Corrigendum: Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process.

[This corrects the article DOI: 10.3389/fgene.2018.00235.].

Cross-Species Meta-Analysis of Transcriptomic Data in Combination With Supervised Machine Learning Models Identifies the Common Gene Signature of Lactation Process.

Lactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals.

Fecal egg counts for gastrointestinal nematodes are associated with a polymorphism in the MHC-DRB1 gene in the Iranian Ghezel sheep breed.

Genetic variation among sheep breeds in resistance to gastrointestinal nematodes (GIN) has been demonstrated in several production environments. Relationships between the ovine major histocompatibility complex and resistance to GIN have been studied, but few studies have systematically examined this issue in less-developed and semi-arid regions. The aim of the current study was to explore associations between fecal worm egg counts (FEC) for several GIN and polymorphisms in the DRB1 gene. One hundred male lambs were selected at 4-6 months of age from weaned animals in five flocks (n = 20 per flock). Body weights were determined, FAMACHA scores based on color of the ocular mucous membranes were assigned as an indicator of anemia, and blood and fecal samples were collected twice to evaluate FEC and blood packed cell volume (PCV) and for DNA isolation. A repeated-measures analysis of variance was used to test effects of genotype on FEC. The model included fixed effects of flock, genotype, time of measurement (1 or 2), and flock × time and genoype × time interactions, and a random (repeated) effect of lamb. Two genotypes (A1A1 and A1A2) were observed following digestion of Region 1 of Ovar-DRB1 with PstI. Genotypic frequencies were 0.73 for A1A1 and 0.27 for A1A2. FEC differed between Ovar_DRB1 genotypes A1A1 and A1A2 for Marshallagia marshalli, Strongyle, and total nematode FEC. Observed FEC were 30-41% lower for genotype A1A1. Differences among genotypes were consistent across measurement times, with no effect of genotype × measurement time interaction for any parasite class (P ≥ 0.34). A significant association was observed between FAMACHA scores and lamb PCV, and the residual correlation between these two variables was -0.51 (P < 0.001). FAMACHA scores can thus be used to detect differences among lambs in PCV, and polymorphic markers of Ovar-DRB1 have potential value as an indicator of parasite resistance in applied animal breeding programs on sheep farms in this region.