High levels of plasma S100A9 at admission indicate an increased risk of death in severe tuberculosis patients

Exploring biomarkers for diagnosing and predicting organ dysfunction in patients with perioperative sepsis: a preliminary investigation

OBJECTIVE

Early diagnosis and prediction of organ dysfunction are critical for intervening and improving the outcomes of septic patients. The study aimed to find novel diagnostic and predictive biomarkers of organ dysfunction for perioperative septic patients.

METHOD

This is a prospective, controlled, preliminary, and single-center study of emergency surgery patients. Mass spectrometry, Gene Ontology (GO) functional analysis, and the protein-protein interaction (PPI) network were performed to identify the differentially expressed proteins (DEPs) from sepsis patients, which were selected for further verification via enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was used to estimate the relative correlation of selected serum protein levels and clinical outcomes of septic patients. Calibration curves were plotted to assess the calibration of the models.

RESULTS

Five randomized serum samples per group were analyzed via mass spectrometry, and 146 DEPs were identified. GO functional analysis and the PPI network were performed to evaluate the molecular mechanisms of the DEPs. Six DEPs were selected for further verification via ELISA. Cathepsin B (CatB), vascular cell adhesion protein 1 (VCAM-1), neutrophil gelatinase-associated lipocalin (NGAL), protein S100-A9, prosaposin, and thrombospondin-1 levels were significantly increased in the patients with sepsis compared with those of the controls (p < 0.001). Logistic regression analysis showed that CatB, S100-A9, VCAM-1, prosaposin, and NGAL could be used for preoperative diagnosis and postoperative prediction of organ dysfunction. CatB and S100-A9 were possible predictive factors for preoperative diagnosis of renal failure in septic patients. Internal validation was assessed using the bootstrapping validation. The preoperative diagnosis of renal failure model displayed good discrimination with a C-index of 0.898 (95% confidence interval 0.843-0.954) and good calibration.

CONCLUSION

Serum CatB, S100-A9, VCAM-1, prosaposin, and NGAL may be novel markers for preoperative diagnosis and postoperative prediction of organ dysfunction. Specifically, S100-A9 and CatB were indicators of preoperative renal dysfunction in septic patients. Combining these two biomarkers may improve the accuracy of predicting preoperative septic renal dysfunction.

TRIAL REGISTRATION

The study was registered at the Chinese Clinical Trials Registry (ChiCTR2200060418) on June 1, 2022.

Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice

Mycobacterium tuberculosis infects two billion people across the globe, and results in 8-9 million new tuberculosis (TB) cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWK/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB.

Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice

Mycobacterium tuberculosis, the bacillus that causes tuberculosis (TB), infects 2 billion people across the globe, and results in 8-9 million new TB cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. We investigated the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using clinical indicators of disease, granuloma histopathological features, and immune response traits identified five new loci on mouse chromosomes 1, 2, 4, 16 and three previously identified loci on chromosomes 3 and 17. Quantitative trait loci (QTLs) on chromosomes 1, 16, and 17, associated with multiple correlated traits and had similar patterns of allele effects, suggesting these QTLs contain important genetic regulators of responses to M. tuberculosis. To narrow the list of candidate genes in QTLs, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks, generating functional scores. The scores were then used to rank candidates for each mapped trait in each locus, resulting in 11 candidates: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Importantly, all 11 candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling. Further, all candidates contain single nucleotide polymorphisms (SNPs), and some but not all SNPs were predicted to have deleterious consequences on protein functions. Multiple methods were used for validation including (i) a statistical method that showed Diversity Outbred mice carrying PWH/PhJ alleles on chromosome 17 QTL have shorter survival; (ii) quantification of S100A8 protein levels, confirming predicted allele effects; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and new functionally relevant gene candidates that may be major regulators of granuloma necrosis and acute inflammation in pulmonary TB.

