The SYSCID map: a graphical and computational resource of molecular mechanisms across rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease.

Chronic inflammatory diseases (CIDs), including inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are thought to emerge from an impaired complex network of inter- and intracellular biochemical interactions among several proteins and small chemical compounds under strong influence of genetic and environmental factors. CIDs are characterised by shared and disease-specific processes, which is reflected by partially overlapping genetic risk maps and pathogenic cells (e.g., T cells). Their pathogenesis involves a plethora of intracellular pathways. The translation of the research findings on CIDs molecular mechanisms into effective treatments is challenging and may explain the low remission rates despite modern targeted therapies. Modelling CID-related causal interactions as networks allows us to tackle the complexity at a systems level and improve our understanding of the interplay of key pathways. Here we report the construction, description, and initial applications of the SYSCID map (https://syscid.elixir-luxembourg.org/), a mechanistic causal interaction network covering the molecular crosstalk between IBD, RA and SLE. We demonstrate that the map serves as an interactive, graphical review of IBD, RA and SLE molecular mechanisms, and helps to understand the complexity of omics data. Examples of such application are illustrated using transcriptome data from time-series gene expression profiles following anti-TNF treatment and data from genome-wide associations studies that enable us to suggest potential effects to altered pathways and propose possible mechanistic biomarkers of treatment response.

Latent Membrane Protein 1 (LMP1) from Epstein-Barr Virus (EBV) Strains M81 and B95.8 Modulate miRNA Expression When Expressed in Immortalized Human Nasopharyngeal Cells.

The Epstein-Barr virus (EBV) is a ubiquitous γ herpesvirus strongly associated with nasopharyngeal carcinomas, and the viral oncogenicity in part relies on cellular effects of the viral latent membrane protein 1 (LMP1). It was previously described that EBV strains B95.8 and M81 differ in cell tropism and the activation of the lytic cycle. Nonetheless, it is unknown whether LMP1 from these strains have different effects when expressed in nasopharyngeal cells. Thus, herein we evaluated the effects of EBV LMP1 derived from viral strains B95.8 and M81 and expressed in immortalized nasopharyngeal cells NP69SV40T in the regulation of 91 selected cellular miRNAs. We found that cells expressing either LMP1 behave similarly in terms of NF-kB activation and cell migration. Nonetheless, the miRs 100-5p, 192-5p, and 574-3p were expressed at higher levels in cells expressing LMP1 B95.8 compared to M81. Additionally, results generated by in silico pathway enrichment analysis indicated that LMP1 M81 distinctly regulate genes involved in cell cycle (i.e., RB1), mRNA processing (i.e., NUP50), and mitochondrial biogenesis (i.e., ATF2). In conclusion, LMP1 M81 was found to distinctively regulate miRs 100-5p, 192-5p, and 574-3p, and the in silico analysis provided valuable clues to dissect the molecular effects of EBV LMP1 expressed in nasopharyngeal cells.

Impacts of the overexpression of a tomato translationally controlled tumor protein (TCTP) in tobacco revealed by phenotypic and transcriptomic analysis.

KEY MESSAGE: Overexpression of a tomato TCTP impacts plant biomass production and performance under stress. These phenotypic alterations were associated with the up-regulation of genes mainly related to photosynthesis, fatty acid metabolism and water transport. The translationally controlled tumor protein (TCTP) is a multifaceted and highly conserved eukaryotic protein. In plants, despite the existence of functional data implicating this protein in cell proliferation and growth, the detailed physiological roles of many plant TCTPs remain poorly understood. Here we focused on a yet uncharacterized TCTP from tomato (SlTCTP). We show that, when overexpressed in tobacco, SlTCTP may promote plant biomass production and affect performance under salt and osmotic stress. Transcriptomic analysis of the transgenic plants revealed the up-regulation of genes mainly related to photosynthesis, fatty acid metabolism and water transport. This induced photosynthetic gene expression was paralleled by an increase in the photosynthetic rate and stomatal conductance of the transgenic plants. Moreover, the transcriptional modulation of genes involved in ABA-mediated regulation of stomatal movement was detected. On the other hand, genes playing a pivotal role in ethylene biosynthesis were found to be down-regulated in the transgenic lines, thus suggesting deregulated ethylene accumulation in these plants. Overall, these results point to a role of TCTP in photosynthesis and hormone signaling.

Transcriptome response signatures associated with the overexpression of a mitochondrial uncoupling protein (AtUCP1) in tobacco.

Mitochondrial inner membrane uncoupling proteins (UCP) dissipate the proton electrochemical gradient established by the respiratory chain, thus affecting the yield of ATP synthesis. UCP overexpression in plants has been correlated with oxidative stress tolerance, improved photosynthetic efficiency and increased mitochondrial biogenesis. This study reports the main transcriptomic responses associated with the overexpression of an UCP (AtUCP1) in tobacco seedlings. Compared to wild-type (WT), AtUCP1 transgenic seedlings showed unaltered ATP levels and higher accumulation of serine. By using RNA-sequencing, a total of 816 differentially expressed genes between the investigated overexpressor lines and the untransformed WT control were identified. Among them, 239 were up-regulated and 577 were down-regulated. As a general response to AtUCP1 overexpression, noticeable changes in the expression of genes involved in energy metabolism and redox homeostasis were detected. A substantial set of differentially expressed genes code for products targeted to the chloroplast and mainly involved in photosynthesis. The overall results demonstrate that the alterations in mitochondrial function provoked by AtUCP1 overexpression require important transcriptomic adjustments to maintain cell homeostasis. Moreover, the occurrence of an important cross-talk between chloroplast and mitochondria, which culminates in the transcriptional regulation of several genes involved in different pathways, was evidenced.

Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype.

The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags.

Whereas genome sequencing defines the genetic potential of an organism, transcript sequencing defines the utilization of this potential and links the genome with most areas of biology. To exploit the information within the human genome in the fight against cancer, we have deposited some two million expressed sequence tags (ESTs) from human tumors and their corresponding normal tissues in the public databases. The data currently define approximately 23,500 genes, of which only approximately 1,250 are still represented only by ESTs. Examination of the EST coverage of known cancer-related (CR) genes reveals that <1% do not have corresponding ESTs, indicating that the representation of genes associated with commonly studied tumors is high. The careful recording of the origin of all ESTs we have produced has enabled detailed definition of where the genes they represent are expressed in the human body. More than 100,000 ESTs are available for seven tissues, indicating a surprising variability of gene usage that has led to the discovery of a significant number of genes with restricted expression, and that may thus be therapeutically useful. The ESTs also reveal novel nonsynonymous germline variants (although the one-pass nature of the data necessitates careful validation) and many alternatively spliced transcripts. Although widely exploited by the scientific community, vindicating our totally open source policy, the EST data generated still provide extensive information that remains to be systematically explored, and that may further facilitate progress toward both the understanding and treatment of human cancers.