MicroRNA expression profile of chicken jejunum in different time points Eimeria maxima infection

Coccidiosis stands as a protozoan disease of notable economic impact, characterized by an intracellular parasite that exerts substantial influence over poultry production. This invasion disrupts the integrity of the enteric mucosa, leading to the emergence of severe lesions and diminishing the efficiency of feed utilization in chickens. MicroRNA (miRNA) are short, non-coding RNA molecules with approximately 21–24 nucleotides long in size that play essential roles in various infectious diseases and inflammatory responses. However, the miRNA’s expression patterns and roles in the context of Eimeria maxima infection of chicken intestines remain unclear. miRNA sequencing was employed to assess the miRNA expression profile in chicken jejunum during E. maxima infection. In this study, we analyzed miRNA expression profiles related to the host’s immune response in the chicken jejunum during E. maxima infection. At 4 days infection and control (J4I versus J4C), 21 differentially expressed miRNAs in the jejunum were identified, comprising 9 upregulated and 12 downregulated miRNAs. Furthermore, in the jejunum, at 7 days infection and control (J7I versus J7C) groups, a total of 35 significantly differentially expressed miRNAs were observed, with 13 upregulated and 22 downregulated miRNAs. The regulatory networks were constructed between differentially expressed miRNA and mRNAs to offer insight into the interaction mechanisms between chickens and E. maxima coccidian infection. Furthermore, within the comparison group, we obtained 946, 897, and 281 GO terms that exhibited significant enrichment associated with host immunity in the following scenarios, J4I vs. J4C, J7I vs. J7C, and J4I vs. J7I, respectively. The KEGG pathway analysis indicated notable enrichment of differentially expressed miRNAs in the jejunum, particularly in J4I vs. J4C; enriched pathways included metabolic pathways, endocytosis, MAPK signaling pathway, regulation of actin cytoskeleton, and cytokine–cytokine receptor interaction. Moreover, in J7I vs. J7C, the KEGG pathway was significantly enriched, including metabolic pathways, protein processing in the endoplasmic reticulum, ubiquitin-mediated proteolysis, and FoxO signaling pathway. A comprehensive understanding of the host genetic basis of resistance with a combination of time-dependent infection to the Eimeria parasite is crucial for pinpointing resistance biomarkers for poultry production

Comprehensive analysis of differentially expressed mRNA profiles in chicken jejunum and cecum following Eimeria maxima infection

Coccidiosis, a protozoan disease that substantially impacts poultry production, is characterized by an intracellular parasite. The study utilized 48 one-day-old Horro chickens, randomly divided into the infected (I) and control (C) groups. The challenge group of chickens were administered Eimeria maxima oocysts via oral gavage at 21-days-old, and each chicken received 2 mL containing 7×104 sporulated oocysts. The total RNAs of chicken jejunum and cecum tissues were isolated from three samples, each from I and C groups. Our study aimed to understand the host immune-parasite interactions and compare immune response mRNA profiles in chicken jejunum and cecum tissues at 4 and 7 days postinfection with Eimeria maxima. The results showed that 823 up- and 737 down-regulated differentially expressed mRNAs (DEmRNAs) in jejunum at 4 d infection and control (J4I vs. J4C), and 710 up- and 368 down-regulated DEmRNAs in jejunum at 7 days infection and control (J7I vs. J7C) were identified. In addition, DEmRNAs in cecum tissue, 1424 up- and 1930 down-regulated genes in cecum at 4 days infection and control (C4I vs. C4C), and 77 up- and 191 down-regulated genes in cecum at 7 days infection and control (C7I vs. C7C) were detected. The crucial DEmRNAs, including SLC7A5, IL1R2, GLDC, ITGB6, ADAMTS4, IL1RAP, TNFRSF11B, IMPG2, WNT9A, and FOXF1, played pivotal roles in the immune response during Eimeria maxima infection of chicken jejunum. In addition, the potential detection of FSTL3, RBP7, CCL20, DPP4, PRKG2, TFPI2, and CDKN1A in the cecum during the host immune response against Eimeria maxima infection is particularly noteworthy. Furthermore, our functional enrichment analysis revealed the primary involvement of DEmRNAs in small molecule metabolic process, immune response function, inflammatory response, and toll-like receptor 10 signaling pathway in the jejunum at 4 and 7 days postinfection. Similarly, in the cecum, DEmRNAs at 4 and 7 days postinfection were enriched in processes related to oxidative stress response and immune responses. Our findings provide new insights and contribute significantly to the field of poultry production and parasitology

Research Note: Association of single nucleotide polymorphism of AKT3 with egg production traits in White Muscovy ducks (Cairina moschata).

