lncRNA BREA2 promotes metastasis by disrupting the WWP2-mediated ubiquitination of Notch1.

Notch has been implicated in human cancers and is a putative therapeutic target. However, the regulation of Notch activation in the nucleus remains largely uncharacterized. Therefore, characterizing the detailed mechanisms governing Notch degradation will identify attractive strategies for treating Notch-activated cancers. Here, we report that the long noncoding RNA (lncRNA) BREA2 drives breast cancer metastasis by stabilizing the Notch1 intracellular domain (NICD1). Moreover, we reveal WW domain containing E3 ubiquitin protein ligase 2 (WWP2) as an E3 ligase for NICD1 at K1821 and a suppressor of breast cancer metastasis. Mechanistically, BREA2 impairs WWP2-NICD1 complex formation and in turn stabilizes NICD1, leading to Notch signaling activation and lung metastasis. BREA2 loss sensitizes breast cancer cells to inhibition of Notch signaling and suppresses the growth of breast cancer patient-derived xenograft tumors, highlighting its therapeutic potential in breast cancer. Taken together, these results reveal the lncRNA BREA2 as a putative regulator of Notch signaling and an oncogenic player driving breast cancer metastasis.

Evaluating genomic inbreeding of two Chinese yak (Bos grunniens) populations.

BACKGROUND: Yaks are a vital livestock in the Qinghai-Tibetan Plateau area for providing food products, maintaining sustainable ecosystems, and promoting cultural heritage. Because of uncontrolled mating, it is impossible to estimate inbreeding level of yak populations using the pedigree-based approaches. With the aims to accurately evaluate inbreeding level of two Chinese yak populations (Maiwa and Jiulong), we obtained genome-wide single nucleotide polymorphisms (SNPs) by DNA sequencing and calculated five SNP-by-SNP estimators ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]), as well as two segment-based estimators of runs of homozygosity (ROH, [Formula: see text]) and homozygous-by-descent (HBD, [Formula: see text]). Functional implications were analyzed for the positional candidate genes located within the related genomic regions. RESULTS: A total of 151,675 and 190,955 high-quality SNPs were obtained from 71 Maiwa and 30 Jiulong yaks, respectively. Jiulong had greater genetic diversity than Maiwa in terms of allele frequency and nucleotide diversity. The two populations could be genetically distinguished by principal component analysis, with the mean differentiation index (Fst) of 0.0054. The greater genomic inbreeding levels of Maiwa yaks were consistently supported by all five SNP-by-SNP estimators. Based on simple proportion of homozygous SNPs ([Formula: see text]), a lower inbreeding level was indicated by three successfully sequenced old leather samples that may represent historical Maiwa yaks about five generations ago. There were 3304 ROH detected among all samples, with mean and median length of 1.97 Mb and 1.0 Mb, respectively. A total of 94 HBD segments were found among all samples, whereas 92 of them belonged to the shortest class with the mean length of 10.9 Kb. Based on the estimates of [Formula: see text] and [Formula: see text], however, there was no difference in inbreeding level between Maiwa and Jiulong yaks. Within the genomic regions with the significant Fst or enriched by ROH, we found several candidate genes and pathways that have been reported to be related to diverse production traits in farm animals. CONCLUSIONS: We successfully evaluated the genomic inbreeding level of two Chinese yak populations. Although different estimators resulted in inconsistent conclusions on their genomic inbreeding levels, our results may be helpful to implement the genetic conservation and utilization programs for the two yak populations.

INHBA promotes tumor growth and induces resistance to PD-L1 blockade by suppressing IFN-γ signaling.

