Vitamin D-related polymorphisms and vitamin D levels as risk biomarkers of COVID-19 disease severity.

Vitamin D is a fundamental regulator of host defences by activating genes related to innate and adaptive immunity. Previous research shows a correlation between the levels of vitamin D in patients infected with SARS-CoV-2 and the degree of disease severity. This work investigates the impact of the genetic background related to vitamin D pathways on COVID-19 severity. For the first time, the Portuguese population was characterized regarding the prevalence of high impact variants in genes associated with the vitamin D pathways. This study enrolled 517 patients admitted to two tertiary Portuguese hospitals. The serum concentration of 25 (OH)D, was measured in the hospital at the time of patient admission. Genetic variants, 18 variants, in the genes AMDHD1, CYP2R1, CYP24A1, DHCR7, GC, SEC23A, and VDR were analysed. The results show that polymorphisms in the vitamin D binding protein encoded by the GC gene are related to the infection severity (p = 0.005). There is an association between vitamin D polygenic risk score and the serum concentration of 25 (OH)D (p = 0.04). There is an association between 25 (OH)D levels and the survival and fatal outcomes (p = 1.5e-4). The Portuguese population has a higher prevalence of the DHCR7 RS12785878 variant when compared with its prevalence in the European population (19% versus 10%). This study shows a genetic susceptibility for vitamin D deficiency that might explain higher severity degrees in COVID-19 patients. These results reinforce the relevance of personalized strategies in the context of viral diseases.Trial registration: NCT04370808.

NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language.

BACKGROUND: Shotgun metagenomes contain a sample of all the genomic material in an environment, allowing for the characterization of a microbial community. In order to understand these communities, bioinformatics methods are crucial. A common first step in processing metagenomes is to compute abundance estimates of different taxonomic or functional groups from the raw sequencing data. Given the breadth of the field, computational solutions need to be flexible and extensible, enabling the combination of different tools into a larger pipeline. RESULTS: We present NGLess and NG-meta-profiler. NGLess is a domain specific language for describing next-generation sequence processing pipelines. It was developed with the goal of enabling user-friendly computational reproducibility. It provides built-in support for many common operations on sequencing data and is extensible with external tools with configuration files. Using this framework, we developed NG-meta-profiler, a fast profiler for metagenomes which performs sequence preprocessing, mapping to bundled databases, filtering of the mapping results, and profiling (taxonomic and functional). It is significantly faster than either MOCAT2 or htseq-count and (as it builds on NGLess) its results are perfectly reproducible. CONCLUSIONS: NG-meta-profiler is a high-performance solution for metagenomics processing built on NGLess. It can be used as-is to execute standard analyses or serve as the starting point for customization in a perfectly reproducible fashion. NGLess and NG-meta-profiler are open source software (under the liberal MIT license) and can be downloaded from https://ngless.embl.de or installed through bioconda.

Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients.

Ischemic stroke is a leading cause of disability and death worldwide among adults. The individual prognosis after stroke is extremely dependent on treatment decisions physicians take during the acute phase. In the last five years, several scores such as the ASTRAL, DRAGON, and THRIVE have been proposed as tools to help physicians predict the patient functional outcome after a stroke. These scores are rule-based classifiers that use features available when the patient is admitted to the emergency room. In this paper, we apply machine learning techniques to the problem of predicting the functional outcome of ischemic stroke patients, three months after admission. We show that a pure machine learning approach achieves only a marginally superior Area Under the ROC Curve (AUC) ( 0.808±0.085) than that of the best score ( 0.771±0.056) when using the features available at admission. However, we observed that by progressively adding features available at further points in time, we can significantly increase the AUC to a value above 0.90. We conclude that the results obtained validate the use of the scores at the time of admission, but also point to the importance of using more features, which require more advanced methods, when possible.

Metagenome enrichment approach used for selection of oil-degrading bacteria consortia for drill cutting residue bioremediation.

Drill cuttings leave behind thousands of tons of residues without adequate treatment, generating a large environmental liability. Therefore knowledge about the microbial community of drilling residue may be useful for developing bioremediation strategies. In this work, samples of drilling residue were enriched in different culture media in the presence of petroleum, aiming to select potentially oil-degrading bacteria and biosurfactant producers. Total DNA was extracted directly from the drill cutting samples and from two enriched consortia and sequenced using the Ion Torrent platform. Taxonomic analysis revealed the predominance of Proteobacteria in the metagenome from the drill cuttings, while Firmicutes was enriched in consortia samples. Functional analysis using the Biosurfactants and Biodegradation Database (BioSurfDB) revealed a similar pattern among the three samples regarding hydrocarbon degradation and biosurfactants production pathways. However, some statistical differences were observed between samples. Namely, the pathways related to the degradation of fatty acids, chloroalkanes, and chloroalkanes were enriched in consortia samples. The degradation colorimetric assay using dichlorophenolindophenol as an indicator was positive for several hydrocarbon substrates. The consortia were also able to produce biosurfactants, with biosynthesis of iturin, lichnysin, and surfactin among the more abundant pathways. A microcosms assay followed by gas chromatography analysis showed the efficacy of the consortia in degrading alkanes, as we observed a reduction of around 66% and 30% for each consortium in total alkanes. These data suggest the potential use of these consortia in the bioremediation of drilling residue based on autochthonous bioaugmentation.

