TY - JOUR A1 - Omolaoye, Temidayo S. A1 - Omolaoye, Victor Adelakun A1 - Kandasamy, Richard K. A1 - Hachim, Mahmood Yaseen A1 - Du Plessis, Stefan S. T1 - Omics and male infertility BT - highlighting the application of transcriptomic data JF - Life : open access journal N2 - Male infertility is a multifaceted disorder affecting approximately 50% of male partners in infertile couples. Over the years, male infertility has been diagnosed mainly through semen analysis, hormone evaluations, medical records and physical examinations, which of course are fundamental, but yet inefficient, because 30% of male infertility cases remain idiopathic. This dilemmatic status of the unknown needs to be addressed with more sophisticated and result-driven technologies and/or techniques. Genetic alterations have been linked with male infertility, thereby unveiling the practicality of investigating this disorder from the "omics" perspective. Omics aims at analyzing the structure and functions of a whole constituent of a given biological function at different levels, including the molecular gene level (genomics), transcript level (transcriptomics), protein level (proteomics) and metabolites level (metabolomics). In the current study, an overview of the four branches of omics and their roles in male infertility are briefly discussed; the potential usefulness of assessing transcriptomic data to understand this pathology is also elucidated. After assessing the publicly obtainable transcriptomic data for datasets on male infertility, a total of 1385 datasets were retrieved, of which 10 datasets met the inclusion criteria and were used for further analysis. These datasets were classified into groups according to the disease or cause of male infertility. The groups include non-obstructive azoospermia (NOA), obstructive azoospermia (OA), non-obstructive and obstructive azoospermia (NOA and OA), spermatogenic dysfunction, sperm dysfunction, and Y chromosome microdeletion. Findings revealed that 8 genes (LDHC, PDHA2, TNP1, TNP2, ODF1, ODF2, SPINK2, PCDHB3) were commonly differentially expressed between all disease groups. Likewise, 56 genes were common between NOA versus NOA and OA (ADAD1, BANF2, BCL2L14, C12orf50, C20orf173, C22orf23, C6orf99, C9orf131, C9orf24, CABS1, CAPZA3, CCDC187, CCDC54, CDKN3, CEP170, CFAP206, CRISP2, CT83, CXorf65, FAM209A, FAM71F1, FAM81B, GALNTL5, GTSF1, H1FNT, HEMGN, HMGB4, KIF2B, LDHC, LOC441601, LYZL2, ODF1, ODF2, PCDHB3, PDHA2, PGK2, PIH1D2, PLCZ1, PROCA1, RIMBP3, ROPN1L, SHCBP1L, SMCP, SPATA16, SPATA19, SPINK2, TEX33, TKTL2, TMCO2, TMCO5A, TNP1, TNP2, TSPAN16, TSSK1B, TTLL2, UBQLN3). These genes, particularly the above-mentioned 8 genes, are involved in diverse biological processes such as germ cell development, spermatid development, spermatid differentiation, regulation of proteolysis, spermatogenesis and metabolic processes. Owing to the stage-specific expression of these genes, any mal-expression can ultimately lead to male infertility. Therefore, currently available data on all branches of omics relating to male fertility can be used to identify biomarkers for diagnosing male infertility, which can potentially help in unravelling some idiopathic cases. KW - male infertility KW - omics KW - genomics KW - transcriptomics KW - proteomics KW - metabolomics Y1 - 2022 U6 - https://doi.org/10.3390/life12020280 SN - 2075-1729 VL - 12 IS - 2 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kraus, Sara Milena A1 - Mathew-Stephen, Mariet A1 - Schapranow, Matthieu-Patrick T1 - Eatomics BT - Shiny exploration of quantitative proteomics data JF - Journal of proteome research N2 - Quantitative proteomics data are becoming increasingly more available, and as a consequence are being analyzed and interpreted by a larger group of users. However, many of these users have less programming experience. Furthermore, experimental designs and setups are getting more complicated, especially when tissue biopsies are analyzed. Luckily, the proteomics community has already established some best practices on how to conduct quality control, differential abundance analysis and enrichment analysis. However, an easy-to-use application that wraps together all steps for the exploration and flexible analysis of quantitative proteomics data is not yet available. For Eatomics, we utilize the R Shiny framework to implement carefully chosen parts of established analysis workflows to (i) make them accessible in a user-friendly way, (ii) add