Pavel Sumazin

An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma.

By analyzing gene expression data in glioblastoma in combination with matched microRNA profiles, we have uncovered a posttranscriptional regulation layer of surprising magnitude, comprising more than 248,000 microRNA (miR)-mediated interactions. These include ∼7,000 genes whose transcripts act as miR sponges and 148 genes that act through alternative, nonsponge interactions. Biochemical analyses in cell lines confirmed that this network regulates established drivers of tumor initiation and subtype implementation, including PTEN, PDGFRA, RB1, VEGFA, STAT3, and RUNX1, suggesting that these interactions mediate crosstalk between canonical oncogenic pathways. siRNA silencing of 13 miR-mediated PTEN regulators, whose locus deletions are predictive of PTEN expression variability, was sufficient to downregulate PTEN in a 3UTR-dependent manner and to increase tumor cell growth rates. Thus, miR-mediated interactions provide a mechanistic, experimentally validated rationale for the loss of PTEN expression in a large number of glioma samples with an intact PTEN locus.

A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers

Assembly of a transcriptional and post‐translational molecular interaction network in B cells, the human B‐cell interactome (HBCI), reveals a hierarchical, transcriptional control module, where MYB and FOXM1 act as synergistic master regulators of proliferation in the germinal center (GC). Eighty percent of genes jointly regulated by these transcription factors are activated in the GC, including those encoding proteins in a complex regulating DNA pre‐replication, replication, and mitosis. These results indicate that the HBCI analysis can be used for the identification of determinants of major human cell phenotypes and provides a paradigm of general applicability to normal and pathologic tissues.

Identification of the Human Mature B Cell miRNome

The full set of microRNAs (miRNAs) in the human genome is not known. Because presently known miRNAs have been identified by virtue of their abundant expression in a few cell types, many tissue-specific miRNAs remain unrevealed. To understand the role of miRNAs in B cell function and lymphomagenesis, we generated short-RNA libraries from normal human B cells at different stages of development (naive, germinal center, memory) and from a Burkitt lymphoma cell line. A combination of cloning and computational analysis identified 178 miRNAs (miRNome) expressed in normal and/or transformed B cell libraries. Most notably, the B cell miRNome included 75 miRNAs which to our knowledge have not been previously reported and of which 66 have been validated by RNA blot and/or RT-PCR analyses. Numerous miRNAs were expressed in a stage- or transformation-specific fashion in B cells, suggesting specific functional or pathologic roles. These results provide a resource for studying the role of miRNAs in B cell development, immune function, and lymphomagenesis.

BCL6 suppression of BCL2 via Miz1 and its disruption in diffuse large B cell lymphoma

The BCL6 proto-oncogene encodes a transcriptional repressor that is required for germinal center (GC) formation and whose deregulation by genomic lesions is implicated in the pathogenesis of GC-derived diffuse large B cell lymphoma (DLBCL) and, less frequently, follicular lymphoma (FL). The biological function of BCL6 is only partially understood because no more than a few genes have been functionally characterized as direct targets of BCL6 transrepression activity. Here we report that the anti-apoptotic proto-oncogene BCL2 is a direct target of BCL6 in GC B cells. BCL6 binds to the BCL2 promoter region by interacting with the transcriptional activator Miz1 and suppresses Miz1-induced activation of BCL2 expression. BCL6-mediated suppression of BCL2 is lost in FL and DLBCL, where the 2 proteins are pathologically coexpressed, because of BCL2 chromosomal translocations and other mechanisms, including Miz1 deregulation and somatic mutations in the BCL2 promoter region. These results identify an important function for BCL6 in facilitating apoptosis of GC B cells via suppression of BCL2, and suggest that blocking this pathway is critical for lymphomagenesis.

A Molecular Signature Predictive of Indolent Prostate Cancer

A three-gene panel derived from mechanistic models of cell senescence predicts outcome of low Gleason score prostate tumors. To Treat or Not to Treat...* ...That is often the question for prostate cancer patients and their caretakers. Now, Irshad et al. describe a gene signature that may guide treatment choices when prognosis is unclear. Along with other clinical and molecular parameters, pathologists use the Gleason grading system to stage prostate cancers and predict patient prognosis. A Gleason score is assigned to a cancer on the basis of its microscopic features and is directly related to tumor aggressiveness and poor prognosis. Most newly diagnosed prostate cancers with low Gleason scores require no treatment intervention and are monitored with active surveillance (indolent tumors). However, the pinpointing of tumors that are aggressive and lethal despite having low Gleason scores is a clinical challenge. In these cases, new tools are needed to answer the title question. Irshad and colleagues show that low Gleason score prostate tumors can be separated into distinct indolent and aggressive subgroups on the basis of their expression of aging and senescence genes. Using patient tissue samples and gene expression data along with computational biology techniques, including a decision tree learning model, the authors identified three genes—FGFR1, PMP22, and CDKN1A—that predicted the clinical outcome of low Gleason score prostate tumors. The prognostic power of the three-gene signature was validated in independent patient cohorts, and expression of the FGFR1, PMP22, and CDKN1A proteins in biopsy samples identified Gleason 6 patients who had failed surveillance over a 10-year period. Just as Hamlet laments in his famous soliloquy, oncologists and patients need more information about the unknown before making a decision. The new signature might aid in the choice between “bear[ing] those ills [they] have” with active surveillance or actively treating—and hopefully thwarting—aggressive tumors. *Paraphrased from the “To be, or not to be” soliloquy in Hamlet by William Shakespeare. Many newly diagnosed prostate cancers present as low Gleason score tumors that require no treatment intervention. Distinguishing the many indolent tumors from the minority of lethal ones remains a major clinical challenge. We now show that low Gleason score prostate tumors can be distinguished as indolent and aggressive subgroups on the basis of their expression of genes associated with aging and senescence. Using gene set enrichment analysis, we identified a 19-gene signature enriched in indolent prostate tumors. We then further classified this signature with a decision tree learning model to identify three genes—FGFR1, PMP22, and CDKN1A—that together accurately predicted outcome of low Gleason score tumors. Validation of this three-gene panel on independent cohorts confirmed its independent prognostic value as well as its ability to improve prognosis with currently used clinical nomograms. Furthermore, protein expression of this three-gene panel in biopsy samples distinguished Gleason 6 patients who failed surveillance over a 10-year period. We propose that this signature may be incorporated into prognostic assays for monitoring patients on active surveillance to facilitate appropriate courses of treatment.

