A Combined Metabonomic and Transcriptomic Approach to Investigate Metabolism during Development in the Chick Chorioallantoic Membrane

Predicting Valproate-Induced Liver Injury Using Metabolomic Analysis of Ex Ovo Chick Embryo Allantoic Fluid

The use of sensitive animals in toxicological studies tends to be limited. Even though cell culture is an attractive alternative, it has some limitations. Therefore, we investigated the potential of the metabolomic profiling of the allantoic fluid (AF) from ex ovo chick embryos to predict the hepatotoxicity of valproate (VPA). To this end, the metabolic changes occurring during embryo development and following exposure to VPA were assessed using ¹H-NMR spectroscopy. During embryonic development, our findings indicated a metabolism progressively moving from anaerobic to aerobic, mainly based on lipids as the energy source. Next, liver histopathology of VPA-exposed embryos revealed abundant microvesicles indicative of steatosis and was metabolically confirmed via the determination of lipid accumulation in AF. VPA-induced hepatotoxicity was further demonstrated by (i) lower glutamine levels, precursors of glutathione, and decreased β-hydroxybutyrate, an endogenous antioxidant; (ii) changes in lysine levels, a precursor of carnitine, which is essential in the transport of fatty acids to the mitochondria and whose synthesis is known to be reduced by VPA; and (iii) choline accumulation that promotes the export of hepatic triglycerides. In conclusion, our results support the use of the ex ovo chick embryo model combined with the metabolomic assessment of AF to rapidly predict drug-induced hepatotoxicity.

Phenology of the transcriptome coincides with the physiology of double-crested cormorant embryonic development

The rigorous timing of the dynamic transcriptome within the embryo has to be well orchestrated for normal development. Identifying the phenology of the transcriptome along with the physiology of embryonic development in birds may suggest periods of increased sensitivity to contaminant exposure depending on the contaminant's mechanism of action. Double-crested cormorants (Nannopterum auritum, formerly Phalacrocorax auritus) are commonly used in ecotoxicological studies, but relatively little is known about their functional transcriptome profile in early development. In this study, we tracked the phenology of the transcriptome during N. auritum embryogenesis. Fresh eggs were collected from a reference site and artificially incubated from collection until four days prior to hatching. Embryos were periodically sampled throughout incubation for a total of seven time points. A custom microarray was designed for cormorants (over 14,000 probes) and used for transcriptome analysis in whole body (days 5, 8) and liver tissue (days 12, 14, 16, 20, 24). Three main developmental periods (early, mid, and late incubation) were identified with differentially expressed genes, gene sets, and pathways within and between each developmental transition. Overall, the timing of differentially expressed genes and enriched pathways corresponded to previously documented changes in morphology, neurology, or physiology during avian embryonic development. Targeted investigation of a subset of genes involved in endogenous and xenobiotic metabolism (e.g., cytochrome P450 cyp1a, cyp1b1, superoxide dismutase 1 sod1) were expressed in a pattern similar to reported endogenous compound levels. These data can provide insights on normal embryonic development in an ecologically relevant species without any environmental contaminant exposure.

The Chicken Embryo Chorioallantoic Membrane as an In Vivo Model for Photodynamic Therapy

For many decades the chicken embryo chorioallantoic membrane (CAM) has been used for research as an in vivo model in a large number of different fields, including toxicology, bioengineering, and cancer research. More specifically, the CAM is also a suitable and convenient model system in the field of photodynamic therapy (PDT), mainly due to the easy access of its membrane and the possibility of grafting or growing tumors on the membrane and, interestingly, to study the PDT effects on its dense vascular network. In addition, the CAM is simple to handle and cheap. Since the CAM is not innervated until later stages of the embryo development, its use in research is simplified compared to other in vivo models as far as ethical and regulatory issues are concerned. In this review different incubation and drug administration protocols of relevance for PDT are presented. Moreover, data regarding the propagation of light at different wavelengths and CAM development stages are provided. Finally, the effects induced by photobiomodulation on the CAM angiogenesis and its impact on PDT treatment outcome are discussed.

Omics as a Window To Unravel the Dynamic Changes of Egg Components during Chicken Embryonic Development

Chicken egg, as a completely aseptic and self-sufficient biological entity, contains all of the components required for embryonic development. As such, it constitutes not only an excellent model to study the mechanisms of early embryo nutrition and disease origin but can also be used to develop egg-based products with specific applications. Different omics disciplines, like transcriptomics, proteomics, and metabolomics, represent promising approaches to assess nutritional and functional molecules in eggs under development. However, these individual molecules do not act in isolation during the dynamic embryogenic process (e.g., migration, transportation, and absorption). Unless we integrate the information from all of these omics disciplines, there will remain an unbridged gap in the systematic and holistic assessment of the information from one omics level to the other. This integrative review of the dynamic molecular processes of the different chicken egg components involved in embryo development describes the critical interplay between the egg components and their implications in immunity, hematopoiesis, organ formation, and nutrient transport functions during the embryonic process.

Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.

Cancer is one of the most heterogeneous diseases, characterized by diverse morphological, phenotypic, and genomic profiles between tumors and their subtypes. Identifying cancer subtypes can help patients receive precise treatments. With the development of high-throughput technologies, genomics, epigenomics, and transcriptomics data have been generated for large cancer patient cohorts. It is believed that the more omics data we use, the more accurate identification of cancer subtypes. To examine this assumption, we first constructed three classes of benchmarking datasets to conduct a comprehensive evaluation and comparison of ten representative multi-omics data integration methods for cancer subtyping by considering their accuracy, robustness, and computational efficiency. Then, we investigated the influence of different omics data and their various combinations on the effectiveness of cancer subtyping. Our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. We hope that our work may help researchers choose a proper method and an effective data combination when identifying cancer subtypes using data integration methods.

Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration

Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article.

Evaluation of integrative clustering methods for the analysis of multi-omics data

Recent advances in sequencing, mass spectrometry and cytometry technologies have enabled researchers to collect large-scale omics data from the same set of biological samples. The joint analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omic layers. In this work, we present a thorough comparison of a selection of recent integrative clustering approaches, including Bayesian (BCC and MDI) and matrix factorization approaches (iCluster, moCluster, JIVE and iNMF). Based on simulations, the methods were evaluated on their sensitivity and their ability to recover both the correct number of clusters and the simulated clustering at the common and data-specific levels. Standard non-integrative approaches were also included to quantify the added value of integrative methods. For most matrix factorization methods and one Bayesian approach (BCC), the shared and specific structures were successfully recovered with high and moderate accuracy, respectively. An opposite behavior was observed on non-integrative approaches, i.e. high performances on specific structures only. Finally, we applied the methods on the Cancer Genome Atlas breast cancer data set to check whether results based on experimental data were consistent with those obtained in the simulations.

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference.

Transcriptomic and metabolomic data integration

Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.

Preliminary evaluation of gene expression profiles in liver of mice exposed to Taihu Lake drinking water for 90 days

Differential gene expression profiling was performed via DNA microarray in the liver tissue of Mus musculus mice after exposure to drinking water of Taihu Lake for 90 days. A total of 75 differentially expressed candidate genes (DEGs) were identified (1.5-fold, p ≤ 0.05), among which the expression of 29 genes was up-regulated and that of 46 genes was down-regulated. Most DEGs were involved in biological process based on gene ontology mapping analysis. The drinking water of Taihu Lake significantly influenced the expression of genes related to cell proliferation, apoptosis, amino acid metabolism, development and immune responses. Long-term exposure to the Taihu drinking water may result in increased carcinogenic risk.

Advantages of tandem LC-MS for the rapid assessment of tissue-specific metabolic complexity using a pentafluorophenylpropyl stationary phase

In this study, a tandem LC-MS (Waters Xevo TQ) MRM-based MS method was developed for rapid, broad profiling of hydrophilic metabolites from biological samples, in either positive or negative ion modes without the need for an ion pairing reagent, using a reversed-phase pentafluorophenylpropyl (PFPP) column. The developed method was successfully applied to analyze various biological samples from C57BL/6 mice, including urine, duodenum, liver, plasma, kidney, heart, and skeletal muscle. As result, a total 112 of hydrophilic metabolites were detected within 8 min of running time to obtain a metabolite profile of the biological samples. The analysis of this number of hydrophilic metabolites is significantly faster than previous studies. Classification separation for metabolites from different tissues was globally analyzed by PCA, PLS-DA and HCA biostatistical methods. Overall, most of the hydrophilic metabolites were found to have a "fingerprint" characteristic of tissue dependency. In general, a higher level of most metabolites was found in urine, duodenum, and kidney. Altogether, these results suggest that this method has potential application for targeted metabolomic analyzes of hydrophilic metabolites in a wide ranges of biological samples.

Dynamic omics approach identifies nutrition-mediated microbial interactions

"Omics" studies reported to date have dealt with either thoroughly characterized single species or poorly explored meta-microbial communities. However, these techniques are capable of producing highly informative data for the analysis of interactions between two organisms. We examined the bacterial interaction between Escherichia coli O157:H7 (O157) and Bifidobacterium longum (BL) as a pathogenic-commensal bacterial model creating a minimum microbial ecosystem in the gut using dynamic omics approaches, consisting of improved time-lapse 2D-nuclear magnetic resonance (NMR) metabolic profiling, transcriptomic, and proteomic analyses. Our study revealed that the minimum ecosystem was established by bacterial adaptation to the changes in the extracellular environment, primarily by O157, but not by BL. Additionally, the relationship between BL and O157 could be partially regarded as that between a producer and a consumer of nutrients, respectively, especially with regard to serine and aspartate metabolism. Taken together, our profiling system can provide a new insight into the primary metabolic dynamics in microbial ecosystems.