Application of single-step single nucleotide polymorphism best linear unbiased predictor model with unknown-parent groups for type traits in Japanese Holsteins.

The objectives of this study were to investigate the computational performance and the predictive ability and bias of a single-step SNP BLUP model (ssSNPBLUP) in genotyped young animals with unknown-parent groups (UPG) for type traits, using national genetic evaluation data from the Japanese Holstein population. The phenotype, genotype, and pedigree data were the same as those used in a national genetic evaluation of linear type traits classified between April 1984 and December 2020. In the current study, 2 data sets were prepared: the full data set containing all entries up to December 2020 and a truncated data set ending with December 2016. Genotyped animals were classified into 3 types: sires with classified daughters (S), cows with records (C), and young animals (Y). The computing performance and prediction accuracy of ssSNPBLUP were compared for the following 3 groups of genotyped animals: sires with classified daughters and young animals (SY); cows with records and young animals (CY); and sires with classified daughters, cows with records, and young animals (SCY). In addition, we tested 3 parameters of residual polygenic variance in ssSNPBLUP (0.1, 0.2, or 0.3). Daughter yield deviations (DYD) for the validation bulls and phenotypes adjusted for all fixed effects and random effects other than animal and residual (Yadj) for the validation cows were obtained using the full data set from the pedigree-based BLUP model. The regression coefficients of DYD for bulls (or Yadj for cows) on the genomic estimated breeding value (GEBV) using the truncated data set were used to measure the inflation of the predictions of young animals. The coefficient of determination of DYD on GEBV was used to measure the predictive ability of the predictions for the validation bulls. The reliability of the predictions for the validation cows was calculated as the square of the correlation between Yadj and GEBV divided by heritability. The predictive ability was highest in the SCY group and lowest in the CY group. However, minimal difference was found in predictive abilities with or without UPG models using different parameters of residual polygenic variance. The regression coefficients approached 1.0 as the parameter of residual polygenic variance increased, but regression coefficients were mostly similar regardless of the use of UPG across the groups of genotyped animals. The ssSNPBLUP model, including UPG, was demonstrated as feasible for implementation in the national evaluation of type traits in Japanese Holsteins.

Invited review: Unknown-parent groups and metafounders in single-step genomic BLUP.

Single-step genomic BLUP (ssGBLUP) is a method for genomic prediction that integrates matrices of pedigree (A) and genomic (G) relationships into a single unified additive relationship matrix whose inverse is incorporated into a set of mixed model equations (MME) to compute genomic predictions. Pedigree information in dairy cattle is often incomplete. Missing pedigree potentially causes biases and inflation in genomic estimated breeding values (GEBV) obtained with ssGBLUP. Three major issues are associated with missing pedigree in ssGBLUP, namely biased predictions by selection, missing inbreeding in pedigree relationships, and incompatibility between G and A in level and scale. These issues can be solved using a proper model for unknown-parent groups (UPG). The theory behind the use of UPG is well established for pedigree BLUP, but not for ssGBLUP. This study reviews the development of the UPG model in pedigree BLUP, the properties of UPG models in ssGBLUP, and the effect of UPG on genetic trends and genomic predictions. Similarities and differences between UPG and metafounder (MF) models, a generalized UPG model, are also reviewed. A UPG model (QP) derived using a transformation of the MME has a good convergence behavior. However, with insufficient data, the QP model may yield biased genetic trends and may underestimate UPG. The QP model can be altered by removing the genomic relationships linking GEBV and UPG effects from MME. This altered QP model exhibits less bias in genetic trends and less inflation in genomic predictions than the QP model, especially with large data sets. Recently, a new model, which encapsulates the UPG equations into the pedigree relationships for genotyped animals, was proposed in simulated purebred populations. The MF model is a comprehensive solution to the missing pedigree issue. This model can be a choice for multibreed or crossbred evaluations if the data set allows the estimation of a reasonable relationship matrix for MF. Missing pedigree influences genetic trends, but its effect on the predictability of genetic merit for genotyped animals should be negligible when many proven bulls are genotyped. The SNP effects can be back-solved using GEBV from older genotyped animals, and these predicted SNP effects can be used to calculate GEBV for young-genotyped animals with missing parents.

Validation of single-step GBLUP genomic predictions from threshold models using the linear regression method: An application in chicken mortality.

