Reverse engineering of regulatory networks in human B cells

Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

BACKGROUND

Transcription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Maize has been a model organism for over one hundred years, but little is known about its tissue-specific gene regulation through TFs. In this study, we used a network approach to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, SAM and seed) in maize. We utilized GENIE3, a machine-learning algorithm combined with large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Unlike some other techniques, this approach is not limited by high-quality Position Weighed Matrix (PWM), and can therefore predict GRNs for over 2000 TFs in maize.

RESULTS

Although many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. Our GRNs were also validated by ChIP-Seq datasets (KN1, FEA4 and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. We also found functional modules in each network by clustering analysis with the MCL algorithm.

CONCLUSIONS

By combining publicly available genome-wide expression data and network analysis, we can uncover GRNs at tissue-level resolution in maize. Since ChIP-Seq and PWMs are still limited in several model organisms, our study provides a uniform platform that can be adapted to any species with genome-wide expression data to construct GRNs. We also present a publicly available database, maize tissue-specific GRN (mGRN, https://www.bio.fsu.edu/mcginnislab/mgrn/ ), for easy querying. All source code and data are available at Github ( https://github.com/timedreamer/maize_tissue-specific_GRN ).

MICMIC: identification of DNA methylation of distal regulatory regions with causal effects on tumorigenesis

Aberrant promoter methylation is a common mechanism for tumor suppressor inactivation in cancer. We develop a set of tools to identify genome-wide DNA methylation in distal regions with causal effect on tumorigenesis called MICMIC. Many predictions are directly validated by dCas9-based epigenetic editing to support the accuracy and efficiency of our tool. Oncogenic and lineage-specific transcription factors are shown to aberrantly shape the methylation landscape by modifying tumor-subtype core regulatory circuitry. Notably, the gene regulatory networks orchestrated by enhancer methylation across different cancer types are seen to converge on a common architecture. MICMIC is available on https://github.com/ZhangJlab/MICMIC .

A Combined PLS and Negative Binomial Regression Model for Inferring Association Networks from Next-Generation Sequencing Count Data

A major challenge of genomics data is to detect interactions displaying functional associations from large-scale observations. In this study, a new cPLS-algorithm combining partial least squares approach with negative binomial regression is suggested to reconstruct a genomic association network for high-dimensional next-generation sequencing count data. The suggested approach is applicable to the raw counts data, without requiring any further pre-processing steps. In the settings investigated, the cPLS-algorithm outperformed the two widely used comparative methods, graphical lasso, and weighted correlation network analysis. In addition, cPLS is able to estimate the full network for thousands of genes without major computational load. Finally, we demonstrate that cPLS is capable of finding biologically meaningful associations by analyzing an example data set from a previously published study to examine the molecular anatomy of the craniofacial development.

Cross-Cohort Analysis Identifies a TEAD4-MYCN Positive Feedback Loop as the Core Regulatory Element of High-Risk Neuroblastoma

High-risk neuroblastomas show a paucity of recurrent somatic mutations at diagnosis. As a result, the molecular basis for this aggressive phenotype remains elusive. Recent progress in regulatory network analysis helped us elucidate disease-driving mechanisms downstream of genomic alterations, including recurrent chromosomal alterations. Our analysis identified three molecular subtypes of high-risk neuroblastomas, consistent with chromosomal alterations, and identified subtype-specific master regulator proteins that were conserved across independent cohorts. A 10-protein transcriptional module-centered around a TEAD4-MYCN positive feedback loop-emerged as the regulatory driver of the high-risk subtype associated with MYCN amplification. Silencing of either gene collapsed MYCN-amplified (MYCNAmp) neuroblastoma transcriptional hallmarks and abrogated viability in vitro and in vivo Consistently, TEAD4 emerged as a robust prognostic marker of poor survival, with activity independent of the canonical Hippo pathway transcriptional coactivators YAP and TAZ. These results suggest novel therapeutic strategies for the large subset of MYCN-deregulated neuroblastomas.Significance: Despite progress in understanding of neuroblastoma genetics, little progress has been made toward personalized treatment. Here, we present a framework to determine the downstream effectors of the genetic alterations sustaining neuroblastoma subtypes, which can be easily extended to other tumor types. We show the critical effect of disrupting a 10-protein module centered around a YAP/TAZ-independent TEAD4-MYCN positive feedback loop in MYCNAmp neuroblastomas, nominating TEAD4 as a novel candidate for therapeutic intervention. Cancer Discov; 8(5); 582-99. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 517.

An Integrated Systems Biology Approach Identifies TRIM25 as a Key Determinant of Breast Cancer Metastasis

At the root of most fatal malignancies are aberrantly activated transcriptional networks that drive metastatic dissemination. Although individual metastasis-associated genes have been described, the complex regulatory networks presiding over the initiation and maintenance of metastatic tumors are still poorly understood. There is untapped value in identifying therapeutic targets that broadly govern coordinated transcriptional modules dictating metastatic progression. Here, we reverse engineered and interrogated a breast cancer-specific transcriptional interaction network (interactome) to define transcriptional control structures causally responsible for regulating genetic programs underlying breast cancer metastasis in individual patients. Our analyses confirmed established pro-metastatic transcription factors, and they uncovered TRIM25 as a key regulator of metastasis-related transcriptional programs. Further, in vivo analyses established TRIM25 as a potent regulator of metastatic disease and poor survival outcome. Our findings suggest that identifying and targeting keystone proteins, like TRIM25, can effectively collapse transcriptional hierarchies necessary for metastasis formation, thus representing an innovative cancer intervention strategy.

Information-theoretic decomposition of embodied and situated systems

The embodied and situated view of cognition stresses the importance of real-time and nonlinear bodily interaction with the environment for developing concepts and structuring knowledge. In this article, populations of robots controlled by an artificial neural network learn a wall-following task through artificial evolution. At the end of the evolutionary process, time series are recorded from perceptual and motor neurons of selected robots. Information-theoretic measures are estimated on pairings of variables to unveil nonlinear interactions that structure the agent-environment system. Specifically, the mutual information is utilized to quantify the degree of dependence and the transfer entropy to detect the direction of the information flow. Furthermore, the system is analyzed with the local form of such measures, thus capturing the underlying dynamics of information. Results show that different measures are interdependent and complementary in uncovering aspects of the robots' interaction with the environment, as well as characteristics of the functional neural structure. Therefore, the set of information-theoretic measures provides a decomposition of the system, capturing the intricacy of nonlinear relationships that characterize robots' behavior and neural dynamics.

