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

Optimisation Models for Pathway Activity Inference in Cancer

BACKGROUND: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. METHODOLOGY: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. RESULTS: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction

Yale Medicine : Alumni Bulletin of the School of Medicine, Winter 2014

This is the Winter 2014 issue of Yale Medicine: alumni bulletin of the School of Medicine, v. 48, no. 1. Prepared in cooperation with the alumni and development offices at the School of Medicine. Earlier volumes are called Yale School of Medicine alumni bulletins, dating from v.1 (1953) through v.13 (1965).https://elischolar.library.yale.edu/yale_med_alumni_newsletters/1031/thumbnail.jp

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields’ immaturity, it may also encumber a newcomer to blend in. In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Identification de nouvelles protéines effectrices dans la signalisation des récepteurs Eph

La réponse cellulaire aux stimuli extracellulaires est souvent médiée par des voies de signalisation qui agissent en aval des récepteurs transmembranaires, comme les récepteurs tyrosine kinases (RTK). Avec quatorze membres, la famille des récepteurs Eph représente la plus grande famille de RTK chez l'humain. Contrairement aux autres RTK, les ligands des récepteurs Eph, les éphrines, sont des protéines associées à la membrane cellulaire. La signalisation Eph-éprhines est donc principalement impliquée dans des événements de communication qui impliquent des contacts cellule-cellule comme la migration cellulaire, la répulsion et l'adhésion cellule-cellule. Ces événements sont cruciaux pour certains processus biologiques tels le guidage axonal et l'organisation tissulaire dans l'organisme en développement et chez l'adulte. Les récepteurs Eph sont fréquemment surexprimés ou dérégulés dans divers cancers, en particulier dans les plus agressifs et mortels. Récemment, la signalisation Eph-éphrines est devenue une nouvelle cible émergente pour le traitement du cancer. Bien que les fonctions biologiques des récepteurs Eph aient été largement étudiées, notre compréhension des mécanismes moléculaires grâce auxquels les récepteurs Eph régulent des phénotypes cellulaires précis demeure incomplète. Pour mieux comprendre le système de signalisation impliquant les Eph, mes travaux ont porté sur l'identification de nouvelles protéines effectrices en aval des récepteurs Eph et sur l'étude de leurs implications dans les fonctions régulées par les récepteurs Eph. Pour mieux comprendre les complexes de signalisation associés aux récepteurs Eph dans des conditions natives, j'ai appliqué une approche basée sur la spectrométrie de masse (MS), le marquage de proximité BioID. Cela m'a permis de surmonter les limites de l'utilisation d'approches conventionnelles de purification par affinité pour cartographier les interactions protéine-protéine liées aux récepteurs transmembranaires. J'ai obtenu un réseau de signalisation dépendant des récepteurs EphA4, - B2, -B3 et -B4, qui comprend 395 protéines, dont la plupart n'avaient jamais été liées à la signalisation Eph-dépendante. Pour tester la pertinence biologique des partenaires identifiés, j'ai examiné la contribution de 17 candidats en utilisant une approche de perte de fonction dans une expérience de tri cellulaire dépendante des récepteurs Eph. J'ai pu montrer que la déplétion de quelques candidats, incluant la protéine Par3, bloque le tri des cellules. En utilisant la purification par affinité combinée à la MS, j'ai aussi identifié un complexe de signalisation impliquant la kinase C-terminal SRC (CSK), dont le recrutement aux complexes Par3 dépend des signaux des récepteurs Eph. Pour mieux comprendre les interactions protéiques suivant la liaison Eph-éphrine, j'ai effectué des expériences de TurboID. Ces études m'ont permis d'identifier des complexes protéiques associés au récepteur EphA4 lorsqu'il est lié à l'éphrine-B2. J'ai également étudié les interactions protéine-protéine dépendantes de la liaison du récepteur EphB2 aux éphrines-B1 et -B2. Pour explorer si l'interaction d'EphB2 avec ces deux ligands peut mener à une réponse de signalisation inverse différente, j'ai identifié les partenaires de des ephrin-B1/-B2 lorsque stimulés par EphB2. Enfin, j'ai cartographié les réseaux de signalisation dépendants