Search

The molecular mechanism underlying embryonic implantation is vital to understand the correct communications between endometrium and developing conceptus during early stages of pregnancy. This study’s objective was to determine molecular changes in the uterine endometrial proteome during the preimplantation and peri-implantation between 9 days (9D), 12 days (12D), and 16 days (16D) of pregnant Polish Large White (PLW) gilts. 2DE-MALDI-TOF/TOF and Clue-GO™ approaches were employed to analyse the biological networks and molecular changes in porcine endometrial proteome during maternal recognition of pregnancy. A total of sixteen differentially expressed proteins (DEPs) were identified using 2-DE gels and MALDI-TOF/TOF mass spectrometry. Comparison between 9D and 12D of pregnancy identified APOA1, CAPZB, LDHB, CCT5, ANXA4, CFB, TTR upregulated DEPs, and ANXA5, SMS downregulated DEPs. Comparison between 9D and 16D of pregnancy identified HP, APOA1, ACTB, CCT5, ANXA4, CFB upregulated DEPs and ANXA5, SMS, LDHB, ACTR3, HP, ENO3, OAT downregulated DEPs. However, a comparison between 12D and 16D of pregnancy identified HP, ACTB upregulated DEPs, and CRYM, ANXA4, ANXA5, CAPZB, LDHB, ACTR3, CCT5, ENO3, OAT, TTR down-regulated DEPs. Outcomes of this study revealed key proteins and their interactions with metabolic pathways involved in the recognition and establishment of early pregnancy in PLW gilts

BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of D

BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of D