Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome

Coordinated Cross-Talk Between the Myc and Mlx Networks in Liver Regeneration and Neoplasia

BACKGROUND & AIMS

The c-Myc (Myc) Basic helix-loop-helix leucine zipper (bHLH-ZIP) transcription factor is deregulated in most cancers. In association with Max, Myc controls target genes that supervise metabolism, ribosome biogenesis, translation, and proliferation. This Myc network crosstalks with the Mlx network, which consists of the Myc-like proteins MondoA and ChREBP, and Max-like Mlx. Together, this extended Myc network regulates both common and distinct gene targets. Here, we studied the consequence of Myc and/or Mlx ablation in the liver, particularly those pertaining to hepatocyte proliferation, metabolism, and spontaneous tumorigenesis.

METHODS

We examined the ability of hepatocytes lacking Mlx (MlxKO) or Myc+Mlx (double KO [DKO]) to repopulate the liver over an extended period of time in a murine model of type I tyrosinemia. We also compared this and other relevant behaviors, phenotypes, and transcriptomes of the livers with those from previously characterized MycKO, ChrebpKO, and MycKO × ChrebpKO mice.

RESULTS

Hepatocyte regenerative potential deteriorated as the Extended Myc Network was progressively dismantled. Genes and pathways dysregulated in MlxKO and DKO hepatocytes included those pertaining to translation, mitochondrial function, and hepatic steatosis resembling nonalcoholic fatty liver disease. The Myc and Mlx Networks were shown to crosstalk, with the latter playing a disproportionate role in target gene regulation. All cohorts also developed steatosis and molecular evidence of early steatohepatitis. Finally, MlxKO and DKO mice showed extensive hepatic adenomatosis.

CONCLUSIONS

In addition to showing cooperation between the Myc and Mlx Networks, this study showed the latter to be more important in maintaining proliferative, metabolic, and translational homeostasis, while concurrently serving as a suppressor of benign tumorigenesis. GEO accession numbers: GSE181371, GSE130178, and GSE114634.

Mathematical Modeling of Avidity Distribution and Estimating General Binding Properties of Transcription Factors from Genome-Wide Binding Profiles

The shape of the experimental frequency distributions (EFD) of diverse molecular interaction events quantifying genome-wide binding is often skewed to the rare but abundant quantities. Such distributions are systematically deviated from standard power-law functions proposed by scale-free network models suggesting that more explanatory and predictive probabilistic model(s) are needed. Identification of the mechanism-based data-driven statistical distributions that provide an estimation and prediction of binding properties of transcription factors from genome-wide binding profiles is the goal of this analytical survey. Here, we review and develop an analytical framework for modeling, analysis, and prediction of transcription factor (TF) DNA binding properties detected at the genome scale. We introduce a mixture probabilistic model of binding avidity function that includes nonspecific and specific binding events. A method for decomposition of specific and nonspecific TF-DNA binding events is proposed. We show that the Kolmogorov-Waring (KW) probability function (PF), modeling the steady state TF binding-dissociation stochastic process, fits well with the EFD for diverse TF-DNA binding datasets. Furthermore, this distribution predicts total number of TF-DNA binding sites (BSs), estimating specificity and sensitivity as well as other basic statistical features of DNA-TF binding when the experimental datasets are noise-rich and essentially incomplete. The KW distribution fits equally well to TF-DNA binding activity for different TFs including ERE, CREB, STAT1, Nanog, and Oct4. Our analysis reveals that the KW distribution and its generalized form provides the family of power-law-like distributions given in terms of hypergeometric series functions, including standard and generalized Pareto and Waring distributions, providing flexible and common skewed forms of the transcription factor binding site (TFBS) avidity distribution function. We suggest that the skewed binding events may be due to a wide range of evolutionary processes of creating weak avidity TFBS associated with random mutations, while the rare high-avidity binding sites (i.e., high-avidity evolutionarily conserved canonical e-boxes) rarely occurred. These, however, may be positively selected in microevolution.

Multiple signatures of a disease in potential biomarker space: Getting the signatures consensus and identification of novel biomarkers

Background

The lack of consensus among reported gene signature subsets (GSSs) in multi-gene biomarker discovery studies is often a concern for researchers and clinicians. Subsequently, it discourages larger scale prospective studies, prevents the translation of such knowledge into a practical clinical setting and ultimately hinders the progress of the field of biomarker-based disease classification, prognosis and prediction.

