Succinate utilisation by Salmonella is inhibited by multiple regulatory systems.

Succinate is a potent immune signalling molecule that is present in the mammalian gut and within macrophages. Both of these infection niches are colonised by the pathogenic bacterium Salmonella enterica serovar Typhimurium during infection. Succinate is a C4-dicarboyxlate that can serve as a source of carbon for bacteria. When succinate is provided as the sole carbon source for in vitro cultivation, Salmonella and other enteric bacteria exhibit a slow growth rate and a long lag phase. This growth inhibition phenomenon was known to involve the sigma factor RpoS, but the genetic basis of the repression of bacterial succinate utilisation was poorly understood. Here, we use an experimental evolution approach to isolate fast-growing mutants during growth of S. Typhimurium on succinate containing minimal medium. Our approach reveals novel RpoS-independent systems that inhibit succinate utilisation. The CspC RNA binding protein restricts succinate utilisation, an inhibition that is antagonised by high levels of the small regulatory RNA (sRNA) OxyS. We discovered that the Fe-S cluster regulatory protein IscR inhibits succinate utilisation by repressing the C4-dicarboyxlate transporter DctA. Furthermore, the ribose operon repressor RbsR is required for the complete RpoS-driven repression of succinate utilisation, suggesting a novel mechanism of RpoS regulation. Our discoveries shed light on the redundant regulatory systems that tightly regulate the utilisation of succinate. We speculate that the control of central carbon metabolism by multiple regulatory systems in Salmonella governs the infection niche-specific utilisation of succinate.

An accessible, efficient and global approach for the large-scale sequencing of bacterial genomes.

We have developed an efficient and inexpensive pipeline for streamlining large-scale collection and genome sequencing of bacterial isolates. Evaluation of this method involved a worldwide research collaboration focused on the model organism Salmonella enterica, the 10KSG consortium. Following the optimization of a logistics pipeline that involved shipping isolates as thermolysates in ambient conditions, the project assembled a diverse collection of 10,419 isolates from low- and middle-income countries. The genomes were sequenced using the LITE pipeline for library construction, with a total reagent cost of less than USD$10 per genome. Our method can be applied to other large bacterial collections to underpin global collaborations.

CLIMB-COVID: continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance.

In response to the ongoing SARS-CoV-2 pandemic in the UK, the COVID-19 Genomics UK (COG-UK) consortium was formed to rapidly sequence SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of CLIMB-COVID, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network.

Genome-Wide Identification and Expression Analysis of SOS Response Genes in Salmonella enterica Serovar Typhimurium.

A bioinformatic search for LexA boxes, combined with transcriptomic detection of loci responsive to DNA damage, identified 48 members of the SOS regulon in the genome of Salmonella enterica serovar Typhimurium. Single cell analysis using fluorescent fusions revealed that heterogeneous expression is a common trait of SOS response genes, with formation of SOSOFF and SOSON subpopulations. Phenotypic cell variants formed in the absence of external DNA damage show gene expression patterns that are mainly determined by the position and the heterology index of the LexA box. SOS induction upon DNA damage produces SOSOFF and SOSON subpopulations that contain live and dead cells. The nature and concentration of the DNA damaging agent and the time of exposure are major factors that influence the population structure upon SOS induction. An analogy can thus be drawn between the SOS response and other bacterial stress responses that produce phenotypic cell variants.

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome's role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

Stepwise evolution of Salmonella Typhimurium ST313 causing bloodstream infection in Africa.

Bloodstream infections caused by nontyphoidal Salmonella are a major public health concern in Africa, causing ~49,600 deaths every year. The most common Salmonella enterica pathovariant associated with invasive nontyphoidal Salmonella disease is Salmonella Typhimurium sequence type (ST)313. It has been proposed that antimicrobial resistance and genome degradation has contributed to the success of ST313 lineages in Africa, but the evolutionary trajectory of such changes was unclear. Here, to define the evolutionary dynamics of ST313, we sub-sampled from two comprehensive collections of Salmonella isolates from African patients with bloodstream infections, spanning 1966 to 2018. The resulting 680 genome sequences led to the discovery of a pan-susceptible ST313 lineage (ST313 L3), which emerged in Malawi in 2016 and is closely related to ST313 variants that cause gastrointestinal disease in the United Kingdom and Brazil. Genomic analysis revealed degradation events in important virulence genes in ST313 L3, which had not occurred in other ST313 lineages. Despite arising only recently in the clinic, ST313 L3 is a phylogenetic intermediate between ST313 L1 and L2, with a characteristic accessory genome. Our in-depth genotypic and phenotypic characterization identifies the crucial loss-of-function genetic events that occurred during the stepwise evolution of invasive S. Typhimurium across Africa.

