Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming.

Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) and spread to humans. Here, we performed a longitudinal study in a large-scale commercial poultry farm in China, collecting E. coli isolates from both farm and slaughterhouse; targeting animals, carcasses, workers and their households and environment. By using whole-genome phylogenetic analysis and network analysis based on single nucleotide polymorphisms (SNPs), we found highly interrelated non-pathogenic and pathogenic E. coli strains with phylogenetic intermixing, and a high prevalence of shared multidrug resistance profiles amongst livestock, human and environment. Through an original data processing pipeline which combines omics, machine learning, gene sharing network and mobile genetic elements analysis, we investigated the resistance to 26 different antimicrobials and identified 361 genes associated to antimicrobial resistance (AMR) phenotypes; 58 of these were known AMR-associated genes and 35 were associated to multidrug resistance. We uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments. We also found several human, livestock and environmental isolates sharing closely related mobile genetic elements carrying ARGs across host species and environments. In a scenario where no consensus exists on how antibiotic use in the livestock may affect antibiotic resistance in the human population, our findings provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming. Moreover, our original data analysis method has the potential to uncover AMR transmission pathways when applied to the study of other pathogens active in other anthropogenic environments characterised by complex interconnections between host species.

Novel SCCmec type XV (7A) and two pseudo-SCCmec variants in foodborne MRSA in China.

BACKGROUND: Staphylococcal cassette chromosome mec (SCCmec) elements are highly diverse and have been classified into 14 types. Novel SCCmec variants have been frequently detected from humans and animals but rarely from food. OBJECTIVES: To characterize a novel SCCmec type and two SCCmec variants identified from food-associated MRSA in China. METHODS: Three MRSA (NV_1, NT_611 and NT_8) collected from retail foods in China were subjected to WGS and the SCCmec elements were determined. RESULTS: The novel SCCmecXV identified in NV_1 carried the mec gene complex class A (mecI-mecR1-mecA-IS431) and the ccr gene complex 7 (ccrA1B6), and a Tn558-mediated phenicol exporter gene fexA was detected in this SCCmecXV cassette. The pseudo-SCCmec elements ΨSCCmecNT_611 and ΨSCCmecNT_8 showed a truncated SCCmec pattern, carrying the class C2 mec gene complex but missing the ccr genes. The ΨSCCmecNT_611 element shared more similarities with those of Staphylococcus haemolyticus (AB478934.1) and carried a heavy metal resistance gene cluster cadD-cadX-arsC-arsB-arsR-copA. The ΨSCCmecNT_8 MRSA exhibited a highly resistant phenotype, showing the absence of a 19.3 kb segment compared with the reference SCCmecXII element (CP019945.1). Notably, a 46 kb region containing multiple transposons encoding antimicrobial or metal resistance genes flanked by IS431 or IS256 was identified ∼30 kb downstream from the mec gene complex in ΨSCCmecNT_8, which might explain such high resistance in MRSA NT_8. CONCLUSIONS: Our finding of novel and pseudo-SCCmec elements reflected the ongoing intra/interspecies genetic rearrangements in staphylococci. Further study will be needed to investigate the biological significance and prevalence of those SCCmec variants along the food chain.

Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis.

Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation-Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen's kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen's kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen's kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Epidemiological Study on Prevalence, Serovar Diversity, Multidrug Resistance, and CTX-M-Type Extended-Spectrum ß-Lactamases of Salmonella spp. from Patients with Diarrhea, Food of Animal Origin, and Pets in Several Provinces of China.

