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Journal ArticleDOI

Proteins involved in mitochondrial metabolic functions and fertilization predominate in stallions with better motility.

TL;DR: In this paper, the authors investigate differences among stallions of variable sperm quality (based on motility and sperm velocities), although all horses had sperm characteristics within normal ranges.
About: This article is published in Journal of Proteomics.The article was published on 2021-07-21 and is currently open access. It has received 4 citations till now. The article focuses on the topics: Sperm motility & Sperm.
Citations
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Journal ArticleDOI
TL;DR: Increasing knowledge on sperm metabolism and its interactions with redox regulation, may improve current sperm technologies in use and shall provide new clues to the understanding of male factor infertility.
Abstract: Proper functionality of the spermatozoa depends on the tight regulation of their redox status, at the same time these cells are very energy demanding, and in the energetic metabolism, reactive oxygen species (ROS) are continuously produced, mainly in the electron transport chain, but also in the Krebs Cycle and during the beta oxidation of fatty acids. Additionally, in the glycolysis, elimination of phosphate groups from the trioses phosphates glyceraldehyde 3-phosphate and dihydroxyacetone phosphate originates as byproducts glyoxal (G) and methylglyoxal (MG); these products are 2-oxoaldehydes and due to their adjacent carbonyl groups are strong electrophiles that react rapidly and spontaneously with nucleophiles of proteins, lipids and DNA, forming advanced glycation end products (AGEs). This mechanism is behind subfertility in diabetic patients; in the animal breeding industry, commercial extenders for stallion semen contain a supraphysiological concentration of glucose that promote the production of methylglyoxal, constituting a potential model of interest. Increasing our knowledge on sperm metabolism and its interactions with redox regulation, may improve current sperm technologies in use and shall provide new clues to the understanding of male factor infertility.

4 citations

Journal ArticleDOI
TL;DR: Silica-coated magnetite nanoparticles (SMNPs) were prepared by a green approach based on waste pickling acid as mentioned in this paper , and a set of techniques were used for their characterization, and for monitoring the levels of heavy metal impurities.

1 citations

Journal ArticleDOI
TL;DR: In this paper , the authors used Tandem Mass Tag (TMT) peptide labeling coupled with LC-MS/MS approach to identify the different abundance sperm proteins in good freezability ejaculates (GFEs) and poor freezable ejaculate (PFEs), and found that upregulated proteins in GF group were mainly involved in N-Glycan biosynthesis and protein processing in endoplasmic reticulum.
Journal ArticleDOI
TL;DR: A detailed set of data on how the stallion sperm proteome differs among stallions with different sperm motilities, although within normal ranges, was provided in this article , which can be used to disclose potential targets to identify good sperm samples and to study specific pathways involved in the regulation of sperm motility.
References
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Journal ArticleDOI
TL;DR: The Kyoto Encyclopedia of Genes and Genomes (KEGG) as discussed by the authors is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules.
Abstract: Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major component of KEGG is the PATHWAY database that consists of graphical diagrams of biochemical pathways including most of the known metabolic pathways and some of the known regulatory pathways. The pathway information is also represented by the ortholog group tables summarizing orthologous and paralogous gene groups among different organisms. KEGG maintains the GENES database for the gene catalogs of all organisms with complete genomes and selected organisms with partial genomes, which are continuously re-annotated, as well as the LIGAND database for chemical compounds and enzymes. Each gene catalog is associated with the graphical genome map for chromosomal locations that is represented by Java applet. In addition to the data collection efforts, KEGG develops and provides various computational tools, such as for reconstructing biochemical pathways from the complete genome sequence and for predicting gene regulatory networks from the gene expression profiles. The KEGG databases are daily updated and made freely available (http://www.genome.ad.jp/kegg/).

24,024 citations

Journal ArticleDOI
TL;DR: This work proposes an approach to measuring statistical significance in genomewide studies based on the concept of the false discovery rate, which offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted.
Abstract: With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.

9,239 citations

Journal ArticleDOI
TL;DR: Key statistics on the current data contents and volume of downloads are outlined, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas are outlined.
Abstract: The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

5,735 citations

Journal ArticleDOI
TL;DR: G:Profiler is now capable of analysing data from any organism, including vertebrates, plants, fungi, insects and parasites, and the 2019 update introduces an extensive technical rewrite making the services faster and more flexible.
Abstract: Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.

2,959 citations

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TL;DR: The authors provide an update to their 2013 protocol for using the PANTHER classification system, detailing how to analyze genome-wide experimental data with the newest version of PANTHER (v.14.0), with improvements in the areas of data quality, data coverage, statistical algorithms and user experience.
Abstract: The PANTHER classification system ( http://www.pantherdb.org ) is a comprehensive system that combines genomes, gene function classifications, pathways and statistical analysis tools to enable biologists to analyze large-scale genome-wide experimental data. The current system (PANTHER v.14.0) covers 131 complete genomes organized into gene families and subfamilies; evolutionary relationships between genes are represented in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models (HMMs)). The families and subfamilies are annotated with Gene Ontology (GO) terms, and sequences are assigned to PANTHER pathways. A suite of tools has been built to allow users to browse and query gene functions and analyze large-scale experimental data with a number of statistical tests. PANTHER is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. Since the protocol for using this tool (v.8.0) was originally published in 2013, there have been substantial improvements and updates in the areas of data quality, data coverage, statistical algorithms and user experience. This Protocol Update provides detailed instructions on how to analyze genome-wide experimental data in the PANTHER classification system.

900 citations