When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data.

Considerable advances in genomics over the past decade have resulted in vast amounts of data being generated and deposited in global archives. The growth of these archives exceeds our ability to process their content, leading to significant analysis bottlenecks. Sketching algorithms produce small, approximate summaries of data and have shown great utility in tackling this flood of genomic data, while using minimal compute resources. This article reviews the current state of the field, focusing on how the algorithms work and how genomicists can utilize them effectively. References to interactive workbooks for explaining concepts and demonstrating workflows are included at https://github.com/will-rowe/genome-sketching .

The diversity, evolution and ecology of Salmonella in venomous snakes.

BACKGROUND: Reptile-associated Salmonella bacteria are a major, but often neglected cause of both gastrointestinal and bloodstream infection in humans globally. The diversity of Salmonella enterica has not yet been determined in venomous snakes, however other ectothermic animals have been reported to carry a broad range of Salmonella bacteria. We investigated the prevalence and diversity of Salmonella in a collection of venomous snakes and non-venomous reptiles. METHODOLOGY/PRINCIPLE FINDINGS: We used a combination of selective enrichment techniques to establish a unique dataset of reptilian isolates to study Salmonella enterica species-level evolution and ecology and used whole-genome sequencing to investigate the relatedness of phylogenetic groups. We observed that 91% of venomous snakes carried Salmonella, and found that a diverse range of serovars (n = 58) were carried by reptiles. The Salmonella serovars belonged to four of the six Salmonella enterica subspecies: diarizonae, enterica, houtanae and salamae. Subspecies enterica isolates were distributed among two distinct phylogenetic clusters, previously described as clade A (52%) and clade B (48%). We identified metabolic differences between S. diarizonae, S. enterica clade A and clade B involving growth on lactose, tartaric acid, dulcitol, myo-inositol and allantoin. SIGNIFICANCE: We present the first whole genome-based comparative study of the Salmonella bacteria that colonise venomous and non-venomous reptiles and shed new light on Salmonella evolution. Venomous snakes examined in this study carried a broad range of Salmonella, including serovars which have been associated with disease in humans such as S. Enteritidis. The findings raise the possibility that venomous snakes could be a reservoir for Salmonella serovars associated with human salmonellosis.

Streaming histogram sketching for rapid microbiome analytics.

BACKGROUND: The growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for tyrhe compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time. RESULTS: We apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed 'histosketch' that can efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using the pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme. Furthermore, we use a 'real life' example to show that histosketches can train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a random forest classifier that could accurately predict whether the neonate had received antibiotic treatment (97% accuracy, 96% precision) and could subsequently be used to classify microbiome data streams in less than 3 s. CONCLUSIONS: Our method offers a new approach to rapidly process microbiome data streams, allowing samples to be rapidly clustered, indexed and classified. We also provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2 GB microbiome in 50 s on a standard laptop using four cores, with the sketch occupying 3000 bytes of disk space. ( https://github.com/will-rowe/hulk ).

Adding function to the genome of African Salmonella Typhimurium ST313 strain D23580.

