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Search Results (2,310)

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10 pages, 3157 KiB  
Article
One Copy Number Variation within the Angiopoietin-1 Gene Is Associated with Leizhou Black Goat Meat Quality
by Qun Wu, Xiaotao Han, Yuelang Zhang, Hu Liu, Hanlin Zhou, Ke Wang and Jiancheng Han
Animals 2024, 14(18), 2682; https://doi.org/10.3390/ani14182682 (registering DOI) - 14 Sep 2024
Viewed by 194
Abstract
The ANGPT1 gene plays a crucial role in the regulation of angiogenesis and muscle growth, with previous studies identifying copy number variations (CNVs) within this gene among Leizhou black goats. In this study, we investigated three ANGPT1 CNVs in 417 individuals of LZBG [...] Read more.
The ANGPT1 gene plays a crucial role in the regulation of angiogenesis and muscle growth, with previous studies identifying copy number variations (CNVs) within this gene among Leizhou black goats. In this study, we investigated three ANGPT1 CNVs in 417 individuals of LZBG using quantitative PCR (qPCR), examining the impact of different CNV types on the ANGPT1 gene expression and their associations with growth and meat quality traits. Notably, the ANGPT1 CNV-1 (ARS1_chr14:24950001-24953600) overlaps with protein-coding regions and conserved domains; its gain-of-copies genotype (copies ≥ 3) was significantly correlated with ANGPT1 mRNA expression in muscle tissue (p < 0.01). Furthermore, the gain-of-copies genotype of CNV-1 demonstrated significant correlations with various phenotypic traits, including carcass weight, body weight, shear stress, chest circumference, and cross-sectional area of longissimus dorsi muscle. These findings indicate that the CNV-1 gain-of-copies genotype in the ANGPT1 gene may serve as a valuable marker for selecting Leizhou black goats exhibiting enhanced growth and muscular development characteristics, thereby holding potential applications in targeted breeding programs aimed at improving meat quality. Full article
22 pages, 3621 KiB  
Article
Estimating Non-Stationary Extreme-Value Probability Distribution Shifts and Their Parameters Under Climate Change Using L-Moments and L-Moment Ratio Diagrams: A Case Study of Hydrologic Drought in the Goat River Near Creston, British Columbia
by Isaac Dekker, Kristian L. Dubrawski, Pearce Jones and Ryan MacDonald
Hydrology 2024, 11(9), 154; https://doi.org/10.3390/hydrology11090154 (registering DOI) - 14 Sep 2024
Viewed by 168
Abstract
Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution [...] Read more.
Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution (PD) shifts under climate change. By employing LMRDs, we analyse changes in PDs and their parameters over time, identifying key environmental predictors such as lagged precipitation for September 5-day low-flows. Our findings indicate a significant relationship between total August precipitation L-moment ratios (LMRs) and September 5-day low-flow LMRs (τ2-Precipitation and τ2-Discharge: R2 = 0.675, p-values < 0.001; τ3-Precipitation and τ3-Discharge: R2 = 0.925, p-value for slope < 0.001, intercept not significant with p = 0.451, assuming α = 0.05 and a 31-year RWLM), which we later refine and use for prediction within our MLR algorithm. The methodology, applied to the Goat River near Creston, British Columbia, aids in understanding the implications of climate change on water resources, particularly for the yaqan nuʔkiy First Nation. We find that future low-flows under climate change will be outside the Natural Range of Variability (NROV) simulated from historical records (assuming a constant PD). This study provides insights that may help in adaptive water management strategies necessary to help preserve Indigenous cultural rights and practices and to help sustain fish and fish habitat into the future. Full article
(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
Show Figures

Figure 1

Figure 1
<p>L-moment ratio diagrams (LMRDs) for: (<b>a</b>) August total precipitation (mm) and (<b>b</b>) September 5-day low-flow (<math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>). Each panel includes the L-Coefficient of Variation (L-CV;<math display="inline"><semantics> <msub> <mi>τ</mi> <mn>2</mn> </msub> </semantics></math>) versus L-skewness (<math display="inline"><semantics> <msub> <mi>τ</mi> <mn>3</mn> </msub> </semantics></math>) and L-kurtosis versus L-skewness ratios.</p>
Full article ">Figure 2
<p>Relationship between L-moment ratios (LMRs) of August total precipitation (mm) and September 5-day low-flow (<math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>): (<b>a</b>) <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>2</mn> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>3</mn> </msub> </semantics></math>. <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>2</mn> </msub> </semantics></math>-Precipitation and <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>2</mn> </msub> </semantics></math>-Discharge: <span class="html-italic">p</span>-values &lt; 0.001; <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>3</mn> </msub> </semantics></math>-Precipitation and <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>3</mn> </msub> </semantics></math>-Discharge: <span class="html-italic">p</span>-value for slope &lt; 0.001, intercept not significant with <span class="html-italic">p</span> = 0.451, assuming <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0.05 and a 31-year rolling-windowed L-moments (RDLMs).</p>
Full article ">Figure 3
<p>Comparison of predicted and observed L-moments (LMs; testing during training) using a 31-year rolling-window. The plots display the predicted (green) and observed (blue) values for the first (<b>a</b>), second (<b>b</b>), third (<b>c</b>), and fourth (<b>d</b>) LMs. Each subplot includes the Overall Mean Squared Error (MSE) between the predicted and observed values computed by summing and averaging the best-fit model Squared Error (SE) for each step in the forward chaining process. The equations plotted alongside the model are derived from the final (best-fit) iteration (index 38), which demonstrated the lowest SE.</p>
Full article ">Figure 4
<p>Location, scale, and shape parameters estimated using [<a href="#B45-hydrology-11-00154" class="html-bibr">45</a>]’s method of L-moments (LMs) for the Generalized Extreme Value (GEV) probability distribution (PD) for the observed (blue) and predicted (testing during training; dashed red) LMs under a 31-year rolling-window.</p>
Full article ">Figure 5
<p>LMRDs using 31-year windows under two Representative Concentration Pathways (RCP) scenarios. Panels (<b>a</b>–<b>c</b>) correspond to the RCP 4.5 scenario, while panels (<b>d</b>–<b>f</b>) correspond to the RCP 8.5 scenario. Diagrams show: (<b>a</b>,<b>d</b>) L-CV/L-skewness, (<b>b</b>,<b>e</b>) L-kurtosis/L-skewness, with theoretical PDs described in [<a href="#B45-hydrology-11-00154" class="html-bibr">45</a>] (distributions include Generalized Logistic (GLO), Generalized Extreme Value (GEV), Generalized Pareto (GPA), Generalized Normal (GNO), Pearson Type III (PE3), Wakeby Lower Bound (WAK_LB), and All Distribution Lower Bound (ALL_LB)). Plots (<b>c</b>,<b>f</b>) show the distribution count for each window. The observed LMRs for the 5-day September low-flow at the Water Survey of Canada (WSC) Goat River Near Erikson Hydrometric Gauge Station (<a href="https://wateroffice.ec.gc.ca/report/data_availability_e.html?type=historical&amp;station=08NH004&amp;parameter_type=Flow&amp;wbdisable=true" target="_blank">08NH004</a>) are plotted alongside simulated future data derived from Multiple Linear Regression (MLR) driven with total August precipitation LMs. Future data are generated using a splice of six Coupled Model Intercomparison Project Phase 5 (CMIP5) series downscaled climate models (median of “ACCESS1-0”, “CanESM2”, “CCSM4”, “CNRM-CM5”, “HadGEM2-ES”, and “MPI-ESM-LR” from 2018 to 2100) downloaded using the single cell extraction tool from the Pacific Climate Impacts Consortium (<a href="https://pacificclimate.org/data/gridded-hydrologic-model-output" target="_blank">PCIC</a>). Historical climate data are downloaded from Historical Climate Data Online (HCDO) repository for the Creston station (Climate ID <a href="https://climate.weather.gc.ca/climate_data/daily_data_e.html?hlyRange=%7C&amp;dlyRange=1912-06-01%7C2017-12-31&amp;mlyRange=1912-01-01%7C2007-02-01&amp;StationID=1111&amp;Prov=BC&amp;urlExtension=_e.html&amp;searchType=stnName&amp;optLimit=yearRange&amp;StartYear=1840&amp;EndYear=2024&amp;selRowPerPage=25&amp;Line=0&amp;searchMethod=contains&amp;Month=12&amp;Day=2&amp;txtStationName=Creston&amp;timeframe=2&amp;Year=2017" target="_blank">1142160</a>; available from 1996 to 2017).</p>
Full article ">Figure 6
<p>Results of the L-moments (LMs) derived from Multiple Linear Regression (MLR) fit to a Generalized Extreme Value (GEV) probability distribution (PD) to produce shape, scale, and location parameters: (<b>a</b>) GEV parameters (shape, scale, and location) over 144 rolling windowed time units under Representative Concentration Pathway (RCP) 4.5 and (<b>b</b>) RCP 8.5.</p>
Full article ">Figure 7
<p>LMs derived from MLR fit to a GEV PD to produce shape, scale, and location parameters to derive median flows (<math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) and confidence intervals (CIs) estimated from percentiles: (<b>a</b>) simulated September 5-day low-flows driven by MLR regression using August total precipitation LMs over a 31-year rolling time window under (<b>a</b>) RCP 4.5 and (<b>b</b>) RCP 8.5. The dashed red line denotes a flow of &lt;1 m<sup>3</sup>/s. Note: each simulation is based on <span class="html-italic">n</span> = 1000 iterations for both panels.</p>
Full article ">Figure 8
<p>Results of the LMs derived from MLR fit to the best-fit probability distribution (PD) (distributions described in [<a href="#B45-hydrology-11-00154" class="html-bibr">45</a>]) to produce shape, scale, and location parameters to derive median flows (<math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) and confidence intervals (CIs) estimated numerically from percentiles: (<b>a</b>) simulated September 5-day low-flows driven by MLR regression using August total precipitation LMs over a 31-year moving window under RCP 4.5 and (<b>b</b>) RCP 8.5. The dashed red line denotes a flow of &lt;1 m<sup>3</sup>/s. Note: each simulation is based on <span class="html-italic">n</span> = 1000 iterations for both panels.</p>
Full article ">Figure 9
<p>Overall standardized Mean Square Error (MSE) across different window sizes during model training.</p>
Full article ">Figure 10
<p>Sensitivity of window size on location, scale, and shape parameters for September 5-day low-flow estimated using the method of LMs described in [<a href="#B45-hydrology-11-00154" class="html-bibr">45</a>] derived from MLR driven by total August precipitation LMs for six common distributions described in [<a href="#B45-hydrology-11-00154" class="html-bibr">45</a>] (Generalized Extreme Value (GEV; (<b>a</b>–<b>c</b>)), Generalized Logistic (GLO; (<b>d</b>–<b>f</b>)), Generalized Normal (GNO; (<b>g</b>–<b>i</b>)), Pearson Type III (PE3; (<b>j</b>–<b>l</b>)), and Generalized Pareto (GPA; (<b>m</b>–<b>o</b>)). The solid line displays data under the Representative Concentration Pathway (RCP) 4.5 emission scenario, while the dashed line displays the RCP 8.5 emissions scenario.</p>
Full article ">Figure 11
<p>Low-flow exceedance and cumulative exceedance probability for the Goat River near Erikson Gauge Station, showing values less than 2.7 cubic meters per second (<math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>) assuming <span class="html-italic">n</span> = 1000 simulations and a Generalized Extreme Value (GEV) probability distribution (PD). Future data assume a Representative Concentration Pathway (RCP) 4.5 emissions scenario.</p>
Full article ">
13 pages, 3650 KiB  
Article
Sirtuin 1 Inhibits Fatty Acid Synthesis through Forkhead Box Protein O1-Mediated Adipose Triglyceride Lipase Expression in Goat Mammary Epithelial Cells
by Qiuya He, Weiwei Yao, Li Lv, Xuelin Zhang, Jiao Wu and Jun Luo
Int. J. Mol. Sci. 2024, 25(18), 9923; https://doi.org/10.3390/ijms25189923 (registering DOI) - 14 Sep 2024
Viewed by 147
Abstract
Sirtuin 1 (SIRT1) is a key upstream regulator of lipid metabolism; however, the molecular mechanisms by which SIRT1 regulates milk fat synthesis in dairy goats remain unclear. This study aimed to investigate the regulatory roles of SIRT1 in modulating lipid metabolism in goat [...] Read more.
