Peptide bonds cleaved by pepsin are affected by the morphology of heat-induced ovalbumin aggregates.

The study aimed to assess the extent to which protein aggregation, and even the modality of aggregation, can affect gastric digestion, down to the nature of the hydrolyzed peptide bonds. By controlling pH and ionic strength during heating, linear or spherical ovalbumin (OVA) aggregates were prepared, then digested with pepsin. Statistical analysis characterized the peptide bonds specifically hydrolyzed versus those not hydrolyzed for a given condition, based on a detailed description of all these bonds. Aggregation limits pepsin access to buried regions of native OVA, but some cleavage sites specific to aggregates reflect specific hydrolysis pathways due to the denaturation-aggregation process. Cleavage sites specific to linear aggregates indicate greater denaturation compared to spherical aggregates, consistent with theoretical models of heat-induced aggregation of OVA. Thus, the peptides released during the gastric phase may vary depending on the aggregation modality. Precisely tuned aggregation may therefore allow subtle control of the digestion process.

Omnibus testing approach for gene-based gene-gene interaction.

Genetic interaction is considered as one of the main heritable component of complex traits. With the emergence of genome-wide association studies (GWAS), a collection of statistical methods dedicated to the identification of interaction at the SNP level have been proposed. More recently, gene-based gene-gene interaction testing has emerged as an attractive alternative as they confer advantage in both statistical power and biological interpretation. Most of the gene-based interaction methods rely on a multidimensional modeling of the interaction, thus facing a lack of robustness against the huge space of interaction patterns. In this paper, we study a global testing approaches to address the issue of gene-based gene-gene interaction. Based on a logistic regression modeling framework, all SNP-SNP interaction tests are combined to produce a gene-level test for interaction. We propose an omnibus test that takes advantage of (1) the heterogeneity between existing global tests and (2) the complementarity between allele-based and genotype-based coding of SNPs. Through an extensive simulation study, it is demonstrated that the proposed omnibus test has the ability to detect with high power the most common interaction genetic models with one causal pair as well as more complex genetic models where more than one causal pair is involved. On the other hand, the flexibility of the proposed approach is shown to be robust and improves power compared to single global tests in replication studies. Furthermore, the application of our procedure to real datasets confirms the adaptability of our approach to replicate various gene-gene interactions.

Genetic architecture and genomic selection of fatty acid composition predicted by Raman spectroscopy in rainbow trout.

BACKGROUND: In response to major challenges regarding the supply and sustainability of marine ingredients in aquafeeds, the aquaculture industry has made a large-scale shift toward plant-based substitutions for fish oil and fish meal. But, this also led to lower levels of healthful n-3 long-chain polyunsaturated fatty acids (PUFAs)-especially eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids-in flesh. One potential solution is to select fish with better abilities to retain or synthesise PUFAs, to increase the efficiency of aquaculture and promote the production of healthier fish products. To this end, we aimed i) to estimate the genetic variability in fatty acid (FA) composition in visceral fat quantified by Raman spectroscopy, with respect to both individual FAs and groups under a feeding regime with limited n-3 PUFAs; ii) to study the genetic and phenotypic correlations between FAs and processing yields- and fat-related traits; iii) to detect QTLs associated with FA composition and identify candidate genes; and iv) to assess the efficiency of genomic selection compared to pedigree-based BLUP selection. RESULTS: Proportions of the various FAs in fish were indirectly estimated using Raman scattering spectroscopy. Fish were genotyped using the 57 K SNP Axiom™ Trout Genotyping Array. Following quality control, the final analysis contained 29,652 SNPs from 1382 fish. Heritability estimates for traits ranged from 0.03 ± 0.03 (n-3 PUFAs) to 0.24 ± 0.05 (n-6 PUFAs), confirming the potential for genomic selection. n-3 PUFAs are positively correlated to a decrease in fat deposition in the fillet and in the viscera but negatively correlated to body weight. This highlights the potential interest to combine selection on FA and against fat deposition to improve nutritional merit of aquaculture products. Several QTLs were identified for FA composition, containing multiple candidate genes with indirect links to FA metabolism. In particular, one region on Omy1 was associated with n-6 PUFAs, monounsaturated FAs, linoleic acid, and EPA, while a region on Omy7 had effects on n-6 PUFAs, EPA, and linoleic acid. When we compared the effectiveness of breeding programmes based on genomic selection (using a reference population of 1000 individuals related to selection candidates) or on pedigree-based selection, we found that the former yielded increases in selection accuracy of 12 to 120% depending on the FA trait. CONCLUSION: This study reveals the polygenic genetic architecture for FA composition in rainbow trout and confirms that genomic selection has potential to improve EPA and DHA proportions in aquaculture species.

