Risk factors for campylobacteriosis in Australia: outcomes of a 2018-2019 case-control study.

BACKGROUND: We aimed to identify risk factors for sporadic campylobacteriosis in Australia, and to compare these for Campylobacter jejuni and Campylobacter coli infections. METHODS: In a multi-jurisdictional case-control study, we recruited culture-confirmed cases of campylobacteriosis reported to state and territory health departments from February 2018 through October 2019. We recruited controls from notified influenza cases in the previous 12 months that were frequency matched to cases by age group, sex, and location. Campylobacter isolates were confirmed to species level by public health laboratories using molecular methods. We conducted backward stepwise multivariable logistic regression to identify significant risk factors. RESULTS: We recruited 571 cases of campylobacteriosis (422 C. jejuni and 84 C. coli) and 586 controls. Important risk factors for campylobacteriosis included eating undercooked chicken (adjusted odds ratio [aOR] 70, 95% CI 13-1296) or cooked chicken (aOR 1.7, 95% CI 1.1-2.8), owning a pet dog aged < 6 months (aOR 6.4, 95% CI 3.4-12), and the regular use of proton-pump inhibitors in the 4 weeks prior to illness (aOR 2.8, 95% CI 1.9-4.3). Risk factors remained similar when analysed specifically for C. jejuni infection. Unique risks for C. coli infection included eating chicken pâté (aOR 6.1, 95% CI 1.5-25) and delicatessen meats (aOR 1.8, 95% CI 1.0-3.3). Eating any chicken carried a high population attributable fraction for campylobacteriosis of 42% (95% CI 13-68), while the attributable fraction for proton-pump inhibitors was 13% (95% CI 8.3-18) and owning a pet dog aged < 6 months was 9.6% (95% CI 6.5-13). The population attributable fractions for these variables were similar when analysed by campylobacter species. Eating delicatessen meats was attributed to 31% (95% CI 0.0-54) of cases for C. coli and eating chicken pâté was attributed to 6.0% (95% CI 0.0-11). CONCLUSIONS: The main risk factor for campylobacteriosis in Australia is consumption of chicken meat. However, contact with young pet dogs may also be an important source of infection. Proton-pump inhibitors are likely to increase vulnerability to infection.

Rewriting yeast central carbon metabolism for industrial isoprenoid production.

A bio-based economy has the potential to provide sustainable substitutes for petroleum-based products and new chemical building blocks for advanced materials. We previously engineered Saccharomyces cerevisiae for industrial production of the isoprenoid artemisinic acid for use in antimalarial treatments. Adapting these strains for biosynthesis of other isoprenoids such as ß-farnesene (C15H24), a plant sesquiterpene with versatile industrial applications, is straightforward. However, S. cerevisiae uses a chemically inefficient pathway for isoprenoid biosynthesis, resulting in yield and productivity limitations incompatible with commodity-scale production. Here we use four non-native metabolic reactions to rewire central carbon metabolism in S. cerevisiae, enabling biosynthesis of cytosolic acetyl coenzyme A (acetyl-CoA, the two-carbon isoprenoid precursor) with a reduced ATP requirement, reduced loss of carbon to CO2-emitting reactions, and improved pathway redox balance. We show that strains with rewired central metabolism can devote an identical quantity of sugar to farnesene production as control strains, yet produce 25% more farnesene with that sugar while requiring 75% less oxygen. These changes lower feedstock costs and dramatically increase productivity in industrial fermentations which are by necessity oxygen-constrained. Despite altering key regulatory nodes, engineered strains grow robustly under taxing industrial conditions, maintaining stable yield for two weeks in broth that reaches >15% farnesene by volume. This illustrates that rewiring yeast central metabolism is a viable strategy for cost-effective, large-scale production of acetyl-CoA-derived molecules.

Histamine fish poisoning in Australia, 2001 to 2013.

