The natural organosulfur compound dipropyltetrasulfide prevents HOCL-induced systemic sclerosis in the mouse

PublishedArticleIntroduction: The aim of this study was to test the naturally occurring organosulfur compound dipropyltetrasulfide (DPTTS) found in plants, which has antibiotic and anti-cancer properties, as a treatment of HOCl-induced systemic sclerosis in the mouse. Methods: The pro-oxidative, anti-proliferative and cytotoxic effects of DPTTS were evaluated ex vivo on fibroblasts from normal and HOCl-mice. In vivo, the anti-fibrotic and immunomodulating properties of DPTTS were evaluated in the skin and lungs of HOCl-mice. Results: H2O2 production was higher in fibroblasts derived from HOCl-mice than in normal fibroblasts (P<0.05). DPTTS did not increase H2O2 production in normal fibroblasts, but DPTTS dose-dependently increased H2O2 production in HOCl-fibroblasts (P<0.001 with 40μM DPTTS). Because H2O2 reached a lethal threshold in cells from HOCl-mice, the anti-proliferative, cytotoxic and pro-apoptotic effects of DPTTS were significantly higher in HOCl-fibroblasts than for normal fibroblasts. In vivo, DPTTS decreased dermal thickness (P<0.001), collagen content in skin (P<0.01) and lungs (P<0.05), SMA (P<0.01) and pSMAD2/3 (P<0.01) expression in skin, formation of advanced oxidation protein products and anti-DNA topoisomerase-1 antibodies in serum (P<0.05) versus untreated HOCl- mice. Moreover, in HOCl-mice, DPTTS reduced splenic B cell counts (P<0.01), the proliferative rates of B-splenocytes stimulated by lipopolysaccharide (P<0.05) and T-splenocytes stimulated by anti-CD3/CD28 mAb (P<0.001). Ex vivo, it also reduced the production of IL-4 and IL-13 by activated T cells (P<0.05 in both cases). Conclusions: The natural organosulfur compound DPTTS prevents skin and lung fibrosis in the mouse through the selective killing of diseased fibroblasts and its immunomodulating properties. DPTTS may be a potential treatment of Systemic sclerosis.This work was supported by European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 215009 RedCat for financial support. The authors are grateful to Ms Agnes for her excellent typing of the manuscript

A global genomic analysis of Salmonella Concord reveals lineages with high antimicrobial resistance in Ethiopia.

Antimicrobial resistant Salmonella enterica serovar Concord (S. Concord) is known to cause severe gastrointestinal and bloodstream infections in patients from Ethiopia and Ethiopian adoptees, and occasional records exist of S. Concord linked to other countries. The evolution and geographical distribution of S. Concord remained unclear. Here, we provide a genomic overview of the population structure and antimicrobial resistance (AMR) of S. Concord by analysing genomes from 284 historical and contemporary isolates obtained between 1944 and 2022 across the globe. We demonstrate that S. Concord is a polyphyletic serovar distributed among three Salmonella super-lineages. Super-lineage A is composed of eight S. Concord lineages, of which four are associated with multiple countries and low levels of AMR. Other lineages are restricted to Ethiopia and horizontally acquired resistance to most antimicrobials used for treating invasive Salmonella infections in low- and middle-income countries. By reconstructing complete genomes for 10 representative strains, we demonstrate the presence of AMR markers integrated in structurally diverse IncHI2 and IncA/C2 plasmids, and/or the chromosome. Molecular surveillance of pathogens such as S. Concord supports the understanding of AMR and the multi-sector response to the global AMR threat. This study provides a comprehensive baseline data set essential for future molecular surveillance

Global diversity and antimicrobial resistance of typhoid fever pathogens : insights from a meta-analysis of 13,000 Salmonella Typhi genomes

