Application of machine learning in bacteriophage research

Ecological and evolutionary dynamics of CRISPR-Cas systems in Clostridium botulinum: Insights from genome mining and comparative analysis

Understanding the prevalence and distribution of CRISPR-Cas systems across different strains can illuminate the ecological and evolutionary dynamics of Clostridium botulinum populations. In this study, we conducted genome mining to characterize the CRISPR-Cas systems of C. botulinum strains. Our analysis involved retrieving complete genome sequences of these strains and assessing the diversity, prevalence, and evolution of their CRISPR-Cas systems. Subsequently, we performed an analysis of homology in spacer sequences from identified CRISPR arrays to investigate and characterize the range of targeted phages and plasmids. Additionally, we investigated the evolutionary trajectory of C. botulinum strains under selective pressures from foreign invasive DNA. Our findings revealed that 306 strains possessed complete CRISPR-Cas structures, comprising 58% of the studied C. botulinum strains. Secondary structure prediction of consensus repeats indicated that subtype II-C, with longer stems compared to subtypes ID and IB, tended to form more stable RNA secondary structures. Moreover, protospacer motif analysis demonstrated that strains with subtype IB CRISPR-Cas systems exhibited 5'-CGG-3', 5'-CC-3', and 5'-CAT-3' motifs in the 3' flanking regions of protospacers. The diversity observed in CRISPR-Cas systems indicated their classification into subtypes IB, ID, II-C, III-B, and III-D. Furthermore, our results showed that systems with subtype ID and III-D frequently harbored similar spacer patterns. Moreover, analysis of spacer sequences homology with phage and prophage genomes highlighted the specific activities exhibited by subtype IB and III-B against phages and plasmids, providing valuable insights into the functional specialization within these systems.

A game of resistance: War between bacteria and phages and how phage cocktails can be the solution

While phages hold promise as an antibiotic alternative, they encounter significant challenges in combating bacterial infections, primarily due to the emergence of phage-resistant bacteria. Bacterial defence mechanisms like superinfection exclusion, CRISPR, and restriction-modification systems can hinder phage effectiveness. Innovative strategies, such as combining different phages into cocktails, have been explored to address these challenges. This review delves into these defence mechanisms and their impact at each stage of the infection cycle, their challenges, and the strategies phages have developed to counteract them. Additionally, we examine the role of phage cocktails in the evolving landscape of antibacterial treatments and discuss recent studies that highlight the effectiveness of diverse phage cocktails in targeting essential bacterial receptors and combating resistant strains.

Occurrence and diversity pattern of CRISPR-Cas systems in Acetobacter genus provides insights on adaptive defense mechanisms against to invasive DNAs

The Acetobacter genus is primarily known for its significance in acetic acid production and its application in various industrial processes. This study aimed to shed light on the prevalence, diversity, and functional implications of CRISPR-Cas systems in the Acetobacter genus using a genome mining approach. The investigation analyzed the CRISPR-Cas architectures and components of 34 Acetobacter species, as well as the evolutionary strategies employed by these bacteria in response to phage invasion and foreign DNA. Furthermore, phylogenetic analysis based on CAS1 protein sequences was performed to gain insights into the evolutionary relationships among Acetobacter strains, with an emphasis on the potential of this protein for genotyping purposes. The results showed that 15 species had orphan, while20 species had complete CRISPR-Cas systems, resulting in an occurrence rate of 38% for complete systems in Acetobacter strains. The predicted complete CRISPR-Cas systems were categorized into I-C, I-F, I-E, and II-C subtypes, with subtype I-E being the most prevalent in Acetobacter. Additionally, spacer homology analysis revealed against such the dynamic interaction between Acetobacter strains and foreign invasive DNAs, emphasizing the pivotal role of CRISPR-Cas systems in defending against such invasions. Furthermore, the investigation of the secondary structures of CRISPR arrays revealed the conserved patterns within subtypes despite variations in repeat sequences. The exploration of protospacer adjacent motifs (PAMs) identified distinct recognition motifs in the flanking regions of protospacers. In conclusion, this research not only contributes to the growing body of knowledge on CRISPR-Cas systems but also establishes a foundation for future studies on the adaptive defense mechanisms of Acetobacter. The findings provide valuable insights into the intricate interplay between bacteria and phages, with implications for industrial applications and potential biotechnological advancements.