Sirtuin 1 (SIRT1) is a key upstream regulator of lipid metabolism; however, the molecular mechanisms by which SIRT1 regulates milk fat synthesis in dairy goats remain unclear. This study aimed to investigate the regulatory roles of SIRT1 in modulating lipid metabolism in goat mammary epithelial cells (GMECs) and its impact on the adipose triglyceride lipase (ATGL) promoter activity using RNA interference (RNAi) and gene overexpression techniques. The results showed that SIRT1 is significantly upregulated during lactation compared to the dry period. Additionally, SIRT1 knockdown notably increased the expressions of genes related to fatty acid synthesis (SREBP1, SCD1, FASN, ELOVL6), triacylglycerol (TAG) production (DGAT2, AGPAT6), and lipid droplet formation (PLIN2). Consistent with the transcriptional changes, SIRT1 knockdown significantly increased the intracellular contents of TAG and cholesterol and the lipid droplet abundance in the GMECs, while SIRT1 overexpression had the opposite effects. Furthermore, the co-overexpression of SIRT1 and Forkhead box protein O1 (FOXO1) led to a more pronounced increase in ATGL promoter activity, and the ability of SIRT1 to enhance ATGL promoter activity was nearly abolished when the FOXO1 binding sites (FKH1 and FKH2) were mutated, indicating that SIRT1 enhances the transcriptional activity of ATGL via the FKH element in the ATGL promoter. Collectively, our data reveal that SIRT1 enhances the transcriptional activity of ATGL through the FOXO1 binding sites located in the ATGL promoter, thereby regulating lipid metabolism. These findings provide novel insights into the role of SIRT1 in fatty acid metabolism in dairy goats. Full article
(This article belongs to the Special Issue Sirtuins as Players in Cell Metabolism and Functions)
Show Figures

Figure 1

Figure 1
<p>SIRT1 mRNA expression in goat tissues. (<b>A</b>) SIRT1 was expressed across multiple goat tissues. Lowercase letters indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.05), while the same letters indicate no significant differences. (<b>B</b>) SIRT1 expression was significantly higher in the mammary gland during peak lactation compared to the dry period. The data were normalized to the dry period. The values are shown as the mean ± standard error of the mean (SEM) for three biological replicates. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 2
<p>Effect of SIRT1 knockdown on the expressions of genes related to fatty acid metabolism in GMECs. (<b>A</b>) The mRNA levels of SIRT1 after infection with siRNA-SIRT1. (<b>B</b>,<b>C</b>) Protein level and quantification of SIRT1 in cells transfected with siRNA-SIRT1. The effect of SIRT1 knockdown on the expressions of genes involved in (<b>D</b>) fatty acid synthesis (SREBP1, SCD1, FASN, and ELOVL6), (<b>E</b>) TAG synthesis (DGAT1, DGAT2, GPAM, and AGPAT6), and (<b>F</b>) lipid droplet formation (PLIN2 and XDH). The RT-qPCR data were calculated using the 2<sup>−ΔΔCt</sup> method. Data shown are mean ± SEM. Statistically significant differences are indicated: ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05. Note: SIRT1, Sirtuin 1; GMECs, goat mammary epithelial cells; SREBP1, Sterol Regulatory Element-Binding Proteins 1; SCD1, stearoyl-coenzyme A desaturase; FASN, fatty acid synthase; ELOVL6, elongase of very long chain fatty acid 6; DGAT1/2, Diacylglycerol-O-Acyltransferase; GPAM, glycerol-3-phosphate acyltransferase; AGPAT6, 1-Acylglycerol-3-Phosphate O-Acyltransferase 6; PLIN2, Perilipin 2; XDH, xanthine dehydrogenase.</p>
Full article ">Figure 3
<p>Effect of SIRT1 overexpression on the expressions of genes related to lipid metabolism in GMECs. (<b>A</b>) The SIRT1 mRNA expression in GMECs that were transfected with SIRT1 overexpression vector. (<b>B</b>,<b>C</b>) Protein abundance of SIRT1 after transfection of the pcDNA3.1-SIRT1 in GMECs (N = 3). The expressions of genes involved in lipid metabolism after SIRT1 overexpression including fatty acid synthesis (SREBP1, PPARG, FASN, and ELOVL6; panel (<b>D</b>)), TAG synthesis (DGAT2, GPAM, AGPAT6, and LIPIN1; panel (<b>E</b>)), and lipid droplet formation (PLIN2 and XDH; panel (<b>F</b>)). Data shown are mean ± SEM. Statistically significant differences are indicated: ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05. Note: SIRT1, Sirtuin 1; SREBP1, Sterol Regulatory Element-Binding Proteins 1; PPARG, peroxisome proliferator-activated receptor gamma; FASN, fatty acid synthase; ELOVL6, elongase of very long chain fatty acid 6; DGAT2, Diacylglycerol-O-Acyltransferase 2; GPAM, glycerol-3-phosphate acyltransferase; AGPAT6, 1-Acylglycerol-3-Phosphate O-Acyltransferase 6; PLIN2, Perilipin 2; XDH, xanthine dehydrogenase.</p>
Full article ">Figure 4
<p>The effect of SIRT1 knockdown on the contents of triglycerides, cholesterol, and lipid droplets. (<b>A</b>) The content of triglycerides in cells transfected with siRNA-SIRT1. (<b>B</b>) Cellular cholesterol levels after knockdown of SIRT1 in GMECs. (<b>C</b>,<b>D</b>) Lipid droplets in SIRT1 knockdown cells were detected by BODIPY 493/503 staining. Scale bar = 200 μm.Values are shown as mean ± SEM. *, <span class="html-italic">p</span> &lt; 0.05. Note: SIRT1, Sirtuin 1.</p>
Full article ">Figure 5
<p>SIRT1 overexpression in GMECs regulates lipid metabolism. (<b>A</b>) SIRT1 overexpression significantly decreased the content of triacylglycerol (TAG) in GMECs. (<b>B</b>) SIRT1 overexpression reduced the cellular cholesterol levels. (<b>C</b>) Overexpression of SIRT1 inhibited the accumulation of lipid droplets. Cellular nuclei were stained with a blue signal, while the lipid droplets were labeled with a green signal. Scale bar = 200 μm. (<b>D</b>) The relative fluorescence intensity of the lipid droplets. Data shown are mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 6
<p>SIRT1 promotes ATGL transcription activity through FOXO1. (<b>A</b>,<b>B</b>) The expression of ATGL after SIRT1 overexpression or inhibition. (<b>C</b>) SIRT1 knockdown significantly decreased the ATGL promoter activity. (<b>D</b>) Effects of SIRT1 and FOXO1 on ATGL promoter activity. GMECs were co-transfected with different lengths of the ATGL promoter and plasmid expressing SIRT1 or empty vector for 48 h. Cells were harvested and luciferase activity assay was performed to detect the effect of SIRT1 on the ATGL promoter activity. In addition, to detect the effect of FOXO1 on the ATGL promoter activity, cells were co-transfected with different lengths of the ATGL promoter and plasmid expressing SIRT1 and FOXO1 for 48 h. (<b>E</b>) SIRT1 increases ATGL promoter activity in a FOXO1-dependent manner. GMECs were co-transfected with wild-type ATGL promoter (−882 + 216 bp) or constructs containing individually or simultaneously mutated FOXO1 binding sites and pcDNA3.1-SIRT1 for 48 h. Data are presented as mean ± SEM; *, <span class="html-italic">p</span> &lt; 0.05. Note: SIRT1, Sirtuin 1; FOXO1, Forkhead box protein O1; ATGL, Adipose Triglyceride Lipase; FKH, FOXO1 binding sites; Red cross, Mutation site.</p>
Full article ">
25 pages, 6783 KiB  
Article
Metabolomic and Transcriptomic Analyses Reveal the Potential Mechanisms of Dynamic Ovarian Development in Goats during Sexual Maturation
by Yanyan Wang, Tianle Chao, Qing Li, Peipei He, Lu Zhang and Jianmin Wang
Int. J. Mol. Sci. 2024, 25(18), 9898; https://doi.org/10.3390/ijms25189898 (registering DOI) - 13 Sep 2024
Viewed by 283
Abstract
The ovary is a crucial reproductive organ in mammals, and its development directly influences an individual’s sexual maturity and reproductive capacity. To comprehensively describe ovarian sexual maturation in goats, we integrated phenotypic, hormonal, metabolomic, and transcriptomic data from four specific time points: after [...] Read more.