Cooperative STAT/NF-κB signaling regulates lymphoma metabolic reprogramming and aberrant GOT2 expression

Knowledge of stromal factors that have a role in the transcriptional regulation of metabolic pathways aside from c-Myc is fundamental to improvements in lymphoma therapy. Using a MYC-inducible human B-cell line, we observed the cooperative activation of STAT3 and NF-κB by IL10 and CpG stimulation. We show that IL10 + CpG-mediated cell proliferation of MYC^low cells depends on glutaminolysis. By ¹³C- and ¹⁵N-tracing of glutamine metabolism and metabolite rescue experiments, we demonstrate that GOT2 provides aspartate and nucleotides to cells with activated or aberrant Jak/STAT and NF-κB signaling. A model of GOT2 transcriptional regulation is proposed, in which the cooperative phosphorylation of STAT3 and direct joint binding of STAT3 and p65/NF-κB to the proximal GOT2 promoter are important. Furthermore, high aberrant GOT2 expression is prognostic in diffuse large B-cell lymphoma underscoring the current findings and importance of stromal factors in lymphoma biology.

Metabolic rewiring of cancer cells can be driven by both extrinsic and intrinsic factors. Here the authors show that microenvironmental factors induce metabolic rewiring of B-cell lymphoma through activation of STAT3 and NF-ΚB resulting in upregulation of the aminotransferase GOT2 and glutamine addiction.

Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm's value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

TRRAP is essential for regulating the accumulation of mutant and wild-type p53 in lymphoma

Tumors accumulate high levels of mutant p53 (mutp53), which contributes to mutp53 gain-of-function properties. The mechanisms that underlie such excessive accumulation are not fully understood. To discover regulators of mutp53 protein accumulation, we performed a large-scale RNA interference screen in a Burkitt lymphoma cell line model. We identified transformation/transcription domain-associated protein (TRRAP), a constituent of several histone acetyltransferase complexes, as a critical positive regulator of both mutp53 and wild-type p53 levels. TRRAP silencing attenuated p53 accumulation in lymphoma and colon cancer models, whereas TRRAP overexpression increased mutp53 levels, suggesting a role for TRRAP across cancer entities and p53 mutations. Through clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 screening, we identified a 109-amino-acid region in the N-terminal HEAT repeat region of TRRAP that was crucial for mutp53 stabilization and cell proliferation. Mass spectrometric analysis of the mutp53 interactome indicated that TRRAP silencing caused degradation of mutp53 via the MDM2-proteasome axis. This suggests that TRRAP is vital for maintaining mutp53 levels by shielding it against the natural p53 degradation machinery. To identify drugs that alleviated p53 accumulation similarly to TRRAP silencing, we performed a small-molecule drug screen and found that inhibition of histone deacetylases (HDACs), specifically HDAC1/2/3, decreased p53 levels to a comparable extent. In summary, here we identify TRRAP as a key regulator of p53 levels and link acetylation-modifying complexes to p53 protein stability. Our findings may provide clues for therapeutic targeting of mutp53 in lymphoma and other cancers.

Multi-focal control of mitochondrial gene expression by oncogenic MYC provides potential therapeutic targets in cancer

Despite ubiquitous activation in human cancer, essential downstream effector pathways of the MYC transcription factor have been difficult to define and target. Using a structure/function-based approach, we identified the mitochondrial RNA polymerase (POLRMT) locus as a critical downstream target of MYC. The multifunctional POLRMT enzyme controls mitochondrial gene expression, a process required both for mitochondrial function and mitochondrial biogenesis. We further demonstrate that inhibition of this newly defined MYC effector pathway causes robust and selective tumor cell apoptosis, via an acute, checkpoint-like mechanism linked to aberrant electron transport chain complex assembly and mitochondrial reactive oxygen species (ROS) production. Fortuitously, MYC-dependent tumor cell death can be induced by inhibiting the mitochondrial gene expression pathway using a variety of strategies, including treatment with FDA-approved antibiotics. In vivo studies using a mouse model of Burkitt's Lymphoma provide pre-clinical evidence that these antibiotics can successfully block progression of MYC-dependent tumors.

Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone

Biological cells express intracellular biomolecular information to the extracellular environment as various physical responses. We show a novel computational approach to estimate intracellular biomolecular pathways from growth cone electrophysiological responses. Previously, it was shown that cGMP signaling regulates membrane potential (MP) shifts that control the growth cone turning direction during neuronal development. We present here an integrated deterministic mathematical model and Bayesian reversed-engineering framework that enables estimation of the molecular signaling pathway from electrical recordings and considers both the system uncertainty and cell-to-cell variability. Our computational method selects the most plausible molecular pathway from multiple candidates while satisfying model simplicity and considering all possible parameter ranges. The model quantitatively reproduces MP shifts depending on cGMP levels and MP variability potential in different experimental conditions. Lastly, our model predicts that chloride channel inhibition by cGMP-dependent protein kinase (PKG) is essential in the core system for regulation of the MP shifts.

Differential gene regulatory networks in development and disease

Gene regulatory networks, in which differential expression of regulator genes induce differential expression of their target genes, underlie diverse biological processes such as embryonic development, organ formation and disease pathogenesis. An archetypical systems biology approach to mapping these networks involves the combined application of (1) high-throughput sequencing-based transcriptome profiling (RNA-seq) of biopsies under diverse network perturbations and (2) network inference based on gene-gene expression correlation analysis. The comparative analysis of such correlation networks across cell types or states, differential correlation network analysis, can identify specific molecular signatures and functional modules that underlie the state transition or have context-specific function. Here, we review the basic concepts of network biology and correlation network inference, and the prevailing methods for differential analysis of correlation networks. We discuss applications of gene expression network analysis in the context of embryonic development, cancer, and congenital diseases.

