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1.  Mesenchymal to amoeboid transition is associated with stem-like features of melanoma cells 
Cellular plasticity confers cancer cells the ability to adapt to microenvironmental changes, a fundamental requirement for tumour progression and metastasis. The epithelial to mesenchymal transition (EMT) is a transcriptional programme associated with increased cell motility and stemness. Besides EMT, the mesenchymal to amoeboid transition (MAT) has been described during tumour progression but to date, little is known about its transcriptional control and involvement in stemness. The aim of this manuscript is to investigate (i) the transcriptional profile associated with the MAT programme and (ii) to study whether MAT acquisition in melanoma cancer cells correlates with clonogenic potential to promote tumour growth.
By using a multidisciplinary approach, we identified four different treatments able to induce MAT in melanoma cells: EphA2 overexpression, Rac1 functional inhibition using its RacN17 dominant negative mutant, stimulation with Ilomastat or treatment with the RhoA activator Calpeptin. First, gene expression profiling identified the transcriptional pathways associated with MAT, independently of the stimulus that induces the MAT programme. Notably, gene sets associated with the repression of mesenchymal traits, decrease in the secretion of extracellular matrix components as well as increase of cellular stemness positively correlate with MAT. Second, the link between MAT and stemness has been investigated in vitro by analysing stemness markers and clonogenic potential of melanoma cells undergoing MAT. Finally, the link between MAT inducing treatments and tumour initiating capability has been validated in vivo.
Taken together, our results demonstrate that MAT programme in melanoma is characterised by increased stemness and clonogenic features of cancer cells, thus sustaining tumour progression. Furthermore, these data suggest that stemness is not an exclusive feature of cells undergoing EMT, but more generally is associated with an increase in cellular plasticity of cancer cells.
PMCID: PMC4022383  PMID: 24690323
Melanoma; Cell plasticity; Amoeboid motility; Stemness; EphA2
2.  Correction: Insight into Molecular and Functional Properties of NMNAT3 Reveals New Hints of NAD Homeostasis within Human Mitochondria 
PLoS ONE  2013;8(12):10.1371/annotation/f5e6107f-a911-4c15-a881-7cb7e4946ff6.
PMCID: PMC3859664
3.  Insight into Molecular and Functional Properties of NMNAT3 Reveals New Hints of NAD Homeostasis within Human Mitochondria 
PLoS ONE  2013;8(10):e76938.
Among the enzymes involved in NAD homeostasis, nicotinamide mononucleotide adenylyltransferases (NMNAT1-3) are central to intracellular NAD formation. Although NMNAT3 is postulated to be a mitochondrial enzyme contributing to NAD-dependent organelle functioning, information on endogenous proteins is lacking. We report that in human cells a single gene nmnat3 localized on chromosome 3 codes for two mRNA splice variants NMNATv1 and FKSG76, whereas the previously reported NMNAT3v2 transcript is not present. However, NMNAT3v1 and FKSG76 proteins are not detectable, consistent with the finding that an upstream ORF in their mRNAs negatively regulates translation. NMNAT3v1 transfection demonstrates that the protein is cytosolic and inactive, whereas FKSG76 is mitochondrial but operates NAD cleavage rather than synthesis. In keeping with the lack of NMNAT3, we show that extracellular NAD, but not its metabolic precursors, sustains mitochondrial NAD pool in an ATP-independent manner. Data of the present study modify the scenario of the origin of mitochondrial NAD by showing that, in human cells, NMNAT3 is absent in mitochondria, and, akin to plants and yeast, cytosolic NAD maintains the mitochondrial NAD pool.
PMCID: PMC3796565  PMID: 24155910
4.  Bioinformatic approaches for functional annotation and pathway inference in metagenomics data 
Briefings in Bioinformatics  2012;13(6):696-710.
