Peptidomics analysis of in vitro digested wheat breads: Effect of genotype and environment on protein digestibility and release of celiac disease and wheat allergy related epitopes.

Wheat proteins can trigger immunogenic reactions due to their resistance to digestion and immunostimulatory epitopes. Here, we investigated the peptidomic map of partially digested bread samples and the fingerprint of epitope diversity from 16 wheat genotypes grown in two environmental conditions. Flour protein content and composition were characterized; gastric and jejunal peptides were quantified using LC-MS/MS, and genotypes were classified into high or low bread protein digestibility. Differences in flour protein content and peptide composition distinguish high from low digestibility genotypes in both growing environments. No common peptide signature was found between high- and low-digestible genotypes; however, the celiac or allergen epitopes were noted not to be higher in low-digestible genotypes. Overall, this study established a peptidomic and epitope diversity map of digested wheat bread and provided new insights and correlations between weather conditions, genotypes, digestibility and wheat sensitivities such as celiac disease and wheat allergy.

PENDAH program for parents with children with attention deficit hyperactivity disorder. French adaptation of a behavioral parent training group: pilot study.

Behavioral parent training (BPT) is recognized as an effective part of the care offered to children with attention deficit hyperactivity disorder (ADHD). The aim of this pilot study was to objectively examine the effect that this intervention may have on motor activity, in addition to the measures classically found in this type of study. Parents of 24 school-aged children (6-12 year) with ADHD who met eligibility criteria were enrolled in the study. Before, after and five months after the intervention, we used three-dimensional accelerometers over one-week periods to measure the children's motor activity, and questionnaires for parental stress, quality of life, ADHD symptoms, anxiety and sensory disorders. To measure motor activity, a control group of normotypic children matched for age, sex and socio-professional category was set up. The experimental group showed slight decreases in motor activity compared with the control group, particularly in the classroom. The intervention showed improvements for parents in average stress and quality of life, and for children in average intensity global ADHD symptom, inattention, opposition and aggression, in line with previous studies on the effectiveness of BPT. The trial is the first clinical study to assess the effects of BPT on motor activity in children with ADHD.

Genomic data integration tutorial, a plant case study.

BACKGROUND: The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts. RESULTS: To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration. CONCLUSION: The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder.

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Asterics: a simple tool for the ExploRation and Integration of omiCS data.

BACKGROUND: The rapid development of omics acquisition techniques has induced the production of a large volume of heterogeneous and multi-level omics datasets, which require specific and sometimes complex analyses to obtain relevant biological information. Here, we present ASTERICS (version 2.5), a publicly available web interface for the analyses of omics datasets. RESULTS: ASTERICS is designed to make both standard and complex exploratory and integration analysis workflows easily available to biologists and to provide high quality interactive plots. Special care has been taken to provide a comprehensive documentation of the implemented analyses and to guide users toward sound analysis choices regarding some specific omics data. Data and analyses are organized in a comprehensive graphical workflow within ASTERICS workspace to facilitate the understanding of successive data editions and analyses leading to a given result. CONCLUSION: ASTERICS provides an easy to use platform for omics data exploration and integration. The modular organization of its open source code makes it easy to incorporate new workflows and analyses by external contributors. ASTERICS is available at https://asterics.miat.inrae.fr and can also be deployed using provided docker images.

Multicolor flow cytometry in clinical samples for platelet signaling assessment.