Inhibin beta A (INHBA) and its homodimer activin A have pleiotropic effects on modulation of immune responses and tumor progression, but it remains uncertain whether tumors may release activin A to regulate anti-tumor immunity. In this study we investigated the effects and mechanisms of tumor intrinsic INHBA on carcinogenesis, tumor immunity and PD-L1 blockade. Bioinformatic analysis on the TCGA database revealed that INHBA expression levels were elevated in 33 cancer types, including breast cancer (BRCA) and colon adenocarcinoma (COAD). In addition, survival analysis also corroborated that INHBA expression was negatively correlated with the prognosis of many types of cancer patients. We demonstrated that gain or loss function of Inhba did not alter in vitro growth of colorectal cancer CT26 cells, but had striking impact on mouse tumor models including CT26, MC38, B16 and 4T1 models. By using the TIMER 2.0 tool, we figured out that in most cancer types, Inhba expression in tumors was inversely associated with the infiltration of CD4+ T and CD8+ T cells. In CT26 tumor-bearing mice, overexpression of tumor INHBA eliminated the anti-tumor effect of the PD-L1 antibody atezolizumab, whereas INHBA deficiency enhanced the efficacy of atezolizumab. We revealed that tumor INHBA significantly downregulated the interferon-γ (IFN-γ) signaling pathway. Tumor INHBA overexpression led to lower expression of PD-L1 induced by IFN-γ, resulting in poor responsiveness to anti-PD-L1 treatment. On the other hand, decreased secretion of IFN-γ-stimulated chemokines, including C-X-C motif chemokine 9 (CXCL9) and 10 (CXCL10), impaired the infiltration of effector T cells into the tumor microenvironment (TME). Furthermore, the activin A-specific antibody garetosmab improved anti-tumor immunity and its combination with the anti-PD-L1 antibody atezolizumab showed a superior therapeutic effect to monotherapy with garetosmab or atezolizumab. We demonstrate that INHBA and activin A are involved in anti-tumor immunity by inhibiting the IFN-γ signaling pathway, which can be considered as potential targets to improve the responsive rate of PD-1/PD-L1 blockade.

Unraveling the genomic background of resilience based on variability in milk yield and milk production levels in North American Holstein cattle through genome-wide association study and Mendelian randomization analyses.

Breeding more resilient animals will benefit the dairy cattle industry in the long term, especially as global climate changes become more severe. Previous studies have reported genetic parameters for various milk yield-based resilience indicators, but the underlying genomic background of these traits remain unknown. In this study, we conducted GWAS of 62,029 SNPs with 4 milk yield-based resilience indicators, including the weighted occurrence frequency (wfPert) and accumulated milk losses (dPert) of milk yield perturbations, and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. These variables were previously derived from 5.6 million daily milk yield records from 21,350 lactations (parities 1-3) of 11,787 North American Holstein cows. The average daily milk yield (ADMY) throughout lactation was also included to compare the shared genetic background of resilience indicators with milk yield. The differential genetic background of these indicators was first revealed by the significant genomic regions identified and significantly enriched biological pathways of positional candidate genes, which confirmed the genetic difference among resilience indicators. Interestingly, the functional analyses of candidate genes suggested that the regulation of intestinal homeostasis is most likely affecting resilience derived based on variability in milk yield. Based on Mendelian randomization analyses of multiple instrumental SNPs, we further found an unfavorable causal association of ADMY with LnVar. In conclusion, the resilience indicators evaluated are genetically different traits, and there are causal associations of milk yield with some of the resilience indicators evaluated. In addition to providing biological insights into the molecular regulation mechanisms of resilience derived based on variability in milk yield, this study also indicates the need for developing selection indexes combining multiple indicator traits and taking into account their genetic relationship for breeding more resilient dairy cattle.

INVITED REVIEW: Phenotyping strategies and genetic background of dairy cattle behavior in intensive production systems - from trait definition to genomic selection.

