Mapping and functional characterization of structural variation in 1060 pig genomes.

BACKGROUND: Structural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence. RESULTS: We present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome. This catalog includes 42,487 deletions, 37,913 mobile element insertions, 3308 duplications, 1664 inversions, and 45,184 break ends. Estimates of breed ancestry and hybridization using genotyped SVs align well with those from single nucleotide polymorphisms. Geographically stratified deletions are observed, along with known duplications of the KIT gene, responsible for white coat color in European pigs. Additionally, we identify a recent SINE element insertion in MYO5A transcripts of European pigs, potentially influencing alternative splicing patterns and coat color alterations. Furthermore, a Yorkshire-specific copy number gain within ABCG2 is found, impacting chromatin interactions and gene expression across multiple tissues over a stretch of genomic region of ~200 kb. Preliminary investigations into SV's impact on gene expression and traits using the Pig Genotype-Tissue Expression (PigGTEx) data reveal SV associations with regulatory variants and gene-trait pairs. For instance, a 51-bp deletion is linked to the lead eQTL of the lipid metabolism regulating gene FADS3, whose expression in embryo may affect loin muscle area, as revealed by our transcriptome-wide association studies. CONCLUSIONS: This SV catalog serves as a valuable resource for studying diversity, evolutionary history, and functional shaping of the pig genome by processes like domestication, trait-based breeding, and adaptive evolution.

Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration.

Meat color traits directly influence consumer acceptability and purchasing decisions. Nevertheless, there is a paucity of comprehensive investigation into the genetic mechanisms underlying meat color traits in pigs. Utilizing genome-wide association studies (GWAS) on five meat color traits and the detection of selection signatures in pig breeds exhibiting distinct meat color characteristics, we identified a promising candidate SNP, 6_69103754, exhibiting varying allele frequencies among pigs with different meat color characteristics. This SNP has the potential to affect the redness and chroma index values of pork. Moreover, transcriptome-wide association studies (TWAS) analysis revealed the expression of candidate genes associated with meat color traits in specific tissues. Notably, the largest number of candidate genes were observed from transcripts derived from adipose, liver, lung, spleen tissues, and macrophage cell type, indicating their crucial role in meat color development. Several shared genes associated with redness, yellowness, and chroma indices traits were identified, including RINL in adipose tissue, ENSSSCG00000034844 and ITIH1 in liver tissue, TPX2 and MFAP2 in lung tissue, and ZBTB17, FAM131C, KIFC3, NTPCR, and ENGSSSCG00000045605 in spleen tissue. Furthermore, single-cell enrichment analysis revealed a significant association between the immune system and meat color. This finding underscores the significance of the immune system associated with meat color. Overall, our study provides a comprehensive analysis of the genetic mechanisms underlying meat color traits, offering valuable insights for future breeding efforts aimed at improving meat quality.

[Quantitative Assessment of the Impact of Climate Change on the Growing Season of Vegetation Gross Primary Productivity in the Middle and Lower Reaches of the Yangtze River].

Quantitatively determining the direct, indirect, and comprehensive effects of climatic factors on the growing season of the vegetation GPP (GPPGS) in the middle and lower reaches of the Yangtze River at the regional and vegetation type scales can provide a scientific basis for the management and restoration of regional vegetation resources under the background of global climate change. Using MODIS GPP data, meteorological data, and vegetation type data, combined with Theil-Sen Median trend analysis and the Mann-Kendall significance test, the spatiotemporal characteristics of the GPPGS in the middle and lower reaches of the Yangtze River were investigated at different temporal and spatial scales. Path analysis was used to further reveal the direct, indirect, and comprehensive effects of climate factors on GPPGS variation in different vegetation types. The results showed that:① from 2000 to 2021, the vegetation GPPGS in the middle and lower reaches of the Yangtze River showed a fluctuating upward trend, with a rising rate (in terms of C, same below) of 2.70 g·(m2·a)-1 (P<0.01). The GPPGS of different vegetation types all showed a significant upward trend (P<0.01), with shrubs having the highest upward rate of 3.31 g·(m2·a)-1 and cultivated vegetation having the lowest upward rate of 2.54 g·(m2·a)-1. ② The proportion of the area with an upward trend in GPPGS in the middle and lower reaches of the Yangtze River was 88.11%. The proportion of the area with an upward trend in GPPGS was greater than 84% for all different vegetation types, with shrubs (49.76%) and cultivated vegetation (44.36%) having significantly higher proportions of the area with an upward trend than that in other vegetation types. ③ The path analysis results showed that precipitation and the maximum temperature had a significant positive direct effect on vegetation GPPGS (P<0.05), whereas solar radiation had a non-significant positive effect (P ≥ 0.05). The indirect effects of maximum temperature, precipitation, and solar radiation on vegetation GPPGS were all non-significantly negative (P ≥ 0.05). Under the combined effects of direct and indirect influences, precipitation and maximum temperature had a non-significant positive effect on vegetation GPPGS (P ≥ 0.05), whereas solar radiation had a non-significant negative effect on vegetation GPPGS (P ≥ 0.05). Among different vegetation types, precipitation was the main climate factor affecting the changes in GPPGS of cultivated vegetation, whereas the maximum temperature was the main climate factor affecting the changes in GPPGS of coniferous forests, broad-leaved forests, shrubs, and grasslands. ④ The changes in vegetation GPPGS in the middle and lower reaches of the Yangtze River were mainly influenced by the direct effects of maximum temperature, precipitation, and solar radiation, with the direct effect of precipitation dominating 56.72% of the changes in GPPGS. The research results can provide a reference for quantifying the carbon sequestration potential of vegetation in the middle and lower reaches of the Yangtze River and formulating ecological restoration governance policies tailored to local conditions under the background of global climate change.