Methods

We define all "gene identificators" (gIDs) as constituents of the entire potential disease biomarker space. For each gID in a GSS of interest ("tested GSS"/tGSS), our method counts the empirical frequency of gID co-occurrences/overlaps in other reference GSSs (rGSSs) and compares it with the expected frequency generated via implementation of a randomized sampling procedure. Comparison of the empirical frequency distribution (EFD) with the expected background frequency distribution (BFD) allows dichotomization of statistically novel (SN) and common (SC) gIDs within the tGSS.

Results

We identify SN or SC biomarkers for tGSSs obtained from previous studies of high-grade serous ovarian cancer (HG-SOC) and breast cancer (BC). For each tGSS, the EFD of gID co-occurrences/overlaps with other rGSSs is characterized by scale and context-dependent Pareto-like frequency distribution function. Our results indicate that while independently there is little overlap between our tGSS with individual rGSSs, comparison of the EFD with BFD suggests that beyond a confidence threshold, tested gIDs become more common in rGSSs than expected. This validates the use of our tGSS as individual or combined prognostic factors. Our method identifies SN and SC genes of a 36-gene prognostic signature that stratify HG-SOC patients into subgroups with low, intermediate or high-risk of the disease outcome. Using 70 BC rGSSs, the method also predicted SN and SC BC prognostic genes from the tested obesity and IGF1 pathway GSSs.

Conclusions

Our method provides a strategy that identify/predict within a tGSS of interest, gID subsets that are either SN or SC when compared to other rGSSs. Practically, our results suggest that there is a stronger association of the IGF1 signature genes with the 70 BC rGSSs, than for the obesity-associated signature. Furthermore, both SC and SN genes, in both signatures could be considered as perspective prognostic biomarkers of BCs that stratify the patients onto low or high risks of cancer development.

Role of IL-9 and STATs in hematological malignancies (Review)

Although interleukin-9 (IL-9) exhibits pleiotropic functions in the immune system, it remains a well-known cytokine in hematological malignancies. Previous cell culture and animal model studies have revealed that the Janus kinase-signal transducer and activator of transcription signaling pathway, which may be activated by a number of cytokines including IL-9, is critical in hematological malignancies. The current review summarizes the characterization of the biological activities of IL-9, highlights the clearly defined roles of the cytokine, and outlines questions with regard to the functions of IL-9 that require further exploration and their downstream signaling proteins, signal transducers and activators of transcription.

NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data

Background

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies.

Results

We propose a rigorous statistical model, the normal-exponential two-peak (NEXT-peak) model, which parallels the physical processes generating the empirical data, and which can naturally incorporate mappability information. The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location. The comparison study with existing programs on real ChIP-seq datasets (STAT1, NRSF, and ZNF143) demonstrates that the NEXT-peak model performs well both in calling peaks and locating them. The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region.

Conclusions

The NEXT-peak program calls peaks on any test dataset about as accurately as any other, but provides unusual accuracy in the estimated location of the peaks it calls. NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data.

Genome-wide identification of Polycomb target genes in human embryonic stem cells

Polycomb group (PcG) proteins are epigenetic regulators that are essential for stem cell differentiation. Identifying PcG binding profiles is important for understanding the mechanisms of PcG-mediated repression in mammals. We used a mapping-convergence (M-C) algorithm using support vector machine (SVM) technology for genome-wide identification of PcG target genes in human embryonic stem cells. The method combined histone modifications and transcription factor binding motifs, eliminating the need for negative training samples as in traditional SVM. Good prediction accuracy comprising 3-fold cross-validation was obtained. In the analysis of 3133 PcG target genes identified by the model, PcG proteins were observed to suppress gene expression during differentiation. The results suggested that PcG and DNA methylation non-redundantly repress gene expression during differentiation. The genome-wide identification of PcG target genes will aid the further analysis of PcG mechanisms.

Promoter hypermethylation-induced transcriptional down-regulation of the gene MYCT1 in laryngeal squamous cell carcinoma

Background

MYCT1, previously named MTLC, is a novel candidate tumor suppressor gene. MYCT1 was cloned from laryngeal squamous cell cancer (LSCC) and has been found to be down-regulated in LSCC; however, the regulatory details have not been fully elucidated.

Methods

Here, we sought to investigate the methylation status of the CpG islands of MYCT1 and mRNA levels by bisulfite-specific PCR (BSP) based on sequencing restriction enzyme digestion, reverse transcription and real-time quantitative polymerase chain reaction (RQ-PCR). The function of specific sites in the proximal promoter of MYCT1 in LSCC was measured by transient transfection, luciferase assays, electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation assay (ChIP).

