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Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities., (© 2024. The Author(s).)

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods., (© 2023. The Author(s).)

Despite significant progress in understanding the genetic landscape of T-cell acute lymphoblastic leukemia (T-ALL), the discovery of novel therapeutic targets has been difficult. Our results demonstrate that the levels of PIM1 protein kinase is elevated in early T-cell precursor ALL (ETP-ALL) but not in mature T-ALL primary samples. Small-molecule PIM inhibitor (PIMi) treatment decreases leukemia burden in ETP-ALL. However, treatment of animals carrying ETP-ALL with PIMi was not curative. To model other pathways that could be targeted to complement PIMi activity, HSB-2 cells, previously characterized as a PIMi-sensitive T-ALL cell line, were grown in increasing doses of PIMi. Gene set enrichment analysis of RNA sequencing data and functional enrichment of network modules demonstrated that the HOXA9, mTOR, MYC, NFκB, and PI3K-AKT pathways were activated in HSB-2 cells after long-term PIM inhibition. Reverse phase protein array-based pathway activation mapping demonstrated alterations in the mTOR, PI3K-AKT, and NFκB pathways, as well. PIMi-tolerant HSB-2 cells contained phosphorylated RelA-S536 consistent with activation of the NFκB pathway. The combination of NFκB and PIMis markedly reduced the proliferation in PIMi-resistant leukemic cells showing that this pathway plays an important role in driving the growth of T-ALL. Together these results demonstrate key pathways that are activated when HSB-2 cell line develop resistance to PIMi and suggest pathways that can be rationally targeted in combination with PIM kinases to inhibit T-ALL growth., (©2020 American Association for Cancer Research.)

Background: Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes., Results: Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses., Conclusions: There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network, based on a single data type, captures the full spectrum of interactions. Greater insight can instead be gained by exploring multiple independent networks and by choosing an appropriate metric on each network. Moreover we can improve our ability to rank phenotypic drivers by combining the information from individual networks. We propose that such integrative network analysis could be used to combine clinical gene expression data with interaction databases to prioritize patient- and disease-specific therapeutic targets.