Productivity and innovation in UK financial services: an intangible assets approach

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How much does the UK employ, spend and invest in design?

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Investigating human mitochondrial genomes in single cells

Mitochondria host multiple copies of their own small circular genome that has been extensively studied to trace the evolution of the modern eukaryotic cell and discover important mutations linked to inherited diseases. Whole genome and exome sequencing have enabled the study of mtDNA in a large number of samples and experimental conditions at single nucleotide resolution, allowing the deciphering of the relationship between inherited mutations and phenotypes and the identification of acquired mtDNA mutations in classical mitochondrial diseases as well as in chronic disorders, ageing and cancer. By applying an ad hoc computational pipeline based on our MToolBox software, we reconstructed mtDNA genomes in single cells using whole genome and exome sequencing data obtained by different amplification methodologies (eWGA, DOP-PCR, MALBAC, MDA) as well as data from single cell Assay for Transposase Accessible Chromatin with high-throughput sequencing (scATAC-seq) in which mtDNA sequences are expected as a byproduct of the technology. We show that assembled mtDNAs, with the exception of those reconstructed by MALBAC and DOP-PCR methods, are quite uniform and suitable for genomic investigations, enabling the study of various biological processes related to cellular heterogeneity such as tumor evolution, neural somatic mosaicism and embryonic development

Management at the service of research: ReOmicS, a quality management system for omics sciences

Management and research represent a binomial almost unknown, whose potentialities and requirements have not yet been fully exploited even if, recently, the scientific and social communities have felt the burden of producing results and data requiring at the same time reproducibility, reliability, safety and efficacy of the discoveries, as well as a profitable use of resources. A Quality Management System (QMS) could represent a valid tool for these purposes, improving the quality of the research. The research community could ask whether and how it is possible to apply this approach in a research laboratory without hindering their creativity, and what the possible benefits might be. On the other hand, an international standard for a quality management system appropriate for a research laboratory is yet to come. The choice, the design and the application of a QMS, inspired by the Good Laboratory Practices, in a research laboratory specialized on “omics” sciences, is fully described in this paper. Its application has already shown good outcomes as testified by specific metric of efficiency and effectiveness. The approach is innovative as there is no obvious requirement for research laboratories to develop and define quality objectives. The paper highlights how the QMS approach enhances the relationship with public and private sectors by increasing customer confidence and loyalty, as well as improving the overall performance of the laboratory in terms of throughput and value of research. These results encourage proposing it as a QMS model providing a new and scalable operational strategy to be applied in a research environment with the same target and even in a generic research laboratory

Draft Genome Sequences of Three Novel Staphylococcus arlettae Strains Isolated from a Disused Biological Safety Cabinet

The genome sequences of three new strains of Staphylococcus arlettae named Bari1, Bari2, and Bari3 are presented. The strains exhibited tolerance to hexavalent chromium ions. An sprC gene encoding a putative chromium transporter was present in each of the three draft genome sequences

Innovation, knowledge spending and productivity growth in the UK: interim report for NESTA 'Innovation Index’ project

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Comparative Genomics Suggests a Taxonomic Revision of the Staphylococcus cohnii Species Complex

Staphylococcus cohnii (SC), a coagulase-negative bacterium, was first isolated in 1975 from human skin. Early phenotypic analyses led to the delineation of two subspecies (subsp.), Staphylococcus cohnii subsp. cohnii (SCC) and Staphylococcus cohnii subsp. urealyticus (SCU). SCC was considered to be specific to humans, whereas SCU apparently demonstrated a wider host range, from lower primates to humans. The type strains ATCC 29974 and ATCC 49330 have been designated for SCC and SCU, respectively. Comparative analysis of 66 complete genome sequences-including a novel SC isolate-revealed unexpected patterns within the SC complex, both in terms of genomic sequence identity and gene content, highlighting the presence of 3 phylogenetically distinct groups. Based on our observations, and on the current guidelines for taxonomic classification for bacterial species, we propose a revision of the SC species complex. We suggest that SCC and SCU should be regarded as two distinct species: SC and SU (Staphylococcus urealyticus), and that two distinct subspecies, SCC and SCB (SC subsp. barensis, represented by the novel strain isolated in Bari) should be recognized within SC. Furthermore, since large-scale comparative genomics studies recurrently suggest inconsistencies or conflicts in taxonomic assignments of bacterial species, we believe that the approach proposed here might be considered for more general application

Genome Sequencing and Comparative Analysis of Three Hanseniaspora uvarum Indigenous Wine Strains Reveal Remarkable Biotechnological Potential

A current trend in winemaking has highlighted the beneficial contribution of non-Saccharomyces yeasts to wine quality. Hanseniaspora uvarum is one of the more represented non-Saccharomyces species onto grape berries and plays a critical role in influencing the wine sensory profile, in terms of complexity and organoleptic richness. In this work, we analyzed a group of H. uvarum indigenous wine strains as for genetic as for technological traits, such as resistance to SO2 and beta-glucosidase activity. Three strains were selected for genome sequencing, assembly and comparative genomic analyses at species and genus level. Hanseniaspora genomes appeared compact and contained a moderate number of genes, while rarefaction analyses suggested an open accessory genome, reflecting a rather incomplete representation of the Hanseniaspora gene pool in the currently available genomes. The analyses of patterns of functional annotation in the three indigenous H. uvarum strains showed distinct enrichment for several PFAM protein domains. In particular, for certain traits, such as flocculation related protein domains, the genetic prediction correlated well with relative flocculation phenotypes at lab-scale. This feature, together with the enrichment for oligo-peptide transport and lipid and amino acid metabolism domains, reveals a promising potential of these indigenous strains to be applied in fermentation processes and modulation of wine flavor and aroma. This study also contributes to increasing the catalog of publicly available genomes from H. uvarum strains isolated from natural grape samples and provides a good roadmap for unraveling the biodiversity and the biotechnological potential of these non-Saccharomyces yeasts

Accurate detection and quantification of SARS-CoV-2 genomic and subgenomic mRNAs by ddPCR and meta-transcriptomics analysis

SARS-CoV-2 replication requires the synthesis of a set of structural proteins expressed through discontinuous transcription of ten subgenomic mRNAs (sgmRNAs). Here, we have fine-tuned droplet digital PCR (ddPCR) assays to accurately detect and quantify SARS-CoV-2 genomic ORF1ab and sgmRNAs for the nucleocapsid (N) and spike (S) proteins. We analyzed 166 RNA samples from anonymized SARS-CoV-2 positive subjects and we observed a recurrent and characteristic pattern of sgmRNAs expression in relation to the total viral RNA content. Additionally, expression profiles of sgmRNAs, as determined by meta-transcriptomics sequencing of a subset of 110 RNA samples, were highly correlated with those obtained by ddPCR. By providing a comprehensive and dynamic snapshot of the levels of SARS-CoV-2 sgmRNAs in infected individuals, our results may contribute a better understanding of the dynamics of transcription and expression of the genome of SARS-CoV-2 and facilitate the development of more accurate molecular diagnostic tools for the stratification of COVID-19 patients

A primer on machine learning techniques for genomic applications

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous “omic” data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available