Results

The results suggested hypermethylation of 12 CpG sites of the promoter in both laryngeal cancer tissues and the laryngeal cancer line Hep-2 cell. The hypermethylation of the site CGCG (−695 to −692), which has been identified as the c-Myc binding site, was identified in laryngeal cancer tissues (59/73) compared to paired mucosa (13/73); in addition, statistical analysis revealed that the methylation status of this site significantly correlated with cancer cell differentiation(p < 0.01). The mRNA level of MYCT1 increased in Hep-2 cells treated with 5-aza-C (p < 0.01). The luciferase activity from mutant transfectants pGL3-MYCT1m (−852/+12, mut-695-C > A, mut-693-C > G) was significantly reduced compared with the wild type pGL3-MYCT1 (−852/+12), while the luciferase activity from wild transfectants pGL3-MYCT1 (−852/+12) rose after 5-aza treatment in Hep-2 cells. Finally, EMSA and ChIP confirmed that the methylation of the CGCG (−695 to −692) site prevented c-Myc from binding of the site and demethylation treatment of the 5′ flanking region of MYCT1 by 5-aza induced the increased occupation of the core promoter by c-Myc (p < 0.01).

Conclusion

In summary, this study concluded that hypermethylation contributed to the transcriptional down-regulation of MYCT1 and could inhibit cancer cell differentiation in LSCC. DNA methylation of the CGCG site (−695 to −692) of MYCT1 altered the promoter activity by interfering with its binding to c-Myc in LSCC. Epigenetic therapy of reactivating MYCT1 by 5-aza should be further evaluated in clinical trails of LSCC.

Quantitative model of R-loop forming structures reveals a novel level of RNA-DNA interactome complexity

R-loop is the structure co-transcriptionally formed between nascent RNA transcript and DNA template, leaving the non-transcribed DNA strand unpaired. This structure can be involved in the hyper-mutation and dsDNA breaks in mammalian immunoglobulin (Ig) genes, oncogenes and neurodegenerative disease related genes. R-loops have not been studied at the genome scale yet. To identify the R-loops, we developed a computational algorithm and mapped R-loop forming sequences (RLFS) onto 66,803 sequences defined by UCSC as 'known' genes. We found that ∼59% of these transcribed sequences contain at least one RLFS. We created R-loopDB (http://rloop.bii.a-star.edu.sg/), the database that collects all RLFS identified within over half of the human genes and links to the UCSC Genome Browser for information integration and visualisation across a variety of bioinformatics sources. We found that many oncogenes and tumour suppressors (e.g. Tp53, BRCA1, BRCA2, Kras and Ptprd) and neurodegenerative diseases related genes (e.g. ATM, Park2, Ptprd and GLDC) could be prone to significant R-loop formation. Our findings suggest that R-loops provide a novel level of RNA-DNA interactome complexity, playing key roles in gene expression controls, mutagenesis, recombination process, chromosomal rearrangement, alternative splicing, DNA-editing and epigenetic modifications. RLFSs could be used as a novel source of prospective therapeutic targets.

MYCT1-TV, a novel MYCT1 transcript, is regulated by c-Myc and may participate in laryngeal carcinogenesis

BACKGROUND

MYCT1, a putative target of c-Myc, is a novel candidate tumor suppressor gene cloned from laryngeal squamous cell carcinoma (LSCC). Its transcriptional regulation and biological effects on LSCC have not been clarified.

METHODOLOGY/PRINCIPAL FINDINGS

Using RACE assay, we cloned a 1106 bp transcript named Myc target 1 transcript variant 1 (MYCT1-TV) and confirmed its transcriptional start site was located at 140 bp upstream of the ATG start codon of MYCT1-TV. Luciferase, electrophoretic mobility shift and chromatin immunoprecipitation assays confirmed c-Myc could regulate the promoter activity of MYCT1-TV by specifically binding to the E-box elements within -886 to -655 bp region. These results were further verified by site-directed mutagenesis and RNA interference (RNAi) assays. MYCT1-TV and MYCT1 expressed lower in LSCC than those in paired adjacent normal laryngeal tissues, and overexpression of MYCT1-TV and MYCT1 could inhibit cell proliferation and invasion and promote apoptosis in LSCC cells.

CONCLUSIONS/SIGNIFICANCE

Our data indicate that MYCT1-TV, a novel MYCT1 transcript, is regulated by c-Myc and down-regulation of MYCT1-TV/MYCT1 could contribute to LSCC development and function.