Prior studies on transcriptomes of hypothalamus and ovary revealed that AKT3 is one of the candidate genes that might affect egg production in White Muscovy ducks. The role of AKT3 in the uterus during reproductive processes cannot be overemphasized. However, functional role of this gene in the tissues and on egg production traits of Muscovy ducks remains unknown. To identify the relationship between AKT3 and egg production traits in ducks, relative expression profile was first examined prior to identifying the variants within AKT3 that may underscore egg production traits [age at first egg (AFE), number of eggs at 300 d (N300D), and number of eggs at 59 wk (N59W)] in 549 ducks. The mRNA expression of AKT3 gene in high producing (HP) ducks was significantly higher than low producing (LP) ducks in the ovary, oviduct, and hypothalamus (P \u3c 0.05 or 0.001). Three variants in AKT3 (C-3631A, C-3766T, and C-3953T) and high linkage block between C-3766T and C-3953T which are significantly (P \u3c 0.05) associated with N300D and N59W were discovered. This study elucidates novel knowledge on the molecular mechanism of AKT3 that might be regulating egg production traits in Muscovy ducks

DNA methylation in poultry: a review

Abstract As an important epigenetic modification, DNA methylation is involved in many biological processes such as animal cell differentiation, embryonic development, genomic imprinting and sex chromosome inactivation. As DNA methylation sequencing becomes more sophisticated, it becomes possible to use it to solve more zoological problems. This paper reviews the characteristics of DNA methylation, with emphasis on the research and application of DNA methylation in poultry

The study of selection signature and its applications on identification of candidate genes using whole genome sequencing data in chicken—a review

ABSTRACT: Chicken is a major source of protein for the increasing human population and is useful for research purposes. There are almost 1,600 distinct regional breeds of chicken across the globe, among which a large body of genetic and phenotypic variations has been accumulated due to extensive natural and artificial selection. Moreover, natural selection is a crucial force for animal domestication. Several approaches have been adopted to detect selection signatures in different breeds of chicken using whole genome sequencing (WGS) data including integrated haplotype score (iHS), cross-populated extend haplotype homozygosity test (XP-EHH), fixation index (FST), cross-population composite likelihood ratio (XP-CLR), nucleotide diversity (Pi), and others. In addition, gene enrichment analyses are utilized to determine KEGG pathways and gene ontology (GO) terms related to traits of interest in chicken. Herein, we review different studies that have adopted diverse approaches to detect selection signatures in different breeds of chicken. This review systematically summarizes different findings on selection signatures and related candidate genes in chickens. Future studies could combine different selection signatures approaches to strengthen the quality of the results thereby providing more affirmative inference. This would further aid in deciphering the importance of selection in chicken conservation for the increasing human population

TAT gene polymorphism and its relationship with production traits in Muscovy ducks (Cairina Moschata)

ABSTRACT: In a previous study, the laying pattern of Muscovy duck was explored by macro-fitting the laying curve of Muscovy duck, and transcriptome sequencing technique of the ovarian tissues was used to screen the egg-related gene “TAT.” Moreover, recent results have shown that TAT is expressed in organs such as oviduct, ovary, and testis. The objective of this study is to examine the effect of TAT gene on egg production traits of Muscovy ducks. First, the expression levels of TAT gene in highest producing (HP) and lowest producing (LP) in 3 tissues related to reproduction were examined, and the results indicated that the expression of TAT gene in hypothalamus was significantly different between HP and LP groups. Then, 6 SNP loci (g. 120G>T, g, 122G>A, g, 254G> A, g. 270C >T, g, 312G>A, and g. 341C>A) were detected in TAT gene. Further, association analysis between the six SNP loci of TAT gene and egg production traits of 652 individual Muscovy ducks was done. The results showed that g. 254G>A and g. 270C>T were significantly correlated (P < 0.05 or 0.001) with the egg production traits of Muscovy ducks. This study elucidated the molecular mechanism that TAT gene might be regulating the egg production traits of Muscovy ducks

Table_2_A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice.xlsx

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.</p

Table_3_A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice.xlsx

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.</p

Table_1_A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice.xlsx

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.</p

Image_1_A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice.tif

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.</p