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Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.

38 p.-5 fig.-2 tab.-8 fig. supl., PREMISE:Plants synthesize information from multiple environmental stimuli when determining their direction of growth. Gravity, being ubiquitous on Earth, plays a major role in determining the direction of growth and overall architecture of the plant. Here, we utilized the microgravity environment on board the International Space Station (ISS) to identify genes involved influencing growth and development of phototropically stimulated seedlings of Arabidopsis thaliana., METHODS:Seedlings were grown on the ISS, and RNA was extracted from 7 samples (pools of 10-15 plants) grown in microgravity (μg) or Earth gravity conditions (1-g). Transcriptomic analyses via RNA sequencing (RNA-seq) of differential gene expression was performed using the HISAT2-Stringtie-DESeq2 RNASeq pipeline. Differentially expressed genes were further characterized by using Pathway Analysis and enrichment for Gene Ontology classifications., RESULTS:For 296 genes that were found significantly differentially expressed between plants in microgravity compared to 1-g controls, Pathway Analysis identified eight molecular pathways that were significantly affected by reduced gravity conditions. Specifically, light-associated pathways (e.g., photosynthesis-antenna proteins, photosynthesis, porphyrin, and chlorophyll metabolism) were significantly downregulated in microgravity., CONCLUSIONS:Gene expression in A. thaliana seedlings grown in microgravity was significantly altered compared to that of the 1-g control. Understanding how plants grow in conditions of microgravity not only aids in our understanding of how plants grow and respond to the environment but will also help to efficiently grow plants during long-range space missions., Funding was provided by grants NNX12A065G and 80NSSC17K0546 from NASA (PI = J. Z. Kiss).

The human brain is a complex organ that consists of several regions each with a unique gene expression pattern. Our intent in this study was to construct a gene co-expression network (GCN) for the normal brain using RNA expression profiles from the Genotype-Tissue Expression (GTEx) project. The brain GCN contains gene correlation relationships that are broadly present in the brain or specific to thirteen brain regions, which we later combined into six overarching brain mini-GCNs based on the brain’s structure. Using the expression profiles of brain region-specific GCN edges, we determined how well the brain region samples could be discriminated from each other, visually with t-SNE plots or quantitatively with the Gene Oracle deep learning classifier. Next, we tested these gene sets on their relevance to human tumors of brain and non-brain origin. Interestingly, we found that genes in the six brain mini-GCNs showed markedly higher mutation rates in tumors relative to matched sets of random genes. Further, we found that cortex genes subdivided Head and Neck Squamous Cell Carcinoma (HNSC) tumors and Pheochromocytoma and Paraganglioma (PCPG) tumors into distinct groups. The brain GCN and mini-GCNs are useful resources for the classification of brain regions and identification of biomarker genes for brain related phenotypes.

Renal cell carcinoma (RCC) subtypes are characterized by distinct molecular profiles. Using RNA expression profiles from 1,009 RCC samples, we constructed a condition-annotated gene coexpression network (GCN). The RCC GCN contains binary gene coexpression relationships (edges) specific to conditions including RCC subtype and tumor stage. As an application of this resource, we discovered RCC GCN edges and modules that were associated with genetic lesions in known RCC driver genes, including VHL, a common initiating clear cell RCC (ccRCC) genetic lesion, and PBRM1 and BAP1 which are early genetic lesions in the Braided Cancer River Model (BCRM). Since ccRCC tumors with PBRM1 mutations respond to targeted therapy differently than tumors with BAP1 mutations, we focused on ccRCC-specific edges associated with tumors that exhibit alternate mutation profiles: VHL-PBRM1 or VHL-BAP1. We found specific blends molecular functions associated with these two mutation paths. Despite these mutation-associated edges having unique genes, they were enriched for the same immunological functions suggesting a convergent functional role for alternate gene sets consistent with the BCRM. The condition annotated RCC GCN described herein is a novel data mining resource for the assignment of polygenic biomarkers and their relationships to RCC tumors with specific molecular and mutational profiles.

