Successful second‐line treatment with cabozantinib for hepatocellular carcinoma harboring c‐MET amplification

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Abstract

A 72-year-old man with metastatic hepatocellular carcinoma (HCC) previously received first-line systemic therapy with atezolizumab plus bevacizumab. His disease was judged to be progressing 5 months after treatment initiation. Comprehensive genomic profiling (CGP) revealed cytoplasmic Mesenchymal Epithelial Transition factor amplification. On the basis of an expert panel’s recommendation, he received cabozantinib as second-line therapy. The tumors shrank markedly and continued to shrink 6 months after treatment. CGP could provide useful information for selecting effective second-line treatments for patients with HCC after first-line immunotherapy.

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Evolutionarily conserved cysteines in plant cytosolic seryl‐tRNA synthetase are important for its resistance to oxidation

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Evolutionarily conserved cysteines in plant cytosolic seryl-tRNA synthetase are important for its resistance to oxidation

We have examined the role of the disulfide link between evolutionarily conserved cysteines in plant cytosolic seryl-tRNA synthetase. We have identified features of the protein microenvironment which may promote disulfide bond formation in oxidizing conditions. Activity assays showed that the disulfide link is important for protein resistance to oxidation, which may be beneficial for translation during oxidative stress conditions in plants.


We have previously identified a unique disulfide bond in the crystal structure of Arabidopsis cytosolic seryl-tRNA synthetase involving cysteines evolutionarily conserved in all green plants. Here, we discovered that both cysteines are important for protein stability, but with opposite effects, and that their microenvironment may promote disulfide bond formation in oxidizing conditions. The crystal structure of the C244S mutant exhibited higher rigidity and an extensive network of noncovalent interactions correlating with its higher thermal stability. The activity of the wild-type showed resistance to oxidation with H2O2, while the activities of cysteine-to-serine mutants were impaired, indicating that the disulfide link may enable the protein to function under oxidative stress conditions which can be beneficial for an efficient plant stress response.

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation

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Abstract

Nitrogen (N) use efficiency (NUE) is an economically important trait for dairy cows. Recently, we proposed a new N efficiency index (NEI), that simultaneously considers both NUE and N pollution. This study aimed to validate the genomic prediction for NEI and its composition traits and investigate the relationship between SNP effects estimated directly from NEI and indirectly from its composition traits. The NEI composition included genomic estimated breeding value of N intake (NINT), milk true protein N (MTPN) and milk urea N yield. The edited data were 132,899 records on 52,064 cows distributed in 773 herds. The pedigree contained 122,368 animals. Genotypic data of 566,294 SNP was available for 4514 individuals. A total of 4413 cows (including 181 genotyped) and 56 bulls (including 32 genotyped) were selected as the validation populations. The linear regression method was used to validate the genomic prediction of NEI and its composition traits using best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP). The mean theoretical accuracies of validation populations obtained from ssGBLUP were higher than those obtained from BLUP for both NEI and its composition traits, ranging from 0.57 (MTPN) to 0.72 (NINT). The highest mean prediction accuracies for NEI and its composition traits were observed for the genotyped cows estimated under ssGBLUP, ranging from 0.48 (MTPN) to 0.66 (NINT). Furthermore, the SNP effects estimated from NEI composition traits, multiplied by the relative weight were the same as those estimated directly from NEI. This study preliminary showed that genomic prediction can be used for NEI, however, we acknowledge the need for further validation of this result in a larger dataset. Moreover, the SNP effects of NEI can be indirectly calculated using the SNP effects estimated from its composition traits. This study provided a basis for adding genomic information to establish NEI as part of future routine genomic evaluation programs.

