A One Health Perspective on Salmonella enterica Serovar Infantis, an Emerging Human Multidrug-Resistant Pathogen.

Salmonella enterica serovar Infantis presents an ever-increasing threat to public health because of its spread throughout many countries and association with high levels of antimicrobial resistance (AMR). We analyzed whole-genome sequences of 5,284 Salmonella Infantis strains from 74 countries, isolated during 1989-2020 from a wide variety of human, animal, and food sources, to compare genetic phylogeny, AMR determinants, and plasmid presence. The global Salmonella Infantis population structure diverged into 3 clusters: a North American cluster, a European cluster, and a global cluster. The levels of AMR varied by Salmonella Infantis cluster and by isolation source; 73% of poultry isolates were multidrug resistant, compared with 35% of human isolates. This finding correlated with the presence of the pESI megaplasmid; 71% of poultry isolates contained pESI, compared with 32% of human isolates. This study provides key information for public health teams engaged in reducing the spread of this pathogen.

COVID-19 therapeutics: stewardship in England and considerations for antimicrobial resistance.

The COVID-19 pandemic saw unprecedented resources and funds driven into research for the development, and subsequent rapid distribution, of vaccines, diagnostics and directly acting antivirals (DAAs). DAAs have undeniably prevented progression and life-threatening conditions in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, there are concerns of antimicrobial resistance (AMR), antiviral resistance specifically, for DAAs. To preserve activity of DAAs for COVID-19 therapy, as well as detect possible mutations conferring resistance, antimicrobial stewardship and surveillance were rapidly implemented in England. This paper expands on the ubiquitous ongoing public health activities carried out in England, including epidemiologic, virologic and genomic surveillance, to support the stewardship of DAAs and assess the deployment, safety, effectiveness and resistance potential of these novel and repurposed therapeutics.

A molnupiravir-associated mutational signature in global SARS-CoV-2 genomes.

Molnupiravir, an antiviral medication widely used against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), acts by inducing mutations in the virus genome during replication. Most random mutations are likely to be deleterious to the virus and many will be lethal; thus, molnupiravir-induced elevated mutation rates reduce viral load1,2. However, if some patients treated with molnupiravir do not fully clear the SARS-CoV-2 infections, there could be the potential for onward transmission of molnupiravir-mutated viruses. Here we show that SARS-CoV-2 sequencing databases contain extensive evidence of molnupiravir mutagenesis. Using a systematic approach, we find that a specific class of long phylogenetic branches, distinguished by a high proportion of G-to-A and C-to-T mutations, are found almost exclusively in sequences from 2022, after the introduction of molnupiravir treatment, and in countries and age groups with widespread use of the drug. We identify a mutational spectrum, with preferred nucleotide contexts, from viruses in patients known to have been treated with molnupiravir and show that its signature matches that seen in these long branches, in some cases with onward transmission of molnupiravir-derived lineages. Finally, we analyse treatment records to confirm a direct association between these high G-to-A branches and the use of molnupiravir.

Generation of SARS-CoV-2 escape mutations by monoclonal antibody therapy.

COVID-19 patients at risk of severe disease may be treated with neutralising monoclonal antibodies (mAbs). To minimise virus escape from neutralisation these are administered as combinations e.g. casirivimab+imdevimab or, for antibodies targeting relatively conserved regions, individually e.g. sotrovimab. Unprecedented genomic surveillance of SARS-CoV-2 in the UK has enabled a genome-first approach to detect emerging drug resistance in Delta and Omicron cases treated with casirivimab+imdevimab and sotrovimab respectively. Mutations occur within the antibody epitopes and for casirivimab+imdevimab multiple mutations are present on contiguous raw reads, simultaneously affecting both components. Using surface plasmon resonance and pseudoviral neutralisation assays we demonstrate these mutations reduce or completely abrogate antibody affinity and neutralising activity, suggesting they are driven by immune evasion. In addition, we show that some mutations also reduce the neutralising activity of vaccine-induced serum.

A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications.

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.

Global responses to oxytetracycline treatment in tetracycline-resistant Escherichia coli.