The ovary is a crucial reproductive organ in mammals, and its development directly influences an individual’s sexual maturity and reproductive capacity. To comprehensively describe ovarian sexual maturation in goats, we integrated phenotypic, hormonal, metabolomic, and transcriptomic data from four specific time points: after birth (D1), at 2 months old (M2), at 4 months old (M4), and at 6 month old (M6). The study showed that during the early stage (D1–M2), ovarian growth was the most rapid, with weight and morphology increasing by 284% and 65%, respectively, and hormone levels rose significantly, with estradiol increasing by 57%. Metabolomic analysis identified 1231 metabolites, primarily lipids, lipid molecules, and organic acids, which can support hormone balance and follicle development by providing energy and participating in signaling transduction. Transcriptomic analysis identified 543 stage-specific differentially expressed genes, mainly enriched in steroid biosynthesis, amino acid metabolism, and the PI3K/AKT pathway, which are key factors influencing ovarian cell proliferation, apoptosis, hormone secretion, and metabolism. The integrated analysis revealed the key processes in the ovarian steroid hormone biosynthesis pathway and gene/metabolite networks associated with ovarian phenotypes and hormone levels, ultimately highlighting scavenger receptor class B type 1 (SCARB1), Cytochrome P450 Family 1 Subfamily A Member 1 (CYP11A1), 3beta-hydroxysteroid dehydrogenase (3BHSD), progesterone, estradiol, and L-phenylalanine as key regulators of ovarian morphological and functional changes at different developmental stages. This study is the first to reveal the metabolic changes and molecular regulatory mechanisms during ovarian sexual maturation in goats, providing valuable insights for understanding reproductive system development and optimizing reproductive performance and breeding efficiency. Full article
(This article belongs to the Section Molecular Endocrinology and Metabolism)
Show Figures

Figure 1

Figure 1
<p>Analysis of ovarian phenotypic characteristics and serum hormone levels at four developmental stages. (<b>A</b>) Analysis of the ovarian weight (OW), ovarian length (OL), ovarian width (OD), and ovarian thickness (OT). (<b>B</b>) Levels of gonadotropin-releasing hormone (GnRH), luteinizing hormone (LH), follicle-stimulating hormone (FSH), progesterone (PROG) and estradiol (E2) in serum. D1: after birth, M2: at 2 months old, M4: at 4 months old, M6: at 6 months old. Values represent mean ± standard error. Different lowercase letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. ns = not statistically significant.</p>
Full article ">Figure 2
<p>Basic metabolomics analysis of ovarian tissues at different developmental stages. (<b>A</b>) Principal component analysis of metabolites at four developmental stages. (<b>B</b>) Top 10 classes of metabolites identified during ovarian development. (<b>C</b>) The number of DAMs in pairwise comparisons. (<b>D</b>) The Venn diagram of DAMs in pairwise comparisons. (<b>E</b>) The heatmap of DAMs during ovarian development.</p>
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<p>The expression patterns and functional enrichment analysis of DAMs. (<b>A</b>) Expression pattern analysis of DAMs. (<b>B</b>) KEGG pathway enrichment analysis of all DAMs.</p>
Full article ">Figure 4
<p>Basic transcriptome analysis of ovarian tissues at different developmental stages. (<b>A</b>) Venn diagram of the number of mRNA in ovary tissues at different developmental stages. (<b>B</b>) Validation of transcriptome data by qRT-PCR. (<b>C</b>) The number of DAMs in pairwise comparisons. (<b>D</b>) The Upset Plot for DEGs in different comparison groups.</p>
Full article ">Figure 5
<p>The expression patterns and functional enrichment analysis of DEGs. (<b>A</b>) The heatmap of DEGs during ovarian development. (<b>B</b>) KEGG pathway enrichment analysis of all DEGs. (<b>C</b>) Expression pattern analysis of DEGs.</p>
Full article ">Figure 6
<p>The integrated metabolomic and transcriptomic analysis of ovarian tissues at different developmental stages. (<b>A</b>) The O2PLS analysis of DAMs and DEGs. (<b>B</b>) Bar plot of the top 20 transcriptomics and metabolites pq1.</p>
Full article ">Figure 7
<p>The pathway related to steroid hormone synthesis and the heatmap of changes in metabolites and genes. (<b>A</b>) The steroid biosynthesis pathway. (<b>B</b>) The ovarian steroid synthesis pathway. (<b>C</b>) Correlation analysis of DAMs and DEGs involved in steroid synthesis. Blue indicates a negative correlation and red indicates a positive correlation. The “*” and “**” represent significant differences at levels of <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01 according to Pearson’s correlation coefficient. (<b>D</b>) The estrogen signaling pathway during ovarian development.</p>
Full article ">Figure 8
<p>Analysis of the WGCNA of the ovarian metabolome at different developmental stages. (<b>A</b>) The clustering dendrogram of all metabolites. The color rows below the dendrogram show module assignments based on the dynamic hybrid branch cutting method. Each color represents a different module of co-expressed metabolites, while the “Merged dynamic” row indicates merged modules, with gray representing unassigned metabolites. The y-axis (Height) represents the dissimilarity between metabolites, with higher values indicating less similarity. (<b>B</b>) Heatmap of the correlations between modules and phenotypic traits. Modules are color-coded on the left, and phenotypic traits are listed below. The numbers in the cells represent correlation coefficients, with red for positive correlations and green for negative correlations. Color intensity indicates correlation strength. (<b>C</b>,<b>D</b>) Visualization of connections of metabolites in pink (<b>C</b>) and brown (<b>D</b>) modules. According to the connection weight value, only the first 100 functional relationships are shown in the figures. Yellow nodes represent hub metabolites with high connectivity, while blue nodes represent others. The thickness of the edges between nodes reflects the strength of their connections, with thicker edges indicating stronger associations, highlights key metabolites in the modules. OW: ovarian weight. OL: ovarian length. OD: ovarian width. OT: ovarian thickness. E2: estradiol. PROG: progesterone.</p>
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<p>Analysis of the WGCNA of ovarian transcriptome at different developmental stages. (<b>A</b>) The clustering dendrogram of all genes. The color rows below the dendrogram show module assignments based on the dynamic hybrid branch cutting method. Each color represents a different module of co-expressed genes, while the “Merged dynamic” row indicates merged modules, with gray representing unassigned metabolites. The y-axis (Height) represents the dissimilarity between genes, with higher values indicating less similarity. (<b>B</b>) Heatmap of the correlations between modules and phenotypic traits. The modules are color-coded on the left, and phenotypic traits are listed below. The numbers in the cells represent correlation coefficients, with red for positive correlations and green for negative correlations. Color intensity indicates correlation strength. (<b>C</b>) KEGG enrichment analysis of genes in the yellow module. The y-axis shows enriched biological pathways, and the x-axis (Count) indicates the number of genes involved. (<b>D</b>) Network visualization of the top 50 genes in the yellow module, based on connection weights. Yellow nodes represent hub genes with high connectivity, while blue nodes represent other genes. Edge thickness reflects connection strength, with thicker edges indicating stronger associations. OL: ovarian length. OD: ovarian width. OT: ovarian thickness. E2: estradiol. PROG: progesterone.</p>
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16 pages, 3405 KiB  
Article
Introduced Herbivores Threaten the Conservation Genetics of Two Critically Endangered Single-Island Endemics, Crambe sventenii and Pleudia herbanica
by Priscila Rodríguez-Rodríguez, Sonia Sarmiento Cabello, Stephan Scholz, Leticia Curbelo and Pedro A. Sosa
Plants 2024, 13(18), 2573; https://doi.org/10.3390/plants13182573 - 13 Sep 2024
Viewed by 216
Abstract
Crambe sventenii Pett. ex Bramwell & Sunding and Pleudia herbanica (A.Santos & M.Fernández) M.Will, N.Schmalz & Class.-Bockh. are two single-island endemic species from Fuerteventura (Canary Islands), inhabiting the same areas and similar habitats. They are under the “Critically Endangered” category due to historical [...] Read more.