The objective of this study was to determine whether the linear regression (LR) method could be used to validate genomic threshold models. Statistics for the LR method were computed from estimated breeding values (EBVs) using the whole and truncated data sets with variances from the reference and validation populations. The method was tested using simulated and real chicken data sets. The simulated data set included 10 generations of 4,500 birds each; genotypes were available for the last three generations. Each animal was assigned a continuous trait, which was converted to a binary score assuming an incidence of failure of 7%. The real data set included the survival status of 186,596 broilers (mortality rate equal to 7.2%) and genotypes of 18,047 birds. Both data sets were analysed using best linear unbiased predictor (BLUP) or single-step GBLUP (ssGBLUP). The whole data set included all phenotypes available, whereas in the partial data set, phenotypes of the most recent generation were removed. In the simulated data set, the accuracies based on the LR formulas were 0.45 for BLUP and 0.76 for ssGBLUP, whereas the correlations between true breeding values and EBVs (i.e. true accuracies) were 0.37 and 0.65, respectively. The gain in accuracy by adding genomic information was overestimated by 0.09 when using the LR method compared to the true increase in accuracy. However, when the estimated ratio between the additive variance computed based on pedigree only and on pedigree and genomic information was considered, the difference between true and estimated gain was <0.02. Accuracies of BLUP and ssGBLUP with the real data set were 0.41 and 0.47, respectively. This small improvement in accuracy when using ssGBLUP with the real data set was due to population structure and lower heritability. The LR method is a useful tool for estimating improvements in accuracy of EBVs due to the inclusion of genomic information when traditional validation methods as k-fold validation and predictive ability are not applicable.

Bcl-2/Bcl-xL inhibitor navitoclax increases the antitumor effect of Chk1 inhibitor prexasertib by inducing apoptosis in pancreatic cancer cells via inhibition of Bcl-xL but not Bcl-2.

In our previous study, we showed that prexasertib, a checkpoint kinase 1 (Chk1) inhibitor, enhances the effects of standard drugs for pancreatic cancer, including gemcitabine (GEM), S-1, and the combination of GEM and S-1 (GS). The combination of prexasertib and GS has a strong antitumor effect and induces apoptosis in pancreatic cancer cells by downregulating anti-apoptotic protein Bcl-2. In the present study, we investigated the combined effect of GEM, S-1, and prexasertib with a selective Bcl-2 inhibitor (venetoclax) and a non-selective Bcl-2 inhibitor (navitoclax) in SUIT-2 pancreatic cancer cells. An MTT assay revealed that the combination of prexasertib with navitoclax showed a synergistic effect but the combination with venetoclax did not. Investigation of the pancreatic cancer cell lines SUIT-2, MIA PaCa-2, and BxPC-3 revealed that BxPC-3 also showed a high synergistic effect when combined with prexasertib and navitoclax but not venetoclax. Mechanistic analysis of the combined effect showed that apoptosis was induced. Bcl-2 knockdown with siRNA and prexasertib treatment did not induce apoptosis, whereas Bcl-xL knockdown with siRNA and prexasertib treatment resulted in strong induction of apoptosis. In addition, among the three cell lines, the combined effect of prexasertib and navitoclax resulted in increased apoptotic cell death because the protein expression levels of Bcl-xL and Chk1 were higher. Our results demonstrate that the combination of prexasertib and navitoclax has a strong antitumor effect and induces apoptosis in pancreatic cancer cells by downregulating Bcl-xL. Simultaneous inhibition of Chk1 and Bcl-xL could be a new strategy for treating pancreatic cancer.

Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups.

BACKGROUND: Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the [Formula: see text] matrix (EUPG) and metafounders (MF)]. METHODS: We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. RESULTS: Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations. CONCLUSIONS: The addition of genomic information increases the accuracy of EBV of young rams in Manech Tête Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years.

Variance components using genomic information for 2 functional traits in Italian Simmental cattle: Calving interval and lactation persistency.

