Exploring dynamic metabolomics data with multiway data analysis: a simulation study

Background Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied. Results We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. Conclusion Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

Diversity Analysis in Horse Gram [Macrotyloma uniflorum (Lam.) Verdc.] Mutants

Background: Horse gram is the potential rainfed legume in Indian farming. The major limitation in horse gram breeding is the narrow variability. Variability induction and its estimation would sustain food security. Methods: Variability was induced using gamma rays, electron beam and ethyl methanesulfonate and their combinations. The induced variation for the economic traits, their inheritance and genetic gain were ascertained. Result: The analysis of variance indicated the induction of significant variation for yield attributing traits. The population was grouped into 10 constellations by the virtue of induced variation. The groups I, II and V were the largest comprising of 38, 31 and 19 mutants respectively. The mutants exhibited significant intra and inter group variation. The mutagens induced the maximum variability for plant height (32.24%), 100 seed weight (25.42%) and number of pods per plant (19.18%). The mutants possessed high genotypic and phenotypic coefficients of variation for all the characters except flowering traits and duration. The induced variability for the yield attributing traits possessed significant breeding value as the heritability (86.66%-99.72%) and genetic advance as percent of mean (10.65-81.94) were high and the environmental influence was the minimum.

Endogenous virus sRNA regulates gene expression following genome shock in tomato hybrids

Hybridization and environmental stress trigger genome shock that perturbs patterns of gene expression leading to phenotypic changes. In extreme examples it is associated to transposon mobilization and genome rearrangement. Here we discover a novel alternative mechanism in interspecific Solanum hybrids in which changes to gene expression were associated with DCL2-mediated small (s)RNAs derived from endogenous (para)retroviruses (EPRVs). Correspondingly, the altered patterns of gene expression overlapped with the effects of dcl2 mutation and the changes to sRNA profiles involved 22nt species produced in the DCL2 biogenesis pathway. These findings implicate hybridization-induced genome shock leading to EPRV activation and sRNA silencing as causing changes in gene expression. Such hybridization-induced variation in gene expression could increase the range of traits available for selection in natural evolution or in breeding for agriculture.