IRE1α RNase-dependent lipid homeostasis promotes survival in Myc-transformed cancers

Myc activation is a primary oncogenic event in many human cancers; however, these transcription factors are difficult to inhibit pharmacologically, suggesting that Myc-dependent downstream effectors may be more tractable therapeutic targets. Here, we show that Myc overexpression induces endoplasmic reticulum (ER) stress and engages the inositol-requiring enzyme 1α (IRE1α)/X-box binding protein 1 (XBP1) pathway through multiple molecular mechanisms in a variety of c-Myc- and N-Myc-dependent cancers. In particular, Myc-overexpressing cells require IRE1α/XBP1 signaling for sustained growth and survival in vitro and in vivo, dependent on elevated stearoyl-CoA-desaturase 1 (SCD1) activity. Pharmacological and genetic XBP1 inhibition induces Myc-dependent apoptosis, which is alleviated by exogenous unsaturated fatty acids. Of note, SCD1 inhibition phenocopies IRE1α RNase activity suppression in vivo. Furthermore, IRE1α inhibition enhances the cytotoxic effects of standard chemotherapy drugs used to treat c-Myc-overexpressing Burkitt's lymphoma, suggesting that inhibiting the IRE1α/XBP1 pathway is a useful general strategy for treatment of Myc-driven cancers.

Targeting the hexosamine biosynthetic pathway and O-linked N-acetylglucosamine cycling for therapeutic and imaging capabilities in diffuse large B-cell lymphoma

The hexosamine biosynthetic pathway (HBP) requires two key nutrients glucose and glutamine for O-linked N-acetylglucosamine (O-GlcNAc) cycling, a post-translational protein modification that adds GlcNAc to nuclear and cytoplasmic proteins. Increased GlcNAc has been linked to regulatory factors involved in cancer cell growth and survival. However, the biological significance of GlcNAc in diffuse large B-cell lymphoma (DLBCL) is not well defined. This study is the first to show that both the substrate and the endpoint O-GlcNAc transferase (OGT) enzyme of the HBP were highly expressed in DLBCL cell lines and in patient tumors compared with normal B-lymphocytes. Notably, high OGT mRNA levels were associated with poor survival of DLBCL patients. Targeting OGT via small interference RNA in DLBCL cells inhibited activation of GlcNAc, nuclear factor kappa B (NF-κB), and nuclear factor of activated T-cells 1 (NFATc1), as well as cell growth. Depleting both glucose and glutamine in DLBCL cells or treating them with an HBP inhibitor (azaserine) diminished O-GlcNAc protein substrate, inhibited constitutive NF-κB and NFATc1 activation, and induced G0/G1 cell-cycle arrest and apoptosis. Replenishing glucose-and glutamine-deprived DLBCL cells with a synthetic glucose analog (ethylenedicysteine-N-acetylglucosamine [ECG]) reversed these phenotypes. Finally, we showed in both in vitro and in vivo murine models that DLBCL cells easily take up radiolabeled technetium-99m-ECG conjugate. These findings suggest that targeting the HBP has therapeutic relevance for DLBCL and underscores the imaging potential of the glucosamine analog ECG in DLBCL.

The pathophysiological significance of PPM1D and therapeutic targeting of PPM1D-mediated signaling by GSK2830371 in mantle cell lymphoma

PPM1D is a serine/threonine phosphatase that negatively regulates key DNA damage response proteins, such as p53, p38 MAPK, histone H2A.X, and ATM. We investigated the pathophysiological significance of PPM1D and its therapeutic targeting by the novel PPM1D inhibitor GSK2830371 in mantle cell lymphoma (MCL). Oncomine-based analyses indicated increased PPM1D mRNA levels in MCL cells compared with their normal counterpart cells. Higher PPM1D expression was associated with higher expression of the proliferation gene signature and poorer prognosis in patients. Eight MCL (three p53 wild-type and five mutant) cell lines were exposed to GSK2830371. GSK2830371 inhibited the cell growth, being prominent in p53 wild-type cells. GSK2830371 induced apoptosis in sensitive cells, as evidenced by induction of phosphatidylserine externalization and loss of mitochondrial membrane potential. p53 knockdown de-sensitized cell sensitivity. GSK2830371 increased the levels of total and Ser15-phosphorylated p53, and p53 targets p21 and PUMA. GSK2830371 and the MDM2 inhibitor Nutlin-3a acted synergistically in p53 wild-type cells. Interestingly, GSK2830371 sensitized MCL cells to bortezomib and doxorubicin in p53 wild-type and mutant cells; p38 signaling appeared to be involved in the GSK2830371/bortezomib lethality. PPM1D inhibition may represent a novel therapeutic strategy for MCL, which can be exploited in combination therapeutic strategies for MCL.

A method for estimating Hill function-based dynamic models of gene regulatory networks

Gene regulatory networks (GRNs) are quite large and complex. To better understand and analyse GRNs, mathematical models are being employed. Different types of models, such as logical, continuous and stochastic models, can be used to describe GRNs. In this paper, we present a new approach to identify continuous models, because they are more suitable for large number of genes and quantitative analysis. One of the most promising techniques for identifying continuous models of GRNs is based on Hill functions and the generalized profiling method (GPM). The advantage of this approach is low computational cost and insensitivity to initial conditions. In the GPM, a constrained nonlinear optimization problem has to be solved that is usually underdetermined. In this paper, we propose a new optimization approach in which we reformulate the optimization problem such that constraints are embedded implicitly in the cost function. Moreover, we propose to split the unknown parameter in two sets based on the structure of Hill functions. These two sets are estimated separately to resolve the issue of the underdetermined problem. As a case study, we apply the proposed technique on the SOS response in Escherichia coli and compare the results with the existing literature.

CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network

The Cancer Target Discovery and Development (CTD²) Network aims to use functional genomics to accelerate the translation of high-throughput and high-content genomic and small-molecule data towards use in precision oncology. As part of this goal, and to share its conclusions with the research community, the Network developed the ‘CTD² Dashboard’ [https://ctd2-dashboard.nci.nih.gov/], which compiles CTD² Network-generated conclusions, termed ‘observations’, associated with experimental entities, collected by its member groups (‘Centers’). Any researcher interested in learning about a given gene, protein, or compound (a ‘subject’) studied by the Network can come to the CTD² Dashboard to quickly and easily find, review, and understand Network-generated experimental results. In particular, the Dashboard allows visitors to connect experiments about the same target, biomarker, etc., carried out by multiple Centers in the Network. The Dashboard’s unique knowledge representation allows information to be compiled around a subject, so as to become greater than the sum of the individual contributions. The CTD² Network has broadly defined levels of validation for evidence (‘Tiers’) pertaining to a particular finding, and the CTD² Dashboard uses these Tiers to indicate the extent to which results have been validated. Researchers can use the Network’s insights and tools to develop a new hypothesis or confirm existing hypotheses, in turn advancing the findings towards clinical applications.

Database URL:https://ctd2-dashboard.nci.nih.gov/

Overexpression of the proneural transcription factor ASCL1 in chronic lymphocytic leukemia with a t(12;14)(q23.2;q32.3)

Background

Translocations of the IGH locus on 14q32.3 are present in about 8% of patients with chronic lymphocytic leukemia (CLL) and contribute to leukemogenesis by deregulating the expression of the IGH-partner genes. Identification of these genes and investigation of the downstream effects of their deregulation can reveal disease-causing mechanisms.

Case presentation

We report on the molecular characterization of a novel t(12;14)(q23.2;q32.3) in CLL. As a consequence of the rearrangement ASCL1 was brought into proximity of the IGHJ-Cμ enhancer and was highly overexpressed in the aberrant B-cells of the patient, as shown by qPCR and immunohistochemistry. ASCL1 encodes for a transcription factor acting as a master regulator of neurogenesis, is overexpressed in neuroendocrine tumors and a promising therapeutic target in small cell lung cancer (SCLC). Its overexpression has also been recently reported in acute adult T-cell leukemia/lymphoma.To examine possible downstream effects of the ASCL1 upregulation in CLL, we compared the gene expression of sorted CD5⁺ cells of the translocation patient to that of CD19⁺ B-cells from seven healthy donors and detected 176 significantly deregulated genes (Fold Change ≥2, FDR p ≤ 0.01). Deregulation of 55 genes in our gene set was concordant with at least two studies comparing gene expression of normal and CLL B-lymphocytes. INSM1, a well-established ASCL1 target in the nervous system and SCLC, was the gene with the strongest upregulation (Fold Change = 209.4, FDR p = 1.37E-4).INSM1 encodes for a transcriptional repressor with extranuclear functions, implicated in neuroendocrine differentiation and overexpressed in the majority of neuroendocrine tumors. It was previously shown to be induced in CLL cells but not in normal B-cells upon treatment with IL-4 and to be overexpressed in CLL cells with unmutated versus mutated IGHV genes. Its role in CLL is still unexplored.

Conclusion

We identified ASCL1 as a novel IGH-partner gene in CLL. The neural transcription factor was strongly overexpressed in the patient's CLL cells. Microarray gene expression analysis revealed the strong upregulation of INSM1, a prominent ASCL1 target, which was previously shown to be induced in CLL cells upon IL-4 treatment. We propose further investigation of the expression and potential role of INSM1 in CLL.

Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

Bronchiolitis obliterans syndrome (BOS), the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group), and 26 samples at or after BOS diagnosis (diagnosis group). An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group). We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1), T-cell leukemia/lymphoma protein 1A (TCL1A), and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p < 0.01) and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS.

Casein kinase 1 is a therapeutic target in chronic lymphocytic leukemia

Casein kinase 1δ/ε (CK1δ/ε) is a key component of noncanonical Wnt signaling pathways, which were shown previously to drive pathogenesis of chronic lymphocytic leukemia (CLL). In this study, we investigated thoroughly the effects of CK1δ/ε inhibition on the primary CLL cells and analyzed the therapeutic potential in vivo using 2 murine model systems based on the Eµ-TCL1-induced leukemia (syngeneic adoptive transfer model and spontaneous disease development), which resembles closely human CLL. We can demonstrate that the CK1δ/ε inhibitor PF-670462 significantly blocks microenvironmental interactions (chemotaxis, invasion and communication with stromal cells) in primary CLL cells in all major subtypes of CLL. In the mouse models, CK1 inhibition slows down accumulation of leukemic cells in the peripheral blood and spleen and prevents onset of anemia. As a consequence, PF-670462 treatment results in a significantly longer overall survival. Importantly, CK1 inhibition has synergistic effects to the B-cell receptor (BCR) inhibitors such as ibrutinib in vitro and significantly improves ibrutinib effects in vivo. Mice treated with a combination of PF-670462 and ibrutinib show the slowest progression of disease and survive significantly longer compared with ibrutinib-only treatment when the therapy is discontinued. In summary, this preclinical testing of CK1δ/ε inhibitor PF-670462 demonstrates that CK1 may serve as a novel therapeutic target in CLL, acting in synergy with BCR inhibitors. Our work provides evidence that targeting CK1 can represent an alternative or addition to the therapeutic strategies based on BCR signaling and antiapoptotic signaling (BCL-2) inhibition.

The Reconstruction and Analysis of Gene Regulatory Networks

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

A PERK-miR-211 axis suppresses circadian regulators and protein synthesis to promote cancer cell survival

The unfolded protein response (UPR) is a stress-activated signalling pathway that regulates cell proliferation, metabolism and survival. The circadian clock coordinates metabolism and signal transduction with light/dark cycles. We explore how UPR signalling interfaces with the circadian clock. UPR activation induces a 10 h phase shift in circadian oscillations through induction of miR-211, a PERK-inducible microRNA that transiently suppresses both Bmal1 and Clock, core circadian regulators. Molecular investigation reveals that miR-211 directly regulates Bmal1 and Clock via distinct mechanisms. Suppression of Bmal1 and Clock has the anticipated impact on expression of select circadian genes, but we also find that repression of Bmal1 is essential for UPR-dependent inhibition of protein synthesis and cell adaptation to stresses that disrupt endoplasmic reticulum homeostasis. Our data demonstrate that c-Myc-dependent activation of the UPR inhibits Bmal1 in Burkitt's lymphoma, thereby suppressing both circadian oscillation and ongoing protein synthesis to facilitate tumour progression.