We report on human illness due to histamine fish poisoning outbreaks in Australia from 2001 to 2013. Histamine fish poisoning results from the ingestion of histamine contained within the flesh of certain fish species that naturally contain histidine, which has been converted to histamine by spoilage bacteria following poor handling or temperature control after harvesting. While symptoms vary, allergic symptoms such as facial flushing, headaches and rashes are frequently reported. Using the OzFoodNet outbreak register, published case reports and surveillance reports, we found data on 57 outbreaks of histamine fish poisoning, which affected 187 people, of whom 14% were hospitalised. There were no deaths reported. Outbreaks were generally small in size, with a median of 2 cases per outbreak (range 1 to 22 people), with 88% of outbreaks comprising less than 5 people. Tuna (in the family Scombridae) was the most frequently reported food vehicle, while 18 outbreaks involved non-scombridae fish. Median incubation periods among the outbreaks were short; being less than 1 hour for 22 outbreaks. The most frequently reported symptoms were diarrhoea and rash. Symptoms of facial/body flushing were reported for at least one case in 19 outbreaks and tingling, burning or swelling of the skin, especially around the lips for at least 1 case in 13 outbreaks. In 3 outbreaks, one or more cases were reported to have had respiratory distress or difficulty breathing. While the condition is often mild, improved recognition and appropriate treatment is important, as it will reduce the possibility of any severe health effects resulting from this condition. Key features of histamine fish poisoning outbreaks are the high attack rate, rapid onset, the typical symptoms and their short duration.

An outbreak of gastroenteritis linked to a buffet lunch served at a Canberra restaurant.

In 2013, an outbreak of gastrointestinal illness occurred following a buffet lunch at a restaurant in Canberra. An investigation was conducted to identify the cause of illness and to implement appropriate public health measures to prevent further disease. We conducted a retrospective cohort study via telephone interviews, using a structured questionnaire developed from the restaurant buffet menu. A case was defined as someone who ate the buffet lunch at the restaurant on the implicated date and developed any symptoms of gastrointestinal illness (such as diarrhoea, abdominal pain and nausea) following the consumption of food. A total of 74% (225/303) of known attendees were interviewed, of whom 56% (125/225) had become ill. The median incubation period and duration of illness were 13 and 19 hours respectively. The most commonly reported symptoms were diarrhoea (94%, 118/125) and abdominal pain (82%, 103/125). A toxin-mediated gastrointestinal illness was suspected based on the incubation period, duration of illness and the symptoms. The environmental health investigation identified a lack of designated hand washing facilities in the kitchen, an absence of thermometers for measuring food temperatures and several maintenance and minor cleaning issues. A number of food samples were taken for microbiological analysis. Multivariable analysis showed that illness was significantly associated with consuming curried prawns (OR 18.4, 95% CI 8.6-39.3, P < 0.001) and Caesar salad (OR 3.6, 95% CI 1.8-7.5, P 0.001). Enterotoxin-producing Staphylococcus aureus and Bacillus cereus were identified in leftover samples of cooked buffet food, but this food was not epidemiologically implicated. The investigation suggested that a breakdown in cleanliness, temperature control and food handling practices may have resulted in contamination of the buffet food. In order to prevent such outbreaks in the future, caterers and restaurateurs need to ensure they have the appropriate facilities and procedures in place if planning to cater for large groups.

Vaccine breakthrough infections in a highly-vaccinated Australian population during a SARS-CoV-2 Delta outbreak.

Abstract: Over 80% of residents in the Australian Capital Territory were fully vaccinated within 10 weeks of a SARS-CoV-2 Delta variant outbreak. Of the outbreak's 1,545 cases, 10% were breakthrough infections. The incidence of infections among fully- and partially-vaccinated people was 98.5% and 90% lower, respectively, than for unvaccinated people.

Quorum-sensing regulation of a copper toxicity system in Pseudomonas aeruginosa.

