Gene Expression Programs Research Articles

ONECUT2 is a druggable driver of luminal to basal breast cancer plasticity.

Tumor heterogeneity complicates patient treatment and can be due to transitioning of cancer cells across phenotypic cell states. This process is associated with the acquisition of independence from an oncogenic driver, such as the estrogen receptor (ER) in breast cancer (BC), resulting in tumor progression, therapeutic failure and metastatic spread. The transcription factor ONECUT2 (OC2) has been shown to be a master regulator protein of metastatic castration-resistant prostate cancer (mCRPC) tumors that promotes lineage plasticity to a drug-resistant neuroendocrine (NEPC) phenotype. Here, we investigate the role of OC2 in the dynamic conversion between different molecular subtypes in BC. We analyze OC2 expression and clinical significance in BC using public databases and immunohistochemical staining. In vitro, we perform RNA-Seq, RT-qPCR and western-blot after OC2 enforced expression. We also assess cellular effects of OC2 silencing and inhibition with a drug-like small molecule in vitro and in vivo. OC2 is highly expressed in a substantial subset of hormone receptor negative human BC tumors and tamoxifen-resistant models, and is associated with poor clinical outcome, lymph node metastasis and heightened clinical stage. OC2 inhibits ER expression and activity, suppresses a gene expression program associated with luminal differentiation and activates a basal-like state at the gene expression level. We also show that OC2 is required for cell growth and survival in metastatic BC models and that it can be targeted with a small molecule inhibitor providing a novel therapeutic strategy for patients with OC2 active tumors. The transcription factor OC2 is a driver of BC heterogeneity and a potential drug target in distinct cell states within the breast tumors.

Developing improved machine learning methods to predict the flow characteristics through vertical and horizontal transitions in open channels

ABSTRACT Transitions in an open channel refer to the change in flow behavior due to changes in the channel geometry. Determining flow characteristics through transitions is an important topic as it is necessary to guarantee the ideal hydraulic performance of water structures with low costs. This research focuses on the flow characteristics through vertical and horizontal transitions through experimental study and then utilizing machine learning to predict the flow characteristics. The proposed framework aims to develop both the cascade-forward artificial neural network (CFANN) model and the regression model to enhance the prediction of flow characteristics. The first model developed modifies the CFANN using dandelion optimizer (DO) to determine the ideal CFANN configuration. The second model used gene expression programming to develop statistical equations. The obtained CFANN–DO model has proven high accuracy in predicting the flow rates at various water loads and speeds achieving a coefficient of determination of approximately 100% for training data and 99.5% for testing data. Finally, predicting the characteristics of vertical and horizontal transitions in open channels is a critical issue. Manipulating these transitions can create a habitat for aquatic organisms, reduce erosion, and improve the overall environment.

A hybrid long-term industrial electrical load forecasting model using optimized ANFIS with gene expression programming

Electric energy demand forecasting is vital in contemporary power systems, especially amidst market deregulation trends and the increasing influence of industrial customers on power dynamics. However, existing forecasting models encounter challenges such as slow convergence and high complexity. Addressing these issues, this study proposes a hybrid forecasting model that combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gene Expression Programming (GEP) to enhance predictions of electrical energy consumption. Validated using real-time monthly electrical load data from an industrial user in Uganda, the hybrid model outperforms individual ANFIS and GEP models, demonstrating reduced errors and minimal computation time. The application of this hybrid model presents promising results, showcasing exceptional predictive capabilities and offering potential improvements in efficiency and precision for electrical energy consumption forecasting amidst market deregulation and evolving industrial dynamics.

An alternative cell cycle coordinates multiciliated cell differentiation.