A total of 2,283 Salmonella isolates were recovered from 18,334 samples, including samples from patients with diarrhea, food of animal origin, and pets, across 5 provinces of China. The highest prevalence of Salmonella spp. was detected in chicken meats (39.3%, 486/1,237). Fifteen serogroups and 66 serovars were identified, with Salmonella enterica serovars Typhimurium and Enteritidis being the most dominant. Most (85.5%, 1,952/2,283) isolates exhibited resistance to ≥1 antimicrobial, and 56.4% were multidrug resistant (MDR). A total of 222 isolates harbored extended-spectrum ß-lactamases (ESBLs), and 200 of these were of the CTX-M type and were mostly detected in isolates from chicken meat and turtle fecal samples. Overall, eight blaCTX-M genes were identified, with blaCTX-M-65, blaCTX-M-123, blaCTX-M-14, blaCTX-M-79, and blaCTX-M-130 being the most prevalent. In total, 166 of the 222 ESBL-producing isolates had amino acid substitutions in GyrA (S83Y, S83F, D87G, D87N, and D87Y) and ParC (S80I), while the plasmid-mediated quinolone resistance (PMQR)-encoding genes oqxA, oqxB, qepA, qnrB, and qnrS were detected in almost all isolates. Of the 15 sequence types (STs) identified in the 222 ESBLs, ST17, ST11, ST34, and ST26 ranked among the top 5 in number of isolates. Our study revealed considerable serovar diversity and a high prevalence of the co-occurrence of MDR determinants, including CTX-M-type ESBLs, quinolone resistance-determining region (QRDR) mutations, and PMQR genes. This is the first report of CTX-M-130 Salmonella spp. from patients with diarrhea and QRDR mutations from turtle fecal samples. Our study emphasizes the importance of actions, both in health care settings and in the veterinary medicine sector, to control the dissemination of MDR, especially the CTX-M-type ESBL-harboring Salmonella isolates.

Radical remodeling of the Y chromosome in a recent radiation of malaria mosquitoes.

Y chromosomes control essential male functions in many species, including sex determination and fertility. However, because of obstacles posed by repeat-rich heterochromatin, knowledge of Y chromosome sequences is limited to a handful of model organisms, constraining our understanding of Y biology across the tree of life. Here, we leverage long single-molecule sequencing to determine the content and structure of the nonrecombining Y chromosome of the primary African malaria mosquito, Anopheles gambiae We find that the An. gambiae Y consists almost entirely of a few massively amplified, tandemly arrayed repeats, some of which can recombine with similar repeats on the X chromosome. Sex-specific genome resequencing in a recent species radiation, the An. gambiae complex, revealed rapid sequence turnover within An. gambiae and among species. Exploiting 52 sex-specific An. gambiae RNA-Seq datasets representing all developmental stages, we identified a small repertoire of Y-linked genes that lack X gametologs and are not Y-linked in any other species except An. gambiae, with the notable exception of YG2, a candidate male-determining gene. YG2 is the only gene conserved and exclusive to the Y in all species examined, yet sequence similarity to YG2 is not detectable in the genome of a more distant mosquito relative, suggesting rapid evolution of Y chromosome genes in this highly dynamic genus of malaria vectors. The extensive characterization of the An. gambiae Y provides a long-awaited foundation for studying male mosquito biology, and will inform novel mosquito control strategies based on the manipulation of Y chromosomes.

Developing and evaluating threshold-based algorithms to detect drinking behavior in dairy cows using reticulorumen temperature.