Biogeographical distribution analysis of hydrocarbon degrading and biosurfactant producing genes suggests that near-equatorial biomes have higher abundance of genes with potential for bioremediation.

BACKGROUND: Bacterial and Archaeal communities have a complex, symbiotic role in crude oil bioremediation. Their biosurfactants and degradation enzymes have been in the spotlight, mainly due to the awareness of ecosystem pollution caused by crude oil accidents and their use. Initially, the scientific community studied the role of individual microbial species by characterizing and optimizing their biosurfactant and oil degradation genes, studying their individual distribution. However, with the advances in genomics, in particular with the use of New-Generation-Sequencing and Metagenomics, it is now possible to have a macro view of the complex pathways related to the symbiotic degradation of hydrocarbons and surfactant production. It is now possible, although more challenging, to obtain the DNA information of an entire microbial community before automatically characterizing it. By characterizing and understanding the interconnected role of microorganisms and the role of degradation and biosurfactant genes in an ecosystem, it becomes possible to develop new biotechnological approaches for bioremediation use. This paper analyzes 46 different metagenome samples, spanning 20 biomes from different geographies obtained from different research projects. RESULTS: A metagenomics bioinformatics pipeline, focused on the biodegradation and biosurfactant-production pathways, genes and organisms, was applied. Our main results show that: (1) surfactation and degradation are correlated events, and therefore should be studied together; (2) terrestrial biomes present more degradation genes, especially cyclic compounds, and less surfactation genes, when compared to water biomes; and (3) latitude has a significant influence on the diversity of genes involved in biodegradation and biosurfactant production. This suggests that microbiomes found near the equator are richer in genes that have a role in these processes and thus have a higher biotechnological potential. CONCLUSION: In this work we have focused on the biogeographical distribution of hydrocarbon degrading and biosurfactant producing genes. Our principle results can be seen as an important step forward in the application of bioremediation techniques, by considering the biostimulation, optimization or manipulation of a starting microbial consortia from the areas with higher degradation and biosurfactant producing genetic diversity.

BioSurfDB: knowledge and algorithms to support biosurfactants and biodegradation studies.

Crude oil extraction, transportation and use provoke the contamination of countless ecosystems. Therefore, bioremediation through surfactants mobilization or biodegradation is an important subject, both economically and environmentally. Bioremediation research had a great boost with the recent advances in Metagenomics, as it enabled the sequencing of uncultured microorganisms providing new insights on surfactant-producing and/or oil-degrading bacteria. Many research studies are making available genomic data from unknown organisms obtained from metagenomics analysis of oil-contaminated environmental samples. These new datasets are presently demanding the development of new tools and data repositories tailored for the biological analysis in a context of bioremediation data analysis. This work presents BioSurfDB, www.biosurfdb.org, a curated relational information system integrating data from: (i) metagenomes; (ii) organisms; (iii) biodegradation relevant genes; proteins and their metabolic pathways; (iv) bioremediation experiments results, with specific pollutants treatment efficiencies by surfactant producing organisms; and (v) a biosurfactant-curated list, grouped by producing organism, surfactant name, class and reference. The main goal of this repository is to gather information on the characterization of biological compounds and mechanisms involved in biosurfactant production and/or biodegradation and make it available in a curated way and associated with a number of computational tools to support studies of genomic and metagenomic data.

Significant Differences Characterise the Correlation Coefficients between Biocide and Antibiotic Susceptibility Profiles in Staphylococcus aureus.