Quantitative analysis of parent‐of‐origin effect in reproductive and morphological selection criteria in the Pura Raza Española horse

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Abstract

It is generally assumed that parents make a genetically equal contribution to their offspring, but this assumption might not always hold. This is because the expression of a gene can be blocked by methylation during gametogenesis, and the degree of methylation can depend on the origin of the parental gene (imprinting) or by preferential management associated with genetic merit. The first consequences of this for quantitative genetics is that the mean phenotypes of reciprocal heterozygotes need no longer be the same, as would be expected according to Mendelian heritage. We analysed three mare reproductive traits (reproductive efficiency, age at first foaling and foaling number) and three morphological traits (height at withers, thoracic circumference, and scapula-ischial length) in the Pura Raza Española (PRE) horse population, which possesses a deep and reliable pedigree, making it a perfect breed for analysing the quantitative effect of parent-of-origin. The number of animals analysed ranged from 44,038 to 144,191, all of them with both parents known. The model comparison between a model without parent-of-origin effects and three different models with parent-of-origin effects revealed that both maternal and paternal gametic effects influence all the analysed traits. The maternal gametic effect had a higher influence on most traits, accounting for between 3% and 11% of the total phenotypic variance, while the paternal gametic effect accounted for a higher proportion of variance in one trait, age at first foaling (4%). As expected, the Pearson's correlations between additive breeding values of models that consider parent-of-origin and that do not consider parent-of-origin were very high; however, the percentage of coincident animals slightly decreases when comparing animals with the highest estimated breeding values. Ultimately, this work demonstrates that parent-of-origin effects exist in horse gene transmission from a quantitative point of view. Additionally, including an estimate of the parent-of-origin effect within the PRE horse breeding program could be a great tool for a better parent's selection and that could be of interest for breeders, as this value will determine whether the animals acquire genetic categories and are much more highly valued.

Inbreeding depression and its effect on sperm quality traits in Pietrain pigs

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Abstract

In most cases, inbreeding is expected to have unfavourable effects on traits in livestock. The consequences of inbreeding depression could be substantial, primarily in reproductive and sperm quality traits, and thus lead to decreased fertility. Therefore, the objectives of this study were (i) to compute inbreeding coefficients using pedigree (F PED) and genomic data based on runs of homozygosity (ROH) in the genome (F ROH) of Austrian Pietrain pigs, and (ii) to assess inbreeding depression on four sperm quality traits. In total, 74,734 ejaculate records from 1034 Pietrain boars were used for inbreeding depression analyses. Traits were regressed on inbreeding coefficients using repeatability animal models. Pedigree-based inbreeding coefficients were lower than ROH-based inbreeding values. The correlations between pedigree and ROH-based inbreeding coefficients ranged from 0.186 to 0.357. Pedigree-based inbreeding affected only sperm motility while ROH-based inbreeding affected semen volume, number of spermatozoa, and motility. For example, a 1% increase in pedigree inbreeding considering 10 ancestor generations (F PED10) was significantly (p < 0.05) associated with a 0.231% decrease in sperm motility. Almost all estimated effects of inbreeding on the traits studied were unfavourable. It is advisable to properly manage the level of inbreeding to avoid high inbreeding depression in the future. Further, analysis of effects of inbreeding depression for other traits, including growth and litter size for the Austrian Pietrain population is strongly advised.

Genomic inbreeding estimation, runs of homozygosity, and heterozygosity‐enriched regions uncover signals of selection in the Quarter Horse racing line

