Principal component approach in variance component estimation for international sire evaluation

<p>Abstract</p> <p>Background</p> <p>The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model.</p> <p>Methods</p> <p>This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix.</p> <p>Results</p> <p>Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time.</p> <p>Conclusions</p> <p>In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.</p

Principal component and factor analytic models in international sire evaluation

<p>Abstract</p> <p>Background</p> <p>Interbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured.</p> <p>Methods</p> <p>Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries.</p> <p>Results</p> <p>In total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared.</p> <p>Conclusions</p> <p>Genetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries.</p

Alternative p38MAPKs as biomarkers in the interplay of colon cancer and inflammatory bowel diseases

Trabajo presentado en el 44º Congreso Nacional de la Sociedad Española de Bioquímica y Biología Molecular (SEBBM), celebrado en Málaga (España) del 06 al 09 de septiembre de 2022.Chronic inflammation in inflammatory bowel disease (IBD) is a risk factor for Colorectal cancer (CRC) development, but our understanding of this interplay at a molecular level is still limited. p38γ and p38δ, are central in the development of mouse colitis-associated CRC (CAC) by modulating the inflammatory immune response. However, their implication in human CRC and IBD is not well defined. In this study we perform an integrative analysis of p38γ and p38δ mRNA and protein expression and activation in human patients; using human CRC derived organoids and plasma samples, as well as data from different human CRC and IBD mRNA databases. We found that, p38δ levels were decreased, whereas p38γ expression and phosphorylation were significantly increased in CRC compared to normal colon samples. This increase correlated with the expression of genes implicated in inflammation. Examine of p38γ/p38δ in IBD patients showed that p38γ mRNA and protein levels were increased in Crohn’s disease and ulcerative colitis patients. Contrary, p38δ mRNA was significantly decreased. We also investigated the expression of miRNAs, miR-128-2, miR133a and miR-155, implicated in inflammation and cancer development. In mouse model of colitis and CAC, miR128-2 level was regulated by p38γ/p38δ. In the plasma of IBD and CRC patients, miR128-2 was increased compared to healthy donors, and this correlated with p38γ and p38δ levels. Our results show an opposite regulation of p38γ and p38δ in both CRC and IBD; and suggest that p38γ acts as a link between colitis and CRC by favouring an inflammatory environment that promotes tumour development. We provided evidence that p38γ/p38δ, together with miR-128-2, can be useful as biomarkers, and as potential treatment targets, for colitis and early-stage CRC

p38γ and p38δ as biomarkers in the interplay of colon cancer and inflammatory bowel diseases

descripción no proporcionada por scopusThis research was funded by the MCIN/AEI/10.13039/501100011033 (PID2019-108349RB100 and SAF2016-79792R) to AC and JJSE; Villum Foundation, grant no. 13152 to KA; by Agencia Estatal de Investigación (PID2019-104867RBI00/AEI/10.13039/501100011033) and the Instituto de Salud Carlos III- Fondo Europeo de Desarrollo Regional (CIBERONC/CB16/12/00273 and ICI20/00057) to AM and AB. PF received MCIN FPI fellowship (BES-2017-080139)

T-cell Receptor (TCR)-Peptide Specificity Overrides Affinity-enhancing TCR-Major Histocompatibility Complex Interactions

αβ T-cell receptors (TCRs) engage antigens using complementarity-determining region (CDR) loops that are either germ line-encoded (CDR1 and CDR2) or somatically rearranged (CDR3). TCR ligands compose a presentation platform (major histocompatibility complex (MHC)) and a variable antigenic component consisting of a short “foreign” peptide. The sequence of events when the TCR engages its peptide-MHC (pMHC) ligand remains unclear. Some studies suggest that the germ line elements of the TCR engage the MHC prior to peptide scanning, but this order of binding is difficult to reconcile with some TCR-pMHC structures. Here, we used TCRs that exhibited enhanced pMHC binding as a result of mutations in either CDR2 and/or CDR3 loops, that bound to the MHC or peptide, respectively, to dissect the roles of these loops in stabilizing TCR-pMHC interactions. Our data show that TCR-peptide interactions play a strongly dominant energetic role providing a binding mode that is both temporally and energetically complementary with a system requiring positive selection by self-pMHC in the thymus and rapid recognition of non-self-pMHC in the periphery