The LasR/LasI quorum-sensing system in Pseudomonas aeruginosa influences global gene expression and mediates pathogenesis. In this study, we show that the quorum-sensing system activates, via the transcriptional regulator PA4778, a copper resistance system composed of 11 genes. The quorum-sensing global regulator LasR was recently shown to directly activate transcription of PA4778, a cueR homolog and a MerR-type transcriptional regulator. Using molecular genetic methods and bioinformatics, we verify the interaction of LasR with the PA4778 promoter and further demonstrate the LasR binding site. We also identify a putative PA4778 binding motif and show that the protein directly binds to and activates five promoters controlling the expression of 11 genes--PA3519 to -15, PA3520, mexPQ-opmE, PA3574.1, and cueA, a virulence factor in a murine model. Using gene disruptions, we show that PA4778, along with 7 of 11 gene targets of PA4778, increases the sensitivity of P. aeruginosa to elevated copper concentrations. This work identifies a cellular function for PA4778 and four other previously unannotated genes (PA3515, PA3516, PA3517, and PA3518) and suggests a potential role for copper in the quorum response. We propose to name PA4778 cueR.

On the choice and number of microarrays for transcriptional regulatory network inference.

BACKGROUND: Transcriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)-gene interactions at the genome-wide level. In correlation-based TRNI, network edges can in principle be evaluated using standard statistical tests. However, while such tests nominally assume independent microarray experiments, we expect dependency between the experiments in microarray compendia, due to both project-specific factors (e.g., microarray preparation, environmental effects) in the multi-project compendium setting and effective dependency induced by gene-gene correlations. Herein, we characterize the nature of dependency in an Escherichia coli microarray compendium and explore its consequences on the problem of determining which and how many arrays to use in correlation-based TRNI. RESULTS: We present evidence of substantial effective dependency among microarrays in this compendium, and characterize that dependency with respect to experimental condition factors. We then introduce a measure neff of the effective number of experiments in a compendium, and find that corresponding to the dependency observed in this particular compendium there is a huge reduction in effective sample size i.e., neff = 14.7 versus n = 376. Furthermore, we found that the neff of select subsets of experiments actually exceeded neff of the full compendium, suggesting that the adage 'less is more' applies here. Consistent with this latter result, we observed improved performance in TRNI using subsets of the data compared to results using the full compendium. We identified experimental condition factors that trend with changes in TRNI performance and neff , including growth phase and media type. Finally, using the set of known E. coli genetic regulatory interactions from RegulonDB, we demonstrated that false discovery rates (FDR) derived from neff -adjusted p-values were well-matched to FDR based on the RegulonDB truth set. CONCLUSIONS: These results support utilization of neff as a potent descriptor of microarray compendia. In addition, they highlight a straightforward correlation-based method for TRNI with demonstrated meaningful statistical testing for significant edges, readily applicable to compendia from any species, even when a truth set is not available. This work facilitates a more refined approach to construction and utilization of mRNA expression compendia in TRNI.

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata.

Many Microbe Microarrays Database (M3D) is designed to facilitate the analysis and visualization of expression data in compendia compiled from multiple laboratories. M3D contains over a thousand Affymetrix microarrays for Escherichia coli, Saccharomyces cerevisiae and Shewanella oneidensis. The expression data is uniformly normalized to make the data generated by different laboratories and researchers more comparable. To facilitate computational analyses, M3D provides raw data (CEL file) and normalized data downloads of each compendium. In addition, web-based construction, visualization and download of custom datasets are provided to facilitate efficient interrogation of the compendium for more focused analyses. The experimental condition metadata in M3D is human curated with each chemical and growth attribute stored as a structured and computable set of experimental features with consistent naming conventions and units. All versions of the normalized compendia constructed for each species are maintained and accessible in perpetuity to facilitate the future interpretation and comparison of results published on M3D data. M3D is accessible at http://m3d.bu.edu/.

Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia.

MOTIVATION: DNA microarrays are routinely applied to study diseased or drug-treated cell populations. A critical challenge is distinguishing the genes directly affected by these perturbations from the hundreds of genes that are indirectly affected. Here, we developed a sparse simultaneous equation model (SSEM) of mRNA expression data and applied Lasso regression to estimate the model parameters, thus constructing a network model of gene interaction effects. This inferred network model was then used to filter data from a given experimental condition of interest and predict the genes directly targeted by that perturbation. RESULTS: Our proposed SSEM-Lasso method demonstrated substantial improvement in sensitivity compared with other tested methods for predicting the targets of perturbations in both simulated datasets and microarray compendia. In simulated data, for two different network types, and over a wide range of signal-to-noise ratios, our algorithm demonstrated a 167% increase in sensitivity on average for the top 100 ranked genes, compared with the next best method. Our method also performed well in identifying targets of genetic perturbations in microarray compendia, with up to a 24% improvement in sensitivity on average for the top 100 ranked genes. The overall performance of our network-filtering method shows promise for identifying the direct targets of genetic dysregulation in cancer and disease from expression profiles. AVAILABILITY: Microarray data are available at the Many Microbe Microarrays Database (M3D, http://m3d.bu.edu). Algorithm scripts are available at the Gardner Lab website (http://gardnerlab.bu.edu/SSEMLasso).

