Biology subjects

Vafaee, F.

Publications and source records attributed to Vafaee, F..

21 records · Page 2Linked to original sources

bioRxiv2020

Lipidome profiles of plasma microvesicles differ in experimental cerebral malaria, compared to malaria without neurological complications

Cerebral malaria (CM), a fatal complication of Plasmodium infection that affects children in sub-Saharan Africa and adults in South-East Asia, results from incompletely understood pathogenetic mechanisms, which include an excessive release of microvesicles (MV). Plasma MV levels have been found elevated in CM patients and in the experimental mouse model. We compared lipid profiles in circulating MV purified from CBA mice infected with P. berghei ANKA (PbA), which causes CM, to those from P. yoelii (Py), which does not. Here we show that plasma MV produced at the time of CM differed dramatically from those from non-CM mice, in spite of identical levels of parasitaemia. Using high-resolution LCMS, we identified over 300 lipid species within 12 lipid classes. Total lysophosphatidylethanolamine (LPE) levels were significantly lower in PbA infection compared to uninfected mice, while they were unchanged in Py MV, and lysophosphatidylcholine (LPC) was more significantly reduced in PbA mice compared to the other two groups. These results suggest, for the time, that experimental CM is characterised by specific changes in lipid composition of circulating MV, pointing towards triglycerides (TG) especially docosahexaenoic acid (DHA 22:6) containing species, phosphatidylethanolamine (PE), LPC, LPE, and diacylglycerol (DG) as potential important players in CM pathogenesis.

pathology↗

bioRxiv2018

CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes

Analyses of large somatic mutation datasets, using advanced computational algorithms, have revealed at least 30 independent mutational signatures in tumor samples. These studies have been instrumental in identification and quantification of responsible endogenous and exogenous molecular processes against cancer. The quantitative approach used to deconvolute mutational signatures is becoming an integral part of cancer research. Therefore, development of a stand-alone tool with a user-friendly graphical interface for analysis of cancer mutational signatures is necessary. In this manuscript, we introduce CANCERSIGN as an open access1 bioinformatics tool that uses raw mutation data (BED files) as input, and generates 3-mer and 5-mer mutational signatures. Additionally, this tool enables users to perform clustering on tumor samples based on the raw mutation counts as well as using the proportion of mutational signatures in each sample. Using this tool, we analysed all the whole genome somatic mutation datasets of International Cancer Genome Consortium (ICGC) samples and identified a number of novel signatures.

bioinformatics↗

bioRxiv2018

Deep sequencing of circulating exosomal microRNA allows non-invasive glioblastoma diagnosis

Exosomes are nano-sized extracellular vesicles released by many cells that contain molecules characteristic of their cell-of-origin, including microRNA. Exosomes released by glioblastoma cross the blood-brain-barrier into the peripheral circulation, and carry molecular cargo distinct to that of free-circulating miRNA. In this pilot study, serum exosomal-microRNAs were isolated from glioblastoma (n=12) patients and analyzed using unbiased deep sequencing. Results were compared to sera from age- and gender-matched healthy controls, and to grades II-III (n=10) glioma patients. Significant differentially expressed microRNAs were identified, and the predictive power of individual and subsets of microRNAs were tested using univariate and multivariate analyses. Additional sera from glioblastoma patients (n=4) and independent sets of healthy (n=9) and non-glioma (n=10) controls were used to further test the specificity and predictive power of this unique exosomal-microRNA signature. Twenty-six microRNAs were differentially expressed in serum exosomes from glioblastoma patients relative to healthy controls. Random forest modeling and data partitioning selected seven miRNAs (miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p and miR-543) as the most stable for classifying glioblastoma. Strikingly, within this model, six iterations of these miRNA classifiers could distinguish glioblastoma patients from controls with perfect accuracy. The seven-miRNA panel was able to correctly classify all specimens in validation cohorts (n=23). Also identified were 23 dysregulated miRNAs in IDHMUT gliomas, a partially overlapping yet distinct signature of lower grade glioma. Serum exosomal-miRNA signatures can accurately diagnose glioblastoma preoperatively. miRNA signatures identified are distinct from previously reported free-circulating miRNA studies in GBM patients, and appear to be superior.

pathology↗