Metagenomic approaches are increasingly recognized as a baseline for understanding the ecology and evolution of microbial ecosystems. The development of methods for pathway inference from metagenomics data is of paramount importance to link a phenotype to a cascade of events stemming from a series of connected sets of genes or proteins. Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism, one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires a clear picture of the potentialities of existing tools and development of new tools to characterize, reconstruct and model biochemical and regulatory pathways as the result of integration of function in complex symbiotic interactions of ontologically and evolutionary distinct cell types.
PMCID: PMC3505041  PMID: 23175748
metagenomics; next-generation sequencing; microbiome; pathway analysis; gene annotation
5.  Polyglutamine Repeats Are Associated to Specific Sequence Biases That Are Conserved among Eukaryotes 
PLoS ONE  2012;7(2):e30824.
Nine human neurodegenerative diseases, including Huntington's disease and several spinocerebellar ataxia, are associated to the aggregation of proteins comprising an extended tract of consecutive glutamine residues (polyQs) once it exceeds a certain length threshold. This event is believed to be the consequence of the expansion of polyCAG codons during the replication process. This is in apparent contradiction with the fact that many polyQs-containing proteins remain soluble and are encoded by invariant genes in a number of eukaryotes. The latter suggests that polyQs expansion and/or aggregation might be counter-selected through a genetic and/or protein context. To identify this context, we designed a software that scrutinize entire proteomes in search for imperfect polyQs. The nature of residues flanking the polyQs and that of residues other than Gln within polyQs (insertions) were assessed. We discovered strong amino acid residue biases robustly associated to polyQs in the 15 eukaryotic proteomes we examined, with an over-representation of Pro, Leu and His and an under-representation of Asp, Cys and Gly amino acid residues. These biases are conserved amongst unrelated proteins and are independent of specific functional classes. Our findings suggest that specific residues have been co-selected with polyQs during evolution. We discuss the possible selective pressures responsible of the observed biases.
PMCID: PMC3270027  PMID: 22312432
6.  A computational pipeline to discover highly phylogenetically informative genes in sequenced genomes: application to Saccharomyces cerevisiae natural strains 
Nucleic Acids Research  2012;40(9):3834-3848.
The quest for genes representing genetic relationships of strains or individuals within populations and their evolutionary history is acquiring a novel dimension of complexity with the advancement of next-generation sequencing (NGS) technologies. In fact, sequencing an entire genome uncovers genetic variation in coding and non-coding regions and offers the possibility of studying Saccharomyces cerevisiae populations at the strain level. Nevertheless, the disadvantageous cost-benefit ratio (the amount of details disclosed by NGS against the time-expensive and expertise-demanding data assembly process) still precludes the application of these techniques to the routinely assignment of yeast strains, making the selection of the most reliable molecular markers greatly desirable. In this work we propose an original computational approach to discover genes that can be used as a descriptor of the population structure. We found 13 genes whose variability can be used to recapitulate the phylogeny obtained from genome-wide sequences. The same approach that we prove to be successful in yeasts can be generalized to any other population of individuals given the availability of high-quality genomic sequences and of a clear population structure to be targeted.
PMCID: PMC3351171  PMID: 22266652
7.  A Computational Approach for Identifying the Chemical Factors Involved in the Glycosaminoglycans-Mediated Acceleration of Amyloid Fibril Formation 
PLoS ONE  2010;5(6):e11363.
Amyloid fibril formation is the hallmark of many human diseases, including Alzheimer's disease, type II diabetes and amyloidosis. Amyloid fibrils deposit in the extracellular space and generally co-localize with the glycosaminoglycans (GAGs) of the basement membrane. GAGs have been shown to accelerate the formation of amyloid fibrils in vitro for a number of protein systems. The high number of data accumulated so far has created the grounds for the construction of a database on the effects of a number of GAGs on different proteins.