Pig pangenome graph reveals functional features of non-reference sequences.

BACKGROUND: The reliance on a solitary linear reference genome has imposed a significant constraint on our comprehensive understanding of genetic variation in animals. This constraint is particularly pronounced for non-reference sequences (NRSs), which have not been extensively studied. RESULTS: In this study, we constructed a pig pangenome graph using 21 pig assemblies and identified 23,831 NRSs with a total length of 105 Mb. Our findings revealed that NRSs were more prevalent in breeds exhibiting greater genetic divergence from the reference genome. Furthermore, we observed that NRSs were rarely found within coding sequences, while NRS insertions were enriched in immune-related Gene Ontology terms. Notably, our investigation also unveiled a close association between novel genes and the immune capacity of pigs. We observed substantial differences in terms of frequencies of NRSs between Eastern and Western pigs, and the heat-resistant pigs exhibited a substantial number of NRS insertions in an 11.6 Mb interval on chromosome X. Additionally, we discovered a 665 bp insertion in the fourth intron of the TNFRSF19 gene that may be associated with the ability of heat tolerance in Southern Chinese pigs. CONCLUSIONS: Our findings demonstrate the potential of a graph genome approach to reveal important functional features of NRSs in pig populations.

[Spatio-temporal Variation in Net Primary Productivity of Different Vegetation Types and Its Influencing Factors Exploration in Southwest China].

Studying the spatiotemporal variation in vegetation net primary productivity (NPP) and exploring its influencing factors are of considerable practical significance for understanding the spatiotemporal variation in vegetation and for guiding ecological restoration and management projects based on local conditions. Based on MODIS NPP data, combined with in situ meteorological data, land use data, and vegetation type data, this study explores the spatiotemporal variation in different types of vegetation NPP in southwest China via the Mann-Kendall significance test and Theil-Sen Median slope estimator. It reveals the influencing factors of spatial differentiation of different types of vegetation NPP and the interaction between influencing factors in combination with stability analysis and Geo Detectors. The results revealed that on the temporal scale, from 2000 to 2021, vegetation NPP, NPPPre (vegetation NPP exclusively under the influence of climate change), and NPPRes (vegetation NPP exclusively under the influence of human activities) in southwest China showed a fluctuating upward trend. Among different vegetation types, NPP, NPPPre, and NPPRes exhibited an upward trend, except for a minor decline in NPPRes of tree vegetation at a rate of -0.183 g·(m2·a)-1. Among them, NPP, NPPPre, and NPPRes of economic vegetation showed the most significant upward rates, 5.96, 3.09, and 2.94 g·(m2·a)-1, respectively. On the spatial scale, the tree vegetation NPP with the most significant downward trend was mainly distributed in Tibet and southern Yunnan, while the economic vegetation NPP with the highest upward trend was primarily distributed in eastern Sichuan Province. The stability of vegetation NPP in southwest China presented a spatial distribution pattern of "low in the south and high in the north," and the average value of the correlation coefficient increased in the ascending order of arbor vegetation (0.101), shrub vegetation (0.105), herb vegetation (0.110), and economic vegetation (0.114). The interaction between surface temperature and relative humidity was the main influencing factor for spatial differentiation of vegetation NPP, while the interaction between sunshine duration and warmth index had the most significant impact on vegetation in southwest China, with an increasing percentage of 30.91%. Different types of vegetation had different requirements for different climatic factors, but their requirements for surface temperature and warmth index were significantly consistent. When the surface temperature was 21.03-28.49℃, and the warmth index was 106.46-167.2, the NPP of different vegetation types peaked. Under natural succession, the impact of climate change on vegetation was inversely proportional to the stability of the vegetation community. The arbor vegetation community with high stability was less affected, while the herb vegetation community with low stability was highly affected by climate. In contrast, the stability of economic vegetation was directly proportional to the impact of climate due to the influence of human activities. This study establishes a theoretical foundation for evaluating the impact of regional climate on the growth of different vegetation types and can be crucial for formulating ecological restoration and management strategies in southwest China that are adapted to the local conditions.