The canonical mitotic cell cycle coordinates DNA replication, centriole duplication and cytokinesis to generate two cells from one1. Some cells, such as mammalian trophoblast giant cells, use cell cycle variants like the endocycle to bypass mitosis2. Differentiating multiciliated cells, found in the mammalian airway, brain ventricles and reproductive tract, are post-mitotic but generate hundreds of centrioles, each of which matures into a basal body and nucleates a motile cilium3,4. Several cell cycle regulators have previously been implicated in specific steps of multiciliated cell differentiation5,6. Here we show that differentiating multiciliated cells integrate cell cycle regulators into a new alternative cell cycle, which we refer to as the multiciliation cycle. The multiciliation cycle redeploys many canonical cell cycle regulators, including cyclin-dependent kinases (CDKs) and their cognate cyclins. For example, cyclin D1, CDK4 and CDK6, which areregulators of mitotic G1-to-S progression, are required to initiate multiciliated cell differentiation. The multiciliation cycle amplifies some aspects of the canonical cell cycle, such as centriole synthesis, and blocks others, such as DNA replication. E2F7, a transcriptional regulator of canonical S-to-G2 progression, is expressed at high levels during the multiciliation cycle. In the multiciliation cycle, E2F7 directly dampens the expression of genes encoding DNA replication machinery and terminates the S phase-like gene expression program. Loss of E2F7 causes aberrantacquisition of DNA synthesis in multiciliated cells and dysregulation of multiciliation cycle progression, which disrupts centriole maturation and ciliogenesis. We conclude that multiciliated cells use an alternative cell cycle that orchestrates differentiation instead of controlling proliferation.

Transcriptional control of the Cryptosporidium life cycle.

The parasite Cryptosporidium is aleading agent of diarrhoeal disease in young children, and a cause and consequence of chronic malnutrition1,2. There are no vaccines and only limited treatment options3. The parasite infects enterocytes, in which it engages in asexual and sexual replication4, both of which are essential to continued infection and transmission. However, their molecular mechanisms remain largely unclear5. Here we use single-cell RNA sequencing to reveal the gene expression programme of the entire Cryptosporidium parvum life cycle in culture and in infected animals. Diverging from the prevailing model6, we find support for only three intracellular stages: asexual type-I meronts, male gamonts and female gametes. We reveal a highly organized program for the assembly of components at each stage. Dissecting the underlying regulatory network, we identify the transcription factor Myb-M as the earliest determinant of male fate, in an organism that lacks genetic sex determination. Conditional expression of this factor overrides the developmental program and induces widespread maleness, while conditional deletion ablates male development. Both have a profound impact on the infection. A large set of stage-specific genes now provides the opportunity to understand, engineer and disrupt parasite sex and life cycle progression to advance the development of vaccines and treatments.

Evolution of diapause in the African turquoise killifish by remodeling the ancient gene regulatory landscape

Suspended animation states allow organisms to survive extreme environments. The African turquoise killifish has evolved diapause as a form of suspended development to survive a complete drought. However, the mechanisms underlying the evolution of extreme survival states are unknown. To understand diapause evolution, we performed integrative multi-omics (gene expression, chromatin accessibility, and lipidomics) in the embryos of multiple killifish species. We find that diapause evolved by a recent remodeling of regulatory elements at very ancient gene duplicates (paralogs) present in all vertebrates. CRISPR-Cas9-based perturbations identify the transcription factors REST/NRSF and FOXOs as critical for the diapause gene expression program, including genes involved in lipid metabolism. Indeed, diapause shows a distinct lipid profile, with an increase in triglycerides with very-long-chain fatty acids. Our work suggests a mechanism for the evolution of complex adaptations and offers strategies to promote long-term survival by activating suspended animation programs in other species.

Gene expression programming (GEP) as novel tool for thermal analysis and kinetic modeling of pyrolysis reactions: coal pyrolysis case study

PurposeIn this study, a different approach is introduced to generate the kinetic sub-model for the modeling of solid-state pyrolysis reactions based on the thermogravimetric (TG) experimental data over a specified range of heating rates. Gene Expression Programming (GEP) is used to produce a correlation for the single-step global reaction rate as a function of determining kinetic variables, namely conversion, temperature, and heating rate.Design/methodology/approachFor a case study on the coal pyrolysis, a coefficient of determination (R2) of 0.99 was obtained using the generated model according to the experimental benchmark data. Comparison of the model results with the experimental data proves the applicability, reliability, and convenience of GEP as a powerful tool for modeling purposes in the solid-state pyrolysis reactions.FindingsThe resulting kinetic sub-model takes advantage of particular characteristics, to be highly efficient, simple, accurate, and computationally attractive, which facilitates the CFD simulation of real pyrolizers under isothermal and non-isothermal conditions.Originality/valueIt should be emphasized that the above-mentioned manuscript is not under evaluation in any journals and submitted exclusively for consideration for possible publication in this journal. The generated kinetic model is in the final form of an algebraic correlation which, in comparison to the conventional kinetic models, suggests several advantages: to be relatively simpler, more accurate, and numerically efficient. These characteristics make the proposed model computationally attractive when used as a sub-model in CFD applications to simulate real pyrolizers under complex heating conditions.

LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.

Dynamic changes in RNA m6A and 5 hmC influence gene expression programs during macrophage differentiation and polarisation

RNA modifications are essential for the establishment of cellular identity. Although increasing evidence indicates that RNA modifications regulate the innate immune response, their role in monocyte-to-macrophage differentiation and polarisation is unclear. While m6A has been widely studied, other RNA modifications, including 5 hmC, remain poorly characterised. We profiled m6A and 5 hmC epitranscriptomes, transcriptomes, translatomes and proteomes of monocytes and macrophages at rest and pro- and anti-inflammatory states. Transcriptome-wide mapping of m6A and 5 hmC reveals enrichment of m6A and/or 5 hmC on specific categories of transcripts essential for macrophage differentiation. Our analyses indicate that m6A and 5 hmC modifications are present in transcripts with critical functions in pro- and anti-inflammatory macrophages. Notably, we also discover the co-occurrence of m6A and 5 hmC on alternatively-spliced isoforms and/or opposing ends of the untranslated regions (UTR) of mRNAs with key roles in macrophage biology. In specific examples, RNA 5 hmC controls the decay of transcripts independently of m6A. This study provides (i) a comprehensive dataset to interrogate the role of RNA modifications in a plastic system (ii) a resource for exploring different layers of gene expression regulation in the context of human monocyte-to-macrophage differentiation and polarisation, (iii) new insights into RNA modifications as central regulators of effector cells in innate immunity.

Single-cell network analysis reveals gene expression programs for Arabidopsis root development and metabolism

Single-cell RNA-sequencing datasets of Arabidopsis roots have been generated, but related comprehensive gene co-expression network analyses are lacking. We conducted a single-cell gene co-expression network analysis with publicly available scRNA-seq datasets of Arabidopsis roots using a SingleCellGGM algorithm. The analysis identified 149 gene co-expression modules, which we considered to be gene expression programs (GEPs). By examining their spatiotemporal expression, we identified GEPs specifically expressed in major root cell types along their developmental trajectories. These GEPs define gene programs regulating root cell development at different stages and are enriched with relevant developmental regulators. As examples, a GEP specific for the quiescent center (QC) contains 20 genes regulating QC and stem cell niche homeostasis, and four GEPs are expressed in sieve elements (SEs) from early to late developmental stages, with the early-stage GEP containing 17 known SE developmental regulators. We also identified GEPs for metabolic pathways with cell-type-specific expression, suggesting the existence of cell-type-specific metabolism in roots. Using the GEPs, we discovered and verified a columella-specific gene, NRL27, as a regulator of the auxin-related root gravitropism response. Our analysis thus systematically reveals GEPs that regulate Arabidopsis root development and metabolism and provides ample resources for root biology studies.

USP44 Overexpression Drives a MYC-Like Gene Expression Program in Neuroblastoma through Epigenetic Reprogramming.

Neuroblastoma is an embryonic cancer that contributes disproportionately to death in young children. Sequencing data have uncovered few recurrently mutated genes in this cancer, although epigenetic pathways have been implicated in disease pathogenesis. We used an expression-based computational screen that examined the impact of deubiquitinating enzymes on patient survival to identify potential new targets. We identified the histone H2B deubiquitinating enzyme USP44 as the enzyme with the greatest impact on survival in patients with neuroblastoma. High levels of USP44 significantly correlate with metastatic disease, unfavorable histology, advanced patient age, and MYCN amplification. The subset of patients with tumors expressing high levels of USP44 had significantly worse survival, including those with tumors lacking MYCN amplification. We showed experimentally that USP44 regulates neuroblastoma cell proliferation, migration, invasion, and neuronal development. Depletion of the histone H2B ubiquitin ligase subunit RNF20 resulted in similar findings, strongly implicating this histone mark as the target of USP44 activity in this disease. Integration of transcriptome and epigenome in analyses demonstrates a distinct set of genes that are regulated by USP44, including those in Hallmark MYC target genes in both murine embryonic fibroblasts and the SH-SY5Y neuroblastoma cell line. We conclude that USP44 is a novel epigenetic regulator that promotes aggressive features and may be a novel target in neuroblastoma. Implications: This study identifies a new genetic marker of aggressive neuroblastoma and identifies the mechanisms by which its overactivity contributes to the pathophysiology of this disease.

