Community cohesion looseness in gene networks reveals individualized drug targets and resistance.

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.

Analysis of pea mutants reveals the conserved role of FRUITFULL controlling the end of flowering and its potential to boost yield.

Monocarpic plants have a single reproductive phase in their life. Therefore, flower and fruit production are restricted to the length of this period. This reproductive strategy involves the regulation of flowering cessation by a coordinated arrest of the growth of the inflorescence meristems, optimizing resource allocation to ensure seed filling. Flowering cessation appears to be a regulated phenomenon in all monocarpic plants. Early studies in several species identified seed production as a major factor triggering inflorescence proliferative arrest. Recently, genetic factors controlling inflorescence arrest, in parallel to the putative signals elicited by seed production, have started to be uncovered in Arabidopsis, with the MADS-box gene FRUITFULL (FUL) playing a central role in the process. However, whether the genetic network regulating arrest is also at play in other species is completely unknown. Here, we show that this role of FUL is not restricted to Arabidopsis but is conserved in another monocarpic species with a different inflorescence structure, field pea, strongly suggesting that the network controlling the end of flowering is common to other plants. Moreover, field trials with lines carrying mutations in pea FUL genes show that they could be used to boost crop yield.

Transcriptome and small RNA analysis unveils novel insights into the C4 gene regulation in sugarcane.

MAIN CONCLUSION: This study reveals miRNA indirect regulation of C4 genes in sugarcane through transcription factors, highlighting potential key regulators like SsHAM3a. C4 photosynthesis is crucial for the high productivity and biomass of sugarcane, however, the miRNA regulation of C4 genes in sugarcane remains elusive. We have identified 384 miRNAs along the leaf gradients, including 293 known miRNAs and 91 novel miRNAs. Among these, 86 unique miRNAs exhibited differential expression patterns, and we identified 3511 potential expressed targets of these differentially expressed miRNAs (DEmiRNAs). Analyses using Pearson correlation coefficient (PCC) and Gene Ontology (GO) enrichment revealed that targets of miRNAs with positive correlations are integral to chlorophyll-related photosynthetic processes. In contrast, negatively correlated pairs are primarily associated with metabolic functions. It is worth noting that no C4 genes were predicted as targets of DEmiRNAs. Our application of weighted gene co-expression network analysis (WGCNA) led to a gene regulatory network (GRN) suggesting miRNAs might indirectly regulate C4 genes via transcription factors (TFs). The GRAS TF SsHAM3a emerged as a potential regulator of C4 genes, targeted by miR171y and miR171am, and exhibiting a negative correlation with miRNA expression along the leaf gradient. This study sheds light on the complex involvement of miRNAs in regulating C4 genes, offering a foundation for future research into enhancing sugarcane's photosynthetic efficiency.

Whole transcriptome sequencing analyses of islets reveal ncRNA regulatory networks underlying impaired insulin secretion and increased ß-cell mass in high fat diet-induced diabetes mellitus.

AIM: Our study aims to identify novel non-coding RNA-mRNA regulatory networks associated with ß-cell dysfunction and compensatory responses in obesity-related diabetes. METHODS: Glucose metabolism, islet architecture and secretion, and insulin sensitivity were characterized in C57BL/6J mice fed on a 60% high-fat diet (HFD) or control for 24 weeks. Islets were isolated for whole transcriptome sequencing to identify differentially expressed (DE) mRNAs, miRNAs, IncRNAs, and circRNAs. Regulatory networks involving miRNA-mRNA, lncRNA-mRNA, and lncRNA-miRNA-mRNA were constructed and functions were assessed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. RESULTS: Despite compensatory hyperinsulinemia and a significant increase in ß-cell mass with a slow rate of proliferation, HFD mice exhibited impaired glucose tolerance. In isolated islets, insulin secretion in response to glucose and palmitic acid deteriorated after 24 weeks of HFD. Whole transcriptomic sequencing identified a total of 1324 DE mRNAs, 14 DE miRNAs, 179 DE lncRNAs, and 680 DE circRNAs. Our transcriptomic dataset unveiled several core regulatory axes involved in the impaired insulin secretion in HFD mice, such as miR-6948-5p/Cacna1c, miR-6964-3p/Cacna1b, miR-3572-5p/Hk2, miR-3572-5p/Cckar and miR-677-5p/Camk2d. Additionally, proliferative and apoptotic targets, including miR-216a-3p/FKBP5, miR-670-3p/Foxo3, miR-677-5p/RIPK1, miR-802-3p/Smad2 and ENSMUST00000176781/Caspase9 possibly contribute to the increased ß-cell mass in HFD islets. Furthermore, competing endogenous RNAs (ceRNA) regulatory network involving 7 DE miRNAs, 15 DE lncRNAs and 38 DE mRNAs might also participate in the development of HFD-induced diabetes. CONCLUSIONS: The comprehensive whole transcriptomic sequencing revealed novel non-coding RNA-mRNA regulatory networks associated with impaired insulin secretion and increased ß-cell mass in obesity-related diabetes.