Statistical modeling of in vitro pepsin specificity.

The specificity of pepsin, the major protease of gastric digestion, has been previously investigated, but only regarding the primary sequence of the protein substrates. The present study aimed to consider in addition physicochemical and structural characteristics, at the molecular and sub-molecular scales. For six different proteins submitted to in vitro gastric digestion, the peptide bonds cleaved were determined from the peptides released and identified by LC-MS/MS. An original statistical approach, based on propensity scores calculated for each amino acid residue on both sides of the peptide bonds, concluded that preferential cleavage occurred after Leu and Phe, and before Ile. Moreover, reliable statistical models developed for predicting peptide bond cleavage, highlighted the predominant role of the amino acid residues at the N-terminal side of the peptide bonds, up to the seventh position (P7 and P7'). The significant influence of hydrophobicity, charge and structural constraints around the peptide bonds was also evidenced.

A functional generalized F-test for signal detection with applications to event-related potentials significance analysis.

Motivated by the analysis of complex dependent functional data such as event-related brain potentials (ERP), this paper considers a time-varying coefficient multivariate regression model with fixed-time covariates for testing global hypotheses about population mean curves. Based on a reduced-rank modeling of the time correlation of the stochastic process of pointwise test statistics, a functional generalized F-test is proposed and its asymptotic null distribution is derived. Our analytical results show that the proposed test is more powerful than functional analysis of variance testing methods and competing signal detection procedures for dependent data. Simulation studies confirm such power gain for data with patterns of dependence similar to those observed in ERPs. The new testing procedure is illustrated with an analysis of the ERP data from a study of neural correlates of impulse control.

A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs.

BACKGROUND: Animal's efficiency in converting feed into lean gain is a critical issue for the profitability of meat industries. This study aimed to describe shared and specific molecular responses in different tissues of pigs divergently selected over eight generations for residual feed intake (RFI). RESULTS: Pigs from the low RFI line had an improved gain-to-feed ratio during the test period and displayed higher leanness but similar adiposity when compared with pigs from the high RFI line at 132 days of age. Transcriptomics data were generated from longissimus muscle, liver and two adipose tissues using a porcine microarray and analyzed for the line effect (n = 24 pigs per line). The most apparent effect of the line was seen in muscle, whereas subcutaneous adipose tissue was the less affected tissue. Molecular data were analyzed by bioinformatics and subjected to multidimensional statistics to identify common biological processes across tissues and key genes participating to differences in the genetics of feed efficiency. Immune response, response to oxidative stress and protein metabolism were the main biological pathways shared by the four tissues that distinguished pigs from the low or high RFI lines. Many immune genes were under-expressed in the four tissues of the most efficient pigs. The main genes contributing to difference between pigs from the low vs high RFI lines were CD40, CTSC and NTN1. Different genes associated with energy use were modulated in a tissue-specific manner between the two lines. The gene expression program related to glycogen utilization was specifically up-regulated in muscle of pigs from the low RFI line (more efficient). Genes involved in fatty acid oxidation were down-regulated in muscle but were promoted in adipose tissues of the same pigs when compared with pigs from the high RFI line (less efficient). This underlined opposite line-associated strategies for energy use in skeletal muscle and adipose tissue. Genes related to cholesterol synthesis and efflux in liver and perirenal fat were also differentially regulated in pigs from the low vs high RFI lines. CONCLUSIONS: Non-productive functions such as immunity, defense against pathogens and oxidative stress contribute likely to inter-individual variations in feed efficiency.

Improving cross-study prediction through addon batch effect adjustment or addon normalization.

Motivation: To date most medical tests derived by applying classification methods to high-dimensional molecular data are hardly used in clinical practice. This is partly because the prediction error resulting when applying them to external data is usually much higher than internal error as evaluated through within-study validation procedures. We suggest the use of addon normalization and addon batch effect removal techniques in this context to reduce systematic differences between external data and the original dataset with the aim to improve prediction performance. Results: We evaluate the impact of addon normalization and seven batch effect removal methods on cross-study prediction performance for several common classifiers using a large collection of microarray gene expression datasets, showing that some of these techniques reduce prediction error. Availability and Implementation: All investigated addon methods are implemented in our R package bapred. Contact: hornung@ibe.med.uni-muenchen.de. Supplementary information: Supplementary data are available at Bioinformatics online.

Sparse factor model for co-expression networks with an application using prior biological knowledge.

