NATbox: a network analysis toolbox in R

Background: There has been recent interest in capturing the functional relationships (FRs) from high-throughput assays using suitable computational techniques. FRs elucidate the working of genes in concert as a system as opposed to independent entities hence may provide preliminary insights into biological pathways and signalling mechanisms. Bayesian structure learning (BSL) techniques and its extensions have been used successfully for modelling FRs from expression profiles. Such techniques are especially useful in discovering undocumented FRs, investigating non-canonical signalling mechanisms and cross-talk between pathways. The objective of the present study is to develop a graphical user interface (GUI), NATbox: Network Analysis Toolbox in the language R that houses a battery of BSL algorithms in conjunction with suitable statistical tools for modelling FRs in the form of acyclic networks from gene expression profiles and their subsequent analysis.Results: NATbox is a menu-driven open-source GUI implemented in the R statistical language for modelling and analysis of FRs from gene expression profiles. It provides options to (i) impute missing observations in the given data (ii) model FRs and network structure from gene expression profiles using a battery of BSL algorithms and identify robust dependencies using a bootstrap procedure, (iii) present the FRs in the form of acyclic graphs for visualization and investigate its topological properties using network analysis metrics, (iv) retrieve FRs of interest from published literature. Subsequently, use these FRs as structural priors in BSL (v) enhance scalability of BSL across high-dimensional data by parallelizing the bootstrap routines.Conclusion: NATbox provides a menu-driven GUI for modelling and analysis of FRs from gene expression profiles. By incorporating readily available functions from existing R-packages, it minimizes redundancy and improves reproducibility, transparency and sustainability, characteristic of open-source environments. NATbox is especially suited for interdisciplinary researchers and biologists with minimal programming experience and would like to use systems biology approaches without delving into the algorithmic aspects. The GUI provides appropriate parameter recommendations for the various menu options including default parameter choices for the user. NATbox can also prove to be a useful demonstration and teaching tool in graduate and undergraduate course in systems biology. It has been tested successfully under Windows and Linux operating systems. The source code along with installation instructions and accompanying tutorial can be found at http://bioinformatics.ualr.edu/natboxWiki/index.php/Main_Page

The spectrum and clinical impact of epigenetic modifier mutations in myeloma

Epigenetic dysregulation is known to be an important contributor to myeloma pathogenesis but, unlike in other B cell malignancies, the full spectrum of somatic mutations in epigenetic modifiers has not been previously reported. We sought to address this using results from whole-exome sequencing in the context of a large prospective clinical trial of newly diagnosed patients and targeted sequencing in a cohort of previously treated patients for comparison.Whole-exome sequencing analysis of 463 presenting myeloma cases entered in the UK NCRI Myeloma XI study and targeted sequencing analysis of 156 previously treated cases from the University of Arkansas for Medical Sciences. We correlated the presence of mutations with clinical outcome from diagnosis and compared the mutations found at diagnosis with later stages of disease.In diagnostic myeloma patient samples we identify significant mutations in genes encoding the histone 1 linker protein, previously identified in other B-cell malignancies. Our data suggest an adverse prognostic impact from the presence of lesions in genes encoding DNA methylation modifiers and the histone demethylase KDM6A/UTX. The frequency of mutations in epigenetic modifiers appears to increase following treatment most notably in genes encoding histone methyltransferases and DNA methylation modifiers.Numerous mutations identified raise the possibility of targeted treatment strategies for patients either at diagnosis or relapse supporting the use of sequencing-based diagnostics in myeloma to help guide therapy as more epigenetic targeted agents become available

Overview of biological database mapping services for interoperation between different 'omics' datasets

Abstract Many primary biological databases are dedicated to providing annotation for a specific type of biological molecule such as a clone, transcript, gene or protein, but often with limited cross-references. Therefore, enhanced mapping is required between these databases to facilitate the correlation of independent experimental datasets. For example, molecular biology experiments conducted on samples (DNA, mRNA or protein) often yield more than one type of 'omics' dataset as an object for analysis (eg a sample can have a genomics as well as proteomics expression dataset available for analysis). Thus, in order to map the two datasets, the identifier type from one dataset is required to be linked to another dataset, so preventing loss of critical information in downstream analysis. This identifier mapping can be performed using identifier converter software relevant to the query and target identifier databases. This review presents the publicly available web-based biological database identifier converters, with comparison of their usage, input and output formats, and the types of available query and target database identifier types.</p

The Mutational and Signaling Landscape of Multiple Myeloma Varies Dependent upon Translocation Cyclin D (TC) Subgroup

Abstract Introduction The spectrum and frequency of mutations in newly diagnosed and relapsed MM has been reported and exhibits a pattern which is distinct from other peripheral lymphoid disorders. A number of data sources have suggested that MM is not a single disease, but rather a collection of molecularly diverse entities which present as malignancies of plasma cells with different clinical courses and response to therapy. To test this hypothesis we examined the spectrum of common mutations and their associations with gene expression in etiological subgroups defined by a simplified TC classification based on translocation subgroup and the deregulation of a D group cyclin. Materials and methods We examined a set of gene expression data from 907 newly diagnosed MM patients and classified them according to an updated TC model. An additional set of 482 cases underwent both sequencing by the FoundationOne Heme targeted panel and gene expression profiling. Mutational data were analyzed for significant associations with gene expression data within five primary TC subgroups: D1, D2, CCND (translocated 11q13 or 6p21), MMSET, and MAF. Results Gene expression signatures of GEP70 risk status, chromosomal aberrations [1q+, 1p-, 13q-, 17p-, HRD], proliferation index, NF-kB activation, and BCL2/MCL1 ratio were not distributed evenly across the TC subgroups, consistent with distinct biological differences amongst groups. Having noted differential expression patterns between subgroups, we hypothesized that distinct patterns of mutation may also exist across TC subgroups. Mutations in RB1 and CDKN2C, seen primarily in the D2 and MMSET subgroups, were mutually exclusive yet both associated with increased proliferation and HR status. Thus two paths to extreme proliferation emerge through either ahomozygous deletion of CDKN2C (with low expression of CDKN2C) or an RB1 alteration (with high expression of CDKN2C). Mutations in KRAS, NRAS, and BRAF were not evenly distributed across subgroups and were significantly inversely associated with an NF-kB signature. RRAS2 was also significantly inversely associated with MAPK mutations in the D1 and D2 subgroups, while genes encoding sprouty-related proteins, SPRED1 and SPRED2, were positively associated with MAPK mutations in the CCND-11q13 subgroup. Overall, MAPK mutations were most significantly associated with elevated expression of DKK1, a known Wnt antagonist, and DUSP6, a known inhibitor of the pathway. These gene expression patterns were primarily localized in the D1, D2, and CCND-11q13 subgroups. In contrast, the MMSET and MAF subgroups had a unique patterns of expression not seen in the D1, D2, or CCND subgroups. A gene set enrichment analysis showed that the DNA replication and cell cycle pathways were significantly enriched in the MMSET and MAF subgroups in the presence of MAPK pathway mutations. Within the MMSET subgroup, MAPK mutations were positively associated with GEP70 HR, proliferation index, and membership of the UAMS-PR subtype, while being negatively associated with FGFR3 expression. This result indicates that the reliance on FGFR3 signaling as an oncogenic driver is lost in the presence of a MAPK activating mutation. Conclusions In MM proliferative signals are delivered via the RAS and NF-kB pathways and activation of these two pathways appears to offer mutually exclusive pathways to disease progression since the majority of cases exhibit a reciprocal relationship between these two signaling pathways. The MMSET and MAF subgroups lack a strong association between NF-kB and MAPK signaling pathways, which may indicate that their initiating translocation event and subsequent genetic patterns provide a unique background in which MAPK alterations accelerate progression. As mutational interactions are differential across TC subgroups, we propose a comprehensive approach to MM classification that includes the etiologic designation by simplified TC subgroups and the subsequent use of genetic and biological markers characteristic of acquired features associated with disease progression, such as MAPK or NF-kB activation. Distinct patterns of RNA expression are associated with DNA mutations in myeloma when contextualized by etiologic subgroups. This indicates that the cellular background in which a mutation occurs has a distinct impact on downstream expression patterns-this observation is particularly relevant to the MMSET and MAF subgroups. Disclosures Pawlyn: Celgene: Consultancy, Honoraria, Other: Travel Support; Takeda Oncology: Consultancy. Morgan:Univ of AR for Medical Sciences: Employment; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Meyers: Consultancy, Honoraria; Janssen: Research Funding

Role of CD38 in anti-tumor immunity of small cell lung cancer

IntroductionImmune checkpoint blockade (ICB) with or without chemotherapy has a very modest benefit in patients with small cell lung cancer (SCLC). SCLC tumors are characterized by high tumor mutation burden (TMB) and low PD-L1 expression. Therefore, TMB and PD-L1 do not serve as biomarkers of ICB response in SCLC. CD38, a transmembrane glycoprotein, mediates immunosuppression in non-small cell lung cancer (NSCLC). In this brief report, we highlight the potential role of CD38 as a probable biomarker of immunotherapy response in SCLC.MethodsWe evaluated the role of CD38 as a determinant of tumor immune microenvironment in SCLC with bulk and single-cell transcriptomic analyses and protein assessments of clinical samples and preclinical models, including CD38 in vivo blockade.ResultsIn SCLC clinical samples, CD38 levels were significantly correlated with the gene expression of the immunosuppressive markers FOXP3, PD-1 and CTLA-4. CD38 expression was significantly enhanced after chemotherapy and ICB treatment in SCLC preclinical models and clinical samples. A combination of cisplatin/etoposide, ICB, and CD38 blockade delayed tumor growth compared to cisplatin/etoposide.ConclusionOur study provides a preliminary but important direction toward exploring CD38 as a potential biomarker of ICB response and CD38 blockade as a combination strategy for chemo-immunotherapy in SCLC

Phosphoproteomic Analyses Reveal Signaling Pathways That Facilitate Lytic Gammaherpesvirus Replication

<div><p>Lytic gammaherpesvirus (GHV) replication facilitates the establishment of lifelong latent infection, which places the infected host at risk for numerous cancers. As obligate intracellular parasites, GHVs must control and usurp cellular signaling pathways in order to successfully replicate, disseminate to stable latency reservoirs in the host, and prevent immune-mediated clearance. To facilitate a systems-level understanding of phosphorylation-dependent signaling events directed by GHVs during lytic replication, we utilized label-free quantitative mass spectrometry to interrogate the lytic replication cycle of murine gammaherpesvirus-68 (MHV68). Compared to controls, MHV68 infection regulated by 2-fold or greater ca. 86% of identified phosphopeptides – a regulatory scale not previously observed in phosphoproteomic evaluations of discrete signal-inducing stimuli. Network analyses demonstrated that the infection-associated induction or repression of specific cellular proteins globally altered the flow of information through the host phosphoprotein network, yielding major changes to functional protein clusters and ontologically associated proteins. A series of orthogonal bioinformatics analyses revealed that MAPK and CDK-related signaling events were overrepresented in the infection-associated phosphoproteome and identified 155 host proteins, such as the transcription factor c-Jun, as putative downstream targets. Importantly, functional tests of bioinformatics-based predictions confirmed ERK1/2 and CDK1/2 as kinases that facilitate MHV68 replication and also demonstrated the importance of c-Jun. Finally, a transposon-mutant virus screen identified the MHV68 cyclin D ortholog as a viral protein that contributes to the prominent MAPK/CDK signature of the infection-associated phosphoproteome. Together, these analyses enhance an understanding of how GHVs reorganize and usurp intracellular signaling networks to facilitate infection and replication.</p></div

Label-free, quantitative phosphoproteomic analyses identify phosphoproteins induced and repressed in MHV68 infection.

<p>(A) Serum-starved 3T3 fibroblasts were mock-infected or infected at MOI = 5 PFU/cell, and cells were harvested 18 h post-infection, the timepoint for which tryptic peptides were enriched by TiO₂ IMAC and identified by high resolution mass spectrometry. Proteins were resolved by SDS-PAGE, and immunoblot analyses were performed using antibodies to p-Ser, p-Thr, or p-Tyr to biochemically demonstrate phosphoproteomic changes induced by MHV68 infection. (B) Pie chart depicts percentages of phosphopeptides that are unique to or shared between control and infected systems. Venn diagram depicts the relative numbers of proteins and overlap of the control and infected proteomic data sets. (C) Scatter plot demonstrating changes in relative abundance for specific phosphopeptides following MHV68 infection. Black squares represent unchanged peptide abundance, blue circles indicate peptides exhibiting >1.5-fold reduction in abundance during infection, and red triangles indicate peptides exhibiting >1.5-fold increased abundance during infection. (D) Phosphoproteins were analyzed using the PANTHER database to classify each protein by “Protein Class” gene ontology (GO). Ontologically-associated proteins are labeled according to their relative abundance in global phosphoproteomic data sets to visualize how infection-related phosphorylation events target each represented GO class. (E) The identities and MaxQuant-defined intensities of the 50 highest scoring phosphopeptides that were either lost (Control ONLY) or induced (Infection ONLY) during MHV68 infection.</p

Comparative phosphorylation motif analyses reveal an infection-associated MAPK/CDK signature.

<p>Unique phosphopeptides present in either mock-infected (A) or infected cells (B) were analyzed using ICE-LOGO. Weighted sequence context for residues flanking either p-Ser or p-Thr at position 0 is provided where a larger size designation corresponds to increased relative abundance of a particular amino acid in the global data set compared to other residues at the same position. (C) Unique phosphopeptides were analyzed using Motif-X to identify motifs over-represented relative to the <i>Mus musculus</i> background proteome at a p-value of <0.000001. (D) 3T3 fibroblasts were mock infected or infected with MHV68 at MOI = 5 PFU/cell. Cells were harvested 18 h post-infection, and proteins were resolved by SDS-PAGE. Immunoblot analyses were performed using antibodies directed against the indicated phosphorylation motifs or proteins. (E) Putative CDK1/2 and ERK1/2 phosphorylated proteins present in infected cells were identified by GPS 2.1 analysis on high confidence settings. A separate STRING analysis was performed to identify proteins that functionally interact with either CDK1/2 or ERK1/2. Arrows connect the kinase to its predicted substrate. STRING-defined substrate interactions are depicted as diamond-shaped nodes, where kinase-substrate interactions are color-coded blue or red to denote CDK or ERK connectivity, respectively.</p

MHV68 D-type cyclin ortholog enhances CDK-related phosphorylation in infected cells.

<p>(A) 3T3 fibroblasts were mock infected or infected with WT or transposon mutant (tn) MHV68 viruses at MOI = 5 PFU/cell. (B) Cells were mock infected or infected with v-cyclin-null (ORF72-null) or genetically repaired WT control (ORF72-MR) MHV68 recombinant viruses. Cells were harvested 18 h post-infection, and proteins were resolved by SDS-PAGE. Immunoblot analyses were performed using antibodies directed against the indicated phosphorylated residues or proteins.</p