We characterized the global transcriptome of Escherichia coli MG1655:: tetA grown in the presence of ½ MIC (14 mg/L) of OTC, and for comparison WT MG1655 strain grown with 1//2 MIC of OTC (0.25 mg/L OTC). 1646 genes changed expression significantly (FDR > 0.05) in the resistant strain, the majority of which (1246) were also regulated in WT strain. Genes involved in purine synthesis and ribosome structure and function were top-enriched among up-regulated genes, and anaerobic respiration, nitrate metabolism and aromatic amino acid biosynthesis genes among down-regulated genes. Blocking of the purine-synthesis- did not affect resistance phenotypes (MIC and growth rate with OTC), while blocking of protein synthesis using low concentrations of chloramphenicol or gentamicin, lowered MIC towards OTC. Metabolic-modeling, using a novel model for MG1655 and continuous weighing factor that reflected the degree of up or down regulation of genes encoding a reaction, identified 102 metabolic reactions with significant change in flux in MG1655:: tetA when grown in the presence of OTC compared to growth without OTC. These pathways could not have been predicted by simply analyzing functions of the up and down regulated genes, and thus this work has provided a novel method for identification of reactions which are essential in the adaptation to growth in the presence of antimicrobials.

Concordance of SNP- and allele-based typing workflows in the context of a large-scale international Salmonella Enteritidis outbreak investigation.

A large European multi-country Salmonella enterica serovar Enteritidis outbreak associated with Polish eggs was characterized by whole-genome sequencing (WGS)-based analysis, with various European institutes using different analysis workflows to identify isolates potentially related to the outbreak. The objective of our study was to compare the output of six of these different typing workflows (distance matrices of either SNP-based or allele-based workflows) in terms of cluster detection and concordance. To this end, we analysed a set of 180 isolates coming from confirmed and probable outbreak cases, which were representative of the genetic variation within the outbreak, supplemented with 22 unrelated contemporaneous S. enterica serovar Enteritidis isolates. Since the definition of a cluster cut-off based on genetic distance requires prior knowledge on the evolutionary processes that govern the bacterial populations in question, we used a variety of hierarchical clustering methods (single, average and complete) and selected the optimal number of clusters based on the consensus of the silhouette, Dunn2, and McClain-Rao internal validation indices. External validation was done by calculating the concordance with the WGS-based case definition (SNP-address) for this outbreak using the Fowlkes-Mallows index. Our analysis indicates that with complete-linkage hierarchical clustering combined with the optimal number of clusters, as defined by three internal validity indices, the six different allele- and SNP-based typing workflows generate clusters with similar compositions. Furthermore, we show that even in the absence of coordinated typing procedures, but by using an unsupervised machine learning methodology for cluster delineation, the various workflows that are currently in use by six European public-health authorities can identify concordant clusters of genetically related S. enterica serovar Enteritidis isolates; thus, providing public-health researchers with comparable tools for detection of infectious-disease outbreaks.

The characterization of mobile colistin resistance (mcr) genes among 33 000 Salmonella enterica genomes from routine public health surveillance in England.

To establish the prevalence of mobile colistin resistance (mcr) genes amongst Salmonella enterica isolates obtained through public health surveillance in England (April 2014 to September 2017), 33 205 S. enterica genome sequences obtained from human, food, animal and environmental isolates were screened for the presence of mcr variants 1 to 8. The mcr-positive genomes were assembled, annotated and characterized according to plasmid type. Nanopore sequencing was performed on six selected isolates with putative novel plasmids, and phylogenetic analysis was used to provide an evolutionary context for the most commonly isolated clones. Fifty-two mcr-positive isolates were identified, of which 32 were positive for mcr-1, 19 for mcr-3 and 1 for mcr-5. The combination of Illumina and Nanopore sequencing identified three novel mcr-3 plasmids and one novel mcr-5 plasmid, as well as the presence of chromosomally integrated mcr-1 and mcr-3. Monophasic S. enterica serovar Typhimurium accounted for 27/52 (52 %) of the mcr-positive isolates, with the majority clustering in clades associated with travel to Southeast Asia. Isolates in these clades were associated with a specific plasmid range and an additional extended-spectrum beta-lactamase genotype. Routine whole-genome sequencing for public health surveillance provides an effective screen for novel and emerging antimicrobial determinants, including mcr. Complementary long-read technologies elucidated the genomic context of resistance determinants, offering insights into plasmid dissemination and linkage to other resistance genes.

