Context-driven discovery of gene cassettes in mobile integrons using a computational grammar

<p>Abstract</p> <p>Background</p> <p>Gene discovery algorithms typically examine sequence data for low level patterns. A novel method to computationally discover higher order DNA structures is presented, using a context sensitive grammar. The algorithm was applied to the discovery of gene cassettes associated with integrons. The discovery and annotation of antibiotic resistance genes in such cassettes is essential for effective monitoring of antibiotic resistance patterns and formulation of public health antibiotic prescription policies.</p> <p>Results</p> <p>We discovered two new putative gene cassettes using the method, from 276 integron features and 978 GenBank sequences. The system achieved <it>κ </it>= 0.972 annotation agreement with an expert gold standard of 300 sequences. In rediscovery experiments, we deleted 789,196 cassette instances over 2030 experiments and correctly relabelled 85.6% (<it>α </it>≥ 95%, <it>E </it>≤ 1%, mean sensitivity = 0.86, specificity = 1, F-score = 0.93), with no false positives.</p> <p>Error analysis demonstrated that for 72,338 missed deletions, two adjacent deleted cassettes were labeled as a single cassette, increasing performance to 94.8% (mean sensitivity = 0.92, specificity = 1, F-score = 0.96).</p> <p>Conclusion</p> <p>Using grammars we were able to represent heuristic background knowledge about large and complex structures in DNA. Importantly, we were also able to use the context embedded in the model to discover new putative antibiotic resistance gene cassettes. The method is complementary to existing automatic annotation systems which operate at the sequence level.</p

IFI27 transcription is an early predictor for COVID-19 outcomes, a multi-cohort observational study

PurposeRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.ResultsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.ConclusionThese data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus

Computational inference of grammars for larger-than-gene structures from annotated gene sequences

Motivation: Larger than gene structures (LGS) are DNA segments that include at least one gene and often other segments such as inverted repeats and gene promoters. Mobile genetic elements (MGE) such as integrons are LGS that play an important role in horizontal gene transfer, primarily in Gram-negative organisms. Known LGS have a profound effect on organism virulence, antibiotic resistance and other properties of the organism due to the number of genes involved. Expert-compiled grammars have been shown to be an effective computational representation of LGS, well suited to automating annotation, and supporting de novo gene discovery. However, development of LGS grammars by experts is labour intensive and restricted to known LGS. Objectives: This study uses computational grammar inference methods to automate LGS discovery. We compare the ability of six algorithms to infer LGS grammars from DNA sequences annotated with genes and other short sequences. We compared the predictive power of learned grammars against an expert-developed grammar for gene cassette arrays found in Class 1, 2 and 3 integrons, which are modular LGS containing up to 9 of about 240 cassette types. Results: Using a Bayesian generalization algorithm our inferred grammar was able to predict >95% of MGE structures in a corpus of 1760 sequences obtained from Genbank (F-score 75%). Even with 100% noise added to the training and test sets, we obtained an F-score of 68%, indicating that the method is robust and has the potential to predict de novo LGS structures when the underlying gene features are known.6 page(s

Horizontal Gene Transfer in a Polyclonal Outbreak of Carbapenem-Resistant Acinetobacter baumannii

In the last few years, phenotypically carbapenem resistant Acinetobacter strains have been identified throughout the world, including in many of the hospitals and intensive care units (ICUs) of Australia. Genotyping of Australian ICU outbreak-associated isolates by pulsed-field gel electrophoresis of whole genomic DNA indicated that different strains were cocirculating within one hospital. The carbapenem-resistant phenotype of these and other Australian isolates was found to be due to carbapenem-hydrolyzing activity associated with the presence of the bla(OXA-23) gene. In all resistant strains examined, the bla(OXA-23) gene was adjacent to the insertion sequence ISAba1 in a structure that has been found in Acinetobacter baumannii strains of a similar phenotype from around the world; bla(OXA-51)-like genes were also found in all A. baumannii strains but were not consistently associated with ISAba1, which is believed to provide the promoter required for expression of linked antibiotic resistance genes. Most isolates were also found to contain additional antibiotic resistance genes within the cassette arrays of class 1 integrons. The same cassette arrays, in addition to the ISAba1-bla(OXA-23) structure, were found within unrelated strains, but no common plasmid carrying these accessory genetic elements could be identified. It therefore appears that antibiotic resistance genes are readily exchanged between cocirculating strains in epidemics of phenotypically indistinguishable organisms. Epidemiological investigation of major outbreaks should include whole-genome typing as well as analysis of potentially transmissible resistance genes and their vehicles

