Dataset Information

Artificial Intelligence in proteomic profiling of Cerebrospinal fluid from extraventricular drainage in child Medulloblastoma

ABSTRACT: Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is the need for biomarkers of residual disease, and recurrence. We analysed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from 6 children bearing various subtypes of MB and 6 controls needing EVD insertion for unrelated causes. Samples included total CSF, Microvesicles, Exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid Chromatography-Coupled Tandem Mass Spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4), and laminin B-type (LMNB1) as proteins that maximize the discrimination between control and MB samples, respectively. Artificial intelligence was able to distinguish between MB vs non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients quality of life.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cerebrospinal Fluid

SUBMITTER: Martina Bartolucci

LAB HEAD: Andrea Petretto

PROVIDER: PXD035292 | Pride | 2022-10-14

REPOSITORIES: Pride

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Publications

Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma.

Bruschi Maurizio M Kajana Xhuliana X Petretto Andrea A Bartolucci Martina M Pavanello Marco M Ghiggeri Gian Marco GM Panfoli Isabella I Candiano Giovanni G

Metabolites 20220805 8

Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is aneed for biomarkers of residual disease and recurrence. We analyzed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from six children bearing various subtypes of MB and six controls needing EVD insertion for unrelated causes. Samples included total CSF, microvesicles, exosomes, and proteins captured by combina ...[more]

PMID: 36005596

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Project description:Treatment failures of antibiotic therapy are of major concern and can be caused by a misalignment of the antibiotic susceptibility determined in vitro with the behaviour of the pathogen in the patient. The aim of this study was to investigate the transcriptomic response of the uropathogenic strain E. coli CFT073 to antibiotic treatment in blood stream infection (BSI) models in order to understand and avoid antibiotic therapy failures in urosepsis treatments. Blood stream infection models were established by growing E. coli CFT073 in pooled human serum with and without ciprofloxacin and compared to Iso-sensitest medium. The antibiotic challenge was introduced at mid-logarithmic phase of growth of the organism to depict a clinical scenario . Global gene expression profiling of these conditions was examined using commercial DNA microarrays. The organismâs metabolic genes appeared to be regulated differently in each medium, this indicated that the bacterial growth regulation were different between the models. Bacterial growth in human serum mainly involved regulations of amino acid synthesis/utilisation such as glycine, arginine, thiamine, regulations of fimbrial proteins and bacteriophage genes. When comparing the responses to antibiotic challenge, bacteria grown in the respective medium displayed specific responses to the antibiotic challenge which were not seen in the other media. The common functions of genes that responded to the ciprofloxacin challenge were SOS response, DNA repair, DNA replication, fimbrial genes and bacteriophage initiation. A subset of the bacteriophage genes showed similar responses between the three models. From genes that were differentially regulated, responses observed in the serum model appeared to have the highest fold changes. In this study we established new models to investigate blood stream infections. They have been used to identify previously unknown differences in the molecular response to antibiotic treatment by the uropathogenic E. coli CFT073 depending on the media. These unique responses will help to unravel the complexity of bloodstream infection and can help to improve the antibiotic therapy that is used. A 20 array study using total RNA recovered from bacteria from bacteria that were either grown in human serum or Iso-Sensitest (IST) broth, with and without ciprofloxacin challenge. Arrays were performed in 5 biological replicates from each condition.Upon QC checks, certain biological repeats were excluded due to poor hybridisation results.

Dataset Information

Artificial Intelligence in proteomic profiling of Cerebrospinal fluid from extraventricular drainage in child Medulloblastoma

Publications

Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma.

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