Background: Availability of multichannel cytometers and specific commercial antibodies makes flow cytometry a new option to simultaneously assess multiple intracellular platelet signaling pathways for clinical purposes, in small volume of blood or low platelet count. Objectives: To describe a multicolor flow cytometry with fluorescent barcoding technique for screening signaling pathways downstream membrane receptors of major platelet agonists (adenosine diphosphate, thrombin, thromboxane, and collagen). Methods: By comparison with immunoblotting, we first selected the target phosphoproteins, AKT, P38MAPK, LIMK, and SPL76; the times of stimulation; and phosphoflow barcoding conditions. We then performed a clinical study on whole blood of patients without evidence of blood platelet disorder on standard biological screening, consulting for trivial or occasionally provoked bleeds without familial antecedent (bleeding of unknown origin, n = 23) or type-1 von Willebrand disease (n = 9). In addition, we included a small group of patients with definite platelet disorders (Glanzmann thrombasthenia, δ-storage pool deficiency, and immune glycoprotein VI-related disease with granule secretion defect). Results: The range, kinetics, and distribution of fluorescence intensity were established for each agonist-target protein combination. Principal component analysis indicates a correlation in response to a target phosphoprotein (AKT and P38MAPK) to different agonists but no correlation in the response of different target phosphoproteins to the same agonist. The heterogeneity of individual responses in the whole population displayed was analyzed using clustering algorithm. Patients with platelet storage pool deficiency were positioned as lowest responders on the heatmap. Conclusion: In complement of functional tests, this study introduces a new approach for rapid platelet signaling profiling in clinical practice.

Improvement of variables interpretability in kernel PCA.

BACKGROUND: Kernel methods have been proven to be a powerful tool for the integration and analysis of high-throughput technologies generated data. Kernels offer a nonlinear version of any linear algorithm solely based on dot products. The kernelized version of principal component analysis is a valid nonlinear alternative to tackle the nonlinearity of biological sample spaces. This paper proposes a novel methodology to obtain a data-driven feature importance based on the kernel PCA representation of the data. RESULTS: The proposed method, kernel PCA Interpretable Gradient (KPCA-IG), provides a data-driven feature importance that is computationally fast and based solely on linear algebra calculations. It has been compared with existing methods on three benchmark datasets. The accuracy obtained using KPCA-IG selected features is equal to or greater than the other methods' average. Also, the computational complexity required demonstrates the high efficiency of the method. An exhaustive literature search has been conducted on the selected genes from a publicly available Hepatocellular carcinoma dataset to validate the retained features from a biological point of view. The results once again remark on the appropriateness of the computed ranking. CONCLUSIONS: The black-box nature of kernel PCA needs new methods to interpret the original features. Our proposed methodology KPCA-IG proved to be a valid alternative to select influential variables in high-dimensional high-throughput datasets, potentially unravelling new biological and medical biomarkers.

Autonomic Nervous System Adaptation and Circadian Rhythm Disturbances of the Cardiovascular System in a Ground-Based Murine Model of Spaceflight.

Whether in real or simulated microgravity, Humans or animals, the kinetics of cardiovascular adaptation and its regulation by the autonomic nervous system (ANS) remain controversial. In this study, we used hindlimb unloading (HU) in 10 conscious mice. Blood pressure (BP), heart rate (HR), temperature, and locomotor activity were continuously monitored with radio-telemetry, during 3 days of control, 5 days of HU, and 2 days of recovery. Six additional mice were used to assess core temperature. ANS activity was indirectly determined by analyzing both heart rate variability (HRV) and baroreflex sensitivity (BRS). Our study showed that HU induced an initial bradycardia, accompanied by an increase in vagal activity markers of HRV and BRS, together with a decrease in water intake, indicating the early adaptation to fluid redistribution. During HU, BRS was reduced; temperature and BP circadian rhythms were altered, showing a loss in day/night differences, a decrease in cycle amplitude, a drop in core body temperature, and an increase in day BP suggestive of a rise in sympathetic activity. Reloading induced resting tachycardia and a decrease in BP, vagal activity, and BRS. In addition to cardiovascular deconditioning, HU induces disruption in day/night rhythmicity of locomotor activity, temperature, and BP.

Multi-omics Data Integration in the Context of Plant Abiotic Stress Signaling.