Detection of Runs of Homozygosity and Identification of Candidate Genes in the Whole Genome of Tunchang Pigs.

Tunchang pigs are an indigenous pig population in China known for their high tolerance to roughage, delicious meat, and fecundity. However, the number of Tunchang pigs has been declining due to the influence of commercial breeds and African swine fever, which could potentially lead to inbreeding. To assess the inbreeding level and the genetic basis of important traits in Tunchang pigs, our research investigated the patterns in "runs of homozygosity" (ROHs) using whole genome resequencing data from 32 Tunchang pigs. The study aimed to determine the length, number, coverage, and distribution model of ROHs in Tunchang pigs, as well as genomic regions with high ROH frequencies. The results of the study revealed that a total of 20,499,374 single-nucleotide polymorphisms (SNPs) and 1953 ROH fragments were recognized in 32 individuals. The ROH fragments in Tunchang pigs were predominantly short, ranging from 0.5 to 1 megabases (Mb) in length. Furthermore, the coverage of ROHs varied across chromosomes, with chromosome 3 having the highest coverage and chromosome 11 having the lowest coverage. The genetic diversity of Tunchang pigs was found to be relatively high based on the values of HE (expected heterozygosity), HO (observed heterozygosity), pi (nucleotide diversity), Ne (effective population size), and MAF (minor allele frequency). The average inbreeding coefficients of Tunchang pigs, as determined by three different methods (FHOM, FGRM, and FROH), were 0.019, 0.0138, and 0.0304, respectively. These values indicate that the level of inbreeding in Tunchang pigs is currently low. Additionally, the study identified a total of 13 ROH islands on all chromosomes, which in total contained 38,913 SNPs and 120 genes. These ROH islands included genes associated with economically important traits, including meat quality (GYS1, PHLPP1, SLC27A5, and CRTC1), growth and development (ANKS1A, TAF11, SPDEF, LHB, and PACSIN1), and environmental adaptation (SLC26A7). The findings of this research offer valuable perspectives on the present status of Tunchang pig resources and offer a reference for breeding conservation plans and the efficient utilization of Tunchang pigs in the future. By understanding the inbreeding level and genetic basis of important traits in Tunchang pigs, conservation efforts can be targeted towards maintaining genetic diversity and promoting the sustainable development of this indigenous pig population.

Holo-omics analysis reveals the influence of gut microbiota on obesity indicators in Jinhua pigs.

BACKGROUND: The mechanisms behind obesity are complex and multi-faceted, involving the interplay of both host genomics and gut microbiome. In recent years, research has largely focused on these factors separately, but rarely from the viewpoint of holo-omics, which considers the host and microbiome as an integrated entity. To address this gap in knowledge, the present study aimed to investigate the holo-omics basis of obesity in Jinhua pigs, a Chinese indigenous breed known for its high degree of fat deposition and superior meat quality. METHODS: Six pigs with extreme obesity phenotype were selected from a larger cohort of eighteen Jinhua pigs, and the contents of the jejunum, cecum, and colon regions were collected after slaughter at 240 days of age. The data obtained was processed, denoised, and annotated using QIIME2, with expression differences being analyzed using edgeR software. RESULTS: The results showed significant differences in jejunal microbial diversity and composition between the two groups, with gut transcriptomics also indicating that differentially expressed genes in the jejunum were enriched in lipid metabolism pathways. These findings provide further evidence of the influence of the gut microbiome and host gene expression on fat deposition in Jinhua pigs. CONCLUSIONS: This study provides valuable insights into the mechanisms of fat deposition in Jinhua pigs from the viewpoint of holo-omics. The integration of host transcriptomics and microbiome data helps shed light on the complex interactions between the host and gut microbiome, and highlights the importance of considering both factors in our understanding of obesity.

