Dataset Information

Proteome profiling identifies urinary biomarkers for Schistosomiasis Haematobium among school-aged children

ABSTRACT: Background: Schistosomiasis is a major public health challenge and one of the neglected tropical diseases globally. Schistosoma haematobium, the causative agent of urogenital schistosomiasis, is primarily endemic in African countries, with school-aged children (7 to 15 years) considered the most vulnerable population. The current diagnostic method relies on identifying parasite eggs in urine through microscopy, which is labor-intensive, requires specialized skills, and is often limited in sensitivity, especially in mild infections. Disease-related biomarkers offer a promising avenue for advancing disease diagnosis and detection. Method: A total of 135 school-aged children (aged 7—15 years) from Zanzibar were recruited for this study. To identify potential host protein biomarkers, data independent acquisition (DIA) proteomics combined with machine learning was used to screen for differentially expressed proteins in the urine samples from individuals infected with schistosome haematobium. Machine learning was used to pinpoint the most discriminative proteins, which were subsequently validated using enzyme-linked immunosorbent assay (ELISA). Results: Proteomic analysis identified 823 common host proteins in urine samples from the infection group, with 269 proteins showing significant differential expression. Of these, 149 were up-regulated and 120 were down-regulated in the infected group. Machine learning further highlighted SYNPO2, CD276, α2M, LCAT, and hnRNPM as the most discriminating biomarkers for Schistosomiasis haematobium infection. ELISA validation confirmed the differential expression trends of these proteins, underscoring their diagnostic potential. Conclusions: This study identified key urinary protein biomarkers associated with Schistosoma haematobium

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Urine

SUBMITTER: yiyun Liu

LAB HEAD: Yuzheng Huang

PROVIDER: PXD059942 | Pride | 2025-06-16

REPOSITORIES: Pride

ACCESS DATA

Similar Datasets

Project description:The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging (TMT)-based shotgun proteomics to characterize proteomes of formalin-fixed paraffin-embedded (FFPE) tumor samples of PAIM (n = 22) and lmCRC (n = 17). Then three machine learning algorithms, namely support vector machine (SVM), random forest and the Least Absolute Shrinkage and Selection Operator (LASSO), were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by immunochemistry (IHC) to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection (UMAP). The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of 10 candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: NARR, MLPH, S100A14, CK7) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

Project description:The Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort in Gómez Palacio, Mexico was recently established to better understand the impacts of prenatal exposure to inorganic arsenic (iAs). In this study, we examined a subset (n = 40) of newborn cord blood samples for microRNA (miRNA) expression changes associated with in utero arsenic exposure. Levels of iAs in maternal drinking water (DW-iAs) and maternal urine were assessed. Levels of DW-iAs ranged from below detectable values to 236 µg/L (mean = 51.7 µg/L). Total arsenic in maternal urine (U-tAs) was defined as the sum of iAs and its monomethylated and dimethylated metabolites (MMAs and DMAs, respectively) and ranged from 6.2 to 319.7 µg/L (mean = 64.5 µg/L). Genome-wide miRNA expression analysis of cord blood revealed 12 miRNAs with increasing expression associated with U-tAs. Transcriptional targets of the miRNAs were computationally predicted and subsequently assessed using transcriptional profiling. Pathway analysis demonstrated that the U-tAs-associated miRNAs are involved in signaling pathways related to known health outcomes of iAs exposure including cancer and diabetes mellitus. Immune response-related mRNAs were also identified with decreased expression levels associated with U-tAs, and predicted to be mediated in part by the arsenic-responsive miRNAs. Results of this study highlight miRNAs as novel responders to prenatal arsenic exposure that may contribute to associated immune response perturbations. We assessed the impact of prenatal exposure to arsenic on genome-wide miRNA expression profiles and their potential influence on gene expression patterns in the Biomarkers of Exposure to ARsenic (BEAR) prospective pregnancy cohort. This cohort includes residents from Gómez Palacio, located in the state of Durango in the Lagunera region of Northern Mexico. A total of 200 pregnant women residing in Gómez Palacio, State of Durango, Mexico, were recruited at the General Hospital of Gómez Palacio to participate in the BEAR prospective pregnancy cohort. The present study focuses on gene expression profiles and utilizes 38 samples obtained from mother-newborn pairs selected from the larger cohort (n=200). The subcohort was selected to include subjects exposed to varying levels of arsenic as determined by both total arsenic in maternal urine (U-tAs) and inorganic arsenic in drinking water (DW-iAs). Cord blood samples were collected from the newborns immediately after infant delivery. Blood samples were collected using PreAnalytix PaxGene RNA tubes and extracted using the PAXgene RNA Kit, per standard protocol (Qiagen, Valencia, CA). Isolated RNA used for microarray analysis were amplified and labeled using the NuGEN Ovation Pico WTA System V2 and Encore Biotin Module, respectively (NuGEN, San Carlos, CA). RNA isolated from 38 cord blood samples were labeled and hybridized to the Affymetrix Human Gene 2.0 ST Array.

