Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes

Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial isolates. The increased resolution significantly enhances the possibility of tracing transmission routes and contamination sources of foodborne pathogens. In addition, machine learning (ML) on WGS data has shown promising applications for predicting important microbial traits such as virulence, growth potential, and resistance to antimicrobials. Many regulatory agencies have already adapted WGS and ML methods. However, the food industry hasn't followed a similarly enthusiastic implementation. Some possible reasons for this might be the lack of computational resources and limited expertise to analyse WGS and ML data and interpret the results. Here, we present ListPred, a ML tool to analyse WGS data of Listeria monocytogenes, a very concerning foodborne pathogen. ListPred relies on genomic markers and pre-trained ML models from two previous studies, and it is able to predict two important bacterial traits, namely virulence potential and disinfectant tolerance. ListPred only requires limited computational resources and practically no bioinformatic expertise, which is essential for a broad application in the food industry.

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation

A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to inclusion of allochthonous strains or metadata errors, the stated sources of strains in public databases are often incorrect, and strains may not be adapted to the habitat from which they were isolated. We describe a new tool, aurora, that identifies autochthonous strains and the genes associated with habitats while acknowledging the potential role of the habitat adaptation trait in shaping phylogeny.

Accelerating eucalypt clone selection pipeline via cloned progeny trials and molecular data

The high productivity of Eucalyptus spp. forest plantations is mainly due to advances in silvicultural techniques and genetic improvement associated with the potential that many species of the genus have for vegetative propagation. However, long reproduction cycles for forest species pose significant challenges for genetic progress via traditional breeding programs. Furthermore, there is often poor correlation between individual (seedling) performance in initial (progeny trials) and final (clonal trials) stages of the breeding program. In this scenario, cloned progeny trials (CPT) offer an alternative to accelerate the eucalypt clone selection pipeline, combining progeny and clonal trials in a single experiment. CPT has the potential to speed up the evaluation process and increase its efficiency by developing new commercial genotypes that were tested as clones from the initial stage of the breeding program. Thus, this study aims to assess the potential of CPT to accelerate eucalypt clone selection programs by estimating the genetic parameters, analyzing responses to selection, and predicting the adequate number of ramets to be used in CPT of Eucalyptus urophylla x Eucalyptus grandis. The results show that when the number of ramets per progeny was decreased from five to one there was a reduction in the estimates of broad-sense heritability and accuracy. However, three ramets/progeny can be used without significant reductions in these estimates. CPT accelerates clonal selection by combining progeny and clonal trial methodologies, enabling an evaluation of performance as both progeny and clone. This capacity is very important for vegetatively propagated crop species such as Eucalyptus. Integrating CPT with SNP markers can offer an alternative to shorten the tree clone selection pipeline, better estimate and decompose the genetic variance components, and improve the correlation between initial and final performance for selected genotypes. This study confirms the potential of CPT to improve selection processes and accelerate genetic gains in the eucalypt clone selection pipeline.

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Utilization of AI - reshaping the future of food safety, agriculture and food security - a critical review

Artificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there is an increased interest in embracing applications associated with Artificial Intelligence to positively contribute to food safety. These applications such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools have been established to contribute to several domains of food safety such as early warning of outbreaks, risk prediction, detection and identification of food associated pathogens. Simultaneously, the ambition toward establishing a sustainable food system has motivated the adoption of cutting-edge technologies such as Artificial Intelligence to strengthen food security. Given the myriad challenges confronting stakeholders in their endeavors to safeguard food security, Artificial Intelligence emerges as a promising tool capable of crafting holistic management strategies for food security. This entails maximizing crop yields, mitigating losses, and trimming operational expenses. AI models present notable benefits in efficiency, precision, uniformity, automation, pattern identification, accessibility, and scalability for food security endeavors. The escalation in the global trend for adopting alternative protein sources such as edible insects and microalgae as a sustainable food source reflects a growing recognition of the need for sustainable and resilient food systems to address the challenges of population growth, environmental degradation, and food insecurity. Artificial Intelligence offers a range of capabilities to enhance food safety in the production and consumption of alternative proteins like microalgae and edible insects, contributing to a sustainable and secure food system.

Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review

Microbial infections pose a substantial global health challenge, particularly impacting immunocompromised individuals and exacerbating the issue of antimicrobial resistance (AMR). High virulence of pathogens can lead to severe infections and prolonged antimicrobial treatment, increasing the risk of developing resistant strains. Integrating machine-learning (ML) with DNA sequencing technologies offers potential solutions by enhancing microbial identification, virulence assessment, and antimicrobial susceptibility evaluation. This review explores recent advancements in these integrated approaches, addressing current limitations and identifying gaps in the literature. A comprehensive literature search was conducted across databases including PubMed, Scopus, Web of Science, and IEEE Xplore, covering publications from January 2014 to June 2024. Using a detailed Boolean search string, relevant studies focusing on ML applications in microorganism identification, antimicrobial susceptibility testing, and microbial virulence were included. The screening process involved a two-stage review of titles, abstracts, and full texts, with data extraction and critical appraisal performed using the QIAO tool. Data were analyzed through narrative synthesis to identify common themes and innovations. Out of 1,650 initially identified records, 19 studies met the inclusion criteria. These studies primarily focused on AMR, with additional research on microbial virulence and identification. Machine learning algorithms such as Random Forest, Support Vector Machines, and Convolutional Neural Networks, combined with DNA sequencing techniques like Whole Genome Sequencing and Metagenomic Sequencing, demonstrated significant advancements in predictive accuracy and efficiency. High-quality studies achieved impressive performance metrics, including F1-scores up to 0.88 and AUC scores up to 0.96. The integration of ML and DNA sequencing technologies has significantly enhanced microbial analysis, improving the identification of pathogens, assessment of virulence, and evaluation of antimicrobial susceptibility. Despite advancements, challenges such as data quality, high costs, and model interpretability persist. This review highlights the need for continued innovation and provides recommendations for future research to address these limitations and improve disease management and public health strategies. The systematic review is registered with PROSPERO (CRD42024571347).

Predictive microbiology through the last century. From paper to Excel and towards AI

This chapter provides a historical perspective on predictive microbiology: from its inception till its current state, and including potential future developments. A look back to its origins in the 1920s underlies that scientists at the time had great ideas that could not be developed due to the lack of proper technologies. Indeed, predictive microbiology advancements mostly halted till the 1980s, when computing machines became broadly available, evidencing how these technologies were an enabler of predictive microbiology. Nowadays, predictive microbiology is a mature scientific field. There is a general consensus on experimental and computational methodologies, with software tools implementing these principles in a user-friendly manner. As a result, predictive microbiology is currently a useful tool for researchers, food industries and food safety legislators. On the other hand, this methodology has some important limitations that would be hard to solve without a reconsideration of some of its basic principles. In this sense, Artificial Intelligence and Data Science present great promise to advance predictive microbiology even further. Nevertheless, this would require the development of a novel conceptual framework that accommodates these novel technologies into predictive microbiology.

Integrating Genomic Data with the Development of CRISPR-Based Point-of-Care-Testing for Bacterial Infections

Purpose of Review

Bacterial infections and antibiotic resistance contribute to global mortality. Despite many infections being preventable and treatable, the lack of reliable and accessible diagnostic tools exacerbates these issues. CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based diagnostics has emerged as a promising solution. However, the development of CRISPR diagnostics has often occurred in isolation, with limited integration of genomic data to guide target selection. In this review, we explore the synergy between bacterial genomics and CRISPR-based point-of-care tests (POCT), highlighting how genomic insights can inform target selection and enhance diagnostic accuracy.

Recent Findings

We review recent advances in CRISPR-based technologies, focusing on the critical role of target sequence selection in improving the sensitivity of CRISPR-based diagnostics. Additionally, we examine the implementation of these technologies in resource-limited settings across Asia and Africa, presenting successful case studies that demonstrate their potential.

Summary

The integration of bacterial genomics with CRISPR technology offers significant promise for the development of effective point-of-care diagnostics.

Immunosenescence: How Aging Increases Susceptibility to Bacterial Infections and Virulence Factors

The process of aging leads to a progressive decline in the immune system function, known as immunosenescence, which compromises both innate and adaptive responses. This includes impairments in phagocytosis and decreased production, activation, and function of T- and B-lymphocytes, among other effects. Bacteria exploit immunosenescence by using various virulence factors to evade the host's defenses, leading to severe and often life-threatening infections. This manuscript explores the complex relationship between immunosenescence and bacterial virulence, focusing on the underlying mechanisms that increase vulnerability to bacterial infections in the elderly. Additionally, it discusses how machine learning methods can provide accurate modeling of interactions between the weakened immune system and bacterial virulence mechanisms, guiding the development of personalized interventions. The development of vaccines, novel antibiotics, and antivirulence therapies for multidrug-resistant bacteria, as well as the investigation of potential immune-boosting therapies, are promising strategies in this field. Future research should focus on how machine learning approaches can be integrated with immunological, microbiological, and clinical data to develop personalized interventions that improve outcomes for bacterial infections in the growing elderly population.