Understanding and assessing dairy cattle behavior is critical for developing sustainable breeding programs and management practices. The behavior of individual animals can provide valuable information on their health and welfare status, improve reproductive management, and predict efficiency traits such as feed efficiency and milking efficiency. Routine genetic evaluations of animal behavior traits can contribute to optimizing breeding and management strategies for dairy cattle but require the identification of traits that capture the most important biological processes involved in behavioral responses. These traits should be heritable, repeatable, and measured in non-invasive and cost-effective ways in many individuals from the breeding populations or related reference populations. While behavior traits are heritable in dairy cattle populations, they are highly polygenic, with no known major genes influencing their phenotypic expression. Genetically selecting dairy cattle based on their behavior can be advantageous because of their relationship with other key traits such as animal health, welfare, and productive efficiency, as well as animal and handlers' safety. Trait definition and longitudinal data collection are still key challenges for breeding for behavioral responses in dairy cattle. However, the more recent developments and adoption of precision technologies in dairy farms provide avenues for more objective phenotyping and genetic selection of behavior traits. Furthermore, there is still a need to standardize phenotyping protocols for existing traits and develop guidelines for recording novel behavioral traits and integrating multiple data sources. This review gives an overview of the most common indicators of dairy cattle behavior, summarizes the main methods used for analyzing animal behavior in commercial settings, describes the genetic and genomic background of previously defined behavioral traits, and discusses strategies for breeding and improving behavior traits coupled with future opportunities for genetic selection for improved behavioral responses.

Machine learning methods for genomic prediction of cow behavioral traits measured by automatic milking systems in North American Holstein cattle.

Identifying genome-enabled methods that provide more accurate genomic prediction is crucial when evaluating complex traits such as dairy cow behavior. In this study, we aimed to compare the predictive performance of traditional genomic prediction methods and deep learning algorithms for genomic prediction of milking refusals (MREF) and milking failures (MFAIL) in North American Holstein cows measured by automatic milking systems (milking robots). A total of 1,993,509 daily records from 4,511 genotyped Holstein cows were collected by 36 milking robot stations. After quality control, 57,600 SNPs were available for the analyses. Four genomic prediction methods were considered: Bayesian least absolute shrinkage and selection operator (LASSO), multiple layer perceptron (MLP), convolutional neural network (CNN), and GBLUP. We implemented the first 3 methods using the Keras and TensorFlow libraries in Python (v.3.9) but the GBLUP method was implemented using the BLUPF90+ family programs. The accuracy of genomic prediction (mean square error) for MREF and MFAIL was 0.34 (0.08) and 0.27 (0.08) based on LASSO, 0.36 (0.09) and 0.32 (0.09) for MLP, 0.37 (0.08) and 0.30 (0.09) for CNN, and 0.35 (0.09) and 0.31(0.09) based on GBLUP, respectively. Additionally, we observed a lower reranking of top selected individuals based on the MLP versus CNN methods compared with the other approaches for both MREF and MFAIL. Although the deep learning methods showed slightly higher accuracies than GBLUP, the results may not be sufficient to justify their use over traditional methods due to their higher computational demand and the difficulty of performing genomic prediction for nongenotyped individuals using deep learning procedures. Overall, this study provides insights into the potential feasibility of using deep learning methods to enhance genomic prediction accuracy for behavioral traits in livestock. Further research is needed to determine their practical applicability to large dairy cattle breeding programs.

Genetic parameters for udder conformation traits derived from Cartesian coordinates generated by robotic milking systems in North American Holstein cattle.