Crambe sventenii Pett. ex Bramwell & Sunding and Pleudia herbanica (A.Santos & M.Fernández) M.Will, N.Schmalz & Class.-Bockh. are two single-island endemic species from Fuerteventura (Canary Islands), inhabiting the same areas and similar habitats. They are under the “Critically Endangered” category due to historical herbivore pressure, mainly goats, leading to habitat fragmentation and poor population recruitment. The main aim of our study was to provide insights into the conservation genetics and habitat suitability of these two species. For this purpose, we sampled all known populations on the island and developed two new sets of microsatellite markers. Moreover, to assist restoration plans, we performed species distribution models to determine the most suitable areas for reintroduction. While Crambe sventenii is highly fragmented, with low genetic diversity indices in some populations, Pleudia herbanica’s genetic structure is quite homogeneous, grouped in three main regions, with signs of inbreeding and an overall low genetic diversity. Both species could present moderate to high levels of autogamy. Our findings can provide guidance to local governments regarding conservation actions to be implemented in the field, like the identification of propagule sources and new suitable areas for restoration. Full article
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<p>(<b>a</b>) <span class="html-italic">Crambe sventenii</span>. (<b>b</b>) <span class="html-italic">Pleudia herbanica</span>. Authorship: Stephan Scholz. (<b>c</b>) Map of The Canary Islands with a black square indicating the study area in Fuerteventura, Spain. (<b>d</b>) Location of the studied populations from <span class="html-italic">C. sventenii</span> (green triangle) and <span class="html-italic">P. herbanica</span> (orange circle).</p>
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<p><span class="html-italic">Crambe sventenii</span> results. (<b>a</b>) Neighbor-joining dendrogram with populations, not including the Botanical Garden individuals. (<b>b</b>) Principal coordinate analysis (PCoA) for all <span class="html-italic">C. sventenii</span> sampled individuals. The first two axes explained 49.08% of the total variation. (<b>c</b>) Bar plots for the proportion of coancestry inferred from Bayesian cluster analysis implemented on STRUCTURE and CLUMPP, representing K = 5 and K = 7, following the highest ∆K (see <a href="#app1-plants-13-02573" class="html-app">Figure S1</a>). Each color represents a different cluster.</p>
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<p><span class="html-italic">Pleudia herbanica</span> results. (<b>a</b>) Neighbor-joining dendrogram with populations, not including the Botanical Garden individuals. (<b>b</b>) Principal coordinate analysis (PCoA) for all <span class="html-italic">P. herbanica</span> sampled individuals. The first two axes explained 50.29% of the total variation. (<b>c</b>) Bar plots for the proportion of coancestry inferred from Bayesian cluster analysis implemented on STRUCTURE and CLUMPP, representing K = 2 and K = 3, following the highest ∆K (see <a href="#app1-plants-13-02573" class="html-app">Figure S1</a>). Each color represents a different cluster.</p>
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<p>Output maps of the ensemble model of topoclimatic suitability of <span class="html-italic">Crambe sventenii</span> (<b>a</b>) and <span class="html-italic">Pleudia herbanica</span> (<b>b</b>). The black dots represent the coordinates used to indicate the presence of the populations.</p>
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18 pages, 2294 KiB  
Article
Goat Cheese Produced with Sunflower (Helianthus annuus L.) Seed Extract and a Native Culture of Limosilactobacillus mucosae: Characterization and Probiotic Survival
by Dôrian Cordeiro Lima Júnior, Viviane Maria da Silva Quirino, Alícia Santos de Moura, Joyceana Oliveira Correia, João Ricardo Furtado, Isanna Menezes Florêncio, Márcia Maria Cândido da Silva, Hévila Oliveira Salles, Karina Maria Olbrich dos Santos, Antonio Silvio do Egito and Flávia Carolina Alonso Buriti
Foods 2024, 13(18), 2905; https://doi.org/10.3390/foods13182905 - 13 Sep 2024
Viewed by 273
Abstract
The microbiological and biochemical properties of a goat cheese produced using Helianthus annuus (sunflower) seed extract as a coagulant and the potentially probiotic autochthonous culture Limosilactobacillus mucosae CNPC007 were examined in comparison to a control cheese devoid of the autochthonous culture. Throughout a [...] Read more.
The microbiological and biochemical properties of a goat cheese produced using Helianthus annuus (sunflower) seed extract as a coagulant and the potentially probiotic autochthonous culture Limosilactobacillus mucosae CNPC007 were examined in comparison to a control cheese devoid of the autochthonous culture. Throughout a 60-day storage period at 6 ± 1 °C, lactobacilli maintained a count of above 8 log CFU/g. Additionally, its viability in cheeses subjected to the in vitro gastrointestinal conditions demonstrated improvement over this period. Specifically, the recovery of lactobacilli above 6 log CFU/g was observed in 16.66% of the samples in the first day, increasing to 66.66% at both 30 and 60 days. While total coliforms were detected in both cheese trials, this sanitary parameter exhibited a decline in L. mucosae cheeses during storage, falling below the method threshold (<3 MPN/g) at 60 days. This observation suggests a potential biopreservative effect exerted by this microorganism, likely attributed to the higher acidity of L. mucosae cheeses at that point (1.80 g/100 g), which was twice that of the control trial (0.97 g/100 g). Furthermore, distinct relative proportions of >30 kDa, 30–20 kDa, and <20 kDa proteins during storage was verified for L. mucosae and control cheeses. Consequently, either the H. annuus seed extract or the L. mucosae CNPC007 autochthonous culture influenced the biochemical properties of the cheese, particularly in terms of proteolysis. Moreover, L. mucosae CNPC007 acidification property resulted in a biopreservative effect throughout the storage period, indicating the potential as a promising source of probiotics for this product. Full article
(This article belongs to the Special Issue Probiotics: Selection, Cultivation, Evaluation and Application)
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<p>Complete cheesemaking process of the cheese trials studied.</p>
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<p>Sodium dodecyl sulfate polyacrylamide gel electrophoresis of control and <span class="html-italic">L. mucosae</span> cheeses in the day of packing (day 1) and after 30 and 60 days of storage at 6 ± 1 °C. The SDS-PAGE pattern of standard mixture Sigma Marker (Wide Range 6500–200,000 Da, Sigma-Aldrich) is shown in lane 1. The pattern of control cheeses on days 1, 30, and 60 is shown in lanes 2, 4 and 6, respectively, while the pattern of <span class="html-italic">L. mucosae</span> cheeses is shown in lanes 3, 5, and 7 for the same sampling periods, respectively. αs-CN = αs-casein. β-CN = β-casein. para-κ-CN = para-κ-casein, * = highlight for the intense bands verified in <span class="html-italic">L. mucosae</span> cheeses.</p>
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<p>Densitometric analysis of bands obtained by SDS-PAGE for cheeses at 1, 30, and 60 days of storage of control (lanes 2, 4, and 6, respectively) and <span class="html-italic">L. mucosae</span> (lanes 3, 5, and 7, respectively) trials. The SDS-PAGE pattern of standard mixture Sigma Marker (Wide Range 6500–200,000 Da, Sigma-Aldrich) is labelled as “S”. * = highlight for the prominent peaks marked with a circle in the region below 6.5 kDa in control cheeses at 30 and 60 days.</p>
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<p>Relative intensity (%) of proteins obtained by SDS-PAGE and quantified by densitometric analysis using GelAnalyzer 23.1.1 in control and <span class="html-italic">L. mucosae</span> cheeses after 1, 30, and 60 days of storage.</p>
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17 pages, 2005 KiB  
Review
An Overview of Interactions between Goat Milk Casein and Other Food Components: Polysaccharides, Polyphenols, and Metal Ions
by Bohan Ma, Majida Al-Wraikat, Qin Shu, Xi Yang and Yongfeng Liu
Foods 2024, 13(18), 2903; https://doi.org/10.3390/foods13182903 - 13 Sep 2024
Viewed by 368
Abstract
Casein is among the most abundant proteins in milk and has high nutritional value. Casein’s interactions with polysaccharides, polyphenols, and metal ions are important for regulating the functional properties and textural quality of dairy foods. To improve the functional properties of casein-based foods, [...] Read more.
Casein is among the most abundant proteins in milk and has high nutritional value. Casein’s interactions with polysaccharides, polyphenols, and metal ions are important for regulating the functional properties and textural quality of dairy foods. To improve the functional properties of casein-based foods, a deep understanding of the interaction mechanisms and the influencing factors between casein and other food components is required. This review started by elucidating the interaction mechanism of casein with polysaccharides, polyphenols, and metal ions. Thermodynamic incompatibility and attraction are the fundamental factors in determining the interaction types between casein and polysaccharides, which leads to different phase behaviors and microstructural types in casein-based foods. Additionally, the interaction of casein with polyphenols primarily occurs through non-covalent (hydrogen bonding, hydrophobic interactions, van der Waals forces, and ionic bonding) or covalent interaction (primarily based on the oxidation of proteins or polyphenols by enzymatic or non-enzymatic (alkaline or free radical grafting) approaches). Moreover, the selectivity of casein to specific metal ions is also introduced. Factors affecting the binding of casein to the above three components, such as temperature, pH, the mixing ratio, and the fine structure of these components, are also summarized to provide a good foundation for casein-based food applications. Full article
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<p>Phase behaviors and network structure of mixed milk casein and polysaccharides. (<b>a</b>) The schematic diagram of the phase transition in mixed casein/polysaccharides; (<b>b</b>) “water in water emulsion” structure of mixed casein and methylcellulose (MC) gels (8.0% casein + 0.2% MC), cited from Li et al. [<a href="#B19-foods-13-02903" class="html-bibr">19</a>] with permission from Elsevier.</p>
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<p>Main categories and chemical structures of fruit and vegetable phenolics.</p>
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<p>Non-covalent conjugation of casein and polyphenol and protein cross-linking via (<b>a</b>) hydrogen bonding, (<b>b</b>) hydrophobic-hydrophobic interaction, and (<b>c</b>) ionic interaction adopted from Quan et al. [<a href="#B51-foods-13-02903" class="html-bibr">51</a>].</p>
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<p>Covalent conjugation of casein and polyphenol and protein cross-links via (<b>a</b>) alkaline, (<b>b</b>) enzymatic, and (<b>c</b>) free-radical grafting reactions adopted from Quan et al. [<a href="#B51-foods-13-02903" class="html-bibr">51</a>].</p>
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<p>The (<b>a</b>) multidentate and (<b>b</b>) monodentate mechanism of protein-polyphenol interaction adopted from Günal-Köroğlu et al. [<a href="#B42-foods-13-02903" class="html-bibr">42</a>].</p>
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20 pages, 14568 KiB  
Article
Combined Genome-Wide Association Study and Haplotype Analysis Identifies Candidate Genes Affecting Growth Traits of Inner Mongolian Cashmere Goats
by Xiaofang Ao, Youjun Rong, Mingxuan Han, Xinle Wang, Qincheng Xia, Fangzheng Shang, Yan Liu, Qi Lv, Zhiying Wang, Rui Su, Yanjun Zhang and Ruijun Wang
Vet. Sci. 2024, 11(9), 428; https://doi.org/10.3390/vetsci11090428 - 12 Sep 2024
Viewed by 303
Abstract
In this study, genome-wide association analysis was performed on the growth traits (body height, body length, chest circumference, chest depth, chest width, tube circumference, and body weight) of Inner Mongolian cashmere goats (Erlangshan type) based on resequencing data. The population genetic parameters were [...] Read more.
