- Volume 9, Issue 8, 2023

The bacillus Calmette–Guérin (BCG) vaccine has been in use for prevention of tuberculosis for over a century. It remains the only widely available tuberculosis vaccine and its protective efficacy has varied across geographical regions. Since it was developed, the BCG vaccine strain has been shared across different laboratories around the world, where use of differing culture methods has resulted in genetically distinct strains over time. Whilst differing BCG vaccine efficacy around the world is well documented, and the reasons for this may be multifactorial, it has been hypothesized that genetic differences in BCG vaccine strains contribute to this variation. Isolates from an historic archive of lyophilized BCG strains were regrown, DNA was extracted and then whole-genome sequenced using Oxford Nanopore Technologies. The resulting whole-genome data were plotted on a phylogenetic tree and analysed to identify the presence or absence of regions of difference (RDs) and single-nucleotide polymorphisms (SNPs) relating to virulence, growth and cell wall structure. Of 50 strains available, 36 were revived in culture and 39 were sequenced. Morphology differed between the strains distributed before and after 1934. There was phylogenetic association amongst certain geographically classified strains, most notably BCG-Russia, BCG-Japan and BCG-Danish. RD2, RD171 and RD713 deletions were associated with late strains (seeded after 1927). When mapped to BCG-Pasteur 1172, the SNPs in sigK, plaA, mmaA3 and eccC5 were associated with early strains. Whilst BCG-Russia, BCG-Japan and BCG-Danish showed strong geographical isolate clustering, the late strains, including BCG-Pasteur, showed more variation. A wide range of SNPs were seen within geographically classified strains, and as much intra-strain variation as between-strain variation was seen. The date of distribution from the original Pasteur laboratory (early pre-1927 or late post-1927) gave the strongest association with genetic differences in regions of difference and virulence-related SNPs, which agrees with the previous literature.

Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44 709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis – including deletion of genes katG and pncA – and suggest mutations that may warrant reclassification as associated with resistance.

Carbohydrate active enzymes (CAZymes) are pivotal in biological processes including energy metabolism, cell structure maintenance, signalling, and pathogen recognition. Bioinformatic prediction and mining of CAZymes improves our understanding of these activities and enables discovery of candidates of interest for industrial biotechnology, particularly the processing of organic waste for biofuel production. CAZy (www.cazy.org) is a high-quality, manually curated, and authoritative database of CAZymes that is often the starting point for these analyses. Automated querying and integration of CAZy data with other public datasets would constitute a powerful resource for mining and exploring CAZyme diversity. However, CAZy does not itself provide methods to automate queries, or integrate annotation data from other sources (except by following hyperlinks) to support further analysis. To overcome these limitations we developed cazy_webscraper, a command-line tool that retrieves data from CAZy and other online resources to build a local, shareable and reproducible database that augments and extends the authoritative CAZy database. cazy_webscraper’s integration of curated CAZyme annotations with their corresponding protein sequences, up-to-date taxonomy assignments, and protein structure data facilitates automated large-scale and targeted bioinformatic CAZyme family analysis and candidate screening. This tool has found widespread uptake in the community, with over 35 000 downloads (from April 2021 to June 2023). We demonstrate the use and application of cazy_webscraper to: (i) augment, update and correct CAZy database accessions; (ii) explore the taxonomic distribution of CAZymes recorded in CAZy, identifying under-represented taxa and unusual CAZy class distributions; and (iii) investigate three CAZymes having potential biotechnological application for degradation of biomass, but lacking a representative structure in the PDB database. We describe in general how cazy_webscraper facilitates functional, structural and evolutionary studies to aid identification of candidate enzymes for further characterization, and specifically note that CAZy provides supporting evidence for recent expansion of the Auxiliary Activities (AA) CAZy family in eukaryotes, consistent with functions potentially specific to eukaryotic lifestyles.

