Gene regulation network inference using k-nearest neighbor-based mutual information estimation- Revisiting an old DREAM

Background: A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past twenty years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization. Results: In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoogbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods. Conclusions: Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction - which combines CMIA, and the KSG-MI estimator - achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or choose gene candidates for experimental validations.

System analysis of VEGFA in renal cell carcinoma: The expression, prognosis, gene regulation network and regulation targets

Background VEGFA is one of the most important regulators of angiogenesis and plays a crucial role in cancer angiogenesis and progression. Recent studies have highlighted a relationship between VEGFA expression and renal cell carcinoma occurrence. However, the expression level, gene regulation network, prognostic value, and target prediction of VEGFA in renal cell carcinoma remain unclear. Therefore, system analysis of the expression, gene regulation network, prognostic value, and target prediction of VEGFA in patients with renal cell carcinoma is of great theoretical significance as there is a clinical demand for the discovery of new renal cell carcinoma treatment targets and strategies to further improve renal cell carcinoma treatment efficacy. Methods This study used multiple free online databases, including cBioPortal, TRRUST, GeneMANIA, GEPIA, Metascape, UALCAN, LinkedOmics, Metascape, and TIMER for the abovementioned analysis. Results VEGFA was upregulated in patients with kidney renal clear cell carcinoma (KIRC) and kidney chromophobe (KICH), and downregulated in patients with kidney renal papillary cell carcinoma (KIRP). Moreover, genetic alterations of VEGFA were found in patients with renal cell carcinoma as follows: 4% (KIRC), 8% (KICH), and 4% (KIRP). The promoter methylation of VEGFA was lower and higher in patients with clinical stages of KIRC and stage 1 KIRP, respectively. VEGFA expression significantly correlated with KIRC and KIRP pathological stages. Furthermore, patients with KICH and KIRP having low VEGFA expression levels had a longer survival than those having high VEGFA expression levels. VEGFA and its neighboring genes functioned in the regulation of protein methylation and glycosylation, as well as muscle fiber growth and differentiation in patients with renal cell carcinoma. Gene Ontology enrichment analysis revealed that the functions of VEGFA and its neighboring genes in patients with renal cell carcinoma are mainly related to cell adhesion molecule binding, catalytic activity, acting on RNA, ATPase activity, actin filament binding, protease binding, transcription coactivator activity, cysteine-type peptidase activity, and calmodulin binding. Transcription factor targets of VEGFA and its neighboring genes in patients with renal cell carcinoma were found: HIF1A, TFAP2A, and ESR1 in KIRC; STAT3, NFKB1, and HIPK2 in KICH; and FOXO3, TFAP2A, and ETS1 in KIRP. We further explored the VEGFA-associated kinase (ATM in KICH as well as CDK1 and AURKB in KIRP) and VEGFA-associated microRNA (miRNA) targets (MIR-21 in KICH as well as MIR-213, MIR-383, and MIR-492 in KIRP). Furthermore, the following genes had the strongest correlation with VEGFA expression in patients with renal cell carcinoma: NOTCH4, GPR4, and TRIB2 in KIRC; CKMT2, RRAGD, and PPARGC1A in KICH; and FLT1, C6orf223, and ESM1 in KIRP. VEGFA expression in patients with renal cell carcinoma was positively associated with immune cell infiltration, including CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells. Conclusions This study revealed VEGFA expression and potential gene regulatory network in patients with renal cell carcinoma, thereby laying a foundation for further research on the role of VEGFA in renal cell carcinoma occurrence. Moreover, the study provides new renal cell carcinoma therapeutic targets and prognostic biomarkers as a reference for fundamental and clinical research.

Development and selective grain make plasticity 'take the lead' in adaptive evolution

Background Biological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation in which natural selection can act. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales. So how do biological systems come to exhibit such extraordinary capacity to evolve? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. Results To better understand the interaction between plasticity and genetic evolvability, we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First, we show that the phenotypic variation resulting from genetic and environmental perturbation are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa. This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity can be effective in promoting the evolution of high genetic evolvability. Conclusions Without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, we show how past selection for plasticity can exercise a disproportionate effect on genetic evolvability and, in turn, influence the course of adaptive evolution.

Transiently increased intercommunity regulation characterizes concerted cell phenotypic transition

Phenotype transition takes place in many biological processes such as differentiation and reprogramming. A fundamental question is how cells coordinate switching of expressions of clusters of genes. Through analyzing single cell RNA sequencing data in the framework of transition path theory, we studied how such a genome-wide expression program switching proceeds in three different cell transition processes. For each process we reconstructed a reaction coordinate describing the transition progression, and inferred the gene regulation network (GRN) along the reaction coordinate. In all three processes we observed common pattern that the effective number and strength of regulation between different communities increase first and then decrease. The change accompanies with similar change of the GRN frustration, defined as overall confliction between the regulation received by genes and their expression states, and GRN heterogeneity. While studies suggest that biological networks are modularized to contain perturbation effects locally, our analyses reveal a general principle that during a cell phenotypic transition intercommunity interactions increase to concertedly coordinate global gene expression reprogramming, and canalize to specific cell phenotype as Waddington visioned.