Gene co-expression networks (GCNs) are constructed from Gene Expression Matrices (GEMs) in a bottom up approach where all gene pairs are tested for correlation within the context of the input sample set. This approach is computationally intensive for many current GEMs and may not be scalable to millions of samples. Further, traditional GCNs do not detect non-linear relationships missed by correlation tests and do not place genetic relationships in a gene expression intensity context. In this report, we propose EdgeScaping, which constructs and analyzes the pairwise gene intensity network in a holistic, top down approach where no edges are filtered. EdgeScaping uses a novel technique to convert traditional pairwise gene expression data to an image based format. This conversion not only performs feature compression, making our algorithm highly scalable, but it also allows for exploring non-linear relationships between genes by leveraging deep learning image analysis algorithms. Using the learned embedded feature space we implement a fast, efficient algorithm to cluster the entire space of gene expression relationships while retaining gene expression intensity. Since EdgeScaping does not eliminate conventionally noisy edges, it extends the identification of co-expression relationships beyond classically correlated edges to facilitate the discovery of novel or unusual expression patterns within the network. We applied EdgeScaping to a human tumor GEM to identify sets of genes that exhibit conventional and non-conventional interdependent non-linear behavior associated with brain specific tumor sub-types that would be eliminated in conventional bottom-up construction of GCNs. Edgescaping source code is available at https://github.com/bhusain/EdgeScaping under the MIT license.

Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call “candidate genes”, by evaluating the ability of gene combinations to classify samples from a dataset, which we call “classification potential”. Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.

A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.

Different modes of gene duplication including whole-genome duplication (WGD), and tandem, proximal and dispersed duplications are widespread in angiosperm genomes. Small-scale, stochastic gene relocations and transposed gene duplications are widely accepted to be the primary mechanisms for the creation of dispersed duplicates. However, here we show that most surviving ancient dispersed duplicates in core eudicots originated from large-scale gene relocations within a narrow window of time following a genome triplication (γ) event that occurred in the stem lineage of core eudicots. We name these surviving ancient dispersed duplicates as relocated γ duplicates. In Arabidopsis thaliana, relocated γ, WGD and single-gene duplicates have distinct features with regard to gene functions, essentiality, and protein interactions. Relative to γ duplicates, relocated γ duplicates have higher non-synonymous substitution rates, but comparable levels of expression and regulation divergence. Thus, relocated γ duplicates should be distinguished from WGD and single-gene duplicates for evolutionary investigations. Our results suggest large-scale gene relocations following the γ event were associated with the diversification of core eudicots.

• Whole genome duplication events provide a lineage with a large reservoir of genes that can be molded by evolutionary forces into phenotypes that fit alternative environments. A well-studied whole genome duplication, the α-event, occurred in an ancestor of the model plant Arabidopsis thaliana. Retained segments of the α-event have been defined in recent years in the form of duplicate protein coding sequences (α-pairs) and associated conserved noncoding DNA sequences (CNSs). Our aim was to identify any association between CNSs and α-pair co-functionality at the gene expression level. • Here, we tested for correlation between CNS counts and α-pair co-expression and expression intensity across nine expression datasets: aerial tissue, flowers, leaves, roots, rosettes, seedlings, seeds, shoots and whole plants. • We provide evidence for a putative regulatory role of the CNSs. The association of CNSs with α-pair co-expression and expression intensity varied by gene function, subgene position and the presence of transcription factor binding motifs. A range of possible CNS regulatory mechanisms, including intron-mediated enhancement, messenger RNA fold stability and transcriptional regulation, are discussed. • This study provides a framework to understand how CNS motifs are involved in the maintenance of gene expression after a whole genome duplication event.

Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes.

Papaya, a fruit crop cultivated in tropical and subtropical regions, is known for its nutritional benefits and medicinal applications. Here we report a 3× draft genome sequence of ‘SunUp’ papaya, the first commercial virus-resistant transgenic fruit tree1 to be sequenced. The papaya genome is three times the size of the Arabidopsis genome, but contains fewer genes, including significantly fewer disease-resistance gene analogues. Comparison of the five sequenced genomes suggests a minimal angiosperm gene set of 13,311. A lack of recent genome duplication, atypical of other angiosperm genomes sequenced so far2–5, may account for the smaller papaya gene number in most functional groups. Nonetheless, striking amplifications in gene number within particular functional groups suggest roles in the evolution of tree-like habit, deposition and remobilization of starch reserves, attraction of seed dispersal agents, and adaptation to tropical daylengths. Transgenesis at three locations is closely associated with chloroplast insertions into the nuclear genome, and with topoisomerase I recognition sites. Papaya offers numerous advantages as a system for fruit-tree functional genomics, and this draft genome sequence provides the foundation for revealing the basis of Carica's distinguishing morpho-physiological, medicinal and nutritional properties.