Predictive phage therapy for Escherichia coli urinary tract infections: Cocktail selection for therapy based on machine learning models

This study supports the development of predictive bacteriophage (phage) therapy: the concept of phage cocktail selection to treat a bacterial infection based on machine learning (ML) models. For this purpose, ML models were trained on thousands of measured interactions between a panel of phage and sequenced bacterial isolates. The concept was applied to Escherichia coli associated with urinary tract infections. This is an important common infection in humans and companion animals from which multidrug-resistant (MDR) bloodstream infections can originate. The global threat of MDR infection has reinvigorated international efforts into alternatives to antibiotics including phage therapy. E. coli exhibit extensive genome-level variation due to horizontal gene transfer via phage and plasmids. Associated with this, phage selection for E. coli is difficult as individual isolates can exhibit considerable variation in phage susceptibility due to differences in factors important to phage infection including phage receptor profiles and resistance mechanisms. The activity of 31 phage was measured on 314 isolates with growth curves in artificial urine. Random Forest models were built for each phage from bacterial genome features, and the more generalist phage, acting on over 20% of the bacterial population, exhibited F1 scores of >0.6 and could be used to predict phage cocktails effective against previously untested strains. The study demonstrates the potential of predictive ML models which integrate bacterial genomics with phage activity datasets allowing their use on data derived from direct sequencing of clinical samples to inform rapid and effective phage therapy.

Probiotic Characterization of LAB isolated from Sourdough and Different Traditional Dairy Products Using Biochemical, Molecular and Computational Approaches

The aim of this study was to identify and isolate lactic acid bacteria (LAB) from indigenous sourdough and dairy samples in Iran, and to assess their probiotic properties in vitro. A total of 560 potential LAB isolates were examined, and 87 demonstrated high survival rates in artificial gastrointestinal fluids without hemolytic activity. The selected isolates exhibited significant auto-aggregation (18.35 to 79.42%) and co-aggregation abilities (20.16 to 71.26%). Additionally, the isolates displayed varying degrees of cell surface hydrophobicity (12.32 to 76.24%). Results indicated that 19 LAB isolates had cholesterol assimilation rates exceeding 30%. Moreover, forty strains tested negative for all twelve assessed pathogenic genes and exhibited good adhesion to human intestinal epithelial cells (13.47 to 49.12%). Furthermore, 24 isolates formed strong biofilms, 29 formed moderate biofilms, and 23 formed weak biofilms. Except for isolates ABRIIFBI-8, ABRIIFBI-16, ABRIIFBI-23, ABRIIFBI-43, ABRIIFBI-56, and ABRIIFBI-62, most isolates were capable of producing exopolysaccharides. Consequently, LAB strains naturally occurring in sourdough and traditional dairy samples were suggested as potential probiotic candidates for incorporation into functional foods.

Genome analysis of triple phages that curtails MDR E. coli with ML based host receptor prediction and its evaluation

Infections by multidrug resistant bacteria (MDR) are becoming increasingly difficult to treat and alternative approaches like phage therapy, which is unhindered by drug resistance, are urgently needed to tackle MDR bacterial infections. During phage therapy phage cocktails targeting different receptors are likely to be more effective than monophages. In the present study, phages targeting carbapenem resistant clinical isolate of E. coli U1007 was isolated from Ganges River (U1G), Cooum River (CR) and Hospital waste water (M). Capsid architecture discerned using TEM identified the phage families as Podoviridae for U1G, Myoviridae for CR and Siphoviridae for M phage. Genome sequencing showed the phage genomes varied in size U1G (73,275 bp) CR (45,236 bp) and M (45,294 bp). All three genomes lacked genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by receptor binding protein of all 3 phages and the best performing algorithm Random Forest predicted LPS O antigen, LamB or OmpC for U1G; FhuA, OmpC for CR phage; and FhuA, LamB, TonB or OmpF for the M phage. OmpC was validated as receptor for U1G by physiological experiments. In vivo intramuscular infection study in zebrafish showed that cocktail of dual phages (U1G + M) along with colsitin resulted in a significant 3.5 log decline in cell counts. Our study highlights the potential of ML tool to predict host receptor and proves the utility of phage cocktail to restrict E. coli U1007 in vivo.

