Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma

Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity

BACKGROUND

Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets.

METHODS

Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/ ) was used for gene interaction relationship mapping.

RESULTS

We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P = 0.04 in PFC and P = 1.227e-09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway.

CONCLUSION

Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.

Community-based network analyses reveal emerging connectivity patterns of protein-protein interactions in murine melanoma secretome

Protein-protein interaction networks (PPINs) are static representations of protein connections in which topological features such as subgraphs (communities) may contain proteins functionally related, revealing an additional layer of interactome complexity. We created two PPINs from the secretomes of a paired set of murine melanocytes (a normal melanocyte and its transformed phenotype). Community structures, identified by a graph clustering algorithm, resulted in the identification of subgraphs in both networks. Interestingly, the underlying structure of such communities revealed shared and exclusive proteins (core and exclusive nodes, respectively), in addition to proteins that changed their location within each community (rewired nodes). Functional enrichment analysis of core nodes revealed conserved biological functions in both networks whereas exclusive and rewired nodes in the tumoral phenotype network were enriched in cancer-related processes, including TGFβ signaling. We found a remarkable shift in the tumoral interactome, resulting in an emerging pattern which was driven by the presence of exclusive nodes and may represent functional network motifs. Our findings suggest that the rearrangement in the tumoral interactome may be correlated with the malignant transformation of melanocytes associated with substrate adhesion impediment. The interactions found in core and new/rewired nodes might potentially be targeted for therapeutic intervention in melanoma treatment. SIGNIFICANCE: Malignant transformation is a result of synergistic action of multiple molecular factors in which genetic alterations as well as protein expression play paramount roles. During oncogenesis, cellular crosstalk through the secretion of soluble mediators modulates the phenotype of transformed cells which ultimately enables them to successfully disrupt important signaling pathways, including those related to cell growth and proliferation. Therefore, in this work we profiled the secretomes of a paired set of normal and transformed phenotypes of a murine melanocyte. After assembling the two interactomes, clusters of functionally related proteins (network communities) were observed as well as emerging patterns of network rewiring which may represent an interactome signature of transformed cells. In summary, the significance of this study relies on the understanding of the repertoire of 'normal' and 'tumoral' secretomes and, more importantly, the set of interacting proteins (the interactome) in both of these conditions, which may reveal key components that might be potentially targeted for therapeutic intervention.

Computational models of melanoma

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research.

Differential coexpression in human tissues and the confounding effect of mean expression levels

Motivation

Differential coexpression-the alteration of gene coexpression patterns observed in different biological conditions-has been proposed to be a mechanism for revealing rewiring of transcription regulatory networks. Despite wide use of methods for differential coexpression analysis, the phenomenon has not been well-studied. In particular, in many applications, differential coexpression is confounded with differential expression, that is, changes in average levels of expression across conditions. This confounding, despite affecting the interpretation of the differential coexpression, has rarely been studied.

Results

We constructed high-quality coexpression networks for five human tissues and identified coexpression links (gene pairs) that were specific to each tissue. Between 3 and 32% of coexpression links were tissue-specific (differentially coexpressed) and this specificity is reproducible in an external dataset. However, we show that up to 75% of the observed differential coexpression is substantially explained by average expression levels of the genes. 'Pure' differential coexpression independent from differential expression is a minority and is less reproducible in external datasets. We also investigated the functional relevance of pure differential coexpression. Our conclusion is that to a large extent, differential coexpression is more parsimoniously explained by changes in average expression levels and pure links have little impact on network-based functional analysis.

Supplementary information

Supplementary data are available at Bioinformatics online.

HVEM has a broader expression than PD-L1 and constitutes a negative prognostic marker and potential treatment target for melanoma

HVEM (Herpes Virus Entry Mediator) engagement of BTLA (B and T Lymphocyte Attenuator) triggers inhibitory signals in T cells and could play a role in evading antitumor immunity. Here, HVEM expression levels in melanoma metastases were analyzed by immunohistochemistry, correlated with overall survival (OS) in 116 patients, and validated by TCGA transcriptomic data. Coincident expression of HVEM and its ligand BTLA was studied in tumor cells and tumor-infiltrating lymphocytes (TILs) by flow cytometry (n = 21) and immunofluorescence (n = 5). Candidate genes controlling HVEM expression in melanoma were defined by bioinformatics studies and validated by siRNA gene silencing. We found that in patients with AJCC stage III and IV melanoma, OS was poorer in those with high HVEM expression on melanoma cells, than in those with a low expression, by immunohistochemistry (p = .0160) or TCGA transcriptomics (p = .0282). We showed a coincident expression of HVEM at the surface of melanoma cells and of BTLA on TILs. HVEM was more widely expressed than PD-L1 in melanoma cells. From a mechanistic perspective, in contrast to PDL1, HVEM expression did not correlate with an IFNγ signature but with an aggressive gene signature. Interestingly, this signature contained MITF, a key player in melanoma biology, whose expression correlated strongly with HVEM. Finally, siRNA gene silencing validated MITF control of HVEM expression. In conclusion, HVEM expression seems to be a prognosis marker and targeting this axis by checkpoint-inhibitors may be of interest in metastatic melanoma.

Toxicogenomic and bioinformatics platforms to identify key molecular mechanisms of a curcumin-analogue DM-1 toxicity in melanoma cells

Melanoma is a highly invasive and metastatic cancer with high mortality rates and chemoresistance. Around 50% of melanomas are driven by activating mutations in BRAF that has led to the development of potent anti-BRAF inhibitors. However resistance to anti-BRAF therapy usually develops within a few months and consequently there is a need to identify alternative therapies that will bypass BRAF inhibitor resistance. The curcumin analogue DM-1 (sodium 4-[5-(4-hydroxy-3-methoxy-phenyl)-3-oxo-penta-1,4-dienyl]-2-methoxy-phenolate) has substantial anti-tumor activity in melanoma, but its mechanism of action remains unclear. Here we use a synthetic lethal genetic screen in Saccharomyces cerevisiae to identify 211 genes implicated in sensitivity to DM-1 toxicity. From these 211 genes, 74 had close human orthologues implicated in oxidative phosphorylation, insulin signaling and iron and RNA metabolism. Further analysis identified 7 target genes (ADK, ATP6V0B, PEMT, TOP1, ZFP36, ZFP36L1, ZFP36L2) with differential expression during melanoma progression implicated in regulation of tumor progression, cell differentiation, and epithelial-mesenchymal transition. Of these TOP1 and ADK were regulated by DM-1 in treatment-naïve and vemurafenib-resistant melanoma cells respectively. These data reveal that the anticancer effect of curcumin analogues is likely to be mediated via multiple targets and identify several genes that represent candidates for combinatorial targeting in melanoma.

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA.

Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.