A Medicago truncatula 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 TML1 and TML2.

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 Medicago truncatula. In the absence of functional SUNN, the autoregulation feedback loop is disrupted, resulting in hypernodulation. To elucidate early autoregulation mechanisms disrupted in SUNN mutants, we searched for genes with altered expression in the loss-of-function sunn-4 mutant and included the rdn1-2 autoregulation mutant for comparison. We identified constitutively altered expression of small groups of genes in sunn-4 roots and in sunn-4 shoots. All genes with verified roles in nodulation that were induced in wild-type roots during the establishment of nodules were also induced in sunn-4, including autoregulation genes TML2 and TML1. Only an isoflavone-7-O-methyltransferase gene was induced in response to rhizobia in wild-type roots but not induced in sunn-4. In shoot tissues of wild-type, eight rhizobia-responsive genes were identified, including a MYB family transcription factor gene that remained at a baseline level in sunn-4; three genes were induced by rhizobia in shoots of sunn-4 but not wild-type. We cataloged the temporal induction profiles of many small secreted peptide (MtSSP) genes in nodulating root tissues, encompassing members of twenty-four peptide families, including the CLE and IRON MAN families. The discovery that expression of TML2 in roots, a key factor in inhibiting nodulation in response to autoregulation signals, is also triggered in sunn-4 in the section of roots analyzed, suggests that the mechanism of TML regulation of nodulation in M. truncatula may be more complex than published models.

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. Thirty significant DNA variants associated with 174 tissue-specific eQTLs from ASD individuals in the SPARK project were identified. Several significant variants fell within brain-specific regulatory regions or had been associated with a significant change in gene expression in the brain. These eQTLs are a new class of biomarkers that could control the myriad of brain and non-brain phenotypic traits seen in ASD-affected individuals.

Time Series Transcriptome Analysis in Medicago truncatula 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 Medicago truncatula over a 72-h time course. To detect temporal shifts in gene expression, we developed GeneShift, a novel computational statistics and machine learning workflow that addresses the time series replicate the averaging issue for detecting gene expression pattern shifts under different conditions. We identified both known and novel genes that are regulated dynamically in both tissues during early nodulation including leginsulin, defensins, root transporters, nodulin-related, and circadian clock genes. We validated over 70% of the expression patterns that GeneShift discovered using an independent M. truncatula RNA-Seq study. GeneShift facilitated the discovery of condition-specific temporally differentially expressed genes in the symbiotic nodulation biological system. In principle, GeneShift should work for time-series gene expression profiling studies from other systems.

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

Introduction: 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. Materials and Methods: Here, we present results from our latest space experiments on the ISS, in which seeds of Arabidopsis thaliana were germinated, and seedlings grew for six days under different gravity levels, namely micro-g, several intermediate partial-g levels, and 1g, and were subjected to irradiation with blue light for the last 48 h. RNA was extracted from 20 samples for subsequent RNAseq analysis. Transcriptomic analysis was performed using the HISAT2-Stringtie-DESeq pipeline. Differentially expressed genes were further characterized for global responses using the GEDI tool, gene networks and for Gene Ontology (GO) enrichment. Results: Differential gene expression analysis revealed only one differentially expressed gene (AT4G21560, VPS28-1 a vacuolar protein) across all gravity conditions using FDR correction (q < 0.05). However, the same 14 genes appeared differentially expressed when comparing either micro-g, low-g level (< 0.1g) or the Moon g-level with 1g control conditions. Apart from these 14-shared genes, the number of differentially expressed genes was similar in microgravity and the Moon g-level and increased in the intermediate g-level (< 0.1g), but it was then progressively reduced as the difference with the Earth gravity became smaller. The GO groups were differentially affected at each g-level: light and photosynthesis GO under microgravity, genes belonged to general stress, chemical and hormone responses under low-g, and a response related to cell wall and membrane structure and function under the Moon g-level. Discussion: Transcriptional analyses of plants under blue light stimulation suggests that root blue-light phototropism may be enough to reduce the gravitational stress response caused by the lack of gravitropism in microgravity. Competition among tropisms induces an intense perturbation at the micro-g level, which shows an extensive stress response that is progressively attenuated. Our results show a major effect on cell wall/membrane remodeling (detected at the interval from the Moon to Mars gravity), which can be potentially related to graviresistance mechanisms.