Effects of alkaline activators and other factors on the properties of geopolymer concrete using industrial wastes based on GEP-based models

This study aims to develop reliable and practical models for estimating the compressive strength and CO2 emission of geopolymer concrete (GPC) using the Gene-expression programming (GEP) technique. Approximately 274 data points on the strength and CO2 emission of GPC using fly ash (FA) and ground granulated blast furnace slag (GGBFS) were used to generate two prediction models considering the effects of 19 input variables. The research results show that the proposed GEP-based models perform well with high correlation coefficients (R > 0.95) and reasonable errors. The parametric study demonstrates that increasing the GGBFS content, the Na2O (dry) content, and the molarity of NaOH solution, and decreasing the FA content, the FA/GGBFS ratio, and the added water or total water/binder ratio could significantly enhance the strength of GPC. Besides, the proposed GEP-based models were used to optimise the mix proportions of GPC concrete with minimisation of CO2 footprint. The range and reasonable values of the FA and GGBFS contents, the alkaline activators, the aggregates, and other parameters were suggested based on the in-depth analyses. This significant reference data source could promote the use of GPC widely in the construction industry as green construction materials for sustainable development.

Spatial genomic, biochemical and cellular mechanisms underlying meningioma heterogeneity and evolution.

Intratumor heterogeneity underlies cancer evolution and treatment resistance, but targetable mechanisms driving intratumor heterogeneity are poorly understood. Meningiomas are the most common primary intracranial tumors and are resistant to all medical therapies, and high-grade meningiomas have significant intratumor heterogeneity. Here we use spatial approaches to identify genomic, biochemical and cellular mechanisms linking intratumor heterogeneity to the molecular, temporal and spatial evolution of high-grade meningiomas. We show that divergent intratumor gene and protein expression programs distinguish high-grade meningiomas that are otherwise grouped together by current classification systems. Analyses of matched pairs of primary and recurrent meningiomas reveal spatial expansion of subclonal copy number variants associated with treatment resistance. Multiplexed sequential immunofluorescence and deconvolution of meningioma spatial transcriptomes using cell types from single-cell RNA sequencing show decreased immune infiltration, decreased MAPK signaling, increased PI3K-AKT signaling and increased cell proliferation, which are associated with meningioma recurrence. To translate these findings to preclinical models, we use CRISPR interference and lineage tracing approaches to identify combination therapies that target intratumor heterogeneity in meningioma cell co-cultures.

Pathways for macrophage uptake of cell-free circular RNAs

Circular RNAs (circRNAs) are stable RNAs present in cell-free RNA, which may comprise cellular debris and pathogen genomes. Here, we investigate the phenomenon and mechanism of cellular uptake and intracellular fate of exogenous circRNAs. Human myeloid cells and B cells selectively internalize extracellular circRNAs. Macrophage uptake of circRNA is rapid, energy dependent, and saturable. CircRNA uptake can lead to translation of encoded sequences and antigen presentation. The route of internalization influences immune activation after circRNA uptake, with distinct gene expression programs depending on the route of RNA delivery. Genome-scale CRISPR screens and chemical inhibitor studies nominate macrophage scavenger receptor MSR1, Toll-like receptors, and mTOR signaling as key regulators of receptor-mediated phagocytosis of circRNAs, a dominant pathway to internalize circRNAs in parallel to macropinocytosis. These results suggest that cell-free circRNA serves as an "eat me" signal and danger-associated molecular pattern, indicating orderly pathways of recognition and disposal.