Plasticity-led and mutation-led evolutions are different modes of the same developmental gene regulatory network.

The standard theory of evolution proposes that mutations cause heritable variations, which are naturally selected, leading to evolution. However, this mutation-led evolution (MLE) is being questioned by an alternative theory called plasticity-led evolution (PLE). PLE suggests that an environmental change induces adaptive phenotypes, which are later genetically accommodated. According to PLE, developmental systems should be able to respond to environmental changes adaptively. However, developmental systems are known to be robust against environmental and mutational perturbations. Thus, we expect a transition from a robust state to a plastic one. To test this hypothesis, we constructed a gene regulatory network (GRN) model that integrates developmental processes, hierarchical regulation, and environmental cues. We then simulated its evolution over different magnitudes of environmental changes. Our findings indicate that this GRN model exhibits PLE under large environmental changes and MLE under small environmental changes. Furthermore, we observed that the GRN model is susceptible to environmental or genetic fluctuations under large environmental changes but is robust under small environmental changes. This indicates a breakdown of robustness due to large environmental changes. Before the breakdown of robustness, the distribution of phenotypes is biased and aligned to the environmental changes, which would facilitate rapid adaptation should a large environmental change occur. These observations suggest that the evolutionary transition from mutation-led to plasticity-led evolution is due to a developmental transition from robust to susceptible regimes over increasing magnitudes of environmental change. Thus, the GRN model can reconcile these conflicting theories of evolution.

Mining key circRNA-associated-ceRNA networks for milk fat metabolism in cows with varying milk fat percentages.

BACKGROUND: Cow milk fat is an essential indicator for evaluating and measuring milk quality and cow performance. Growing research has identified the molecular functions of circular RNAs (circRNAs) necessary for mammary gland development and lactation in mammals. METHOD: The present study analyzed circRNA expression profiling data in mammary epithelial cells (MECs) from cows with highly variable milk fat percentage (MFP) using differential expression analysis and weighted gene co-expression network analysis (WGCNA). RESULTS: A total of 309 differentially expressed circRNAs (DE-circRNAs) were identified in the high and low MFP groups. WGCNA analysis revealed that the pink module was significantly associated with MFP (r = - 0.85, P = 0.007). Parental genes of circRNAs in this module were enriched mainly in lipid metabolism-related signaling pathways, such as focal adhesion, ECM-receptor interaction, adherens junction and AMPK. Finally, six DE-circRNAs were screened from the pink module: circ_0010571, circ_0007797, circ_0002746, circ_0003052, circ_0004319, and circ_0012840. Among them, circ_0002746, circ_0003052, circ_0004319, and circ_0012840 had circular structures and were highly expressed in mammary tissues. Subcellular localization revealed that these four DE-circRNAs may play a regulatory role in the mammary glands of dairy cows, mainly as competitive endogenous RNAs (ceRNAs). Seven hub target genes (GNB1, GNG2, PLCB1, PLCG1, ATP6V0C, NDUFS4, and PIGH) were obtained by constructing the regulatory network of their ceRNAs and then analyzed by CytoHubba and MCODE plugins in Cytoscape. Functional enrichment analysis revealed that these genes are crucial and most probable ceRNA regulators in milk fat metabolism. CONCLUSIONS: Our study identified several vital circRNAs and ceRNAs affecting milk fat synthesis, providing new research ideas and a theoretical basis for cow lactation, milk quality, and breed improvement.

Network-based identification of key proteins and repositioning of drugs for non-small cell lung cancer.