Factors affecting the accuracy of genomic prediction in joint pig populations.

Genomic prediction (GP) has greatly advanced animal and plant breeding over the past two decades. GP in joint populations is a feasible method to improve the accuracy of genomic estimated breeding values in small populations. However, there is still a need to understand the factors that influence GP in joint populations. This study used simulated data and real data from Duroc pig populations to examine the impact of linkage disequilibrium (LD), causal variants effect sizes (CVESs), and minor allele frequencies (MAF) of SNPs on the accuracy of genomic prediction in joint populations. Three prediction methods were used: genomic best linear unbiased prediction (GBLUP), single-step GBLUP and multi-trait GBLUP. Results from the simulated datasets showed that the accuracies of GP in joint populations were always higher than those in a single population when only LD inconsistencies existed. However, single-step GBLUP accuracy in joint populations decreased as the correlation of MAF between populations decreased, while the accuracy of GBLUP is consistently higher in joint populations than in a single population. As the correlation of CVES between populations decreased, the accuracy of both GBLUP and single-step GBLUP in joint populations declined. Analysis of real Duroc populations showed low genetic correlation, similar to the simulated relationship between the most distant populations. In most cases in Duroc populations, GP have higher accuracies in joint populations than in individual population. In conclusion, the consistency of CVES plays a more important role in multi-population GP. The genetic relatedness of the Duroc populations is so weak that the prediction accuracy of GP in joint populations is reduced in some traits. Multi-trait GBLUP is a competitive method for the joint breeding evaluation.

[Chromosomal microarray analysis of 17 patients with unbalanced reciprocal translocations].

OBJECTIVE: To retrospectively analyze the results of chromosomal microarray analysis (CMA) and parental origins of unbalanced translocations among 17 patients, so as to provide reference for their genetic counseling. METHODS: The results of CMA for 7 001 samples tested in Chengdu Women and Children's Central Hospital from January 2019 to January 2022 were retrospectively reviewed. Unbalanced reciprocal translocation was defined as two non-homologous chromosomes with lost and gained segments respectively or both with gained segments, and their parental origins were identified by parental chromosomal karyotyping and/or fluorescence in situ hybridization (FISH). RESULTS: In total 17 unbalanced translocations were identified. In three cases, two non-homologous chromosomes both had gained segments, which constituted a derivative chromosome, with the total number of chromosomes being 47. In the remaining 14 cases, there was a terminal deletion on one chromosome and a terminal duplication on the other, 10 of which were confirmed by karyotyping, with the total number of chromosomes being 46. In the derivative chromosome, the lost segment was replaced by a gained segment from another chromosome. Among 15 cases undergoing parental origin analysis, 12 had paternal or maternal chromosomal abnormalities, including 11 balanced translocations and 1 unbalanced translocation. The unbalanced gametes therefore may form through meiosis. In 3 cases, the parental chromosomes were normal, indicating a de novo origin. CONCLUSION: Discovery of terminal duplication and deletion or gained segments on two non-homologous chromosomes by CMA is suggestive of parental balanced translocation, which can facilitate genetic counseling and assessment the recurrence risk for subsequent pregnancies.

Multi-Omics Reveals the Effect of Crossbreeding on Some Precursors of Flavor and Nutritional Quality of Pork.