Cereulide production capacities and genetic properties of 31 emetic Bacillus cereus group strains

The highly potent toxin cereulide is a frequent cause of foodborne intoxications. This extremely resistant toxin is produced by Bacillus cereus group strains carrying the plasmid encoded cesHPTABCD gene cluster. It is known that the capacities to produce cereulide vary greatly between different strains but the genetic background of these variations is not clear. In this study, cereulide production capacities were associated with genetic characteristics. For this, cereulide levels in cultures of 31 strains were determined after incubation in tryptic soy broth for 24 h at 24 °C, 30 °C and 37 °C. Whole genome sequencing based data were used for an in-depth characterization of gene sequences related to cereulide production. The taxonomy, population structure and phylogenetic relationships of the strains were evaluated based on average nucleotide identity, multi-locus sequence typing (MLST), core genome MLST and single nucleotide polymorphism analyses. Despite a limited strain number, the approach of a genome wide association study (GWAS) was tested to link genetic variation with cereulide quantities. Our study confirms strain-dependent differences in cereulide production. For most strains, these differences were not explainable by sequence variations in the cesHPTABCD gene cluster or the regulatory genes abrB, spo0A, codY and pagRBc. Likewise, the population structure and phylogeny of the tested strains did not comprehensively reflect the cereulide production capacities. GWAS yielded first hints for associated proteins, while their possible effect on cereulide synthesis remains to be further investigated.

High-throughput omics technologies in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic, relapsing intestinal disease. Elucidation of the pathogenic mechanisms of IBD requires high-throughput technologies (HTTs) to effectively obtain and analyze large amounts of data. Recently, HTTs have been widely used in IBD, including genomics, transcriptomics, proteomics, microbiomics, metabolomics and single-cell sequencing. When combined with endoscopy, the application of these technologies can provide an in-depth understanding on the alterations of intestinal microbe diversity and abundance, the abnormalities of signaling pathway-mediated immune responses and functionality, and the evaluation of therapeutic effects, improving the accuracy of early diagnosis and treatment of IBD. This review comprehensively summarizes the development and advancement of HTTs, and also highlights the challenges and future directions of these technologies in IBD research. Although HTTs have made striking breakthrough in IBD, more standardized methods and large-scale dataset processing are still needed to achieve the goal of personalized medicine.

Identifying virulence factors using graph transformer autoencoder with ESMFold-predicted structures

With the increasing resistance of bacterial pathogens to conventional antibiotics, antivirulence strategies targeting virulence factors (VFs) have become an effective new therapy for the treatment of pathogenic bacterial infections. Therefore, the identification and prediction of VFs can provide ideal candidate targets for the implementation of antivirulence strategies in treating infections caused by pathogenic bacteria. Currently, the existing computational models predominantly rely on the amino acid sequences of virulence proteins while overlooking structural information. Here, we propose a novel graph transformer autoencoder for VF identification (GTAE-VF), which utilizes ESMFold-predicted 3D structures and converts the VF identification problem into a graph-level prediction task. In an encoder-decoder framework, GTAE-VF adaptively learns both local and global information by integrating a graph convolutional network and a transformer to implement all-pair message passing, which can better capture long-range correlations and potential relationships. Extensive experiments on an independent test dataset demonstrate that GTAE-VF achieves reliable and robust prediction accuracy with an AUC of 0.963, which is consistently better than that of other structure-based and sequence-based approaches. We believe that GTAE-VF has the potential to emerge as a valuable tool for assessing VFs and devising antivirulence strategies.

