Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves an epithelial-mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments have proposed that cancer cells can be transformed into adipocytes via a combination of drugs. However, the underlying mechanisms for how these drugs work, from a molecular network perspective, remain elusive. To reveal the mechanism of cancer-adipose conversion (CAC), this study adopts a systems biology approach by combing mathematical modeling and molecular experiments, based on underlying molecular regulatory networks. Four types of attractors are identified, corresponding to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that intermediate states play critical roles in the cancer to adipose transition. Through a landscape control approach, two new therapeutic strategies for drug combinations are identified, that promote CAC. These predictions are verified by molecular experiments in different cell lines. The combined computational and experimental approach provides a powerful tool to explore molecular mechanisms for cell fate transitions in cancer networks. The results reveal underlying mechanisms of intermediate cell states that govern the CAC, and identified new potential drug combinations to induce cancer adipogenesis.

Caenorhabditis elegans males undergo sex-specific tail tip morphogenesis (TTM) under the control of the DM-domain transcription factor DMD-3. To find genes regulated by DMD-3, We performed RNA-seq of laser-dissected tail tips. We identified 564 genes differentially expressed (DE) in wild-type males vs. dmd-3(-) males and hermaphrodites. The transcription profile of dmd-3(-) tail tips is similar to that in hermaphrodites. For validation, we analyzed transcriptional reporters for 49 genes and found male-specific or male-biased expression for 26 genes. Only 11 DE genes overlapped with genes found in a previous RNAi screen for defective TTM. GO enrichment analysis of DE genes finds upregulation of genes within the unfolded protein response pathway and downregulation of genes involved in cuticle maintenance. Of the DE genes, 40 are transcription factors, indicating that the gene network downstream of DMD-3 is complex and potentially modular. We propose modules of genes that act together in TTM and are coregulated by DMD-3, among them the chondroitin synthesis pathway and the hypertonic stress response.

Most organisms have evolved specific mechanisms to respond to changes in environmental conditions such as light and temperature over the course of day. These periodic changes in the physiology and behaviour of organisms, referred to as circadian rhythms, are a consequence of intricate molecular mechanisms in the form of transcription and translational feedback loops. The plant circadian regulatory network is a complex web of interconnected feedback loops involving various transcription factors such as CCA1, LHY, PRRs, TOC1, LUX, ELF3, ELF4, RVE8, and more. This network enables plants to adapt and thrive in diverse environmental conditions. It responds to entrainment signals, including light, temperature, and nutrient concentrations and interacts with most of the physiological functions such as flowering, growth and stress response. Mathematical modelling of these gene regulatory networks enables a deeper understanding of not only the function but also the perturbations that may affect the plant growth and function with changing climate. Over the years, numerous mathematical models have been developed to understand the diverse aspects of plant circadian regulation. In this review, we have delved into the systematic development of these models, outlining the model components and refinements over time. We have also highlighted strengths and limitations of each of the models developed so far. Finally, we conclude the review by describing the prospects for investigation and advancement of these models for better understanding of plant circadian regulation.

The skeleton plays a fundamental role in the maintenance of organ function and daily activities. The insulin-like growth factor (IGF) family is a group of polypeptide substances with a pronounced role in osteoblast differentiation, bone development, and metabolism. Disturbance of the IGFs and the IGF signaling pathway is inextricably linked with assorted developmental defects, growth irregularities, and jeopardized skeletal structure. Recent findings have illustrated the significance of the action of the IGF signaling pathway via growth factors and receptors and its interactions with dissimilar signaling pathways (Wnt/β-catenin, BMP, TGF-β, and Hh/PTH signaling pathways) in promoting the growth, survival, and differentiation of osteoblasts. IGF signaling also exhibits profound influences on cartilage and bone development and skeletal homeostasis via versatile cell-cell interactions in an autocrine, paracrine, and endocrine manner systemically and locally. Our review summarizes the role and regulatory function as well as a potentially integrated gene network of the IGF signaling pathway with other signaling pathways in bone and cartilage development and skeletal homeostasis, which in turn provides an enlightening insight into visualizing bright molecular targets to be eligible for designing effective drugs to handle bone diseases and maladies, such as osteoporosis, osteoarthritis, and dwarfism.