Salmonella Typhimurium sequence type (ST) 313 causes invasive nontyphoidal Salmonella (iNTS) disease in sub-Saharan Africa, targeting susceptible HIV+, malarial, or malnourished individuals. An in-depth genomic comparison between the ST313 isolate D23580 and the well-characterized ST19 isolate 4/74 that causes gastroenteritis across the globe revealed extensive synteny. To understand how the 856 nucleotide variations generated phenotypic differences, we devised a large-scale experimental approach that involved the global gene expression analysis of strains D23580 and 4/74 grown in 16 infection-relevant growth conditions. Comparison of transcriptional patterns identified virulence and metabolic genes that were differentially expressed between D23580 versus 4/74, many of which were validated by proteomics. We also uncovered the S. Typhimurium D23580 and 4/74 genes that showed expression differences during infection of murine macrophages. Our comparative transcriptomic data are presented in a new enhanced version of the Salmonella expression compendium, SalComD23580: http://bioinf.gen.tcd.ie/cgi-bin/salcom_v2.pl. We discovered that the ablation of melibiose utilization was caused by three independent SNP mutations in D23580 that are shared across ST313 lineage 2, suggesting that the ability to catabolize this carbon source has been negatively selected during ST313 evolution. The data revealed a novel, to our knowledge, plasmid maintenance system involving a plasmid-encoded CysS cysteinyl-tRNA synthetase, highlighting the power of large-scale comparative multicondition analyses to pinpoint key phenotypic differences between bacterial pathovariants.

International phase I study protocol to develop a patient-reported outcome measure for adolescents and adults receiving gender-affirming treatments (the GENDER-Q).

INTRODUCTION: A critical barrier to outcome assessment in gender-affirming healthcare is the lack of a specific patient-reported outcome measure (PROM). This phase I protocol describes an international collaboration between investigators in Canada, Denmark, the Netherlands and the USA who have coalesced to develop a new PROM (ie, the GENDER-Q) to evaluate outcomes of psychological, hormonal and surgical gender-affirming treatments. METHODS AND ANALYSIS: This phase I study uses an interpretive description approach. Participants aged 16 years and older seeking any form of gender-affirming treatments in centres located in Canada, Denmark, the Netherlands and the USA will be invited to take part in qualitative interviews. Participants will review BREAST-Q and FACE-Q scales hypothesised to contain content relevant to specific gender-affirming treatments. Interviews will elicit new concepts for additional scale development. Each interview will be digitally recorded, transcribed and coded. The main outcome of this phase I study will be the development of a conceptual framework and set of scales to measure outcomes important to evaluating gender-affirming treatments. To this end, analysis will be used to add/drop/revise items of existing scales to achieve content validity. For new concepts, coding will assign top-level domains and themes/subthemes to participant quotes. Codes will be used to develop an item pool to inform scale development. Draft scales will be shown to transgender and gender diverse persons and experts to obtain feedback that will be used to refine and finalise the scales. The field-test version of the GENDER-Q will be translated by following rigorous methods to prepare for the international field-test study. ETHICS AND DISSEMINATION: This study is coordinated at McMaster University (Canada). Ethics board approval was received from the Hamilton Integrated Ethics Board (Canada), the Medical Ethical Committee at VUmc (The Netherlands) and Advarra (USA). Findings will be published in peer-reviewed journals and presented at national and international conferences and meetings.

Indexed variation graphs for efficient and accurate resistome profiling.

Motivation: Antimicrobial resistance (AMR) remains a major threat to global health. Profiling the collective AMR genes within a metagenome (the 'resistome') facilitates greater understanding of AMR gene diversity and dynamics. In turn, this can allow for gene surveillance, individualized treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of analysis workflows. We have developed an efficient and accurate method for resistome profiling that addresses these complications and improves upon currently available tools. Results: Our method combines a variation graph representation of gene sets with a locality-sensitive hashing Forest indexing scheme to allow for fast classification of metagenomic sequence reads using similarity-search queries. Subsequent hierarchical local alignment of classified reads against graph traversals enables accurate reconstruction of full-length gene sequences using a scoring scheme. We provide our implementation, graphing Resistance Out Of meTagenomes (GROOT), and show it to be both faster and more accurate than a current reference-dependent tool for resistome profiling. GROOT runs on a laptop and can process a typical 2 gigabyte metagenome in 2 min using a single CPU. Our method is not restricted to resistome profiling and has the potential to improve current metagenomic workflows. Availability and implementation: GROOT is written in Go and is available at https://github.com/will-rowe/groot (MIT license). Supplementary information: Supplementary data are available at Bioinformatics online.

Public health surveillance in the UK revolutionises our understanding of the invasive Salmonella Typhimurium epidemic in Africa.