Udder conformation is directly related to milk yield, cow health, workability, and welfare. Automatic milking systems (AMS, also known as milking robots) have become popular worldwide, and the number of dairy farms adopting these systems has increased considerably over the past years. In each milking visit, AMS record the location of the 4 teats as Cartesian coordinates in an xyz plan, which can then be used to derive udder conformation traits. Because AMS generate a large amount of data for individual cows per milking visit, they can contribute to an accurate assessment of important traits such as udder conformation without the addition of human classifier errors (in subjective scoring systems). Therefore, the primary objectives of this study were to estimate genomic-based genetic parameters for udder conformation traits derived from AMS records in North American Holstein cattle and to assess the genetic correlation between the derived traits for evaluating the feasibility of multitrait genomic selection for breeding cows that are more suitable for milking in AMS. The Cartesian teat coordinates measured during each milking visit were collected by 36 milking robots in 4,480 Holstein cows from 2017 to 2021, resulting in 5,317,488 records. A total of 4,118 of these Holstein cows were also genotyped for 57,600 SNPs. Five udder conformation traits were derived: udder balance (UB, mm), udder depth (UD, mm), front teat distance (FTD, mm), rear teat distance (RTD, mm), and distance front-rear (DFR, mm). In addition, 2 traits directly related to cow productivity in the system were added to the study: daily milk yield (DY) and milk electroconductivity (EC; as an indicator of mastitis). Variance components and genetic parameters for UB, UD, FTD, RTD, DFR, DY, and EC were estimated based on repeatability animal models. The estimates of heritability (± SE) for UB, UD, FTD, RTD, DFR, DY, and EC were 0.41 ± 0.02, 0.79 ± 0.01, 0.53 ± 0.02, 0.40 ± 0.02, 0.65 ± 0.02, 0.20 ± 0.02, and 0.46 ± 0.02, respectively. The repeatability estimates (± SE) for UB, UD, FTD, RTD, and DFR were 0.82 ± 0.01, 0.93 ± 0.01, 0.87 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. The strongest genetic correlations were observed between FTD and RTD (0.54 ± 0.03), UD and DFR (-0.47 ± 0.03), DFR and FTD (0.32 ± 0.03), and UD and FTD (-0.31 ± 0.03). These results suggest that udder conformation traits derived from Cartesian coordinates from AMS are moderately to highly heritable. Furthermore, the moderate genetic correlations between these traits should be considered when developing selection subindexes. The most relevant genetic correlations between traits related to cow milk productivity and udder conformation traits were between UD and EC (-0.25 ± 0.03) and between DFR and DY (0.30 ± 0.04), in which both genetic correlations are favorable. These findings will contribute to the design of genomic selection schemes for improving udder conformation in North American Holstein cattle, especially in precision dairy farms.

Mechanically Adaptive Polymers Constructed from Dynamic Coordination Equilibria.

Designing materials capable of adapting their mechanical properties in response to external stimuli is the key to preventing failure and extending their service life. However, existing mechanically adaptive polymers are hindered by limitations such as inadequate load-bearing capacity, difficulty in achieving reversible changes, high cost, and a lack of multiple responsiveness. Herein, we address these challenges using dynamic coordination bonds. A new type of mechanically adaptive material with both rate- and temperature-responsiveness was developed. Owing to the stimuli-responsiveness of the coordination equilibria, the prepared polymers, PBMBD-Fe and PBMBD-Co, exhibit mechanically adaptive properties, including temperature-sensitive strength modulation and rate-dependent impact hardening. Benefitting from the dynamic nature of the coordination bonds, the polymers exhibited impressive energy dissipation, damping capacity (loss factors of 1.15 and 2.09 at 1.0 Hz), self-healing, and 3D printing abilities, offering durable and customizable impact resistance and protective performance. The development of impact-resistant materials with comprehensive properties has potential applications in the sustainable and intelligent protection fields.

Insufficient proximal medullary filling of cephalomedullary nails in intertrochanteric femur fractures predicts excessive postoperative sliding: a case-control study.

OBJECTIVE: Excessive postoperative sliding is a common complication of intramedullary nails in the treatment of intertrochanteric femur fractures. The aim of this study was to identify risk factors for excessive postoperative sliding in the intertrochanteric fractures treated with an intramedullary nail. METHODS: A retrospective analysis of 369 patients with femoral intertrochanteric fractures treated with short intramedullary nails between February 2017 and September 2020 was performed. Patients were classified into an excessive sliding group (ES group) and a control group according to the sliding distance after 6 months of follow-up. The proximal medullary filling degree (MFD), fracture reduction patterns in the anteroposterior (AP) view and lateral view, and tip-apex distance (TAD) were evaluated and compared in each group. RESULTS: Thirty-three cases were included in the ES group, and 336 cases were included in the control group. No significant differences in age, sex, fracture side, AO Foundation and Orthopaedic Trauma Association (AO/OTA) classification, Dorr classification, Singh Osteoporosis Index (SOI), American Society of Anesthesiologists classification (ASA), TAD or fracture reduction patterns in the AP view were noted between the two groups. The negative reduction pattern can strongly predict excessive postoperative sliding (OR 4.286, 95% CI 1.637-11.216, P = 0.003). The incidence of excessive postoperative sliding increased by 8.713-fold when the MFD decreased by 10% (OR 8.713, 95% CI 1.925-39.437, P = 0.005). CONCLUSIONS: A low medullary filling degree and negative fracture reduction pattern in the lateral view were both independent risk factors for excessive postoperative sliding.

Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records.

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01-0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88-0.96) than between lactations 1 and 2 or 3 (0.34-0.88) for dPert, LnVar, and rauto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and rauto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle.

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Tough, Reprocessable, and Recyclable Dynamic Covalent Polymers with Ultrastable Long-Lived Room-Temperature Phosphorescence.

Room-temperature phosphorescence (RTP) polymers, whose emission can persist for a long period after photoexcitation, are of great importance for practical applications. Herein, dynamic covalent boronic ester linkages with internal B-N coordination are incorporated into a commercial epoxy matrix. The reversible dissociation of B-N bonds upon loading provides an efficient energy dissipation pathway for the epoxy network, while the rigid epoxy matrix can inhibit the quenching of triplet excitons in boronic esters. The obtained polymers exhibit enhanced mechanical toughness (12.26 MJ m-3 ), ultralong RTP (τ=540.4 ms), and shape memory behavior. Notably, there is no apparent decrease in the RTP property upon prolonged immersion in various solvents because the networks are robust. Moreover, the dynamic bonds endow the polymers with superior reprocessablity and recyclability. These novel properties have led to their potential application for information encryption and anti-counterfeiting.

Integrated analysis of microRNAs, circular RNAs, long non-coding RNAs, and mRNAs revealed competing endogenous RNA networks involved in brown adipose tissue whitening in rabbits.

BACKGROUND: The brown adipose tissue (BAT) is a target for treating obesity. BAT losses thermogenic capacity and gains a "white adipose tissue-like" phenotype ("BAT whitening") under thermoneutral environments, which is a potential factor causing a low curative effect in BAT-related obesity treatments. Circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNA) to mRNAs and function in various processes by sponging shared microRNAs (miRNAs). However, the roles of circRNA- and lncRNA-related ceRNA networks in regulating BAT whitening remain litter known. RESULTS: In this study, BATs were collected from rabbits at day0 (D0), D15, D85, and 2 years (Y2). MiRNA-seq was performed to investigate miRNA changes during BAT whitening. Then, a combined analysis of circRNA-seq and whole-transcriptome sequencing was used for circRNA assembly and quantification during BAT whitening. Our data showed that 1187 miRNAs and 6204 circRNAs were expressed in the samples, and many of which were identified as significantly changed during BAT whitening. Target prediction showed that D0-selective miRNAs were significantly enriched in the Ras, MAPK, and PI3K-Akt signaling pathways, and Y2-selective miRNAs were predicted to be involved in cell proliferation. The cyclization of several circRNAs could form novel response elements of key thermogenesis miRNAs at the back-splicing junction (BSJ) sites, and in combination with a dual-luciferase reporter assay confirmed the binding between the BSJ site of novel_circ_0013792 and ocu-miR-378-5p. CircRNAs and lncRNAs have high cooperativity in sponging miRNAs during BAT whitening. Both circRNA-miRNA-mRNA and lncRNA-miRNA-mRNA triple networks were significantly involved in immune response-associated biological processes. The D15-selective circRNA might promote BAT whitening by increasing the expression of IDH2. The Y2-selective circRNA-related ceRNA network and lncRNA-related ceRNA network might regulate the formation of the WAT-like phenotype of BAT via MAPK and Ras signaling pathways, respectively. CONCLUSIONS: Our work systematically revealed ceRNA networks during BAT whitening in rabbits and might provide new insight into BAT-based obesity treatments.

Identifying pleiotropic variants and candidate genes for fertility and reproduction traits in Holstein cattle via association studies based on imputed whole-genome sequence genotypes.