In this study, genome-wide association analysis was performed on the growth traits (body height, body length, chest circumference, chest depth, chest width, tube circumference, and body weight) of Inner Mongolian cashmere goats (Erlangshan type) based on resequencing data. The population genetic parameters were estimated, haplotypes were constructed for the significant sites, and association analysis was conducted between the haplotypes and phenotypes. A total of two hundred and eighty-four SNPs and eight candidate genes were identified by genome-wide association analysis, gene annotation, and enrichment analysis. The phenotypes of 16 haplotype combinations were significantly different by haplotype analysis. Combined with the above results, the TGFB2, BAG3, ZEB2, KCNJ12, MIF, MAP2K3, HACD3, and MEGF11 functional candidate genes and the haplotype combinations A2A2, C2C2, E2E2, F2F2, I2I2, J2J2, K2K2, N2N2, O2O2, P2P2, R1R1, T1T1, W1W1, X1X1, Y1Y1, and Z1Z1 affected the growth traits of the cashmere goats and could be used as molecular markers to improve the accuracy of early selection and the economic benefits of breeding. Full article
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<p>Analysis process. Body Height (BH), Body Length (BL), Chest Circumference (CC), Chest Depth (CD), Chest Width (CW), Tube Circumference (TC), Body Weight (BW). Considering that multiple body weight measurements were recorded for the same individual, in order to dissect their permanent environmental effects, their breeding values (breeding values + residuals) were derived using the repetitive force model for subsequent GWAS analyses.</p>
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<p>Correlation analysis of growth indexes of IMCGs.</p>
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<p>Distribution of SNPs in the 1 Mb window of chromosomes, with the left Y axis representing chromosome names and the upper X axis representing window sizes.</p>
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<p>Population structure and relationship analysis of IMCGs (ErIangshan type). (<b>a</b>) Principal component analysis results diagram of IMCGs (ErIangshan type); (<b>b</b>) G matrix Heat map of IMCGs (ErIangshan type) in the conserved population. Each small square indicates the kinship value between different individuals. The closer the color of the square to red, the closer the kinship between individuals.</p>
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<p>Manhattan plot and quantile-quantile (Q-Q) plot for growth traits. Body Height (BH), Body Length (BL), Chest Circumference (CC), Chest Depth (CD), Chest Width (CW), Tube Circumference (TC), Body Weight (BW). In the Manhattan plot (left), single nucleotide polymorphisms (SNPs) on different chromosomes are denoted by different colors (markers). Density is shown at the bottom of the Manhattan plot; the horizontal black line indicates a significant genome-wide association threshold (<span class="html-italic">p</span> = 1.0 × 10<sup>−6</sup>). Q-Q plots are displayed as scatter plots of observed and expected log <span class="html-italic">p</span>-values (right).</p>
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<p>Enrichment analysis of growth traits of IMCGs (Erlangshan type). (<b>a</b>) Secondary classification histogram of Gene ontology (GO) enrichment analysis of candidate genes. (<b>b</b>) KEGG enrichment analysis diagram.</p>
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<p>Association analysis of haplotype combinations with growth traits in IMCGs (Erlangshan type) (<b>a</b>–<b>c</b>); significant differences between genotypes indicated with different lowercase letters (<span class="html-italic">p</span> &lt; 0.05). <span class="html-italic">x</span>-axis indicates haplotype combinations, <span class="html-italic">y</span>-axis indicates phenotypes corresponding to growth traits, and markers at the top of the graph are annotated candidate genes, which include <span class="html-italic">PBX1</span>, <span class="html-italic">GABGR1</span>, <span class="html-italic">AADAT</span>, <span class="html-italic">TRNAS-GGA-82</span>, <span class="html-italic">KIAA1109</span>, <span class="html-italic">IGFBP3</span>, <span class="html-italic">REV3L</span>, <span class="html-italic">ARMC2</span>, <span class="html-italic">BAG3</span>, <span class="html-italic">TRNAG-UCC-59</span>, <span class="html-italic">TGFB2</span>, <span class="html-italic">TRNAG-UCC-34</span>, <span class="html-italic">KCNK9</span>, <span class="html-italic">FASTKD2</span>, <span class="html-italic">HACD3</span>, <span class="html-italic">MEGF11</span>, and <span class="html-italic">SLC8A1</span>. Body Height (BH), Body Length (BL), Chest Circumference (CC), Chest Depth (CD), Chest Width (CW), Tube Circumference (TC), Body Weight (BW).</p>
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16 pages, 754 KiB  
Review
Caprine and Ovine Genomic Selection—Progress and Application
by Linyun Zhang, Yixin Duan, Shengnan Zhao, Naiyi Xu and Yongju Zhao
Animals 2024, 14(18), 2659; https://doi.org/10.3390/ani14182659 - 12 Sep 2024
Viewed by 284
Abstract
The advancement of sequencing technology and molecular breeding methods has provided technical support and assurance for accurate breeding. Genomic Selection (GS) utilizes genomic information to improve livestock breeding, and it is more accurate and more efficient than traditional selection methods. GS has been [...] Read more.
The advancement of sequencing technology and molecular breeding methods has provided technical support and assurance for accurate breeding. Genomic Selection (GS) utilizes genomic information to improve livestock breeding, and it is more accurate and more efficient than traditional selection methods. GS has been widely applied in domestic animal breeding, especially in cattle. However, there are still limited studies on the application and research of GS in sheep and goats. This paper outlines the principles, analysis methods, and influential factors of GS and elaborates on the research progress, challenges, and prospects of applying GS in sheep and goat breeding. Through the review of these aspects, this paper is expected to provide valuable references for the implementation of GS in the field of sheep and goat breeding. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>GS technical route.</p>
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14 pages, 401 KiB  
Review
Review of the Current Status on Ruminant Abortigenic Pathogen Surveillance in Africa and Asia
by George Peter Semango and Joram Buza
Vet. Sci. 2024, 11(9), 425; https://doi.org/10.3390/vetsci11090425 - 12 Sep 2024
Viewed by 481
Abstract
Ruminant abortion events cause economic losses. Despite the importance of livestock production for food security and the livelihoods of millions of people in the world’s poorest communities, very little is known about the scale, magnitude, or causes of these abortions in Africa and [...] Read more.
Ruminant abortion events cause economic losses. Despite the importance of livestock production for food security and the livelihoods of millions of people in the world’s poorest communities, very little is known about the scale, magnitude, or causes of these abortions in Africa and Asia. The aim of this review was to determine the current status of surveillance measures adopted for ruminant abortigenic pathogens in Africa and Asia and to explore feasible surveillance technologies. A systematic literature search was conducted using PRISMA guidelines for studies published between 1 January 1990 and 1 May 2024 that reported epidemiological surveys of abortigenic pathogens Africa and Asia. A meta-analysis was used to estimate the species-specific sero-prevalence of the abortigenic agents and the regions where they were detected. In the systematic literature search, 39 full-text manuscripts were included. The most prevalent abortigenic pathogens with sero-prevalence greater than 10% were BHV-1, Brucella, Chlamydia abortus, Neospora caninum, RVFV, and Waddlia chondrophila in cattle, BVDV in sheep, and RVFV and Toxoplasma gondii in goats in Africa. In Asia, Anaplasma, BHV-1, Bluetongue virus, Brucella, and BVDV were prevalent in cattle, whereas Mycoplasma was important in goats and sheep. Full article
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<p>PRISMA flow diagram showing identification, screening, and selection of eligible articles for inclusion in the systematic review, 1990–2002.</p>
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18 pages, 1001 KiB  
Article
Factors Affecting the Adoption of Anti-Predation Measures by Livestock Farmers: The Case of Northern Chile
by Camila Núñez, Lisandro Roco and Victor Moreira
Diversity 2024, 16(9), 567; https://doi.org/10.3390/d16090567 - 11 Sep 2024
Viewed by 329
Abstract
Livestock farming has been a practice of great importance for the evolution of civilization, not only influencing social, economic, and cultural aspects at a global level, but also food, the economy, and sustainability, especially in developing countries, where it generates significant pressure on [...] Read more.
