Coccidiosis is an economically significant protozoan disease and an intracellular parasite that significantly impacts poultry production. The gastrointestinal tract microbiota plays a central role in host health and metabolism, and these microbes enhance chickens’ immune systems and nutrient absorption. In this study, we analyzed the abundance and diversity of microbiota of the jejunum and cecum of a dual-purpose indigenous Horro chicken following Eimeria maxima infection. We compared microbial abundance, composition, and diversity at the 4- and 7- days post-infection using 16S rRNA gene sequencing. We obtained, on average, 147,742 and 132,986 high-quality sequences per sample for jejunum and cecum content, respectively. Firmicutes, Proteobacteria, Campilobacterota and Bacteroidota were the major microbial phylum detected in the jejunum content. Firmicutes were the dominant phylum for 4- and 7-days jejunum control groups accounting for (>60% of the sequences). In the infected group Campilobacterota was the dominant phylum in the jejunum (> 24% of sequences) at 4-and 7-days post-infection groups, while Proteobacteria was predominant at 4- and 7-days post-infection of the cecum (> 40% of the sequences). The microbial genus Lactobacillus and Helicobacter were found in the jejunum, while Alistipes, Barnesiella and Faecalibacterium were detected in the cecum. In the jejunum, Helicobacter was dominant at 4 -and-7 days post-infection (≥24%), and Lactobacillus was dominant at 4 -and 7- days in the control group (> 50%). In 4- and 7-days post-infection, Alistipes genus was the more prevalent (> 38%) in the cecum. Thus, clear differences were observed in the bacterial microbiota distribution and abundance between the jejunum and cecum, as well as between infected and control groups for both tissues. The results indicate that chicken intestinal microbial imbalance (dysbiosis) is associated with Eimeria parasite infection and will likely affect the host-microbial non-pathogenic and pathogenic molecular interactions.
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.
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