Transcriptional Regulatory Network Topology with Applications to Bio-inspired Networking: A Survey

The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment—the very optimization challenges that are dealt with by biological networks as a consequence of millions of years of evolution. The transcriptional regulatory network (TRN) is a biological network whose innate topological robustness is a function of its underlying graph topology. In this article, we survey these properties of TRN and the metrics derived therefrom that lend themselves to the design of smart networking protocols and architectures. We then review a body of literature on bio-inspired networking solutions that leverage the stated properties of TRN. Finally, we present a vision for specific aspects of TRNs that may inspire future research directions in the fields of large-scale social and communication networks.

Comparative Analysis of Proanthocyanidin Metabolism and Genes Regulatory Network in Fresh Leaves of Two Different Ecotypes of Tetrastigma hemsleyanum

Tetrastigma hemsleyanum Diels et Gilg is a rare and wild medicinal resource. Metabolites, especially secondary metabolites, have an important influence on T. hemsleyanum adaptability and its medicinal quality. The metabolite proanthocyanidin (PA) is a polyphenol compound widely distributed in land plants, which can be used as antioxidants and anticancer agents. Here, we discovered that three types of PA accumulated in large amounts in purple leaves (PL), but not in green leaves (RG), based on widely non-targeted metabolomics. In addition, we further found that catechins and their derivatives, which are the structural units of PA, are also enriched in PL. Afterwards, we screened and obtained five key genes, DNR1/2, ANS, ANR and LAR closely related to PA biosynthesis through transcriptome analysis and found they were all highly expressed in PL compared to RG. Therefore, observed the regulatory relationship between the main compounds and genes network, and the PA metabolism regulatory pathway was complicated, which may be different to other species.

A High-Throughput Screening System for Populus Wood-Associated Transcription Factors and Its Application to Lignin Regulation

Wood formation of trees is a complex and costly developmental process, whose regulatory network is involved in the protein-protein and protein-DNA interactions. To detect such interactions in wood development, we developed a high-throughput screening system with 517 Gal4-AD-wood-associated transcription factors (TFs) library from Populus alba × P. glandulosa cv “84K.” This system can be used for screening the upstream regulators and interacting proteins of targets by mating-based yeast-one hybrid (Y1H) and yeast-two-hybrid (Y2H) method, respectively. Multiple regulatory modules of lignin biosynthesis were identified based on this Populus system. Five TFs interacted with the 500-bp promoter fragment of PHENYLALANINE AMMONIA-LYASE 2 (PAL2), the first rate-limiting enzyme gene in the lignin biosynthesis pathway, and 10 TFs interacted with PaMYB4/LTF1, a key regulator of lignin biosynthesis. Some of these interactions were further validated by EMSA and BiFC assays. The TF-PaPAL2 promoter interaction and TF-PaMYB4 interaction revealed a complex mechanism governing the regulation of lignin synthesis in wood cells. Our high-throughput Y1H/Y2H screening system may be an efficient tool for studying regulatory network of wood formation in tree species.

Exosomal and Plasma Non-Coding RNA Signature Associated with Urinary Albumin Excretion in Hypertension

Non-coding RNA (ncRNA), released into circulation or packaged into exosomes, plays important roles in many biological processes in the kidney. The purpose of the present study is to identify a common ncRNA signature associated with early renal damage and its related molecular pathways. Three individual libraries (plasma and urinary exosomes, and total plasma) were prepared from each hypertensive patient (with or without albuminuria) for ncRNA sequencing analysis. Next, an RNA-based transcriptional regulatory network was constructed. The three RNA biotypes with the greatest number of differentially expressed transcripts were long-ncRNA (lncRNA), microRNA (miRNA) and piwi-interacting RNA (piRNAs). We identified a common 24 ncRNA molecular signature related to hypertension-associated urinary albumin excretion, of which lncRNAs were the most representative. In addition, the transcriptional regulatory network showed five lncRNAs (LINC02614, BAALC-AS1, FAM230B, LOC100505824 and LINC01484) and the miR-301a-3p to play a significant role in network organization and targeting critical pathways regulating filtration barrier integrity and tubule reabsorption. Our study found an ncRNA profile associated with albuminuria, independent of biofluid origin (urine or plasma, circulating or in exosomes) that identifies a handful of potential targets, which may be utilized to study mechanisms of albuminuria and cardiovascular damage.

Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer

Background: Gastric cancer (GC) is aggressive cancer with a poor prognosis. Previously bulk transcriptome analysis was utilized to identify key genes correlated with the development, progression and prognosis of GC. However, due to the complexity of the genetic mutations, there is still an urgent need to recognize core genes in the regulatory network of GC.Methods: Gene expression profiles (GSE66229) were retrieved from the GEO database. Weighted correlation network analysis (WGCNA) was employed to identify gene modules mostly correlated with GC carcinogenesis. R package ‘DiffCorr’ was applied to identify differentially correlated gene pairs in tumor and normal tissues. Cytoscape was adopted to construct and visualize the gene regulatory network.Results: A total of 15 modules were detected in WGCNA analysis, among which three modules were significantly correlated with GC. Then genes in these modules were analyzed separately by “DiffCorr”. Multiple differentially correlated gene pairs were recognized and the network was visualized by the software Cytoscape. Moreover, GEMIN5 and PFDN2, which were rarely discussed in GC, were identified as key genes in the regulatory network and the differential expression was validated by real-time qPCR, WB and IHC in cell lines and GC patient tissues.Conclusions: Our research has shed light on the carcinogenesis mechanism by revealing differentially correlated gene pairs during transition from normal to tumor. We believe the application of this network-based algorithm holds great potential in inferring relationships and detecting candidate biomarkers.

Approximated Gene Expression Trajectories (AGETs) for Gene Regulatory Network Inference on Cell Tracks

The study of pattern formation has benefited from reverse-engineering gene regulatory network (GRN) structure from spatio-temporal quantitative gene expression data. Traditional approaches omit tissue morphogenesis, hence focusing on systems where the timescales of pattern formation and morphogenesis can be separated. In such systems, pattern forms as an emergent property of the underlying GRN. This is not the case in many animal patterning systems, where patterning and morphogenesis are simultaneous. To address pattern formation in these systems we need to adapt our methodologies to explicitly accommodate cell movements and tissue shape changes. In this work we present a novel framework to reverse-engineer GRNs underlying pattern formation in tissues experiencing morphogenetic changes and cell rearrangements. By combination of quantitative data from live and fixed embryos we approximate gene expression trajectories (AGETs) in single cells and use a subset to reverse-engineer candidate GRNs using a Markov Chain Monte Carlo approach. GRN fit is assessed by simulating on cell tracks (live-modelling) and comparing the output to quantitative data-sets. This framework outputs candidate GRNs that recapitulate pattern formation at the level of the tissue and the single cell. To our knowledge, this inference methodology is the first to integrate cell movements and gene expression data, making it possible to reverse-engineer GRNs patterning tissues undergoing morphogenetic changes.

Machine-learning from Pseudomonas putida transcriptomes reveals its transcriptional regulatory network

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1,993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

Identification of the Q Gene Playing a Role in Spike Morphology Variation in Wheat Mutants and Its Regulatory Network

The wheat AP2 family gene Q controls domestication traits, including spike morphology and threshability, which are critical for the widespread cultivation and yield improvement of wheat. Although many studies have investigated the molecular mechanisms of the Q gene, its direct target genes, especially those controlling spike morphology, are not clear, and its regulatory pathways are not well established. In this study, we conducted gene mapping of a wheat speltoid spike mutant and found that a new allele of the Q gene with protein truncation played a role in spike morphology variation in the mutant. Dynamic expression levels of the Q gene throughout the spike development process suggested that the transcript abundances of the mutant were decreased at the W6 and W7 scales compared to those of the WT. We identified several mutation sites on the Q gene and showed that mutations in different domains resulted in distinct phenotypes. In addition, we found that the Q gene produced three transcripts via alternative splicing and that they exhibited differential expression patterns in nodes, internodes, flag leaves, and spikes. Finally, we identified several target genes directly downstream of Q, including TaGRF1-2D and TaMGD-6B, and proposed a possible regulatory network. This study uncovered the target genes of Q, and the results can help to clarify the mechanism of wheat spike morphology and thereby improve wheat grain yield.

Transcriptional regulation of KCS gene by bZIP29 and MYB70 transcription factors during ABA-stimulated wound suberization of kiwifruit (Actinidia deliciosa)

Background Our previous study has demonstrated that the transcription of AchnKCS involved in suberin biosynthesis was up-regulated by exogenous abscisic acid (ABA) during the wound suberization of kiwifruit, but the regulatory mechanism has not been fully elucidated. Results Through subcellular localization analysis in this work, AchnbZIP29 and AchnMYB70 transcription factors were observed to be localized in the nucleus. Yeast one-hybrid and dual-luciferase assay proved the transcriptional activation of AchnMYB70 and transcriptional suppression of AchnbZIP29 on AchnKCS promoter. Furthermore, the transcription level of AchnMYB70 was enhanced by ABA during wound suberization of kiwifruit, but AchnbZIP29 transcription was reduced by ABA. Conclusions Therefore, it was believed that ABA enhanced the transcriptional activation of AchnMYB70 on AchnKCS by increasing AchnMYB70 expression. On the contrary, ABA relieved the inhibitory effect of AchnbZIP29 on transcription of AchnKCS by inhibiting AchnbZIP29 expression. These results gave further insight into the molecular regulatory network of ABA in wound suberization of kiwifruit.