Inference on gene regulatory networks from high-throughput expression data turns out to be one of the main current challenges in systems biology. Such networks can be very insightful for the deep understanding of interactions between genes. Because genes-gene interactions is often viewed as joint contributions to known biological mechanisms, inference on the dependence among gene expressions is expected to be consistent to some extent with the functional characterization of genes which can be derived from ontologies (GO, KEGG, …). The present paper introduces a sparse factor model as a general framework either to account for a prior knowledge on joint contributions of modules of genes to latent biological processes or to infer on the corresponding co-expression network. We propose an ℓ1 - regularized EM algorithm to fit a sparse factor model for correlation. We demonstrate how it helps extracting modules of genes and more generally improves the gene clustering performance. The method is compared to alternative estimation procedures for sparse factor models of relevance networks in a simulation study. The integration of a biological knowledge based on the gene ontology (GO) is also illustrated on a liver expression data generated to understand adiposity variability in chicken.

Molecular alterations induced by a high-fat high-fiber diet in porcine adipose tissues: variations according to the anatomical fat location.

BACKGROUND: Changing the energy and nutrient source for growing animals may be an effective way of limiting adipose tissue expansion, a response which may depend on the genetic background of the animals. This study aims to describe the transcriptional modulations present in the adipose tissues of two pig lines divergently selected for residual feed intake which were either fed a high-fat high-fiber (HF) diet or an isocaloric low-fat high-starch diet (LF). RESULTS: Transcriptomic analysis using a porcine microarray was performed on 48 pigs (n = 12 per diet and per line) in both perirenal (PRAT) and subcutaneous (SCAT) adipose tissues. There was no interaction between diet and line on either adiposity or transcriptional profiles, so that the diet effect was inferred independently of the line. Irrespective of line, the relative weights of the two fat depots were lower in HF pigs than in LF pigs after 58 days on dietary treatment. In the two adipose tissues, the most apparent effect of the HF diet was the down-regulation of several genes associated with the ubiquitin-proteasome system, which therefore may be associated with dietary-induced modulations in genes acting in apoptotic and cell cycle regulatory pathways. Genes involved in glucose metabolic processes were also down-regulated by the HF diet, with no significant variation or decreased expression of important lipid-related genes such as the low-density lipoprotein receptor and leptin in the two fat pads. The master regulators of glucose and fatty acid homeostasis SREBF1 and MLXIPL, and peroxisome proliferator-activated receptor (PPAR)δ and its heterodimeric partner RXRA were down-regulated by the HF diet. PPARγ which has pleiotropic functions including lipid metabolism and adipocyte differentiation, was however up-regulated by this diet in PRAT and SCAT. Dietary-related modulations in the expression of genes associated with immunity and inflammation were mainly revealed in PRAT. CONCLUSION: A high-fat high-fiber diet depressed glucose and lipid anabolic molecular pathways, thus counteracting adipose tissue expansion. Interaction effects between dietary intake of fiber and lipids on gene expression may modulate innate immunity and inflammation, a response which is of interest with regard to chronic inflammation and its adverse effects on health and performance.

Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment.

BACKGROUND: In the context of high-throughput molecular data analysis it is common that the observations included in a dataset form distinct groups; for example, measured at different times, under different conditions or even in different labs. These groups are generally denoted as batches. Systematic differences between these batches not attributable to the biological signal of interest are denoted as batch effects. If ignored when conducting analyses on the combined data, batch effects can lead to distortions in the results. In this paper we present FAbatch, a general, model-based method for correcting for such batch effects in the case of an analysis involving a binary target variable. It is a combination of two commonly used approaches: location-and-scale adjustment and data cleaning by adjustment for distortions due to latent factors. We compare FAbatch extensively to the most commonly applied competitors on the basis of several performance metrics. FAbatch can also be used in the context of prediction modelling to eliminate batch effects from new test data. This important application is illustrated using real and simulated data. We implemented FAbatch and various other functionalities in the R package bapred available online from CRAN. RESULTS: FAbatch is seen to be competitive in many cases and above average in others. In our analyses, the only cases where it failed to adequately preserve the biological signal were when there were extremely outlying batches and when the batch effects were very weak compared to the biological signal. CONCLUSIONS: As seen in this paper batch effect structures found in real datasets are diverse. Current batch effect adjustment methods are often either too simplistic or make restrictive assumptions, which can be violated in real datasets. Due to the generality of its underlying model and its ability to perform well FAbatch represents a reliable tool for batch effect adjustment for most situations found in practice.

A factor-adjusted multiple testing procedure for ERP data analysis.