a multitude of interactive exploration possibilities, and (iii) develop a unique experimental design setup module, which interactively translates a given research hypothesis into a differential abundance and enrichment analysis formula. In this, we aim to fulfill the needs of a growing group of inexperienced quantitative proteomics data analysts. Eatomics may be tested with demo data directly online via https://we.analyzegenomes.com/now/eatomics/or with the user's own data by installation from the Github repository at https://github.com/Millchmaedchen/Eatomics. KW - R Shiny KW - application KW - label-free KW - proteomics KW - analysis KW - differential KW - abundance KW - experimental design Y1 - 2021 U6 - https://doi.org/10.1021/acs.jproteome.0c00398 SN - 1535-3893 SN - 1535-3907 VL - 20 IS - 1 SP - 1070 EP - 1078 PB - American Chemical Society CY - Washington ER - TY - JOUR A1 - Christopher Ashwood, Wout Bittremieux A1 - Bittremieux, Wout A1 - Deutsch, Eric W. A1 - Doncheva, Nadezhda T. A1 - Dorfer, Viktoria A1 - Gabriels, Ralf A1 - Gorshkov, Vladimir A1 - Gupta, Surya A1 - Jones, Andrew R. A1 - Käll, Lukas A1 - Kopczynski, Dominik A1 - Lane, Lydie A1 - Lautenbacher, Ludwig A1 - Legeay, Marc A1 - Locard-Paulet, Marie A1 - Mesuere, Bart A1 - Sachsenberg, Timo A1 - Salz, Renee A1 - Samaras, Patroklos A1 - Schiebenhoefer, Henning A1 - Schmidt, Tobias A1 - Schwämmle, Veit A1 - Soggiu, Alessio A1 - Uszkoreit, Julian A1 - Van Den Bossche, Tim A1 - Van Puyvelde, Bart A1 - Van Strien, Joeri A1 - Verschaffelt, Pieter A1 - Webel, Henry A1 - Willems, Sander A1 - Perez-Riverolab, Yasset A1 - Netz, Eugen A1 - Pfeuffer, Julianus T1 - Proceedings of the EuBIC-MS 2020 Developers’ Meeting JF - EuPA Open Proteomics N2 - The 2020 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers’ meeting was held from January 13th to January 17th 2020 in Nyborg, Denmark. Among the participants were scientists as well as developers working in the field of computational mass spectrometry (MS) and proteomics. The 4-day program was split between introductory keynote lectures and parallel hackathon sessions. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts, and to actively contribute to highly relevant research projects. We successfully produced several new tools that will be useful to the proteomics community by improving data analysis as well as facilitating future research. All keynote recordings are available on https://doi.org/10.5281/zenodo.3890181. KW - computational mass spectrometry KW - proteomics KW - bioinformatics KW - spectrum clustering KW - phosphoproteomics KW - XIC extraction KW - proteomics graph networks KW - predicted spectra Y1 - 2020 U6 - https://doi.org/10.1016/j.euprot.2020.11.001 SN - 2212-9685 VL - 24 SP - 1 EP - 6 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Winck, Flavia Vischi A1 - Kwasniewski, Miroslaw A1 - Wienkoop, Stefanie A1 - Müller-Röber, Bernd T1 - An optimized method for the isolation of nuclei from chlamydomas Reinhardtii (Chlorophyceae) JF - Journal of phycology N2 - The cell nucleus harbors a large number of proteins involved in transcription, RNA processing, chromatin remodeling, nuclear signaling, and ribosome assembly. The nuclear genome of the model alga Chlamydomonas reinhardtii P. A. Dang. was recently sequenced, and many genes encoding nuclear proteins, including transcription factors and transcription regulators, have been identified through computational discovery tools. However, elucidating the specific biological roles of nuclear proteins will require support from biochemical and proteomics data. Cellular preparations with enriched nuclei are important to assist in such analyses. Here, we describe a simple protocol for the isolation of nuclei from Chlamydomonas, based on a commercially available kit. The modifications done in the original protocol mainly include alterations of the differential centrifugation parameters and detergent-based cell lysis. The nuclei-enriched fractions obtained with the optimized protocol show low contamination with mitochondrial and plastid proteins. The protocol can be concluded within only 3 h, and the proteins extracted can be used for gel-based and non-gel-based proteomic approaches. KW - 2D gel electrophoresis KW - algae KW - Chlamydomonas KW - nuclear proteins KW - nucleus KW - proteomics Y1 - 2011 U6 - https://doi.org/10.1111/j.1529-8817.2011.00967.x SN - 0022-3646 VL - 47 IS - 2 SP - 333 EP - 340 PB - Wiley-Blackwell CY - Malden ER -