BACKGROUND: Genetic progress for fertility and reproduction traits in dairy cattle has been limited due to the low heritability of most indicator traits. Moreover, most of the quantitative trait loci (QTL) and candidate genes associated with these traits remain unknown. In this study, we used 5.6 million imputed DNA sequence variants (single nucleotide polymorphisms, SNPs) for genome-wide association studies (GWAS) of 18 fertility and reproduction traits in Holstein cattle. Aiming to identify pleiotropic variants and increase detection power, multiple-trait analyses were performed using a method to efficiently combine the estimated SNP effects of single-trait GWAS based on a chi-square statistic. RESULTS: There were 87, 72, and 84 significant SNPs identified for heifer, cow, and sire traits, respectively, which showed a wide and distinct distribution across the genome, suggesting that they have relatively distinct polygenic nature. The biological functions of immune response and fatty acid metabolism were significantly enriched for the 184 and 124 positional candidate genes identified for heifer and cow traits, respectively. No known biological function was significantly enriched for the 147 positional candidate genes found for sire traits. The most important chromosomes that had three or more significant QTL identified are BTA22 and BTA23 for heifer traits, BTA8 and BTA17 for cow traits, and BTA4, BTA7, BTA17, BTA22, BTA25, and BTA28 for sire traits. Several novel and biologically important positional candidate genes were strongly suggested for heifer (SOD2, WTAP, DLEC1, PFKFB4, TRIM27, HECW1, DNAH17, and ADAM3A), cow (ANXA1, PCSK5, SPESP1, and JMJD1C), and sire (ELMO1, CFAP70, SOX30, DGCR8, SEPTIN14, PAPOLB, JMJD1C, and NELL2) traits. CONCLUSIONS: These findings contribute to better understand the underlying biological mechanisms of fertility and reproduction traits measured in heifers, cows, and sires, which may contribute to improve genomic evaluation for these traits in dairy cattle.

Structural characteristics of Heparan sulfate required for the binding with the virus processing Enzyme Furin.

Furin is one of the nine-member proprotein convertase family. Furin cleaves proteins with polybasic residues, which includes many viral glycoproteins such as SARS-Cov-2 spike protein. The cleavage is required for the activation of the proteins. Currently, the mechanisms that regulate Furin activity remain largely unknown. Here we demonstrated that Furin is a novel heparin/heparan sulfate binding protein by the use of biochemical and genetic assays. The KD is 9.78 nM based on the biolayer interferometry assay. Moreover, we found that sulfation degree, site-specific sulfation (N-sulfation and 3-O-sulfation), and iduronic acid are the major structural determinants for the binding. Furthermore, we found that heparin inhibits the enzymatic activity of Furin when pre-mixes heparin with either Furin or Furin substrate. We also found that the Furin binds with cells of different origin and the binding with the cells of lung origin is the strongest one. These data could advance our understanding of the working mechanism of Furin and will benefit the Furin based drug discovery such as inhibitors targeting the interaction between heparan sulfate and Furin for inhibition of viral infection.

Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine.

Rapid analysis of components in complex matrices has always been a major challenge in constructing sensing methods, especially concerning time and cost. The detection of pesticide residues is an important task in food safety monitoring, which needs efficient methods. Here, we constructed a machine learning-assisted synchronous fluorescence sensing approach for the rapid and simultaneous quantitative detection of two important benzimidazole pesticides, thiabendazole (TBZ) and fuberidazole (FBZ), in red wine. First, fluorescence spectra data were collected using a second derivative constant-energy synchronous fluorescence sensor. Next, we established a prediction model through the machine learning approach. With this approach, the recovery rate of TBZ and FBZ detection of pesticide residues in red wine was 101% ± 5% and 101% ± 15%, respectively, without resorting complicated pretreatment procedures. This work provides a new way for the combination of machine learning and fluorescence techniques to solve the complexity in multi-component analysis in practical applications.

Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms.

BACKGROUND: There is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait. RESULTS: The average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs. CONCLUSIONS: We provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.

Genome-wide SNP discovery and genetic diversity evaluation of Liangshan cattle in China.