New Insights and Approach Toward the Genetic Diversity and Strain Typing of Erwinia pyrifoliae Based on rsxC, an Electron Transport Gene

Erwinia pyrifoliae, a causal agent of black shoot blight in apple and pear trees, is a plant pathogenic bacterium first reported in South Korea. The symptoms of black shoot blight are very similar to those of the fire blight disease in apple and pear trees caused by E. amylovora, as E. pyrifoliae has a genetically very close relationship with E. amylovora. Recently, there have been reports that E. pyrifoliae causes disease in European strawberries, resulting in severe fruit loss that aroused great concern about its spread, distribution, and host range. Therefore, it is essential to establish a trustworthy approach to understanding the distribution patterns of E. pyrifoliae based on the genetic background to strengthen the barrier of potential spreading risks, although advanced methods have been provided to accurately detect E. pyrifoliae and E. amylovora. Consequently, this study discovered a noble and noteworthy gene, rsxC, capable of providing the pathogen genotype by comparing E. pyrifoliae genomic sequences in the international representative genome archive. Different numbers of 40-unit amino acid repeats in this gene among the strains induced intraspecific traits in RsxC. By comparing their repeat pattern, E. pyrifoliae isolates were divided into two main groups, branching into several clades via sequence alignment of 35 E. pyrifoliae isolates from various apple orchards from 2020 to 2021 in South Korea. The newly discovered quadraginta amino acid repeat within this gene would be a valuable genetic touchstone for determining the genotype and distribution pattern of E. pyrifoliae strains, ultimately leading to exploring their evolution. The function of amino acid repeats and the biological significance of strains need to be elucidated further.

Step-by-Step Bacterial Genome Comparison

Thanks to advancements in genome sequencing and bioinformatics, thousands of bacterial genome sequences are available in public databases. This presents an opportunity to study bacterial diversity in unprecedented detail. This chapter describes a complete bioinformatics workflow for comparative genomics of bacterial genomes, including genome annotation, pangenome reconstruction and visualization, phylogenetic analysis, and identification of sequences of interest such as antimicrobial-resistance genes, virulence factors, and phage sequences. The workflow uses state-of-the-art, open-source tools. The workflow is presented by means of a comparative analysis of Salmonella enterica serovar Typhimurium genomes. The workflow is based on Linux commands and scripts, and result visualization relies on the R environment. The chapter provides a step-by-step protocol that researchers with basic expertise in bioinformatics can easily follow to conduct investigations on their own genome datasets.

Exploring clonality and virulence gene associations in bloodstream infections using whole-genome sequencing and clinical data

BACKGROUND

Bloodstream infections (BSIs) remain a significant cause of mortality worldwide. Causative pathogens are routinely identified and susceptibility tested but only very rarely investigated for their resistance genes, virulence factors, and clonality. Our aim was to gain insight into the clonality patterns of different species causing BSI and the clinical relevance of distinct virulence genes.

METHODS

For this study, we whole-genome-sequenced over 400 randomly selected important pathogens isolated from blood cultures in our diagnostic department between 2016 and 2021. Genomic data on virulence factors, resistance genes, and clonality were cross-linked with in-vitro data and demographic and clinical information.

RESULTS

The investigation yielded extensive and informative data on the distribution of genes implicated in BSI as well as on the clonality of isolates across various species.

CONCLUSION

Associations between survival outcomes and the presence of specific genes must be interpreted with caution, and conducting replication studies with larger sample sizes for each species appears mandatory. Likewise, a deeper knowledge of virulence and host factors will aid in the interpretation of results and might lead to more targeted therapeutic and preventive measures. Monitoring transmission dynamics more efficiently holds promise to serve as a valuable tool in preventing in particular BSI caused by nosocomial pathogens.

Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen

Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., k-mer) approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives

Genome-wide association studies (GWAS) have allowed the discovery of marker-trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, k-mer-based GWAS approaches have recently been developed. k-mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, k-mer-based analyses can be used in species that lack a reference genome. However, the use of k-mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for k-mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant k-mers to sequence variation.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

Candida albicans selection for human commensalism results in substantial within-host diversity without decreasing fitness for invasive disease

Candida albicans is a frequent colonizer of human mucosal surfaces as well as an opportunistic pathogen. C. albicans is remarkably versatile in its ability to colonize diverse host sites with differences in oxygen and nutrient availability, pH, immune responses, and resident microbes, among other cues. It is unclear how the genetic background of a commensal colonizing population can influence the shift to pathogenicity. Therefore, we examined 910 commensal isolates from 35 healthy donors to identify host niche-specific adaptations. We demonstrate that healthy people are reservoirs for genotypically and phenotypically diverse C. albicans strains. Using limited diversity exploitation, we identified a single nucleotide change in the uncharacterized ZMS1 transcription factor that was sufficient to drive hyper invasion into agar. We found that SC5314 was significantly different from the majority of both commensal and bloodstream isolates in its ability to induce host cell death. However, our commensal strains retained the capacity to cause disease in the Galleria model of systemic infection, including outcompeting the SC5314 reference strain during systemic competition assays. This study provides a global view of commensal strain variation and within-host strain diversity of C. albicans and suggests that selection for commensalism in humans does not result in a fitness cost for invasive disease.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Genome Sequences of 17 Strains from Eight Races of Xanthomonas campestris pv. campestris

Xanthomonas campestris pv. campestris is a group of phytopathogenic bacteria causing black rot disease on Brassicaceae crops. Here, we report on draft genome sequences of 17 strains representing eight of nine known races of this pathogen, including the pathotype strain CFBP 6865.