Precision Medicine for Acute Kidney Injury (AKI): Redefining AKI by Agnostic Kidney Tissue Interrogation and Genetics

Acute kidney injury (AKI) currently is diagnosed by a temporal trend of a single blood analyte: serum creatinine. This measurement is neither sensitive nor specific to kidney injury or its protean forms. Newer biomarkers, neutrophil gelatinase-associated lipocalin (NGAL, Lipocalin 2, Siderocalin), or kidney injury molecule-1 (KIM-1, Hepatitis A Virus Cellular Receptor 1), accelerate the diagnosis of AKI as well as prospectively distinguish rapidly reversible from prolonged causes of serum creatinine increase. Nonetheless, these biomarkers lack the capacity to subfractionate AKI further (eg, sepsis versus ischemia versus nephrotoxicity from medications, enzymes, or metals) or inform us about the primary and secondary sites of injury. It also is unknown whether all nephrons are injured in AKI, whether all cells in a nephron are affected, and whether injury responses can be stimulus-specific or cell type-specific or both. In this review, we summarize fully agnostic tissue interrogation approaches that may help to redefine AKI in cellular and molecular terms, including single-cell and single-nuclei RNA sequencing technology. These approaches will empower a shift in the current paradigm of AKI diagnosis, classification, and staging, and provide the renal community with a significant advance toward precision medicine in the analysis AKI.

Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response

The availability of increasing volumes of multi-omics profiles across many cancers promises to improve our understanding of the regulatory mechanisms underlying cancer. The main challenge is to integrate these multiple levels of omics profiles and especially to analyze them across many cancers. Here we present AMARETTO, an algorithm that addresses both challenges in three steps. First, AMARETTO identifies potential cancer driver genes through integration of copy number, DNA methylation and gene expression data. Then AMARETTO connects these driver genes with co-expressed target genes that they control, defined as regulatory modules. Thirdly, we connect AMARETTO modules identified from different cancer sites into a pancancer network to identify cancer driver genes. Here we applied AMARETTO in a pancancer study comprising eleven cancer sites and confirmed that AMARETTO captures hallmarks of cancer. We also demonstrated that AMARETTO enables the identification of novel pancancer driver genes. In particular, our analysis led to the identification of pancancer driver genes of smoking-induced cancers and 'antiviral' interferon-modulated innate immune response.

SOFTWARE AVAILABILITY

AMARETTO is available as an R package at https://bitbucket.org/gevaertlab/pancanceramaretto.

Model-Based Clustering With Data Correction For Removing Artifacts In Gene Expression Data

The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 colors are used to measure the expression levels of the 1,000 landmark genes measured, and the data for the resulting pairs of genes are deconvolved. The raw data are sometimes inadequate for reliable deconvolution, leading to artifacts in the final processed data. These include the expression levels of paired genes being flipped or given the same value, and clusters of values that are not at the true expression level. We propose a new method called model-based clustering with data correction (MCDC) that is able to identify and correct these three kinds of artifacts simultaneously. We show that MCDC improves the resulting gene expression data in terms of agreement with external baselines, as well as improving results from subsequent analysis.

Systematic analysis of gene expression alterations and clinical outcomes of STAT3 in cancer

Accumulated studies have provided controversial evidences of prognostic value for signal transducer and activator of transcription proteins 3 (STAT3) in cancers. To address this inconsistency, we performed a systematic analysis to determine whether STAT3 can serve as a prognostic marker in human cancers. STAT3 expression was assessed using Oncomine analysis. cBioPortal, Kaplan-Meier Plotter, and Prognoscan were performed to identify the prognostic roles of STAT3 in human cancers. The copy number alteration, mutation, interactive analysis, and visualize the altered networks were performed by cBioPortal. We found that STAT3 was more frequently overexpressed in lung, ovarian, gastric, blood and brain cancers than their normal tissues and its expression might be negatively related with the prognosis. In addition, STAT3 mutation mainly occurred in uterine cancer and existed in a hotspot in SH2 domain. Those findings suggest that STAT3 might serve as a diagnostic and therapeutic target for certain types of cancer, such as lung, ovarian, gastric, blood and brain cancers. However, future research is required to validate our findings and thus promote the clinical utility of STAT3 in those cancers prognosis evaluation.

Cancer-type dependent expression of CK2 transcripts

A multitude of proteins are aberrantly expressed in cancer cells, including the oncogenic serine-threonine kinase CK2. In a previous report, we found increases in CK2 transcript expression that could explain the increased CK2 protein levels found in tumors from lung and bronchus, prostate, breast, colon and rectum, ovarian and pancreatic cancers. We also found that, contrary to the current notions about CK2, some CK2 transcripts were downregulated in several cancers. Here, we investigate all other cancers using Oncomine to determine whether they also display significant CK2 transcript dysregulation. As anticipated from our previous analysis, we found cancers with all CK2 transcripts upregulated (e.g. cervical), and cancers where there was a combination of upregulation and/or downregulation of the CK2 transcripts (e.g. sarcoma). Unexpectedly, we found some cancers with significant downregulation of all CK2 transcripts (e.g. testicular cancer). We also found that, in some cases, CK2 transcript levels were already dysregulated in benign lesions (e.g. Barrett’s esophagus). We also found that CK2 transcript upregulation correlated with lower patient survival in most cases where data was significant. However, there were two cancer types, glioblastoma and renal cell carcinoma, where CK2 transcript upregulation correlated with higher survival. Overall, these data show that the expression levels of CK2 genes is highly variable in cancers and can lead to different patient outcomes.

Gene network analysis: from heart development to cardiac therapy

Networks offer a flexible framework to represent and analyse the complex interactions between components of cellular systems. In particular gene networks inferred from expression data can support the identification of novel hypotheses on regulatory processes. In this review we focus on the use of gene network analysis in the study of heart development. Understanding heart development will promote the elucidation of the aetiology of congenital heart disease and thus possibly improve diagnostics. Moreover, it will help to establish cardiac therapies. For example, understanding cardiac differentiation during development will help to guide stem cell differentiation required for cardiac tissue engineering or to enhance endogenous repair mechanisms. We introduce different methodological frameworks to infer networks from expression data such as Boolean and Bayesian networks. Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies. Collectively, our literature-based analysis indicates that gene network analysis constitutes a promising opportunity to infer therapy-relevant regulatory processes in heart development. However, the use of network-based approaches has so far been limited by the small amount of samples in available datasets. Thus, we propose to acquire high-resolution temporal expression data to improve the mathematical descriptions of regulatory processes obtained with gene network inference methodologies. Especially probabilistic methods that accommodate the intrinsic variability of biological systems have the potential to contribute to a deeper understanding of heart development.

