An integrative method to decode regulatory logics in gene transcription

Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms

There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.

Abundant binary promoter switches in lineage-determining transcription factors indicate a digital component of cell fate determination

Previous studies of the murine Ly49 and human KIR gene clusters implicated competing sense and antisense promoters in the control of variegated gene expression. In the current study, an examination of transcription factor genes defines an abundance of convergent and divergent sense/antisense promoter pairs, suggesting that competing promoters may control cell fate determination. Differentiation of CD34+ hematopoietic progenitors in vitro shows that cells with GATA1 antisense transcription have enhanced GATA2 transcription and a mast cell phenotype, whereas cells with GATA2 antisense transcription have increased GATA1 transcripts and an erythroblast phenotype. Detailed analyses of the AHR and RORC genes demonstrate the ability of competing promoters to act as binary switches and the association of antisense transcription with an immature/progenitor cell phenotype. These data indicate that alternative cell fates generated by promoter competition in lineage-determining transcription factors contribute to the programming of cell differentiation.

Integrated modeling framework reveals co-regulation of transcription factors, miRNAs and lncRNAs on cardiac developmental dynamics

AIMS

Dissecting complex interactions among transcription factors (TFs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are central for understanding heart development and function. Although computational approaches and platforms have been described to infer relationships among regulatory factors and genes, current approaches do not adequately account for how highly diverse, interacting regulators that include noncoding RNAs (ncRNAs) control cardiac gene expression dynamics over time.

METHODS

To overcome this limitation, we devised an integrated framework, cardiac gene regulatory modeling (CGRM) that integrates LogicTRN and regulatory component analysis bioinformatics modeling platforms to infer complex regulatory mechanisms. We then used CGRM to identify and compare the TF-ncRNA gene regulatory networks that govern early- and late-stage cardiomyocytes (CMs) generated by in vitro differentiation of human pluripotent stem cells (hPSC) and ventricular and atrial CMs isolated during in vivo human cardiac development.

RESULTS

Comparisons of in vitro versus in vivo derived CMs revealed conserved regulatory networks among TFs and ncRNAs in early cells that significantly diverged in late staged cells. We report that cardiac genes ("heart targets") expressed in early-stage hPSC-CMs are primarily regulated by MESP1, miR-1, miR-23, lncRNAs NEAT1 and MALAT1, while GATA6, HAND2, miR-200c, NEAT1 and MALAT1 are critical for late hPSC-CMs. The inferred TF-miRNA-lncRNA networks regulating heart development and contraction were similar among early-stage CMs, among individual hPSC-CM datasets and between in vitro and in vivo samples. However, genes related to apoptosis, cell cycle and proliferation, and transmembrane transport showed a high degree of divergence between in vitro and in vivo derived late-stage CMs. Overall, late-, but not early-stage CMs diverged greatly in the expression of "heart target" transcripts and their regulatory mechanisms.

CONCLUSIONS

In conclusion, we find that hPSC-CMs are regulated in a cell autonomous manner during early development that diverges significantly as a function of time when compared to in vivo derived CMs. These findings demonstrate the feasibility of using CGRM to reveal dynamic and complex transcriptional and posttranscriptional regulatory interactions that underlie cell directed versus environment-dependent CM development. These results with in vitro versus in vivo derived CMs thus establish this approach for detailed analyses of heart disease and for the analysis of cell regulatory systems in other biomedical fields.

Novel Association of IGF2BP2 Gene Variants With Altered Risk of Breast Cancer and as Potential Molecular Biomarker of Triple Negative Breast Cancer

BACKGROUND

Several studies documented that insulin-like growth factor-2 mRNA-binding protein 2 (IGF2BP2) contributes to carcinogenesis, and 1 report documented the association of IGF2BP2 rs4402960 with increased risk of breast cancer (BC). This study investigated the association of rs4402960 and rs1470579 IGF2BP2 variants with BC and triple negative BC (TNBC).