Pseudomonas aeruginosa clinical blood isolates display significant phenotypic variability

Pseudomonas aeruginosa is a significant threat in healthcare settings where it deploys a wide host of virulence factors to cause disease. Many virulence-related phenotypes such as pyocyanin production, biofilm formation, and twitching motility have been implicated in causing disease in a number of hosts. In this study, we investigate these three virulence factors in a collection of 22 clinical strains isolated from blood stream infections. Despite the fact that all 22 strains caused disease and came from the same body site of different patients, they show significant variability in assays for each of the three specific phenotypes examined. There was no significant correlation between the strength of the three phenotypes across our collection, suggesting that they can be independently modulated. Furthermore, strains deficient in each of the virulence-associated phenotypes examined could be identified. To understand the genetic basis of this variability we sequenced the genomes of the 22 strains. We found that the majority of genes responsible for pyocyanin production, biofilm formation, and twitching motility were highly conserved among the strains despite their phenotypic variability, suggesting that the phenotypic variability is likely due to regulatory changes. Our findings thus demonstrate that no one lab-assayed phenotype of pyocyanin production, biofilm production, and twitching motility is necessary for a P. aeruginosa strain to cause blood stream infection and that additional factors may be needed to fully predict what strains will lead to specific human diseases.

Classification of the plant-associated lifestyle of Pseudomonas strains using genome properties and machine learning

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between beneficial and pathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here we used the Genome properties (GP) common biological pathways annotation system and Machine Learning (ML) to establish the relationship between the genome wide GP composition and the plant-associated lifestyle of 91 Pseudomonas strains isolated from the rhizosphere and the phyllosphere representing both plant-associated phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A test set of 75 new strains confirmed the importance of the selected features for classification. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated lifestyle.

Phenotype-Based Threat Assessment

Assessing the threat posed by bacterial samples is fundamentally important to safeguarding human health. Whole-genome sequence analysis of bacteria provides a route to achieving this goal. However, this approach is fundamentally constrained by the scope, the diversity, and our understanding of the bacterial genome sequences that are available for devising threat assessment schemes. For example, genome-based strategies offer limited utility for assessing the threat associated with pathogens that exploit novel virulence mechanisms or are recently emergent. To address these limitations, we developed PathEngine, a machine learning strategy that features the use of phenotypic hallmarks of pathogenesis to assess pathogenic threat. PathEngine successfully classified potential pathogenic threats with high accuracy and thereby establishes a phenotype-based, sequence-independent pipeline for threat assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

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.

Livestock have been suggested as an important source of antimicrobial-resistant (AMR) Escherichia coli, capable of infecting humans and carrying resistance to drugs used in human medicine. China has a large intensive livestock farming industry, poultry being the second most important source of meat in the country, and is the largest user of antibiotics for food production in the world. Here we studied antimicrobial resistance gene overlap between E. coli isolates collected from humans, livestock and their shared environments in a large-scale Chinese poultry farm and associated slaughterhouse. By using a computational approach that integrates machine learning, whole-genome sequencing, gene sharing network and mobile genetic elements analysis we characterized the E. coli community structure, antimicrobial resistance phenotypes and the genetic relatedness of non-pathogenic and pathogenic E. coli strains. We uncovered the network of genes, associated with AMR, shared across host species (animals and workers) and environments (farm and slaughterhouse). Our approach opens up new avenues for the development of a fast, affordable and effective computational solutions that provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming.