There is a growing concern by regulatory authorities for the selection of antibiotic resistance caused by the use of biocidal products. We aimed to complete the detailed information on large surveys by investigating the relationship between biocide and antibiotic susceptibility profiles of a large number of Staphylococcus aureus isolates using four biocides and antibiotics commonly used in clinical practice. The minimal inhibitory concentration (MIC) for most clinically-relevant antibiotics was determined according to the standardized methodology for over 1600 clinical S. aureus isolates and compared to susceptibility profiles of benzalkonium chloride, chlorhexidine, triclosan, and sodium hypochlorite. The relationship between antibiotic and biocide susceptibility profiles was evaluated using non-linear correlations. The main outcome evidenced was an absence of any strong or moderate statistically significant correlation when susceptibilities of either triclosan or sodium hypochlorite were compared for any of the tested antibiotics. On the other hand, correlation coefficients for MICs of benzalkonium chloride and chlorhexidine were calculated above 0.4 for susceptibility to quinolones, beta-lactams, and also macrolides. Our data do not support any selective pressure for association between biocides and antibiotics resistance and furthermore do not allow for a defined risk evaluation for some of the compounds. Importantly, our data clearly indicate that there does not involve any risk of selection for antibiotic resistance for the compounds triclosan and sodium hypochlorite. These data hence infer that biocide selection for antibiotic resistance has had so far a less significant impact than feared.

The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae.

The YEASTRACT (http://www.yeastract.com) information system is a tool for the analysis and prediction of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2013, this database contains over 200,000 regulatory associations between transcription factors (TFs) and target genes, including 326 DNA binding sites for 113 TFs. All regulatory associations stored in YEASTRACT were revisited and new information was added on the experimental conditions in which those associations take place and on whether the TF is acting on its target genes as activator or repressor. Based on this information, new queries were developed allowing the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. This release further offers tools to rank the TFs controlling a gene or genome-wide response by their relative importance, based on (i) the percentage of target genes in the data set; (ii) the enrichment of the TF regulon in the data set when compared with the genome; or (iii) the score computed using the TFRank system, which selects and prioritizes the relevant TFs by walking through the yeast regulatory network. We expect that with the new data and services made available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

Evaluation of reduced susceptibility to quaternary ammonium compounds and bisbiguanides in clinical isolates and laboratory-generated mutants of Staphylococcus aureus.

The MICs and minimum bactericidal concentrations (MBCs) for the biocides benzalkonium chloride and chlorhexidine were determined against 1,602 clinical isolates of Staphylococcus aureus. Both compounds showed unimodal MIC and MBC distributions (2 and 4 or 8 mg/liter, respectively) with no apparent subpopulation with reduced susceptibility. To investigate further, all isolates were screened for qac genes, and 39 of these also had the promoter region of the NorA multidrug-resistant (MDR) efflux pump sequenced. The presence of qacA, qacB, qacC, and qacG genes increased the mode MIC, but not MBC, to benzalkonium chloride, while only qacA and qacB increased the chlorhexidine mode MIC. Isolates with a wild-type norA promoter or mutations in the norA promoter had similar biocide MIC distributions; notably, not all clinical isolates with norA mutations were resistant to fluoroquinolones. In vitro efflux mutants could be readily selected with ethidium bromide and acriflavine. Multiple passages were necessary to select mutants with biocides, but these mutants showed phenotypes comparable to those of mutants selected by dyes. All mutants showed changes in the promoter region of norA, but these were distinct from this region of the clinical isolates. Still, none of the in vitro mutants displayed fitness defects in a killing assay in Galleria mellonella larvae. In conclusion, our data provide an in-depth comparative overview on efflux in S. aureus mutants and clinical isolates, showing also that plasmid-encoded efflux pumps did not affect bactericidal activity of biocides. In addition, current in vitro tests appear not to be suitable for predicting levels of resistance that are clinically relevant.

The use of machine learning methodologies to analyse antibiotic and biocide susceptibility in Staphylococcus aureus.

BACKGROUND: The rise of antibiotic resistance in pathogenic bacteria is a significant problem for the treatment of infectious diseases. Resistance is usually selected by the antibiotic itself; however, biocides might also co-select for resistance to antibiotics. Although resistance to biocides is poorly defined, different in vitro studies have shown that mutants presenting low susceptibility to biocides also have reduced susceptibility to antibiotics. However, studies with natural bacterial isolates are more limited and there are no clear conclusions as to whether the use of biocides results in the development of multidrug resistant bacteria. METHODS: The main goal is to perform an unbiased blind-based evaluation of the relationship between antibiotic and biocide reduced susceptibility in natural isolates of Staphylococcus aureus. One of the largest data sets ever studied comprising 1632 human clinical isolates of S. aureus originated worldwide was analysed. The phenotypic characterization of 13 antibiotics and 4 biocides was performed for all the strains. Complex links between reduced susceptibility to biocides and antibiotics are difficult to elucidate using the standard statistical approaches in phenotypic data. Therefore, machine learning techniques were applied to explore the data. RESULTS: In this pioneer study, we demonstrated that reduced susceptibility to two common biocides, chlorhexidine and benzalkonium chloride, which belong to different structural families, is associated to multidrug resistance. We have consistently found that a minimum inhibitory concentration greater than 2 mg/L for both biocides is related to antibiotic non-susceptibility in S. aureus. CONCLUSIONS: Two important results emerged from our work, one methodological and one other with relevance in the field of antibiotic resistance. We could not conclude on whether the use of antibiotics selects for biocide resistance or vice versa. However, the observation of association between multiple resistance and two biocides commonly used may be of concern for the treatment of infectious diseases in the future.

A novel resistance mechanism to triclosan that suggests horizontal gene transfer and demonstrates a potential selective pressure for reduced biocide susceptibility in clinical strains of Staphylococcus aureus.

The widely used biocide triclosan selectively targets FabI, the NADH-dependent trans-2-enoyl-acyl carrier protein reductase, which is an important target for narrow-spectrum antimicrobial drug development. In relation to the growing concern about biocide resistance, we compared in vitro mutants and clinical isolates of Staphylococcus aureus with reduced triclosan susceptibility. Clinical isolates of S. aureus as well as laboratory-generated mutants were assayed for minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) phenotypes and genotypes related to reduced triclosan susceptibility. A potential epidemiological cut-off (ECOFF) MBC of >4 mg/L was observed for triclosan in clinical isolates of S. aureus. These showed significantly lower MICs and higher MBCs than laboratory mutants. These groups of strains also had few similarities in the triclosan resistance mechanism. Molecular analysis identified novel resistance mechanisms linked to the presence of an additional sh-fabI allele derived from Staphylococcus haemolyticus. The lack of predictive value of in-vitro-selected mutations for clinical isolates indicates that laboratory tests in the present form appear to be of limited value. More importantly, detection of sh-fabI as a novel resistance mechanism with high potential for horizontal gene transfer demonstrates for the first time that a biocide could exert a selective pressure able to drive the spread of a resistance determinant in a human pathogen.

YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface.

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT) information system (http://www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2010, this database contains over 48,200 regulatory associations between transcription factors (TFs) and target genes, including 298 specific DNA-binding sites for 110 characterized TFs. All regulatory associations stored in the database were revisited and detailed information on the experimental evidences that sustain those associations was added and classified as direct or indirect evidences. The inclusion of this new data, gathered in response to the requests of YEASTRACT users, allows the user to restrict its queries to subsets of the data based on the existence or not of experimental evidences for the direct action of the TFs in the promoter region of their target genes. Another new feature of this release is the availability of all data through a machine readable web-service interface. Users are no longer restricted to the set of available queries made available through the existing web interface, and can use the web service interface to query, retrieve and exploit the YEASTRACT data using their own implementation of additional functionalities. The YEASTRACT information system is further complemented with several computational tools that facilitate the use of the curated data when answering a number of important biological questions. Since its first release in 2006, YEASTRACT has been extensively used by hundreds of researchers from all over the world. We expect that by making the new data and services available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

A novel phenotypic expression associated with a new mutation in LMNA gene, characterized by partial lipodystrophy, insulin resistance, aortic stenosis and hypertrophic cardiomyopathy.

BACKGROUND: Lipodystrophies are a heterogeneous group of diseases characterized by abnormal fat distribution. Familial partial lipodystrophy 2 (FPLD2) is due to mutations in the LMNA gene. Previous studies have suggested that LMNA mutations 5' to the nuclear localization signal (NLS) are more likely to underlie laminopathies with cardiac or skeletal muscle involvement, while mutations 3' to the NLS are more likely to underlie lipodystrophy and progeroid syndromes. OBJECTIVE: To study the clinical and molecular features of a subject with FPLD. SUBJECTS AND METHODS: We carried out mutational analysis of LMNA gene in a woman with FPLD phenotype and in her relatives. Insulin resistance was evaluated by minimal model. Body composition was evaluated by dual-energy X-ray absorptiometry (DEXA). Echocardiography was done in affected subjects. 3T3-L1 preadipocytes were transfected with wild-type or mutant prelamin A constructs. In transfected cells, lamin A was detected using a Cy3-conjugated monoclonal anti-FLAG antibody. RESULTS: The patient showed atypical fat distribution, insulin resistance, severe aortic stenosis and hypertrophic cardiomyopathy. She has an affected 11-year-old son, not yet lipodystrophic but with an incipient aortic disease. LMNA sequencing showed that mother and son were both heterozygous for a novel c.1772G > T missense mutation in exon 11, which causes the substitution of the cysteine at residue 591 by a phenylalanine (C591F). In mouse preadipocytes transfected with the mutant human LMNA gene, the mutant lamin A isoform was mislocated in the nucleus. CONCLUSIONS: This patient shows a novel clinical form of FPLD2, due to a mutation affecting lamin A only, with cardiac involvement.