DATA AVAILABILITY : All data analysed during this study are publicly accessible. Raw Illumina sequence reads have been submitted to the European Nucleotide Archive (ENA), and individual sequence accession numbers are listed in Supplementary file 2. The full set of n=13,000 genome assemblies generated for this study are available for download from FigShare: https://doi.org/10.26180/21431883. All assemblies of suitable quality (n=12,849) are included as public data in the online platform Pathogenwatch (https://pathogen.watch). The data are organised into collections, which each comprise a neighbour-joining phylogeny annotated with metadata, genotype, AMR determinants, and a linked map. Each contributing study has its own collection, browsable at https://pathogen.watch/collections/all?organismId= 90370. In addition, we have provided three large collections, each representing roughly a third of the total dataset presented in this study: Typhi 4.3.1.1 (https://pathogen.watch/collection/ 2b7mp173dd57-clade-4311), Typhi lineage 4 (excluding 4.3.1.1) (https://pathogen.watch/collection/ wgn6bp1c8bh6-clade-4-excluding-4311), and Typhi lineages 0-3 (https://pathogen.watch/collection/ 9o4bpn0418n3-clades-0-1-2-and-3). In addition, users can browse the full set of Typhi genomes in Pathogenwatch and select subsets of interest (e.g. by country, genotype, and/or resistance) to generate a collection including neighbour-joining tree for interactive exploration.SUPPLEMENTARY FILES : Available at https://elifesciences.org/articles/85867/figures#content. SUPPLEMENTARY FILE 1. Details of local ethical approvals provided for studies that were unpublished at the time of contributing data to this consortium project. Most data are now published, and the citations for the original studies are provided here. National surveillance programs in Chile (Maes et al., 2022), Colombia (Guevara et al., 2021), France, New Zealand, and Nigeria (Ikhimiukor et al., 2022b) were exempt from local ethical approvals as these countries allow sharing of non-identifiable pathogen sequence data for surveillance purposes. The US CDC Internal Review Board confirmed their approval was not required for use in this project (#NCEZID-ARLT- 10/ 20/21-fa687). SUPPLEMENTARY FILE 2. Line list of 13,000 genomes included in the study. SUPPLEMENTARY FILE 3. Source information recorded for genomes included in the study. ^Indicates cases included in the definition of ‘assumed acute illness’. SUPPLEMENTARY FILE 4. Summary of genomes by country. SUPPLEMENTARY FILE 5. Genotype frequencies per region (N, %, 95% confidence interval; annual and aggregated, 2010–2020). SUPPLEMENTARY FILE 6. Genotype frequencies per country (N, %, 95% confidence interval; annual and aggregated, 2010–2020). SUPPLEMENTARY FILE 7. Antimicrobial resistance (AMR) frequencies per region (N, %, 95% confidence interval; aggregated 2010–2020). SUPPLEMENTARY FILE 8. Antimicrobial resistance (AMR) frequencies per country (N, %, 95% confidence interval; annual and aggregated, 2010–2020). SUPPLEMENTARY FILE 9. Laboratory code master list. Three letter laboratory codes assigned by the consortium.BACKGROUND : The Global Typhoid Genomics Consortium was established to bring together the typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi) genomic data to inform public health action. This analysis, which marks 22 years since the publication of the first Typhi genome, represents the largest Typhi genome sequence collection to date (n=13,000). METHODS : This is a meta-analysis of global genotype and antimicrobial resistance (AMR) determinants extracted from previously sequenced genome data and analysed using consistent methods implemented in open analysis platforms GenoTyphi and Pathogenwatch. RESULTS : Compared with previous global snapshots, the data highlight that genotype 4.3.1 (H58) has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate and have independently evolved AMR. Data gaps remain in many parts of the world, and we show the potential of travel-associated sequences to provide informal ‘sentinel’ surveillance for such locations. The data indicate that ciprofloxacin non-susceptibility (>1 resistance determinant) is widespread across geographies and genotypes, with high-level ciprofloxacin resistance (≥3 determinants) reaching 20% prevalence in South Asia. Extensively drug-resistant (XDR) typhoid has become dominant in Pakistan (70% in 2020) but has not yet become established elsewhere. Ceftriaxone resistance has emerged in eight non-XDR genotypes, including a ciprofloxacin-resistant lineage (4.3.1.2.1) in India. Azithromycin resistance mutations were detected at low prevalence in South Asia, including in two common ciprofloxacin-resistant genotypes. CONCLUSIONS : The consortium’s aim is to encourage continued data sharing and collaboration to monitor the emergence and global spread of AMR Typhi, and to inform decision-making around the introduction of typhoid conjugate vaccines (TCVs) and other prevention and control strategies.Fellowships from the European Union (funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 845681), the Wellcome Trust (SB, Wellcome Trust Senior Fellowship), and the National Health and Medical Research Council.https://elifesciences.org/am2024Medical MicrobiologySDG-03:Good heatlh and well-bein

Enhancing the Accuracy of Chemogenomic Models with a Three-Dimensional Binding Site Kernel

International audienceComputational chemogenomic (or proteochemometric) methods predict target-ligand interactions by training machine learning algorithms on known experimental data in order to distinguish attributes of true from false target-ligand pairs. Many ligand and target descriptors can be used for training and predicting binary associations or even binding affinities. Several chemogenomic studies have not noticed any real benefit in using 3-D structural target descriptors with respect to simpler sequence-based or property-based information. To assess whether this observation results from inaccurate target description or from the fact that 3-D information is simply not required in chemogenomic modeling, we used a target kernel measuring the distance between target-ligand binding sites of known X-ray structures. When used in combination with a standard ligand kernel in a support vector machine (SVM) classifier, the 3-D target kernel significantly outperforms a sequence-based target kernel in discriminating 2882 target-ligand PDB complexes from 9128 false pairs, whatever the modeling procedure (local or global). The best SVM models could be successfully applied to predict, with very high recall (70%), precision (99%), and specificity (99%), target-ligand associations for an external set of 14 117 ligands and 531 targets. In most of the cases, pooling all data in a global model gave better statistics than just discretizing specific target-ligand subspaces in local models. The current study clearly demonstrates that chemogenomic models taking both ligand and target information outperform simpler ligand-based models. It also permits one to design good modeling practices in predicting target-ligand pairing for a large array of targets: (i) ligand-based models are precise enough if sufficient ligand information (>40-50 diverse ligands) is known; (ii) if not, structure-based chemogenomic models (associating a ligand kernel to a structure-based target kernel) are recommended for proteins of known holostructures; (iii) sequence-based chemogenomic models (associating a ligand kernel to a sequence-based target kernel) can still be used with a very good accuracy for the remaining targets

How We Type: Eye and Finger Movement Strategies in Mobile Typing

| openaire: EC/H2020/637991/EU//COMPUTEDRelatively little is known about eye and finger movement in typing with mobile devices. Most prior studies of mobile typing rely on log data, while data on finger and eye movements in typing come from studies with physical keyboards. This paper presents new findings from a transcription task with mobile touchscreen devices. Movement strategies were found to emerge in response to sharing of visual attention: attention is needed for guiding finger movements and detecting typing errors. In contrast to typing on physical keyboards, visual attention is kept mostly on the virtual keyboard, and glances at the text display are associated with performance. When typing with two fingers, although users make more errors, they manage to detect and correct them more quickly. This explains part of the known superiority of two-thumb typing over one-finger typing. We release the extensive dataset on everyday typing on smartphones.Peer reviewe