In this study, we assessed for the first time the use of a reticuloruminal temperature bolus and a thresholding method to detect drinking events and investigated different factors that can affect drinking behavior. First, we validated the detection of drinking events using 16 cows that received a reticuloruminal bolus. For this, we collected continuous drinking behavior data for 4 d using video recordings and ambient and water temperature for the same 4 d. After all the data were synchronized, we performed 2 threshold algorithms: a general-fixed threshold and a cow-day specific threshold algorithm. In the general-fixed threshold, a positive test was considered if the temperature of any cow fell below a fixed threshold; in the cow-day specific threshold, a positive test was considered when the temperature of specific cows fell below the threshold value deviations around the mean temperature of the cow for that day. The former was evaluated using a threshold varying between 35.7 and 39.5°C, and the latter using the formula µ-n10σ, where µ = mean of the temperature of each cow for one day, n = 1, 2, …, 20, and σ = standard deviation of the temperature of each cow on that day. The performance of the validation of detection using each of the threshold types was computed using different metrics, including overall accuracy, precision, recall (also known as sensitivity), F-score, positive predictive value, negative predictive value, false discovery rate, false omission rate, and Cohen's kappa statistic. The findings of the first study showed that the cow-day specific threshold of n = 10 performed better (true positives = 466; false positives = 167; false negatives = 165; true negatives = 8,416) than using a general-fixed threshold of 38.1°C (true positives = 449; false positives = 181; false negatives = 182; true negatives = 8,402). With the information gained in this first study, we investigated the different factors associated with temperature drop characteristics per cow: number of drops, mean amplitude of the drop, and mean recovery time. For this, we used data from 54 cows collected for almost 1 yr to build a mixed-effect multilevel model that included days in milk, parity, average monthly milk production, and ambient temperature as explanatory variables. Cow characteristics and ambient temperature had significant effects on drinking events. Our results provide a platform for automated monitoring of drinking behavior, which has potential value in prediction of health and welfare in dairy cattle.

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep.

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

Regulation of Anopheles gambiae male accessory gland genes influences postmating response in female.

In Drosophila, the accessory gland proteins (Acps) secreted from the male accessory glands (MAGs) and transferred along with sperm into the female reproductive tract have been implicated in triggering postmating behavioral changes, including refractoriness to subsequent mating and propensity to egg laying. Recently, Acps have been found also in Anopheles, suggesting similar functions. Understanding the mechanisms underlying transcriptional regulation of Acps and their functional role in modulating Anopheles postmating behavior may lead to the identification of novel vector control strategies to reduce mosquito populations. We identified heat-shock factor (HSF) binding sites within the Acp promoters of male Anopheles gambiae and discovered three distinct Hsf isoforms; one being significantly up-regulated in the MAGs after mating. Through genome-wide transcription analysis of Hsf-silenced males, we observed significant down-regulation in 50% of the Acp genes if compared to control males treated with a construct directed against an unrelated bacterial sequence. Treated males retained normal life span and reproductive behavior compared to control males. However, mated wild-type females showed a ∼46% reduction of egg deposition rate and a ∼23% reduction of hatching rate (∼58% combined reduction of progeny). Our results highlight an unsuspected role of HSF in regulating Acp transcription in A. gambiae and provide evidence that Acp down-regulation in males leads a significant reduction of progeny, thus opening new avenues toward the development of novel vector control strategies.

Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China.

Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods. Most isolates within same hosts possessed the same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Supervised machine learning classifiers revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials, primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.

Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics.

Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in wastewater treatment, soil, or through natural processes in the environment. If a metabolite is bioactive, even at sub-lethal levels, and also stable in the environment, then it could provide selection pressure for resistance. (5S)-penicilloic acid of piperacillin has previously been found complexed to the binding pocket of penicillin binding protein 3 (PBP3) of Pseudomonas aeruginosa. Here, we predicted the affinities of all potentially relevant antibiotic metabolites of ten different penicillins to that target protein, using molecular docking and molecular dynamics simulations. Docking predicts that, in addition to penicilloic acid, pseudopenicillin derivatives of these penicillins, as well as 6-aminopenicillanic acid (6APA), could also bind to this target. MD simulations further confirmed that (5R)-pseudopenicillin and 6APA bind the target protein, in addition to (5S)-penicilloic acid. Thus, it is possible that these metabolites are bioactive, and, if stable in the environment, could be contaminants selective for antibiotic resistance. This could have considerable significance for environmental surveillance for antibiotics as a means to reduce antimicrobial resistance, because targeted mass spectrometry could be required for relevant metabolites as well as the native antibiotics.

Accurate prediction of calving in dairy cows by applying feature engineering and machine learning.