Project description:The Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort in Gómez Palacio, Mexico was recently established to better understand the impacts of prenatal exposure to inorganic arsenic (iAs). In this study, we examined a subset (n = 40) of newborn cord blood samples for microRNA (miRNA) expression changes associated with in utero arsenic exposure. Levels of iAs in maternal drinking water (DW-iAs) and maternal urine were assessed. Levels of DW-iAs ranged from below detectable values to 236 µg/L (mean = 51.7 µg/L). Total arsenic in maternal urine (U-tAs) was defined as the sum of iAs and its monomethylated and dimethylated metabolites (MMAs and DMAs, respectively) and ranged from 6.2 to 319.7 µg/L (mean = 64.5 µg/L). Genome-wide miRNA expression analysis of cord blood revealed 12 miRNAs with increasing expression associated with U-tAs. Transcriptional targets of the miRNAs were computationally predicted and subsequently assessed using transcriptional profiling. Pathway analysis demonstrated that the U-tAs-associated miRNAs are involved in signaling pathways related to known health outcomes of iAs exposure including cancer and diabetes mellitus. Immune response-related mRNAs were also identified with decreased expression levels associated with U-tAs, and predicted to be mediated in part by the arsenic-responsive miRNAs. Results of this study highlight miRNAs as novel responders to prenatal arsenic exposure that may contribute to associated immune response perturbations. We assessed the impact of prenatal exposure to arsenic on genome-wide miRNA expression profiles and their potential influence on gene expression patterns in the Biomarkers of Exposure to ARsenic (BEAR) prospective pregnancy cohort. This cohort includes residents from Gómez Palacio, located in the state of Durango in the Lagunera region of Northern Mexico. A total of 200 pregnant women residing in Gómez Palacio, State of Durango, Mexico, were recruited at the General Hospital of Gómez Palacio to participate in the BEAR prospective pregnancy cohort. The present study focuses on miRNA expression profiles and utilizes 40 samples obtained from mother-newborn pairs selected from the larger cohort (n=200). The subcohort was selected to include subjects exposed to varying levels of arsenic as determined by both total arsenic in maternal urine (U-tAs) and inorganic arsenic in drinking water (DW-iAs). Cord blood samples were collected from the newborns immediately after infant delivery. Blood samples were collected using PreAnalytix PaxGene RNA tubes and extracted using the PAXgene RNA Kit, per standard protocol (Qiagen, Valencia, CA). Isolated RNA used for microarray analysis were amplified and labeled using the NuGEN Ovation Pico WTA System V2 and Encore Biotin Module, respectively (NuGEN, San Carlos, CA). RNA isolated from 40 cord blood samples were labeled and hybridized to the Agilent Human miRNA Microarray, based off miRBase v16.0.

			Action	DRS
	03.raw	Raw
	05.raw	Raw
	06.raw	Raw
	10.raw	Raw
	102.raw	Raw

Dataset Information

Proteome profiling identifies urinary biomarkers for Schistosomiasis Haematobium among school-aged children

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets