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Background: Dysbiosis is associated with colorectal cancer (CRC) and adenomas (CRA). However, the robustness of diagnostic models based on microbial signatures in multiple cohorts remains unsatisfactory.Materials and Methods: In this study, we used machine learning models to screen metagenomic signatures from the respective cross-cohort datasets of CRC and CRA (selected from CuratedMetagenomicData, each disease included 4 datasets). Then select a CRC and CRA data set from the CuratedMetagenomicData database and meet the requirements of having both metagenomic data and clinical data. This data set will be used to verify the inference that integrating clinical features can improve the performance of microbial disease prediction models.Results: After repeated verification, we selected 20 metagenomic features that performed well and were stably expressed within cross-cohorts to represent the diagnostic role of bacterial communities in CRC/CRA. The performance of the selected cross-cohort metagenomic features was stable for multi-regional and multi-ethnic populations (CRC, AUC: 0.817–0.867; CRA, AUC: 0.766–0.833). After clinical feature combination, AUC of our integrated CRC diagnostic model reached 0.939 (95% CI: 0.932–0.947, NRI=30%), and that of the CRA integrated model reached 0.925 (95%CI: 0.917–0.935, NRI=18%).Conclusion: In conclusion, the integrated model performed significantly better than single microbiome or clinical feature models in all cohorts. Integrating cross-cohort common discriminative microbial features with clinical features could help construct stable diagnostic models for early non-invasive screening for CRC and CRA.

Obesity, a key contributor to metabolic disorders, necessitates an in-depth understanding of its pathogenesis and prerequisites for prevention. Guangxi Bama miniature pig (GBM) offers an apt model for obesity-related studies. In this research, we used transcriptomics and 16S rRNA gene sequencing to discern the differentially expressed genes (DEGs) within intestinal (jejunum, ileum, and colon) tissues and variations in microbial communities in intestinal contents of GBM subjected to normal diets (ND) and high-fat, high-carbohydrate diets (HFHCD). After a feeding duration of 26 weeks, the HFHCD-fed experimental group demonstrated notable increases in backfat thickness, BMI, abnormal blood glucose metabolism, and blood lipid levels alongside the escalated serum expression of pro-inflammatory factors and a marked decline in intestinal health status when compared to the ND group. Transcriptomic analysis revealed a total of 1669 DEGs, of which 27 had similar differences in three intestinal segments across different groups, including five immune related genes: COL6A6, CYP1A1, EIF2AK2, NMI, and LGALS3B. Further, we found significant changes in the microbiota composition, with a significant decrease in beneficial bacterial populations within the HFHCD group. Finally, the results of integrated analysis of microbial diversity with transcriptomics show a positive link between certain microbial abundance (Solibacillus, norank_f__Saccharimonadaceae, Candidatus_Saccharimonas, and unclassified_f__Butyricicoccaceae) and changes in gene expression (COL6A6 and NMI). Overall, HFHCD appears to co-contribute to the initiation and progression of obesity in GBM by aggravating inflammatory responses, disrupting immune homeostasis, and creating imbalances in intestinal flora.

Abstract Background and aims Previous study disclosed Fucosyltransferase 2 (Fut2) gene as a IBD risk locus. This study aimed to explore the mechanism of Fut2 in IBD susceptibility and to propose a new strategy for the treatment of IBD. Methods Intestinal epithelium-specific Fut2 knockout (Fut2 △IEC) mice was used. Colitis was induced by dextran sulfate sodium (DSS). The composition and diversity of gut microbiota were assessed via 16S rRNA analysis and the metabolomic findings was obtained from mice feces via metabolite profiling. The fecal microbiota transplantation (FMT) experiment was performed to confirm the association of gut microbiota and LPC. WT mice were treated with Lysophosphatidylcholine (LPC) to verify its impact on colitis. Results The expression of Fut2 and α-1,2-fucosylation in colonic tissues were decreased in patients with UC (UC vs. control, P = 0.036) and CD (CD vs. control, P = 0.031). When treated with DSS, in comparison to WT mice, more severe intestinal inflammation and destructive barrier functions in Fut2 △IEC mice was noted. Lower gut microbiota diversity was observed in Fut2 △IEC mice compared with WT mice (p

Identifying and verifying appropriate biomarkers is instrumental in improving the prediction of early-stage pig production performance while reducing the cost of breeding and production. The main factor that affects the production cost and environmental protection cost of the pig industry is the feed efficiency of pigs. This study aimed to detect the differentially expressed proteins in the early blood index determination serum between high-feed efficiency and low-feed efficiency pigs and to provide a basis for further identification of biomarkers using the isobaric tandem mass tag and parallel reaction monitoring approach. In total, 350 (age, 90 ± 2 d; body weight, 41.20 ± 4.60 kg) purebred Yorkshire pigs were included in the study, and their serum samples were obtained during the early blood index determination. The pigs were then arranged based on their feed efficiency; 24 pigs with extreme phenotypes were grouped as high-feed efficiency and low-feed efficiency, with 12 pigs in each group. A total of 1364 proteins were found in the serum, and 137 of them showed differential expression between the groups with high- and low-feed efficiency, with 44 of them being upregulated and 93 being downregulated. PRM (parallel reaction monitoring) was used to verify 10 randomly chosen differentially expressed proteins. The proteins that were differentially expressed were shown to be involved in nine pathways, including the immune system, digestive system, human diseases, metabolism, cellular processing, and genetic information processing, according to the KEGG and GO analyses. Moreover, all of the proteins enriched in the immune system were downregulated in the high-feed efficiency pigs, suggesting that a higher immune level may not be conducive to improving feed efficiency in pigs. This study provides insights into the important feed efficiency proteins and pathways in pigs, promoting the further development of protein biomarkers for predicting and improving porcine feed efficiency.

The epigenetic regulation mechanism of porcine skeletal muscle development relies on the openness of chromatin and is also precisely regulated by transcriptional machinery. However, fewer studies have exploited the temporal changes in gene expression and the landscape of accessible chromatin to reveal the underlying molecular mechanisms controlling muscle development. To address this, skeletal muscle biopsy samples were taken from Landrace pigs at days 0 (D0), 60 (D60), 120 (D120), and 180 (D180) after birth and were then analyzed using RNA-seq and ATAC-seq. The RNA-seq analysis identified 8554 effective differential genes, among which ACBD7, TMEM220, and ATP1A2 were identified as key genes related to the development of porcine skeletal muscle. Some potential cis-regulatory elements identified by ATAC-seq analysis contain binding sites for many transcription factors, including SP1 and EGR1, which are also the predicted transcription factors regulating the expression of ACBD7 genes. Moreover, the omics analyses revealed regulatory regions that become ectopically active after birth during porcine skeletal muscle development after birth and identified 151,245, 53,435, 30,494, and 40,911 peaks. The enriched functional elements are related to the cell cycle, muscle development, and lipid metabolism. In summary, comprehensive high-resolution gene expression maps were developed for the transcriptome and accessible chromatin during postnatal skeletal muscle development in pigs.

Litter size and teat number are economically important traits in the porcine industry. However, the genetic mechanisms influencing these traits remain unknown. In this study, we analyzed the genetic basis of litter size and teat number in Bama Xiang pigs and evaluated the genomic inbreeding coefficients of this breed. We conducted a genome-wide association study to identify runs of homozygosity (ROH), and copy number variation (CNV) using the novel Illumina PorcineSNP50 BeadChip array in Bama Xiang pigs and annotated the related genes in significant single nucleotide polymorphisms and common copy number variation region (CCNVR). We calculated the ROH-based genomic inbreeding coefficients (FROH) and the Spearman coefficient between FROH and reproduction traits. We completed a mixed linear model association analysis to identify the effect of high-frequency copy number variation (HCNVR; over 5%) on Bama Xiang pig reproductive traits using TASSEL software. Across eight chromosomes, we identified 29 significant single nucleotide polymorphisms, and 12 genes were considered important candidates for litter-size traits based on their vital roles in sperm structure, spermatogenesis, sperm function, ovarian or follicular function, and male/female infertility. We identified 9,322 ROHs; the litter-size traits had a significant negative correlation to FROH. A total of 3,317 CNVs, 24 CCNVR, and 50 HCNVR were identified using cnvPartition and PennCNV. Eleven genes related to reproduction were identified in CCNVRs, including seven genes related to the testis and sperm function in CCNVR1 (chr1 from 311585283 to 315307620). Two candidate genes (NEURL1 and SH3PXD2A) related to reproduction traits were identified in HCNVR34. The result suggests that these genes may improve the litter size of Bama Xiang by marker-assisted selection. However, attention should be paid to deter inbreeding in Bama Xiang pigs to conserve their genetic diversity.

Abstract Improving the predication efficiency of porcine production performance at early stage will contribute to reducing the breeding and production costs. The intestinal microbiota had received plenty of attention in recent years due to their influence on host health and performance. The purpose of this study was to investigate the relationship between the fecal microbiota at early growth period and porcine feed efficiency (FE) under a commercial feeding environment. Ninety-one pigs were reordered according to the residual feed intake (RFI) values between day 90 on test and day 160 off test, 9 lowest RFI pigs and 9 highest RFI pigs were selected as the LRFI group and the HRFI group, respectively. Fecal samples from pigs in the early grower phase (day 80) were performed for microbial diversity, composition, and predicted functionality by using 16S rRNA sequencing. The results showed that no significant differences in microbial alpha diversity were observed between two RFI groups, whereas, some RFI-associated compositional differences were revealed. In particular, the microbiota of the LRFI group (more feed-efficient) had significantly higher levels of some members of Clostridiales and Bacteroidales (e.g., g_1_68 and g_norank_f_p_2534_18B5), which may promoted FE through protecting gut barrier function, compared with those of the HRFI pigs. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis found that the LRFI pigs were likely have microbiota with higher levels of amino acid metabolism. Moreover, redundancy analysis (RDA) showed that litter size, parity, and date of birth had significant effects on the bacterial community structure. These results improved our knowledge of the porcine early-life fecal microbiota and its potential link underlying RFI, which would be useful for future development of microbial biomarkers for predicting and improving porcine FE as well as investigation of targets for dietary strategies.

Summary: Pig has been proved to be a valuable large animal model used for research on diabetic disease. However, their translational value is limited given their distinct anatomy and physiology. For the last 30 years, we have been developing a laboratory Asian miniature pig inbred line (Bama miniature pig [BM]) from the primitive Bama xiang pig via long-term selective inbreeding. Here, we assembled a BM reference genome at full chromosome-scale resolution with a total length of 2.49 Gb. Comparative and evolutionary genomic analyses identified numerous variations between the BM and commercial pig (Duroc), particularly those in the genetic loci associated with the features advantageous to diabetes studies. Resequencing analyses revealed many differentiated gene loci associated with inbreeding and other selective forces. These together with transcriptome analyses of diabetic pig models provide a comprehensive genetic basis for resistance to diabetogenic environment, especially related to energy metabolism. : Biological Sciences; Gene Ontology; Genomic Analysis; Genomics; Omics; Sequence Analysis; Transcriptomics Subject Areas: Biological Sciences, Gene Ontology, Genomic Analysis, Genomics, Omics, Sequence Analysis, Transcriptomics

RNA-binding proteins (RBPs) are involved in regulating all aspects of RNA metabolism, including processing, transport, translation, and degradation. Dysregulation of RNA metabolism is linked to a plethora of diseases, such as cancer, neurodegenerative diseases, and neuromuscular disorders. Recent years have seen a dramatic shift in the knowledge base, with RNA increasingly being recognised as an attractive target for precision medicine therapies. In this article, we are going to review current RNA-targeted therapies. Furthermore, we will scrutinise a range of drug discoveries targeting protein-RNA interactions. In particular, we will focus on the interplay between Lin28 and let-7, splicing regulatory proteins and survival motor neuron (SMN) pre-mRNA, as well as HuR, Musashi, proteins and their RNA targets. We will highlight the mechanisms RBPs utilise to modulate RNA metabolism and discuss current high-throughput screening strategies. This review provides evidence that we are entering a new era of RNA-targeted medicine.