Over the last several decades, China has continuously introduced Duroc boars and used them as breeding boars. Although this crossbreeding method has increased pork production, it has affected pork quality. Nowadays, one of the primary goals of industrial breeding and production systems is to enhance the quality of meat. This research analyzed the molecular mechanisms that control the quality of pork and may be used as a guide for future efforts to enhance meat quality. The genetic mechanisms of cross-breeding for meat quality improvement were investigated by combining transcriptome and metabolome analysis, using Chinese native Jiaxing black (JXB) pigs and crossbred Duroc × Duroc × Berkshire × JXB (DDBJ) pigs. In the longissimus Dorsi muscle, the content of inosine monophosphate, polyunsaturated fatty acid, and amino acids were considerably higher in JXB pigs in contrast with that of DDBJ pigs, whereas DDBJ pigs have remarkably greater levels of polyunsaturated fatty acids than JXB pigs. Differentially expressed genes (DEGs) and differential metabolites were identified using transcriptomic and metabolomic KEGG enrichment analyses. Differential metabolites mainly include amino acids, fatty acids, and phospholipids. In addition, several DEGs that may explain differences in meat quality between the two pig types were found, including genes associated with the metabolism of lipids (e.g., DGKA, LIPG, and LPINI), fatty acid (e.g., ELOVL5, ELOVL4, and ACAT2), and amino acid (e.g., SLC7A2, SLC7A4). Combined with the DEGS-enriched signaling pathways, the regulatory mechanisms related to amino acids, fatty acids, and phospholipids were mapped. The abundant metabolic pathways and DEGs may provide insight into the specific molecular mechanism that regulates meat quality. Optimizing the composition of fatty acids, phospholipids, amino acids, and other compounds in pork is conducive to improving meat quality. Overall, these findings will provide useful information and further groundwork for enhancing the meat quality that may be achieved via hybrid breeding.

Genetics and breeding of a black-bone and blue eggshell chicken line. 3. Visual eggshell color and colorimeter parameters in 3 consecutive generations.

The BG line, originated by crossing 2 Chinese indigenous breeds, Dongxiang blue eggshell and Jiangshan black-bone, has been bred for black carcass and blue-greenish eggs. Aiming to study the genetic parameters and selection aspects of these eggshell colors, the 4 colorimeter parameters (L*, a*, b*, SCI = L*-a*-b*) were measured on ∼5 eggs/hen/age (200 d and 300 d) from each hen in 3 generations (G4 = 452, G5 = 508, G6 = 498). Visual eggshell color was classified as either "Light," "Blue," "Green," or "Olive," and data from G4 and G5 indicated that visual eggshell color was more accurately determined by combining the classifications of single representative egg/hen by 4 independent observers. Based on the apparent gradual variation in visual color, the 4 colors were expressed numerically (Light = 1, Blue = 2, Green = 3, Olive = 4) and the averages of the 4 observers (AveObs) were used as quantitative expression of the visual color of each egg. This expression, in the range from Blue to Olive, was highly significantly correlated with L*, b* and SCI. The a* values were also associated with AveObs, but not linearly; AveObs between 2 (Blue) and 3 (Green) had lowest a*, and it increased as AveObs was more Light (<2) or more Olive (>3). The heritability estimates of the colorimeter parameters were mostly very high; those of b* and SCI ranged between 0.7 and 0.8, and those of L* and a* between 0.6 and 0.7, indicating that they can serve as criterions to select for blue and/or green eggshells. The phenotypic and genetic correlations between the colorimeter parameters were highly significant and favorable. It is suggested that effective breeding for blue eggs can be done by selecting hens laying eggs with highest SCI/L* or lowest b* (against green and olive shades), followed by selection for low a* (against light shades). Breeding for green eggs can be done by selecting hens laying eggs with SCI ≈ 75 and/or L* ≈ 80 and/or b* ≈ 12. Breeding for hens that lay either blue or green eggs can be done by selection for low a* values.

The Role of SOCS3 in Regulating Meat Quality in Jinhua Pigs.

Meat quality is an important economic trait that influences the development of the pig industry. Skeletal muscle development and glycolytic potential (GP) are two crucial aspects that significantly impact meat quality. It has been reported that abnormal skeletal muscle development and high glycogen content results in low meat quality. However, the genetic mechanisms underlying these factors are still unclear. Compared with intensive pig breeds, Chinese indigenous pig breeds, such as the Jinhua pig, express superior meat quality characteristics. The differences in the meat quality traits between Jinhua and intensive pig breeds make them suitable for uncovering the genetic mechanisms that regulate meat quality traits. In this study, the Jinhua pig breed and five intensive pig breeds, including Duroc, Landrace, Yorkshire, Berkshire, and Pietrain pig breeds, were selected as experimental materials. First, the FST and XP-EHH methods were used to screen the selective signatures on the genome in the Jinhua population. Then, combined with RNA-Seq data, the study further confirmed that SOCS3 could be a key candidate gene that influences meat quality by mediating myoblast proliferation and glycometabolism because of the down-regulated expression of SOCS3 in Jinhua pigs compared with Landrace pigs. Finally, through SOCS3 knockout (KO) and overexpression (OE) experiments in mouse C2C12 cells, the results showed that SOCS3 regulated the cell proliferation of myoblasts. Moreover, SOCS3 is involved in regulating glucose uptake by the IRS1/PI3K/AKT signaling pathway. Overall, these findings provide a basis for the genetic improvement of meat quality traits in the pig industry.