BACKGROUND: NSCLC is a lethal cancer that is highly prevalent and accounts for 85% of cases of lung cancer. Conventional cancer treatments, such as chemotherapy and radiation, frequently exhibit limited efficacy and notable adverse reactions. Therefore, a drug repurposing method is proposed for effective NSCLC treatment. AIMS: This study aims to evaluate candidate drugs that are effective for NSCLC at the clinical level using a systems biology and network analysis approach. METHODS: Differentially expressed genes in transcriptomics data were identified using the systems biology and network analysis approaches. A network of gene co-expression was developed with the aim of detecting two modules of gene co-expression. Following that, the Drug-Gene Interaction Database was used to find possible drugs that target important genes within two gene co-expression modules linked to non-small cell lung cancer (NSCLC). The use of Cytoscape facilitated the creation of a drug-gene interaction network. Finally, gene set enrichment analysis was done to validate candidate drugs. RESULTS: Unlike previous research on repositioning drugs for NSCLC, which uses a gene co-expression network, this project is the first to research both gene co-expression and co-occurrence networks. And the co-occurrence network also accounts for differentially expressed genes in cancer cells and their adjacent normal cells. For effective management of non-small cell lung cancer (NSCLC), drugs that show higher gene regulation and gene affinity within the drug-gene interaction network are thought to be important. According to the discourse, NSCLC genes have a lot of control over medicines like vincristine, fluorouracil, methotrexate, clotrimazole, etoposide, tamoxifen, sorafenib, doxorubicin, and pazopanib. CONCLUSION: Hence, there is a possibility of repurposing these drugs for the treatment of non-small-cell lung cancer.

Cis-regulatory interfaces reveal the molecular mechanisms underlying the notochord gene regulatory network of Ciona.

Tissue-specific gene expression is fundamental in development and evolution, and is mediated by transcription factors and by the cis-regulatory regions (enhancers) that they control. Transcription factors and their respective tissue-specific enhancers are essential components of gene regulatory networks responsible for the development of tissues and organs. Although numerous transcription factors have been characterized from different organisms, the knowledge of the enhancers responsible for their tissue-specific expression remains fragmentary. Here we use Ciona to study the enhancers associated with ten transcription factors expressed in the notochord, an evolutionary hallmark of the chordate phylum. Our results illustrate how two evolutionarily conserved transcription factors, Brachyury and Foxa2, coordinate the deployment of other notochord transcription factors. The results of these detailed cis-regulatory analyses delineate a high-resolution view of the essential notochord gene regulatory network of Ciona, and provide a reference for studies of transcription factors, enhancers, and their roles in development, disease, and evolution.

PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization.

Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.

Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs.

BACKGROUND: Enhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking. RESULTS: Here, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model. CONCLUSIONS: Our work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.

An integrated approach to understand the regulatory role of miR-27 family in breast cancer metastasis.

One of the prime reasons of increasing breast cancer mortality is metastasizing cancer cells. Owing to the side effects of clinically available drugs to treat breast cancer metastasis, it is of utmost importance to understand the underlying biogenesis of breast cancer tumorigenesis. In-silico identification of potential RNAs might help in utilizing the miR-27 family as a therapeutic target in breast cancer. The experimentally verified common interacting mRNAs for miR27 family are retrieved from three publicly available databases- TargetScan, miRDB and miRTarBase. Finally on comparing the common genes with HCMDB and GEPIA data, four breast cancer-associated differentially expressed metastatic mRNAs (GATA3, ENAH, ITGA2 and SEMA4D) are obtained. Corresponding to the miR27 family and associated mRNAs, interacting drugs are retrieved from Sm2mir and CTDbase, respectively. The interaction network-based approach was utilized to obtain the hub RNAs and triad modules by employing the 'Cytohubba' and 'MClique' plugins, respectively in Cytoscape. Further, sample-, subclass- and promoter methylation-based expression analyses reveals GATA3 and ENAH to be the most significant mRNAs in breast cancer metastasis having >10% genetic alteration in both METABRIC Vs TCGA datasets as per their oncoprint analysis via cBioPortal. Additionally, survival analysis in Oncolnc reveals SEMA4D as survival biomarker. Interactions among the miR27 family, their target mRNAs and drugs interacting with miRNAs and mRNAs can be extensively explored in both in-vivo and in-vitro setups to assess their therapeutic potential in the diminution of breast cancer.

Long noncoding RNAs and mRNAs profiling in ovary during laying and broodiness in Taihe Black-Bone Silky Fowls (Gallus gallus Domesticus Brisson).