MATERIALS AND METHODS

This case-control study included 488 BC patients comprising 130 TNBC and 358 non-TNBC patients, and 476 cancer-free controls. Genomic DNA was obtained from peripheral venous blood, and genotyping was done by allelic exclusion method on real-time PCR.

RESULTS

The rs440960, but not rs1470579, minor allele was significantly associated with BC, and significantly higher rs4402960 T/T genotype frequency was noted in BC patients than controls; the distribution of rs1470579 genotypes were comparable between BC patients and controls. In contrast, significantly lower rs1470579 minor allele frequency, and reduced rs1470579 A/C and C/C, and rs4402960 T/T genotype frequencies were seen in TNBC cases. Among TNBC cases, rs4402960 and rs1470579 correlated with menses pattern, histological type, breastfeeding, oral contraceptive use and hormonotherapy. Among non-TNBC patients, and rs1470579 correlated significantly with breast feeding, oral contraceptive use, hormonotherapy, and nodal status; rs4402960 also correlated with menses pattern. Two-locus (rs440960-rs1470579) haplotype analysis confirmed the positive association of TC, and negative association of GC and TA haplotypes with BC, while TC and GC haplotypes were negatively associated with TNBC.

CONCLUSION

Whereas rs440960 was positively associated with BC, both rs4402960 and rs1470579 were negatively associated with TNBC, suggesting potential diagnostic/prognostic role in BC and its complications.

RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique

Background

Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs.

Results

To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data.

Conclusions

In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04696-w.

Three topological features of regulatory networks control life-essential and specialized subsystems

Gene regulatory networks (GRNs) play key roles in development, phenotype plasticity, and evolution. Although graph theory has been used to explore GRNs, associations amongst topological features, transcription factors (TFs), and systems essentiality are poorly understood. Here we sought the relationship amongst the main GRN topological features that influence the control of essential and specific subsystems. We found that the K_nn, page rank, and degree are the most relevant GRN features: the ones are conserved along the evolution and are also relevant in pluripotent cells. Interestingly, life-essential subsystems are governed mainly by TFs with intermediary K_nn and high page rank or degree, whereas specialized subsystems are mainly regulated by TFs with low K_nn. Hence, we suggest that the high probability of TFs be toured by a random signal, and the high probability of the signal propagation to target genes ensures the life-essential subsystems' robustness. Gene/genome duplication is the main evolutionary process to rise K_nn as the most relevant feature. Herein, we shed light on unexplored topological GRN features to assess how they are related to subsystems and how the duplications shaped the regulatory systems along the evolution. The classification model generated can be found here: https://github.com/ivanrwolf/NoC/ .

Cis-Regulatory Logic Produces Gene-Expression Noise Describing Phenotypic Heterogeneity in Bacteria

Gene transcriptional process is random. It occurs in bursts and follows single-molecular kinetics. Intermittent bursts are measured based on their frequency and size. They influence temporal fluctuations in the abundance of total mRNA and proteins by generating distinct transcriptional variations referred to as “noise”. Noisy expression induces uncertainty because the association between transcriptional variation and the extent of gene expression fluctuation is ambiguous. The promoter architecture and remote interference of different cis-regulatory elements are the crucial determinants of noise, which is reflected in phenotypic heterogeneity. An alternative perspective considers that cellular parameters dictating genome-wide transcriptional kinetics follow a universal pattern. Research on noise and systematic perturbations of promoter sequences reinforces that both gene-specific and genome-wide regulation occur across species ranging from bacteria and yeast to animal cells. Thus, deciphering gene-expression noise is essential across different genomics applications. Amidst the mounting conflict, it is imperative to reconsider the scope, progression, and rational construction of diversified viewpoints underlying the origin of the noise. Here, we have established an indication connecting noise, gene expression variations, and bacterial phenotypic variability. This review will enhance the understanding of gene-expression noise in various scientific contexts and applications.