Evaluation of the Genetic Diversity, Population Structure and Selection Signatures of Three Native Chinese Pig Populations.

Indigenous pig populations in Hainan Province live in tropical climate conditions and a relatively closed geographical environment, which has contributed to the formation of some excellent characteristics, such as heat tolerance, strong disease resistance and excellent meat quality. Over the past few decades, the number of these pig populations has decreased sharply, largely due to a decrease in growth rate and poor lean meat percentage. For effective conservation of these genetic resources (such as heat tolerance, meat quality and disease resistance), the whole-genome sequencing data of 78 individuals from 3 native Chinese pig populations, including Wuzhishan (WZS), Tunchang (TC) and Dingan (DA), were obtained using a 150 bp paired-end platform, and 25 individuals from two foreign breeds, including Landrace (LR) and Large White (LW), were downloaded from a public database. A total of 28,384,282 SNPs were identified, of which 27,134,233 SNPs were identified in native Chinese pig populations. Both genetic diversity statistics and linkage disequilibrium (LD) analysis indicated that indigenous pig populations displayed high genetic diversity. The result of population structure implied the uniqueness of each native Chinese pig population. The selection signatures were detected between indigenous pig populations and foreign breeds by using the population differentiation index (FST) method. A total of 359 candidate genes were identified, and some genes may affect characteristics such as immunity (IL-2, IL-21 and ZFYVE16), adaptability (APBA1), reproduction (FGF2, RNF17, ADAD1 and HIPK4), meat quality (ABCA1, ADIG, TLE4 and IRX5), and heat tolerance (VPS13A, HSPA4). Overall, the findings of this study will provide some valuable insights for the future breeding, conservation and utilization of these three Chinese indigenous pig populations.

Integrated microbiome-metabolome-genome axis data of Laiwu and Lulai pigs.

Excessive fat deposition can trigger metabolic diseases, and it is crucial to identify factors that can break the link between fat deposition and metabolic diseases. Healthy obese Laiwu pigs (LW) are high in fat content but resistant to metabolic diseases. In this study, we compared the fecal microbiome, fecal and blood metabolome, and genome of LW and Lulai pigs (LU) to identify factors that can block the link between fat deposition and metabolic diseases. Our results show significant differences in Spirochetes and Treponema, which are involved in carbohydrate metabolism, between LW and LU. The fecal and blood metabolome composition was similar, and some anti-metabolic disease components of blood metabolites were different between the two breeds of pigs. The predicted differential RNA is mainly enriched in lipid metabolism and glucose metabolism, which is consistent with the functions of differential microbiota and metabolites. The down-regulated gene RGP1 is strongly negatively correlated with Treponema. Our omics data would provide valuable resources for further scientific research on healthy obesity in both human and porcine.

[Spatial-temporal Variation in Net Primary Productivity in Terrestrial Vegetation Ecosystems and Its Driving Forces in Southwest China].

Studying the spatial-temporal variation in net primary productivity (NPP) in terrestrial vegetation ecosystems and its driving forces in southwest China is of great importance for regional eco-environmental protection. The spatial and temporal changes in net primary productivity (NPP) in terrestrial vegetation ecosystems and its responding characteristics to climate change and human activities were explored in this study on the basis of the Moderate Resolution Imaging Spectroradiometer (MODIS) NPP from 2000 to 2021, in situ meteorological data from 1999 to 2021, and land use type datasets from 2000 to 2020 using principal component analysis, residual analysis, Theil-Sen Median analysis, and partial correlation analysis. The results showed that on a temporal scale, the vegetation NPP showed a fluctuating upward trend, with a rate of 3.54 g·(m2·a)-1in southwest China from 2000 to 2021. Meanwhile, under the influence of climate change and human activities, NPP of farmland, grassland, and forests all showed an upward trend, but the magnitude of the increasing trends of farmland NPP was the most significant. On the spatial scale, the areas with an upward trend in vegetation NPP accounted for 89.06% in southwest China, and the areas with significant and extremely significant increases were mainly distributed in southern Guangxi, eastern Sichuan, western Chongqing, and the junction areas of Yunnan and Guizhou. Climate change and human activities had dual effects on vegetation growth in southwest China, and the proportions of the areas with upward trends in farmland NPP were higher than that of grassland and forests both under the influences of climate change and human activities. The correlations between vegetation NPP and climate factors showed obvious regional differences in southwest China. On the regional scale, the areas with a positive correlation between vegetation NPP and temperature, precipitation, and sunshine duration were greater than that of the areas with a negative correlation. However, an opposite relationship could be found between vegetation NPP and biological aridity/humidity index. Among them, the areas with a positive correlation between vegetation NPP and temperature were greater than that with other climate factors. In terms of different vegetation ecosystems, temperature, precipitation, and sunshine duration had a stronger role in promoting NPP variation in the grassland ecosystem than in farmland and forest ecosystems. The transformation of other land use types to forest land had contributed to vegetation improvement in southwest China.