Liangshan cattle are a very small indigenous breed with adult weight of less than 300 Kg and have been mainly distributed in the Liangshan Yi Autonomous Prefecture of Southwestern Sichuan, China. Due to its long-term adaptation to local environments, Liangshan cattle is a valuable genetic resource and should be paid with more attentions. However, the genetic diversity of Liangshan cattle have not been specifically investigated yet, which would be required when designing the appropriate conservation and utilization programs. In this study, we successfully employed the restriction-site-associated DNA sequencing (RAD-seq) approach to explore a total of 84,854 genome-wide and high-confidence SNPs of Liangshan cattle. All these SNPs were evenly distributed through all chromosomes with an average of 98 SNPs per 1-Mb region. The nucleotide diversity, expected heterozygosity, polymorphism information content of Liangshan cattle were 0.227, 0.223 and 0.183, respectively. Furthermore, there was no obvious difference on the genetic diversity among the three studied geographical populations. In conclusion, we provided a list of SNPs that could be used in the follow-up studies for Liangshan cattle and revealed a relatively high genetic variation in this gene pool.

The anterior talofibular ligament-posterior talofibular ligament angle decreased after ankle lateral stabilization surgery.

PURPOSE: The angle between the anterior talofibular ligament (ATFL) and the posterior talofibular ligament (PTFL) is increased in patients with chronic ATFL injury. This study aimed to compare the AFTL-PTFL angle before versus after ankle lateral stabilization surgery, and to evaluate whether the ATFL-PTFL angle correlates with the ligament injury severity. METHODS: This retrospective study included 48 patients with mechanical ankle instability treated between 2016 and 2018. After arthroscopic evaluation, all patients underwent ankle lateral stabilization surgery comprising ligament repair (n = 28) or reconstruction (n = 20). The ATFL-PTFL angle was measured in the axial plane on pre- and postoperative MRI. Comparisons were made of the pre- versus postoperative ATFL-PTFL angles, and the ATFL-PTFL angle of the repair versus reconstruction groups. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of the ATFL-PTFL angle in selecting the surgical technique. RESULTS: The postoperative ATFL-PTFL angle was significantly decreased compared with preoperatively. The ATFL-PTFL angle was significantly smaller in the repair group than the reconstruction group preoperatively and postoperatively. The area under the ROC curve was 0.741 (P < 0.01). The optimal cutoff point for the selection of ligament reconstruction was an ATFL-PTFL angle of 89.4° (sensitivity 0.85, specificity 0.61). CONCLUSION: The ATFL-PTFL angle decreases after ankle lateral stabilization surgery. The ATFL-PTFL angle is related to the severity of the ATFL injury. Ankle lateral ligament reconstruction should be considered when the ATFL-PTFL angle is > 89.4°. LEVEL OF EVIDENCE: Level III.

Genome-wide identification and characterization of long non-coding RNAs during differentiation of visceral preadipocytes in rabbit.

Emerging evidence suggests that long non-coding RNAs (lncRNAs) are critical regulators of diverse biological processes, including adipogenesis. Despite being considered an ideal animal model for studying adipogenesis, little is known about the roles of lncRNAs in the regulation of rabbit preadipocyte differentiation. In the present study, visceral preadipocytes isolated from newborn rabbits were cultured in vitro and induced for differentiation, and global lncRNA expression profiles of adipocytes collected at days 0, 3, and 9 of differentiation were analyzed by RNA-seq. A total of 2066 lncRNAs were identified from nine RNA-seq libraries. Compared to protein-coding transcripts, lncRNA transcripts exhibited characteristics of a longer length and lower expression level. Furthermore, 486 and 357 differentially expressed (DE) lncRNAs were identified when comparing day 3 vs. day 0 and day 9 vs. day 3, respectively. Target genes of DE lncRNAs were predicted by the cis-regulating approach. Prediction of functions revealed that DE lncRNAs when comparing day 3 vs. day 0 were involved in gene ontology (GO) terms of developmental growth, growth, developmental cell growth, and stem cell proliferation, and involved in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of PI3K-Akt signaling pathway, fatty acid biosynthesis, and the insulin signaling pathway. The DE lncRNAs when comparing day 9 vs. day 3 were involved in GO terms that associated with epigenetic modification and were involved in the KEGG pathway of cAMP signaling pathway. This study provides further insight into the regulatory function of lncRNAs in rabbit visceral adipose and facilitates a better understanding of different stages of preadipocyte differentiation.