Noncoding mutations cause super-enhancer retargeting resulting in protein synthesis dysregulation during B cell lymphoma progression.

Whole-genome sequencing of longitudinal tumor pairs representing transformation of follicular lymphoma to high-grade B cell lymphoma with MYC and BCL2 rearrangements (double-hit lymphoma) identified coding and noncoding genomic alterations acquired during lymphoma progression. Many of these transformation-associated alterations recurrently and focally occur at topologically associating domain resident regulatory DNA elements, including H3K4me3 promoter marks located within H3K27ac super-enhancer clusters in B cell non-Hodgkin lymphoma. One region found to undergo recurrent alteration upon transformation overlaps a super-enhancer affecting the expression of the PAX5/ZCCHC7 gene pair. ZCCHC7 encodes a subunit of the Trf4/5-Air1/2-Mtr4 polyadenylation-like complex and demonstrated copy number gain, chromosomal translocation and enhancer retargeting-mediated transcriptional upregulation upon lymphoma transformation. Consequently, lymphoma cells demonstrate nucleolar dysregulation via altered noncoding 5.8S ribosomal RNA processing. We find that a noncoding mutation acquired during lymphoma progression affects noncoding rRNA processing, thereby rewiring protein synthesis leading to oncogenic changes in the lymphoma proteome.

Specific regulation of BACH1 by the hotspot mutant p53R175H reveals a distinct gain-of-function mechanism.

Although the gain of function (GOF) of p53 mutants is well recognized, it remains unclear whether different p53 mutants share the same cofactors to induce GOFs. In a proteomic screen, we identified BACH1 as a cellular factor that recognizes the p53 DNA-binding domain depending on its mutation status. BACH1 strongly interacts with p53R175H but fails to effectively bind wild-type p53 or other hotspot mutants in vivo for functional regulation. Notably, p53R175H acts as a repressor for ferroptosis by abrogating BACH1-mediated downregulation of SLC7A11 to enhance tumor growth; conversely, p53R175H promotes BACH1-dependent tumor metastasis by upregulating expression of pro-metastatic targets. Mechanistically, p53R175H-mediated bidirectional regulation of BACH1 function is dependent on its ability to recruit the histone demethylase LSD2 to target promoters and differentially modulate transcription. These data demonstrate that BACH1 acts as a unique partner for p53R175H in executing its specific GOFs and suggest that different p53 mutants induce their GOFs through distinct mechanisms.

Mechanism of noncoding RNA-associated N6-methyladenosine recognition by an RNA processing complex during IgH DNA recombination.

Immunoglobulin heavy chain (IgH) locus-associated G-rich long noncoding RNA (SµGLT) is important for physiological and pathological B cell DNA recombination. We demonstrate that the METTL3 enzyme-catalyzed N6-methyladenosine (m6A) RNA modification drives recognition and 3' end processing of SµGLT by the RNA exosome, promoting class switch recombination (CSR) and suppressing chromosomal translocations. The recognition is driven by interaction of the MPP6 adaptor protein with nuclear m6A reader YTHDC1. MPP6 and YTHDC1 promote CSR by recruiting AID and the RNA exosome to actively transcribe SµGLT. Direct suppression of m6A modification of SµGLT or of m6A reader YTHDC1 reduces CSR. Moreover, METTL3, an essential gene for B cell development in the bone marrow and germinal center, suppresses IgH-associated aberrant DNA breaks and prevents genomic instability. Taken together, we propose coordinated and central roles for MPP6, m6A modification, and m6A reader proteins in controlling long noncoding RNA processing, DNA recombination, and development in B cells.

Proteasomal Regulation of Mammalian SPT16 in Controlling Transcription.

FACT (facilitates chromatin transcription), an essential and evolutionarily conserved heterodimer from yeast to humans, controls transcription and is found to be upregulated in various cancers. However, the basis for such upregulation is not clearly understood. Our recent results deciphering a new ubiquitin-proteasome system regulation of the FACT subunit SPT16 in orchestrating transcription in yeast hint at the involvement of the proteasome in controlling FACT in humans, with a link to cancer. To test this, we carried out experiments in human embryonic kidney (HEK293) cells, which revealed that human SPT16 undergoes ubiquitylation and that its abundance is increased following inhibition of the proteolytic activity of the proteasome, thus implying proteasomal regulation of human SPT16. Furthermore, we find that the increased abundance/expression of SPT16 in HEK293 cells alters the transcription of genes, including ones associated with cancer, and that the proteasomal degradation of SPT16 is impaired in kidney cancer (Caki-2) cells to upregulate SPT16. Like human SPT16, murine SPT16 in C2C12 cells also undergoes ubiquitylation and proteasomal degradation to regulate transcription. Collectively, our results reveal a proteasomal regulation of mammalian SPT16, with physiological relevance in controlling transcription, and implicate such proteasomal control in the upregulation of SPT16 in cancer.

Lineage specification of human dendritic cells is marked by IRF8 expression in hematopoietic stem cells and multipotent progenitors.

The origin and specification of human dendritic cells (DCs) have not been investigated at the clonal level. Through the use of clonal assays, combined with statistical computation, to quantify the yield of granulocytes, monocytes, lymphocytes and three subsets of DCs from single human CD34+ progenitor cells, we found that specification to the DC lineage occurred in parallel with specification of hematopoietic stem cells (HSCs) to the myeloid and lymphoid lineages. This started as a lineage bias defined by specific transcriptional programs that correlated with the combinatorial 'dose' of the transcription factors IRF8 and PU.1, which was transmitted to most progeny cells and was reinforced by upregulation of IRF8 expression driven by the hematopoietic cytokine FLT3L during cell division. We propose a model in which specification to the DC lineage is driven by parallel and inheritable transcriptional programs in HSCs and is reinforced over cell division by recursive interactions between transcriptional programs and extrinsic signals.

