cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models

The increasing availability of sequencing data of cancer samples is fueling the development of algorithmic strategies to investigate tumor heterogeneity and infer reliable models of cancer evolution. We here build up on previous works on cancer progression inference from genomic alteration data, to deliver two distinct Cytoscape-based applications, which allow to produce, visualize and manipulate cancer evolution models, also by interacting with public genomic and proteomics databases. In particular, we here introduce cyTRON, a stand-alone Cytoscape app, and cyTRON/JS, a web application which employs the functionalities of Cytoscape/JS. cyTRON was developed in Java; the code is available at https://github.com/BIMIB-DISCo/cyTRON and on the Cytoscape App Store http://apps.cytoscape.org/apps/cytron. cyTRON/JS was developed in JavaScript and R; the source code of the tool is available at https://github.com/BIMIB-DISCo/cyTRON-js and the tool is accessible from https://bimib.disco.unimib.it/cytronjs/welcome

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science

Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models

The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with a functional selective advantage. In this work, we model the order of accumulation of such mutations during the progression, which eventually leads to the disease, by means of probabilistic graphic models, i.e., Bayesian Networks (BNs). We investigate how to perform the task of learning the structure of such BNs, according to experimental evidence, adopting a global optimization meta-heuristics. In particular, in this work we rely on Genetic Algorithms, and to strongly reduce the execution time of the inference -- which can also involve multiple repetitions to collect statistically significant assessments of the data -- we distribute the calculations using both multi-threading and a multi-node architecture. The results show that our approach is characterized by good accuracy and specificity; we also demonstrate its feasibility, thanks to a 84x reduction of the overall execution time with respect to a traditional sequential implementation

Expression of genes associated with anthocyanin synthesis in red-purplish, pink, pinkish-green and green grape berries from mutated 'Sangiovese' biotypes: A case study

Using normal red-purplish grape bunches and pink, pink-green and green berry colour-mutated biotypes of cv. Sangiovese (V. vinifera L.), we investigated their anthocyanin metabolism via biochemical and molecular assays. The number and composition of the different types of anthocyanins were analysed by spectrophotometry and chromatography. The expression of six structural genes of the biosynthetic pathway (chalcone synthase [CHS], chalcone isomerase [CHI], flavanon-3-hydroxylase [F3H], dihydroflavonol 4-reductase [DFR], leucoanthocyanidin dioxygenase [LDOX] and UDP-glucose 3-O-flavonoid:glucosyltransferase [UFGT]) was determined over the four weeks subsequent to veraison via Northen blot and Real Time PCR.The grapes from the non-mutated biotype showed a prevailing accumulation of monoglycoside anthocyanin fractions, with only traces of acetyl and p-coumaroyl derivatives. The berries of the mutated biotypes showed a gradual berry pigment loss associated with a reduction in total anthocyanin content, although anthocyanin composition was the same of the non-mutant biotype. Indeed, the Northern blot assay data, as confirmed by the quantitative Real Time PCR tests, showed a differential expression in the berries of the non mutated and mutated biotypes for the UFGT gene, proving normal in the red-purplish, lower in the biotypes with pink and pink-green berries and wholly lacking in the green one. Thus, the UFGT gene in berry skin of colour-mutated 'Sangiovese' biotypes is controlled independently of the other structural genes encoding enzymes in the anthocyanin biosynthetic pathway and its capacity of expression is a critical factor in the synthesis and storage of these compounds.

Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks

Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables. In general, learning the structure of such models is a problem with considerable theoretical interest that still poses many challenges. On the one hand, this is a well-known NP-complete problem, which is practically hardened by the huge search space of possible solutions. On the other hand, the phenomenon of I-equivalence, i.e., different graphical structures underpinning the same set of statistical dependencies, may lead to multimodal fitness landscapes further hindering maximum likelihood approaches to solve the task. Despite all these difficulties, greedy search methods based on a likelihood score coupled with a regularization term to account for model complexity, have been shown to be surprisingly effective in practice. In this paper, we consider the formulation of the task of learning the structure of Bayesian Networks as an optimization problem based on a likelihood score. Nevertheless, our approach do not adjust this score by means of any of the complexity terms proposed in the literature; instead, it accounts directly for the complexity of the discovered solutions by exploiting a multi-objective optimization procedure. To this extent, we adopt NSGA-II and define the first objective function to be the likelihood of a solution and the second to be the number of selected arcs. We thoroughly analyze the behavior of our method on a wide set of simulated data, and we discuss the performance considering the goodness of the inferred solutions both in terms of their objective functions and with respect to the retrieved structure. Our results show that NSGA-II can converge to solutions characterized by better likelihood and less arcs than classic approaches, although paradoxically frequently characterized by a lower similarity to the target network

Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients

We considered observational data available from the MIMIC-III open-access ICU database and collected within a study period between year 2002 up to 2011. If a patient had multiple admissions to the ICU during the 30 days before death, only the first stay was analyzed, leading to a final set of 6,436 unique ICU admissions during the study period. We tested two hypotheses: (i) administration of invasive intervention during the ICU stay immediately preceding end-of-life would decrease over the study time period and (ii) time-to-death from ICU admission would also decrease, due to the decrease in invasive intervention administration. To investigate the latter hypothesis, we performed a subgroups analysis by considering patients with lowest and highest severity. To do so, we stratified the patients based on their SAPS I scores, and we considered patients within the first and the third tertiles of the score. We then assessed differences in trends within these groups between years 2002-05 vs. 2008-11. Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in endotracheal ventilation among patients who died within 30 days of ICU admission (120.8 vs. 68.5 hours for the lowest severity patients, p<0.001; 47.7 vs. 46.0 hours for the highest severity patients, p=0.004). This is explained in part by an increase in the use of non-invasive ventilation. Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in the use of vasopressors and inotropes among patients with the lowest severity who died within 30 days of ICU admission (41.8 vs. 36.2 hours, p<0.001) but not among those with the highest severity. Despite a reduction in the use of invasive interventions, we did not find a reduction in the time to death between 2002-2005 vs. 2008-2011 (7.8 days vs. 8.2 days for the lowest severity patients, p=0.32; 2.1 days vs. 2.0 days for the highest severity patients, p=0.74)

Osservazioni sul paradigma dei learning object

No abstract availabl

Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and Rural Africa

peer reviewe

Population genomics reveals evolution and variation of Saccharomyces cerevisiae in the human and insects gut

The quest to discover the variety of ecological niches inhabited by Saccharomyces cerevisiae has led to research in areas as diverse as wineries, oak trees, and insect guts. The discovery of fungal communities in the human gastrointestinal tract suggested the host's gut as a potential reservoir for yeast adaptation. Here we report the existence of yeast populations associated with the human gut (HG) that differ from those isolated from other human body sites. Phylogenetic analysis on 12 microsatellite loci and 1,715 combined CDSs from whole‐genome sequencing revealed three subclusters of HG strains with further evidence of clonal colonization within the host's gut. The presence of such subclusters was supported by other genomic features, such as copy number variation, absence/introgressions of CDSs and relative polymorphism frequency. Functional analysis of CDSs specific of the different subclusters suggested possible alterations in cell wall composition and sporulation features. The phenotypic analysis combined with immunological profiling of these strains further showed that sporulation was related with strain‐specific genomic characteristics in the immune recognition pattern. We conclude that both genetic and environmental factors involved in cell wall remodeling and sporulation are the main drivers of adaptation in S. cerevisiae populations in the human gut

Phospholipases in Gliomas: Current Knowledge and Future Perspectives from Bench to Bedside

Phospholipases are essential intermediaries that work as hydrolyzing enzymes of phospholipids (PLs), which represent the most abundant species contributing to the biological membranes of nervous cells of the healthy human brain. They generate different lipid mediators, such as diacylglycerol, phosphatidic acid, lysophosphatidic acid, and arachidonic acid, representing key elements of intra- and inter-cellular signaling and being involved in the regulation of several cellular mechanisms that can promote tumor progression and aggressiveness. In this review, it is summarized the current knowledge about the role of phospholipases in brain tumor progression, focusing on low- and high-grade gliomas, representing promising prognostic or therapeutic targets in cancer therapies due to their influential roles in cell proliferation, migration, growth, and survival. A deeper understanding of the phospholipases-related signaling pathways could be necessary to pave the way for new targeted therapeutic strategies