The Transformation of Reference Microbiology Methods and Surveillance for Salmonella With the Use of Whole Genome Sequencing in England and Wales.

The use of whole genome sequencing (WGS) as a method for supporting outbreak investigations, studying Salmonella microbial populations and improving understanding of pathogenicity has been well-described (1-3). However, performing WGS on a discrete dataset does not pose the same challenges as implementing WGS as a routine, reference microbiology service for public health surveillance. Challenges include translating WGS data into a useable format for laboratory reporting, clinical case management, Salmonella surveillance, and outbreak investigation as well as meeting the requirement to communicate that information in an understandable and universal language for clinical and public health action. Public Health England have been routinely sequencing all referred presumptive Salmonella isolates since 2014 which has transformed our approach to reference microbiology and surveillance. Here we describe an overview of the integrated methods for cross-disciplinary working, describe the challenges and provide a perspective on how WGS has impacted the laboratory and surveillance processes in England and Wales.

Informal genomic surveillance of regional distribution of Salmonella Typhi genotypes and antimicrobial resistance via returning travellers.

Salmonella enterica serovar Typhi (S. Typhi) is the causative agent of typhoid fever, a systemic human infection with a burden exceeding 20 million cases each year that occurs disproportionately among children in low and middle income countries. Antimicrobial therapy is the mainstay for treatment, but resistance to multiple agents is common. Here we report genotypes and antimicrobial resistance (AMR) determinants detected from routine whole-genome sequencing (WGS) of 533 S. Typhi isolates referred to Public Health England between April 2014 and March 2017, 488 (92%) of which had accompanying patient travel information obtained via an enhanced surveillance questionnaire. The majority of cases involved S. Typhi 4.3.1 (H58) linked with travel to South Asia (59%). Travel to East and West Africa were associated with genotypes 4.3.1 and 3.3.1, respectively. Point mutations in the quinolone resistance determining region (QRDR), associated with reduced susceptibility to fluoroquinolones, were very common (85% of all cases) but the frequency varied significantly by region of travel: 95% in South Asia, 43% in East Africa, 27% in West Africa. QRDR triple mutants, resistant to ciprofloxacin, were restricted to 4.3.1 lineage II and associated with travel to India, accounting for 23% of cases reporting travel to the country. Overall 24% of isolates were MDR, however the frequency varied significantly by region and country of travel: 27% in West Africa, 52% in East Africa, 55% in Pakistan, 24% in Bangladesh, 3% in India. MDR determinants were plasmid-borne (IncHI1 PST2 plasmids) in S. Typhi 3.1.1 linked to West Africa, but in all other regions MDR was chromosomally integrated in 4.3.1 lineage I. We propose that routine WGS data from travel-associated cases in industrialised countries could serve as informal sentinel AMR genomic surveillance data for countries where WGS is not available or routinely performed.

Emergence of phylogenetically diverse and fluoroquinolone resistant Salmonella Enteritidis as a cause of invasive nontyphoidal Salmonella disease in Ghana.

