A Autoregulation of Nodulation Mutant Transcriptome Analysis Reveals Disruption of the SUNN Pathway Causes Constitutive Expression Changes in Some Genes, but Overall Response to Rhizobia Resembles Wild-Type, Including Induction of and .

Nodule number regulation in legumes is controlled by a feedback loop that integrates nutrient and rhizobia symbiont status signals to regulate nodule development. Signals from the roots are perceived by shoot receptors, including a CLV1-like receptor-like kinase known as SUNN in . In the absence of functional SUNN, the autoregulation feedback loop is disrupted, resulting in hypernodulation.

Discovery of eQTL Alleles Associated with Autism Spectrum Disorder: A Case-Control Study.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by challenges in social communication as well as repetitive or restrictive behaviors. Many genetic associations with ASD have been identified, but most associations occur in a fraction of the ASD population. Here, we searched for eQTL-associated DNA variants with significantly different allele distributions between ASD-affected and control.

Time Series Transcriptome Analysis in Shoot and Root Tissue During Early Nodulation.

In response to colonization by rhizobia bacteria, legumes are able to form nitrogen-fixing nodules in their roots, allowing the plants to grow efficiently in nitrogen-depleted environments. Legumes utilize a complex, long-distance signaling pathway to regulate nodulation that involves signals in both roots and shoots. We measured the transcriptional response to treatment with rhizobia in both the shoots and roots of over a 72-h time course.

EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks.

The mechanisms that coordinate cellular gene expression are highly complex and intricately interconnected. Thus, it is necessary to move beyond a fully reductionist approach to understanding genetic information flow and begin focusing on the networked connections between genes that organize cellular function. Continued advancements in computational hardware, coupled with the development of gene correlation network algorithms, provide the capacity to study networked interactions between genes rather than their isolated functions.

RNAseq Analysis of the Response of to Fractional Gravity Under Blue-Light Stimulation During Spaceflight.

Traveling to nearby extraterrestrial objects having a reduced gravity level (partial gravity) compared to Earth's gravity is becoming a realistic objective for space agencies. The use of plants as part of life support systems will require a better understanding of the interactions among plant growth responses including tropisms, under partial gravity conditions. Here, we present results from our latest space experiments on the ISS, in which seeds of were germinated, and seedlings grew for six days under different gravity levels, namely micro-, several intermediate partial- levels, and 1, and were subjected to irradiation with blue light for the last 48 h.

Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis.

Root nodulation results from a symbiotic relationship between a plant host and bacteria. Synchronized gene expression patterns over the course of rhizobial infection result in activation of pathways that are unique but overlapping with the highly conserved pathways that enable mycorrhizal symbiosis. We performed RNA sequencing of 30 root maturation zone samples at five distinct time points.

Autism risk genes are evolutionarily ancient and maintain a unique feature landscape that echoes their function.

Previous research on autism risk (ASD), developmental regulatory (DevReg), and central nervous system (CNS) genes suggests they tend to be large in size, enriched in nested repeats, and mutation intolerant. The relevance of these genomic features is intriguing yet poorly understood. In this study, we investigated the feature landscape of these gene groups to discover structural themes useful in interpreting their function, developmental patterns, and evolutionary history.

Widespread Genotype-Phenotype Correlations in Intellectual Disability.

Linking genotype to phenotype is a major aim of genetics research, yet the underlying biochemical mechanisms of many complex conditions continue to remain elusive. Recent research provides evidence that relevant gene-phenotype associations are discoverable in the study of intellectual disability (ID). Here we expand on that work, identifying distinctive gene interaction modules with unique enrichment patterns reflective of associated clinical features in ID.

A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa.

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions.

Conserved Non-Coding Sequences are Associated with Rates of mRNA Decay in Arabidopsis.

Steady-state mRNA levels are tightly regulated through a combination of transcriptional and post-transcriptional control mechanisms. The discovery of cis-acting DNA elements that encode these control mechanisms is of high importance. We have investigated the influence of conserved non-coding sequences (CNSs), DNA patterns retained after an ancient whole genome duplication event, on the breadth of gene expression and the rates of mRNA decay in Arabidopsis thaliana.

Bioenergy grass feedstock: current options and prospects for trait improvement using emerging genetic, genomic, and systems biology toolkits.

For lignocellulosic bioenergy to become a viable alternative to traditional energy production methods, rapid increases in conversion efficiency and biomass yield must be achieved. Increased productivity in bioenergy production can be achieved through concomitant gains in processing efficiency as well as genetic improvement of feedstock that have the potential for bioenergy production at an industrial scale. The purpose of this review is to explore the genetic and genomic resource landscape for the improvement of a specific bioenergy feedstock group, the C4 bioenergy grasses.