In order to answer new biological questions, high-throughput data generated by new biotechnologies can be very meaningful but require specific and adapted statistical treatments. Thus, in the context of abiotic stress signaling studies, understanding the integration of cascading mechanisms from stress perception to biochemical and physiological adjustments necessarily entails efficient and valid analysis of multilevel and heterogeneous data. In this chapter, we propose examples to manage, analyze, and integrate multi-omics heterogeneous data. This workflow suggests and follows different general biological questions or issues answered with detailed code, data analysis, multiple visualizations, and always followed by brief interpretations. We illustrated this using the mixOmics package for the R software, as it specifically provides tools to address vertical and horizontal data integration issues. In order to illustrate this workflow, we used the usual omics datasets biologists can generate (phenomics, metabolomics, proteomics, and transcriptomics). These data were collected from two organs (leaf rosettes, floral stems) of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. They are available in the R package WallOmicsData. The workflow presented here is not limited to Arabidopsis thaliana and can be applied to any plant species. It can even be largely deployed to whatever the organisms of interest and the biological questions may be.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior.

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Multivariate Analysis with the R Package mixOmics.

The high-dimensional nature of proteomics data presents challenges for statistical analysis and biological interpretation. Multivariate analysis, combined with insightful visualization can help to reveal the underlying patterns in complex biological data. This chapter introduces the R package mixOmics which focuses on data exploration and integration. We first introduce methods for single data sets: both Principal Component Analysis, which can identify the patterns of variance present in data, and sparse Partial Least Squares Discriminant Analysis, which aims to identify variables that can classify samples into known groups. We then present integrative methods with Projection to Latent Structures and further extensions for discriminant analysis. We illustrate each technique on a breast cancer multi-omics study and provide the R code and data as online supplementary material for readers interested in reproducing these analyses.

Toxic effect and inability of L-homoserine to be a nitrogen source for growth of Escherichia coli resolved by a combination of in vivo evolution engineering and omics analyses.

L-homoserine is a pivotal intermediate in the carbon and nitrogen metabolism of E. coli. However, this non-canonical amino acid cannot be used as a nitrogen source for growth. Furthermore, growth of this bacterium in a synthetic media is potently inhibited by L-homoserine. To understand this dual effect, an adapted laboratory evolution (ALE) was applied, which allowed the isolation of a strain able to grow with L-homoserine as the nitrogen source and was, at the same time, desensitized to growth inhibition by this amino acid. Sequencing of this evolved strain identified only four genomic modifications, including a 49 bp truncation starting from the stop codon of thrL. This mutation resulted in a modified thrL locus carrying a thrL* allele encoding a polypeptide 9 amino acids longer than the thrL encoded leader peptide. Remarkably, the replacement of thrL with thrL* in the original strain MG1655 alleviated L-homoserine inhibition to the same extent as strain 4E, but did not allow growth with this amino acid as a nitrogen source. The loss of L-homoserine toxic effect could be explained by the rapid conversion of L-homoserine into threonine via the thrL*-dependent transcriptional activation of the threonine operon thrABC. On the other hand, the growth of E. coli on a mineral medium with L-homoserine required an activation of the threonine degradation pathway II and glycine cleavage system, resulting in the release of ammonium ions that were likely recaptured by NAD(P)-dependent glutamate dehydrogenase. To infer about the direct molecular targets of L-homoserine toxicity, a transcriptomic analysis of wild-type MG1655 in the presence of 10 mM L-homoserine was performed, which notably identified a potent repression of locomotion-motility-chemotaxis process and of branched-chain amino acids synthesis. Since the magnitude of these effects was lower in a ΔthrL mutant, concomitant with a twofold lower sensitivity of this mutant to L-homoserine, it could be argued that growth inhibition by L-homoserine is due to the repression of these biological processes. In addition, L-homoserine induced a strong upregulation of genes in the sulfate reductive assimilation pathway, including those encoding its transport. How this non-canonical amino acid triggers these transcriptomic changes is discussed.

Muscle atrophy phenotype gene expression during spaceflight is linked to a metabolic crosstalk in both the liver and the muscle in mice.

