An integrative method to decode regulatory logics in gene transcription

abstract: Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF–TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.The final version of this article, as published in Nature Communications, can be viewed online at: http://www.nature.com/articles/s41467-017-01193-

An integrative method to decode regulatory logics in gene transcription

Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF-TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.published_or_final_versio

Mechanisms of receptor tyrosine kinase signaling diversity: a focus in cardiac growth

To understand organism function and disease and to target perturbed processes for therapy, comprehensive knowledge of the underlying cell signaling networks is required. However, mapping the interplay of the vast number of biomolecules involved in these networks remains challenging. As a result, efforts have focused on identifying the structural elements within biomolecules that facilitate signal transmission. Receptor tyrosine kinases (RTKs) regulate the function of several important organs and are most recognized as oncogenes in cancer. Research into the structural determinants of RTKs that govern their signaling has led to clinically approved therapies. However, some structural regions of these kinases remain poorly understood. In this thesis, the diversity of cell signaling arising from variation in an overlooked region in RTKs known as the extracellular juxtamembrane region was explored. A sequence motif that controls the cell surface location and the signaling of RTKs was identified, presenting a potential novel way to target RTKs for therapy. The cell signaling pathways that regulate myocardial growth could be putatively re-activated to treat heart failure or inhibited to treat pathological hypertrophy. Additionally, these pathways may hold the key to regenerating the myocardium post-injury. A pathway promoting myocardial growth involving STAT5b and the RTK ErbB4 was uncovered in this thesis. VEGFB, traditionally associated with endothelial cells, was additionally observed to elicit myocardial growth through paracrine signaling involving ErbB RTKs. Activation of ErbB4 pathways in the heart with NRG-1 has improved the cardiac function of heart failure patients implying that the discoveries made in this thesis may aid in heart failure therapy development. Finally, recent developments in omics technologies have facilitated the detection and quantification of the different layers of cell signaling networks. Consequently, a growing need for computational analyses capable of reverse-engineering cell signaling pathways from multi-omics data has emerged. In this thesis, a new computational approach specifically designed to discover cell signaling pathways from multi-omics data without the use of prior information was developed. These types of de novo methods remain essential for uncovering new cell signaling connections, which, in turn, can unveil potential new drug targets to treat disease. Reseptorityrosiinikinaasien viestinnän monimuotoisuuden mekanismit: painotus sydänlihaksen kasvussa Elimistön toiminnan ja sairauksien ymmärtäminen sekä lääkekehitys edellyttää kattavaa tietoa solujen soluviestintäverkostoista. Koska soluviestintämolekyylejä on lukuisia, soluviestinnän tutkimus on keskittynyt löytämään toistuvia rakenteellisia soluviestintää välittäviä alueita soluviestintämolekyyleistä. Reseptorityrosiinikinaasit (RTK:t) ovat solun pinnan soluviestintämolekyylejä, jotka säätelevät useita elimistön tärkeitä toimintoja ja joiden rakenteen tutkimus on johtanut useisiin käytössä oleviin lääkkeisiin. RTK:iden rakenteessa sijaitsee alue solun ulkopuolella, jonka merkitystä ei ole aikaisemmin juurikaan selvitetty. Tämän alueen vaikutusta RTK:iden viestinnän monimuotoisuudelle tutkittiin tässä väitöskirjassa. Alueelta löydettiin sekvenssimotiivi, joka säätelee RTK:iden sijaintia solun pinnalla sekä niiden viestintää. Alueelle voidaan tulevaisuudessa mahdollisesti kohdentaa RTK:iden viestintää muuttavia lääkkeitä. Soluviestintäreittejä, jotka säätelevät sydänlihaksen kasvua, voidaan mahdollisesti aktivoida sydämen vajaatoiminnan hoitamiseksi tai estää vahingollisen sydämen liikakasvun lieventämiseksi. Lisäksi näitä soluviestintäreittejä voidaan hyödyntää vaurion jälkeiseen sydänlihassolujen regeneraatioon. Sydänlihaksen kasvun soluviestintäreitteihin liittyviä havaintoja tehtiin tässä väitöskirjassa. RTK ErbB4:n todettiin aiheuttavan sydänlihaksen kasvua STAT5b viestinnän kautta. RTK ligandi VEGF-B:n puolestaan todettiin vaikuttavan sydänlihaksen kasvuun ErbB RTK:iden viestinnän avulla. Koska ErbB4 viestinnän aktivointi on parantanut sydämen vajaatoimintapotilaiden sydämen toimintaa, nämä havainnot saattavat edesauttaa sydämen vajaatoiminnan hoitojen kehitystä. Omiikka-teknologioilla voidaan mitata soluviestintäverkostojen eri tasoja lähes kattavasti. Laskennallisia työkaluja kuitenkin tarvitaan, jotta omiikka-teknologioilla tuotettu tieto voidaan mallintaa soluviestintäreiteiksi. Uusi soluviestintäreittien mallinnusohjelma kehitettiin tässä väitöskirjassa. Mallinnusohjelma käyttää ainoastaan omiikka-teknologioilla saatua tietoa soluviestintäreittien mallinnukseen. Tämän kaltaisia vain mitattuun tietoon perustuvia menetelmiä tarvitaan uusien soluviestintäreittien löytämiseksi. Uudet soluviestintäreittien yhteydet puolestaan voivat paljastaa uusia tautimekanismeja ja toimia uusina lääkekohteina