Identifying Transcription Factor Combinations to Modulate Circadian Rhythms by Leveraging Virtual Knockouts on Transcription Networks

The mammalian circadian systems consist of indigenous, self-sustained 24-h rhythm generators. They comprise many genes, molecules, and regulators. To decode their systematic controls, a robust computational approach was employed. It integrates transcription-factor-occupancy and time-series gene-expression data as input. The model equations were constructed and solved to determine the transcriptional regulatory logics in the mouse transcriptome network. This hypothesizes to explore the underlying mechanisms of combinatorial transcriptional regulations for circadian rhythms in mouse. We reconstructed the quantitative transcriptional-regulatory networks for circadian gene regulation at a dynamic scale. Transcriptional-simulations with virtually knocked-out mutants were performed to estimate their influence on networks. The potential transcriptional-regulators-combinations modulating the circadian rhythms were identified. Of them, CLOCK/CRY1 double knockout preserves the highest modulating capacity. Our quantitative framework offers a quick, robust, and physiologically relevant way to characterize the druggable targets to modulate the circadian rhythms at a dynamic scale effectively.

IGF2BP2 Polymorphisms Are Associated with Clinical Characteristics and Development of Oral Cancer

Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is associated with insulin resistance, lipid metabolism, and tumorigenesis. However, the association between the IGF2BP2 polymorphism and oral cancer risk remains unclear. We recruited 1349 male patients with oral cancer and 1198 cancer-free controls. Three single nucleotide polymorphisms IGF2BP2 rs11705701, rs4402960, and rs1470579 were assessed using real-time polymerase chain reaction. The results indicate that the male patients with oral cancer and with the rs11705701 GA+AA, rs4402960 GT+TT, and rs1470579 AC+CC genotypes had increased risk of advanced clinical stage, larger tumor, and progression of lymph node metastasis compared with those with wild-type IGF2BP2. Moreover, according to The Cancer Genome Atlas dataset, high expression of the IGF2BP2 gene is associated with poor survival in patients with head and neck squamous cell carcinoma. In conclusion, our results suggest that IGF2BP2 polymorphisms are associated with less favorable oral cancer clinical characteristics.

Understanding Quantitative Circadian Regulations Are Crucial Towards Advancing Chronotherapy

Circadian rhythms have a deep impact on most aspects of physiology. In most organisms, especially mammals, the biological rhythms are maintained by the indigenous circadian clockwork around geophysical time (~24-h). These rhythms originate inside cells. Several core components are interconnected through transcriptional/translational feedback loops to generate molecular oscillations. They are tightly controlled over time. Also, they exert temporal controls over many fundamental physiological activities. This helps in coordinating the body’s internal time with the external environments. The mammalian circadian clockwork is composed of a hierarchy of oscillators, which play roles at molecular, cellular, and higher levels. The master oscillation has been found to be developed at the hypothalamic suprachiasmatic nucleus in the brain. It acts as the core pacemaker and drives the transmission of the oscillation signals. These signals are distributed across different peripheral tissues through humoral and neural connections. The synchronization among the master oscillator and tissue-specific oscillators offer overall temporal stability to mammals. Recent technological advancements help us to study the circadian rhythms at dynamic scale and systems level. Here, we outline the current understanding of circadian clockwork in terms of molecular mechanisms and interdisciplinary concepts. We have also focused on the importance of the integrative approach to decode several crucial intricacies. This review indicates the emergence of such a comprehensive approach. It will essentially accelerate the circadian research with more innovative strategies, such as developing evidence-based chronotherapeutics to restore de-synchronized circadian rhythms.