Individual deletions of three genes encoding orphan DNA methyltransferases resulted in the occurrence of growth defect only in the aamA (encoding Acinetobacter Adenine Methylase A) mutant of A. baumannii strain ATCC 17978. Our single-molecule real-time sequencing-based methylome analysis revealed multiple AamA-mediated DNA methylation sites and proposed a potent census target motif (TTTRAATTYAAA). Loss of Dam led to modulation of genome-wide gene expression, and several Dam-target sites including the promoter region of the trmD operon (rpsP, rimM, trmD, and rplS) were identified through our methylome and transcriptome analyses. AamA methylation also appeared to control the expression of many genes linked to membrane functions (lolAB, lpxO), replication (dnaA) and protein synthesis (trmD operon) in the strain ATCC 17978. Interestingly, cellular resistance against several antibiotics and ethidium bromide through functions of efflux pumps diminished in the absence of the aamA gene, and the complementation of aamA gene restored the wild-type phenotypes. Other tested phenotypic traits such as outer-membrane vesicle production, biofilm formation and virulence were also affected in the aamA mutant. Collectively, our data indicated that epigenetic regulation through AamA-mediated DNA methylation of novel target sites mostly in the regulatory regions could contribute significantly to changes in multiple phenotypic traits in A. baumannii ATCC 17978.

Application of whole-genome sequencing (WGS) to characterize foodborne pathogens has advanced our understanding of circulating genotypes and evolutionary relationships. Herein, we used WGS to investigate the genomic epidemiology of Campylobacter jejuni , a leading cause of foodborne disease. Among the 214 strains recovered from patients with gastroenteritis in Michigan, USA, 85 multilocus sequence types (STs) were represented and 135 (63.1 %) were phenotypically resistant to at least one antibiotic. Horizontally acquired antibiotic resistance genes were detected in 128 (59.8 %) strains and the genotypic resistance profiles were mostly consistent with the phenotypes. Core-gene phylogenetic reconstruction identified three sequence clusters that varied in frequency, while a neighbour-net tree detected significant recombination among the genotypes (pairwise homoplasy index P<0.01). Epidemiological analyses revealed that travel was a significant contributor to pangenomic and ST diversity of C. jejuni , while some lineages were unique to rural counties and more commonly possessed clinically important resistance determinants. Variation was also observed in the frequency of lineages over the 4 year period with chicken and cattle specialists predominating. Altogether, these findings highlight the importance of geographically specific factors, recombination and horizontal gene transfer in shaping the population structure of C. jejuni . They also illustrate the usefulness of WGS data for predicting antibiotic susceptibilities and surveillance, which are important for guiding treatment and prevention strategies.

Encephalitis and meningitis are notable global public health concerns, especially among infants or children. Metagenomic next-generation sequencing (mNGS) has greatly advanced our understanding of the viruses responsible for these diseases. However, the detection rate of the aetiology remains low. We conducted RNA sequencing and virome analysis on cerebrospinal fluid (CSF) and serum samples commonly used in the clinical diagnosis to detect viral pathogens. In total, 226 paired CSF and serum samples from 113 children with encephalitis and meningitis were enrolled. The results showed that the diversity of viruses was higher in CSF, with a total of 12 viral taxa detected, including one case each of herpesvirus, coronavirus and enterovirus, and six cases of adenovirus related to human diseases. In contrast, the Anelloviridae was the most abundant viral family detected in serum, and only a few samples contained human viral pathogens, including one case of enterovirus and two cases of adenovirus. The detection rate for human viral pathogens increases to 10.6 %(12/113) when both types of samples are used simultaneously, compared to CSF along 7.9 % (9/113) or serum alone 2.6 % (3/113). However, we did not detect these viruses simultaneously in paired samples from the same case. These results suggest that CSF samples still have irreplaceable advantages for using mNGS to detect viruses in patients with meningitis and encephalitis, and serum can supplement to improve the detection rate of viral encephalitis and meningitis. The findings of this study could help improve the etiological diagnosis, clinical management and prognosis of patients with meningitis and encephalitis in children.

