Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE.

The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.

Draft genome sequences of 14 Lacticaseibacillus spp. strains, representatives of a collection of 200 strains.

Lactobacilli have a fundamental role in the food industry as starters and probiotics, therefore, requiring special attention concerning food safety. In this work, 14 strains selected accordingly to their genetic fingerprint and physiologic characteristics are presented as representatives of a collection of 200 strains.

LINC01605 Is a Novel Target of Mutant p53 in Breast and Ovarian Cancer Cell Lines.

TP53 is the most frequently mutated gene in human cancers. Most TP53 genomic alterations are missense mutations, which cause a loss of its tumour suppressor functions while providing mutant p53 (mut_p53) with oncogenic features (gain-of-function). Loss of p53 tumour suppressor functions alters the transcription of both protein-coding and non-protein-coding genes. Gain-of-function of mut_p53 triggers modification in gene expression as well; however, the impact of mut_p53 on the transcription of the non-protein-coding genes and whether these non-protein-coding genes affect oncogenic properties of cancer cell lines are not fully explored. In this study, we suggested that LINC01605 (also known as lincDUSP) is a long non-coding RNA regulated by mut_p53 and proved that mut_p53 directly regulates LINC01605 by binding to an enhancer region downstream of the LINC01605 locus. We also showed that the loss or downregulation of LINC01605 impairs cell migration in a breast cancer cell line. Eventually, by performing a combined analysis of RNA-seq data generated in mut_TP53-silenced and LINC01605 knockout cells, we showed that LINC01605 and mut_p53 share common gene pathways. Overall, our findings underline the importance of ncRNAs in the mut_p53 network in breast and ovarian cancer cell lines and in particular the importance of LINC01605 in mut_p53 pro-migratory pathways.

A sense of life: computational and experimental investigations with models of biochemical and evolutionary processes.

We collaborate in a research program aimed at creating a rigorous framework, experimental infrastructure, and computational environment for understanding, experimenting with, manipulating, and modifying a diverse set of fundamental biological processes at multiple scales and spatio-temporal modes. The novelty of our research is based on an approach that (i) requires coevolution of experimental science and theoretical techniques and (ii) exploits a certain universality in biology guided by a parsimonious model of evolutionary mechanisms operating at the genomic level and manifesting at the proteomic, transcriptomic, phylogenic, and other higher levels. Our current program in "systems biology" endeavors to marry large-scale biological experiments with the tools to ponder and reason about large, complex, and subtle natural systems. To achieve this ambitious goal, ideas and concepts are combined from many different fields: biological experimentation, applied mathematical modeling, computational reasoning schemes, and large-scale numerical and symbolic simulations. From a biological viewpoint, the basic issues are many: (i) understanding common and shared structural motifs among biological processes; (ii) modeling biological noise due to interactions among a small number of key molecules or loss of synchrony; (iii) explaining the robustness of these systems in spite of such noise; and (iv) cataloging multistatic behavior and adaptation exhibited by many biological processes.