An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection

BACKGROUND

The reconstruction of gene regulatory network (GRN) from gene expression data can discover regulatory relationships among genes and gain deep insights into the complicated regulation mechanism of life. However, it is still a great challenge in systems biology and bioinformatics. During the past years, numerous computational approaches have been developed for this goal, and Bayesian network (BN) methods draw most of attention among these methods because of its inherent probability characteristics. However, Bayesian network methods are time consuming and cannot handle large-scale networks due to their high computational complexity, while the mutual information-based methods are highly effective but directionless and have a high false-positive rate.

RESULTS

To solve these problems, we propose a Candidate Auto Selection algorithm (CAS) based on mutual information and breakpoint detection to restrict the search space in order to accelerate the learning process of Bayesian network. First, the proposed CAS algorithm automatically selects the neighbor candidates of each node before searching the best structure of GRN. Then based on CAS algorithm, we propose a globally optimal greedy search method (CAS + G), which focuses on finding the highest rated network structure, and a local learning method (CAS + L), which focuses on faster learning the structure with little loss of quality.

CONCLUSION

Results show that the proposed CAS algorithm can effectively reduce the search space of Bayesian networks through identifying the neighbor candidates of each node. In our experiments, the CAS + G method outperforms the state-of-the-art method on simulation data for inferring GRNs, and the CAS + L method is significantly faster than the state-of-the-art method with little loss of accuracy. Hence, the CAS based methods effectively decrease the computational complexity of Bayesian network and are more suitable for GRN inference.

Deconvolution of Transcriptional Networks in Post-Traumatic Stress Disorder Uncovers Master Regulators Driving Innate Immune System Function

Post-Traumatic Stress Disorder (PTSD) is a psychiatric disorder that develops in individuals experiencing a shocking incident, but the underlying disease susceptibility gene networks remain poorly understood. Breen et al. conducted a Weighted Gene Co-expression Network Analysis on PTSD, and identified a dysregulated innate immune module associated with PTSD development. To further identify the Master Regulators (MRs) driving the network function, here we deconvoluted the transcriptional networks on the same datasets using ARACNe (Algorithm for Reconstruction of Accurate Cellular Networks) followed by protein activity analysis. We successfully identified several MRs including SOX3, TNFAIP3, TRAFD1, POU3F3, STAT2, and PML that govern the expression of a large collection of genes. Transcription factor binding site enrichment analysis verified the binding of these MRs to their predicted targets. Notably, the sub-networks regulated by TNFAIP3, TRAFD1 and PML are involved in innate immune response, suggesting that these MRs may correlate with the innate immune module identified by Breen et al. These findings were replicated in an independent dataset generated on expression microarrays. In conclusion, our analysis corroborated previous findings that innate immunity may be involved in the progression of PTSD, yet also identified candidate MRs driving the disease progression in the innate immunity pathways.

The best models of metabolism

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

Pharmacological inhibition of the transcription factor PU.1 in leukemia

The transcription factor PU.1 is often impaired in patients with acute myeloid leukemia (AML). Here, we used AML cells that already had low PU.1 levels and further inhibited PU.1 using either RNA interference or, to our knowledge, first-in-class small-molecule inhibitors of PU.1 that we developed specifically to allosterically interfere with PU.1-chromatin binding through interaction with the DNA minor groove that flanks PU.1-binding motifs. These small molecules of the heterocyclic diamidine family disrupted the interaction of PU.1 with target gene promoters and led to downregulation of canonical PU.1 transcriptional targets. shRNA or small-molecule inhibition of PU.1 in AML cells from either PU.1lo mutant mice or human patients with AML-inhibited cell growth and clonogenicity and induced apoptosis. In murine and human AML (xeno)transplantation models, treatment with our PU.1 inhibitors decreased tumor burden and resulted in increased survival. Thus, our study provides proof of concept that PU.1 inhibition has potential as a therapeutic strategy for the treatment of AML and for the development of small-molecule inhibitors of PU.1.

Network analysis of EMT and MET micro-RNA regulation in breast cancer

Over the last years, microRNAs (miRs) have shown to be crucial for breast tumour establishment and progression. To understand the influence that miRs have over transcriptional regulation in breast cancer, we constructed mutual information networks from 86 TCGA matched breast invasive carcinoma and control tissue RNA-Seq and miRNA-Seq sequencing data. We show that miRs are determinant for tumour and control data network structure. In tumour data network, miR-200, miR-199 and neighbour miRs seem to cooperate on the regulation of the acquisition of epithelial and mesenchymal traits by the biological processes: Epithelial-Mesenchymal Transition (EMT) and Mesenchymal to Epithelial Transition (MET). Despite structural differences between tumour and control networks, we found a conserved set of associations between miR-200 family members and genes such as VIM, ZEB-1/2 and TWIST-1/2. Further, a large number of miRs observed in tumour network mapped to a specific chromosomal location in DLK1-DIO3 (Chr14q32); some of those miRs have also been associated with EMT and MET regulation. Pathways related to EMT and TGF-beta reinforce the relevance of miR-200, miR-199 and DLK1-DIO3 cluster in breast cancer. With this approach, we stress that miR inclusion in gene regulatory network construction improves our understanding of the regulatory mechanisms underlying breast cancer biology.

Contextual tumor suppressor function of T cell death-associated gene 8 (TDAG8) in hematological malignancies

Background

Extracellular acidosis is a condition found within the tumor microenvironment due to inadequate blood perfusion, hypoxia, and altered tumor cell metabolism. Acidosis has pleiotropic effects on malignant progression; therefore it is essential to understand how acidosis exerts its diverse effects. TDAG8 is a proton-sensing G-protein-coupled receptor that can be activated by extracellular acidosis.