QTL mapping experiments yield heterogeneous results due to the use of different genotypes, environments, and sampling variation. Compilation of QTL mapping results yields a more complete picture of the genetic control of a trait and reveals patterns in organization of trait variation. A total of 432 QTL mapped in one diploid and 10 tetraploid interspecific cotton populations were aligned using a reference map and depicted in a CMap resource. Early demonstrations that genes from the non-fiber-producing diploid ancestor contribute to tetraploid lint fiber genetics gain further support from multiple populations and environments and advanced-generation studies detecting QTL of small phenotypic effect. Both tetraploid subgenomes contribute QTL at largely non-homeologous locations, suggesting divergent selection acting on many corresponding genes before and/or after polyploid formation. QTL correspondence across studies was only modest, suggesting that additional QTL for the target traits remain to be discovered. Crosses between closely-related genotypes differing by single-gene mutants yield profoundly different QTL landscapes, suggesting that fiber variation involves a complex network of interacting genes. Members of the lint fiber development network appear clustered, with cluster members showing heterogeneous phenotypic effects. Meta-analysis linked to synteny-based and expression-based information provides clues about specific genes and families involved in QTL networks.

The 3beta-hydroxysteroid dehydrogenase/Delta(5)-Delta(4) isomerase (3beta-HSD) isoenzymes are responsible for the oxidation and isomerization of Delta(5)-3beta-hydroxysteroid precursors into Delta(4)-ketosteroids, thus catalyzing an essential step in the formation of all classes of active steroid hormones. In humans, expression of the type I isoenzyme accounts for the 3beta-HSD activity found in placenta and peripheral tissues, whereas the type II 3beta-HSD isoenzyme is predominantly expressed in the adrenal gland, ovary, and testis, and its deficiency is responsible for a rare form of congenital adrenal hyperplasia. Phylogeny analyses of the 3beta-HSD gene family strongly suggest that the need for different 3beta-HSD genes occurred very late in mammals, with subsequent evolution in a similar manner in other lineages. Therefore, to a large extent, the 3beta-HSD gene family should have evolved to facilitate differential patterns of tissue- and cell-specific expression and regulation involving multiple signal transduction pathways, which are activated by several growth factors, steroids, and cytokines. Recent studies indicate that HSD3B2 gene regulation involves the orphan nuclear receptors steroidogenic factor-1 and dosage-sensitive sex reversal adrenal hypoplasia congenita critical region on the X chromosome gene 1 (DAX-1). Other findings suggest a potential regulatory role for STAT5 and STAT6 in transcriptional activation of HSD3B2 promoter. It was shown that epidermal growth factor (EGF) requires intact STAT5; on the other hand IL-4 induces HSD3B1 gene expression, along with IL-13, through STAT 6 activation. However, evidence suggests that multiple signal transduction pathways are involved in IL-4 mediated HSD3B1 gene expression. Indeed, a better understanding of the transcriptional factors responsible for the fine control of 3beta-HSD gene expression may provide insight into mechanisms involved in the functional cooperation between STATs and nuclear receptors as well as their potential interaction with other signaling transduction pathways such as GATA proteins. Finally, the elucidation of the molecular basis of 3beta-HSD deficiency has highlighted the fact that mutations in the HSD3B2 gene can result in a wide spectrum of molecular repercussions, which are associated with the different phenotypic manifestations of classical 3beta-HSD deficiency and also provide valuable information concerning the structure-function relationships of the 3beta-HSD superfamily. Furthermore, several recent studies using type I and type II purified enzymes have elegantly further characterized structure-function relationships responsible for kinetic differences and coenzyme specificity.

Understanding the control of any trait optimally requires the detection of causal genes, gene interaction, and mechanism of action to discover and model the biochemical pathways underlying the expressed phenotype. Functional genomics techniques, including RNA expression profiling via microarray and high-throughput DNA sequencing, allow for the precise genome localization of biological information. Powerful genetic approaches, including quantitative trait locus (QTL) and genome-wide association study mapping, link phenotype with genome positions, yet genetics is less precise in localizing the relevant mechanistic information encoded in DNA. The coupling of salient functional genomic signals with genetically mapped positions is an appealing approach to discover meaningful gene-phenotype relationships. Techniques used to define this genetic-genomic convergence comprise the field of systems genetics. This short review will address an application of systems genetics where RNA profiles are associated with genetically mapped genome positions of individual genes (eQTL mapping) or as gene sets (co-expression network modules). Both approaches can be applied for knowledge independent selection of candidate genes (and possible control mechanisms) underlying complex traits where multiple, likely unlinked, genomic regions might control specific complex traits.

