Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays.

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants

Web-based visualisation of the transcriptional control network of Escherichia coli

Transcription is one of the basic processes of gene expression, controlled by a complex network of biochemical reactions. Despite its importance, most work on the visualisation of biochemical networks focuses on the representation of metabolic pathways. The visualisation of the complex networks controlling transcription requires the implementation of a hierarchical approach that allows the display of the structure of each regulatory region with its transcription factors and regulated operons. This paper presents a web-based application for the visualisation of transcriptional control networks. It takes as case study the organism Escherichia coli. The definition of the visual components implemented is mainly based on those proposed by Shen-Orr et al., 2002, slightly extended to visualise complex networks. © 2004 - IOS Press and Bioinformation Systems e.V. and the authors. All rights reserved. [accessed 2014 October 15

Analysis of the mtDNA insertion site on chromosome 9L in maize inbreds using fluorescence in situ hybridization

Abstract only availableAlmost all eukaryotic nuclear genomes show evidence of organellar DNA insertions originating from mitochondrial DNA (mtDNA) and chloroplast DNA (cpDNA). While the precise mechanisms of incorporation remain unknown, the phenomenon is frequent and ongoing in many species. In Zea mays, mtDNA insertions differ among inbred lines. A very large mtDNA insertion is found near the centromere of the long arm of chromosome 9 in the B73 inbred. This insertion contains the majority of the mitochondrial genome, while a similarly positioned insertion in the Mo17 inbred line is much smaller. We used recombinant inbred lines from the intermated B73 x Mo17 (IBM) population to determine if the insertions are indeed at the same position. We selected lines with recombination in this region of chromosome 9L. Using two mtDNA probes present in the insertions in both B73 and Mo17, we applied a chromosome painting technique called fluorescence in situ hybridization (FISH) to root-tip metaphase chromosomes and looked for the presence of the mtDNA site on chromosome 9L in the selected IBM lines. If the mtDNA insertion sites in B73 and Mo17 are at different locations, then at least one of the recombinant IBM lines should not display a mtDNA insertion at the chromosome 9 location. However, all of the recombinant IBM lines examined displayed the mtDNA insertion site on chromosome 9L. This indicates that the Mo17 and B73 insertions likely occupy the same region on the chromosome. Furthermore, this suggests that the large mtDNA insertion occurred recently in B73 at a pre-existing site present in both B73 and Mo17.NSF-REU Program in Biological Sciences & Biochemistr

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity

Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence

Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: (i) gene-family–guided splitting and (ii) ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction and have the area under the ROC curve (auROC) values ranging from 0.75 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the hypothesis that the 3′ UTR is more important for fine-tuning mRNA abundance levels while the 5′ UTR is more important for large-scale changes

In silico evolution of diauxic growth

The glucose effect is a well known phenomenon whereby cells, when presented with two different nutrients, show a diauxic growth pattern, i.e. an episode of exponential growth followed by a lag phase of reduced growth followed by a second phase of exponential growth. Diauxic growth is usually thought of as a an adaptation to maximise biomass production in an environment offering two or more carbon sources. While diauxic growth has been studied widely both experimentally and theoretically, the hypothesis that diauxic growth is a strategy to increase overall growth has remained an unconfirmed conjecture. Here, we present a minimal mathematical model of a bacterial nutrient uptake system and metabolism. We subject this model to artificial evolution to test under which conditions diauxic growth evolves. As a result, we find that, indeed, sequential uptake of nutrients emerges if there is competition for nutrients and the metabolism/uptake system is capacity limited. However, we also find that diauxic growth is a secondary effect of this system and that the speed-up of nutrient uptake is a much larger effect. Notably, this speed-up of nutrient uptake coincides with an overall reduction of efficiency. Our two main conclusions are: (i) Cells competing for the same nutrients evolve rapid but inefficient growth dynamics. (ii) In the deterministic models we use here no substantial lag-phase evolves. This suggests that the lag-phase is a consequence of stochastic gene expression