Human expansion in space is hampered by the physiological risks of spaceflight. The muscle and the liver are among the most affected tissues during spaceflight and their relationships in response to space exposure have never been studied. We compared the transcriptome response of liver and quadriceps from mice on NASA RR1 mission, after 37 days of exposure to spaceflight using GSEA, ORA, and sparse partial least square-differential analysis. We found that lipid metabolism is the most affected biological process between the two organs. A specific gene cluster expression pattern in the liver strongly correlated with glucose sparing and an energy-saving response affecting high energy demand process gene expression such as DNA repair, autophagy, and translation in the muscle. Our results show that impaired lipid metabolism gene expression in the liver and muscle atrophy gene expression are two paired events during spaceflight, for which dietary changes represent a possible countermeasure.

Plasma proteomics reveals crosstalk between lipid metabolism and immunity in dairy cows receiving essential fatty acids and conjugated linoleic acid.

Essential fatty acids (EFA) and conjugated linoleic acids (CLA) are unsaturated fatty acids with immune-modulatory effects, yet their synergistic effect is poorly understood in dairy cows. This study aimed at identifying differentially abundant proteins (DAP) and their associated pathways in dairy cows supplied with a combination of EFA and CLA during the transition from antepartum (AP) to early postpartum (PP). Sixteen Holstein cows were abomasally infused with coconut oil as a control (CTRL) or a mixture of EFA (linseed + safflower oil) and CLA (Lutalin, BASF) (EFA + CLA) from - 63 to + 63 days relative to parturition. Label-free quantitative proteomics was performed on plasma samples collected at days - 21, + 1, + 28, and + 63. During the transition time, DAP, consisting of a cluster of apolipoproteins (APO), including APOE, APOH, and APOB, along with a cluster of immune-related proteins, were related to complement and coagulation cascades, inflammatory response, and cholesterol metabolism. In response to EFA + CLA, specific APO comprising APOC3, APOA1, APOA4, and APOC4 were increased in a time-dependent manner; they were linked to triglyceride-enriched lipoprotein metabolisms and immune function. Altogether, these results provide new insights into metabolic and immune adaptation and crosstalk between them in transition dairy cows divergent in EFA + CLA status.

Longitudinal liver proteome profiling in dairy cows during the transition from gestation to lactation: Investigating metabolic adaptations and their interactions with fatty acids supplementation via repeated measurements ANOVA-simultaneous component analysis.

Repeated measurements analysis of variance - simultaneous component analysis (ASCA) has been developed to handle complex longitudinal omics datasets and combine novel information with existing data. Herein, we aimed at applying ASCA to 64 liver proteomes collected at 4-time points (day -21, +1, +28, and + 63 relative to parturition) from 16 Holstein cows treated from 9 wk. antepartum to 9 wk. postpartum (PP) with coconut oil (CTRL) or a mixture of essential fatty acids (EFA) and conjugated linoleic acid (CLA) (EFA + CLA). The ASCA modeled 116, 43, and 97 differentially abundant proteins (DAP) during the transition to lactation, between CTRL and EFA + CLA, and their interaction, respectively. Time-dependent DAP were annotated to pathways related to the metabolism of carbohydrates, FA, and amino acid in the PP period. The DAP between FA and the interaction effect were annotated to the metabolism of xenobiotics by cytochrome P450, drug metabolism - cytochrome P450, retinol metabolism, and steroid hormone biosynthesis. Collectively, ASCA provided novel information on molecular markers of metabolic adaptations and their interactions with EFA + CLA supplementation. Bioinformatics analysis suggested that supplemental EFA + CLA amplified hepatic FA oxidation; cytochrome P450 was enriched to maintain metabolic homeostasis by oxidation/detoxification of endogenous compounds and xenobiotics. SIGNIFICANCE: This report is among the first ones applying repeated measurement analysis of variance-simultaneous component analysis (ASCA) to deal with longitudinal proteomics results. ASCA separately identified differentially abundant proteins (DAP) in 'transition time', 'between fatty acid treatments', and 'their interaction'. We first identified the molecular signature of hepatic metabolic adaptations during postpartum negative energy balance; the enriched pathways were well-known pathways related to mobilizing fatty acids (FA) and amino acids to support continuous energy production through fatty acid oxidation, TCA cycle, and gluconeogenesis. Some of the DAP were not previously reported in transition dairy cows. Secondly, we provide novel information on the mechanisms by which supplemented essential FA and conjugated linoleic acids interact with hepatic metabolism. In this regard, FA amplified hepatic detoxifying and oxidation capacity through ligand activation of nuclear receptors. Finally, we briefly compared the strengths and weaknesses of the ASCA model with PLS-DA and outlined why these methods are complementary.

