Functional dissection of inherited non-coding variation influencing multiple myeloma risk

Thousands of non-coding variants have been associated with increased risk of human diseases, yet the causal variants and their mechanisms-of-action remain obscure. In an integrative study combining massively parallel reporter assays (MPRA), expression analyses (eQTL, meQTL, PCHiC) and chromatin accessibility analyses in primary cells (caQTL), we investigate 1,039 variants associated with multiple myeloma (MM). We demonstrate that MM susceptibility is mediated by gene-regulatory changes in plasma cells and B-cells, and identify putative causal variants at six risk loci (SMARCD3, WAC, ELL2, CDCA7L, CEP120, and PREX1). Notably, three of these variants co-localize with significant plasma cell caQTLs, signaling the presence of causal activity at these precise genomic positions in an endogenous chromosomal context in vivo. Our results provide a systematic functional dissection of risk loci for a hematologic malignancy.

Joint reconstruction of multiple gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity

MOTIVATION

Reconstruction of cancer gene networks from gene expression data is important for understanding the mechanisms underlying human cancer. Due to heterogeneity, the tumor tissue samples for a single cancer type can be divided into multiple distinct subtypes (inter-tumor heterogeneity) and are composed of non-cancerous and cancerous cells (intra-tumor heterogeneity). If tumor heterogeneity is ignored when inferring gene networks, the edges specific to individual cancer subtypes and cell types cannot be characterized. However, most existing network reconstruction methods do not simultaneously take inter-tumor and intra-tumor heterogeneity into account.

RESULTS

In this article, we propose a new Gaussian graphical model-based method for jointly estimating multiple cancer gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity. Given gene expression data of heterogeneous samples for different cancer subtypes, a non-cancerous network shared across different cancer subtypes and multiple subtype-specific cancerous networks are estimated jointly. Tumor heterogeneity can be revealed by the difference in the estimated networks. The performance of our method is first evaluated using simulated data, and the results indicate that our method outperforms other state-of-the-art methods. We also apply our method to The Cancer Genome Atlas breast cancer data to reconstruct non-cancerous and subtype-specific cancerous gene networks. Hub nodes in the networks estimated by our method perform important biological functions associated with breast cancer development and subtype classification.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/Zhangxf-ccnu/NETI2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The transcriptional coregulator NAB2 is a target gene for the Wilms' tumor gene 1 protein (WT1) in leukemic cells

The Wilms’ tumor gene 1 (WT1) is recurrently mutated in acute myeloid leukemia. Mutations and high expression of WT1 associate with a poor prognosis. In mice, WT1 cooperates with the RUNX1/RUNX1T1 (AML1/ETO) fusion gene in the induction of acute leukemia, further emphasizing a role for WT1 in leukemia development. Molecular mechanisms for WT1 are, however, incompletely understood. Here, we identify the transcriptional coregulator NAB2 as a target gene of WT1. Analysis of gene expression profiles of leukemic samples revealed a positive correlation between the expression of WT1 and NAB2, as well as a non-zero partial correlation. Overexpression of WT1 in hematopoietic cells resulted in increased NAB2 levels, while suppression of WT1 decreased NAB2 expression. WT1 bound and transactivated the proximal NAB2 promoter, as shown by ChIP and reporter experiments, respectively. ChIP experiments also revealed that WT1 can recruit NAB2 to the IRF8 promoter, thus modulating the transcriptional activity of WT1, as shown by reporter experiments. Our results implicate NAB2 as a previously unreported target gene of WT1 and that NAB2 acts as a transcriptional cofactor of WT1.

Databases and tools for constructing signal transduction networks in cancer

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., “big data”), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called “systems biology”. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.

Anti-apoptotic quinolinate phosphoribosyltransferase (QPRT) is a target gene of Wilms' tumor gene 1 (WT1) protein in leukemic cells

Wilms' tumor gene 1 (WT1) is a zinc finger transcription factor that has been implicated as an oncogene in leukemia and several other malignancies. When investigating possible gene expression network partners of WT1 in a large acute myeloid leukemia (AML) patient cohort, one of the genes with the highest correlation to WT1 was quinolinate phosphoribosyltransferase (QPRT), a key enzyme in the de novo nicotinamide adenine dinucleotide (NAD+) synthesis pathway. To investigate the possible relationship between WT1 and QPRT, we overexpressed WT1 in hematopoietic progenitor cells and cell lines, resulting in an increase of QPRT expression. WT1 knock-down gave a corresponding decrease in QPRT gene and protein expression. Chromatin-immunoprecipitation revealed WT1 binding to a conserved site in the first intron of the QPRT gene. Upon overexpression in leukemic K562 cells, QPRT conferred partial resistance to the anti-leukemic drug imatinib, indicating possible anti-apoptotic functions, consistent with previous reports on glioma cells. Interestingly, the rescue effect of QPRT overexpression was not correlated to increased NAD + levels, suggesting NAD + independent mechanisms. We conclude that QPRT, encoding a protein with anti-apoptotic properties, is a novel and direct target gene of WT1 in leukemic cells.

Homozygosity for a null allele of SMIM1 defines the Vel-negative blood group phenotype

The Vel antigen is present on red blood cells (RBCs) from all humans except rare Vel-negative individuals who can form antibodies to Vel in response to transfusion or pregnancy. These antibodies may cause severe hemolytic reactions in blood recipients. We combined SNP profiling and transcriptional network modeling to link the Vel-negative phenotype to SMIM1, located in a 97-kb haplotype block on chromosome 1p36. This gene encodes a previously undiscovered, evolutionarily conserved transmembrane protein expressed on RBCs. Notably, 35 of 35 Vel-negative individuals were homozygous for a frameshift deletion of 17 bp in exon 3. Functional studies using antibodies raised against SMIM1 peptides confirmed a null phenotype in RBC membranes, and SMIM1 overexpression induced Vel expression. Genotype screening estimated that ~1 of 17 Swedish blood donors is a heterozygous deletion carrier and ~1 of 1,200 is a homozygous deletion knockout and enabled identification of Vel-negative donors. Our results establish SMIM1 as a new erythroid gene and Vel as a new blood group system.