Event-related potentials (ERPs) are now widely collected in psychological research to determine the time courses of mental events. When event-related potentials from treatment conditions are compared, often there is no a priori information on when or how long the differences should occur. Testing simultaneously for differences over the entire set of time points creates a serious multiple comparison problem in which the probability of false positive errors must be controlled, while maintaining reasonable power for correct detection. In this work, we extend the factor-adjusted multiple testing procedure developed by Friguet, Kloareg, and Causeur (Journal of the American Statistical Association, 104, 1406-1415, 2009) to manage the multiplicity problem in ERP data analysis and compare its performance with that of the Benjamini and Hochberg (Journal of the Royal Statistical Society B, 57, 289-300, 1995) false discovery rate procedure, using simulations. The proposed procedure outperformed the latter in detecting more truly significant time points, in addition to reducing the variability of the false discovery rate, suggesting that corrections for mass multiple testings of ERPs can be much improved by modeling the strong local temporal dependencies.

Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken.

BACKGROUND: Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones.In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM. RESULTS: Using this methodology which accounts for hidden dependence structure among phenotypes, we identified 688 genes that are significantly correlated to the AF trait and we distinguished 5 subtypes for AF trait, which are not observed with gene lists obtained by classical approaches. After exclusion of one of the two lean bird subtypes, linkage analysis revealed a previously undetected QTL on chromosome 5 around 100 cM. Interestingly, the animals of this subtype presented the same q paternal haplotype at the 168 cM QTL. This result strongly suggests that the two QTL are in interaction. In other words, the "q configuration" at the 168 cM QTL could hide the QTL existence in the proximal region at 100 cM. We further show that the proximal QTL interacts with the previous one detected on the chromosome 5 distal region. CONCLUSION: Our results demonstrate that stratifying genetic population by molecular phenotypes followed by QTL analysis on various subtypes can lead to identification of novel and interacting QTL.

A factor model to analyze heterogeneity in gene expression.

BACKGROUND: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene dependence structure. This leads to correlation among test statistics which affects a strong control of the false discovery proportion. A recent method called FAMT allows capturing the gene dependence into factors in order to improve high-dimensional multiple testing procedures. In the subsequent analyses aiming at a functional characterization of the differentially expressed genes, our study shows how these factors can be used both to identify the components of expression heterogeneity and to give more insight into the underlying biological processes. RESULTS: The use of factors to characterize simple patterns of heterogeneity is first demonstrated on illustrative gene expression data sets. An expression data set primarily generated to map QTL for fatness in chickens is then analyzed. Contrarily to the analysis based on the raw data, a relevant functional information about a QTL region is revealed by factor-adjustment of the gene expressions. Additionally, the interpretation of the independent factors regarding known information about both experimental design and genes shows that some factors may have different and complex origins. CONCLUSIONS: As biological information and technological biases are identified in what was before simply considered as statistical noise, analyzing heterogeneity in gene expression yields a new point of view on transcriptomic data.

Hierarchy of factors affecting behavioural signs used for oestrus detection of Holstein and Normande dairy cows in a seasonal calving system.

As oestrous expression of dairy cows has decreased over the last decades oestrus detection has become more difficult. The objective of this study is to identify the main factors that affect oestrus detection in seasonal calving dairy cows, and to establish their relative importance. In each of 5 years 36 Normande and 36 Holstein cows were assigned to a Low or High winter-feeding level group. Half of each group was then assigned to a Low or High pasture-feeding group. The Low-Low strategy resulted in the lowest milk yield and the greatest body condition (BC) loss from calving to nadir BC score (6302 kg; -0.98 unit). The High-High strategy had the converse effect (7549 kg; -0.75 units). Low-High and High-Low strategies had intermediate values. The Normande cows had lower milk yield and BC loss than Holstein cows (6153 kg versus 7620 kg; -0.82 unit versus -1.20 unit). A database of 415 observed spontaneous oestruses was created. Oestruses were classified according to detection signs: (1) standing to be mounted, (2) mounting without standing, (3) other signs without standing or mounting (slight signs). Presence of another cow in oestrus, access to pasture, Normande breed and Low-Low strategy increased standing detection. In the Normande breed, 97% of oestruses were detected by standing while combining the presence of a herdmate in oestrus and access to pasture with a milk production of less than 6550 kg. Holstein cows had a higher frequency of slight signs oestruses than Normande ones, which was associated with a decreased subsequent calving rate (P<0.05). In multiparous Holstein cows, the odds of slight signs detection was multiplied by 7.8 for the High-High group in comparison with the Low-Low group (P<0.05). In our study milk yield had an effect on oestrus detection which was not explained by BC loss. As High-High cows produced more milk than others, we logically found that an increase in milk yield increased slight signs detection. Conversely, as they lost less BC than others, BC loss improved the chance of standing or mounting detection. These two results show that an increase in milk yield may reduce oestrous behaviour even if BC loss is moderate. Oestrus detection is crucial in seasonal compact calving systems. High phenotypic milk yields appear unsuitable with such systems in regard to depressed oestrous behaviour.