Prediction of calving is key to dairy cow management. Current trends of increasing herd sizes globally can directly impact the time that farmers spend monitoring individual animals. Automated monitoring on behavioural and physiological changes prior to parturition can be used to develop machine learning solutions for calving prediction. In this study, we developed a machine learning algorithm for the prediction of calving in dairy cows. We demonstrated that temperature and activity index information retrieved from a commercial reticuloruminal bolus sensor can accurately predict calving from 1-day to 5-days in advance. The best prediction solution using data from 82 dairy cows, achieved up to 87.81 % in accuracy, 92.99 % in specificity, 75.84 % in sensitivity, 82.99 % in positive predictive value (PPV), 78.85 % in F-score, and 90.02 % in negative predictive value (NPV) on the test dataset when using information from 2-days in advance and all the subsets of feature characteristics (temperature + drinking + activity). The performance only decreased by 2.45 % points in accuracy, 0.74 % points in specificity, 6.41 % points in sensitivity, 2.45 % points in positive predictive value, 4.91 % points in F-score, and 2.44 % points in negative predictive value on the test dataset when using all feature characteristics and 5-days in advance information compared to using all features and information from 2-days in advance. Full evaluation of the performance of the prediction showed an improvement when using all the different subsets of feature characteristics together (temperature, activity, and drinking) compared to using temperature features only. When adding activity and drinking to the subset of temperature features, an average increase of 2.70, 1.52, 5.40, 4.39, 5.02, 2.13 % points in accuracy, specificity, sensitivity, PPV, F-score, and NPV, respectively, was obtained. Notably, evaluation of feature importance (i.e., relative weight of any given feature in relation to model prediction) showed that 3-5 (depending on the selected days in advance model) of the top ten features were derived from drinking behaviour, showing the relevance that this behaviour can have in the prediction of calving. This algorithm can provide a useful tool for automated calving prediction in dairy cows which has potential for improvement of health, welfare, and productivity in the dairy industry.

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock.

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, machine learning (ML), and culture-based methods, we focused on a poultry farm and connected slaughterhouse in China, investigating the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil. For both the microbiome and resistomes in this study, differences are observed across environments and hosts. However, at a finer scale, several similar clinically relevant antimicrobial resistance genes (ARGs) and similar associated mobile genetic elements were found in both human and broiler chicken samples. Next, we focused on Escherichia coli, an important indicator for the surveillance of AMR on the farm. Strains of E. coli were found intermixed between humans and chickens. We observed that several ARGs present in the chicken faecal resistome showed correlation to resistance/susceptibility profiles of E. coli isolates cultured from the same samples. Finally, by using environmental sensing these ARGs were found to be correlated to variations in environmental temperature and humidity. Our results show the importance of adopting a multi-domain and multi-scale approach when studying microbial communities and AMR in complex, interconnected environments.

Activated tissue resident memory T-cells (CD8+CD103+CD39+) uniquely predict survival in left sided "immune-hot" colorectal cancers.

Introduction: Characterization of the tumour immune infiltrate (notably CD8+ T-cells) has strong predictive survival value for cancer patients. Quantification of CD8 T-cells alone cannot determine antigenic experience, as not all infiltrating T-cells recognize tumour antigens. Activated tumour-specific tissue resident memory CD8 T-cells (TRM) can be defined by the co-express of CD103, CD39 and CD8. We investigated the hypothesis that the abundance and localization of TRM provides a higher-resolution route to patient stratification. Methods: A comprehensive series of 1000 colorectal cancer (CRC) were arrayed on a tissue microarray, with representative cores from three tumour locations and the adjacent normal mucosa. Using multiplex immunohistochemistry we quantified and determined the localization of TRM. Results: Across all patients, activated TRM were an independent predictor of survival, and superior to CD8 alone. Patients with the best survival had immune-hot tumours heavily infiltrated throughout with activated TRM. Interestingly, differences between right- and left-sided tumours were apparent. In left-sided CRC, only the presence of activated TRM (and not CD8 alone) was prognostically significant. Patients with low numbers of activated TRM cells had a poor prognosis even with high CD8 T-cell infiltration. In contrast, in right-sided CRC, high CD8 T-cell infiltration with low numbers of activated TRM was a good prognosis. Conclusion: The presence of high intra-tumoural CD8 T-cells alone is not a predictor of survival in left-sided CRC and potentially risks under treatment of patients. Measuring both high tumour-associated TRM and total CD8 T-cells in left-sided disease has the potential to minimize current under-treatment of patients. The challenge will be to design immunotherapies, for left-sided CRC patients with high CD8 T-cells and low activate TRM,that result in effective immune responses and thereby improve patient survival.

Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China.

China is the largest global consumer of antimicrobials and improving surveillance methods could help to reduce antimicrobial resistance (AMR) spread. Here we report the surveillance of ten large-scale chicken farms and four connected abattoirs in three Chinese provinces over 2.5 years. Using a data mining approach based on machine learning, we analysed 461 microbiomes from birds, carcasses and environments, identifying 145 potentially mobile antibiotic resistance genes (ARGs) shared between chickens and environments across all farms. A core set of 233 ARGs and 186 microbial species extracted from the chicken gut microbiome correlated with the AMR profiles of Escherichia coli colonizing the same gut, including Arcobacter, Acinetobacter and Sphingobacterium, clinically relevant for humans, and 38 clinically relevant ARGs. Temperature and humidity in the barns were also correlated with ARG presence. We reveal an intricate network of correlations between environments, microbial communities and AMR, suggesting multiple routes to improving AMR surveillance in livestock production.

Identifying associations between management practices and antimicrobial resistances of sentinel bacteria recovered from bulk tank milk on dairy farms.

There is increasing emphasis on the need to reduce antimicrobial use (AMU) on dairy farms to reduce the emergence of resistant bacteria which could compromise animal health and impact human medicine. In addition to AMU, the role of farm management is an area of growing interest and represents an alternative route for possible interventions. The aim of this study was to evaluate the impact of farm management practices and AMU on resistances of sentinel bacteria in bulk milk. Dairy farms from two, geographically separate locations within the British Isles were recruited as part of two study groups. Farm management data from study group 1 (n = 125) and study group 2 (n = 16) were collected by means of a face-to-face questionnaire with farmers carried out during farm visits. For study group 2, additional data on AMU was collated from veterinary medicine sales records. Sentinel bacterial species (Enterococcus spp. and E. coli), which have been reported to be of value in antimicrobial resistance (AMR) studies, were isolated from bulk tank milk to monitor antimicrobial susceptibilities by means of minimum inhibitory concentrations (MICs). MIC data for both groups was used to generate an overall "score" for each farm. For both groups, this overall farm mean MIC was used as the outcome variable to evaluate the impact of farm management and AMU. This was achieved through use of elastic net modelling, a regularised regression method which also featured a bootstrapping procedure to produce robust models. Inference of models was based on covariate stabilities and bootstrapped P-values to identify farm management and AMU practices that have significant effects on MICs of sentinel bacteria. Practices which were found to be of importance with respect to Enterococcus spp. included management of slurry, external entry of livestock to the dairy herd, use of bedding materials and conditioners, cubicle cleaning routines and antibiotic practices, including use of ß-lactams and fluoroquinolones. Practices deemed to be of importance for E. coli MICs included cubicle and bedding management practices, teat preparation routines at milking and the milking procedure itself. We conclude that a variety of routine farm management practices are associated with MICs of sentinel bacteria in bulk milk. Amendment of these practices offers additional possible routes of intervention, alongside alterations to AMU, to mitigate the emergence and dissemination of AMR on dairy farms.

Gepoclu: a software tool for identifying and analyzing gene positional clusters in large-scale gene expression analysis.