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Metastability and Dynamics of Stem Cells: From Direct Observations to Inference at the Single Cell Level

Organismal development, homeostasis, and pathology are rooted in inherently probabilistic events. From gene expression to cellular differentiation, rates and likelihoods shape the form and function of biology. Processes ranging from growth to cancer homeostasis to reprogramming of stem cells all require transitions between distinct phenotypic states, and these occur at defined rates. Therefore, measuring the fidelity and dynamics with which such transitions occur is central to understanding natural biological phenomena and is critical for therapeutic interventions. While these processes may produce robust population-level behaviors, decisions are made by individual cells. In certain circumstances, these minuscule computing units effectively roll dice to determine their fate. And while the 'omics' era has provided vast amounts of data on what these populations are doing en masse, the behaviors of the underlying units of these processes get washed out in averages. Therefore, in order to understand the behavior of a sample of cells, it is critical to reveal how its underlying components, or mixture of cells in distinct states, each contribute to the overall phenotype. As such, we must first define what states exist in the population, determine what controls the stability of these states, and measure in high dimensionality the dynamics with which these cells transition between states. To address a specific example of this general problem, we investigate the heterogeneity and dynamics of mouse embryonic stem cells (mESCs). While a number of reports have identified particular genes in ES cells that switch between 'high' and 'low' metastable expression states in culture, it remains unclear how levels of many of these regulators combine to form states in transcriptional space. Using a method called single molecule mRNA fluorescent in situ hybridization (smFISH), we quantitatively measure and fit distributions of core pluripotency regulators in single cells, identifying a wide range of variabilities between genes, but each explained by a simple model of bursty transcription. From this data, we also observed that strongly bimodal genes appear to be co-expressed, effectively limiting the occupancy of transcriptional space to two primary states across genes studied here. However, these states also appear punctuated by the conditional expression of the most highly variable genes, potentially defining smaller substates of pluripotency. Having defined the transcriptional states, we next asked what might control their stability or persistence. Surprisingly, we found that DNA methylation, a mark normally associated with irreversible developmental progression, was itself differentially regulated between these two primary states. Furthermore, both acute or chronic inhibition of DNA methyltransferase activity led to reduced heterogeneity among the population, suggesting that metastability can be modulated by this strong epigenetic mark. Finally, because understanding the dynamics of state transitions is fundamental to a variety of biological problems, we sought to develop a high-throughput method for the identification of cellular trajectories without the need for cell-line engineering. We achieved this by combining cell-lineage information gathered from time-lapse microscopy with endpoint smFISH for measurements of final expression states. Applying a simple mathematical framework to these lineage-tree associated expression states enables the inference of dynamic transitions. We apply our novel approach in order to infer temporal sequences of events, quantitative switching rates, and network topology among a set of ESC states. Taken together, we identify distinct expression states in ES cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method for the identification of cellular trajectories using scalable in situ readouts of cellular state.</p

Computational identification of the normal and perturbed genetic networks involved in myeloid differentiation and acute promyelocytic leukemia

A dissection of the genetic networks and circuitries is described for two form of leukaemia. Integrating transcription factor binding and gene expression profiling, networks are revealed that underly this important human disease

Systems Modeling to Predict Mechano-Chemo Interactions In Cardiac Fibrosis

Cardiac fibrosis poses a central challenge in preventing heart failure for patients who have suffered a cardiac injury such as myocardial infarction or aortic valve stenosis. This chronic condition is characterized by a reduction in contractile function through combined hypertrophy and excessive scar formation, and although currently prescribed therapeutics targeting hypertrophy have shown improvements in patient outcomes, pathological fibrosis remains a leading cause of reduced cardiac function for patients long-term. Cardiac fibroblasts play a key role in regulating scar formation during heart failure progression, and interacting biochemical and biomechanical cues within the myocardium guide the activation of fibroblasts and expression of extracellular matrix proteins. While targeted experimental studies of fibroblast activation have elucidated the roles of individual pathways in fibroblast activation, intracellular crosstalk between mechanotransduction and chemotransduction pathways from multiple biochemical cues has largely confounded efforts to control overall cell behavior within the myocardial environment. Computational networks of intracellular signaling can account for complex interactions between signaling pathways and provide a promising approach for identifying influential mechanisms mediating cell behavior. The overarching goal of this dissertation is to improve our understanding of complex signaling in fibroblasts by investigating the role of mechano-chemo interactions in cardiac fibroblast-mediated fibrosis using a combination of experimental studies and systems-level computational models. Firstly, using an in vitro screen of cardiac fibroblast-secreted proteins in response to combinations of biochemical stimuli and mechanical tension, we found that tension modulated cell sensitivity towards biochemical stimuli, thereby altering cell behavior based on the mechanical context. Secondly, using a curated model of fibroblast intracellular signaling, we expanded model topology to include robust mechanotransduction pathways, improved accuracy of model predictions compared to independent experimental studies, and identified mechanically dependent mechanisms-of- ction and mechano-adaptive drug candidates in a post-infarction scenario. Lastly, using an inferred network of fibroblast transcriptional regulation and model fitting to patient-specific data, we showed the utility of model-based approaches in identifying influential pathways underlying fibrotic protein expression during aortic valve stenosis and predicting patient-specific responses to pharmacological intervention. Our work suggests that computational-based approaches can generate insight into influential mechanisms among complex systems, and such tools may be promising for further therapeutic development and precision medicine