Liver proteome profiling in dairy cows during the transition from gestation to lactation: Effects of supplementation with essential fatty acids and conjugated linoleic acids as explored by PLS-DA.

This study aimed at investigating the synergistic effects of essential fatty acids (EFA) and conjugated linoleic acids (CLA) on the liver proteome profile of dairy cows during the transition to lactation. 16 Holstein cows were infused from 9 wk. antepartum to 9 wk. postpartum into the abomasum with either coconut oil (CTRL) or a mixture of EFA (linseed + safflower oil) and CLA (EFA + CLA). Label-free quantitative proteomics was performed in liver tissue biopsied at days -21, +1, +28, and + 63 relative to calving. Differentially abundant proteins (DAP) between treatment groups were identified at the intersection between a multivariate and a univariate analysis. In total, 1680 proteins were identified at each time point, of which between groups DAP were assigned to the metabolism of xenobiotics by cytochrome P450, drug metabolism - cytochrome P450, steroid hormone biosynthesis, glycolysis/gluconeogenesis, and glutathione metabolism. Cytochrome P450, as a central hub, enriched with specific CYP enzymes comprising: CYP51A1 (d - 21), CYP1A1 & CYP4F2 (d + 28), and CYP4V2 (d + 63). Collectively, supplementation of EFA + CLA in transition cows impacted hepatic lipid metabolism and enriched several common biological pathways at all time points that were mainly related to ω-oxidation of fatty acids through the Cytochrome p450 pathway. SIGNIFICANCE: In three aspects this manuscript is notable. First, this is among the first longitudinal proteomics studies in nutrition of dairy cows. The selected time points are critical periods around parturition with profound endocrine and metabolic adaptations. Second, our findings provided novel information on key drivers of biologically relevant pathways suggested according to previously reported performance, zootechnical, and metabolism data (already published elsewhere). Third, our results revealed the role of cytochrome P450 that is hardly investigated, and of ω-oxidation pathways in the metabolism of fatty acids with the involvement of specific enzymes.

Rv0180c contributes to Mycobacterium tuberculosis cell shape and to infectivity in mice and macrophages.

Mycobacterium tuberculosis, the main causative agent of human tuberculosis, is transmitted from person to person via small droplets containing very few bacteria. Optimizing the chance to seed in the lungs is therefore a major adaptation to favor survival and dissemination in the human population. Here we used TnSeq to identify genes important for the early events leading to bacterial seeding in the lungs. Beside several genes encoding known virulence factors, we found three new candidates not previously described: rv0180c, rv1779c and rv1592c. We focused on the gene, rv0180c, of unknown function. First, we found that deletion of rv0180c in M. tuberculosis substantially reduced the initiation of infection in the lungs of mice. Next, we established that Rv0180c enhances entry into macrophages through the use of complement-receptor 3 (CR3), a major phagocytic receptor for M. tuberculosis. Silencing CR3 or blocking the CR3 lectin site abolished the difference in entry between the wild-type parental strain and the Δrv0180c::km mutant. However, we detected no difference in the production of both CR3-known carbohydrate ligands (glucan, arabinomannan, mannan), CR3-modulating lipids (phthiocerol dimycocerosate), or proteins in the capsule of the Δrv0180c::km mutant in comparison to the wild-type or complemented strains. By contrast, we established that Rv0180c contributes to the functionality of the bacterial cell envelope regarding resistance to toxic molecule attack and cell shape. This alteration of bacterial shape could impair the engagement of membrane receptors that M. tuberculosis uses to invade host cells, and open a new perspective on the modulation of bacterial infectivity.