Methods

TDAG8 gene expression was analyzed by bioinformatic analyses and quantitative RT-PCR in human hematological malignancies. Retroviral transduction was used to restore TDAG8 expression in U937, Ramos and other blood cancer cells. Multiple in vitro and in vivo tumorigenesis and metastasis assays were employed to evaluate the effects of TDAG8 expression on blood cancer progression. Western blotting, immunohistochemistry and biochemical approaches were applied to elucidate the underlying mechanisms associated with the TDAG8 receptor pathway.

Results

TDAG8 expression is significantly reduced in human blood cancers in comparison to normal blood cells. Severe acidosis, pH 6.4, inhibited U937 cancer cell proliferation while mild acidosis, pH 6.9, stimulated its proliferation. However, restoring TDAG8 gene expression modulated the U937 cell response to mild extracellular acidosis and physiological pH by reducing cell proliferation. Tumor xenograft experiments further revealed that restoring TDAG8 expression in U937 and Ramos cancer cells reduced tumor growth. It was also shown U937 cells with restored TDAG8 expression attached less to Matrigel, migrated slower toward a chemoattractant, and metastasized less in severe combined immunodeficient mice. These effects correlated with a reduction in c-myc oncogene expression. The mechanistic investigation indicated that Gα13/Rho signaling arbitrated the TDAG8-mediated c-myc oncogene repression in response to acidosis.

Conclusions

This study provides data to support the concept that TDAG8 functions as a contextual tumor suppressor down-regulated in hematological malignancies and potentiation of the TDAG8 receptor pathway may be explored as a potential anti-tumorigenic approach in blood cancers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-017-1305-6) contains supplementary material, which is available to authorized users.

Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.

The FOXN3-NEAT1-SIN3A repressor complex promotes progression of hormonally responsive breast cancer

The pathophysiological function of the forkhead transcription factor FOXN3 remains to be explored. Here we report that FOXN3 is a transcriptional repressor that is physically associated with the SIN3A repressor complex in estrogen receptor-positive (ER+) cells. RNA immunoprecipitation-coupled high-throughput sequencing identified that NEAT1, an estrogen-inducible long noncoding RNA, is required for FOXN3 interactions with the SIN3A complex. ChIP-Seq and deep sequencing of RNA genomic targets revealed that the FOXN3-NEAT1-SIN3A complex represses genes including GATA3 that are critically involved in epithelial-to-mesenchymal transition (EMT). We demonstrated that the FOXN3-NEAT1-SIN3A complex promotes EMT and invasion of breast cancer cells in vitro as well as dissemination and metastasis of breast cancer in vivo. Interestingly, the FOXN3-NEAT1-SIN3A complex transrepresses ER itself, forming a negative-feedback loop in transcription regulation. Elevation of both FOXN3 and NEAT1 expression during breast cancer progression corresponded to diminished GATA3 expression, and high levels of FOXN3 and NEAT1 strongly correlated with higher histological grades and poor prognosis. Our experiments uncovered that NEAT1 is a facultative component of the SIN3A complex, shedding light on the mechanistic actions of NEAT1 and the SIN3A complex. Further, our study identified the ERα-NEAT1-FOXN3/NEAT1/SIN3A-GATA3 axis that is implicated in breast cancer metastasis, providing a mechanistic insight into the pathophysiological function of FOXN3.

Epigenetic Regulation of ZBTB18 Promotes Glioblastoma Progression

Glioblastoma (GBM) comprises distinct subtypes characterized by their molecular profile. Mesenchymal identity in GBM has been associated with a comparatively unfavorable prognosis, primarily due to inherent resistance of these tumors to current therapies. The identification of molecular determinants of mesenchymal transformation could potentially allow for the discovery of new therapeutic targets. Zinc Finger and BTB Domain Containing 18 (ZBTB18/ZNF238/RP58) is a zinc finger transcriptional repressor with a crucial role in brain development and neuronal differentiation. Here, ZBTB18 is primarily silenced in the mesenchymal subtype of GBM through aberrant promoter methylation. Loss of ZBTB18 contributes to the aggressive phenotype of glioblastoma through regulation of poor prognosis-associated signatures. Restitution of ZBTB18 expression reverses the phenotype and impairs tumor-forming ability. These results indicate that ZBTB18 functions as a tumor suppressor in GBM through the regulation of genes associated with phenotypically aggressive properties.Implications: This study characterizes the role of the putative tumor suppressor ZBTB18 and its regulation by promoter hypermethylation, which appears to be a common mechanism to silence ZBTB18 in the mesenchymal subtype of GBM and provides a new mechanistic opportunity to specifically target this tumor subclass. Mol Cancer Res; 15(8); 998-1011. ©2017 AACR.

Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network

We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes. We report known prostate tumor regulatory drivers and nominate novel transcription factors (ERF, CREB3L1, and POU2F2), which are supported by functional validation.

Reconstructing the regulatory circuit of cell fate determination in yeast mating response

Massive technological advances enabled high-throughput measurements of proteomic changes in biological processes. However, retrieving biological insights from large-scale protein dynamics data remains a challenging task. Here we used the mating differentiation in yeast Saccharomyces cerevisiae as a model and developed integrated experimental and computational approaches to analyze the proteomic dynamics during the process of cell fate determination. When exposed to a high dose of mating pheromone, the yeast cell undergoes growth arrest and forms a shmoo-like morphology; however, at intermediate doses, chemotropic elongated growth is initialized. To understand the gene regulatory networks that control this differentiation switch, we employed a high-throughput microfluidic imaging system that allows real-time and simultaneous measurements of cell growth and protein expression. Using kinetic modeling of protein dynamics, we classified the stimulus-dependent changes in protein abundance into two sources: global changes due to physiological alterations and gene-specific changes. A quantitative framework was proposed to decouple gene-specific regulatory modes from the growth-dependent global modulation of protein abundance. Based on the temporal patterns of gene-specific regulation, we established the network architectures underlying distinct cell fates using a reverse engineering method and uncovered the dose-dependent rewiring of gene regulatory network during mating differentiation. Furthermore, our results suggested a potential crosstalk between the pheromone response pathway and the target of rapamycin (TOR)-regulated ribosomal biogenesis pathway, which might underlie a cell differentiation switch in yeast mating response. In summary, our modeling approach addresses the distinct impacts of the global and gene-specific regulation on the control of protein dynamics and provides new insights into the mechanisms of cell fate determination. We anticipate that our integrated experimental and modeling strategies could be widely applicable to other biological systems.

