New perspective on conceptus estrogens in maternal recognition and pregnancy establishment in the pig†

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Abstract

The proposed signal for maternal recognition of pregnancy in pigs is estrogen (E2), produced by the elongating conceptuses between days 11 to 12 of pregnancy with a more sustained increase during conceptus attachment and placental development on days 15 to 30. To understand the role of E2 in porcine conceptus elongation and pregnancy establishment, a loss-of-function study was conducted by editing aromatase (CYP19A1) using CRISPR/Cas9 technology. Wild-type (CYP19A1+/+) and (CYP19A1−/−) fibroblast cells were used to create embryos through somatic cell nuclear transfer, which were transferred into recipient gilts. Elongated and attaching conceptuses were recovered from gilts containing CYP19A1+/+ or CYP19A1−/− embryos on day 14 and 17 of pregnancy. Total E2 in the uterine flushings of gilts with CYP19A1−/− embryos was lower than recipients containing CYP19A1+/+ embryos with no difference in testosterone, PGF2α, or PGE2 on either day 14 or 17. Despite the loss of conceptus E2 production, CYP19A1−/− conceptuses were capable of maintaining the corpora lutea. However, gilts gestating CYP19A1−/− embryos aborted between days 27 and 31 of gestation. Attempts to rescue the pregnancy of CYP19A1−/− gestating gilts with exogenous E2 failed to maintain pregnancy. However, CYP19A1−/− embryos could be rescued when co-transferred with embryos derived by in vitro fertilization. Endometrial transcriptome analysis revealed that ablation of conceptus E2 resulted in disruption of a number biological pathways. Results demonstrate that intrinsic E2 conceptus production is not essential for pre-implantation development, conceptus elongation, and early CL maintenance, but is essential for maintenance of pregnancy beyond 30 days .

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featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

Y. Liao, G K Smyth, W Shi (2014)

Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

Jing Chen, Eric Bardes, Bruce J Aronow … (2009)

ToppGene Suite (http://toppgene.cchmc.org; this web site is free and open to all users and does not require a login to access) is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein–protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method). We demonstrate the utility of ToppGene Suite using 20 recently reported GWAS-based gene–disease associations (including novel disease genes) representing five diseases. ToppGene ranked 19 of 20 (95%) candidate genes within the top 20%, while ToppNet ranked 12 of 16 (75%) candidate genes among the top 20%.