Functional traits, such as fertility and lactation persistency, are becoming relevant breeding goals for dairy cattle. Fertility is a key element for herd profitability and animal welfare; in particular, calving interval (CIN) is an indicator of female fertility that can be easily recorded. Lactation persistency (LPE; i.e., the ability of a cow to maintain a high milk yield after the lactation peak) is economically important and is related to several other traits, such as feed efficiency, health, and reproduction. The selection of these functional traits is constrained by their low heritability. In this study, variance components for CIN and LPE in the Italian Simmental cattle breed were estimated using genomic and pedigree information under the single-step genomic framework. A data set of 594,257 CIN records (from 275,399 cows) and 285,213 LPE records (from 1563,389 cows) was considered. Phenotypes were limited up to the third parity. The pedigree contained about 2 million animals, and 7,246 genotypes were available. Lactation persistency was estimated using principal component analysis on test day records, with higher values of the second extracted principal component (PC2) values associated with lower LPE, and lower PC2 values associated with higher LPE. Heritability of CIN and LPE were estimated using single-trait repeatability models. A multiple-trait analysis using CIN and production traits (milk, fat, and protein yields) was performed to estimate genetic correlations among these traits. Heritability for CIN in the single-trait model was low (0.06 ± 0.002). Unfavorable genetic correlations were found between CIN and production traits. A measure of LPE was derived using principal component analysis on test day records. The heritability and repeatability of LPE were 0.11 ± 0.004 and 0.20 ± 0.02, respectively. Genetic correlation between CIN and LPE was weak but had a favorable direction. Despite the low heritability estimates, results of the present work suggest the possibility of including these traits in the Italian Simmental breeding program. The use of a single-step approach may provide better results for young genotyped animals without their own phenotypes.

A genome-wide single nucleotide polymorphism and copy number variation analysis for number of piglets born alive.

BACKGROUND: In this study we integrated the CNV (copy number variation) and WssGWAS (weighted single-step approach for genome-wide association) analyses to increase the knowledge about number of piglets born alive, an economically important reproductive trait with significant impact on production efficiency of pigs. RESULTS: A total of 3892 samples were genotyped with the Porcine SNP80 BeadChip. After quality control, a total of 57,962 high-quality SNPs from 3520 Duroc pigs were retained. The PennCNV algorithm identified 46,118 CNVs, which were aggregated by overlapping in 425 CNV regions (CNVRs) ranging from 2.5 Kb to 9718.4 Kb and covering 197 Mb (~ 7.01%) of the pig autosomal genome. The WssGWAS identified 16 genomic regions explaining more than 1% of the additive genetic variance for number of piglets born alive. The overlap between CNVR and WssGWAS analyses identified common regions on SSC2 (4.2-5.2 Mb), SSC3 (3.9-4.9 Mb), SSC12 (56.6-57.6 Mb), and SSC17 (17.3-18.3 Mb). Those regions are known for harboring important causative variants for pig reproductive traits based on their crucial functions in fertilization, development of gametes and embryos. Functional analysis by the Panther software identified 13 gene ontology biological processes significantly represented in this study such as reproduction, developmental process, cellular component organization or biogenesis, and immune system process, which plays relevant roles in swine reproductive traits. CONCLUSION: Our research helps to improve the understanding of the genetic architecture of number of piglets born alive, given that the combination of GWAS and CNV analyses allows for a more efficient identification of the genomic regions and biological processes associated with this trait in Duroc pigs. Pig breeding programs could potentially benefit from a more accurate discovery of important genomic regions.

Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: a simulation study.

BACKGROUND: The dimensionality of genomic information is limited by the number of independent chromosome segments (Me), which is a function of the effective population size. This dimensionality can be determined approximately by singular value decomposition of the gene content matrix, by eigenvalue decomposition of the genomic relationship matrix (GRM), or by the number of core animals in the algorithm for proven and young (APY) that maximizes the accuracy of genomic prediction. In the latter, core animals act as proxies to linear combinations of Me. Field studies indicate that a moderate accuracy of genomic selection is achieved with a small dataset, but that further improvement of the accuracy requires much more data. When only one quarter of the optimal number of core animals are used in the APY algorithm, the accuracy of genomic selection is only slightly below the optimal value. This suggests that genomic selection works on clusters of Me. RESULTS: The simulation included datasets with different population sizes and amounts of phenotypic information. Computations were done by genomic best linear unbiased prediction (GBLUP) with selected eigenvalues and corresponding eigenvectors of the GRM set to zero. About four eigenvalues in the GRM explained 10% of the genomic variation, and less than 2% of the total eigenvalues explained 50% of the genomic variation. With limited phenotypic information, the accuracy of GBLUP was close to the peak where most of the smallest eigenvalues were set to zero. With a large amount of phenotypic information, accuracy increased as smaller eigenvalues were added. CONCLUSIONS: A small amount of phenotypic data is sufficient to estimate only the effects of the largest eigenvalues and the associated eigenvectors that contain a large fraction of the genomic information, and a very large amount of data is required to estimate the remaining eigenvalues that account for a limited amount of genomic information. Core animals in the APY algorithm act as proxies of almost the same number of eigenvalues. By using an eigenvalues-based approach, it was possible to explain why the moderate accuracy of genomic selection based on small datasets only increases slowly as more data are added.

Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle.

BACKGROUND: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. METHODS: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. RESULTS: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. CONCLUSIONS: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.

Effects of proton pump inhibitors on severe haematotoxicity induced after first course of pemetrexed/carboplatin combination chemotherapy.

WHAT IS KNOWN AND OBJECTIVE: Pemetrexed/carboplatin combination chemotherapy has shown efficacy as a first-line treatment for advanced non-small-cell lung cancer. However, severe haematotoxicity is often observed during this combination chemotherapy. Some studies have suggested that concomitant drugs may be the risk factors for severe adverse events. However, those studies identified the predictive risk factors without paying attention to the relative dose intensities (RDIs) of the anticancer drugs. The objective of this study was to clarify the effects of concomitant drugs on the severe haematotoxicity induced by pemetrexed/carboplatin combination chemotherapy using multiple logistic regression analysis incorporating RDIs of the anticancer drugs. METHODS: We retrospectively reviewed the records of 61 patients who had received first-line treatment with this combination chemotherapy at Yamato Municipal Hospital between April 2011 and May 2017. Severe haematotoxicity was defined as grade 3 or 4 according to the Common Terminology Criteria for Adverse Events, version 4.0. To clarify the influence of concomitant drugs on haematotoxicity, we performed multiple logistic regression analysis. RESULTS: Among the 61 patients, 18 (29.5%) developed grade 3 or 4 haematotoxicity. Multiple logistic regression analysis showed that body weight <54.5 kg [odds ratio: 5.21, 95% confidence interval (CI): 1.17-23.08, P = 0.030], haemoglobin <12.0 g/dL [odds ratio: 7.13, 95% CI: 1.54-33.11, P = 0.012], and coadministration of proton pump inhibitors (PPIs) [odds ratio: 5.34, 95% CI: 1.06-26.94, P = 0.042] were significantly associated with severe haematotoxicity in patients receiving pemetrexed/carboplatin combination chemotherapy after adjustment using non-steroidal anti-inflammatory drugs and RDIs of the anticancer drugs. WHAT IS NEW AND CONCLUSION: Multiple logistic regression analysis incorporating RDIs of the anticancer drugs revealed that low baseline body weight, low baseline haemoglobin level, and coadministration of PPIs were the independent risk factors for predicting severe haematotoxicity induced by pemetrexed/carboplatin combination chemotherapy.

Incorporation of causative quantitative trait nucleotides in single-step GBLUP.

BACKGROUND: Much effort is put into identifying causative quantitative trait nucleotides (QTN) in animal breeding, empowered by the availability of dense single nucleotide polymorphism (SNP) information. Genomic selection using traditional SNP information is easily implemented for any number of genotyped individuals using single-step genomic best linear unbiased predictor (ssGBLUP) with the algorithm for proven and young (APY). Our aim was to investigate whether ssGBLUP is useful for genomic prediction when some or all QTN are known. METHODS: Simulations included 180,000 animals across 11 generations. Phenotypes were available for all animals in generations 6 to 10. Genotypes for 60,000 SNPs across 10 chromosomes were available for 29,000 individuals. The genetic variance was fully accounted for by 100 or 1000 biallelic QTN. Raw genomic relationship matrices (GRM) were computed from (a) unweighted SNPs, (b) unweighted SNPs and causative QTN, (c) SNPs and causative QTN weighted with results obtained with genome-wide association studies, (d) unweighted SNPs and causative QTN with simulated weights, (e) only unweighted causative QTN, (f-h) as in (b-d) but using only the top 10% causative QTN, and (i) using only causative QTN with simulated weight. Predictions were computed by pedigree-based BLUP (PBLUP) and ssGBLUP. Raw GRM were blended with 1 or 5% of the numerator relationship matrix, or 1% of the identity matrix. Inverses of GRM were obtained directly or with APY. RESULTS: Accuracy of breeding values for 5000 genotyped animals in the last generation with PBLUP was 0.32, and for ssGBLUP it increased to 0.49 with an unweighted GRM, 0.53 after adding unweighted QTN, 0.63 when QTN weights were estimated, and 0.89 when QTN weights were based on true effects known from the simulation. When the GRM was constructed from causative QTN only, accuracy was 0.95 and 0.99 with blending at 5 and 1%, respectively. Accuracies simulating 1000 QTN were generally lower, with a similar trend. Accuracies using the APY inverse were equal or higher than those with a regular inverse. CONCLUSIONS: Single-step GBLUP can account for causative QTN via a weighted GRM. Accuracy gains are maximum when variances of causative QTN are known and blending is at 1%.

Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species.

BACKGROUND: A genomic relationship matrix (GRM) can be inverted efficiently with the Algorithm for Proven and Young (APY) through recursion on a small number of core animals. The number of core animals is theoretically linked to effective population size (N e ). In a simulation study, the optimal number of core animals was equal to the number of largest eigenvalues of GRM that explained 98% of its variation. The purpose of this study was to find the optimal number of core animals and estimate N e for different species. METHODS: Datasets included phenotypes, pedigrees, and genotypes for populations of Holstein, Jersey, and Angus cattle, pigs, and broiler chickens. The number of genotyped animals varied from 15,000 for broiler chickens to 77,000 for Holsteins, and the number of single-nucleotide polymorphisms used for genomic prediction varied from 37,000 to 61,000. Eigenvalue decomposition of the GRM for each population determined numbers of largest eigenvalues corresponding to 90, 95, 98, and 99% of variation. RESULTS: The number of eigenvalues corresponding to 90% (98%) of variation was 4527 (14,026) for Holstein, 3325 (11,500) for Jersey, 3654 (10,605) for Angus, 1239 (4103) for pig, and 1655 (4171) for broiler chicken. Each trait in each species was analyzed using the APY inverse of the GRM with randomly selected core animals, and their number was equal to the number of largest eigenvalues. Realized accuracies peaked with the number of core animals corresponding to 98% of variation for Holstein and Jersey and closer to 99% for other breed/species. N e was estimated based on comparisons of eigenvalue decomposition in a simulation study. Assuming a genome length of 30 Morgan, N e was equal to 149 for Holsteins, 101 for Jerseys, 113 for Angus, 32 for pigs, and 44 for broilers. CONCLUSIONS: Eigenvalue profiles of GRM for common species are similar to those in simulation studies although they are affected by number of genotyped animals and genotyping quality. For all investigated species, the APY required less than 15,000 core animals. Realized accuracies were equal or greater with the APY inverse than with regular inversion. Eigenvalue analysis of GRM can provide a realistic estimate of N e .

PI3-Kinase Inhibitor LY294002 Repressed the Expression of Thrombin-Activatable Fibrinolysis Inhibitor in Human Hepatoma HepG2 Cells.

Thrombin-activatable fibrinolysis inhibitor (TAFI) is a carboxypeptidase B-like proenzyme biosynthesized in the liver and released into the blood circulation. Activated TAFI (TAFIa) has been implicated as an important player in maintaining the balance between blood coagulation and fibrinolysis. In the present study, regulation of TAFI (CPB2) gene expression was investigated using cultured human hepatoma HepG2 cells. HepG2 cells were treated with the phosphoinositide 3-kinase (PI3K) inhibitor LY294002, and the levels of TAFI antigen and CPB2 mRNA were measured. HepG2 cells treated with LY29400 decreased their release of TAFI antigen into the conditioned medium (CM). In parallel, there were decreased levels of CPB2 mRNA and TAFI antigen in the cells. However, CPB2 gene promoter activity was not influenced by treatment of the cells with LY294002. The half-life of the CPB2 transcript was shortened by treatment with LY294002 compared with control. The present results suggest that the PI3K inhibitor LY294002 suppresses expression of TAFI, a prothrombotic factor, by decreasing the stability of CPB2 transcripts.

