Genome-Wide Network Analysis of Above- and Below-Ground Co-growth in Populus euphratica.

Tree growth is the consequence of developmental interactions between above- and below-ground compartments. However, a comprehensive view of the genetic architecture of growth as a cohesive whole is poorly understood. We propose a systems biology approach for mapping growth trajectories in genome-wide association studies viewing growth as a complex (phenotypic) system in which above- and below-ground components (or traits) interact with each other to mediate systems behavior. We further assume that trait-trait interactions are controlled by a genetic system composed of many different interactive genes and integrate the Lotka-Volterra predator-prey model to dissect phenotypic and genetic systems into pleiotropic and epistatic interaction components by which the detailed genetic mechanism of above- and below-ground co-growth can be charted. We apply the approach to analyze linkage mapping data of Populus euphratica, which is the only tree species that can grow in the desert, and characterize several loci that govern how above- and below-ground growth is cooperated or competed over development. We reconstruct multilayer and multiplex genetic interactome networks for the developmental trajectories of each trait and their developmental covariation. Many significant loci and epistatic effects detected can be annotated to candidate genes for growth and developmental processes. The results from our model may potentially be useful for marker-assisted selection and genetic editing in applied tree breeding programs. The model provides a general tool to characterize a complete picture of pleiotropic and epistatic genetic architecture in growth traits in forest trees and any other organisms.

The genetic architecture of trait covariation in Populus euphratica, a desert tree.

Introduction: The cooperative strategy of phenotypic traits during the growth of plants reflects how plants allocate photosynthesis products, which is the most favorable decision for them to optimize growth, survival, and reproduction response to changing environment. Up to now, we still know little about why plants make such decision from the perspective of biological genetic mechanisms. Methods: In this study, we construct an analytical mapping framework to explore the genetic mechanism regulating the interaction of two complex traits. The framework describes the dynamic growth of two traits and their interaction as Differential Interaction Regulatory Equations (DIRE), then DIRE is embedded into QTL mapping model to identify the key quantitative trait loci (QTLs) that regulate this interaction and clarify the genetic effect, genetic contribution and genetic network structure of these key QTLs. Computer simulation experiment proves the reliability and practicability of our framework. Results: In order to verify that our framework is universal and flexible, we applied it to two sets of data from Populus euphratica, namely, aboveground stem length - underground taproot length, underground root number - underground root length, which represent relationships of phenotypic traits in two spatial dimensions of plant architecture. The analytical result shows that our model is well applicable to datasets of two dimensions. Discussion: Our model helps to better illustrate the cooperation-competition patterns between phenotypic traits, and understand the decisions that plants make in a specific environment that are most conducive to their growth from the genetic perspective.

Circular RNA-related CeRNA network and prognostic signature for patients with oral squamous cell carcinoma.

Background: Circular RNA (circRNA) has an important influence on oral squamous cell carcinoma (OSCC) progression as competing endogenous RNAs (ceRNAs). However, the link between ceRNAs and the OSCC immune microenvironment is unknown. The research aimed to find circRNAs implicated in OSCC carcinogenesis and progression and build a circRNA-based ceRNA network to create a reliable OSCC risk prediction model. Methods: The expression profiles of circRNA in OSCC tumors and normal tissues were assessed through RNA sequencing. From the TCGA database, clinicopathological data and expression patterns of microRNAs (miRNAs) and mRNAs were obtained. A network of circRNA-miRNA-mRNA ceRNA was prepared according to these differentially expressed RNAs and was analyzed through functional enrichment. Subsequently, based on the mRNA in the ceRNA network, the influence of the model on prognosis was then evaluated using a risk prediction model. Finally, considering survival, tumor-infiltrating immune cells (TICs), clinicopathological features, immunosuppressive molecules, and chemotherapy efficacy were analyzed. Results: Eleven differentially expressed circRNAs were found in cancer tissues relative to healthy tissues. We established a network of circRNA-miRNA-mRNA ceRNA, and the ceRNA network includes 123 mRNAs, six miRNAs, and four circRNAs. By the assessment of Genomes pathway and Kyoto Encyclopedia of Genes, it is found that in the cellular senescence, PI3K-AKT and mTOR signaling pathway mRNAs were mainly enrichment. An immune-related signature was created utilizing seven immune-related genes in the ceRNA network after univariate and multivariate analysis. The receiver operating characteristic of the nomogram exhibited satisfactory accuracy and predictive potential. According to a Kaplan-Meier analysis, the high-risk group's survival rate was signally lower than the group with low-risk. In addition, risk models were linked to clinicopathological characteristics, TICs, immune checkpoints, and antitumor drug susceptibility. Conclusion: The profiles of circRNAs expression of OSCC tissues differ significantly from normal tissues. Our study established a circRNA-associated ceRNA network associated with OSCC and identified essential prognostic genes. Furthermore, our proposed immune-based signature aims to help research OSCC etiology, prognostic marker screening, and immune response evaluation.

FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits.

We describe a statistical protocol of how to reconstruct and dissect functional omnigenic multilayer interactome networks that mediate complex dynamic traits in a genome-wide association study (GWAS). This protocol, named FunGraph, can analyze how each locus affects phenotypic variation through its own direct effect and a complete set of indirect effects due to regulation by other loci co-existing in large-scale networks. FunGraph is applicable to any GWAS aimed to characterize the genetic architecture of dynamic phenotypic traits. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).

Inferring multilayer interactome networks shaping phenotypic plasticity and evolution.

Phenotypic plasticity represents a capacity by which the organism changes its phenotypes in response to environmental stimuli. Despite its pivotal role in adaptive evolution, how phenotypic plasticity is genetically controlled remains elusive. Here, we develop a unified framework for coalescing all single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) into a quantitative graph. This framework integrates functional genetic mapping, evolutionary game theory, and predator-prey theory to decompose the net genetic effect of each SNP into its independent and dependent components. The independent effect arises from the intrinsic capacity of a SNP, only expressed when it is in isolation, whereas the dependent effect results from the extrinsic influence of other SNPs. The dependent effect is conceptually beyond the traditional definition of epistasis by not only characterizing the strength of epistasis but also capturing the bi-causality of epistasis and the sign of the causality. We implement functional clustering and variable selection to infer multilayer, sparse, and multiplex interactome networks from any dimension of genetic data. We design and conduct two GWAS experiments using Staphylococcus aureus, aimed to test the genetic mechanisms underlying the phenotypic plasticity of this species to vancomycin exposure and Escherichia coli coexistence. We reconstruct the two most comprehensive genetic networks for abiotic and biotic phenotypic plasticity. Pathway analysis shows that SNP-SNP epistasis for phenotypic plasticity can be annotated to protein-protein interactions through coding genes. Our model can unveil the regulatory mechanisms of significant loci and excavate missing heritability from some insignificant loci. Our multilayer genetic networks provide a systems tool for dissecting environment-induced evolution.

Functional Mapping of Phenotypic Plasticity of Staphylococcus aureus Under Vancomycin Pressure.

Phenotypic plasticity is the exhibition of various phenotypic traits produced by a single genotype in response to environmental changes, enabling organisms to adapt to environmental changes by maintaining growth and reproduction. Despite its significance in evolutionary studies, we still know little about the genetic control of phenotypic plasticity. In this study, we designed and conducted a genome-wide association study (GWAS) to reveal genetic architecture of how Staphylococcus aureus strains respond to increasing concentrations of vancomycin (0, 2, 4, and 6 µg/mL) in a time course. We implemented functional mapping, a dynamic model for genetic mapping using longitudinal data, to map specific loci that mediate the growth trajectories of abundance of vancomycin-exposed S. aureus strains. 78 significant single nucleotide polymorphisms were identified following analysis of the whole growth and development process, and seven genes might play a pivotal role in governing phenotypic plasticity to the pressure of vancomycin. These seven genes, SAOUHSC_00020 (walR), SAOUHSC_00176, SAOUHSC_00544 (sdrC), SAOUHSC_02998, SAOUHSC_00025, SAOUHSC_00169, and SAOUHSC_02023, were found to help S. aureus regulate antibiotic pressure. Our dynamic gene mapping technique provides a tool for dissecting the phenotypic plasticity mechanisms of S. aureus under vancomycin pressure, emphasizing the feasibility and potential of functional mapping in the study of bacterial phenotypic plasticity.

Long non-coding RNAs in Oral squamous cell carcinoma: biologic function, mechanisms and clinical implications.

There is growing evidence that regions of the genome that cannot encode proteins play an important role in diseases. These regions are usually transcribed into long non-coding RNAs (lncRNAs). LncRNAs, little or no coding potential, are defined as capped transcripts longer than 200 nucleotides. New sequencing technologies have shown that a large number of aberrantly expressed lncRNAs are associated with multiple cancer types and indicated they have emerged as an important class of pervasive genes during the development and progression of cancer. However, the underlying mechanism in cancer is still unknown. Therefore, it is necessary to elucidate the lncRNA function. Notably, many lncRNAs dysregulation are associated with Oral squamous cell carcinoma (OSCC) and affect various aspects of cellular homeostasis, including proliferation, survival, migration or genomic stability. This review expounds the up- or down-regulation of lncRNAs in OSCC and the molecular mechanisms by which lncRNAs perform their function in the malignant cell. Finally, the potential of lncRNAs as non-invasive biomarkers for OSCC diagnosis are also described. LncRNAs hold promise as prospective novel therapeutic targets, but more research is needed to gain a better understanding of their biologic function.