Livestock farming has been a practice of great importance for the evolution of civilization, not only influencing social, economic, and cultural aspects at a global level, but also food, the economy, and sustainability, especially in developing countries, where it generates significant pressure on natural resources and biodiversity. In this context, conflict arises between wildlife, mainly top predators, and livestock farmers. Despite the efforts of different communities to implement measures against predation, the conflict continues to increase. In Latin America, the livestock sector is growing at a much higher rate than in the rest of the world, particularly in Chile, where around a third of agricultural production units use livestock as their main source of livelihood. To understand the factors influencing the behavior of goat farmers when adopting measures, we applied a hurdle model with social, spatial, economic, and productive information to assess the decision to adopt measures and the intensity of the adoption of such practices. To perform this, we used data from a survey, administered in 2014 to 476 farmers located in the three provinces of the Coquimbo Region. Our dependent variable was defined by six measures: a protection dog, night confinement of the herd, supervised grazing, anti-carnivore corral, the death or capture of the predator, and repelling the predator. The adoption decision, as well as the intensity of adoption, were influenced by the location, household size, the type of livestock, the income generated by the livestock, health management, and access to technical advice. The decision to adopt measures was influenced by the production system and whether it was self-sustaining, while the intensity of adoption was influenced by herd size and the number of losses due to predation. The results showed the importance of developing and adjusting livestock support initiatives in the study area, including those that could be created, based on differentiated measures according to the profiles of farmers in the territory. Full article
(This article belongs to the Special Issue Human-Wildlife Conflicts)
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<p>Map of the Coquimbo Region with provincial boundaries.</p>
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<p>Mean perception of damage depending on the type of predator (0 to 4, <span class="html-italic">n</span> = 476).</p>
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<p>Number of measures adopted according to province (<span class="html-italic">n</span> = 476).</p>
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12 pages, 279 KiB  
Article
Fortification of Goat Milk Yogurts with Encapsulated Postbiotic Active Lactococci
by Andrea Lauková, Marián Maďar, Natália Zábolyová, Aleksandra Troscianczyk and Monika Pogány Simonová
Life 2024, 14(9), 1147; https://doi.org/10.3390/life14091147 - 11 Sep 2024
Viewed by 272
Abstract
The species Lactococcus lactis is a bacterium extensively used in the dairy industry. This bacterium is Generally Recognized as Safe and was added to the European Food Safety Authority’s Qualified Presumption of Safety list. The major functions of this species in dairy fermentation [...] Read more.
The species Lactococcus lactis is a bacterium extensively used in the dairy industry. This bacterium is Generally Recognized as Safe and was added to the European Food Safety Authority’s Qualified Presumption of Safety list. The major functions of this species in dairy fermentation are the production of lactic acid from lactose, citric acid fermentation, and the hydrolysis of casein. But, the representatives of this species that produce bacteriocin substances can also exert an inhibitory effect against spoilage bacteria. The aims of this study were to test three lactococcal strains isolated from raw goat milk for their postbiotic activity and to test their stability in goat milk yogurts after their application in encapsulated form for their further application. To achieve these aims, validated methods were used. Three Lactococcus lactis strains (identified by Blastn 16S rRNA analysis) produced bacteriocin substances/postbiotics. These concentrated postbiotics inhibited the growth of enterococci and staphylococci (by up to 97.8%), reaching an inhibitory activity of up to 800 AU/mL. The encapsulated (freeze-dried) lactococci survived in the goat milk yogurts with sufficient stability. Strain MK2/8 fortified the yogurts in the highest amount (8.1 ± 0.0 cfu/g log 10). It did not influence the pH of the yogurt. Full article
(This article belongs to the Special Issue Food Microbiological Contamination)
16 pages, 4797 KiB  
Article
Fat Mass- and Obesity-Associated Protein (FTO) Promotes the Proliferation of Goat Skeletal Muscle Satellite Cells by Stabilizing DAG1 mRNA in an IGF2BP1-Related m6A Manner
by Jiangzhen Yao, Liang Xu, Zihao Zhao, Dinghui Dai, Siyuan Zhan, Jiaxue Cao, Jiazhong Guo, Tao Zhong, Linjie Wang, Li Li and Hongping Zhang
Int. J. Mol. Sci. 2024, 25(18), 9804; https://doi.org/10.3390/ijms25189804 - 11 Sep 2024
Viewed by 201
Abstract
Skeletal muscle development is spotlighted in mammals since it closely relates to animal health and economic benefits to the breeding industry. Researchers have successfully unveiled many regulatory factors and mechanisms involving myogenesis. However, the effect of N6-methyladenosine (m6A) modification, [...] Read more.
Skeletal muscle development is spotlighted in mammals since it closely relates to animal health and economic benefits to the breeding industry. Researchers have successfully unveiled many regulatory factors and mechanisms involving myogenesis. However, the effect of N6-methyladenosine (m6A) modification, especially demethylase and its regulated genes, on muscle development remains to be further explored. Here, we found that the typical demethylase FTO (fat mass- and obesity-associated protein) was highly enriched in goats’ longissimus dorsi (LD) muscles. In addition, the level of m6A modification on transcripts was negatively regulated by FTO during the proliferation of goat skeletal muscle satellite cells (MuSCs). Moreover, a deficiency of FTO in MuSCs significantly retarded their proliferation and promoted the expression of dystrophin-associated protein 1 (DAG1). m6A modifications of DAG1 mRNA were efficiently altered by FTO. Intriguingly, the results of DAG1 levels and its m6A enrichment from FB23-2 (FTO demethylase inhibitor)-treated cells were consistent with those of the FTO knockdown, indicating that the regulation of FTO on DAG1 depended on m6A modification. Further experiments showed that interfering FTO improved m6A modification at site DAG1-122, recognized by Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) and consequently stabilized DAG1 transcripts. Our study suggests that FTO promotes the proliferation of MuSCs by regulating the expression of DAG1 through m6A modification. This will extend our knowledge of the m6A-related mechanism of skeletal muscle development in animals. Full article
(This article belongs to the Section Molecular Biology)
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<p>Deficiency of FTO suppresses the proliferation of goat MuSCs. (<b>A</b>) Expression of <span class="html-italic">FTO</span> in different tissues of goats. (<b>B</b>) Cells immunofluorescent stained with anti-PAX7 (MuSCs cultured in growth medium (GM) for 2 days) and anti-MYHC (MuSCs cultured in differentiation medium (DM) for 6 days). Scale bar: 200 μm. (<b>C</b>) <span class="html-italic">FTO</span> mRNA and m<sup>6</sup>A changes during MuSCs (cultured in the growth medium for 1 and 2 days and differentiation medium for 1, 3, 5, and 7 days). (<b>D</b>) mRNA level of <span class="html-italic">FTO</span> in cells treated with siFTO. (<b>E</b>) The m<sup>6</sup>A of total RNA affected by FTO knockdown. (<b>F</b>) Effect of FTO knockdown on gene expression of m<sup>6</sup>A modified enzymes. (<b>G</b>) mRNA changes in myoblast proliferation marker genes after FTO knockdown. (<b>H</b>) Protein of myoblast proliferation genes affected by deficiency of FTO. (<b>I</b>) CCK8 assay of the viability of MuSCs. (<b>J</b>) The number of new cells stained with EdU. Scale bar: 200 μm. Results are represented as the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and ns indicate insignificance.</p>
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<p>The FTO-targeted gene DAG1 inhibits cell proliferation. (<b>A</b>) <span class="html-italic">DAG1</span> mRNA increased by FTO knockdown. (<b>B</b>) DAG1 protein elevated by inhibiting FTO. (<b>C</b>) <span class="html-italic">DAG1</span> mRNA stability affected by FTO knockdown. (<b>D</b>) The profile of DAG1 in cell proliferation and differentiation. (<b>E</b>) siRNA targeting <span class="html-italic">DAG1</span> knockdown on mRNA expression. (<b>F</b>) mRNA changes in cell proliferation marker genes in cells deficiency of DAG1. (<b>G</b>) Effect of DAG1 knockdown on protein of cell proliferation marker genes. (<b>H</b>) Viability of cells tested by CCK8. (<b>I</b>) EdU staining cells altered by siDAG1. Scale bar: 200 μm. Results are represented as the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and ns indicates no significance. In the picture marked with lower case letters, means shared at least one letter indicate no significance (<span class="html-italic">p</span> &gt; 0.05), and on the contrary, no common letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>FTO regulates DAG1 and other proliferation genes in an m<sup>6</sup>A-dependent manner. (<b>A</b>) The m<sup>6</sup>A modification sites on <span class="html-italic">DAG1</span> mRNA predicted using the SRAMP. (<b>B</b>) The m<sup>6</sup>A modification of <span class="html-italic">DAG1</span> mRNA verified by MeRIP-qPCR. (<b>C</b>) The FTO binding sites on <span class="html-italic">DAG1</span> mRNA predicted by RBPsuite. (<b>D</b>) FTO binding on <span class="html-italic">DAG1</span> mRNA verified by RIP-qPCR. (<b>E</b>) The wild-type (WT) and mutant (MUT) m<sup>6</sup>A motif dual luciferase reporter vectors. (<b>F</b>) Effect of interfering FTO on luciferase activity in m<sup>6</sup>A-modified fragments of <span class="html-italic">DAG1</span> mRNA. (<b>G</b>) DAG1 m<sup>6</sup>A modification levels affected by interfering FTO. (<b>H</b>) Changes in total RNA m<sup>6</sup>A modification of cells treated by FB23-2. (<b>I</b>) MeRIP-qPCR of DAG1-122 after FB23-2 treatment. (<b>J</b>) FTO and <span class="html-italic">DAG1</span> mRNA altered by FB23-2. (<b>K</b>) Effect of FB23-2 on <span class="html-italic">DAG1</span> stability. (<b>L</b>) mRNA levels of cell proliferation marker genes changed by FB23-2. (<b>M</b>) Protein of proliferation marker genes influenced by FB23-2. Results are represented as the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and ns indicates no significance.</p>
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<p>IGF2BP1 stabilizes <span class="html-italic">DAG1</span> mRNA through recognizing its m<sup>6</sup>A modification. (<b>A</b>) The DAG1-IGF2BP1 interaction sites predicted by RBPsuite. (<b>B</b>) <span class="html-italic">DAG1</span> mRNA enriched by IGF2BP1 protein. (<b>C</b>) Luciferase activity of DAG1-122 altered by interfering IGF2BP1. (<b>D</b>) IGF2BP1 and <span class="html-italic">DAG1</span> mRNA altered by interfering IGF2BP1. (<b>E</b>) <span class="html-italic">DAG1</span> mRNA stability caused by knockdown of IGF2BP1. (<b>F</b>) mRNA profiles of cell proliferation marker genes altered by deficiency of IGF2BP1. (<b>G</b>) Protein of cell proliferation marker genes affected by interfering IGF2BP1. (<b>H</b>) Expression of IGF2BP1 transcripts in cells treated with FB23-2 combined with siIGF2BP1. (<b>I</b>) <span class="html-italic">DAG1</span> mRNA, (<b>J</b>) <span class="html-italic">mki67</span> mRNA, and <span class="html-italic">PCNA</span> mRNA in cells cotransfected with FB23-2 and siIGF2BP1. Results are represented as the mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and ns indicates no significance. Means with totally different lowercase letters indicate <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Proposed mechanism of FTO/IGF2BP1/DAG1 on myocyte proliferation.</p>
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25 pages, 6695 KiB  
Article
Challenge Dose Titration in a Mycobacterium bovis Infection Model in Goats
by Elisabeth M. Liebler-Tenorio, Nadine Wedlich, Julia Figl, Heike Köhler, Reiner Ulrich, Charlotte Schröder, Melanie Rissmann, Leander Grode, Stefan H. E. Kaufmann and Christian Menge
Int. J. Mol. Sci. 2024, 25(18), 9799; https://doi.org/10.3390/ijms25189799 - 10 Sep 2024
Viewed by 510
Abstract
Goats are natural hosts of Mycobacterium (M.) bovis, and affected herds can be the cause of significant economic losses. Similarites in disease course and lesions of M. bovis infections in goats and M. tuberculosis in humans make goats good models for human [...] Read more.