Neurophysiological and immunohistochemical studies of IgG anti-GM1 monoclonal antibody on neuromuscular transmission: effects in rat neuromuscular junctions.

Guillain-Barré syndrome, which is a variant of acute inflammatory neuropathy, is associated with anti-GM1 antibodies and causes ataxia. We investigated the effects of IgG anti-GM1 monoclonal antibody (IgG anti-GM1 mAb) on spontaneous muscle action potentials in a rat spinal cord-muscle co-culture system and the localization of IgG anti-GM1 mAb binding in the rat hemi-diaphragm. The frequency of spontaneous muscle action potentials in innervated muscle cells was acutely inhibited by IgG anti-GM1 mAb. When cultures were pretreated with GM2 synthase antisense oligodeoxynucleotide, IgG anti-GM1 mAb failed to inhibit spontaneous muscle action potentials, demonstrating the importance of the GM1 epitope in the action of IgG anti-GM1 mAb. Immunohistochemistry of rat hemi-diaphragm showed that IgG anti-GM1 mAb binding overlapped with neurofilament 200 (NF200) antibodies staining, but not α-bungarotoxin (α-BuTx) staining, demonstrating that IgG anti-GM1 mAb was localized at the presynaptic nerve terminal. IgG anti-GM1 mAb binding overlapped with syntaxin antibody and S-100 antibody in the nerve terminal. After collagenase treatment, IgG anti-GM1 mAb and NF200 antibodies did not show staining, but α-BuTx selectively stained the hemi-diaphragm. IgG anti-GM1 mAb binds to the presynaptic nerve terminal of neuromuscular junctions. Therefore, we suggest that the inhibitory effect of IgG anti-GM1 mAb on spontaneous muscle action potentials is related to the GM1 epitope in presynaptic motor nerve terminals at the NMJs.

MAOA and TNF-ß gene polymorphisms are associated with photophobia but not osmophobia in patients with migraine.

PURPOSE: Photophobia and osmophobia are typical symptoms associated with migraine, but the contributions of gene polymorphisms to these symptoms are not fully elucidated. We investigated whether the gene polymorphisms are involved in photophobia and osmophobia in patients with migraine. METHODS: Ninety-one migraine patients and 119 non-headache healthy volunteers were enrolled. The 12 gene polymorphisms were determined by polymerase-chain-reaction (PCR) and PCR restriction-fragment-length polymorphism analysis. RESULTS: Photophobia and osmophobia were observed in 49 (54%) and 31 patients (34%), respectively. Distributions of monoamine oxidase A (MAOA) T941G and tumour necrosis factor-ß (TNF-ß) G252A polymorphisms were significantly different between patients with photophobia and controls. However, no gene polymorphism differences were observed between patients with osmophobia and controls. CONCLUSION: The MAOA T941G and TNF-ß G252A gene polymorphisms appear to contribute to photophobia but not to osmophobia. We propose that different gene polymorphisms are responsible for photophobia and osmophobia symptoms during migraine.

Meta-model of Human Recognition-behavioral Adaptation System.

Scientific modeling is a syllogistic system of definitive premise, sound inference and consistent explanation to understand, define, quantify, visualize or simulate feature of the target. Single-model is defined to an informative representation for identifying a property of a target object/phenomenon, and meta-model integrates the relevant single-models to explain phenomenological realities. Human recognition-behavioral adaptation is an information-metabolism system to maintain homeostasis of human-self, and that has been investigated in neurological, psychiatric and psychological aspects. I analyzed human recognition-behavioral adaptation-system via scientific modeling. Neurological meta-model of human recognition-behavioral adaptation system was synthesized as complex-network of the functional neuronal modules, and the meta-model was integrated to Mentality-model in the psychiatric aspect, and to Personality-model in the psychological aspect. The integrated meta-models successfully explained phenomenological realities in the aspects. From the above, I comprehended that the meta-model of human recognition-behavioral adaptation-system has been developed to Biopsychosocial model integrating the biological, psychological and socio-environmental factors.

Genetic relationship of energy balance predicted from milk traits with fertility in Japanese Holsteins.