Iron is essential for growth in most bacteria due to its redox activity and its role in essential metabolic reactions; it is a cofactor for many bacterial enzymes. The bacterium   Acinetobacter baumannii   is a multidrug-resistant nosocomial pathogen.   A. baumannii   responds to low iron availability imposed by the host through the exploitation of multiple iron-acquisition strategies, which are likely to deliver iron to the cell under a variety of environmental conditions, including human and animal infection. To date, six different gene clusters for active iron uptake have been described in   A. baumannii  , encoding protein systems involved in (i) ferrous iron uptake (feo); (ii) haem uptake (hemT and hemO); and (iii) synthesis and transport of the baumannoferrin(s) (bfn), acinetobactin (bas/bau) and fimsbactin(s) (fbs) siderophores. Here we describe the structure, distribution and phylogeny of iron-uptake gene clusters among >1000 genotypically diverse   A. baumannii   isolates, showing that feo, hemT, bfn and bas/bau clusters are very prevalent across the dataset, whereas the additional haem-uptake system hemO is only present in a portion of the dataset and the fbs gene cluster is very rare. Since the expression of multiple iron-uptake clusters can be linked to virulence, the presence of the additional haem-uptake system hemO may have contributed to the success of some   A. baumannii   clones.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) has emerged as a major clinical and public health threat. The rapid dissemination of this pathogen is driven by several successful clones worldwide. We aimed to investigate the CRKP clonal lineages, their antibiotic resistance determinants and their potential transmissions in a tertiary care hospital located in Athens, Greece. Between 2003 and 2018, 392 CRKP isolates from bloodstream infections were recovered from hospitalized patients. Whole genome sequencing (WGS) was performed on the Illumina platform to characterize 209 of these isolates. In total, 74 % (n=155) of 209 isolates belonged to three major clonal lineages: ST258 (n=108), ST147 (n=29) and ST11 (n=18). Acquired carbapenemase genes were the mechanisms of resistance in 205 isolates (bla KPC, n=123; bla VIM, n=56; bla NDM, n=20; bla OXA-48, n=6). Strong associations (P=0.0004) were observed between carbapenemase genes and clonal lineages. We first isolated bla VIM-1-carrying ST147 strains during the early sampling period in 2003, followed by the emergence of bla KPC-2-carrying ST258 in 2006 and bla NDM-1-carrying ST11 in 2013. Analysis of genetic distances between the isolates revealed six potential transmission events. When contextualizing the current collection with published data, ST147 reflected the global diversity, ST258 clustered with isolates representing the first introduction into Europe and ST11 formed a distinct geographically restricted lineage indicative of local spread. This study demonstrates the changing profile of bloodstream CRKP in a tertiary care hospital over a 15 year period and underlines the need for continued genomic surveys to develop strategies to contain further dissemination. This article contains data hosted by Microreact.

Rapid respiratory viral whole genome sequencing (WGS) in a clinical setting can inform real-time outbreak and patient treatment decisions, but the feasibility and clinical utility of influenza A virus (IAV) WGS using Nanopore technology has not been demonstrated. A 24 h turnaround Nanopore IAV WGS protocol was performed on 128 reverse transcriptase PCR IAV-positive nasopharyngeal samples taken over seven weeks of the 2022–2023 winter influenza season, including 25 from patients with nosocomial IAV infections and 102 from patients attending the Emergency Department. WGS results were reviewed collectively alongside clinical details for interpretation and reported to clinical teams. All eight segments of the IAV genome were recovered for 97/128 samples (75.8 %) and the haemagglutinin gene for 117/128 samples (91.4 %). Infection prevention and control identified nosocomial IAV infections in 19 patients across five wards. IAV WGS revealed two separate clusters on one ward and excluded transmission across different wards with contemporaneous outbreaks. IAV WGS also identified neuraminidase inhibitor resistance in a persistently infected patient and excluded avian influenza in a sample taken from an immunosuppressed patient with a history of travel to Singapore which had failed PCR subtyping. Accurate IAV genomes can be generated in 24 h using a Nanopore protocol accessible to any laboratory with SARS-CoV-2 Nanopore sequencing capacity. In addition to replicating reference laboratory surveillance results, IAV WGS can identify antiviral resistance and exclude avian influenza. IAV WGS also informs management of nosocomial outbreaks, though molecular and clinical epidemiology were concordant in this study, limiting the impact on decision-making.