Nuclear Proximity of Mtr4 to RNA Exosome Restricts DNA Mutational Asymmetry.

The distribution of sense and antisense strand DNA mutations on transcribed duplex DNA contributes to the development of immune and neural systems along with the progression of cancer. Because developmentally matured B cells undergo biologically programmed strand-specific DNA mutagenesis at focal DNA/RNA hybrid structures, they make a convenient system to investigate strand-specific mutagenesis mechanisms. We demonstrate that the sense and antisense strand DNA mutagenesis at the immunoglobulin heavy chain locus and some other regions of the B cell genome depends upon localized RNA processing protein complex formation in the nucleus. Both the physical proximity and coupled activities of RNA helicase Mtr4 (and senataxin) with the noncoding RNA processing function of RNA exosome determine the strand-specific distribution of DNA mutations. Our study suggests that strand-specific DNA mutagenesis-associated mechanisms will play major roles in other undiscovered aspects of organismic development.

Diagnosing phenotypes of single-sample individuals by edge biomarkers.

Network or edge biomarkers are a reliable form to characterize phenotypes or diseases. However, obtaining edges or correlations between molecules for an individual requires measurement of multiple samples of that individual, which are generally unavailable in clinical practice. Thus, it is strongly demanded to diagnose a disease by edge or network biomarkers in one-sample-for-one-individual context. Here, we developed a new computational framework, EdgeBiomarker, to integrate edge and node biomarkers to diagnose phenotype of each single test sample. By applying the method to datasets of lung and breast cancer, it reveals new marker genes/gene-pairs and related sub-networks for distinguishing earlier and advanced cancer stages. Our method shows advantages over traditional methods: (i) edge biomarkers extracted from non-differentially expressed genes achieve better cross-validation accuracy of diagnosis than molecule or node biomarkers from differentially expressed genes, suggesting that certain pathogenic information is only present at the level of network and under-estimated by traditional methods; (ii) edge biomarkers categorize patients into low/high survival rate in a more reliable manner; (iii) edge biomarkers are significantly enriched in relevant biological functions or pathways, implying that the association changes in a network, rather than expression changes in individual molecules, tend to be causally related to cancer development. The new framework of edge biomarkers paves the way for diagnosing diseases and analyzing their molecular mechanisms by edges or networks in one-sample-for-one-individual basis. This also provides a powerful tool for precision medicine or big-data medicine.

MCentridFS: a tool for identifying module biomarkers for multi-phenotypes from high-throughput data.

Systematically identifying biomarkers, in particular, network biomarkers, from high-throughput data is an important and challenging task, and many methods for two-class comparison have been developed to exploit information of high-throughput data. However, as the high-throughput data with multi-phenotypes are available, there is a great need to develop effective multi-classification models. In this study, we proposed a novel approach, called MCentridFS (Multi-class Centroid Feature Selection), to systematically identify responsive modules or network biomarkers for classifying multi-phenotypes from high-throughput data. MCentridFS formulated the multi-classification model by network modules as a binary integer linear programming problem, which can be solved efficiently and effectively in an accurate manner. The approach is evaluated with respect to two diseases, i.e., multi-stages HCV-induced dysplasia and hepatocellular carcinoma and multi-tissues breast cancer, both of which demonstrated the high classification rate and the cross-validation rate of the approach. The computational results of the five-fold cross-validation of the two data show that MCentridFS outperforms the state-of-the-art multi-classification methods. We further verified the effectiveness of MCentridFS to characterize the multi-phenotype processes using module biomarkers by two independent datasets. In addition, functional enrichment analysis revealed that the identified network modules are strongly related to the corresponding biological processes and pathways. All these results suggest that it can serve as a useful tool for module biomarker detection in multiple biological processes or multi-classification problems by exploring both big biological data and network information. The Matlab code for MCentridFS is freely available from http://www.sysbio.ac.cn/cb/chenlab/images/MCentridFS.rar.

EdgeMarker: Identifying differentially correlated molecule pairs as edge-biomarkers.

Biomarker discovery is one of the major topics in translational biomedicine study based on high-throughput biological data analysis. Traditional methods focus on differentially expressed genes (or node-biomarkers) but ignore non-differentials. However, non-differentially expressed genes also play important roles in the biological processes and the rewired interactions / edges among non-differential genes may reveal fundamental difference between variable conditions. Therefore, it is necessary to identify relevant interactions or gene pairs to elucidate the molecular mechanism of complex biological phenomena, e.g. distinguish different phenotypes. To address this issue, we proposed a new method based on a new vector representation of an edge, EdgeMarker, to (1) identify edge-biomarkers, i.e. the differentially correlated molecular pairs (e.g., gene pairs) with optimal classification ability, and (2) transform the 'node expression' data in node space into the 'edge expression' data in edge space and classify the phenotype of each single sample in edge space, which generally cannot be achieved in traditional methods. Unlike the traditional methods which analyze the node space (i.e. molecular expression space) or higher dimensional space using arbitrary kernel methods, this study provides a mathematical model to explore the edge space (i.e. correlation space) for classification of a single sample. In this work, we show that the identified edge-biomarkers indeed have strong ability in distinguishing normal and disease samples even when all involved genes are not significantly differentially expressed. The analysis of human cholangiocarcinoma dataset and diabetes dataset also suggested that the identified edge-biomarkers may cast new biological insights into the pathogenesis of human complex diseases.