We predicted the energy balance of cows from milk traits and estimated the genetic correlations of predicted energy balance (PEB) with fertility traits for the first three lactations. Data included 9,646,606 test-day records of 576,555 Holstein cows in Japan from 2015 to 2019. Genetic parameters were estimated with a multiple-trait model in which the records among lactation stages and parities were treated as separate traits. Fertility traits were conception rate at first insemination (CR), number of inseminations (NI), and days open (DO). Heritability estimates of PEB were 0.28-0.35 (first lactation), 0.15-0.29 (second), and 0.09-0.23 (third). Estimated genetic correlations among lactation stages were 0.85-1.00 (first lactation), 0.73-1.00 (second), and 0.64-1.00 (third). Estimated genetic correlations among parities were 0.82-0.96 (between first and second), 0.97-0.99 (second and third), and 0.69-0.92 (first and third). Estimated genetic correlations of PEB in early lactation with fertility were 0.04 to 0.19 for CR, -0.03 to -0.19 for NI, and -0.01 to -0.24 for DO. Genetic improvement of PEB is possible. Lower PEB in early lactation was associated with worse fertility, suggesting that improving PEB in early lactation may improve reproductive performance.

[Early Prediction of Support Necessity for Pharmacy Clinical Internship Using Deep Learning].

The duration of undergraduate study was extended in 2006 to six years for pharmaceutical education aimed at training highly qualified pharmacists. Clinical internship in current pharmaceutical education is positioned as being important for fostering the qualities required of a pharmacist, and the support of faculty members is essential. Based on the above, we thought that support from faculty members should be provided easily and positively, which would enrich community pharmacy clinical internships. This study aimed to examine the method of predicting the need for support from weekly reports of community pharmacy practice trainees at Showa Pharmaceutical University. It became evident that the level of necessary support could not be predicted by using the support needs listed. However, application of deep learning to the contents of the weekly report for the first to fifth weeks in 2019 enabled the prediction of the level of support needed in 2020 with 97% accuracy. Although this research is currently limited to predicting the level of support required for community pharmacy practical internship at our university, it demonstrates the use of deep learning to predict the level of support needed based on five weeks' worth of weekly reports.

Simultaneous inhibition of Chk1 and Bcl-xL induces apoptosis in vitro and represses tumour growth in an in vivo xenograft model.

We previously showed that prexasertib, a checkpoint kinase 1 (Chk1) inhibitor, and navitoclax, a Bcl-2 and Bcl-xL inhibitor, induced a synergistic inhibitory effect on cell proliferation in vitro. Here, we investigated the effect of the simultaneous knockdown of Chk1 and each antiapoptotic protein of the Bcl-2 family (Bcl-2, Bcl-xL, or Mcl-1) with small interfering RNAs on apoptosis in three pancreatic cancer cell lines. Only simultaneous knockdown of Chk1 and Bcl-xL induced significant apoptosis compared with single knockdown in all three cell lines. We evaluated the anti-tumour effects of combined prexasertib and navitoclax treatment in a mouse xenograft model. Treatment to control volume ratios were calculated as 63.2% for prexasertib, 79.4% for navitoclax, and 36.8% for prexasertib and navitoclax. These findings suggest that the simultaneous inhibition of Chk1 and Bcl-xL may be an effective treatment for pancreatic cancer.

Genome-wide search reveals the uniqueness of DNA regions associated with coat color and innate immunity in Hokkaido Native Horse.

Hokkaido Native Horse (HKD) is a horse breed native to Hokkaido in Japan known for the traits such as coat color with no white spots and adaptability to the local cold climate. To examine whether those traits of HKD are conferred at the DNA level, we attempted to identify fixed DNA regions in HKD individuals, that is, the selection signatures of HKD. A comparison of genome-wide single nucleotide polymorphism genotypes in 58 HKD individuals by principal component analysis, and cluster analysis between breeds, including HKD, and within the HKD individuals indicated the genetic independence of HKD as a breed. Tajima's D analysis and runs of homozygosity analysis identified 23 selection signatures unique to HKD (P < 0.05), and following database search found 20 traits that were associated with those selection signatures; among these traits, coat color traits, face and body markings, showed the highest important value (0.50 and 0.46). Enrichment analysis of genes in the selection signatures identified six gene ontology terms (P < 0.05), and a term related to innate immunity (regulation of defense response; GO:0031347) showed the highest positive fold enrichment value (7.13). These results provide the first scientific evidence of a genetic basis for the traits of HKD.