Multidrug-Resistant Salmonella enterica 4,[5],12:i:- Sequence Type 34, New South Wales, Australia, 2016–2017

Multidrug- and colistin-resistant Salmonella enterica serotype 4,[5],12:i:- sequence type 34 is present in Europe and Asia. Using genomic surveillance, we determined that this sequence type is also endemic to Australia. Our findings highlight the public health benefits of genome sequencing–guided surveillance for monitoring the spread of multidrug-resistant mobile genes and isolates

Meropenem versus piperacillin-tazobactam for definitive treatment of bloodstream infections due to ceftriaxone non-susceptible Escherichia coli and Klebsiella spp (the MERINO trial): study protocol for a randomised controlled trial

Background: Gram-negative bacteria such as Escherichia coli or Klebsiella spp. frequently cause bloodstream infections. There has been a worldwide increase in resistance in these species to antibiotics such as third generation cephalosporins, largely driven by the acquisition of extended-spectrum beta-lactamase or plasmid-mediated AmpC enzymes. Carbapenems have been considered the most effective therapy for serious infections caused by such resistant bacteria; however, increased use creates selection pressure for carbapenem resistance, an emerging threat arising predominantly from the dissemination of genes encoding carbapenemases. Recent retrospective data suggest that beta-lactam/beta-lactamase inhibitor combinations, such as piperacillin-tazobactam, may be non-inferior to carbapenems for the treatment of bloodstream infection caused by extended-spectrum beta-lactamase-producers, if susceptible in vitro. This study aims to test this hypothesis in an effort to define carbapenem-sparing alternatives for these infections.\ud \ud Methods/Design: The study will use a multicentre randomised controlled open-label non-inferiority trial design comparing two treatments, meropenem (standard arm) and piperacillin-tazobactam (carbapenem-sparing arm) in adult patients with bacteraemia caused by E. coli or Klebsiella spp. demonstrating non-susceptibility to third generation cephalosporins. Recruitment is planned to occur in sites across three countries (Australia, New Zealand and Singapore). A total sample size of 454 patients will be required to achieve 80% power to determine non-inferiority with a margin of 5%. Once randomised, definitive treatment will be for a minimum of 4 days, but up to 14 days with total duration determined by treating clinicians. Data describing demographic information, antibiotic use, co-morbid conditions, illness severity, source of infection and other risk factors will be collected. Vital signs, white cell count, use of vasopressors and days to bacteraemia clearance will be recorded up to day 7. The primary outcome measure will be mortality at 30 days, with secondary outcomes including days to clinical and microbiological resolution, microbiological failure or relapse, isolation of a multi-resistant organism or Clostridium difficile infection

IFI27 transcription is an early predictor for COVID-19 outcomes; a multi-cohort observational study

Robust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness. We conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients. We show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression is associated with the presence of a high viral load. We further demonstrate that systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 severity. For clinical outcome prediction (e.g. respiratory failure), IFI27 expression displays a high positive (0.83) and negative (0.95) predictive value, outperforming all other known predictors of COVID-19 severity. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 swine influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients. These data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus. We searched the scientific literature using PubMed to identify studies that used the IFI27 biomarker to predict outcomes in COVID-19 patients. We used the search terms “IFI27”, “COVID-19, “gene expression” and “outcome prediction”. We did not identify any study that investigated the role of IFI27 biomarker in outcome prediction. Although ten studies were identified using the general terms of “gene expression” and “COVID-19”, IFI27 was only mentioned in passing as one of the identified genes. All these studies addressed the broader question of the host response to COVID-19; none focused solely on using IFI27 to improve the risk stratification of infected patients in a pandemic. Here, we present the findings of a multi-cohort study of the IFI27 biomarker in COVID-19 patients. Our findings show that the host response, as reflected by blood IFI27 gene expression, accurately predicts COVID-19 disease progression (positive and negative predictive values; 0.83 and 0.95, respectively), outperforming age, comorbidity, C-reactive protein and all other known risk factors. The strong association of IFI27 with disease severity occurs not only in SARS-CoV-2 infection, but also in other respiratory viruses with pandemic potential, such as the influenza virus. These findings suggest that host response biomarkers, such as IFI27, could help identify high-risk COVID-19 patients - those who are more likely to develop infection complications - and therefore may help improve patient triage in a pandemic. This is the first systemic study of the clinical role of IFI27 in the current COVID-19 pandemic and its possible future application in other respiratory virus pandemics. The findings not only could help improve the current management of COVID-19 patients but may also improve future pandemic preparedness