BACKGROUND: The notion that genes are non-randomly organized within the chromosomes of eukaryotic organisms has recently received strong experimental support. Clusters of co-expressed and co-localized genes have been recognized as playing key roles in a number of functional pathways and adaptive responses including organism development, differentiation, disease states and aging. The identification of genes arranged in close proximity with each other within a particular temporal and spatial transcriptional program is anticipated to unravel possible functional links and reciprocal interactions. RESULTS: We developed a novel software tool Gepoclu (Gene Positional Clustering) that automatically selects genes based on expression values from multiple sources, including microarray, EST and qRT-PCR, and performs positional clustering. Gepoclu provides expression-based gene selection from multiple experimental sources, position-based gene clustering and cluster visualization functionalities, all as parts of the same fully integrated, and interactive, package. This means rapid iterations while exploring for emergent behavior, and full programmability of the filtering and clustering steps. CONCLUSIONS: Gepoclu is a useful data-mining tool for exploring relationships among transcriptional data deriving form different sources. It provides an easy interactive environment for analyzing positional clustering behavior of co-expressed genes, and at the same time it is fully programmable, so that it can be customized and extended to support specific analysis needs.

Plasmepsin 4-deficient Plasmodium berghei are virulence attenuated and induce protective immunity against experimental malaria.

Plasmodium parasites lacking plasmepsin 4 (PM4), an aspartic protease that functions in the lysosomal compartment and contributes to hemoglobin digestion, have only a modest decrease in the asexual blood-stage growth rate; however, PM4 deficiency in the rodent malaria parasite Plasmodium berghei results in significantly less virulence than that for the parental parasite. P. berghei Deltapm4 parasites failed to induce experimental cerebral malaria (ECM) in ECM-susceptible mice, and ECM-resistant mice were able to clear infections. Furthermore, after a single infection, all convalescent mice were protected against subsequent parasite challenge for at least 1 year. Real-time in vivo parasite imaging and splenectomy experiments demonstrated that protective immunity acted through antibody-mediated parasite clearance in the spleen. This work demonstrates, for the first time, that a single Plasmodium gene disruption can generate virulence-attenuated parasites that do not induce cerebral complications and, moreover, are able to stimulate strong protective immunity against subsequent challenge with wild-type parasites. Parasite blood-stage attenuation should help identify protective immune responses against malaria, unravel parasite-derived factors involved in malarial pathologies, such as cerebral malaria, and potentially pave the way for blood-stage whole organism vaccines.

Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning.

Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen's kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen's kappa to 92.2% and 84.1% respectively. A computational framework integrating protein-protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.

Comparative Genomic Analysis of the Foodborne Pathogen Burkholderia gladioli pv. cocovenenans Harboring a Bongkrekic Acid Biosynthesis Gene Cluster.

The environmental bacterium Burkholderia gladioli pv. cocovenenans (B. cocovenenans) has been linked to fatal food poisoning cases in Asia and Africa. Bongkrekic acid (BA), a mitochondrial toxin produced by B. cocovenenans, is thought to be responsible for these outbreaks. While there are over 80 species in the Burkholderia genus, B. cocovenenans is the only pathovar capable of producing BA and causing human death. However, the genomic features of B. gladioli and the evolution of the BA biosynthesis gene cluster, bon, in B. cocovenenans remain elusive. In this study, 239 whole genome sequences (WGSs) of B. gladioli, isolated from 12 countries collected over 100 years, were used to analyze the intra-species genomic diversity and phylogenetic relationships of B. gladioli and to explore the origin and evolution of the bon gene cluster. Our results showed that the genome-wide average nucleotide identity (ANI) values were above 97.29% for pairs of B. gladioli genomes. Thirty-six of the 239 (15.06%) B. gladioli genomes, isolated from corn, rice, fruits, soil, and patients from Asia, Europe, North America, and South America, contained the bon gene cluster and formed three clades within the phylogenetic tree. Pan- and core-genome analysis suggested that the BA biosynthesis genes were recently acquired. Comparative genome analysis of the bon gene cluster showed that complex recombination events contributed to this toxin biosynthesis gene cluster's evolution and formation. This study suggests that a better understanding of the genomic diversity and evolution of this lethal foodborne pathovar will potentially contribute to B. cocovenenans food poisoning outbreak prevention.

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms.

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens. IMPORTANCE Escherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.