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Abstract

With the advent of genomics, significant progress has been made in the genetic improvement of livestock species, particularly through increased accuracy in predicting breeding values for selecting superior animals and the possibility of performing a high-resolution genetic scan throughout the genome of an individual. The main objectives of this study were to estimate the individual genomic inbreeding coefficient based on runs of homozygosity (F ROH), to identify and characterize runs of homozygosity and heterozygosity (ROH and ROHet, respectively; length and distribution) throughout the genome, and to map selection signatures in relevant chromosomal regions in the Quarter Horse racing line. A total of 336 animals registered with the Brazilian Association of Quarter Horse Breeders (ABQM) were genotyped. One hundred and twelve animals were genotyped using the Equine SNP50 BeadChip (Illumina, USA), with 54,602 single nucleotide polymorphisms (SNPs; 54K). The remaining 224 samples were genotyped using the Equine SNP70 BeadChip (Illumina, USA) with 65,157 SNPs (65K). To ensure data quality, we excluded animals with a call rate below 0.9. We also excluded SNPs located on non-autosomal chromosomes, as well as those with a call rate below 0.9 or a p-value below 1 × 10−5 for Hardy–Weinberg equilibrium. The results indicate moderate to high genomic inbreeding, with 46,594 ROH and 16,101 ROHet detected. In total, 30 and 14 candidate genes overlap with ROH and ROHet regions, respectively. The ROH islands showed genes linked to crucial biological processes, such as cell differentiation (CTBP1, WNT5B, and TMEM120B), regulation of glucose metabolic process (MAEA and NKX1-1), heme transport (PGRMC2), and negative regulation of calcium ion import (VDAC1). In ROHet, the islands showed genes related to respiratory capacity (OR7D19, OR7D4G, OR7D4E, and OR7D4J) and muscle repair (EGFR and BCL9). These findings could aid in selecting animals with greater regenerative capacity and developing treatments for muscle disorders in the QH breed. This study serves as a foundation for future research on equine breeds. It can contribute to developing reproductive strategies in animal breeding programs to improve and preserve the Quarter Horse breed.

Genome‐wide association studies for epistatic genetic effects on fertility and reproduction traits in Holstein cattle

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Abstract

Non-additive genetic effects are well known to play an important role in the phenotypic expression of complex traits, such as fertility and reproduction. In this study, a genome scan was performed using 41,640 single nucleotide polymorphism (SNP) markers to identify genomic regions associated with epistatic (additive-by-additive) effects in fertility and reproduction traits in Holstein cattle. Nine fertility and reproduction traits were analysed on 5825 and 6090 Holstein heifers and cows with phenotypes and genotypes, respectively. The Marginal Epistasis Test (MAPIT) was used to identify SNPs with significant marginal epistatic effects at a chromosome-wise 5% and 10% false discovery rate (FDR) level. The −log10(p) values were adjusted by the genomic inflation factor (λ) to correct for the potential bias on the p-values and minimize the possible effects of population stratification. After adjustments, MAPIT enabled the identification of genomic regions with significant marginal epistatic effects for heifers on BTA5 for age at first insemination, BTA3 and BTA24 for non-return rate (NRR); BTA16 and BTA28 for gestation length (GL); BTA1, BTA4 and BTA17 for stillbirth (SB). For the cow traits, MAPIT enabled the identification of regions on BTA11 for GL, BTA11 and BTA16 for SB and BTA19 for calf size (CZ). An additional approach for mapping epistasis in a genome-wide association study was also proposed, in which the genome scan was performed using estimates of epistatic values as the input pseudo-phenotypes, computed using single-trait animal models. Significant SNPs were identified at the chromosome-wise 5% and 10% FDR levels for all traits. For the heifer traits, significant regions were found on BTA7 for AFS; BTA12 for NRR; BTA14 and BTA19 for GL; BTA19 for calving ease (CE); BTA5, BTA24, BTA25 and in the X chromosome for SB; BTA23 and in the X chromosome for CZ and in the X chromosome for the number of services (NS). For the cow traits, significant regions were found on BTA29 and in the X chromosome for NRR, BTA11, BTA16 and in the X chromosome for SB, BTA2 for GL, BTA28 for CZ, BTA19 for calving to first insemination, and in the X chromosome for NS and first insemination to conception. The results suggest that the epistatic genetic effects are likely due to many loci with a small effect rather than few loci with a large effect and/or a single SNP marker alone do not capture the epistatic effects well. The genomic architecture of fertility and reproduction traits is complex, and these results should be validated in independent dairy cattle populations and using alternative statistical models.