Integration of Selection Signatures and Protein Interactions Reveals NR6A1, PAPPA2, and PIK3C2B as the Promising Candidate Genes Underlying the Characteristics of Licha Black Pig.

Licha black (LI) pig has the specific characteristics of larger body length and appropriate fat deposition among Chinese indigenous pigs. Body length is one of the external traits that affect production performance, and fat deposition influences meat quality. However, the genetic characteristics of LI pigs have not yet been systematically uncovered. Here, the genomic information from 891 individuals of LI pigs, commercial pigs, and other Chinese indigenous pigs was used to analyze the breed characteristics of the LI pig with runs of homozygosity, haplotype, and FST selection signatures. The results showed the growth traits-related genes (i.e., NR6A1 and PAPPA2) and the fatness traits-related gene (i.e., PIK3C2B) were the promising candidate genes that closely related to the characteristics of LI pigs. In addition, the protein-protein interaction network revealed the potential interactions between the promising candidate genes and the FASN gene. The RNA expression data from FarmGTEx indicated that the RNA expression levels of NR6A1, PAPPA2, PIK3C2B, and FASN were highly correlated in the ileum. This study provides valuable molecular insights into the mechanisms that affect pig body length and fat deposition, which can be used in the further breeding process to improve meat quality and commercial profitability.

Molecular Design-Based Breeding: A Kinship Index-Based Selection Method for Complex Traits in Small Livestock Populations.

Genomic selection (GS) techniques have improved animal breeding by enhancing the prediction accuracy of breeding values, particularly for traits that are difficult to measure and have low heritability, as well as reducing generation intervals. However, the requirement to establish genetic reference populations can limit the application of GS in pig breeds with small populations, especially when small populations make up most of the pig breeds worldwide. We aimed to propose a kinship index based selection (KIS) method, which defines an ideal individual with information on the beneficial genotypes for the target trait. Herein, the metric for assessing selection decisions is a beneficial genotypic similarity between the candidate and the ideal individual; thus, the KIS method can overcome the need for establishing genetic reference groups and continuous phenotype determination. We also performed a robustness test to make the method more aligned with reality. Simulation results revealed that compared to conventional genomic selection methods, the KIS method is feasible, particularly, when the population size is relatively small.

The pigments in eggshell with different colour and the pigment regulatory gene expression in corresponding chicken's shell gland.

Eggshell colour is the unique appearance and economically valuable trait of eggs, whereas the colour is often short of uniformity, especially in the blue-shelled breeds, hence, their pigment differences and molecular mechanism need clarity. To investigate the relationship between the pigment content of eggshells and related gene expression in the eggshell glands of chickens, four subtypes of blue-shelled eggs ('Olive', 'Green', 'Blue', and 'Light') from the same blue-eggshell chicken line were selected; Hy-Line 'White' and 'Brown'-shelled eggs were used as control groups. The L*, a*, b* values, and protoporphyrin-IX and biliverdin contents in each group of eggshells were measured. In addition, the shell glands of the corresponding hens were collected to detect SLCO1B3 genotype and mRNA expression, and ABCG2 and HMOX1 transcription and protein expression. Eggshell colour L* values were negatively correlated with protoporphyrin-IX, b* values were positively correlated with total pigment content (P < 0.001), and a* values were positively correlated with protoporphyrin-IX (P < 0.001) but negatively with biliverdin. Moreover, all four blue-eggshell subtypes were SLCO1B3 homozygous, with SLCO1B3 mRNA expression in shell glands being significantly higher than in the White and Brown groups. ABCG2 and HMOX1 mRNA expression were highest in the Brown and Green groups, respectively (P < 0.05), and were positively correlated with protoporphyrin-IX (P < 0.001) and biliverdin contents in eggshells, respectively. Western blot and immunohistochemical results demonstrated that the Brown group had the highest ABCG2 expression (P < 0.05), followed by the Green and Olive groups. HMOX1 protein expression was higher in the Olive and Green groups (P < 0.05), and lowest in the White group. This study suggests that ABCG2 and HMOX1 have important regulatory roles in the production and transport of protoporphyrin-IX and biliverdin in blue-shelled chicken eggs, respectively.

