A web platform for the network analysis of high-throughput data in melanoma and its use to investigate mechanisms of resistance to anti-PD1 immunotherapy

Cellular phenotypes are established and controlled by complex and precisely orchestrated molecular networks. In cancer, mutations and dysregulations of multiple molecular factors perturb the regulation of these networks and lead to malignant transformation. High-throughput technologies are a valuable source of information to establish the complex molecular relationships behind the emergence of malignancy, but full exploitation of this massive amount of data requires bioinformatics tools that rely on network-based analyses.In this report we present the Virtual Melanoma Cell, an online tool developed to facilitate the mining and interpretation of high-throughput data on melanoma by biomedical researches. The platform is based on a comprehensive, manually generated and expert-validated regulatory map composed of signaling pathways important in malignant melanoma. The Virtual Melanoma Cell is a tool designed to accept, visualize and analyze user-generated datasets. It is available at: https://www.vcells.net/melanoma. To illustrate the utilization of the web platform and the regulatory map, we have analyzed a large publicly available dataset accounting for anti-PD1 immunotherapy treatment of malignant melanoma patients.     

Extending biochemical databases by metabolomic surveys

Metabolomics can map the large metabolic diversity in species, organs, or cell types. In addition to gains in enzyme specificity, many enzymes have retained substrate and reaction promiscuity. Enzyme promiscuity and the large number of enzymes with unknown enzyme function may explain the presence of a plethora of unidentified compounds in metabolomic studies. Cataloguing the identity and differential abundance of all detectable metabolites in metabolomic repositories may detail which compounds and pathways contribute to vital biological functions. The current status in metabolic databases is reviewed concomitant with tools to map and visualize the metabolome.     

On-line resources for bacterial micro-evolution studies using MLVA or CRISPR typing

The control of bacterial pathogens requires the development of tools allowing the precise identification of strains at the subspecies level. It is now widely accepted that these tools will need to be DNA-based assays (in contrast to identification at the species level, where biochemical based assays are still widely used, even though very powerful 16S DNA sequence databases exist). Typing assays need to be cheap and amenable to the designing of international databases. The success of such subspecies typing tools will eventually be measured by the size of the associated reference databases accessible over the internet. Three methods have shown some potential in this direction, the so-called spoligotyping assay (Mycobacterium tuberculosis, 40,000 entries database), Multiple Loci Sequence Typing (MLST; up to a few thousands entries for the more than 20 bacterial species), and more recently Multiple Loci VNTR Analysis (MLVA; up to a few hundred entries, assays available for more than 20 pathogens). In the present report we will review the current status of the tools and resources we have developed along the past seven years to help in the setting-up or the use of MLVA assays or lately for analysing Clustered Regularly Interspaced Short Palindromic Repeats called CRISPRs which are the basis for spoligotyping assays.     

The proteome and secretome of human arterial smooth muscle cells

Smooth muscle cells (SMCs) play a crucial role in cardiovascular disorders. A differential proteomic approach should help to elucidate SMC dysfunctions involved in these diseases. With this goal in mind, we plotted the first 2-dimensional (2-D) maps of the proteome and secretome of human arterial smooth muscle cell (ASMC). Intracellular and secreted proteins were extracted from a primary culture of SMCs obtained from patients undergoing coronary artery bypass surgery (n = 11) and separated by 2-dimensional gel electrophoresis. Silver-stained gels were analyzed using Progenesis software. A high level of between-gel reproducibility was obtained, allowing us to generate two protein patterns specific to the ASMC proteome and secretome, respectively. A total of 121 and 40 distinct intracellular and secreted polypeptide spots, corresponding to 83 and 18 different proteins, respectively, were identified by matrix-assisted laser desorption/ionization mass spectrometry. The 2-D reference maps and database resulting from this study confirm that SMCs are involved in a wide range of biological functions. They could constitute a useful tool for a wide range of investigators involved in vascular biology, allowing them to investigate SMC protein changes associated with cardiovascular disorders or environmental stimuli.     