BACKGROUND: The ST313 sequence type of Salmonella Typhimurium causes invasive non-typhoidal salmonellosis and was thought to be confined to sub-Saharan Africa. Two distinct phylogenetic lineages of African ST313 have been identified. METHODS: We analysed the whole genome sequences of S. Typhimurium isolates from UK patients that were generated following the introduction of routine whole-genome sequencing (WGS) of Salmonella enterica by Public Health England in 2014. RESULTS: We found that 2.7% (84/3147) of S. Typhimurium from patients in England and Wales were ST313 and were associated with gastrointestinal infection. Phylogenetic analysis revealed novel diversity of ST313 that distinguished UK-linked gastrointestinal isolates from African-associated extra-intestinal isolates. The majority of genome degradation of African ST313 lineage 2 was conserved in the UK-ST313, but the African lineages carried a characteristic prophage and antibiotic resistance gene repertoire. These findings suggest that a strong selection pressure exists for certain horizontally acquired genetic elements in the African setting. One UK-isolated lineage 2 strain that probably originated in Kenya carried a chromosomally located bla CTX-M-15, demonstrating the continual evolution of this sequence type in Africa in response to widespread antibiotic usage. CONCLUSIONS: The discovery of ST313 isolates responsible for gastroenteritis in the UK reveals new diversity in this important sequence type. This study highlights the power of routine WGS by public health agencies to make epidemiologically significant deductions that would be missed by conventional microbiological methods. We speculate that the niche specialisation of sub-Saharan African ST313 lineages is driven in part by the acquisition of accessory genome elements.

Overexpression of antibiotic resistance genes in hospital effluents over time.

Objectives: Effluents contain a diverse abundance of antibiotic resistance genes that augment the resistome of receiving aquatic environments. However, uncertainty remains regarding their temporal persistence, transcription and response to anthropogenic factors, such as antibiotic usage. We present a spatiotemporal study within a river catchment (River Cam, UK) that aims to determine the contribution of antibiotic resistance gene-containing effluents originating from sites of varying antibiotic usage to the receiving environment. Methods: Gene abundance in effluents (municipal hospital and dairy farm) was compared against background samples of the receiving aquatic environment (i.e. the catchment source) to determine the resistome contribution of effluents. We used metagenomics and metatranscriptomics to correlate DNA and RNA abundance and identified differentially regulated gene transcripts. Results: We found that mean antibiotic resistance gene and transcript abundances were correlated for both hospital ( ρ = 0.9, two-tailed P <0.0001) and farm ( ρ = 0.5, two-tailed P <0.0001) effluents and that two ß-lactam resistance genes ( bla GES and bla OXA ) were overexpressed in all hospital effluent samples. High ß-lactam resistance gene transcript abundance was related to hospital antibiotic usage over time and hospital effluents contained antibiotic residues. Conclusions: We conclude that effluents contribute high levels of antibiotic resistance genes to the aquatic environment; these genes are expressed at significant levels and are possibly related to the level of antibiotic usage at the effluent source.

Comparative metagenomics reveals a diverse range of antimicrobial resistance genes in effluents entering a river catchment.

The aquatic environment has been implicated as a reservoir for antimicrobial resistance genes (ARGs). In order to identify sources that are contributing to these gene reservoirs, it is crucial to assess effluents that are entering the aquatic environment. Here we describe a metagenomic assessment for two types of effluent entering a river catchment. We investigated the diversity and abundance of resistance genes, mobile genetic elements (MGEs) and pathogenic bacteria. Findings were normalised to a background sample of river source water. Our results show that effluent contributed an array of genes to the river catchment, the most abundant being tetracycline resistance genes tetC and tetW from farm effluents and the sulfonamide resistance gene sul2 from wastewater treatment plant (WWTP) effluents. In nine separate samples taken across 3 years, we found 53 different genes conferring resistance to seven classes of antimicrobial. Compared to the background sample taken up river from effluent entry, the average abundance of genes was three times greater in the farm effluent and two times greater in the WWTP effluent. We conclude that effluents disperse ARGs, MGEs and pathogenic bacteria within a river catchment, thereby contributing to environmental reservoirs of ARGs.

Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data.

BACKGROUND: Antimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring. RESULTS: Here we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR's application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei. CONCLUSIONS: We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates.