Shotgun metagenomics has become a pivotal technology in microbiome research, enabling in-depth analysis of microbial communities at both the high-resolution taxonomic and functional levels. This approach provides valuable insights of microbial diversity, interactions, and their roles in health and disease. However, the complexity of data processing and the need for reproducibility pose significant challenges to researchers. To address these challenges, we developed EasyMetagenome, a user-friendly pipeline that supports multiple analysis methods, including quality control and host removal, read-based, assembly-based, and binning, along with advanced genome analysis. The pipeline also features customizable settings, comprehensive data visualizations, and detailed parameter explanations, ensuring its adaptability across a wide range of data scenarios. Looking forward, we aim to refine the pipeline by addressing host contamination issues, optimizing workflows for third-generation sequencing data, and integrating emerging technologies like deep learning and network analysis, to further enhance microbiome insights and data accuracy. EasyMetageonome is freely available at https://github.com/YongxinLiu/EasyMetagenome.

Type 2 diabetes mellitus (T2DM) is an obesity-related disease claiming substantial global mortality annually. Current animal models of T2DM remain limited, with low success rates in establishing porcine models of high-fat diet (HFD)-induced T2DM. Our experimental design employed 35 Guizhou mini-pigs to develop a T2DM model via HFD induction, aiming to identify microbial and metabolic signatures associated with disease pathogenesis and resistance. At month 10, five individuals from the control (CTR), T2DM (DM), and T2DM resistant (anti-DM) groups were slaughtered, samples were collected, and relevant indices were measured. Metagenomics, metabolomics, and 16S rRNA sequencing were performed to identify microbes and metabolites linked to T2DM progression and resistance. Key findings demonstrated anti-DM group parameters-including metabolic indices (fasting blood glucose, insulin levels, HbA1c, IVGTT), histopathology (HE-stained pancreatic/hepatic tissues), microbial profiles (structural, compositional, functional), and metabolomic signatures-occupied intermediate positions between CTR and DM groups. Network analyses revealed: (1) Lactobacillus, L. amylovorus, fingolimod, polyoxyethylene sorbitan monooleate, thiamine, and atrazine in HFD-associated networks; (2) Limosilactobacillus reuteri, N-oleoyl-L-serine, tolbutamide, tetradecanoyl carnitine, 3′-sulfogalactosylceramide, and guggulsterone in T2DM resistance networks; (3) Ruminococcaceae NK4A214 group, diethyl phthalate, zingerone, enalapril, 5-hydroxytryptophol, 2′-deoxyinosine, icariin, and emetine in T2DM progression networks. These results further clarify the role of the gut microbiota and serum metabolites in the development of T2DM in the Guizhou mini-pig model.

Background: Persicaria capitata (Buch.-Ham. ex D.Don) H.Gross (P. P. capitata, , PCB), a traditional drug of the Miao people in China, is potential traditional drug used for the treatment of diabetic nephropathy (DN). Purpose: The purpose of this study is to investigate the function of P. capitata and clarify its protective mechanism against DN. Methods: We induced DN in the Guizhou miniature pig with injections of streptozotocin, and P. capitata was added to the pigs' diet to treat DN. In week 16, all the animals were slaughtered, samples were collected, and the relative DN indices were measured. 16S rRNA sequencing, metagenomics, metabolomics, RNA sequencing, and proteomics were used to explore the protective mechanism of P. capitata against DN. Results: Dietary supplementation with P. capitata significantly reduced the extent of the disease, not only in term of the relative disease indices but also in hematoxylin-eosin-stained tissues. A multiomic analysis showed that two microbes ( Clostridium baratii and Escherichia coli), ), five metabolites (oleic acid, linoleic acid, 4-phenylbutyric acid, 18-beta-glycyrrhetinic acid, and ergosterol peroxide), four proteins (ENTPD5, EPHX1, ARVCF and TREH), four important mRNAs (encoding ENTPD5, EPHX1, ARVCF, , and TREH), ), six lncRNAs (TCONS_00024194, TCONS_00085825, TCONS_00006937, TCONS_00070981, TCONS_00074099, and TCONS_00097913), and two circRNAs (novel_circ_0001514 and novel_circ_0017507) are all involved in the protective mechanism of P. capitata against DN. Conclusions: Our results provide multidimensional theoretical support for the study and application of P. capitata. .

The average daily weight gain (ADG) is considered a crucial indicator for assessing growth rates in the swine industry. Therefore, investigating the gastrointestinal microbiota and serum metabolites influencing the ADG in pigs is pivotal for swine breed selection. This study involved the inclusion of 350 purebred Yorkshire pigs (age: 90 ± 2 days; body weight: 41.20 ± 4.60 kg). Concurrently, serum and fecal samples were collected during initial measurements of blood and serum indices. The pigs were categorized based on their ADG, with 27 male pigs divided into high-ADG (HADG) and low-ADG (LADG) groups based on their phenotype values. There were 12 pigs in LADG and 15 pigs in HADG. Feces and serum samples were collected on the 90th day. Microbiome and non-targeted metabolomics analyses were conducted using 16S rRNA sequencing and liquid chromatography-mass spectrometry (LC-MS). Pearson correlation, with Benjamini–Hochberg (BH) adjustment, was employed to assess the associations between these variables. The abundance of Lactobacillus and Prevotella in LADG was significantly higher than in HADG, while Erysipelothrix, Streptomyces, Dubosiella, Parolsenella, and Adlercreutzia in LADG were significantly lower than in HADG. The concentration of glutamine, etiocholanolone glucuronide, and retinoyl beta-glucuronide in LADG was significantly higher than in HADG, while arachidonic acid, allocholic acid, oleic acid, phenylalanine, and methyltestosterone in LADG were significantly lower than in HADG. The Lactobacillus–Streptomyces networks (Lactobacillus, Streptomyces, methyltestosterone, phenylalanine, oleic acid, arachidonic acid, glutamine, 3-ketosphingosine, L-octanoylcarnitine, camylofin, 4-guanidinobutyrate 3-methylcyclopentadecanone) were identified as the most influential at regulating swine weight gain. These findings suggest that the gastrointestinal tract regulates the daily weight gain of pigs through the network of Lactobacillus and Streptomyces. However, this study was limited to fecal and serum samples from growing and fattening boars. A comprehensive consideration of factors affecting the daily weight gain in pig production, including gender, parity, season, and breed, is warranted.

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.

Abstract Background Bama miniature pigs aged between six (6 M) and twelve months (12 M) are usually used in human medical research as laboratory pigs. However, the difference in serum metabolic profiles from 6 to 12 M-old pigs remains unclear. This study aimed to identify the metabolic and physiological profiles present in the blood to further explain changes in Bama miniature pig growth. We collected blood samples from 6 M-, eight-month- (8 M-), ten-month- (10 M-), and 12 M-old healthy Guangxi Bama miniature pigs. A total of 20 blood physiological indices (BPIs) were measured: seven for white blood cells, eight for red blood cells, and five for platelet indices. Liquid chromatography and mass spectrometry-based non-targeted metabolomic approaches were used to analyze the difference in metabolites. The associations between the differences were calculated using Spearman correlations with Benjamini–Hochberg adjustment. The 100 most abundant differential metabolites were selected for analysis of their metabolic profiles. Results There were no significant differences in BPIs at different ages, but the mid cell ratio and red blood cell number increased with age. Seven BPIs in Bama miniature pigs were closer to human BPIs than to mouse BPIs. A total of 14 and 25 significant differential metabolites were identified in 6 M vs. 12 M and 8 M vs. 12 M, respectively. In total, 9 and 18 amino acids and their derivatives showed significantly lower concentrations in 6 M- and 8 M-old pigs than in 12 M-old pigs. They were identified as the core significantly different metabolites between the age groups 6 M vs. 12 M and 8 M vs. 12 M. Half of the enriched pathways were the amino acids metabolism pathways. The concentration of six amino acids (dl-tryptophan, phenylacetylglycine, muramic acid, N-acetylornithine, l(−)-pipecolinic acid, and creatine) and their derivatives increased with age. A total of 61 of the top 100 most abundant metabolites were annotated. The metabolic profiles contained 14 amino acids and derivatives, six bile acids and derivatives, 19 fatty acids and derivatives, and 22 others. The concentrations of fatty acids and derivatives were found to be inversely proportional to those of amino acids and derivatives. Conclusion These findings suggest high levels of MID cell ratio, red blood count, and amino acids in 12 M-old pigs as indicators for improved body function over time in Bama miniature pigs, similar to those in human development. This makes the pig a more suitable medical model organism than the mouse. The results of this study are limited to the characteristics of blood metabolism in the inbred Bama miniature pigs, and the effects of impacting factors such as breed, age, sex, health status and nutritional level should be considered when studying other pig populations.

高能饮食是肥胖发生的重要诱因之一,肠道微生物在其中发挥重要作用.为探究高脂高糖饮食过程中肠道微生物组成的动态变化并筛选与肥胖发展密切相关的肠道微生物,本研究以广西巴马小型猪为动物模型,随机分为普通饮食组(CN组)和高脂高糖饮食组(HFD组),进行为期30 d的饮食干预,分别在干预0、7、15和30 d时收集粪便样品,利用16S rRNA基因测序技术分析高脂高糖饮食对广西巴马小型猪肠道微生物组成、结构和功能的影响.结果显示:HFD组个体肠道微生物的多样性普遍低于CN组,高脂高糖饮食改变了小型猪肠道微生物的结构;随着高脂高糖饮食干预时间的增加,观察到了肠道微生物组的总体变化趋势:在门水平,HFD组个体肠道微生物中Firmicutes丰度增加而Bacteroidetes丰度逐渐下降;在属水平,HFD组中Lactobacillus丰度逐渐减少而Ruminococcus丰度则不断升高;CN组和HFD组之间存在一些显著差异的肠道微生物,其中g__Lactobacillus,g__norank_f__p_251_o5和 g__unclassif ied_c__Bacilli 等 12个菌群在 CN 组显著富集,g-Peptococcus、g-Col-linsella和g__Senegalimassilia等8个菌群在HFD组显著富集,可能是与肥胖发生相关的潜在微生物;微生物功能预测分析发现,两组之间微生物功能无显著性差异,均主要富集在氨基酸的生物合成、淀粉与蔗糖的代谢、氨基糖和核苷酸糖代谢以及糖酵解和糖异生等方面.本研究发现,高脂高糖饮食降低了肠道微生物的多样性,使微生物结构发生了改变,存在一定菌群失调的现象,发现了与肥胖发展密切相关的潜在微生物.

Litter size is an important economic trait in pig production. However, the genetic mechanisms underlying varying litter size in Guangxi Bama Xiang pigs remain unknown. To identify selection signatures for litter size in Guangxi Bama Xiang pigs, we obtained 297 Illumina PorcineSNP50 BeadChip array data and the average born number (ABN) from parity one to nine in Guangxi Bama Xiang pigs. Fixation index (Fst) methods were used to identify the selection signature of the litter size, and three phenotypic gradient differential population pairs (according to the ABN) in individuals were used to reduce the false positives of signature selections. Single nucleotide polymorphisms (SNPs) were identified in the VEGFA promoter and exons. The general linear model was used to analyse the differences in distinct genotypes after they were typed using three-round multiplex PCR technology. Finally, the transcriptome factor and CpG island in the VEGFA promoter were predicted. A total of 328, 328 and 317 significant loci were identified in the 1st, 2nd and 3rd population pairs, respectively. After removing the false positives, 25 SNPs were defined as the selection signatures in relation to litter size. Ten (VEGFA, USP49, USP25, SRPK1, SLC26A8, RPL10A, PPARD, MAPK14, HMGA1 and CHRDL2) out of 52 genes in the selection regions were annotated as the candidate genes of litter size, respectively, VEGFA. There were no SNPs in the VEGFA exon region, but we obtained three SNPs (rs786889605, rs343769603 and rs323942424) in the VEGFA promoter regions. The ABN in CC was significantly higher than that in TT in rs786889605, and the ABN in TT was significantly lower than that in GG in rs323942424. Meanwhile, the mutation of the VEGFA promoter result in the loss of Sp1 and NF-1 and the formation of Oct-1. In summary, we obtained ten candidate genes, and two mutations in the VEGFA promoter that could be important potential molecular biomarkers for litter size in Bama Xiang pigs.

[目的]检测隆林猪的全基因组拷贝数变异.[方法]采集33头隆林猪的耳组织样本,通过酚-氯仿法提取DNA后,使用猪中芯一号50K SNP芯片进行基因分型,得到的原始数据通过Genomestudio软件和Linux系统进行处理,使用CNVPartition和PennCNV软件分别检测拷贝数变异(copy number variation,CNV),并利用Bedtools软件将CNV合并为拷贝数变异区域(copy number variation region,CNVR),使用Biomart对CNVR进行基因定位,利用David网站对定位到的基因进行GO和KEGG富集分析,使用猪QTL数据库对共同CNVR进行QTL注释.[结果]CNVPartition软件共检测到260个CNVs,合并为47个CNVRs,其中缺失型40个、获得型5个、混合型2个,共定位到84个基因,显著富集到13条信号通路;PennCNV软件共检测到96个CNVs,合并为15个CNVRs,其中缺失型9个、获得型1个、混合型5个,共定位到8个基因,显著富集到8条信号通路;2个软件检测结果定位到的基因主要富集在嗅觉相关通路和G-蛋白偶联相关通路中,其中INPP5B、NEURL1和GAPDHS基因显著富集到精子活力通路;2个软件获得了 3个共同CNVRs,其中缺失型、获得型和混合型均为1个,共定位到8个基因,显著富集到涉及嗅觉感官知觉的化学刺激检测通路、嗅觉受体活性通路、嗅觉转导通路、G-蛋白偶联受体活性通路、G-蛋白偶联受体信号通路、膜整体组件通路和质膜通路共7条信号通路;共有130个QTLs与3个共同CNVRs重叠,其中与背膘厚、肉质和乳头数相关的QTLs分别有11、9和6个.[结论]隆林猪CNV可能与嗅觉功能、繁殖性能、背膘厚、肉质和乳头数性状相关.

Lactobacillus delbrueckii subsp. bulgaricus (LDB) is an approved feed additive on the Chinese ‘Approved Feed Additives’ list. However, the possibility of LDB as an antibiotic replacement remains unclear. Particularly, the effect of LDB on microbiota and metabolites in the gastrointestinal tract (GIT) requires further explanation. This study aimed to identify the microbiota and metabolites present in fecal samples and investigate the relationship between the microbiota and metabolites to evaluate the potential of LDB as an antibiotic replacement in pig production. A total of 42 female growing-finishing pigs were randomly allocated into the antibiotic group (basal diet + 75 mg/kg aureomycin) and LDB (basal diet + 3.0 × 109 cfu/kg LDB) groups. Fecal samples were collected on days 0 and 30. Growth performance was recorded and assessed. 16S rRNA sequencing and liquid chromatography-mass spectrometry-based non-targeted metabolomics approaches were used to analyze the differences in microbiota and metabolites. Associations between the differences were calculated using Spearman correlations with the Benjamini–Hochberg adjustment. The LDB diet had no adverse effect on feed efficiency but slightly enhanced the average daily weight gain and average daily feed intake (p > 0.05). The diet supplemented with LDB increased Lactobacillus abundance and decreased that of Prevotellaceae_NK3B31_group spp. Dietary-supplemented LDB enhanced the concentrations of pyridoxine, tyramine, D-(+)-pyroglutamic acid, hypoxanthine, putrescine and 5-hydroxyindole-3-acetic acid and decreased the lithocholic acid concentration. The Lactobacillus networks (Lactobacillus, Peptococcus, Ruminococcaceae_UCG-004, Escherichia-Shigella, acetophenone, tyramine, putrescine, N-methylisopelletierine, N1-acetylspermine) and Prevotellaceae_NK3B31_group networks (Prevotellaceae_NK3B31_group, Treponema_2, monolaurin, penciclovir, N-(5-acetamidopentyl)acetamide, glycerol 3-phosphate) were the most important in the LDB effect on pig GIT health in our study. These findings indicate that LDB may regulate GIT function through the Lactobacillus and Prevotellaceae_NK3B31_group networks. However, our results were restrained to fecal samples of female growing-finishing pigs; gender, growth stages, breeds and other factors should be considered to comprehensively assess LDB as an antibiotic replacement in pig production.

Piglets are susceptible to weaning stress, which weakens the barrier and immune function of the intestinal mucosa, causes inflammation, and ultimately affects animal growth and development. Ellagic acid (EA) is a natural polyphenol dilactone with various biological functions. However, The mechanisms underlying the effects of EA on animal health are still poorly known. Herein, we examined whether dietary supplementation with EA has a positive effect on growth performance, intestinal health, immune response, microbiota, or inflammation in weaned piglets. Sixty weaned piglets (age, 30 days) were randomly divided into two groups: the control group (basic diet) and the test group (basic diet + 500 g/t EA). The pigs were fed for 40 days under the same feeding and management conditions, and the growth performance of each individual was measured. At the end of the feeding period, samples were collected from the small intestinal mucosa for further analysis. Using these tissues, the transcriptome sequences and intestinal microbial diversity were analyzed in both groups. An inflammation model using small intestinal mucosal epithelial cells (IPEC-J2) was also constructed. Dietary EA supplementation significantly increased the average daily weight gain (ADG) and reduced diarrhea rate and serum diamine oxidase (DAO) levels of weaned piglets. Transcriptome sequencing results revealed 401 differentially expressed genes in the jejunum mucosal tissue of pigs in the control and test groups. Of these, 163 genes were up-regulated and 238 were down-regulated. The down-regulated genes were significantly enriched in 10 pathways (false discovery rate < 0.05), including seven pathways related to immune response. The results of bacterial 16s rDNA sequencing show that EA affects the composition of the intestinal microbiota in the cecum and rectum, and reveal significant differences in the abundances of Prevotella_9, Lactobacillus delbrueckii, and Lactobacillus reuteri between the test and control groups (P < 0.05). Experiments using the inflammation model showed that certain doses of EA promote the proliferation of IPEC-J2 cells, increase the relative mRNA expression levels of tight junction-related proteins (ZO-1 and Occludin), improve the compactness of the intestine, reduce the expression of inflammatory factors TNF-α and IL-6, and significantly reduce LPS-induced inflammation in IPEC-J2 cells. In conclusion, we found for the first time that dietary supplementation of EA affects the gut immune response and promotes the beneficial gut microbiota in weaned piglets, reduces the occurrence of inflammatory responses, and thereby promotes the growth and intestinal health of piglets.

In China there are approximately 100 pig breeds, which show great diversity in their appearance. However, information on genome selection signatures, such as spine curvature, is scarce. Therefore, we used the fixation index (FST ) and cross-population extended haplotype homozygosity (XPEHH) methods to explore the genome selection signatures of spine curvature in six breeds of Chinese indigenous pig. We identified 396 and 389 single nucleotide polymorphisms using the FST and XPEHH methods, respectively. We detected 19 selection signatures and 28 genes located in the selected regions. Five candidate genes (MAP3K7, CUX1, GRIN2B, ALPL and MACF1) were identified in the selection signatures. Additionally, 719 high-frequency runs of homozygosity regions, 17 unique runs of homozygosity regions, 78 genes and 27 pathways were identified in the runs of homozygosity analysis. The TGF-beta signaling pathway and eight genes related to the spine formation, spine defects and intervertebral disk degeneration were identified, comprising ACVR1, FMOD, ITGA4, MAPK8, PDGF, RPL3, SULF1 and UBE2D1. In summary, we identified 13 candidate genes related to spine curvature in Chinese indigenous pigs.

A total 403 Bama Xiang pig Illumina PorcineSNP50 BeadChip array.

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 (F-ROH) and the Spearman coefficient between F-ROH 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 F-ROH. 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.

旨在对几个中国地方猪品种进行群体遗传结构分析,并筛选与中国地方猪产仔数相关的基因组选择信号及候选基因.本研究下载了6个中国地方品种猪共计102头个体的Illumina PorcineSNP60芯片数据,构建了包括19头迪庆藏猪、16头明光小耳猪、16头五指山猪在内的低产仔数组和包括11头姜曲海猪、20头蓝塘猪、20头梅山猪在内的高产仔数组,经质控和基因型填充后,使用软件进行亲缘关系分析、主成分分析、连锁不平衡衰减分析、群体遗传结构分析、进化树构建和选择信号分析,并对受选择位点进行基因定位和功能富集分析.对数据进行质控和基因型填充后共获得33285个SNPs位点,6个中国地方猪品种间个体的亲缘关系较远,品种内个体亲缘关系较近;6个群体的LD衰减速度都非常快,依次为梅山猪＜蓝塘猪＜姜曲海猪＜明光小耳猪＜五指山猪或迪庆藏猪;梅山猪群体内存在3个亲缘关系较高的小群体,明光小耳猪有部分个体与迪庆藏猪聚集在一起,各个猪种在进化树的距离与其地理距离较为一致.选择信号分析共筛选到176个受选择的SNPs位点,在其上、下游12.68 kb范围内共检测到66个候选基因,富集到6条信号通路.通过文献查阅发现5个可能与繁殖性状相关的基因,其中CHD7与生长发育迟缓和生殖器异常相关,FBXO43与繁殖力相关并且影响呈圆周运动的精子数量,RUNX1控制雌激素、雄激素和前列腺素的分泌,STRBP与卵巢发育有关,TEDDM1在附睾中特异性表达.CHD7、FBXO43、RUNX1、STRBP和TEDDM1基因可能作为中国地方猪产仔数性状的候选基因.

旨在评估两个不同来源大白猪群体经过近8个世代的选育后总产仔数(total number of piglets born,TNB)近交衰退的程度.本研究对1 937头大白猪使用GeneSeek GGP Porcine HD芯片进行分型,其中1 039头来自加系大白猪和898头来自法系大白猪,且两品系均有表型记录和系谱记录,系谱共由3 086头大白猪组成.分别使用系谱、SNP和ROH进行个体近交系数估计,并将近交系数作为协变量利用动物模型对总产仔数进行近交衰退评估.为了精准定位导致总产仔数衰退的基因组片段,又进一步对每条染色体以及显著染色体分段计算近交系数并估计其效应,检测是否能引起总产仔数发生近交衰退现象.对于加系群体,FROH、FGRM和FPED估计的近交系数均值分别为0.124、0.042和0.013,其中FROH和FPED相关最高,相关系数为0.358;对于法系群体,FROH、FGRM和FPED均值分别为0.123、0.052和0.007,其中FROH和FGRM相关最高,相关系数为0.371.利用3种不同计算方法所得近交系数用于估计近交衰退时,加系群体的总产仔数均检测到显著的近交衰退,而且当FROH、FGRM和FPED每增加10％时,总产仔数分别减少0.571、0.341和0.823头;但法系群体仅有FRO H估计的总产仔数检测到显著近交衰退,FROH每增加10％时,总产仔数减少0.690头.为了锁定相关的染色体和基因组区段,首先利用ROH估计每条染色体近交系数并进行近交衰退分析发现,加系群体中检测到第6、7、8和13号染色体产生了显著近的总产仔数交衰退,而法系群体未检测到与近交衰退相关的染色体.然后,又将与加系总产仔数近交衰退显著相关的4条染色体平均分为2、4、6、8个片段进行近交衰退检测,其中平均分成8段后的染色片段的长度范围为15.1～25.8 Mb.在第6、7和8号染色体分别检测到1、2和3个与总产仔数相关的近交衰退染色体片段.这些区域注释到了CUL7、MAPK14和PPARD基因与胎盘发育相关,AREG和EREG基因与卵母细胞成熟有关.本研究利用3种近交系数计算方法对两个不同来源的大白猪总产仔数进行近交衰退评估,在加系大白猪中3种估计方法都能检测到近交衰退的现象,而法系群体中只有FROH才能检测到.而且通过ROH方法进一步确定了能引起加系大白猪总产仔数衰退的4条染色体和6个特定的染色体区段,还注释到了与繁殖相关的候选基因.这为揭示近交衰退的遗传机制提供了新的研究手段,也为基因组选种选配提供了参考依据.

肠道是机体重要的消化与免疫器官,维持肠道健康对猪的生长发育和疾病预防具有十分重要的意义.高通量测序技术的发展极大地促进了人们对肠道微生物功能的认知,猪肠道微生物组的研究正逐步成为热点.目前,尽管对某一生长阶段猪的肠道微生物组已有较为深入的理解,但仍缺乏有关商品猪整个生命周期范围内肠道微生物组动态变化的全面纵向研究.而从出生到出栏,猪在整个生长周期内的肠道微生物组并不是一成不变的,是一个动态的发育过程.作者综述了猪哺乳期、断奶期、育肥期和妊娠期等从出生到育肥过程中不同阶段肠道微生物组的纵向变化及其主要影响因素:一方面,肠道微生物群落结构的显著变化主要发生在断奶期;另一方面,虽然肠道微生物组成随着时间始终在不断变化,但仍有一部分优势菌是一直存在的,这部分优势菌被称为核心菌群,而只在特定时期才出现的菌只是胃肠道中的"过客",也最易受外界因素影响.肠道微生物组与多种因素相关,如年龄、饮食、环境、抗生素使用等,其中饮食对塑造肠道微生物起到至关重要的作用.本文可为理解猪在不同生长发育阶段肠道微生物的动态变化规律及改善猪生长性能和健康水平的微生物技术手段提供理论参考.

为了探讨杜洛克公猪肠道微生物菌群与日增重的关系,根据91头杜洛克公猪平均日增重的高低,选择极端高低组各10头分为低组(LADG)和高组(HADG),收集粪便,利用16S rRNA测序分析LADG和HADG 2组猪的粪便微生物多样性和组成,并对其功能进行预测.结果:2组的alpha多样性差异不显著(P＞0.05);杜洛克猪的2个优势菌门是厚壁菌门(Firmicutes)和拟杆菌门(Bacteroidetes),优势菌属是普氏菌属(Prevotella)和乳酸菌属(Lactobacillus);LADG组中螺旋体门(Spirochaetes)、软壁菌门(Tenericutes)、WPS-2和疣微菌门(Verrucomi-crobi)4个菌群的丰度要显著高于HADG组(P＜0.05),HADG组中的巨球型菌(Megasphaere)、粪杆菌属(Faecalibacterium)和韦荣氏菌(unclassified_f _Veilionellacece)3个菌群的丰度显著高于LADG组;HADG组中的微生物在疾病代谢通路,如嘌呤代谢和核黄素代谢高度富集,LADG组微生物在分泌系统等通路高度富集.试验结果提示:HADG组可能有较高的代谢水平,LADG组的分泌系统可能更发达.本研究丰富了杜洛克猪的早期选种的理论依据,并为理解早期宿主与微生物的相互作用提供了帮助.

目的 近期研究证据表明,肠道微生物与2型糖尿病(T2DM)等代谢性疾病有关,相关机制研究多以啮齿类动物为模型,而在更适合研究人类疾病的猪模型上却鲜有报道.方法 为了探究小型猪T2DM模型中肠道微生物的组成和结构的变化,本研究以广西巴马小型猪为动物模型,通过高脂高糖饮食诱导T2DM,建模成功后采集T2DM发病组(T2DM组)和普通饮食饲喂的对照组(CN组)个体的新鲜粪便样品,采用16S rRNA基因测序技术进行肠道微生物组成与结构的比较分析.结果 结果发现,T2DM组个体的肠道微生物的多样性发生了明显下降;在门水平上,与CN组相比,T2DM组厚壁菌门丰度显著增加(P<0.05),而拟杆菌门丰度下降;在属水平,PCoA分析发现CN组和T2DM组组成显著差异,T2DM组独有与产琥珀酸相关的光冈菌属,两组比较和LEfSe分析发现两组间存在多个显著性差异的菌属:颤螺旋菌属、普氏菌属和消化球菌属等在CN组显著富集,g_Intestinibacter在T2DM组中显著富集;功能预测未发现两组存在显著差异的代谢通路,两组均主要富集在氨基酸的生物合成、碳新陈代谢、氨基糖和核苷酸糖代谢以及糖酵解和糖异生等方面.结论 本研究发现了2型糖尿病小型猪肠道微生物的变化特征和与糖尿病发生密切关联的潜在微生物,为研究肠道微生物与2型糖尿病的关联及其作用机制提供了理论依据.

Follicular atresia is one of the main factors limiting the reproductive power of domestic animals. At present, the molecular mechanisms involved in porcine follicular atresia at the metabolic level remain unclear. In this study, we divided the follicles of Bama Xiang pigs into healthy follicles (HFs) and atretic follicles (AFs) based on the follicle morphology. The expression of genes related to atresia in granulosa cells (GCs) and the concentration of hormones in the follicular fluid (FF) from HFs and AFs were detected. We then used liquid chromatography–mass spectrometry-based non-targeted metabolomic approach to analyze the metabolites in the FF from HFs and AFs. The results showed that the content of estradiol was significantly lower in AFs than in HFs, whereas that of progesterone was significantly higher in AFs than that in HFs. The expression of BCL2, VEGFA, and CYP19A1 was significantly higher in HFs than in AFs. In contrast, the expression of BAX and CASPASE3 was significantly lower in HFs. A total of 18 differential metabolites (DMs) were identified, including phospholipids, bioactive substances, and amino acids. The DMs were involved in 12 metabolic pathways, including arginine biosynthesis and primary bile acid biosynthesis. The levels of eight DMs were higher in the HF group than those in the AF group (p < 0.01), and those of 10 DMs were higher in the AF group than those in the HF group (p < 0.01). These findings indicate that the metabolic characteristics of porcine AFs are lower levels of lipids such as phospholipids and higher levels of amino acids and bile acids than those in HFs. Disorders of amino acid metabolism and cholic acid metabolism may contribute to porcine follicular atresia.