BACKGROUND: Salmonella enterica serovar Enteritidis is a cause of both poultry- and egg-associated enterocolitis globally and bloodstream-invasive nontyphoidal Salmonella (iNTS) disease in sub-Saharan Africa (sSA). Distinct, multi-drug resistant genotypes associated with iNTS disease in sSA have recently been described, often requiring treatment with fluoroquinolone antibiotics. In industrialised countries, antimicrobial use in poultry production has led to frequent fluoroquinolone resistance amongst globally prevalent enterocolitis-associated lineages. METHODOLOGY/PRINCIPAL FINDINGS: Twenty seven S. Enteritidis isolates from patients with iNTS disease and two poultry isolates, collected between 2007 and 2015 in the Ashanti region of Ghana, were whole-genome sequenced. These isolates, notable for a high rate of diminished ciprofloxacin susceptibility (DCS), were placed in the phyletic context of 1,067 sequences from the Public Health England (PHE) S. Enteritidis genome database to understand whether DCS was associated with African or globally-circulating clades of S. Enteritidis. Analysis showed four of the major S. Enteritidis clades were represented, two global and two African. All thirteen DCS isolates, containing a single gyrA mutation at codon 87, belonged to a global PT4-like clade responsible for epidemics of poultry-associated enterocolitis. Apart from two DCS isolates, which clustered with PHE isolates associated with travel to Spain and Brazil, the remaining DCS isolates, including one poultry isolate, belonged to two monophyletic clusters in which gyrA 87 mutations appear to have developed within the region. CONCLUSIONS/SIGNIFICANCE: Extensive phylogenetic diversity is evident amongst iNTS disease-associated S. Enteritidis in Ghana. Antimicrobial resistance profiles differed by clade, highlighting the challenges of devising empirical sepsis guidelines. The detection of fluoroquinolone resistance in phyletically-related poultry and human isolates is of major concern and surveillance and control measures within the region's burgeoning poultry industry are required to protect a human population at high risk of iNTS disease.

An international outbreak of Salmonella enterica serotype Enteritidis linked to eggs from Poland: a microbiological and epidemiological study.

BACKGROUND: Salmonella spp are a major cause of food-borne outbreaks in Europe. We investigated a large multi-country outbreak of Salmonella enterica serotype Enteritidis in the EU and European Economic Area (EEA). METHODS: A confirmed case was defined as a laboratory-confirmed infection with the outbreak strains of S Enteritidis based on whole-genome sequencing (WGS), occurring between May 1, 2015, and Oct 31, 2018. A probable case was defined as laboratory-confirmed infection with S Enteritidis with the multiple-locus variable-number tandem repeat analysis outbreak profile. Multi-country epidemiological, trace-back, trace-forward, and environmental investigations were done. We did a case-control study including confirmed and probable cases and controls randomly sampled from the population registry (frequency matched by age, sex, and postal code). Odds ratios (ORs) for exposure rates between cases and controls were calculated with unmatched univariable and multivariable logistic regression. FINDINGS: 18 EU and EEA countries reported 838 confirmed and 371 probable cases. 509 (42%) cases were reported in 2016, after which the number of cases steadily increased. The case-control study results showed that cases more often ate in food establishments than did controls (OR 3·4 [95% CI 1·6-7·3]), but no specific food item was identified. Recipe-based food trace-back investigations among cases who ate in food establishments identified eggs from Poland as the vehicle of infection in October, 2016. Phylogenetic analysis identified two strains of S Enteritidis in human cases that were subsequently identified in salmonella-positive eggs and primary production premises in Poland, confirming the source of the outbreak. After control measures were implemented, the number of cases decreased, but increased again in March, 2017, and the increase continued into 2018. INTERPRETATION: This outbreak highlights the public health value of multi-country sharing of epidemiological, trace-back, and microbiological data. The re-emergence of cases suggests that outbreak strains have continued to enter the food chain, although changes in strain population dynamics and fewer cases indicate that control measures had some effect. Routine use of WGS in salmonella surveillance and outbreak response promises to identify and stop outbreaks in the future. FUNDING: European Centre for Disease Prevention and Control; Directorate General for Health and Food Safety, European Commission; and National Public Health and Food Safety Institutes of the authors' countries (see Acknowledgments for full list).

Outbreak of Shiga toxin-producing Escherichia coli O157:H7 linked to raw drinking milk resolved by rapid application of advanced pathogen characterisation methods, England, August to October 2017.

An outbreak of Shiga toxin-producing Escherichia coli (STEC) O157:H7 occurred on the Isle of Wight between August and October 2017. Of the seven cases linked to the outbreak, five were identified through the statutory notification process and two were identified through national surveillance of whole genome sequencing data. Enhanced surveillance questionnaires established a common link to a farm, and link to the likely food vehicle, raw drinking milk (RDM). Microbiological investigations, including PCR, identified the presence of STEC O157:H7 in samples of RDM. Analysis of core genome single nucleotide polymorphism (SNP) data of STEC O157:H7 from human stool specimens, animal faecal samples and RDM demonstrated a one SNP difference between isolates, and therefore close genetic relatedness. Control measures that were put in place included suspension of sales and recall of RDM, as well as restrictions on public access to parts of the farm. Successful integration of traditional epidemiological surveillance and advanced laboratory methods for the detection and characterisation of STEC O157:H7 from human, animal and environmental samples enabled prompt identification of the outbreak vehicle and provided evidence to support the outbreak control team's decision-making, leading to implementation of effective control measures in a timely manner.