Impact of multi‐output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms

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Abstract

Feeding represents the largest economic cost in meat production; therefore, selection to improve traits related to feed efficiency is a goal in most livestock breeding programs. Residual feed intake (RFI), that is, the difference between the actual and the expected feed intake based on animal's requirements, has been used as the selection criteria to improve feed efficiency since it was proposed by Kotch in 1963. In growing pigs, it is computed as the residual of the multiple regression model of daily feed intake (DFI), on average daily gain (ADG), backfat thickness (BFT), and metabolic body weight (MW). Recently, prediction using single-output machine learning algorithms and information from SNPs as predictor variables have been proposed for genomic selection in growing pigs, but like in other species, the prediction quality achieved for RFI has been generally poor. However, it has been suggested that it could be improved through multi-output or stacking methods. For this purpose, four strategies were implemented to predict RFI. Two of them correspond to the computation of RFI in an indirect way using the predicted values of its components obtained from (i) individual (multiple single-output strategy) or (ii) simultaneous predictions (multi-output strategy). The other two correspond to the direct prediction of RFI using (iii) the individual predictions of its components as predictor variables jointly with the genotype (stacking strategy), or (iv) using only the genotypes as predictors of RFI (single-output strategy). The single-output strategy was considered the benchmark. This research aimed to test the former three hypotheses using data recorded from 5828 growing pigs and 45,610 SNPs. For all the strategies two different learning methods were fitted: random forest (RF) and support vector regression (SVR). A nested cross-validation (CV) with an outer 10-folds CV and an inner threefold CV for hyperparameter tuning was implemented to test all strategies. This scheme was repeated using as predictor variables different subsets with an increasing number (from 200 to 3000) of the most informative SNPs identified with RF. Results showed that the highest prediction performance was achieved with 1000 SNPs, although the stability of feature selection was poor (0.13 points out of 1). For all SNP subsets, the benchmark showed the best prediction performance. Using the RF as a learner and the 1000 most informative SNPs as predictors, the mean (SD) of the 10 values obtained in the test sets were: 0.23 (0.04) for the Spearman correlation, 0.83 (0.04) for the zero–one loss, and 0.33 (0.03) for the rank distance loss. We conclude that the information on predicted components of RFI (DFI, ADG, MW, and BFT) does not contribute to improve the quality of the prediction of this trait in relation to the one obtained with the single-output strategy.

Genetic parameter estimation for pork production and litter performance traits of Landrace, Large White, and Duroc pigs in Japan

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Abstract

We estimated genetic parameters for two pork production and six litter performance traits of Landrace, Large White, and Duroc pigs reared in Japan. Pork production traits were average daily gain from birth to end of performance testing and backfat thickness at end of testing (46,042 records for Landrace, 40,467 records for Large White, and 42,920 records for Duroc). Litter performance traits were number born alive, litter size at weaning (LSW), number of piglets dead during suckling (ND), survival rate of piglets during suckling (SV), total piglet weight at weaning (TWW), and average piglet weight at weaning (AWW) (27,410, 26,716, and 12,430 records for Landrace, Large White, and Duroc, respectively). ND was calculated as the difference between LSW and litter size at start of suckling (LSS). SV was calculated as LSW/LSS. AWW was calculated as TWW/LSW. Pedigree data for Landrace, Large White, and Duroc breeds contained 50,193, 44,077, and 45,336 pigs, respectively. Trait heritability was estimated via single-trait analysis and genetic correlation between two traits was estimated via two-trait analysis. When considering the linear covariate of LSS in the statistical model for LSW and TWW, for all breeds, the heritability was estimated to be 0.4–0.5 for pork production traits and below 0.2 for litter performance traits. Estimated genetic correlation between average daily gain and backfat thickness was small, ranging from 0.057 to 0.112, and those between pork production traits and litter performance traits were negligible to moderate, ranging from −0.493 to 0.487. A wide range of genetic correlation values among the litter performance traits was estimated, while that between LSW and ND could not be obtained. The results of genetic parameter estimation were affected by whether the linear covariate of LSS was included in the statistical model for LSW and TWW or not. This finding implies the necessity of carefully interpreting the results according to the choice of statistical model. Our results could give fundamental information on simultaneously improving productivity and female reproductivity for pigs.