Sex determination in papaya is controlled by a recently evolved XY chromosome pair, with two slightly different Y chromosomes controlling the development of males (Y) and hermaphrodites (Y h ). To study the events of early sex chromosome evolution, we sequenced the hermaphrodite-specific region of the Y h chromosome (HSY) and its X counterpart, yielding an 8.1-megabase (Mb) HSY pseudomolecule, and a 3.5-Mb sequence for the corresponding X region. The HSY is larger than the X region, mostly due to retrotransposon insertions. The papaya HSY differs from the X region by two large-scale inversions, the first of which likely caused the recombination suppression between the X and Y h chromosomes, followed by numerous additional chromosomal rearrangements. Altogether, including the X and/or HSY regions, 124 transcription units were annotated, including 50 functional pairs present in both the X and HSY. Ten HSY genes had functional homologs elsewhere in the papaya autosomal regions, suggesting movement of genes onto the HSY, whereas the X region had none. Sequence divergence between 70 transcripts shared by the X and HSY revealed two evolutionary strata in the X chromosome, corresponding to the two inversions on the HSY, the older of which evolved about 7.0 million years ago. Gene content differences between the HSY and X are greatest in the older stratum, whereas the gene content and order of the collinear regions are identical. Our findings support theoretical models of early sex chromosome evolution.

Sorghum, an African grass related to sugar cane and maize, is grown for food, feed, fibre and fuel. We present an initial analysis of the approximately 730-megabase Sorghum bicolor (L.) Moench genome, placing approximately 98% of genes in their chromosomal context using whole-genome shotgun sequence validated by genetic, physical and syntenic information. Genetic recombination is largely confined to about one-third of the sorghum genome with gene order and density similar to those of rice. Retrotransposon accumulation in recombinationally recalcitrant heterochromatin explains the approximately 75% larger genome size of sorghum compared with rice. Although gene and repetitive DNA distributions have been preserved since palaeopolyploidization approximately 70 million years ago, most duplicated gene sets lost one member before the sorghum-rice divergence. Concerted evolution makes one duplicated chromosomal segment appear to be only a few million years old. About 24% of genes are grass-specific and 7% are sorghum-specific. Recent gene and microRNA duplications may contribute to sorghum's drought tolerance.

Complex traits and other polygenic processes require coordinated gene expression. Co-expression networks model mRNA co-expression: the product of gene regulatory networks. To identify regulatory mechanisms underlying coordinated gene expression in a tissue-enriched context, ten Arabidopsis thaliana co-expression networks were constructed after manually sorting 4,566 RNA profiling datasets into aerial, flower, leaf, root, rosette, seedling, seed, shoot, whole plant, and global (all samples combined) groups. Collectively, the ten networks contained 30% of the measurable genes of Arabidopsis and were circumscribed into 5,491 modules. Modules were scrutinized for cis regulatory mechanisms putatively encoded in conserved non-coding sequences (CNSs) previously identified as remnants of a whole genome duplication event. We determined the non-random association of 1,361 unique CNSs to 1,904 co-expression network gene modules. Furthermore, the CNS elements were placed in the context of known gene regulatory networks (GRNs) by connecting 250 CNS motifs with known GRN cis elements. Our results provide support for a regulatory role of some CNS elements and suggest the functional consequences of CNS activation of co-expression in specific gene sets dispersed throughout the genome.

Background Both single gene and whole genome duplications (WGD) have recurred in angiosperm evolution. However, the evolutionary effects of different modes of gene duplication, especially regarding their contributions to genetic novelty or redundancy, have been inadequately explored. Results In Arabidopsis thaliana and Oryza sativa (rice), species that deeply sample botanical diversity and for which expression data are available from a wide range of tissues and physiological conditions, we have compared expression divergence between genes duplicated by six different mechanisms (WGD, tandem, proximal, DNA based transposed, retrotransposed and dispersed), and between positional orthologs. Both neo-functionalization and genetic redundancy appear to contribute to retention of duplicate genes. Genes resulting from WGD and tandem duplications diverge slowest in both coding sequences and gene expression, and contribute most to genetic redundancy, while other duplication modes contribute more to evolutionary novelty. WGD duplicates may more frequently be retained due to dosage amplification, while inferred transposon mediated gene duplications tend to reduce gene expression levels. The extent of expression divergence between duplicates is discernibly related to duplication modes, different WGD events, amino acid divergence, and putatively neutral divergence (time), but the contribution of each factor is heterogeneous among duplication modes. Gene loss may retard inter-species expression divergence. Members of different gene families may have non-random patterns of origin that are similar in Arabidopsis and rice, suggesting the action of pan-taxon principles of molecular evolution. Conclusion Gene duplication modes differ in contribution to genetic novelty and redundancy, but show some parallels in taxa separated by hundreds of millions of years of evolution.