SnapperDB: a database solution for routine sequencing analysis of bacterial isolates.

Summary: Real-time surveillance of infectious disease using whole genome sequencing data poses challenges in both result generation and communication. SnapperDB represents a set of tools to store bacterial variant data and facilitate reproducible and scalable analysis of bacterial populations. We also introduce the 'SNP address' nomenclature to describe the relationship between isolates in a population to the single nucleotide resolution. We announce the release of SnapperDB v1.0 a program for scalable routine SNP analysis and storage of microbial populations. Availability and implementation: SnapperDB is implemented as a python application under the open source BSD license. All code and user guides are available at https://github.com/phe-bioinformatics/snapperdb. Reference genomes and SnapperDB configs are available at https://github.com/phe-bioinformatics/snapperdb_references.

Model-assisted metabolic engineering of Escherichia coli for long chain alkane and alcohol production.

Biologically-derived hydrocarbons are considered to have great potential as next-generation biofuels owing to the similarity of their chemical properties to contemporary diesel and jet fuels. However, the low yield of these hydrocarbons in biotechnological production is a major obstacle for commercialization. Several genetic and process engineering approaches have been adopted to increase the yield of hydrocarbon, but a model driven approach has not been implemented so far. Here, we applied a constraint-based metabolic modeling approach in which a variable demand for alkane biosynthesis was imposed, and co-varying reactions were considered as potential targets for further engineering of an E. coli strain already expressing cyanobacterial enzymes towards higher chain alkane production. The reactions that co-varied with the imposed alkane production were found to be mainly associated with the pentose phosphate pathway (PPP) and the lower half of glycolysis. An optimal modeling solution was achieved by imposing increased flux through the reaction catalyzed by glucose-6-phosphate dehydrogenase (zwf) and iteratively removing 7 reactions from the network, leading to an alkane yield of 94.2% of the theoretical maximum conversion determined by in silico analysis at a given biomass rate. To validate the in silico findings, we first performed pathway optimization of the cyanobacterial enzymes in E. coli via different dosages of genes, promoting substrate channelling through protein fusion and inducing substantial equivalent protein expression, which led to a 36-fold increase in alka(e)ne production from 2.8 mg/L to 102 mg/L. Further, engineering of E. coli based on in silico findings, including biomass constraint, led to an increase in the alka(e)ne titer to 425 mg/L (major components being 249 mg/L pentadecane and 160 mg/L heptadecene), a 148.6-fold improvement over the initial strain, respectively; with a yield of 34.2% of the theoretical maximum. The impact of model-assisted engineering was also tested for the production of long chain fatty alcohol, another commercially important molecule sharing the same pathway while differing only at the terminal reaction, and a titer of 1506 mg/L was achieved with a yield of 86.4% of the theoretical maximum. Moreover, the model assisted engineered strains had produced 2.54 g/L and 12.5 g/L of long chain alkane and fatty alcohol, respectively, in the bioreactor under fed-batch cultivation condition. Our study demonstrated successful implementation of a combined in silico modeling approach along with the pathway and process optimization in achieving the highest reported titers of long chain hydrocarbons in E. coli.

Whole-Genome Sequencing for National Surveillance of Shigella flexneri.