A systematic characterization of the proteomic changes during the process of cell differentiation is critical for understanding the underlying molecular mechanisms. However, protein expression can be largely affected by changes in cell physiological state, which hampers the detection of regulatory interactions. Here we proposed an integrated experimental and computational framework to reconstruct regulatory circuits in mating differentiation of budding yeast Saccharomyces cerevisiae, in which distinct cell fates are triggered by alteration in pheromone concentration. A modeling approach was developed to decouple gene-specific regulation from growth-dependent global regulation of protein expression, allowing us to reverse engineering the gene regulatory circuits underlying distinct cell fates. Our work highlights the importance of model-based analysis of proteomic data and delivers new insight into dose-dependent differentiation behavior of budding yeast.

PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens

Pharmacological and functional genomic screens play an essential role in the discovery and characterization of therapeutic targets and associated pharmacological inhibitors. Although these screens affect thousands of gene products, the typical readout is based on low complexity rather than genome-wide assays. To address this limitation, we introduce pooled library amplification for transcriptome expression (PLATE-Seq), a low-cost, genome-wide mRNA profiling methodology specifically designed to complement high-throughput screening assays. Introduction of sample-specific barcodes during reverse transcription supports pooled library construction and low-depth sequencing that is 10- to 20-fold less expensive than conventional RNA-Seq. The use of network-based algorithms to infer protein activity from PLATE-Seq data results in comparable reproducibility to 30 M read sequencing. Indeed, PLATE-Seq reproducibility compares favorably to other large-scale perturbational profiling studies such as the connectivity map and library of integrated network-based cellular signatures.

Despite the importance of pharmacological and functional genomic screens the readouts are of low complexity. Here the authors introduce PLATE-Seq, a low-cost genome-wide mRNA profiling method to complement high-throughput screening.

Voltage-gated calcium channels: Novel targets for cancer therapy

Voltage-gated calcium channels (VGCCs) comprise five subtypes: The L-type; R-type; N-type; P/Q-type; and T-type, which are encoded by α₁ subunit genes. Calcium ion channels also have confirmed roles in cellular functions, including mitogenesis, proliferation, differentiation, apoptosis and metastasis. An association between VGCCs, a reduction in proliferation and an increase in apoptosis in prostate cancer cells has also been reported. Therefore, in the present study, the online clinical database Oncomine was used to identify the alterations in the mRNA expression level of VGCCs in 19 cancer subtypes. Overall, VGCC family genes exhibited under-expression in numerous types of cancer, including brain, breast, kidney and lung cancers. Notably, the majority of VGCC family members (CACNA1C, CACNA1D, CACNA1A, CACNA1B, CACNA1E, CACNA1H and CACNA1I) exhibited low expression in brain tumors, with mRNA expression levels in the top 1–9% of downregulated gene rankings. A total of 5 VGCC family members (CACNA1A, CACNA1B, CACNA1E, CACNA1G and CACNA1I) were under-expressed in breast cancer, with a gene ranking in the top 1–10% of the low-expressed genes compared with normal tissue. In kidney and lung cancers, CACNA1S, CACNA1C, CACNA1D, CACNA1A and CACNA1H exhibited low expression, with gene rankings in the top 1–8% of downregulated genes. In conclusion, the present findings may contribute to the development of new cancer treatment approaches by identifying target genes involved in specific types of cancer.

ICT1 predicts a poor survival and correlated with cell proliferation in diffuse large B-cell lymphoma

Immature colon carcinoma transcript-1 (ICT1) is a crucial member of the large mitoribosomal subunit in mitochondrial ribosome, which has been shown to be closely related to tumorigenesis. Its expression and function in human diffuse large B-cell lymphoma (DLBCL), however, remained elusive. In this study, analysis of public available Oncomine database suggested that the expression levels of ICT1 mRNA was significantly upregulated in DLBCL tissues. Consistently, we described ICT1 was remarkably upregulated in fresh DLBCL samples compared with the corresponding normal tissues using quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Western blotting. Moreover, ICT1 overexpression was associated with the poor overall survival (OS) of DLBCL patients. Finally, we used DLBCL cell lines to further probe the potential mechanisms, and found shRNA-mediated knockdown of ICT1 significantly suppressed DLBCL cell proliferation, induced cell cycle arrest at G0/G1 phase and apoptosis in vitro. Further verification showed that inhibition of ICT1 gene expression caused the upregulation of the p21, Bad and caspase-3, and downregulation of PCNA, Survivin, CDK4, CDK6 and Cyclin D1. Taken together, this study suggested that ICT1 may play an oncogenic role in human DLBCL by promoting cell proliferation and it might be a biomarker of unfavorable prognosis in DLBCL patients.

Transcriptional Network Analysis Reveals Drought Resistance Mechanisms of AP2/ERF Transgenic Rice

This study was designed to investigate at the molecular level how a transgenic version of rice "Nipponbare" obtained a drought-resistant phenotype. Using multi-omics sequencing data, we compared wild-type rice (WT) and a transgenic version (erf71) that had obtained a drought-resistant phenotype by overexpressing OsERF71, a member of the AP2/ERF transcription factor (TF) family. A comprehensive bioinformatics analysis pipeline, including TF networks and a cascade tree, was developed for the analysis of multi-omics data. The results of the analysis showed that the presence of OsERF71 at the source of the network controlled global gene expression levels in a specific manner to make erf71 survive longer than WT. Our analysis of the time-series transcriptome data suggests that erf71 diverted more energy to survival-critical mechanisms related to translation, oxidative response, and DNA replication, while further suppressing energy-consuming mechanisms, such as photosynthesis. To support this hypothesis further, we measured the net photosynthesis level under physiological conditions, which confirmed the further suppression of photosynthesis in erf71. In summary, our work presents a comprehensive snapshot of transcriptional modification in transgenic rice and shows how this induced the plants to acquire a drought-resistant phenotype.