How did the complex structure of the telencephalon evolve? Existing explanations are based on phenomena and lack a first-principles account. The Darwinian dynamics and endogenous network theory-established decades ago-provides a mathematical and theoretical framework and a general constitutive structure for theory-experiment coupling for answering this question from a first-principles perspective. By revisiting a gene network that explains the anterior-posterior patterning of the vertebrate telencephalon, we found that upon increasing the cooperative effect within this network, fixed points gradually evolve, accompanied by the occurrence of two bifurcations. The dynamic behavior of this network is informed by the knowledge obtained from experiments on telencephalic evolution. Our work provides a quantitative explanation for how telencephalon anterior-posterior patterning evolved from the pre-vertebrate chordate to the vertebrate and provides a series of verifiable predictions from a first-principles perspective.

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.

Larvacean tunicates feature a spectacular innovation not seen in other animals - the trunk oikoplastic epithelium (OE). This epithelium produces a house, a large and complex extracellular structure used for filtering and concentrating food particles. Previously we identified several homeobox transcription factor genes expressed during early OE patterning. Among these are two Pax3/7 copies that we named pax37A and pax37B. The vertebrate homologs, PAX3 and PAX7 are involved in developmental processes related to neural crest and muscles. In the ascidian tunicate Ciona intestinalis, Pax3/7 plays a role in the development of cells deriving from the neural plate border, including trunk epidermal sensory neurons and tail nerve cord neurons, as well as in the neural tube closure. Here we have investigated the roles of Oikopleura dioica pax37A and pax37B in the development of the OE, by using CRISPR-Cas9 mutant lines and analyzing scRNA-seq data from wild-type animals. We found that pax37B but not pax37A is essential for the differentiation of cell fields that produce the food concentrating filter of the house: the anterior Fol, giant Fol and Nasse cells. Trajectory analysis supported a neuroepithelial-like or a preplacodal ectoderm transcriptional signature in these cells. We propose that the highly specialized secretory epithelial cells of the Fol region either maintained or evolved neuroepithelial features. This is supported by a fragmented gene regulatory network involved in their development that also operates in ascidian epidermal neurons.

BACKGROUND: Jasmonic acid (JA) is a phytohormone involved in regulating responses to biotic and abiotic stress. Although the JA pathway is well characterized in model plants such as Arabidopsis thaliana, less is known about many non-model plants. Phytolacca americana (pokeweed) is native to eastern North Americana and is resilient to environmental stress. The goal of this study was to produce a publicly available pokeweed genome assembly and annotations and use this resource to determine how early response to JA changes gene expression, with particular focus on genes involved in defense.
RESULTS: We assembled the pokeweed genome de novo from approximately 30 Gb of PacBio Hifi long reads and achieved an NG50 of ~ 13.2 Mb and a minimum 93.9% complete BUSCO score for gene annotations. With this reference, we investigated the early changes in pokeweed gene expression following JA treatment. Approximately 5,100 genes were differentially expressed during the 0-6 h time course with almost equal number of genes with increased and decreased transcript levels. Cluster and gene ontology analyses indicated the downregulation of genes associated with photosynthesis and upregulation of genes involved in hormone signaling and defense. We identified orthologues of key transcription factors and constructed the first JA gene response network integrated with our transcriptomic data from orthologues of Arabidopsis genes. We discovered that pokeweed did not use leaf senescence as a means of reallocating resources during stress; rather, most secondary metabolite synthesis genes were constitutively expressed, suggesting that pokeweed directs its resources for survival over the long term. In addition, pokeweed synthesizes several RNA N-glycosylases hypothesized to function in defense, each with unique expression profiles in response to JA.
CONCLUSIONS: Our investigation of the early response of pokeweed to JA illustrates patterns of gene expression involved in defence and stress tolerance. Pokeweed provides insight into the defense mechanisms of plants beyond those observed in research models and crops, and further study may yield novel approaches to improving the resilience of plants to environmental changes. Our assembled pokeweed genome is the first within the taxonomic family Phytolaccaceae to be publicly available for continued research.

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring \"causal sufficiency,\" meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper\'s transparent peer review process is included in the supplemental information.