Quantitative Analysis of Cell Aggregation Dynamics Identifies HDAC Inhibitors as Potential Regulators of Cancer Cell Clustering.

Characterization of the molecular mechanisms involved in tumor cell clustering could open the way to new therapeutic strategies. Towards this aim, we used an in vitro quantitative procedure to monitor the anchorage-independent cell aggregation kinetics in a panel of 25 cancer cell lines. The analysis of the relationship between selected aggregation dynamic parameters and the gene expression data for these cell lines from the CCLE database allowed identifying genes with expression significantly associated with aggregation parameter variations. Comparison of these transcripts with the perturbagen signatures from the Connectivity Map resource highlighted that they were strongly correlated with the transcriptional signature of most histone deacetylase (HDAC) inhibitors. Experimental evaluation of two HDAC inhibitors (SAHA and ISOX) showed that they inhibited the initial step of in vitro tumor cell aggregation. This validates our findings and reinforces the potential interest of HDCA inhibitors to prevent metastasis spreading.

Gene Expression Signature Associated with Clinical Outcome in ALK-Positive Anaplastic Large Cell Lymphoma.

Anaplastic large cell lymphomas associated with ALK translocation have a good outcome after CHOP treatment; however, the 2-year relapse rate remains at 30%. Microarray gene-expression profiling of 48 samples obtained at diagnosis was used to identify 47 genes that were differentially expressed between patients with early relapse/progression and no relapse. In the relapsing group, the most significant overrepresented genes were related to the regulation of the immune response and T-cell activation while those in the non-relapsing group were involved in the extracellular matrix. Fluidigm technology gave concordant results for 29 genes, of which FN1, FAM179A, and SLC40A1 had the strongest predictive power after logistic regression and two classification algorithms. In parallel with 39 samples, we used a Kallisto/Sleuth pipeline to analyze RNA sequencing data and identified 20 genes common to the 28 genes validated by Fluidigm technology-notably, the FAM179A and FN1 genes. Interestingly, FN1 also belongs to the gene signature predicting longer survival in diffuse large B-cell lymphomas treated with CHOP. Thus, our molecular signatures indicate that the FN1 gene, a matrix key regulator, might also be involved in the prognosis and the therapeutic response in anaplastic lymphomas.

When Bigger Is Better: 3D RNA Profiling of the Developing Head in the Catshark Scyliorhinus canicula.

We report the adaptation of RNA tomography, a technique allowing spatially resolved, genome-wide expression profiling, to a species occupying a key phylogenetic position in gnathostomes, the catshark Scyliorhinus canicula. We focused analysis on head explants at an embryonic stage, shortly following neural tube closure and of interest for a number of developmental processes, including early brain patterning, placode specification or the establishment of epithalamic asymmetry. As described in the zebrafish, we have sequenced RNAs extracted from serial sections along transverse, horizontal and sagittal planes, mapped the data onto a gene reference taking advantage of the high continuity genome recently released in the catshark, and projected read counts onto a digital model of the head obtained by confocal microscopy. This results in the generation of a genome-wide 3D atlas, containing expression data for most protein-coding genes in a digital model of the embryonic head. The digital profiles obtained for candidate forebrain regional markers along antero-posterior, dorso-ventral and left-right axes reproduce those obtained by in situ hybridization (ISH), with expected relative organizations. We also use spatial autocorrelation and correlation as measures to analyze these data and show that they provide adequate statistical tools to extract novel expression information from the model. These data and tools allow exhaustive searches of genes exhibiting any predefined expression characteristic, such a restriction to a territory of interest, thus providing a reference for comparative analyses across gnathostomes. This methodology appears best suited to species endowed with large embryo or organ sizes and opens novel perspectives to a wide range of evo-devo model organisms, traditionally counter-selected on size criterion.