Expression and Gene Regulation Network of Adenosine Receptor A2B in Lung Adenocarcinoma: A Potential Diagnostic and Prognostic Biomarker

Adenosinereceptor A2B (ADORA2B) encodes a protein belonging to the G protein–coupled receptor superfamily. Abnormal expression of ADORA2B may play a pathophysiological role in some human cancers. We investigated whether ADORA2B is a potential diagnostic and prognostic biomarker for lung adenocarcinoma (LUAD). The expression, various mutations, copy number variations, mRNA expression levels, and related network signaling pathways of ADORA2B were analyzed using bioinformatics-related websites, including Oncomine, UALCAN, cBioPortal, GeneMANIA, LinkedOmics, KM Plotter, and TIMER. We found that ADORA2B was overexpressed and amplified in LUAD, and a high ADORA2B expression predicted a poor prognosis for LUAD patients. Pathway analyses of ADORA2B in LUAD revealed ADORA2B-correlated signaling pathways, and the expression level of ADORA2B was associated with immune cell infiltration. Furthermore, ADORA2B mRNA and protein levels were significantly higher in human LUAD cell lines (A549 cells and NCl-H1299 cells) than in normal human bronchial epithelial (HBE) cells, and the transcript levels of genes positively or negatively correlated with ADORA2B were consistent and statistically significant. siRNA transfection experiments and functional experiments further confirmed these results. In vitro results were also consistent with those of bioinformatics analysis. Our findings provide a foundation for studying the role of ADORA2B in tumorigenesis and support the development of new drug targets for LUAD.

Expression and gene regulation network of TYMS and BCL2L1 in colorectal cancer based on data mining

Background The purpose of this study was to study the role of thymidylate synthetase (TYMS) and B-cell lymphoma-2 like 1 (BCL2L1) in the occurrence and development of colorectal cancer and its potential regulatory mechanism. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to examine the expression and prognostic value of TYMS and BCL2L1 in colorectal cancer. C-BioPortal analysis was used to detect the TYMS and BCL2L1 alterations. Through The Human Protein Atlas (THPA), the TYMS and BCL2L1 protein levels were also assessed. The protein protein interaction (PPI) network was built using GeneMANIA analysis, while co-expression genes correlated with TYMS and BCL2L1 were identified using LinkedOmics analysis. Finally, we collected clinical samples to verify the expressions of TYMS and BCL2L1 in colorectal cancer. Results TYMS and BCL2L1 were up-regulated, and TYMS and BCL2L1 genomic alterations were not associated with the occurrence of colorectal cancer. TYMS and BCL2L1 were significantly connected with the prognosis of colorectal cancer patients. The genes interacted with TYMS and BCL2L1 were linked to functional networks involving pathway of apoptosis, apoptosis-multiple species, colorectal cancer, platinum drug resistance and p53 signaling pathway. qRT-PCR verification results of TYMS were consistent with the result of TCGA and GEO analysis. Conclusions This study display that data mining can efficiently provide information on expression of TYMS and BCL2L1, correlated genes of TYMS and BCL2L1, core pathways and potential functional networks in colorectal cancer, suggesting that TYMS and BCL2L1 may become new prognostic and therapeutic targets for colorectal cancer.

Relationship between gene regulation network structure and prediction accuracy in high dimensional regression

The least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance structure, which is characterized by gene regulation networks. However, the manner in which the structure of a gene regulation network together with the sample size affects prediction accuracy has not yet been sufficiently investigated. In this study, Monte Carlo simulations are conducted to investigate the prediction accuracy for several network structures under various sample sizes. When the gene regulation network is a random graph, a sufficiently large number of observations are required to ensure good prediction accuracy with the lasso. The PCR provided poor prediction accuracy regardless of the sample size. However, a real gene regulation network is likely to exhibit a scale-free structure. In such cases, the simulation indicates that a relatively small number of observations, such as $$N=300$$ N = 300 , is sufficient to allow the accurate prediction of traits from a transcriptome with the lasso.

Development and Selective Grain Make Plasticity ‘Take the Lead’ in Adaptive Evolution

Background: Biological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation in which natural selection can act. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales . So how do biological systems come to exhibit such extraordinary capacity to evolve ? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. Results: To better understand the interaction between plasticity and genetic evolvability , we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First , we show that the phenotypic variation resulting from genetic and environmental perturbation are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa . This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity can be effective in promoting the evolution of high genetic evolvability. Conclusions: Without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, we show how past selection for plasticity can exercise a disproportionate effect on genetic evolvability and, in turn, influence the course of adaptive evolution.