Lessons Learnt From Using the Machine Learning Random Forest Algorithm to Predict Virulence in Streptococcus pyogenes

Group A Streptococcus is a globally significant human pathogen. The extensive variability of the GAS genome, virulence phenotypes and clinical outcomes, render it an excellent candidate for the application of genotype-phenotype association studies in the era of whole-genome sequencing. We have catalogued the distribution and diversity of the transcription regulators of GAS, and employed phylogenetics, concordance metrics and machine learning (ML) to test for associations. In this review, we communicate the lessons learnt in the context of the recent bacteria genotype-phenotype association studies of others that have utilised both genome-wide association studies (GWAS) and ML. We envisage a promising future for the application GWAS in bacteria genotype-phenotype association studies and foresee the increasing use of ML. However, progress in this field is hindered by several outstanding bottlenecks. These include the shortcomings that are observed when GWAS techniques that have been fine-tuned on human genomes, are applied to bacterial genomes. Furthermore, there is a deficit of easy-to-use end-to-end workflows, and a lag in the collection of detailed phenotype and clinical genomic metadata. We propose a novel quality control protocol for the collection of high-quality GAS virulence phenotype coupled to clinical outcome data. Finally, we incorporate this protocol into a workflow for testing genotype-phenotype associations using ML and ‘linked’ patient-microbe genome sets that better represent the infection event.

Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BACKGROUND

The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step.

RESULTS

Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest.

CONCLUSION

The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence

Despite being considered the simplest form of life, bacteria remain enigmatic, particularly in light of pathogenesis and evolving antimicrobial resistance. After three decades of genomics, we remain some way from understanding these organisms, and a substantial proportion of genes remain functionally unknown. Methodological advances, principally mass spectrometry (MS), are paving the way for parallel analysis of the proteome, metabolome and lipidome. Each provides a global, complementary assay, in addition to genomics, and the ability to better comprehend how pathogens respond to changes in their internal (e.g. mutation) and external environments consistent with infection-like conditions. Such responses include accessing necessary nutrients for survival in a hostile environment where co-colonizing bacteria and normal flora are acclimated to the prevailing conditions. Multi-omics can be harnessed across temporal and spatial (sub-cellular) dimensions to understand adaptation at the molecular level. Gene deletion libraries, in conjunction with large-scale approaches and evolving bioinformatics integration, will greatly facilitate next-generation vaccines and antimicrobial interventions by highlighting novel targets and pathogen-specific pathways. MS is also central in phenotypic characterization of surface biomolecules such as lipid A, as well as aiding in the determination of protein interactions and complexes. There is increasing evidence that bacteria are capable of widespread post-translational modification, including phosphorylation, glycosylation and acetylation; with each contributing to virulence. This review focuses on the bacterial genotype to phenotype transition and surveys the recent literature showing how the genome can be validated at the proteome, metabolome and lipidome levels to provide an integrated view of organism response to host conditions.

Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models

Group A Streptococcus (GAS) is a globally significant bacterial pathogen. The GAS genotyping gold standard characterises the nucleotide variation of emm, which encodes a surface-exposed protein that is recombinogenic and under immune-based selection pressure. Within a supervised learning methodology, we tested three random forest (RF) algorithms (Guided, Ordinary, and Regularized) and 53 GAS response regulator (RR) allele types to infer six genomic traits (emm-type, emm-subtype, tissue and country of sample, clinical outcomes, and isolate invasiveness). The Guided, Ordinary, and Regularized RF classifiers inferred the emm-type with accuracies of 96.7%, 95.7%, and 95.2%, using ten, three, and four RR alleles in the feature set, respectively. Notably, we inferred the emm-type with 93.7% accuracy using only mga2 and lrp. We demonstrated a utility for inferring emm-subtype (89.9%), country (88.6%), invasiveness (84.7%), but not clinical (56.9%), or tissue (56.4%), which is consistent with the complexity of GAS pathophysiology. We identified a novel cell wall-spanning domain (SF5), and proposed evolutionary pathways depicting the 'contrariwise' and 'likewise' chimeric deletion-fusion of emm and enn. We identified an intermediate strain, which provides evidence of the time-dependent excision of mga regulon genes. Overall, our workflow advances the understanding of the GAS mga regulon and its plasticity.

Load Forecasting in an Office Building with Different Data Structure and Learning Parameters

Energy efficiency topics have been covered by several energy management approaches in the literature, including participation in demand response programs where the consumers provide load reduction upon request or price signals. In such approaches, it is very important to know in advance the electricity consumption for the future to adequately perform the energy management. In the present paper, a load forecasting service designed for office buildings is implemented. In the building, using several available sensors, different learning parameters and structures are tested for artificial neural networks and the K-nearest neighbor algorithm. Deep focus is given to the individual period errors. In the case study, the forecasting of one week of electricity consumption is tested. It has been concluded that it is impossible to identify a single combination of learning parameters as different parts of the day have different consumption patterns.