National surveillance of Shigella flexneri ensures the rapid detection of outbreaks to facilitate public health investigation and intervention strategies. In this study, we used whole-genome sequencing (WGS) to type S. flexneri in order to detect linked cases and support epidemiological investigations. We prospectively analyzed 330 isolates of S. flexneri received at the Gastrointestinal Bacteria Reference Unit at Public Health England between August 2015 and January 2016. Traditional phenotypic and WGS sub-typing methods were compared. PCR was carried out on isolates exhibiting phenotypic/genotypic discrepancies with respect to serotype. Phylogenetic relationships between isolates were analyzed by WGS using single nucleotide polymorphism (SNP) typing to facilitate cluster detection. For 306/330 (93%) isolates there was concordance between serotype derived from the genome and phenotypic serology. Discrepant results between the phenotypic and genotypic tests were attributed to novel O-antigen synthesis/modification gene combinations or indels identified in O-antigen synthesis/modification genes rendering them dysfunctional. SNP typing identified 36 clusters of two isolates or more. WGS provided microbiological evidence of epidemiologically linked clusters and detected novel O-antigen synthesis/modification gene combinations associated with two outbreaks. WGS provided reliable and robust data for monitoring trends in the incidence of different serotypes over time. SNP typing can be used to facilitate outbreak investigations in real-time thereby informing surveillance strategies and providing the opportunities for implementing timely public health interventions.

A Genome Scale Model of Geobacillus thermoglucosidasius (C56-YS93) reveals its biotechnological potential on rice straw hydrolysate.

Rice straw is a major crop residue which is burnt in many countries, creating significant air pollution. Thus, alternative routes for disposal of rice straw are needed. Biotechnological treatment of rice straw hydrolysate has potential to convert this agriculture waste into valuable biofuel(s) and platform chemicals. Geobacillus thermoglucosidasius is a thermophile with properties specially suited for use as a biocatalyst in lignocellulosic bioprocesses, such as high optimal temperature and tolerance to high levels of ethanol. However, the capabilities of G. thermoglucosidasius to utilise sugars in rice straw hydrolysate for making bioethanol and other platform chemicals have not been fully explored. In this work, we have created a genome scale metabolic model (denoted iGT736) of the organism containing 736 gene products, 1159 reactions and 1163 metabolites. The model was validated both by purely theoretical approaches and by comparing the behaviour of the model to previously published experimental results. The model was then used to determine the yields of a variety of platform chemicals from glucose and xylose - two primary sugars in rice straw hydrolysate. A comparison with results from a model of Escherichia coli shows that G. thermoglucosidasius is capable of producing a wider range of products, and that for the products also produced by E. coli, the yields are comparable. We also discuss strategies to utilise arabinose, a minor component of rice straw hydrolysate, and propose additional reactions to lead to the synthesis of xylitol, not currently produced by G. thermoglucosidasius. Our results provide additional motivation for the current exploration of the industrial potential of G. thermoglucosidasius and we make our model publicly available to aid the development of metabolic engineering strategies for this organism.

Identification of metabolic pathways essential for fitness of Salmonella Typhimurium in vivo.

Bacterial infections remain a threat to human and animal health worldwide, and there is an urgent need to find novel targets for intervention. In the current study we used a computer model of the metabolic network of Salmonella enterica serovar Typhimurium and identified pairs of reactions (cut sets) predicted to be required for growth in vivo. We termed such cut sets synthetic auxotrophic pairs. We tested whether these would reveal possible combined targets for new antibiotics by analyzing the performance of selected single and double mutants in systemic mouse infections. One hundred and two cut sets were identified. Sixty-three of these included only pathways encoded by fully annotated genes, and from this sub-set we selected five cut sets involved in amino acid or polyamine biosynthesis. One cut set (asnA/asnB) demonstrated redundancy in vitro and in vivo and showed that asparagine is essential for S. Typhimurium during infection. trpB/trpA as well as single mutants were attenuated for growth in vitro, while only the double mutant was a cut set in vivo, underlining previous observations that tryptophan is essential for successful outcome of infection. speB/speF,speC was not affected in vitro but was attenuated during infection showing that polyamines are essential for virulence apparently in a growth independent manner. The serA/glyA cut-set was found to be growth attenuated as predicted by the model. However, not only the double mutant, but also the glyA mutant, were found to be attenuated for virulence. This adds glycine production or conversion of glycine to THF to the list of essential reactions during infection. One pair (thrC/kbl) showed true redundancy in vitro but not in vivo demonstrating that threonine is available to the bacterium during infection. These data add to the existing knowledge of available nutrients in the intra-host environment, and have identified possible new targets for antibiotics.

Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation.

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.