Bacterial healthcare-associated infections (HAIs) are a substantial source of global morbidity and mortality. The estimated cost associated with HAIs ranges from $35 to $45 billion in the USA alone. The costs and accessibility of whole genome sequencing (WGS) of bacteria and the lack of sufficiently accurate, high-resolution, scalable and accessible analysis for strain identification are being addressed. Thus, it is timely to determine the economic viability and impact of routine diagnostic bacterial genomics. The aim of this study was to model the economic impact of a WGS surveillance system that proactively detects and directs interventions for nosocomial infections and outbreaks compared to the current standard of care, without WGS. Using a synthesis of published models, inputs from national statistics, and peer-reviewed articles, the economic impacts of conducting a WGS-led surveillance system addressing the 11 most common nosocomial pathogen groups in England and the USA were modelled. This was followed by a series of sensitivity analyses. England was used to establish the baseline model because of the greater availability of underpinning data, and this was then modified using USA-specific parameters where available. The model for the NHS in England shows bacterial HAIs currently cost the NHS around £3 billion. WGS-based surveillance delivery is predicted to cost £61.1 million associated with the prevention of 74 408 HAIs and 1257 deaths. The net cost saving was £478.3 million, of which £65.8 million were from directly incurred savings (antibiotics, consumables, etc.) and £412.5 million from opportunity cost savings due to re-allocation of hospital beds and healthcare professionals. The USA model indicates that the bacterial HAI care baseline costs are around $18.3 billion. WGS surveillance costs $169.2 million, and resulted in a net saving of ca.$3.2 billion, while preventing 169 260 HAIs and 4862 deaths. From a ‘return on investment’ perspective, the model predicts a return to the hospitals of £7.83 per £1 invested in diagnostic WGS in the UK, and US$18.74 per $1 in the USA. Sensitivity analyses show that substantial savings are retained when inputs to the model are varied within a wide range of upper and lower limits. Modelling a proactive WGS system addressing HAI pathogens shows significant improvement in morbidity and mortality while simultaneously achieving substantial savings to healthcare facilities that more than offset the cost of implementing diagnostic genomics surveillance.

This study investigated antimicrobial resistance (AMR) phenotypes and genotypes exhibited by Neisseria gonorrhoeae from Yaoundé, Cameroon. AMR to tetracycline, penicillin and ciprofloxacin was observed although none of the isolates had reduced susceptibility to azithromycin, cefixime or ceftriaxone. Whole genome sequence (WGS) data were obtained and, using a threshold of 300 or fewer locus differences in the N. gonorrhoeae core gene multilocus sequence typing (cgMLST) scheme, four distinct core genome lineages were identified. Publicly available WGS data from 1355 gonococci belonging to these four lineages were retrieved from the PubMLST database, allowing the Cameroonian isolates to be examined in the context of existing data and compared with related gonococci. Examination of AMR genotypes in this dataset found an association between the core genome and AMR with, for example, isolates belonging to the core genome group, Ng_cgc_300 : 21, possessing GyrA and ParC alleles with amino acid substitutions conferring high-level resistance to ciprofloxacin while lineages Ng_cgc_300 : 41 and Ng_cgc_300 : 243 were predicted to be susceptible to several antimicrobials. A core genome lineage, Ng_cgc_300 : 498, was observed which largely consisted of gonococci originating from Africa. Analyses from this study demonstrate the advantages of using the N. gonorrhoeae cgMLST scheme to find related gonococci to carry out genomic analyses that enhance our understanding of the population biology of this important pathogen.

Inferring the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via Bayesian phylogeography has been complicated by the overwhelming sampling bias present in the global genomic dataset. Previous work has demonstrated the utility of metadata in addressing this bias. Specifically, the inclusion of recent travel history of SARS-CoV-2-positive individuals into extended phylogeographical models has demonstrated increased accuracy of estimates, along with proposing alternative hypotheses that were not apparent using only genomic and geographical data. However, as the availability of comprehensive epidemiological metadata is limited, many of the current estimates rely on sequence data and basic metadata (i.e. sample date and location). As the bias within the SARS-CoV-2 sequence dataset is extensive, the degree to which we can rely on results drawn from standard phylogeographical models (i.e. discrete trait analysis) that lack integrated metadata is of great concern. This is particularly important when estimates influence and inform public health policy. We compared results generated from the same dataset, using two discrete phylogeographical models: one including travel history metadata and one without. We utilized sequences from Victoria, Australia, in this case study for two unique properties. Firstly, the high proportion of cases sequenced throughout 2020 within Victoria and the rest of Australia. Secondly, individual travel history was collected from returning travellers in Victoria during the first wave (January to May) of the coronavirus disease 2019 (COVID-19) pandemic. We found that the implementation of individual travel history was essential for the estimation of SARS-CoV-2 movement via discrete phylogeography models. Without the additional information provided by the travel history metadata, the discrete trait analysis could not be fit to the data due to numerical instability. We also suggest that during the first wave of the COVID-19 pandemic in Australia, the primary driving force behind the spread of SARS-CoV-2 was viral importation from international locations. This case study demonstrates the necessity of robust genomic datasets supplemented with epidemiological metadata for generating accurate estimates from phylogeographical models in datasets that have significant sampling bias. For future work, we recommend the collection of metadata in conjunction with genomic data. Furthermore, we highlight the risk of applying phylogeographical models to biased datasets without incorporating appropriate metadata, especially when estimates influence public health policy decision making.