Decreased S100A9 expression alleviates Clostridium perfringens beta2 toxin-induced inflammatory injury in IPEC-J2 cells

Background

S100 calcium-binding protein A9 (S100A9) is a commonly known pro-inflammatory factor involved in various inflammatory responses. Clostridium perfringens (C. perfringens ) type C is known to cause diarrhea in piglets. However, the role of S100A9 in C. perfringens type C-induced infectious diarrhea is unclear.

Methods

Here, the S100A9 gene was overexpressed and knocked down in the IPEC-J2 cells, which were treated with C. perfringens beta2 (CPB2) toxin. The role of S100A9 in CPB2 toxin-induced injury in IPEC-J2 cells was assessed by measuring the levels of inflammatory cytokines, reactive oxygen species (ROS), lactate dehydrogenase (LDH), cell proliferation, and tight junction-related proteins.

Results

The results showed elevated expression of S100A9 in diarrhea-affected piglet tissues, and the elevation of S100A9 expression after CPB2 toxin treatment of IPEC-J2 was time-dependent. In CPB2 toxin-induced IPEC-J2 cells, overexpression of S100A9 had the following effects: the relative expression of inflammatory factors IL-6, IL8, TNF-α, and IL-1β was increased; the ROS levels and LDH viability were significantly increased; cell viability and proliferation were inhibited; the G0/G1 phase cell ratio was significantly increased. Furthermore, overexpression of S100A9 reduced the expression of tight junction proteins in CPB2-induced IPEC-J2 cells. The knockdown of S100A9 had an inverse effect. In conclusion, our results confirmed that S100A9 exacerbated inflammatory injury in CPB2 toxin-induced IPEC-J2 cells, inhibited cell viability and cell proliferation, and disrupted the tight junctions between cells. Thus, decreased S100A9 expression alleviates CPB2 toxin-induced inflammatory injury in IPEC-J2 cells.

Association Between Plasma Exosomes S100A9/C4BPA and Latent Tuberculosis Infection Treatment: Proteomic Analysis Based on a Randomized Controlled Study

Background

Identifying host plasma exosome proteins associated with host response to latent tuberculosis infection (LTBI) treatment might promote our understanding of tuberculosis (TB) pathogenesis and provide useful tools for implementing the precise intervention.

Methods

Based on an open-label randomized controlled trial (RCT) aiming to evaluate the short-course regimens for LTBI treatment, plasma exosomes from pre- and post-LTBI treatment were retrospectively detected by label-free quantitative protein mass spectrometry and validated by a parallel reaction monitoring method for participants with changed or not changed infection testing results after LTBI treatment. Eligible participants for both screening and verification sets were randomly selected from the based-RCT in a 1:1 ratio by age and gender. Reversion was defined as a decrease in IFN-γ levels from &gt;0.70 IU/ml prior to treatment to 0.20 IU/ml within 1 week of treatment. The predictive ability of the candidate proteins was evaluated by receiver operating characteristic (ROC) analysis.

Results

Totally, two sample sets for screening (n = 40) and validation (n = 60) were included. Each of them included an equal number of subjects with persistent positive or reversed QuantiFERON-TB Gold In-Tube (QFT) results after LTBI. A total of 2,321 exosome proteins were detected and 102 differentially expressed proteins were identified to be associated with QFT reversion. Proteins with high confidence and original values intact were selected to be further verified. Totally, 9 downregulated proteins met the criteria and were validated. After verification, C4BPA and S100A9 were confirmed to be still significantly downregulated (fold change &lt;0.67, p &lt; 0.05). The respective areas under the ROC curve were 0.73 (95% CI: 0.57–0.89) and 0.69 (95% CI: 0.52–0.86) for C4BPA and S100A9, with a combined value of 0.78 (95% CI: 0.63–0.93). The positive and negative predictive values for combined markers were 70.10% (95% CI: 50.22–86.30%) and 55.63% (95% CI: 29.17–61.00%).

Conclusion

Our findings suggest that downregulated C4BPA and S100A9 in plasma exosomes might be associated with a host positive response to LTBI treatment. Further studies are warranted to verify the findings and potential underlying mechanisms in varied populations with a larger sample size.