Free range eggs does not mean safe eggs: an outbreak of Salmonella Typhimurium linked to free range eggs.

An outbreak of Salmonella enterica serovar Typhimurium with closely related Multiple Locus Variable-number Tandem Repeat Analysis (MLVA) patterns was detected by routine surveillance by the Australian Capital Territory Health Protection Service in May 2018. The outbreak consisted of three cases in 2018 (MLVA 03-10-10-09-496) and one in 2016 (MLVA 03-10-09-09-496), who reported eating home-cooked eggs from the same local producer. Environmental investigations found significant problems with egg cleaning, hand hygiene and documentation of food safety procedures on farm. Environmental samples collected from the farm were found to have the same MLVA pattern as the 2018 cases. Although poor farm practices most likely led to contamination of the eggs, this outbreak highlights the need for consumer education about safe handling of eggs in the home.

An outbreak of Bacillus cereus toxin-mediated emetic and diarrhoeal syndromes at a restaurant in Canberra, Australia 2018.

A cluster of gastrointestinal illness was detected following receipt of a complaint of becoming ill after a multi-course dinner at a restaurant in Canberra, Australian Capital Territory (ACT), Australia. The complaint led to an investigation by ACT Health. Food samples retained by the restaurant for microbiological analysis returned an unsatisfactory level of Bacillus cereus in beef (19,000 colony forming units/gram [cfu/g]) and a satisfactory level in arancini (50 cfu/g). These positive samples underwent whole genome sequencing and genes encoding diarrhoeal toxins were detected with no laboratory evidence of the emetic toxin. No stool specimens were collected. A cohort study was undertaken and 80% (33/41) of patrons took part in a structured interview. There was no significant difference in age or sex between those ill and not ill. Due to universal exposure most foods were unable to be statistically analysed and no significant results were found from the food history. The ill cohort diverged into two distinct groups based on incubation period and symptoms suggesting this outbreak involved B. cereus intoxication with both diarrhoeal and potentially emetic toxins. Some hygiene practices during food preparation were noted to be inadequate and heating and cooling procedures were unverified when questioned. A combination of the incubation periods and symptom profile, food laboratory evidence, and genomic sequencing of the B. cereus diarrhoeal gene suggest a probable aetiology of B. cereus intoxication. Public health action included the restaurant rectifying hygiene practices and documenting heating/cooling procedures.

Predicting sigma28 promoters in eleven Shewanella genomes.

An iterative position-specific score matrix (PSSM)-based approach was used to predict sigma(28) promoters in 11 Shewanella genomes. The Shewanella Correlation Browser was used to distinguish true-positive predictions from false-positive predictions in Shewanella oneidensis MR-1 by generating a sigma(28)-regulated transcriptional network from transcriptional profiling data. This dual-pronged approach identified several genes that have sigma(28) promoters and that may be involved with motility or chemotaxis in Shewanella.

Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks.

A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 515 whole-genome yeast expression profiles resulting from a variety of treatments (compounds, knockouts and induced expression), and correctly enrich for the known targets and associated pathways in the majority of compounds examined. We demonstrate our approach with PTSB, a growth inhibitory compound with a previously unknown mode of action, by predicting and validating thioredoxin and thioredoxin reductase as its target.

Digital publishing isn't enough: the case for 'blueprints' in scientific communication.