Benchmarking of 16S rRNA gene databases using known strain sequences

16S rRNA gene analysis is the most convenient and robust method for microbiome studies. Inaccurate taxonomic assignment of bacterial strains could have deleterious effects as all downstream analyses rely heavily on the accurate assessment of microbial taxonomy. The use of mock communities to check the reliability of the results has been suggested. However, often the mock communities used in most of the studies represent only a small fraction of taxa and are used mostly as validation of sequencing run to estimate sequencing artifacts. Moreover, a large number of databases and tools available for classification and taxonomic assignment of the 16S rRNA gene make it challenging to select the best-suited method for a particular dataset. In the present study, we used authentic and validly published 16S rRNA gene type strain sequences (full length, V3-V4 region) and analyzed them using a widely used QIIME pipeline along with different parameters of OTU clustering and QIIME compatible databases. Data Analysis Measures (DAM) revealed a high discrepancy in ratifying the taxonomy at different taxonomic hierarchies. Beta diversity analysis showed clear segregation of different DAMs. Limited differences were observed in reference data set analysis using partial (V3-V4) and full-length 16S rRNA gene sequences, which signify the reliability of partial 16S rRNA gene sequences in microbiome studies. Our analysis also highlights common discrepancies observed at various taxonomic levels using various methods and databases.     

Data Mining for Bioprocess Optimization

Although developed for completely different applications, the great technological potential of data analysis methods called “data mining” has increasingly been realized as a method for efficiently analyzing potentials for optimization and for troubleshooting within many application areas of process, technology. This paper presents the successful application of data mining methods for the optimization of a fermentation process, and discusses diverse characteristics of data mining for biological processes. For the optimization of biological processes a huge amount of possibly relevant process parameters exist. Those input variables can be parameters from devices as well as process control parameters. The main challenge of such optimizations is to robustly identify relevant combinations of parameters among a huge amount of process parameters. For the underlying process we found with the application of data mining methods, that the moment a special carbohydrate component is added has a strong impact on the formation of secondary components. The yield could also be increased by using 2 m³ fermentors instead of 1 m³ fermentors.     

Classification of habitats highlights priorities for conservation policies: The case of Spanish Mediterranean tall humid herb grasslands

The Mediterranean grasslands of Molinio-Holoschoenion are one of the lesser known natural habitat types of interest for conservation in the European Community. A study on their content with regard to their plant-communities and interest for plant conservation is conducted on a regional scale for the first time. A new comprehensive classification is proposed for this habitat type in Spain (Iberian Peninsula and Balearic and Canary Islands) where they have their highest European diversity. Twenty-five plant-community types are floristically identified by k-means clustering. Cluster analysis reveals a major hierarchical aggregation in two vegetation groups: (i) rush meadows of Scirpoides holoschoenus (L.) Soják; and (ii) communities of herbs and forbs dominated by Molinia caerulea (L.) Moench subsp. arundinacea (Schrank) H. Paul and/or Schoenus nigricans L. These groups are associated with different positions along a soil moisture gradient where rush formations occupy the positions farthest from the water table. Cluster Analysis also shows an uneven distribution of species with conservation interest. The highest content in endangered species is found in halophilous rushes. Grasslands on mineralised soils host the highest number of vulnerable species. Spanish Molinio-Holoschoenion plant communities were ranked using three criteria (regional responsibility, local rarity and habitat vulnerability) in order to set conservation priorities. The highest conservation values were achieved by coastal rush communities developing on dune slacks, and by forbs and rushes in the Baetic System growing on banks of streams with oligotrophic waters.     

Testing the predictivity of ecological indicator values. A comparison of real and `virtual' relevés of lichen vegetation

A complex database on the lichens of Italy was used to combine ecological indicator values assigned to each species (pH, eutrophication, light and aridity), in such a way as to simulate `virtual habitats' from a beech forest of northern Italy. For each habitat, a list of species was obtained (`virtual relevé'). A matrix of real and virtual relevés was submitted to classification and ordination, obtaining six main community-types, all of which include both real and virtual relevés. Two ordinations of species were carried out, one on the matrix of real relevés, the other on that of virtual relevés: their comparison shows that the consistency and the resolving power of the indicator values was high, but it also permits to detect and correct some errors. The results indicate a high predictivity of the indicator values for constructing ecological scenarios.     

Floristics of Higher Plants in China——Report from Catalogue of Life: Higher Plants in China Database

Biodiversity research increasingly relies on distribution networks for dealing with large-scale primary data. Up-to-date information on biodiversity is critical for the proper management and conservation of any area. The first step towards conservation should be to compile an authoritative species inventory or checklist. Catalogue of Life: Higher Plants in China (CNPC) is an ongoing biodiversity informatics project with the aim to integrate existing higher plants inventory data, and provide access via an internet based web service to public user and the scientific community. The CNPC, for the first time, provides integrated and authoritative taxonomic information on higher plant species found in China, and this database will be permanent, free and continously updated. Presently, a total of 34377 species have been included in the database. Among of them, 16620 species are only found in China. Taxa are classified into 432 families and 3941 genera. The CNPC will be an important source for scientists working on Chinese flora, and will play an important role in helping to achieve the targets set under the Global Strategy for Plant Conservation in the future.