Biology subjects

Wild, P. J.

Publications and source records attributed to Wild, P. J..

6 recordsLinked to original sources

bioRxiv2026

Stroke-induced lipocalin-2-expressing red pulp macrophages reprogram peripheral immunity

Acute ischemic stroke (AIS) induces profound systemic immune alterations that contribute to infection susceptibility. Here, we identify lipocalin-2 (LCN-2) as a rapidly induced and conserved regulator of stroke-associated immunosuppression. Using 3-MACE-Seq, cytokine profiling, and immunofluorescence in C57BL/6J mice subjected to transient middle cerebral artery occlusion (tMCAO), we show that LCN-2 is strongly upregulated in splenic red pulp macrophages (RPMs) within 24 hours and again 7 days post-tMCAO. LCN-2-expressing RPMs form immunological synapses with CD3+ T cells, thereby impacting T cell trafficking. Recombinant LCN-2 directly reprogrammed T cells and monocytes toward hyporesponsive, tolerogenic phenotypes by suppressing inflammatory cytokines, impairing chemotaxis, enhancing phagocytosis, and uncoupling oxidative burst. Human spleens likewise displayed LCN-2-expressing CD68+ RPMs, and LCN-2 preconditioning of monocytes reproduced reduced HLA-DR, CD80, CD206, and ROS with increased uptake of E. coli bioparticles. These findings identify LCN-2 signaling as a central orchestrator of stroke-induced peripheral immunoreprogramming and a potential therapeutic target to mitigate post-stroke immunodepression. SummaryAcute ischemic stroke induces LCN-2 in splenic red pulp macrophages, which reprogram T cells and monocytes toward tolerogenic, hyporesponsive states. Mouse and human data identify LCN-2 as a driver of peripheral immunodepression and a potential target to reduce infection risk. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/733904v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@fb0caforg.highwire.dtl.DTLVardef@1fdcd41org.highwire.dtl.DTLVardef@1ae99b0org.highwire.dtl.DTLVardef@1ec0837_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

immunology↗

bioRxiv2025

Exosomal cargo genes as biomarkers and potential mediators of exosome-driven cell communication in Pleural Mesothelioma

Pleural mesothelioma (PM) is a rare yet aggressive and heterogeneous cancer type with very poor survival rates. Due to its long latency period and nonspecific symptoms, the disease is usually detected at advanced stages, limiting available treatment options and leading to poor survival rates. So far, the disease can only be confirmed through invasive thoracoscopic biopsy. Moreover, the proposed circulating protein biomarkers lack sensitivity and specificity, highlighting the urgent need for novel approaches. In our previous work, we characterized the transcriptomic profile of extracellular vesicles secreted by PM cells and demonstrated their feasibility as a source of circulating biomarkers. To further investigate the role of circulating RNA in tumor progression and its potential use in non-invasive testing for PM, here we present a detailed characterization of RNA cargo carried by PM exosomes - a distinct subset of extracellular vesicles known to serve as key mediators of intercellular communication. By utilizing primary cell cultures established from tissue samples of 11 PM and 6 non-PM patients, followed by exosome isolation, and total RNA sequencing of RNA from isolated exosomes, matching cells, and tissues, we provide new evidence on exosomal RNA secretion, regulation of PM-exosome cargo genes, and their potential functions in recipient cells. We show that PM-exosomal cargo is enriched in genes associated with proliferation, whose key transcriptional factors are SETDB1, FOXM1 and GATA2. We identified Pcbp2, Srsf1 and Srsf9, RNA-binding proteins, which may be involved in selective cargo sorting into PM-exosomes. Our analysis identified six secreted genes, including GAS5, AL031666.3, RSLD1D1, AC103740.2, ADAM10, and AC020892.1, whose exosomal expression was associated with patient survival, as promising biomarkers for patient diagnosis, stratification, and prognosis assessment. Finally, we identify potential target genes of candidate LncRNAs biomarkers and processes in which they are involved.

cancer biology↗

bioRxiv2023

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

cancer biology↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗

bioRxiv2018

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations.\n\nIn this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohens quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa=0.71). Furthermore, the models Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort.\n\nOverall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.

bioinformatics↗

bioRxiv2018

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Many tumors are characterized by large genomic heterogeneity and it remains unclear to what extent this impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of 30 biopsy-scale prostate tissues using pressure cycling technology and SWATH mass spectrometry. We quantified 8,248 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied significantly depending on proteins and tissue types. Benign tissues exhibited generally more complex ITH patterns than malignant tissues. Spatial variability of ten prostate biomarkers was further validated by immunohistochemistry in an independent cohort (n=83) using tissue microarrays. PSA was preferentially variable in benign prostatic hyperplasia, while GDF15 substantially varied in prostate adenocarcinomas. Further, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery by quantifying spatial proteome variation and it demonstrates the feasibility by exploiting recent technological progress.

systems biology↗