Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Integrated analysis of smRNAome, transcriptome, and degradome data to decipher microRNAs regulating costunolide biosynthesis in Saussurea lappa

Saussurea lappa (S. lappa) has been known to synthesize medicinally important, costunolide. Due to its immense therapeutic importance, understanding of regulatory mechanism associated with its biosynthesis is crucial. The identification of genes and transcription factors (TFs) in S. lappa, created a clear picture of costunolide biosynthesis pathways. Further to understand the regulation of costunolide biosynthesis by miRNAs, an integrated study of transcriptome, miRNAs, and degradome was performed. Identified candidate miRNAs and associated feed-forward loops (FFLs) illustrates their regulatory role in secondary metabolite biosynthesis. Small RNA and degradome sequencing were performed for leaf and root tissues to determine miRNAs-targets pairs. A total of 711 and 525 such targets were obtained for novel and known miRNAs respectively. This data was used to generate costunolide-specific miRNA-TF-gene interactome to perform systematic analyses through graph theoretical approach. Interestingly, miR171c.1 and sla-miR121 were identified as key regulators to connect and co-regulate both mevalonate and sesquiterpenoid pathways to bio-synthesize costunolide. Tissue-specific FFLs were identified to be involved in costunolide biosynthesis which further suggests the evolutionary co-relation of root-specific networks in synthesis of secondary metabolites in addition to leaf-specific networks. This integrative approach allowed us to determine candidate miRNAs and associated tissue-specific motifs involved in the diversification of secondary metabolites. MiRNAs identified in present study can provide alternatives for bioengineering tool to enhance the synthesis of costunolide and other secondary metabolites in S. lappa.

Integrated physiological and weighted gene co-expression network analysis reveals the hub genes engaged in nitrate-regulated alleviation of ammonium toxicity at the seedling stage in wheat (Triticum aestivum L.)

Wheat has a specific preference for NO₃ ^- and shows toxicity symptoms under high NH₄ ⁺ concentrations. Increasing the nitrate supply may alleviate ammonium stress. Nevertheless, the mechanisms underlying the nitrate regulation of wheat root growth to alleviate ammonium toxicity remain unclear. In this study, we integrated physiological and weighted gene co-expression network analysis (WGCNA) to identify the hub genes involved in nitrate alleviation of ammonium toxicity at the wheat seedling stage. Five NH₄ ⁺/NO₃ ^- ratio treatments, including 100/0 (N_a), 75/25 (N_r1), 50/50 (N_r2), 25/75 (N_r3), and 0/100 (N_n) were tested in this study. The results showed that sole ammonium treatment (N_a) increased the lateral root number but reduced root biomass. Increasing the nitrate supply significantly increased the root biomass. Increasing nitrate levels decreased abscisic acid (ABA) content and increased auxin (IAA) content. Furthermore, we identified two modules (blue and turquoise) using transcriptome data that were significantly related to root physiological growth indicators. TraesCS6A02G178000 and TraesCS2B02G056300 were identified as hub genes in the two modules which coded for plastidic ATP/ADP-transporter and WRKY62 transcription factors, respectively. Additionally, network analysis showed that in the blue module, TraesCS6A02G178000 interacts with downregulated genes that coded for indolin-2-one monooxygenase, SRG1, DETOXIFICATION, and wall-associated receptor kinase. In the turquoise module, TraesCS2B02G056300 was highly related to the genes that encoded ERD4, ERF109, CIGR2, and WD40 proteins, and transcription factors including WRKY24, WRKY22, MYB30, and JAMYB, which were all upregulated by increasing nitrate supply. These studies suggest that increasing the nitrate supply could improve root growth and alleviate ammonium toxicity through physiological and molecular regulation networks, including ROS, hormonal crosstalk, and transcription factors.

Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

Background

Thyroid eye disease (TED) is the most common orbital pathology that occurs in up to 50% of patients with Graves’ disease. Herein, we aimed at discovering the possible hub genes and pathways involved in TED based on bioinformatical approaches.