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 Rhizobium 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 Medicago truncatula root maturation zone samples at five distinct time points. These samples included plants inoculated with Sinorhizobium medicae and control plants that did not receive any Rhizobium. Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. One LCM included genes that were up-regulated at 24 h following inoculation, suggesting an activation of allergen family genes and carbohydrate-binding gene products in response to Rhizobium. We also identified two LCMs that were comprised entirely of genes that were down regulated at 24 and 48 h post-inoculation. The identity of the genes in these modules suggest that down-regulating specific genes at 24 h may result in decreased jasmonic acid production with an increase in cytokinin production. At 48 h, coordinated down-regulation of a specific set of genes involved in lipid biosynthesis may play a role in nodulation. We show that GCN-LCM analysis is an effective method to preliminarily identify polygenic candidate biomarkers of root nodulation and develop hypotheses for future discovery.

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. ASD, DevReg, CNS, housekeeping, and whole genome control (WGC) groups were compiled using various resources. Multiple gene features of interest were extracted from NCBI/UCSC Bioinformatics. Residual variation intolerance scores, Exome Aggregation Consortium pLI scores, and copy number variation data from Decipher were used to estimate variation intolerance. Gene age and protein-protein interactions (PPI) were estimated using Ensembl and EBI Intact databases, respectively. Compared to WGC: ASD, DevReg, and CNS genes are longer, produce larger proteins, maintain greater numbers/density of conserved noncoding elements and transposable elements, produce more transcript variants, and are comparatively variation intolerant. After controlling for gene size, mutation tolerance, and clinical association, ASD genes still retain many of these same features. In addition, we also found that ASD genes that are extremely mutation intolerant have larger PPI networks. These data support many of the recent findings within the field of autism genetics but also expand our understanding of the evolution of these broad gene groups, their potential regulatory complexity, and the extent to which they interact with the cellular network. Autism Res 2019, 12: 860-869. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism risk genes are more ancient compared to other genes in the genome. As such, they exhibit physical features related to their age, including long gene and protein size and regulatory sequences that help to control gene expression. They share many of these same features with other genes that are expressed in the brain and/or are associated with prenatal development.

Widespread Genotype-Phenotype Correlations in Intellectual Disability.

Background: 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. Methods: Two hundred twelve forms of monogenic ID were curated according to comorbidities with autism and epilepsy. These groups were further subdivided according to secondary clinical manifestations of complex vs. simple facial dysmorphia and neurodegenerative-like features due to their clinical prominence, modest symptom overlap, and probable etiological divergence. An aggregate gene interaction ID network for these phenotype subgroups was discovered via a public database of known gene interactions: protein-protein, genetic, and mRNA coexpression. Additional annotation resources (Gene Ontology, Human Phenotype Ontology, TRANSFAC/JASPAR, and KEGG/WikiPathways) were utilized to assess functional and phenotypic enrichment patterns within subgroups. Results: Phenotypic analysis revealed high rates of complex facial dysmorphia in ID with comorbid autism. In contrast, neurodegenerative-like features were overrepresented in ID with epilepsy. Network analysis subsequently showed that gene groups divided according to clinical features of interest resulted in distinctive interaction clusters, with unique functional enrichments according to gene set. Conclusions: These data suggest that specific comorbid and secondary clinical features in ID are predictive of underlying genotype. In summary, ID form unique clusters, which are comprised of individual conditions with remarkable genotypic and phenotypic overlap.

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. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

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. The absence of CNSs near α duplicate genes was associated with a decrease in breadth of gene expression and slower mRNA decay rates while the presence CNSs near α duplicates was associated with an increase in breadth of gene expression and faster mRNA decay rates. The observed difference in mRNA decay rate was fastest in genes with CNSs in both non-transcribed and transcribed regions, albeit through an unknown mechanism. This study supports the notion that some Arabidopsis CNSs regulate the steady-state mRNA levels through post-transcriptional control mechanisms and that CNSs also play a role in controlling the breadth of gene expression.

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. First, bioenergy grass feedstock traits relevant to biochemical conversion are examined. Then we outline genetic resources available bioenergy grasses for mapping bioenergy traits to DNA markers and genes. This is followed by a discussion of genomic tools and how they can be applied to understanding bioenergy grass feedstock trait genetic mechanisms leading to further improvement opportunities.