ASD cases and 27,969 controls). Single nucleotide polymorphisms robustly associated with the 91 inflammatory factors were used as instrumental variables. The random-effects inverse-variance weighted method was used as the primary analysis, and the Bonferroni correction for multiple comparisons was applied. Sensitivity tests were carried out to assess the validity of the causal relationship.

Results

The forward MR analysis results suggest that levels of sulfotransferase 1A1, natural killer cell receptor 2B4, T-cell surface glycoprotein CD5, Fms-related tyrosine kinase 3 ligand, and tumor necrosis factor-related apoptosis-inducing ligand are positively associated with the occurrence of ASD, while levels of interleukin-7, interleukin-2 receptor subunit beta, and interleukin-2 are inversely associated with the occurrence of ASD. In addition, matrix metalloproteinase-10, caspase 8, tumor necrosis factor-related activation-induced cytokine, and C-C motif chemokine 19 were considered downstream consequences of ASD.

Conclusion

This MR study identified additional inflammatory factors in patients with ASD relative to previous studies, and raised a possibility of ASD-caused immune abnormalities. These identified inflammatory factors may be potential biomarkers of immunologic dysfunction in ASD.

MUC16 can Predict the Pregnancy Outcomes in Human and Intraperitoneal Administration of MUC16 can Rescue Pregnancy Losses in Mouse Models

Mucin 16 (MUC16) participates in the process of embryo implantation, but few studies have examined the association between MUC16 and pregnancy loss. To investigate this association, the expression of MUC16 in serum and decidua was compared between women with pregnancy loss and ongoing pregnancies. In vitro experiments and animal models were used to explore the role and underlying mechanisms of MUC16 in pregnancy loss. In human study, the expression of MUC16 in serum and decidua was both consistently lower in the women with pregnancy loss compared with those in women with ongoing pregnancies. In vitro experiments revealed the interaction of MUC16 with peripheral blood natural killer (pNK) cells. MUC16 changed the phenotype and reduced the pro-inflammation ability of pNK cells. MUC16 also inhibited the cytotoxicity of pNK cells through the Src homology region 2 domain-containing phosphatase-1/extracellular signal-regulated kinase (SHP-ERK) pathway. Furthermore, MUC16 promoted the migration, invasion and tube formation of trophoblast cells by co-culturing together with pNK cells. In vivo experiments, the mouse model of abortion was used to further confirm that intraperitoneal administration of MUC16 could rescue the pregnancy loss. This study reveals the still-unknown connection between MUC16 and pNK cells and indicates that MUC16 provides a novel method for future prediction and treatment of unfavorable pregnancy outcomes.

Combined Plasma Olink Proteomics and Transcriptomics Identifies CXCL1 and TNFRSF12A as Potential Predictive and Diagnostic Inflammatory Markers for Acute Kidney Injury

Acute kidney injury (AKI) poses a significant global public health challenge. Current methods for detecting AKI rely on monitoring changes in serum creatinine (Scr), blood urea nitrogen (BUN), urinary output and some commonly employed biomarkers. However, these indicators are usually neither specific nor sensitive to AKI, especially in cases of mild kidney injury. AKI is accompanied by severe inflammatory reactions, resulting in the upregulation of numerous inflammation-associated proteins in the plasma. Plasma biomarkers are a noninvasive method for detecting kidney injury, and to date, plasma inflammation-associated cytokines have not been adequately studied in AKI patients. The objective of our research was to identify novel inflammatory biomarkers for AKI. We utilized Olink proteomics to analyze the alterations in plasma inflammation-related proteins in the serum of healthy mice (n = 2) or mice treated with cisplatin (n = 6). Additionally, transcriptome datasets for the lipopolysaccharide (LPS), cisplatin, and ischemia‒reperfusion injury (IRI) groups were obtained from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. We calculated the intersection of differentially expressed proteins (DEPs) and genes (DEGs) from both datasets. In the Olink proteomics analysis, the AKI group had significantly greater levels of 11 DEPs than did the control group. In addition, 56 common upregulated DEGs were obtained from the transcriptome dataset. The expression of CXCL1 and TNFRSF12A overlapped across all the datasets. The transcription and protein expression levels of CXCL1 and TNFRSF12A were detected in vivo. The gene and protein levels of CXCL1 and TNFRSF12A were significantly increased in different AKI mouse models and clinical patients, suggesting that these genes and proteins could be potential specific biomarkers for the identification of AKI.

Identification of molecular markers for predicting the severity of heart failure after AMI: An Olink precision proteomic study

BACKGROUND

Acute myocardial infarction (AMI) still has a high incidence of varying degrees of heart failure (HF). The aim of this study is to identify new molecular markers for predicting the severity of HF after AMI.

METHODS

We analyzed demographic indicators, past medical history, clinical indicators, major adverse cardiac events (MACEs) and molecular markers in patients with different Killip classifications after AMI. Olink proteomics was used to explore new molecular markers for predicting different severity of HF after AMI.

RESULTS

Neutrophil count was the independent risk factors for in-hospital MACEs. Nineteen differentially expressed proteins (DEPs) increased significantly with increasing Killip classification. Five DEPs were also found to have an AUC (95 % CI) value greater than 0.8: GDF-15, NT-pro BNP, TNF-R2, TNF-R1 and TFF3.

CONCLUSIONS

Neutrophil count, GDF-15, TNF-R2, TNF-R1 and TFF3 were closely related to the Killip classification of HF after AMI, which suggests that the inflammatory response plays an important role in the severity of HF after AMI and that regulating inflammation might become a new target for controlling HF.

Cerebrospinal fluid metabolomic and proteomic characterization of neurologic post-acute sequelae of SARS-CoV-2 infection

The mechanism by which SARS-CoV-2 causes neurological post-acute sequelae of SARS-CoV-2 (neuro-PASC) remains unclear. Herein, we conducted proteomic and metabolomic analyses of cerebrospinal fluid (CSF) samples from 21 neuro-PASC patients, 45 healthy volunteers, and 26 inflammatory neurological diseases patients. Our data showed 69 differentially expressed metabolites and six differentially expressed proteins between neuro-PASC patients and healthy individuals. Elevated sphinganine and ST1A1, sphingolipid metabolism disorder, and attenuated inflammatory responses may contribute to the occurrence of neuro-PASC, whereas decreased levels of 7,8-dihydropterin and activation of steroid hormone biosynthesis may play a role in the repair process. Additionally, a biomarker cohort consisting of sphinganine, 7,8-dihydroneopterin, and ST1A1 was preliminarily demonstrated to have high value in diagnosing neuro-PASC. In summary, our study represents the first attempt to integrate the diagnostic benefits of CSF with the methodological advantages of multi-omics, thereby offering valuable insights into the pathogenesis of neuro-PASC and facilitating the work of neuroscientists in disclosing different neurological dimensions associated with COVID-19.