Prediction of Gene Expression Patterns With Generalized Linear Regression Model

Cell reprogramming has played important roles in medical science, such as tissue repair, organ reconstruction, disease treatment, new drug development, and new species breeding. Oct4, a core pluripotency factor, has especially played a key role in somatic cell reprogramming through transcriptional control and affects the expression level of genes by its combination intensity. However, the quantitative relationship between Oct4 combination intensity and target gene expression is still not clear. Therefore, firstly, a generalized linear regression method was constructed to predict gene expression values in promoter regions affected by Oct4 combination intensity. Training data, including Oct4 combination intensity and target gene expression, were from promoter regions of genes with different cell development stages. Additionally, the quantitative relationship between gene expression and Oct4 combination intensity was analyzed with the proposed model. Then, the quantitative relationship between gene expression and Oct4 combination intensity at each stage of cell development was classified into high and low levels. Experimental analysis showed that the combination height of Oct4-inhibited gene expression decremented by a temporal exponential value, whereas the combination width of Oct4-promoted gene expression incremented by a temporal logarithmic value. Experimental results showed that the proposed method can achieve goodness of fit with high confidence.

Relationship between IGF2BP2 and IGFBP3 polymorphisms and susceptibility to non-small-cell lung cancer: a case-control study in Eastern Chinese Han population

Background

IGF2BP2 and IGFBP3 polymorphisms may be associated with cancer risk.

Methods

With an aim to determine the association of variations in IGF2BP2 and IGFBP3 genes with risk of non-small-cell lung cancer (NSCLC), IGF2BP2 rs1470579 A>C, rs4402960 G>T and IGFBP3 rs2270628 C>T, rs3110697 G>A, and rs6953668 G>A polymorphisms were selected and genotyped in 521 NSCLC patients and 1,030 controls.

Results

We found that there was no difference in IGF2BP2 and IGFBP3 genotype distribution among the NSCLC patients and controls. The stratified analyses suggested that IGF2BP2 rs1470579 A>C polymorphism decreased the risk of NSCLC in some subgroups (female subgroup: CC vs AA: adjusted P=0.032 and CC vs AC/AA: adjusted P=0.028; <60 years subgroup: CC vs AA: adjusted P=0.012 and CC vs AC/AA: adjusted P=0.013; and never drinking subgroup: CC vs AA: adjusted P=0.046 and CC vs AC/AA: adjusted P=0.031). The stratified analyses also found that IGF2BP2 rs4402960 G>T polymorphism decreased the risk of NSCLC in some subgroups (female subgroup: TT vs GG: adjusted P=0.031 and TT vs GT/GG: adjusted P=0.026; <60 subgroup: TT vs GG: adjusted P=0.037 and TT vs GT/GG: adjusted P=0.038; and never drinking subgroup: TT vs GT/GG: adjusted P=0.046). Haplotype analysis indicated A_rs1470579C_rs2270628G_rs3110697G_rs4402960A_rs6953668 haplotype decreased susceptibility of NSCLC (P=0.007).

Conclusion

Our study suggests that IGF2BP2 rs1470579 A>C, rs4402960 G>T single-nucleotide polymorphisms are candidates for decreased susceptibility to NSCLC among female, <60 years, and never drinking subgroups. In the future, more case–control studies with functional analysis are needed to confirm these preliminary findings.

Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data

Mathematical modeling and analysis of biochemical reaction networks are key routines in computational systems biology and biophysics; however, it remains difficult to choose the most valid model. Here, we propose a computational framework for data-driven and systematic inference of a nonlinear biochemical network model. The framework is based on the expectation-maximization algorithm combined with particle smoother and sparse regularization techniques. In this method, a “redundant” model consisting of an excessive number of nodes and regulatory paths is iteratively updated by eliminating unnecessary paths, resulting in an inference of the most likely model. Using artificial single-cell time-course data showing heterogeneous oscillatory behaviors, we demonstrated that this algorithm successfully inferred the true network without any prior knowledge of network topology or parameter values. Furthermore, we showed that both the regulatory paths among nodes and the optimal number of nodes in the network could be systematically determined. The method presented in this study provides a general framework for inferring a nonlinear biochemical network model from heterogeneous single-cell time-course data.