Goats are natural hosts of Mycobacterium (M.) bovis, and affected herds can be the cause of significant economic losses. Similarites in disease course and lesions of M. bovis infections in goats and M. tuberculosis in humans make goats good models for human tuberculosis. The aim of this investigation was to characterize M. bovis challenge models in goats. For this, goats were endobronchially inoculated with three doses of M. bovis or culture medium. Clinical signs, shedding, and immune responses were monitored until 146 days post inoculation (dpi). At necropsy, lesions were examined by computed tomography, histology, and bacteriological culture. Infected goats did not develop clinical signs. M. bovis was cultured from feces, but never from nasal swabs. IGRAs were positive from 28 dpi onwards, antibodies at 140 dpi, and SICCT at 146 dpi. The increase in CD25+, IFN-γ+, and IFN-γ-releasing T-cell subpopulations was time-related, but not dose-dependent. All infected goats developed paucibacillary granulomas in the lungs and regional lymph nodes. M. bovis was regularly cultured. Dose-dependent effects included the size of pulmonary lesions, caverns, intestinal lesions, and early generalization in the high-dose group. In summary, reproducible challenge models with dose-dependent differences in lesions were established, which may serve for testing vaccines for veterinary or medical use. Full article
(This article belongs to the Special Issue Cellular and Molecular Mechanisms in Mycobacterial Infection 3.0)
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Graphical abstract

Graphical abstract
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<p>(<b>A</b>) Time course of the antibody response against mycobacterial antigens (MPB70 and MPB83) in <span class="html-italic">M. bovis</span>-inoculated and mock-inoculated goats. Serum samples were tested with a modified IDEXX <span class="html-italic">M. bovis</span> antibody ELISA. Significant differences between infected and mock-inoculated goats were detected only at 140 dpi. (<b>B</b>) Time course of the antigen-specific IFN-γ release response of whole blood samples after re-stimulation with bPPD. Significant differences between infected and mock-inoculated goats were detected at 28 dpi, 112 dpi, and 140 dpi. Box–whisker plots represent median, 25%, and 75% quartiles, lowest and highest values and outliers as dots for n = 4 animals per group. Dose groups are color-coded. Significant group differences detected by the Kruskal–Wallis test (<span class="html-italic">p</span> ≤ 0.05) are labeled with an asterisk, significant differences detected by pairwise comparison with the Mann–Whitney U test (<span class="html-italic">p</span> ≤ 0.05) with identical lower-case letters.</p>
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<p>(<b>A</b>) Time course of the antibody response against mycobacterial antigens (MPB70 and MPB83) in <span class="html-italic">M. bovis</span>-inoculated and mock-inoculated goats. Serum samples were tested with a modified IDEXX <span class="html-italic">M. bovis</span> antibody ELISA. Significant differences between infected and mock-inoculated goats were detected only at 140 dpi. (<b>B</b>) Time course of the antigen-specific IFN-γ release response of whole blood samples after re-stimulation with bPPD. Significant differences between infected and mock-inoculated goats were detected at 28 dpi, 112 dpi, and 140 dpi. Box–whisker plots represent median, 25%, and 75% quartiles, lowest and highest values and outliers as dots for n = 4 animals per group. Dose groups are color-coded. Significant group differences detected by the Kruskal–Wallis test (<span class="html-italic">p</span> ≤ 0.05) are labeled with an asterisk, significant differences detected by pairwise comparison with the Mann–Whitney U test (<span class="html-italic">p</span> ≤ 0.05) with identical lower-case letters.</p>
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<p>Results of the intradermal skin test (SICCT) 146 days after intrabronchial inoculation of goats with <span class="html-italic">M. bovis</span>. The test is considered positive if the increase in skin fold thickness after application of bPPD is ≥4 mm more than after application of aPPD (red line). Box–whisker plots represent median, 25%, and 75% quartiles and the lowest and highest values for n = 4 animals per group. Dose groups are color coded. Significant group differences detected by the Kruskal–Wallis test (<span class="html-italic">p</span> ≤ 0.05) are labeled with an asterisk, significant differences detected by pairwise comparison with the Mann–Whitney U test (<span class="html-italic">p</span> ≤ 0.05) with identical lower-case letters.</p>
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<p>Percentage of IFN-γ<sup>+</sup> T cells (<b>A</b>–<b>C</b>) and median fluorescence intensity (MFI) of intracellular IFN-γ (<b>D</b>–<b>F</b>) in CD4<sup>+</sup> (<b>A</b>,<b>D</b>), CD8<sup>+</sup> (<b>B</b>,<b>E</b>), and γδ (<b>C</b>,<b>F</b>) T-cells from PBMC after re-stimulation with <span class="html-italic">M. bovis</span> antigen in vitro, normalized as p/u ratio (bPPD stimulated cells/unstimulated cells). Significant increases in IFN-γ expression were detected in CD8<sup>+</sup> and γδ T cells 28 dpi, and intracellular IFN-γ was increased in CD4<sup>+</sup> T cells at 140 dpi. Box–whisker plots represent median, 25%, and 75% quartiles, lowest and highest values and outliers as dots for n = 4 animals per group. Dose groups are color coded. Significant group differences detected by the Kruskal-Wallis test (<span class="html-italic">p</span> ≤ 0.05) are labeled with an asterisk, significant differences detected by pairwise comparison with the Mann-Whitney-U test (<span class="html-italic">p</span> ≤ 0.05) by identical lower-case letters.</p>
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<p>Median fluorescence intensity (MFI) of CD25 on the surface of CD45RO<sup>+</sup> T cells, analyzed for CD4<sup>+</sup> (<b>A</b>), CD8<sup>+</sup> (<b>B</b>) and γδ (<b>C</b>) T cells from PBMC after re-stimulation with <span class="html-italic">M. bovis</span> antigen in vitro. There was a transient increase in all T-cell subsets from 28 dpi to 84 dpi. Box–whisker-plots represent median, 25%, and 75% quartiles, lowest and highest values and outliers as dots for n = 4 animals per group. Dose groups are color coded. Significant group differences detected by the Kruskal–Wallis test (<span class="html-italic">p</span> ≤ 0.05) are labeled with an asterisk, significant differences detected by pairwise comparison with the Mann–Whitney U test (<span class="html-italic">p</span> ≤ 0.05) with identical lower-case letters.</p>
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<p>Computed tomography scans of lungs from the <span class="html-italic">M. bovis</span>-inoculated goats. (<b>A</b>) Examples of multiple micronodules (MNs, thin arrows) in the right and left basal lobe of a goat from the LD group (goat 6). Most MNs are located adjacent to airways. (<b>B</b>) Example of a unicentric consolidation &gt;5 mm (delineated by a hatched line) in the right basal lobe of a goat from the MD group (goat 9). MNs are present adjacent to the UC and in the left basal lobe (thin arrows). A lesion in a tracheobronchial LN is marked with a thick arrow. (<b>C</b>) Example of three multicentric consolidations (delineated by hatched lines) in the right cranial and basal lobes of a goat from the HD group (goat 15). Several MNs are present in addition (thin arrows). Lesions in the cranial tracheobronchial LN are marked with a thick arrow. (<b>D</b>) Example of a cavern (C) partitioned by septae (S) in the center of a multicentric consolidation (delineated by a hatched line) in the left basal lobe of a goat from the HD group (goat 14).</p>
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<p>Number of lesions detected by computed tomography imaging. (<b>A</b>) Micronodules (small &lt; 5 mm). (<b>B</b>) Unicentric consolidations (&gt;5 mm, focal mineralization). (<b>C</b>) Multicentric consolidation (&gt;5 mm, multiple mineralizations). n = 4 animals per group, except for HD group (n = 3); box–whisker plots represent medians, 25%, and 75% quartiles, the lowest and highest values, and counts of individual goats. Significant group differences detected by the Kruskal–Wallis test (<span class="html-italic">p</span> ≤ 0.05) are labeled with an asterisk, significant differences detected by pairwise comparison via the Mann–Whitney U test (<span class="html-italic">p</span> ≤ 0.05) with identical lower-case letters.</p>
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<p>Graphical visualization of size and location of pulmonary TB lesions detected by computed tomography imaging. Micronodules and consolidations (black) and caverns (black with white centers) are represented as spheres of the measured volume and located at the site where they were identified in the individual goats of the LD, MD, and HD groups. No pulmonary lesions were detected by computed tomography imaging in the intratracheally inoculated goat.</p>