ChIP-on-chip significance analysis reveals large-scale binding and regulation by human transcription factor oncogenes.

ChIP-on-chip has emerged as a powerful tool to dissect the complex network of regulatory interactions between transcription factors and their targets. However, most ChIP-on-chip analysis methods use conservative approaches aimed at minimizing false-positive transcription factor targets. We present a model with improved sensitivity in detecting binding events from ChIP-on-chip data. Its application to human T cells, followed by extensive biochemical validation, reveals that 3 oncogenic transcription factors, NOTCH1, MYC, and HES1, bind to several thousand target gene promoters, up to an order of magnitude increase over conventional analysis methods. Gene expression profiling upon NOTCH1 inhibition shows broad-scale functional regulation across the entire range of predicted target genes, establishing a closer link between occupancy and regulation. Finally, the increased sensitivity reveals a combinatorial regulatory program in which MYC cobinds to virtually all NOTCH1-bound promoters. Overall, these results suggest an unappreciated complexity of transcriptional regulatory networks and highlight the fundamental importance of genome-scale analysis to represent transcriptional programs.

The miR-424(322)/503 cluster orchestrates remodeling of the epithelium in the involuting mammary gland

The mammary gland is a very dynamic organ that undergoes continuous remodeling. The critical regulators of this process are not fully understood. Here we identify the microRNA cluster miR-424(322)/503 as an important regulator of epithelial involution after pregnancy. Through the generation of a knockout mouse model, we found that regression of the secretory acini of the mammary gland was compromised in the absence of miR-424(322)/503. Mechanistically, we show that miR-424(322)/503 orchestrates cell life and death decisions by targeting BCL-2 and IGF1R (insulin growth factor-1 receptor). Furthermore, we demonstrate that the expression of this microRNA cluster is regulated by TGF-β, a well-characterized regulator of mammary involution. Overall, our data suggest a model in which activation of the TGF-β pathway after weaning induces the transcription of miR-424(322)/503, which in turn down-regulates the expression of key genes. Here, we unveil a previously unknown, multilayered regulation of epithelial tissue remodeling coordinated by the microRNA cluster miR-424(322)/503.

Interrogation of a Context-Specific Transcription Factor Network Identifies Novel Regulators of Pluripotency

The predominant view of pluripotency regulation proposes a stable ground state with coordinated expression of key transcription factors (TFs) that prohibit differentiation. Another perspective suggests a more complexly regulated state involving competition between multiple lineage‐specifying TFs that define pluripotency. These contrasting views were developed from extensive analyses of TFs in pluripotent cells in vitro. An experimentally validated, genome‐wide repertoire of the regulatory interactions that control pluripotency within the in vivo cellular contexts is yet to be developed. To address this limitation, we assembled a TF interactome of adult human male germ cell tumors (GCTs) using the Algorithm for the Accurate Reconstruction of Cellular Pathways (ARACNe) to analyze gene expression profiles of 141 tumors comprising pluripotent and differentiated subsets. The network (GCTNet) comprised 1,305 TFs, and its ingenuity pathway analysis identified pluripotency and embryonal development as the top functional pathways. We experimentally validated GCTNet by functional (silencing) and biochemical (ChIP‐seq) analysis of the core pluripotency regulatory TFs POU5F1, NANOG, and SOX2 in relation to their targets predicted by ARACNe. To define the extent of the in vivo pluripotency network in this system, we ranked all TFs in the GCTNet according to sharing of ARACNe‐predicted targets with those of POU5F1 and NANOG using an odds‐ratio analysis method. To validate this network, we silenced the top 10 TFs in the network in H9 embryonic stem cells. Silencing of each led to downregulation of pluripotency and induction of lineage; 7 of the 10 TFs were identified as pluripotency regulators for the first time. Stem Cells 2015;33:367–377

A Systems Biology Approach to Transcription Factor Binding Site Prediction

Background The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs), identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates. Methodology/Principal Findings We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs) and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data. Conclusions/Significance Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy of our framework on a human B-cell specific test set, which may help guide future methodological development.

Correlating measurements across samples improves accuracy of large-scale expression profile experiments

Gene expression profiling technologies suffer from poor reproducibility across replicate experiments. However, when analyzing large datasets, probe-level expression profile correlation can help identify flawed probes and lead to the construction of truer probe sets with improved reproducibility. We describe methods to eliminate uninformative and flawed probes, account for dependence between probes, and address variability due to transcript-isoform mixtures. We test and validate our approach on Affymetrix microarrays and outline their future adaptation to other technologies.