PACOM: A Versatile Tool for Integrating, Filtering, Visualizing, and Comparing Multiple Large Mass Spectrometry Proteomics Data Sets.

Mass-spectrometry-based proteomics has evolved into a high-throughput technology in which numerous large-scale data sets are generated from diverse analytical platforms. Furthermore, several scientific journals and funding agencies have emphasized the storage of proteomics data in public repositories to facilitate its evaluation, inspection, and reanalysis. (1) As a consequence, public proteomics data repositories are growing rapidly. However, tools are needed to integrate multiple proteomics data sets to compare different experimental features or to perform quality control analysis. Here, we present a new Java stand-alone tool, Proteomics Assay COMparator (PACOM), that is able to import, combine, and simultaneously compare numerous proteomics experiments to check the integrity of the proteomic data as well as verify data quality. With PACOM, the user can detect source of errors that may have been introduced in any step of a proteomics workflow and that influence the final results. Data sets can be easily compared and integrated, and data quality and reproducibility can be visually assessed through a rich set of graphical representations of proteomics data features as well as a wide variety of data filters. Its flexibility and easy-to-use interface make PACOM a unique tool for daily use in a proteomics laboratory. PACOM is available at https://github.com/smdb21/pacom .

MDR-1 and MRP2 Gene Polymorphisms in Mexican Epileptic Pediatric Patients with Complex Partial Seizures.

Although the Pgp efflux transport protein is overexpressed in resected tissue of patients with epilepsy, the presence of polymorphisms in MDR1/ABCB1 and MRP2/ABCC2 in patients with antiepileptic-drugs resistant epilepsy (ADR) is controversial. The aim of this study was to perform an exploratory study to identify nucleotide changes and search new and reported mutations in patients with ADR and patients with good response (CTR) to antiepileptic drugs (AEDs) in a rigorously selected population. We analyzed 22 samples In Material and Methods, from drug-resistant patients with epilepsy and 7 samples from patients with good response to AEDs. Genomic DNA was obtained from leukocytes. Eleven exons in both genes were genotyped. The concentration of drugs in saliva and plasma was determined. The concentration of valproic acid in saliva was lower in ADR than in CRT. In ABCB1, five reported SNPs and five unreported nucleotide changes were identified; rs2229109 (GA) and rs2032582 (AT and AG) were found only in the ADR. Of six SNPs associated with the ABCC2 that were found in the study population, rs3740066 (TT) and 66744T > A (TG) were found only in the ADR. The strongest risk factor in the ABCB1 gene was identified as the TA genotype of rs2032582, whereas for the ABCC2 gene the strongest risk factor was the T allele of rs3740066. The screening of SNPs in ACBC1 and ABCC2 indicates that the Mexican patients with epilepsy in this study display frequently reported ABCC1 polymorphisms; however, in the study subjects with a higher risk factor for drug resistance, new nucleotide changes were found in the ABCC2 gene. Thus, the population of Mexican patients with AED-resistant epilepsy (ADR) used in this study exhibits genetic variability with respect to those reported in other study populations; however, it is necessary to explore this polymorphism in a larger population of patients with ADR.