des récepteurs EphA4 et EphB2 sauvages ou kinase-inactifs, ce qui m'a permis de conclure que la perte de leur activité catalytique a conduit à des changements majeurs dans les interactomes dépendants de ces récepteurs. L'ensemble de mes résultats a permis de mieux définir les complexes protéiques dépendants des récepteurs Eph. Mes études ont mené à une meilleure compréhension des mécanismes moléculaires sous-jacents aux récepteurs Eph et de leur contribution dans le processus de délimitation des tissus, un processus souvent perturbé dans des maladies comme le cancer.The cellular response to extracellular stimuli is often mediated by signaling pathways that act downstream of transmembrane receptors, such as receptor tyrosine kinases (RTKs). With fourteen members, the Eph family of RTKs is the largest in humans. In contrast to other RTKs, Eph receptor cognate ligands, ephrins, are tethered to the cell surface. This results in Eph receptor-ephrin signaling being mainly involved in short-range cell-cell communication events that regulate cell migration, repulsion and cell-cell adhesion. These events are crucial in biological processes such as axon guidance and tissue boundary formation in the developing and adult organisms. Eph receptors are frequently overexpressed or deregulated in a variety of human tumors, especially in the more aggressive and lethal ones. In recent years, the Eph-ephrin signaling system became an emerging new target for cancer treatment. Although a plethora of Eph receptor biological functions have been extensively studied, we still have a vague idea on the molecular mechanisms of Eph receptor signal transduction, underlying how Eph receptors regulate precise cellular phenotypes. To better understand the Eph receptor signaling system, my studies focused on the identification of novel Eph receptor downstream effector proteins and the determination of their requirement for Eph receptor-regulated functions. To unravel Eph receptor-associated signaling complexes under native conditions, I applied a mass spectrometry (MS)-based approach, namely BioID proximity labeling. This allowed me to overcome the limitations of conventional affinity purification approaches for mapping protein-protein interactions of transmembrane receptors. I obtained a composite signaling network from EphA4, -B2, -B3 and -B4 receptors that comprises 395 proteins, most of which not previously linked to Eph signaling. To test the biological relevance of the identified Eph receptor proximity interactors, I examined the contribution of 17 candidates using a loss-of-function approach in an Eph receptor-dependent cell sorting assay. I showed that depletion of a few candidates, including the signaling scaffold Par3, blocks Eph receptordependent cell sorting. Using affinity purification combined with MS, I further delineated a signaling complex involving C-terminal SRC kinase (CSK), whose recruitment to Par3 complexes is dependent on Eph receptor signals. To further elucidate Eph receptor-centric signaling complexes that are formed upon ephrin binding and are affected by Eph receptor catalytic activity I performed TurboID experiments. I systematically mapped ligand stimulation-dependent signaling networks downstream of EphA4 and EphB2 receptors. I dissected the impact of ephrin-B2 stimulation on the formation of EphA4- nucleated proximal protein complexes. Moreover, I showed the differential recruitment of EphB2 partners upon receptor binding to the same subclass of ligands, ephrin-B1 and ephrin-B2. To explore whether the EphB2 interactions with these two ephrin-B ligands elicit different reverse signaling responses, I delineated ephrin-B1/-B2 proximity partners recruited upon EphB2 stimulation. I also determined that the kinase domain of EphA4/-B2 plays a major role in determining the composition of signaling networks around the receptors, as a loss of catalytic activity led to a drastic decrease in a number of interactors with the receptors. Collectively, my definition of Eph receptor signaling networks sheds light on physiologically relevant Eph receptor-centered protein complexes that occur in living cells. These studies will lead to a better understanding of the mechanisms by which Eph receptors transmit signals at the membrane and give insight into how Eph receptor-mediated signaling pathways contribute to boundary formation, a process often disrupted in diseases like cancer