AID-targeting and hypermutation of non-immunoglobulin genes does not correlate with proximity to immunoglobulin genes in germinal center B cells

Upon activation, B cells divide, form a germinal center, and express the activation induced deaminase (AID), an enzyme that triggers somatic hypermutation of the variable regions of immunoglobulin (Ig) loci. Recent evidence indicates that at least 25% of expressed genes in germinal center B cells are mutated or deaminated by AID. One of the most deaminated genes, c-Myc, frequently appears as a translocation partner with the Ig heavy chain gene (Igh) in mouse plasmacytomas and human Burkitt's lymphomas. This indicates that the two genes or their double-strand break ends come into close proximity at a biologically relevant frequency. However, the proximity of c-Myc and Igh has never been measured in germinal center B cells, where many such translocations are thought to occur. We hypothesized that in germinal center B cells, not only is c-Myc near Igh, but other mutating non-Ig genes are deaminated by AID because they are near Ig genes, the primary targets of AID. We tested this "collateral damage" model using 3D-fluorescence in situ hybridization (3D-FISH) to measure the distance from non-Ig genes to Ig genes in germinal center B cells. We also made mice transgenic for human MYC and measured expression and mutation of the transgenes. We found that there is no correlation between proximity to Ig genes and levels of AID targeting or gene mutation, and that c-Myc was not closer to Igh than were other non-Ig genes. In addition, the human MYC transgenes did not accumulate mutations and were not deaminated by AID. We conclude that proximity to Ig loci is unlikely to be a major determinant of AID targeting or mutation of non-Ig genes, and that the MYC transgenes are either missing important regulatory elements that allow mutation or are unable to mutate because their new nuclear position is not conducive to AID deamination.     

Understanding signaling in yeast: Insights from network analysis

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.     

Distinguishing primary and secondary translocations in multiple myeloma

Multiple myeloma (MM) is a malignant post-germinal center tumor of somatically-mutated, isotype-switched plasma cells that accumulate in the bone marrow. It often is preceded by a stable pre-malignant tumor called monoclonal gammopathy of undetermined significance (MGUS), which can sporadically progress to MM. Five recurrent primary translocations involving the immunoglobulin heavy chain (IgH) locus on chromosome 14q32 have been identified in MGUS and MM tumors. The five partner loci include 11q13, 6p21, 4p16, 16q23, and 20q12, with corresponding dysregulation of CYCLIN D1, CYCLIN D3, FGFR3/MMSET, c-MAF, and MAFB, respectively, by strong enhancers in the IgH locus. The five recurrent translocations, which are present in 40% of MM tumors, typically are simple reciprocal translocations, mostly having breakpoints within or near IgH switch regions but sometimes within or near VDJ or JH sequences. It is thought that these translocations are caused by aberrant IgH switch recombination, and possibly by aberrant somatic hypermutation in germinal center B cells, thus providing an early and perhaps initiating event in transformation. A MYC gene is dysregulated by complex translocations and insertions as a very late event during the progression of MM tumors. Since the IgH switch recombination and somatic hypermutation mechanism are turned off in plasma cells and plasma cell tumors, the MYC rearrangements are thought to be mediated by unknown mechanisms that contribute to structural genomic instability in all kinds of tumors. These rearrangements, which often but not always juxtapose MYC near one of the strong immunoglobulin enhancers, provide a paradigm for secondary translocations. It is hypothesized that secondary translocations not involving a MYC gene can occur at any stage of tumorigenesis, including in pre-malignant MGUS tumor cells.     

Modulation of specific cell cycle phases in human embryonic stem cells by lncRNA RNA decoys

Recent studies have shown that long noncoding RNAs (lncRNAs) are crucial regulators of human embryonic stem cells (hESCs). However, modes of actions of lncRNAs in hESCs are not well illustrated. Here, we predicted a regulatory network in hESCs in which lncRNAs interact with TFs and thereby control the expressions of downstream targets of TFs. The predicted network is comprised of 2289 3‐motif subgraphs which are characterized by 3 nodes: (i) a lncRNA which is predicted to interact with (ii) a TF and (iii) a gene which is a target of TF and coexpressing with lncRNA. We performed functional annotation of the network by identifying hub nodes followed by pathway enrichment study, which unveiled an active G1‐S cell cycle phase transition‐specific subnetwork that encompasses 2 lncRNAs, MALAT1 and DANCR. Our analysis revealed that MALAT1 and DANCR might be playing key roles in G1‐S phase transition by acting as RNA decoy via interacting with crucial stemness maintaining TFs. We predicted that MALAT1 possibly compete with DNMT1 and CDCA7 genes to bind to E2F1 thereby interrupting repression of DNMT1 and activation of CDCA7 by E2F1 in hESCs, whereas DANCR possibly competes with IPO7 gene to bind to MYC thereby interrupting MYC‐mediated activation of IPO7 in hESCs. Both of these are conjectured to contribute to rapid G1‐S phase transition aiding in stemness maintenance of hESCs. This study presents a crucial TF target cross talks mediated by lncRNAs in hESCs regulating its properties which needs further investigation.     

Discovering functions and revealing mechanisms at molecular level from biological networks

With the increasingly accumulated data from high-throughput technologies, study on biomolecular networks has become one of key focuses in systems biology and bioinformatics. In particular, various types of molecular networks (e.g., protein-protein interaction (PPI) network; gene regulatory network (GRN); metabolic network (MN); gene coexpression network (GCEN)) have been extensively investigated, and those studies demonstrate great potentials to discover basic functions and to reveal essential mechanisms for various biological phenomena, by understanding biological systems not at individual component level but at a system-wide level. Recent studies on networks have created very prolific researches on many aspects of living organisms. In this paper, we aim to review the recent developments on topics related to molecular networks in a comprehensive manner, with the special emphasis on the computational aspect. The contents of the survey cover global topological properties and local structural characteristics, network motifs, network comparison and query, detection of functional modules and network motifs, function prediction from network analysis, inferring molecular networks from biological data as well as representative databases and software tools.     

High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.