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Background: The microbiome is emerging as a crucial player of the immune checkpoint in cancer. Melanoma is a highly immunogenic tumour, and the composition of the gut microbiome has been correlated to prognosis and evolution of advanced melanoma and proposed as a biomarker for immune checkpoint therapy., Objectives: We investigated the gut fungal and bacterial compositions in early-stage melanoma and correlated microbial profiles with histopathological features., Methods: Sequencing of bacterial 16S rRNA and the fungal internal transcribed spacer region was performed on faecal samples of patients with stage I and II melanoma, and healthy controls. A meta-analysis with gut microbiota data from patients with metastatic melanoma was also carried out., Results: We found a combination of gut fungal and bacterial profiles significantly discriminating patients with melanoma from controls. In patients with melanoma, we observed an abundance of Prevotella copri and yeasts belonging to the order Saccharomycetales. We found that the bacterial and fungal community correlated to melanoma invasiveness, whereas the specific fungal profile correlated to melanoma regression. Bacteroides was identified as general marker of immunogenicity, being shared by regressive and invasive melanoma. In addition, the bacterial communities in patients with stage I and II melanoma were different in structure and richer than those from patients with metastatic melanoma., Conclusions: The composition of the gut microbiota in early-stage melanoma changes along the gradient from in situ to invasive (and metastatic) melanoma. Changes in the microbiota and mycobiota are correlated to the histological features of early-stage melanoma, and to the clinical course and response to immune therapies of advanced-stage melanoma, through direct or indirect immunomodulation., (© 2021 The Authors. British Journal of Dermatology published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.)

Background and Aims: A personalized approach to therapy hold great promise to improve disease outcomes. To this end, the identification of different subsets of patients according to the prevalent pathogenic process might guide the choice of therapeutic strategy. We hypothesize that ulcerative colitis [UC] patients might be stratified according to distinctive cytokine profiles and/or to a specific mucosa-associated microbiota., Methods: In a cohort of clinically and endoscopic active UC patients and controls, we used quantitative PCR to analyse the mucosal cytokine mRNA content and 16S rRNA gene sequencing to assess the mucosa-associated microbiota composition., Results: We demonstrate, by means of data-driven approach, the existence of a specific UC patient subgroup characterized by elevated IL-13 mRNA tissue content separate from patients with low IL-13 mRNA tissue content. The two subsets differ in clinical-pathological characteristics. High IL-13 mRNA patients are younger at diagnosis and have a higher prevalence of extensive colitis than low IL-13 mRNA patients. They also show more frequent use of steroid/immunosuppressant/anti-tumour necrosis factor α therapy during 1 year of follow-up. The two subgroups show differential enrichment of mucosa-associated microbiota genera with a prevalence of Prevotella in patients with high IL-13 mRNA tissue content and Sutterella and Acidaminococcus in patients with low IL-13 mRNA tissue content., Conclusion: Assessment of mucosal IL-13 mRNA might help in the identification of a patient subgroup that might benefit from a therapeutic approach modulating IL-13., Podcast: This article has an associated podcast which can be accessed at https://academic.oup.com/ecco-jcc/pages/podcast., (Copyright © 2019 European Crohn's and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Summary: Pathway Inspector is an easy-to-use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology., Availability and Implementation: Pathway Inspector is available at the website http://admiral.fmach.it/PI and has been developed in Python, making use of the Django Web Framework., Contact: Contact:paolo.fontana@fmach.it

Support for Amborella as the sole survivor of an evolutionary lineage that is sister to all other angiosperms comes from positions in DNA multiple-sequence alignments that have a poor fit to time-reversible substitution models. These sites exhibit significant levels of homoplasy, compositional heterogeneity, and strong heterotachy. We report phylogenetic analyses with observed, randomized, and simulated data which show there is little or no expectation that these sites provide useful information for understanding relationships among basal angiosperms. Their inclusion in phylogenetic analyses leads to a long-branch attraction artifact that favors Amborella as sister to other angiosperms in reconstructed phylogenies. Using parametric simulations, we show that sites in chloroplast sequences that exhibit less homoplasy between angiosperms and gymnosperms provide more reliable information for inferring basal angiosperm relationships. We confirm our earlier findings that the basal angiosperm Amborella is most closely related to aquatic herbs. Our current and previously reported (Goremykin et al. 2013) analyses highlight an essential aspect of the total evidence approach to phylogenetic inference. They suggest that data partitioning aimed at identifying components of the data that better fit evolutionary models is a more reliable approach to phylogeny reconstruction at deep taxonomic levels., (© The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Both the origin of domesticated apple and the overall phylogeny of the genus Malus are still not completely resolved. Having this as a target, we built a 134,553-position-long alignment including two previously published chloroplast DNAs (cpDNAs) and 45 de novo sequenced, fully colinear chloroplast genomes from cultivated apple varieties and wild apple species. The data produced are free from compositional heterogeneity and from substitutional saturation, which can adversely affect phylogeny reconstruction. Phylogenetic analyses based on this alignment recovered a branch, having the maximum bootstrap support, subtending a large group of the cultivated apple sorts together with all analyzed European wild apple (Malus sylvestris) accessions. One apple cultivar was embedded in a monophylum comprising wild M. sieversii accessions and other Asian apple species. The data demonstrate that M. sylvestris has contributed chloroplast genome to a substantial fraction of domesticated apple varieties, supporting the conclusion that different wild species should have contributed the organelle and nuclear genomes to the domesticated apple.