Proposal and evaluation of new models for predicting the FRP contribution to shear strength in reinforced concrete beams using gene expression programming

Fiber-reinforced polymers (FRP) have been widely used in shear strengthening applications of reinforced concrete (RC) beams. The accurate prediction of the FRP contribution to the shear strength of beams is essential for reliable design. Gene expression programming (GEP) has been widely utilized because it reliably expresses complex relationships between experimental variables. In this study, three new GEP models are proposed for three different strengthening configurations of FRP such as fully-wrapping, U wrapping, and side-bonding to predict the FRP contribution to shear strength. These models are developed using the most comprehensive database containing a total of 811 strengthened beams (350 fully-wrapped, 328 U-wrapped, and 133 side-bonded. Many variables have been considered in the proposed GEP models, including those that have been experimentally effective but are often neglected in existing literature equations, such as the shear span-to-effective depth ratio (a/d)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(a/d)$$\\end{document} and the stirrup ratio (ρw\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\rho }_{w}$$\\end{document}). Additionally, the reliability of existing equations in the literature and the proposed GEP models for predicting the FRP contribution to shear strength was statistically evaluated. As a result of this evaluation, the proposed GEP models for each strengthening configuration of FRP yielded the most accurate statistical results, with the lowest coefficient of variation (COV), and the highest coefficient of correlation (R).

Enhancing permeability prediction in tight and deep carbonate formations: new insights from pore description and electrical property using gene expression programming

Enhancing permeability prediction in tight and deep carbonate formations: new insights from pore description and electrical property using gene expression programming

Uncovering the dynamics and consequences of RNA isoform changes during neuronal differentiation

Static gene expression programs have been extensively characterized in stem cells and mature human cells. However, the dynamics of RNA isoform changes upon cell-state-transitions during cell differentiation, the determinants and functional consequences have largely remained unclear. Here, we established an improved model for human neurogenesis in vitro that is amenable for systems-wide analyses of gene expression. Our multi-omics analysis reveals that the pronounced alterations in cell morphology correlate strongly with widespread changes in RNA isoform expression. Our approach identifies thousands of new RNA isoforms that are expressed at distinct differentiation stages. RNA isoforms mainly arise from exon skipping and the alternative usage of transcription start and polyadenylation sites during human neurogenesis. The transcript isoform changes can remodel the identity and functions of protein isoforms. Finally, our study identifies a set of RNA binding proteins as a potential determinant of differentiation stage-specific global isoform changes. This work supports the view of regulated isoform changes that underlie state-transitions during neurogenesis.

Unconfined compressive strength of geopolymers based soil: Model development using gene expression programming and a comparison with other computer-based approaches

The present work aims to propose a model for calculating unconfined compressive strength (UCS) of soil stabilized by geopolymer based on gene expression programming (GEP). The new model is compared with earlier models developed from artificial neural networks (ANN), multivariable regression analysis (MVR), multi-gene genetic programming (MGGP), and support vector machines (SVM) models. The results indicate that the GEP model is superior to MVR and MGGP models and quite comparable to ANN and SVM models. The study demonstrates the robustness of the GEP model through parametric analysis. Additionally, this model is simpler and easier to use in practice.

The people behind the papers - Jingjing Sun and Angelike Stathopoulos.

Collective migration of caudal visceral mesoderm (CVM) cells in Drosophila embryos helps form the longitudinal muscles of the larval gut. In their study, Angelike Stathopoulos and colleagues reveal that cell division coordinates two gene expression programmes in migrating CVM cells. To know more about their work, we spoke to the first author, Jingjing Sun, and the corresponding author, Angelike Stathopoulos, Professor in the Division of Biology at the California Institute of Technology, USA.

Compressive strength of waste-derived cementitious composites using machine learning

Abstract Marble cement (MC) is a new binding material for concrete, and the strength assessment of the resulting materials is the subject of this investigation. MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithms can help with the formulation of better MC-based concrete. ML models that could predict the compressive strength (CS) of MC-based concrete that contained FA and RHA were built. Gene expression programming (GEP) and multi-expression programming (MEP) were used to build these models. Additionally, models were evaluated by calculating R 2 values, carrying out statistical tests, creating Taylor’s diagram, and comparing theoretical and experimental readings. When comparing the MEP and GEP models, MEP yielded a slightly better-fitted model and better prediction performance (R 2 = 0.96, mean absolute error = 0.646, root mean square error = 0.900, and Nash–Sutcliffe efficiency = 0.960). According to the sensitivity analysis, the prediction of CS was most affected by curing age and MC content, then by FA and RHA contents. Incorporating waste materials such as marble powder, RHA, and FA into building materials can help reduce environmental impacts and encourage sustainable development.