Biology subjects

Wright, V. J.

Publications and source records attributed to Wright, V. J..

4 recordsLinked to original sources

bioRxiv2026

Bacterial histone-like proteins released during antibiotic treatment mediate vascular injury in meningococcal sepsis.

Vascular injury and coagulopathy are key drivers of mortality in bacterial sepsis. In Neisseria meningitidis infection, endothelial adhesion and thrombosis cause the characteristic petechial rash and, in the most severe cases, purpura fulminans. Although antibiotics rapidly kill bacteria, inflammation and vascular injury often persist or worsen after bacterial clearance, suggesting ongoing toxicity from released bacterial components. Here we identify bacterial histone-like proteins (HLPs), small positively charged DNA-binding proteins conserved across bacterial species, as previously unrecognized mediators of vascular damage. In vitro HLPs are released following antibiotic exposure, disrupting endothelial integrity. In patients with severe sepsis, they are detectable in plasma and tissue, colocalising with areas of vascular leak and coagulopathy. Non-anticoagulant heparins and anti-HLP antibodies neutralize HLP-induced endothelial disruption and toxicity in vitro and in vivo. These findings reveal HLPs as antibiotic-released bacterial toxins and suggest new therapeutic strategies to prevent vascular injury in sepsis. One sentence summaryAntibiotic treatment of meningococcal and other sepsis causative bacteria triggers the release of histone-like proteins (HLPs) that damage the endothelium and drive coagulopathy, but these effects can be neutralized by non-anticoagulant heparins or anti-HLP antibodies.

microbiology↗

bioRxiv2026

Multi-omics integration identifies a reproducible inflammatory host-response axis in pediatric sepsis

Sepsis is a major cause of morbidity and mortality in children, yet biological heterogeneity in host responses has limited progress toward targeted therapies and patient stratification. Multiomics integration can combine complementary molecular layers to identify coordinated disease programs not captured by individual assays. Here, we integrated genomic, bulk transcriptomic, proteomic and metabolomic data from blood samples of 22 children with culture-confirmed bacterial sepsis enrolled in the Swiss Pediatric Sepsis Study. Multi-Omics Factor Analysis identified a dominant host-response axis reflecting systemic inflammation. This axis was driven primarily by transcriptomic variation and supported by coordinated proteomic and metabolomic signals, including circulating inflammatory mediators and altered amino-acid metabolism. It was associated with C-reactive protein and a severity score proxy. Projection into an independent pediatric sepsis cohort (n = 22) reproduced the inflammatory and severity-related interpretation of this axis. Single-omic projections showed that the integrated signal could be approximated from individual layers, particularly transcriptomics. In three external pediatric whole-blood transcriptomic datasets, the RNA-derived projection separated septic shock from healthy controls and increased across clinical inflammatory syndromes. These findings define a reproducible inflammatory host-response axis in pediatric sepsis and support multi-omics-guided selection of molecular readouts suitable for clinical translation.

immunology↗

bioRxiv2023

Competitive Amplification Networks enable molecular pattern recognition with PCR

Gene expression has great potential to be used as a clinical diagnostic tool. However, despite the progress in identifying these gene expression signatures, clinical translation has been hampered by a lack of purpose-built. readily deployable testing platforms. We have developed Competitive Amplification Networks. CANs to enable analysis of an entire gene expression signature in a single PCR reaction. CANs consist of natural and synthetic amplicons that compete for shared primers during amplification, forming a reaction network that leverages the molecular machinery of PCR. These reaction components are tuned such that the final fluorescent signal from the assay is exactly calibrated to the conclusion of a statistical model. In essence, the reaction acts as a biological computer, simultaneously detecting the RNA targets, interpreting their level in the context of the gene expression signature, and aggregating their contributions to the final diagnosis. We illustrate the clinical validity of this technique, demonstrating perfect diagnostic agreement with the gold-standard approach of measuring each gene independently. Crucially, CAN assays are compatible with existing qPCR instruments and workflows. CANs hold the potential to enable rapid deployment and massive scalability of gene expression analysis to clinical laboratories around the world, in highly developed and low-resource J settings alike. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/546934v3_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@79d1c5org.highwire.dtl.DTLVardef@1bafb87org.highwire.dtl.DTLVardef@d78e31org.highwire.dtl.DTLVardef@1b85125_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2019

Identification of reduced host transcriptomic signatures for tuberculosis and digital PCR-based validation and quantification

Recently, host whole blood gene expression signatures have been identified for diagnosis of tuberculosis (TB). Absolute quantification of the concentrations of signature transcripts in blood have not been reported, but would facilitate the development of diagnostic tests.\n\nTo identify minimal transcript signatures, we applied a novel transcript selection procedure to microarray data from African adults comprising 536 patients with TB, other diseases (OD) and latent TB (LTBI), divided into training and test sets. Signatures were validated using reverse transcriptase (RT) - digital PCR (dPCR).\n\nA four-transcript signature (GBP6, TMCC1, PRDM1, ARG1) measured using RT-dPCR distinguished TB patients from those with OD (area under the curve (AUC) 93.8% (CI95% 82.2 - 100%). A three-transcript signature (FCGR1A, ZNF296, C1QB) differentiated TB from LTBI (AUC 97.3%, CI95%: 93.3 - 100%), regardless of HIV.\n\nThese signatures have been validated across platforms and across samples offering strong, quantitative support for their use as diagnostic biomarkers for TB.

molecular biology↗