BACKGROUND: Broodiness significantly impacts poultry egg production, particularly notable in specific breeds such as the black-boned Silky, characterized by pronounced broodiness. An understanding of the alterations in ovarian signaling is essential for elucidating the mechanisms that influence broodiness. However, comparative research on the characteristics of long non-coding RNAs (lncRNAs) in the ovaries of broody chickens (BC) and high egg-laying chickens (GC) remains scant. In this investigation, we employed RNA sequencing to assess the ovarian transcriptomes, which include both lncRNAs and mRNAs, in eight Taihe Black-Bone Silky Fowls (TBsf), categorized into broody and high egg-laying groups. This study aims to provide a clearer understanding of the genetic underpinnings associated with broodiness and egg production. RESULTS: We have identified a total of 16,444 mRNAs and 18,756 lncRNAs, of which 349 mRNAs and 651 lncRNAs exhibited significantly different expression (DE) between the BC and GC groups. Furthermore, we have identified the cis-regulated and trans-regulated target genes of differentially abundant lncRNA transcripts and have constructed an lncRNA-mRNA trans-regulated interaction network linked to ovarian follicle development. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analyses have revealed that DE mRNAs and the target genes of DE lncRNAs are associated with pathways including neuroactive ligand-receptor interaction, CCR6 chemokine receptor binding, G-protein coupled receptor binding, cytokine-cytokine receptor interaction, and ECM-receptor interaction. CONCLUSION: Our research presents a comprehensive compilation of lncRNAs and mRNAs linked to ovarian development. Additionally, it establishes a predictive interaction network involving differentially abundant lncRNAs and differentially expressed genes (DEGs) within TBsf. This significantly contributes to our understanding of the intricate interactions between lncRNAs and genes governing brooding behavior.

Automatic design of gene regulatory mechanisms for spatial pattern formation.

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

Novel potential lncRNA biomarker in B cells indicates essential pathogenic pathway activation in patients with SLE.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a highly heterogeneous disease, and B cell abnormalities play a central role in the pathogenesis of SLE. Long non-coding RNAs (lncRNAs) have also been implicated in the pathogenesis of SLE. The expression of lncRNAs is finely regulated and cell-type dependent, so we aimed to identify B cell-expressing lncRNAs as biomarkers for SLE, and to explore their ability to reflect the status of SLE critical pathway and disease activity. METHODS: Weighted gene coexpression network analysis (WGCNA) was used to cluster B cell-expressing genes of patients with SLE into different gene modules and relate them to clinical features. Based on the results of WGCNA, candidate lncRNA levels were further explored in public bulk and single-cell RNA-sequencing data. In another independent cohort, the levels of the candidate were detected by RT-qPCR and the correlation with disease activity was analysed. RESULTS: WGCNA analysis revealed one gene module significantly correlated with clinical features, which was enriched in type I interferon (IFN) pathway. Among non-coding genes in this module, lncRNA RP11-273G15.2 was differentially expressed in all five subsets of B cells from patients with SLE compared with healthy controls and other autoimmune diseases. RT-qPCR validated that RP11-273G15.2 was highly expressed in SLE B cells and positively correlated with IFN scores (r=0.7329, p<0.0001) and disease activity (r=0.4710, p=0.0005). CONCLUSION: RP11-273G15.2 could act as a diagnostic and disease activity monitoring biomarker for SLE, which might have the potential to guide clinical management.

Analyses of lncRNA and mRNA profiles in recurrent atrial fibrillation after catheter ablation.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. Catheter ablation has become a crucial treatment for AF. However, there is a possibility of atrial fibrillation recurrence after catheter ablation. Our study sought to elucidate the role of lncRNA‒mRNA regulatory networks in late AF recurrence after catheter ablation. METHODS: We conducted RNA sequencing to profile the transcriptomes of 5 samples from the presence of recurrence after AF ablation (P-RAF) and 5 samples from the absence of recurrence after AF ablation (A-RAF). Differentially expressed genes (DEGs) and long noncoding RNAs (DE-lncRNAs) were analyzed using the DESeq2 R package. The functional correlations of the DEGs were assessed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein‒protein interaction (PPI) network was constructed using STRING and Cytoscape. We also established a lncRNA‒mRNA regulatory network between DE-lncRNAs and DEGs using BEDTools v2.1.2 software and the Pearson correlation coefficient method. To validate the high-throughput sequencing results of the hub genes, we conducted quantitative real-time polymerase chain reaction (qRT‒PCR) experiments. RESULTS: A total of 28,528 mRNAs and 42,333 lncRNAs were detected. A total of 96 DEGs and 203 DE-lncRNAs were identified between the two groups. GO analysis revealed that the DEGs were enriched in the biological processes (BPs) of "regulation of immune response" and "regulation of immune system process", the cellular components (CCs) of "extracellular matrix" and "cell‒cell junction", and the molecular functions (MFs) of "signaling adaptor activity" and "protein-macromolecule adaptor activity". According to the KEGG analysis, the DEGs were associated with the "PI3K-Akt signaling pathway" and "MAPK signaling pathway." Nine hub genes (MMP9, IGF2, FGFR1, HSPG2, GZMB, PEG10, GNLY, COL6A1, and KCNE3) were identified through the PPI network. lncRNA-TMEM51-AS1-201 was identified as a core regulator in the lncRNA‒mRNA regulatory network, suggesting its potential impact on the recurrence of AF after catheter ablation through the regulation of COL6A1, FGFR1, HSPG2, and IGF2. CONCLUSIONS: The recurrence of atrial fibrillation after catheter ablation may be associated with immune responses and fibrosis, with the extracellular matrix playing a crucial role. TMEM51-AS1-201 has been identified as a potential key target for AF recurrence after catheter ablation.

Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

A muti-modal feature fusion method based on deep learning for predicting immunotherapy response.

Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respond to ICT. To predict ICT response, we developed a multi-modal feature fusion model based on deep learning (MFMDL). This model utilizes graph neural networks to map gene-gene relationships in gene networks to low dimensional vector spaces, and then fuses biological pathway features and immune cell infiltration features to make robust predictions of ICT. We used five datasets to validate the predictive performance of the MFMDL. These five datasets span multiple types of cancer, including melanoma, lung cancer, and gastric cancer. We found that the prediction performance of multi-modal feature fusion model based on deep learning is superior to other traditional ICT biomarkers, such as ICT targets or tumor microenvironment-associated markers. In addition, we also conducted ablation experiments to demonstrate the necessity of fusing different modal features, which can improve the prediction accuracy of the model.

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML.

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

Bioinformatics analysis of the potentially functional circRNA-miRNA-mRNA network in breast cancer.

Breast cancer (BC) is the most common cancer among women with high morbidity and mortality. Therefore, new research is still needed for biomarker detection. GSE101124 and GSE182471 datasets were obtained from the Gene Expression Omnibus (GEO) database to evaluate differentially expressed circular RNAs (circRNAs). The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used to identify the significantly dysregulated microRNAs (miRNAs) and genes considering the Prediction Analysis of Microarray classification (PAM50). The circRNA-miRNA-mRNA relationship was investigated using the Cancer-Specific CircRNA, miRDB, miRTarBase, and miRWalk databases. The circRNA-miRNA-mRNA regulatory network was annotated using Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. The protein-protein interaction network was constructed by the STRING database and visualized by the Cytoscape tool. Then, raw miRNA data and genes were filtered using some selection criteria according to a specific expression level in PAM50 subgroups. A bottleneck method was utilized to obtain highly interacted hub genes using cytoHubba Cytoscape plugin. The Disease-Free Survival and Overall Survival analysis were performed for these hub genes, which are detected within the miRNA and circRNA axis in our study. We identified three circRNAs, three miRNAs, and eighteen candidate target genes that may play an important role in BC. In addition, it has been determined that these molecules can be useful in the classification of BC, especially in determining the basal-like breast cancer (BLBC) subtype. We conclude that hsa_circ_0000515/miR-486-5p/SDC1 axis may be an important biomarker candidate in distinguishing patients in the BLBC subgroup of BC.

Engineering a synthetic gene circuit for high-performance inducible expression in mammalian systems.

Inducible gene expression systems can be used to control the expression of a gene of interest by means of a small-molecule. One of the most common designs involves engineering a small-molecule responsive transcription factor (TF) and its cognate promoter, which often results in a compromise between minimal uninduced background expression (leakiness) and maximal induced expression. Here, we focus on an alternative strategy using quantitative synthetic biology to mitigate leakiness while maintaining high expression, without modifying neither the TF nor the promoter. Through mathematical modelling and experimental validations, we design the CASwitch, a mammalian synthetic gene circuit based on combining two well-known network motifs: the Coherent Feed-Forward Loop (CFFL) and the Mutual Inhibition (MI). The CASwitch combines the CRISPR-Cas endoribonuclease CasRx with the state-of-the-art Tet-On3G inducible gene system to achieve high performances. To demonstrate the potentialities of the CASwitch, we apply it to three different scenarios: enhancing a whole-cell biosensor, controlling expression of a toxic gene and inducible production of Adeno-Associated Virus (AAV) vectors.