Methodology/Principal Findings
In this study, we have constructed such a database and have used a computational approach that uses a combination of single parameter and multivariate analyses to identify the main chemical factors that determine the GAG-induced acceleration of amyloid formation. We show that the GAG accelerating effect is mainly governed by three parameters that account for three-fourths of the observed experimental variability: the GAG sulfation state, the solute molarity, and the ratio of protein and GAG molar concentrations. We then combined these three parameters into a single equation that predicts, with reasonable accuracy, the acceleration provided by a given GAG in a given condition.
In addition to shedding light on the chemical determinants of the protein∶GAG interaction and to providing a novel mathematical predictive tool, our findings highlight the possibility that GAGs may not have such an accelerating effect on protein aggregation under the conditions existing in the basement membrane, given the values of salt molarity and protein∶GAG molar ratio existing under such conditions.
PMCID: PMC2894048  PMID: 20613870
8.  Aggregation Propensity of the Human Proteome 
PLoS Computational Biology  2008;4(10):e1000199.
Formation of amyloid-like fibrils is involved in numerous human protein deposition diseases, but is also an intrinsic property of polypeptide chains in general. Progress achieved recently now allows the aggregation propensity of proteins to be analyzed over large scales. In this work we used a previously developed predictive algorithm to analyze the propensity of the 34,180 protein sequences of the human proteome to form amyloid-like fibrils. We show that long proteins have, on average, less intense aggregation peaks than short ones. Human proteins involved in protein deposition diseases do not differ extensively from the rest of the proteome, further demonstrating the generality of protein aggregation. We were also able to reproduce some of the results obtained with other algorithms, demonstrating that they do not depend on the type of computational tool employed. For example, proteins with different subcellular localizations were found to have different aggregation propensities, in relation to the various efficiencies of quality control mechanisms. Membrane proteins, intrinsically disordered proteins, and folded proteins were confirmed to have very different aggregation propensities, as a consequence of their different structures and cellular microenvironments. In addition, gatekeeper residues at strategic positions of the sequences were found to protect human proteins from aggregation. The results of these comparative analyses highlight the existence of intimate links between the propensity of proteins to form aggregates with β-structure and their biology. In particular, they emphasize the existence of a negative selection pressure that finely modulates protein sequences in order to adapt their aggregation propensity to their biological context.
Author Summary
Amyloid-like fibrils are insoluble proteinaceous fibrillar aggregates with a characteristic structure (the cross-β core) that form and deposit in more than 40 pathological conditions in humans. These include Alzheimer's disease, Parkinson's disease, type II diabetes, and the spongiform encephalopathies. A number of proteins not involved in any disease can also form amyloid-like fibrils in vitro, suggesting that amyloid fibril formation is an intrinsic property of proteins in general. Recent efforts in understanding the physico-chemical grounds of amyloid fibril formation has led to the development of several algorithms, capable of predicting a number of aggregation-related parameters of a protein directly from its amino acid sequence. In order to study the predicted aggregation behavior of the human proteome, we have run one of these algorithms on the 34,180 human protein sequences. Our results demonstrate that molecular evolution has acted on protein sequences to finely modulate their aggregation propensities, depending on different parameters related to their in vivo environment. Together with cellular control mechanisms, this natural selection protects proteins from aggregation during their lifetime.
PMCID: PMC2557143  PMID: 18927604
9.  XYLab: an interactive plotting tool for mixed multivariate data observation and interpretation 
Bioinformation  2008;2(9):392-394.
The correct display of data is often a key point for interpreting the results of experimental procedures. Multivariate data sets suffer from the problem of representation, since a dimensionality above 3 is beyond the capability of plotting programs. Moreover, non numerical variables such as protein annotations are usually fundamental for a full comprehension of biological data. Here we present a novel interactive XY plotter designed to take the full control of large datasets containing mixed-type variables, provided with an intuitive data management, a powerful labelling system and other features aimed at facilitating data interpretation and sub-setting.
XYLab program, test dataset and manual is available at XYLab.html.
PMCID: PMC2533058  PMID: 18795112
multivariate data; scatter plot; labels; search; subset

Results 1-9 (9)