Since the time of Newton and Galileo, the tools for capturing and communicating science have remained conceptually unchanged - in essence, they consist of observations on paper (or electronic variants), followed by a 'letter' to the community to report your findings. These age-old tools are inadequate for the complexity of today's scientific challenges. If modern software engineering worked like science, programmers would not share open source code; they would take notes on their work and then publish long-form articles about their software. Months or years later, their colleagues would attempt to reproduce the software based on the article. It sounds a bit silly, and yet even, this level of prose-based methodological discourse has deteriorated in science communication. Materials and Methods sections of papers are often a vaguely written afterthought, leaving researchers baffled when they try to repeat a published finding. It's time for a fundamental shift in scientific communication and sharing, a shift akin to the advent of computer-aided design and source code versioning. Science needs reusable 'blueprints' for experiments replete with the experiment designs, material flows, reaction parameters, data, and analytical procedures. Such an approach could establish the foundations for truly open source science where these scientific blueprints form the digital 'source code' for a supply chain of high-quality innovations and discoveries.

Evaluation of a novel cloud-based software platform for structured experiment design and linked data analytics.

Open data in science requires precise definition of experimental procedures used in data generation, but traditional practices for sharing protocols and data cannot provide the required data contextualization. Here, we explore implementation, in an academic research setting, of a novel cloud-based software system designed to address this challenge. The software supports systematic definition of experimental procedures as visual processes, acquisition and analysis of primary data, and linking of data and procedures in machine-computable form. The software was tested on a set of quantitative microbial-physiology experiments. Though time-intensive, definition of experimental procedures in the software enabled much more precise, unambiguous definitions of experiments than conventional protocols. Once defined, processes were easily reusable and composable into more complex experimental flows. Automatic coupling of process definitions to experimental data enables immediate identification of correlations between procedural details, intended and unintended experimental perturbations, and experimental outcomes. Software-based experiment descriptions could ultimately replace terse and ambiguous 'Materials and Methods' sections in scientific journals, thus promoting reproducibility and reusability of published studies.

Lightweight genome viewer: portable software for browsing genomics data in its chromosomal context.

BACKGROUND: Lightweight genome viewer (lwgv) is a web-based tool for visualization of sequence annotations in their chromosomal context. It performs most of the functions of larger genome browsers, while relying on standard flat-file formats and bypassing the database needs of most visualization tools. Visualization as an aide to discovery requires display of novel data in conjunction with static annotations in their chromosomal context. With database-based systems, displaying dynamic results requires temporary tables that need to be tracked for removal. RESULTS: lwgv simplifies the visualization of user-generated results on a local computer. The dynamic results of these analyses are written to transient files, which can import static content from a more permanent file. lwgv is currently used in many different applications, from whole genome browsers to single-gene RNAi design visualization, demonstrating its applicability in a large variety of contexts and scales. CONCLUSION: lwgv provides a lightweight alternative to large genome browsers for visualizing biological annotations and dynamic analyses in their chromosomal context. It is particularly suited for applications ranging from short sequences to medium-sized genomes when the creation and maintenance of a large software and database infrastructure is not necessary or desired.

Identification of diverse carbon utilization pathways in Shewanella oneidensis MR-1 via expression profiling.

To identify pathways of carbon utilization in the metal-reducing marine bacterium Shewanella oneidensis MR-1, we assayed the expression of cells grown with various carbon sources using a high-density oligonucleotide Affymetrix microarray. Our expression profiles reveal genes and regulatory mechanisms which govern the sensing, import, and utilization of the nucleoside inosine, the chitin monomer N-acetylglucosamine, and a casein-derived mixture of amino acids. Our analysis suggests a prominent role for the pentose-phosphate and Entner-Doudoroff pathways in energy metabolism, and regulatory coupling between carbon catabolism and electron acceptor pathways. In sum, these results indicate that S. oneidensis possesses a broader capacity for carbon utilization than previously reported, a view with implications for optimizing its role in microbial fuel cell and bioremediative applications.

Reverse-engineering transcription control networks.

Microarray technologies, which enable the simultaneous measurement of all RNA transcripts in a cell, have spawned the development of algorithms for reverse-engineering transcription control networks. In this article, we classify the algorithms into two general strategies: physical modeling and influence modeling. We discuss the biological and computational principles underlying each strategy, and provide leading examples of each. We also discuss the practical considerations for developing and applying the various methods.