Summary: Pathway Processor 2.0 is a web application designed to analyze high-throughput datasets, including but not limited to microarray and next-generation sequencing, using a pathway centric logic. In addition to well-established methods such as the Fisher's test and impact analysis, Pathway Processor 2.0 offers innovative methods that convert gene expression into pathway expression, leading to the identification of differentially regulated pathways in a dataset of choice., Availability and Implementation: Pathway Processor 2.0 is available as a web service at http://compbiotoolbox.fmach.it/pathwayProcessor/. Sample datasets to test the functionality can be used directly from the application., Contact: duccio.cavalieri@fmach.it, Supplementary Information: Supplementary data are available at Bioinformatics online.

Drosophilid fruit flies have provided science with striking cases of behavioral adaptation and genetic innovation. A recent example is the invasive pest Drosophila suzukii, which, unlike most other Drosophila, lays eggs and feeds on undamaged, ripening fruits. This not only poses a serious threat for fruit cultivation but also offers an interesting model to study evolution of behavioral innovation. We developed genome and transcriptome resources for D. suzukii. Coupling analyses of these data with field observations, we propose a hypothesis of the origin of its peculiar ecology. Using nuclear and mitochondrial phylogenetic analyses, we confirm its Asian origin and reveal a surprising sister relationship between the eugracilis and the melanogaster subgroups. Although the D. suzukii genome is comparable in size and repeat content to other Drosophila species, it has the lowest nucleotide substitution rate among the species analyzed in this study. This finding is compatible with the overwintering diapause of D. suzukii, which results in a reduced number of generations per year compared with its sister species. Genome-scale relaxed clock analyses support a late Miocene origin of D. suzukii, concomitant with paleogeological and climatic conditions that suggest an adaptation to temperate montane forests, a hypothesis confirmed by field trapping. We propose a causal link between the ecological adaptations of D. suzukii in its native habitat and its invasive success in Europe and North America.

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.

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.

Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of 'big data' research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European 'Network of Excellence' (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science.

Motivation: Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions., Results: The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and 'wet lab' scientists., Availability and Implementation: The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.

In the mucosa, the immune pathways discriminating between colonizing and invasive Candida, thus inducing tolerance or inflammation, are poorly understood. Th17 responses induced by Candida albicans hyphae are central for the activation of mucosal antifungal immunity. An essential step for the discrimination between yeasts and hyphae and induction of Th17 responses is the activation of the inflammasome by C. albicans hyphae and the subsequent release of active IL-1β in macrophages. Inflammasome activation in macrophages results from differences in cell-wall architecture between yeasts and hyphae and is partly mediated by the dectin-1/Syk pathway. These results define the dectin-1/inflammasome pathway as the mechanism that enables the host immune system to mount a protective Th17 response and distinguish between colonization and tissue invasion by C. albicans.

Motivation: Eu.Gene Analyzer is an easy-to-use, stand-alone application that allows rapid and powerful microarray data analysis in the context of biological pathways. Its intuitive graphical user interface makes it an easy and flexible tool, even for the first-time user. Eu.Gene supports a variety of array platforms, organisms and pathway ontologies, transparently deals with multiple nomenclature systems and seamlessly integrates data from different sources. Two different statistical methods, the Fisher Exact Test and the Gene Set Enrichment Analysis (GSEA), are implemented to identify biological pathways transcriptionally affected under experimental conditions. A suite of tools is offered to define, visualize and share custom non-redundant pathway sets. In conclusion, Eu.Gene Analyzer is a new software application that takes advantage of information from multiple pathway databases to build a comprehensive interpretation of experimental results in a simple, intuitive environment.

Evolutionary biologists seek to understand which traits display variation, are heritable, and influence differential reproduction, because such traits respond to natural selection and underlie organic evolution. Selection acts upon individual differences within a population. Whether individual differences within a natural population include variation in gene expression levels has not yet been addressed on a genome-wide scale. Here we use DNA microarray technology for measuring comparative gene expression and a refined statistical analysis for the purpose of comparing gene expression levels in natural isolates of the wine yeast Saccharomyces cerevisiae. A method for the Bayesian analysis of gene expression levels is used to compare four natural isolates of S. cerevisiae from Montalcino, Italy. Widespread variation in amino acid metabolism, sulfur assimilation and processing, and protein degradation-primarily consisting of differences in expression level smaller than a factor of 2-is demonstrated. Genetic variation in gene expression among isolates from a natural population is present on a genomic scale. It remains to be determined what role differential gene expression may play in adaptation to new or changing environments.

The resistance to 5,5,5-trifluoro-DL-leucine, encoded by the dominant allele LEU4-1, was used as a selectable marker to transform laboratory and natural Saccharomyces cerevisiae strains by the lithium acetate procedure. Results of transformation of S. cerevisiae laboratory and wine natural strains showed that trifluoroleucine resistance is a very effective selection marker and can be widely used to transform prototrophic S. cerevisiae strains. The LEU4-1 gene could also be exploited to improve wine flavour, as indicated by the higher isoamyl alcohol content of the transformants compared to the parental strains.