[Spatio-temporal Variation in PM2.5 Concentration and Its Relationship with Vegetation Landscape Patterns in Typical Economic Zones in China from 2000 to 2020].

This study explored the temporal and spatial variation in PM2.5 concentration and its relationship with the vegetation landscape pattern in three typical economic zones in China, which is of great significance for regional PM2.5pollution control and atmospheric environmental protection. In this study, the pixel binary model, Getis-Ord Gi* analysis, Theil-Sen Median analysis, Mann-Kendall significance test, Pearson correlation analysis, and multiple correlation analysis were used to explore the spatial cluster and spatio-temporal variation in PM2.5 and its correlation with the vegetation landscape index in the three economic zones of China on the basis of PM2.5 concentration data and MODIS NDVI data set. The results showed that PM2.5 in the Bohai Economic Rim was mainly dominated by the expansion of hot spots and the reduction in cold spots from 2000 to 2020. The proportion of cold spots and hot spots in the Yangtze River Delta showed insignificant changes. Both cold and hot spots in the Pearl River Delta had expanded. PM2.5 showed a downward trend in the three major economic zones from 2000 to 2020, and the magnitudes of increasing rates were higher in the Pearl River Delta, followed by those in the Yangtze River Delta and Bohai Economic Rim. From 2000 to 2020, PM2.5 exhibited a downward trend in the context of all vegetation coverage grades, and PM2.5 had most significantly improved within extremely low vegetation coverage in the three economic zones. On the landscape scale, PM2.5 values were mostly correlated with aggregation index in the Bohai Economic Rim, with the largest patch index in the Yangtze River Delta and Shannon's diversity in the Pearl River Delta, respectively. Under the context of different vegetation coverage levels, PM2.5showed the highest correlation with aggregation index in the Bohai Economic Rim, landscape shape index in the Yangtze River Delta, and percent of landscape in the Pearl River Delta, respectively. PM2.5 showed significant differences with vegetation landscape indices in the three economic zones. The combined effect of multiple vegetation landscape pattern indices on PM2.5 was stronger than that of the single vegetation landscape pattern index. The above results indicated that the spatial cluster of PM2.5 in the three major economic zones had changed, and PM2.5 showed a decreasing trend in the three economic zones during the study period. The relationship between PM2.5 and vegetation landscape indices exhibited obvious spatial heterogeneity in the three economic zones.

PyAGH: a python package to fast construct kinship matrices based on different levels of omic data.

BACKGROUND: Construction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are becoming diverse and different methods have their specific appropriate scenes. However, software that can comprehensively calculate kinship matrices for a variety of scenarios is still in an urgent demand. RESULTS: In this study, we developed an efficient and user-friendly python module, PyAGH, that can accomplish (1) conventional additive kinship matrces construction based on pedigree, genotypes, abundance data from transcriptome or microbiome; (2) genomic kinship matrices construction in combined population; (3) dominant and epistatic effects kinship matrices construction; (4) pedigree selection, tracing, detection and visualization; (5) visualization of cluster, heatmap and PCA analysis based on kinship matrices. The output from PyAGH can be easily integrated in other mainstream software based on users' purposes. Compared with other softwares, PyAGH integrates multiple methods for calculating the kinship matrix and has advantages in terms of speed and data size compared to other software. PyAGH is developed in python and C + + and can be easily installed by pip tool. Installation instructions and a manual document can be freely available from https://github.com/zhaow-01/PyAGH . CONCLUSION: PyAGH is a fast and user-friendly Python package for calculating kinship matrices using pedigree, genotype, microbiome and transcriptome data as well as processing, analyzing and visualizing data and results. This package makes it easier to perform predictions and association studies processes based on different levels of omic data.