Transcriptional characterization of macrophages reveals unexpected novel biology

Macrophages are very plastic and versatile immune cells in response to different environmental signals. Similar phenomenon has been observed for other myeloid compartment, such as monocytes. Moreover, it has been described that different tissue macrophage subpopulations have distinct origins. In this dissertation, I systematically analyzed a large resource dataset to assess transcriptional regulation during human macrophage activation by comparing a diverse set of stimuli on a single microarray platform under highly standardized conditions. Network modeling of this dataset led to the extension of the current M1 versus M2 polarization model to a “multi-dimensional model” with at least nine distinct macrophage activation programs. Applying these transcriptional programs to human in vivo alveolar macrophages from smokers and patients with chronic obstructive pulmonary disease (COPD) revealed an unexpected biology. Reverse engineering of large transcriptional dataset by integrating multiple network inference approaches sharpens the resolution of the common macrophage activation regulatory networks. And the refined network indicated that transcription factors are the most important components in regulatory circuits involved in macrophage activation. Furthermore, by applying the same computational methodologies to a transcriptomic dataset of infected human peripheral blood mononuclear cells (PBMC), I extended my studies to identify common and stimulus-specific transcriptional programs in host defense against bacteria and fungi. By combination of knowledge-based and data-driven analysis, I propose refined pathway models for these microbial infections on transcriptional level. Finally, computational studies on gene expression profiles for embryonic and adult tissue macrophages from both wild type and Irf8-deficient mice revealed distinct origins and transcriptional profiles of different tissue macrophage subpopulations and a crucial role of Irf8 in macrophage ontogeny and homeostasis. Overall, applying systems biology approaches, especially advanced methods on large enough transcriptional datasets enables robust and accurate statistical predictions. Thus, the studies on macrophages or myeloid cells using these approaches successfully uncovered the complex dynamic regulatory networks of these cells and reflected a hitherto unexplored biology

How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification

Biological and Soft Matter Physic

Interpretable Mechanistic and Machine Learning Models for Pre-dicting Cardiac Remodeling from Biochemical and Biomechanical Features

Biochemical and biomechanical signals drive cardiac remodeling, resulting in altered heart physiology and the precursor for several cardiac diseases, the leading cause of death for most racial groups in the USA. Reversing cardiac remodeling requires medication and device-assisted treatment such as Cardiac Resynchronization Therapy (CRT), but current interventions produce highly variable responses from patient to patient. Mechanistic modeling and Machine learning (ML) approaches have the functionality to aid diagnosis and therapy selection using various input features. Moreover, \u27Interpretable\u27 machine learning methods have helped make machine learning models fairer and more suited for clinical application. The overarching objective of this doctoral work is to develop computational models that combine an extensive array of clinically measured biochemical and biomechanical variables to enable more accurate identification of heart failure patients prone to respond positively to therapeutic interventions. In the first aim, we built an ensemble ML classification algorithm using previously acquired data from the SMART-AV CRT clinical trial. Our classification algorithm incorporated 26 patient demographic and medical history variables, 12 biomarker variables, and 18 LV functional variables, yielding correct CRT response prediction in 71% of patients. In the second aim, we employed a machine learning-based method to infer the fibrosis-related gene regulatory network from RNA-seq data from the MAGNet cohort of heart failure patients. This network identified significant interactions between transcription factors and cell synthesis outputs related to cardiac fibrosis - a critical driver of heart failure. Novel filtering methods helped us prioritize the most critical regulatory interactions of mechanistic forward simulations. In the third aim, we developed a logic-based model for the mechanistic network of cardiac fibrosis, integrating the gene regulatory network derived from aim two into a previously constructed cardiac fibrosis signaling network model. This integrated model implemented biochemical and biomechanical reactions as ordinary differential equations based on normalized Hill functions. The model elucidated the semi-quantitative behavior of cardiac fibrosis signaling complexity by capturing multi-pathway crosstalk and feedback loops. Perturbation analysis predicted the most critical nodes in the mechanistic model. Patient-specific simulations helped identify which biochemical species highly correlate with clinical measures of patient cardiac function