TCR‐induced alteration of primary MHC peptide anchor residue

The HLA‐A*02:01‐restricted decapeptide EAAGIGILTV, derived from melanoma antigen recognized by T‐cells‐1 (MART‐1) protein, represents one of the best‐studied tumor associated T‐cell epitopes, but clinical results targeting this peptide have been disappointing. This limitation may reflect the dominance of the nonapeptide, AAGIGILTV, at the melanoma cell surface. The decapeptide and nonapeptide are presented in distinct conformations by HLA‐A*02:01 and TCRs from clinically relevant T‐cell clones recognize the nonapeptide poorly. Here, we studied the MEL5 TCR that potently recognizes the nonapeptide. The structure of the MEL5‐HLA‐A*02:01‐AAGIGILTV complex revealed an induced fit mechanism of antigen recognition involving altered peptide–MHC anchoring. This “flexing” at the TCR–peptide–MHC interface to accommodate the peptide antigen explains previously observed incongruences in this well‐studied system and has important implications for future therapeutic approaches. Finally, this study expands upon the mechanisms by which molecular plasticity can influence antigen recognition by T cells

ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning

Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensive, error-prone, and becomes unfeasible with large data volumes. Therefore, machine-driven algorithms are increasingly applied to various bioacoustic signal identification challenges. Nevertheless, biologists still have major difficulties trying to transfer existing animal- and/or scenario-related machine learning approaches to their specific animal datasets and scientific questions. This study presents an animal-independent, open-source deep learning framework, along with a detailed user guide. Three signal identification tasks, commonly encountered in bioacoustics research, were investigated: (1) target signal vs. background noise detection, (2) species classification, and (3) call type categorization. ANIMAL-SPOT successfully segmented human-annotated target signals in data volumes representing 10 distinct animal species and 1 additional genus, resulting in a mean test accuracy of 97.9%, together with an average area under the ROC curve (AUC) of 95.9%, when predicting on unseen recordings. Moreover, an average segmentation accuracy and F1-score of 95.4% was achieved on the publicly available BirdVox-Full-Night data corpus. In addition, multi-class species and call type classification resulted in 96.6% and 92.7% accuracy on unseen test data, as well as 95.2% and 88.4% regarding previous animal-specific machine-based detection excerpts. Furthermore, an Unweighted Average Recall (UAR) of 89.3% outperformed the multi-species classification baseline system of the ComParE 2021 Primate Sub-Challenge. Besides animal independence, ANIMAL-SPOT does not rely on expert knowledge or special computing resources, thereby making deep-learning-based bioacoustic signal identification accessible to a broad audience

Validation of an open source, remote web‐based eye‐tracking method (WebGazer) for research in early childhood

Measuring eye movements remotely via the participant's webcam promises to be an attractive methodological addition to in-person eye-tracking in the lab. However, there is a lack of systematic research comparing remote web-based eye-tracking with in-lab eye-tracking in young children. We report a multi-lab study that compared these two measures in an anticipatory looking task with toddlers using WebGazer.js and jsPsych. Results of our remotely tested sample of 18-27-month-old toddlers (N = 125) revealed that web-based eye-tracking successfully captured goal-based action predictions, although the proportion of the goal-directed anticipatory looking was lower compared to the in-lab sample (N = 70). As expected, attrition rate was substantially higher in the web-based (42%) than the in-lab sample (10%). Excluding trials based on visual inspection of the match of time-locked gaze coordinates and the participant's webcam video overlayed on the stimuli was an important preprocessing step to reduce noise in the data. We discuss the use of this remote web-based method in comparison with other current methodological innovations. Our study demonstrates that remote web-based eye-tracking can be a useful tool for testing toddlers, facilitating recruitment of larger and more diverse samples; a caveat to consider is the larger drop-out rate