Results

The GSE105149 and GSE58331 datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged for identifying TED-associated modules by weighted gene coexpression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. EdgeR was run to screen differentially expressed genes (DEGs). Transcription factor (TF), microRNA (miR) and drug prediction analyses were performed using ToppGene suite. Function enrichment analysis was used to investigate the biological function of genes. Protein–protein interaction (PPI) analysis was performed based on the intersection between the list of genes obtained by WGCNA, lmQCM and DEGs, and hub genes were identified using the MCODE plugin. Based on the overlap of 497 genes retrieved from the different approaches, a robust TED coexpression network was constructed and 11 genes (ATP6V1A, PTGES3, PSMD12, PSMA4, METAP2, DNAJA1, PSMA1, UBQLN1, CCT2, VBP1 and NAA50) were identified as hub genes. Key TFs regulating genes in the TED-associated coexpression network, including NFRKB, ZNF711, ZNF407 and MORC2, and miRs including hsa-miR-144, hsa-miR-3662, hsa-miR-12136 and hsa-miR-3646, were identified. Genes in the coexpression network were enriched in the biological processes including proteasomal protein catabolic process and proteasome-mediated ubiquitin-dependent protein catabolic process and the pathways of endocytosis and ubiquitin-mediated proteolysis. Drugs perturbing genes in the coexpression network were also predicted and included enzyme inhibitors, chlorodiphenyl and finasteride.

Conclusions

For the first time, TED-associated coexpression network was constructed and key genes and their functions, as well as TFs, miRs and drugs, were predicted. The results of the present work may be relevant in the treatment and diagnosis of TED and may boost molecular studies regarding TED.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00412-0.

Gene Coexpression Connectivity Predicts Gene Targets Underlying High Ionic-Liquid Tolerance in Yarrowia lipolytica

Microbial tolerance to organic solvents such as ionic liquids (ILs) is a robust phenotype beneficial for novel biotransformation. While most microbes become inhibited in 1% to 5% (vol/vol) IL (e.g., 1-ethyl-3-methylimidazolium acetate), we engineered a robust Yarrowia lipolytica strain (YlCW001) that tolerates a record high of 18% (vol/vol) IL via adaptive laboratory evolution. Yet, genotypes conferring high IL tolerance in YlCW001 remain to be discovered. In this study, we shed light on the underlying cellular processes that enable robust Y. lipolytica to thrive in inhibitory ILs. By using dynamic transcriptome sequencing (RNA-Seq) data, we introduced Gene Coexpression Connectivity (GeCCo) as a metric to discover genotypes conferring desirable phenotypes that might not be found by the conventional differential expression (DE) approaches. GeCCo selects genes based on their number of coexpressed genes in a subnetwork of upregulated genes by the target phenotype. We experimentally validated GeCCo by reverse engineering a high-IL-tolerance phenotype in wild-type Y. lipolytica. We found that gene targets selected by both DE and GeCCo exhibited the best statistical chance at increasing IL tolerance when individually overexpressed. Remarkably, the best combination of dual-overexpression genes was genes selected by GeCCo alone. This nonintuitive combination of genes, BRN1 and OYE2, is involved in guiding/regulating mitotic cell division, chromatin segregation/condensation, microtubule and cytoskeletal organization, and Golgi vesicle transport.

IMPORTANCE Cellular robustness to cope with stressors is an important phenotype. Y. lipolytica is an industrial robust oleaginous yeast that has recently been discovered to tolerate record high concentrations of ILs, beneficial for novel biotransformation in organic solvents. However, genotypes that link to IL tolerance in Y. lipolytica are largely unknown. Due to the complex IL-tolerant phenotype, conventional gene discovery and validation based on differential gene expression approaches are time-consuming due to a large search space and might encounter a high false-discovery rate. Here, using the developed Gene Coexpression Connectivity (GeCCo) method, we identified and validated a subset of most promising gene targets conferring the IL-tolerant phenotypes and shed light on their potential mechanisms. We anticipate GeCCo being a useful method to discover the genotype-to-phenotype link.

An interolog-based barley interactome as an integration framework for immune signaling

The barley MLA nucleotide-binding leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many fungal diseases, including powdery mildew, stem-, and stripe rust. We used interolog inference to construct a barley protein interactome (Hordeum vulgare predicted interactome, HvInt) comprising 66,133 edges and 7,181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared with the experimentally validated Arabidopsis interactome of 11,253 proteins and 73,960 interactions, verifying that the 2 networks share scale-free properties, including a power-law distribution and small-world network. Then, by successive layering of defense-specific "omics" datasets, HvInt was customized to model cellular response to powdery mildew infection. Integration of HvInt with expression quantitative trait loci (eQTL) enabled us to infer disease modules and responses associated with fungal penetration and haustorial development. Next, using HvInt and infection-time-course RNA sequencing of immune signaling mutants, we assembled resistant and susceptible subnetworks. The resulting differentially coexpressed (resistant - susceptible) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to mildew resistance locus a (Mla) through trans eQTL associations. Lastly, we anchored HvInt with new and previously identified interactors of the MLA coiled coli + nucleotide-binding domains and extended these to additional MLA alleles, orthologs, and NLR outgroups to predict receptor localization and conservation of signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.