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<p>Number of pulmonary lobes with lesions. Groups are color-coded with 4 animals per group, except for HD group (n = 3); box–whisker plots represent medians, 25%, and 75% quartiles, the lowest and highest values, and counts in individual goats. Significant group differences detected by the Kruskal–Wallis test (<span class="html-italic">p</span> ≤ 0.05) are labeled with an asterisk, significant differences detected by pairwise comparison via the Mann–Whitney U test (<span class="html-italic">p</span> ≤ 0.05) with identical lower-case letters.</p>
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<p>Volume of pulmonary lesions as percentage of lung volume. Groups are color-coded with 4 animals per group, except for HD group (n = 3); box–whisker plots represent medians, 25%, and 75% quartiles, the lowest and highest values, and data for individual goats. Significant group differences detected by the Kruskal–Wallis test (<span class="html-italic">p</span> ≤ 0.05) are labeled with an asterisk, significant differences detected by pairwise comparison via the Mann–Whitney U test (<span class="html-italic">p</span> ≤ 0.05) with identical lower-case letters.</p>
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<p>Sections of formalin-fixed caudal lung lobes of one representative goat per group inoculated with LD (<b>A</b>), MD (<b>B</b>), and HD (<b>C</b>) of <span class="html-italic">M. bovis</span> and of a mock-inoculated goat (<b>D</b>). (<b>A</b>) Small granulomas (arrowhead) and a cavern (open arrow); larger bronchi are indicated by filled arrows, goat 7. (<b>B</b>) Small granulomas (arrowheads, examples) and granulomas larger than 5 mm (thin arrows) with central caseous necrosis (white) next to large bronchi (arrows), goat 9. (<b>C</b>) A large area of confluent granulomas of variable size (light colored) with multiple foci of caseous necrosis (white) has replaced most of the pulmonary tissue (dark brown) and extends multifocally to the serosal surfaces (short black arrows). Small granulomas are present in the adjacent pulmonary tissue (arrowheads, examples). Two caverns are indicated by open arrows, goat 15. (<b>D</b>) Unaltered pulmonary tissue with large bronchi indicated by filled arrows, goat 4. Size bars = 1 cm.</p>
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<p>Pulmonary sites (stars) where inoculum was deposited by spray catheter.</p>
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<p>Gating strategy for quantitation of antigen-specific peripheral T cells by activation marker expression analysis (example). (<b>A</b>) Gating for live and morphologically intact lymphocytes. (<b>B</b>) Gating for the γδ TCR<sup>+</sup> population. (<b>C</b>) Gating for CD45RO<sup>+</sup> T-cells from the γδ TCR<sup>+</sup> population. The median fluorescence intensity (MFI) for CD25 (MFI CD25 (γδ TCR<sup>+</sup>/CD45RO<sup>+</sup>).</p>
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15 pages, 2899 KiB  
Article
The Ruminal Microbiome Alterations Associated with Diet-Induced Milk Fat Depression and Milk Fat Globule Size Reduction in Dairy Goats
by Menglu Zhang, Zhentao Liu, Kuixian Wu, Chuankai Zhang, Tong Fu, Yu Sun, Tengyun Gao and Liqiang Han
Animals 2024, 14(17), 2614; https://doi.org/10.3390/ani14172614 - 9 Sep 2024
Viewed by 406
Abstract
The aim of this study was to evaluate the effect of conjugated linoleic acid (CLA) on milk fat globule (MFG) size and the ruminal microbiome of goats. Twenty-four mid-lactation Saanen dairy goats weighing 49 ± 4.5 kg (168 ± 27 d in milk, [...] Read more.
The aim of this study was to evaluate the effect of conjugated linoleic acid (CLA) on milk fat globule (MFG) size and the ruminal microbiome of goats. Twenty-four mid-lactation Saanen dairy goats weighing 49 ± 4.5 kg (168 ± 27 d in milk, 1.2 ± 0.1 kg milk/d, 2–3 years old) were randomly divided into four groups—a control (CON) group, which was fed a basal diet, and three CLA supplementation groups, in which 30 g CLA (low-dose group, L-CLA), 60 g CLA (medium-dose group, M-CLA), or 90 g CLA (high-dose group, H-CLA) was added to the basal diet daily. The experiment lasted for 21 days, during which time goat milk was collected for composition and MFG size analysis. On day 21 of feeding, ruminal fluid was collected from the CON and H-CLA groups for analysis of the changes in microorganismal abundance. The results showed that CLA supplementation did not affect milk production, milk protein, or lactose content in the dairy goats (p > 0.05), but significantly reduced the milk fat content (p < 0.01) compared with the CON group. The CLA supplementation significantly decreased the D[3,2] and D[4,3] of the MFGs in a dose-dependent manner (p < 0.01). Moreover, dietary CLA inclusion increased the proportion of small-sized MFGs and decreased that of large-sized ones. The results of 16S rRNA gene sequencing showed that CLA-induced milk fat depression in dairy goats was accompanied by significant changes in the relative abundance of ruminal bacterial populations, most of which belonged to the Firmicutes and Bacteroidetes phyla. The relative abundance of Rikenellaceae_RC9_gut_group and Prevolellaceae_UCG-003 in Bacteroidetes and UCG-002, Succiniclasticum, and norank_f__norank_o__Clostridia_vadinBB60_group in Firmicutes was significantly higher in the CON group than in the H-CLA group. In contrast, the relative abundance of norank_f__UCG-011, norank_f_Eubacterium_coprostanoligenes_group, unclassified_f__Lachnospiraceae, and UCG-001 in Firmicutes and norank_f__Muribaculaceae in Bacteroidetes was significantly higher in the H-CLA group than in the CON group. Correlation analysis showed that the milk fat content was negatively correlated with the relative abundance of some bacteria, including members of Firmicutes and Bacteroidetes. Similarly, MFG size (D[3,2] and D[4,3]) was negatively correlated with several members of Firmicutes and Bacteroidetes, including Lachnospiraceae, norank_f__UCG-011, UCG-001, norank_f__Eubacterium_coprostanoligenes_group (Firmicutes), and norank_f__Muribaculaceae (Bacteroidetes), while positively correlated with the relative abundance of some members of Firmicutes and Bacteroidetes, including Mycoplasma, Succiniclasticum, norank_f__norank_o__Clostridia_vadinBB60_group, UCG-002 (Firmicutes), and Rikenellaceae_RC9_gut_group (Bacteroidetes). Overall, our data indicated that CLA treatment affected milk fat content and MFG size in dairy goats, and these effects were correlated with the relative abundance of ruminal bacterial populations. These results provide the first evidence to explain the mechanism underlying diet-induced MFG from the perspective of the ruminal microbiome in dairy goats. Full article
(This article belongs to the Section Small Ruminants)
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Figure 1

Figure 1
<p>The effects of CLA on milk fat globule size. (<b>A</b>) D<sub>[3,2]</sub>: surface area-related equivalent diameter; (<b>B</b>) D<sub>[4,3]</sub>: volume-related equivalent diameter; (<b>C</b>) SSA: specific surface area. CON: control group; L-CLA: low-dose CLA group; M-CLA: medium-dose CLA group; H-CLA: high-dose CLA group.</p>
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<p>The effects of conjugated linoleic acid (CLA) on milk fat globule (MFG) size proportions. H-CLA: high-dose CLA group; M-CLA: medium-dose CLA group; L-CLA: low-dose CLA group; CON: control group.</p>
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<p>Shared and unique operational taxonomic units (OTUs) between the control (CON) and the high-dose conjugated linoleic acid (H-CLA) group.</p>
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<p>Comparative analysis of ruminal bacteria alpha-diversity between the control (CON) and high-dose conjugated linoleic acid (H-CLA) groups. (<b>A</b>) Sob index, (<b>B</b>) ACE index, (<b>C</b>) Chao index, (<b>D</b>) Shannon index, (<b>E</b>) Simpson index.</p>
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<p>The relative abundance of ruminal bacteria at the phylum (<b>A</b>) and genus (<b>B</b>) levels in the control (CON) and high-dose conjugated linoleic acid group (H-CLA) groups.</p>
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<p>Comparison of the abundance of ruminal bacteria at the genus level between the control (CON) and high-dose conjugated linoleic acid (H-CLA) groups.</p>
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<p>Correlations between differential bacteria and milk composition in the control (CON) and high-dose conjugated linoleic acid (H-CLA) groups. The color code indicates the direction of the correlations (red indicates a positive correlation and blue indicates a negative correlation); * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Correlations between differential bacteria and milk fat granule (MFG) size parameters in the control (CON) and high-dose conjugated linoleic acid (H-CLA) groups. The color code indicates the direction of the correlations (red indicates a positive and blue indicates a negative correlation); * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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