Trib2 suppresses tumor initiation in Notch-driven T-ALL

Trib2 is highly expressed in human T cell acute lymphoblastic leukemia (T-ALL) and is a direct transcriptional target of the oncogenic drivers Notch and TAL1. In human TAL1-driven T-ALL cell lines, Trib2 is proposed to function as an important survival factor, but there is limited information about the role of Trib2 in primary T-ALL. In this study, we investigated the role of Trib2 in the initiation and maintenance of Notch-dependent T-ALL. Trib2 had no effect on the growth and survival of murine T-ALL cell lines in vitro when expression was blocked by shRNAs. To test the function of Trib2 on leukemogenesis in vivo, we generated Trib2 knockout mice. Mice were born at the expected Mendelian frequencies without gross developmental anomalies. Adult mice did not develop pathology or shortened survival, and hematopoiesis, including T cell development, was unperturbed. Using a retroviral model of Notch-induced T-ALL, deletion of Trib2 unexpectedly decreased the latency and increased the penetrance of T-ALL development in vivo. Immunoblotting of primary murine T-ALL cells showed that the absence of Trib2 increased C/EBPα expression, a known regulator of cell proliferation, and did not alter AKT or ERK phosphorylation. Although Trib2 was suggested to be highly expressed in T-ALL, transcriptomic analysis of two independent T-ALL cohorts showed that low Trib2 expression correlated with the TLX1-expressing cortical mature T-ALL subtype, whereas high Trib2 expression correlated with the LYL1-expressing early immature T-ALL subtype. These data indicate that Trib2 has a complex role in the pathogenesis of Notch-driven T-ALL, which may vary between different T-ALL subtypes

Intervention in gene regulatory networks via greedy control policies based on long-run behavior

<p>Abstract</p> <p>Background</p> <p>A salient purpose for studying gene regulatory networks is to derive intervention strategies, the goals being to identify potential drug targets and design gene-based therapeutic intervention. Optimal stochastic control based on the transition probability matrix of the underlying Markov chain has been studied extensively for probabilistic Boolean networks. Optimization is based on minimization of a cost function and a key goal of control is to reduce the steady-state probability mass of undesirable network states. Owing to computational complexity, it is difficult to apply optimal control for large networks.</p> <p>Results</p> <p>In this paper, we propose three new greedy stationary control policies by directly investigating the effects on the network long-run behavior. Similar to the recently proposed mean-first-passage-time (MFPT) control policy, these policies do not depend on minimization of a cost function and avoid the computational burden of dynamic programming. They can be used to design stationary control policies that avoid the need for a user-defined cost function because they are based directly on long-run network behavior; they can be used as an alternative to dynamic programming algorithms when the latter are computationally prohibitive; and they can be used to predict the best control gene with reduced computational complexity, even when one is employing dynamic programming to derive the final control policy. We compare the performance of these three greedy control policies and the MFPT policy using randomly generated probabilistic Boolean networks and give a preliminary example for intervening in a mammalian cell cycle network.</p> <p>Conclusion</p> <p>The newly proposed control policies have better performance in general than the MFPT policy and, as indicated by the results on the mammalian cell cycle network, they can potentially serve as future gene therapeutic intervention strategies.</p

Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized.Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy.Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure

The Pediatric Cell Atlas:Defining the Growth Phase of Human Development at Single-Cell Resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

The Pediatric Cell Atlas: defining the growth phase of human development at single-cell resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

Differential Integration of Transcriptome and Proteome Identifies Pan-Cancer Prognostic Biomarkers

High-throughput analysis of the transcriptome and proteome individually are used to interrogate complex oncogenic processes in cancer. However, an outstanding challenge is how to combine these complementary, yet partially disparate data sources to accurately identify tumor-specific gene products and clinical biomarkers. Here, we introduce inteGREAT for robust and scalable differential integration of high-throughput measurements. With inteGREAT, each data source is represented as a co-expression network, which is analyzed to characterize the local and global structure of each node across networks. inteGREAT scores the degree by which the topology of each gene in both transcriptome and proteome networks are conserved within a tumor type, yet different from other normal or malignant cells. We demonstrated the high performance of inteGREAT based on several analyses: deconvolving synthetic networks, rediscovering known diagnostic biomarkers, establishing relationships between tumor lineages, and elucidating putative prognostic biomarkers which we experimentally validated. Furthermore, we introduce the application of a clumpiness measure to quantitatively describe tumor lineage similarity. Together, inteGREAT not only infers functional and clinical insights from the integration of transcriptomic and proteomic data sources in cancer, but also can be readily applied to other heterogeneous high-throughput data sources. inteGREAT is open source and available to download from https://github.com/faryabib/inteGREAT

Proposing a novel oriented genetic algorithm for optimum seismic design of steel moment resisting frames

This paper proposes a new variant of the genetic algorithm (GA), called the oriented genetic algorithm (OGA), for optimum seismic design of steel special moment resisting frames. Since GA is mainly based on random operators, its computational burden is usually high. To overcome this issue, OGA takes advantage of the violation values of design constraints of the problem, such as elements’ demand-to-capacity ratios, strong column-weak beam requirements, and story drift ratios, to direct the search procedure. OGA is applied to a set of steel frames with different geometrical properties to demonstrate, and the results are compared to those of GA. The numerical results indicate that OGA significantly reduces the total number of function evaluations (NFE) required to obtain the optimum solutions. Also, the convergence history of OGA is compared with Particle Swarm Optimization and Ant Colony Optimization algorithms. It is shown that for a specified NFE limit, OGA gives better-optimized results

Image_1_Differential Integration of Transcriptome and Proteome Identifies Pan-Cancer Prognostic Biomarkers.pdf

<p>High-throughput analysis of the transcriptome and proteome individually are used to interrogate complex oncogenic processes in cancer. However, an outstanding challenge is how to combine these complementary, yet partially disparate data sources to accurately identify tumor-specific gene-programs and clinical biomarkers. Here, we introduce inteGREAT for robust and scalable differential integration of high-throughput measurements. With inteGREAT, each data source is represented as a co-expression network, which is analyzed to characterize the local and global structure of each node across networks. inteGREAT scores the degree by which the topology of each gene in both transcriptome and proteome networks are conserved within a tumor type, yet different from other normal or malignant cells. We demonstrated the high performance of inteGREAT based on several analyses: deconvolving synthetic networks, rediscovering known diagnostic biomarkers, establishing relationships between tumor lineages, and elucidating putative prognostic biomarkers which we experimentally validated. Furthermore, we introduce the application of a clumpiness measure to quantitatively describe tumor lineage similarity. Together, inteGREAT not only infers functional and clinical insights from the integration of transcriptomic and proteomic data sources in cancer, but also can be readily applied to other heterogeneous high-throughput data sources. inteGREAT is open source and available to download from https://github.com/faryabib/inteGREAT.</p