CRISPR-Cas systems feature and targeting phages diversity in Lacticaseibacillus rhamnosus strains

One of the most important adaptive immune systems in bacteria against phages is clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (CAS) genes. In this investigation, an approach based on genome mining was employed to characterize the CRISPR-Cas systems of Lacticaseibacillus rhamnosus strains. The analysis involved retrieving complete genome sequences of L. rhamnosus strains, and assessing the diversity, prevalence, and evolution of their CRISPR-Cas systems. Following this, an analysis of homology in spacer sequences from identified CRISPR arrays was carried out to investigate and characterize the range of target phages. The findings revealed that 106 strains possessed valid CRISPR-Cas structures (comprising CRISPR loci and Cas genes), constituting 45% of the examined L. rhamnosus strains. The diversity observed in the CRISPR-Cas systems indicated that all identified systems belonged to subtype II-A. Analyzing the homology of spacer sequences with phage and prophage genomes discovered that strains possessing only CRISPR-Cas subtype II targeted a broader spectrum of foreign phages. In summary, this study suggests that while there is not significant diversity among the CRISPR-Cas systems identified in L. rhamnosus strains, there exists notable variation in subtype II-A systems between L. rhamnosus and other lactobacilli. The diverse nature of these CRISPR-Cas systems underscores their natural activity and importance in adaptive immunity.

CRISPR-Cas systems and diversity of targeting phages in Lactobacillus johnsonii strains; insights from genome mining approach

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (CAS) genes make up bacteria's adaptive immune system. These genes protect bacteria from being eaten by bacteriophages. In this study, CRISPR-Cas systems were characterized using a genomic approach. For this purpose, genome sequences of Lactobacillus johnsonii strains were retrieved, and the diversity, occurrence, and evolution of the CRISPR-Cas systems were analyzed. Then, homology analyses of spacer sequences in identified CRISPR arrays were performed to analyze and characterize the diversity of target phages and plasmids. Finally, the evolutionary paths of spaceromes in each subtype of CRISPR arrays were performed using acquisition and deletion events surveyed under the selective pressure of foreign plasmids and phages. Results showed that 138 strains contain valid CRISPR-Cas structures (CRISPR loci together with the Cas genes) in their genomes, which accounted for about 17% of the L. johnsonii studied strains belonging to subtypes II-A, I-E, and I-C. Moreover, results indicated that some specific groups of plasmids were targeted with specific CRISPR array systems. Homology analysis of spacer sequences with phage genomes also revealed that spacers of strains that harbored only CRISPR-Cas subtype-II targeted a greater diversity of foreign phages. In conclusion, the current study indicates that there is great diversity between the CRISPR-Cas systems identified in L. johnsonii strains. Such diverse CRISPR-Cas systems indicate that these systems are naturally active and important in terms of adaptive immunity and evolutionary relationships.

Probiotic properties and antimicrobial evaluation of silymarin-enriched Lactobacillus bacteria isolated from traditional curd

Nowadays, the increasing use of medicinal plants in the treatment and prevention of diseases has attracted the attention of researchers. The aim of this work was to investigate the probiotic properties and antibacterial and antifungal activity of silymarin-enriched Lactobacillus bacteria against several important pathogenic bacteria and also Aspergillus flavus as one of the harmful molds in the food and health industries. For this purpose, 52 g-positive and catalase-negative bacteria were isolated from 60 traditional curd samples from Ilam province. Five of the 52 bacterial strains had more than 90% viability in high bile salt and acidic conditions and were selected for further investigation. The five strains with positive results showed good hydrophobicity (≥ 50.30%), auto-aggregation (≥ 53.70%), coaggregation (≥ 28.20%), and high cholesterol removal ability (from 09.20 to 67.20%) and therefore can be considered potential probiotics. The tested strains displayed acceptable antibacterial and antifungal activity against all 12 pathogenic bacteria and A. flavus. Also, the results of the simultaneous antifungal activity of probiotic strains and silymarin showed that the combination of silymarin and probiotics has a significantly better (P < 0.05) antifungal effect than the control group or the probiotic groups alone. Interestingly, in addition to the Limosilactobacillus fermentum C3 strain, the Limosilactobacillus fermentum C18 and Lactiplantibacillus pentosus C20 strains also had significant inhibitory effects against A. flavus when used with silymarin extract in methanol. Meanwhile, silymarin extract in DMSO and PEG increased the antagonistic activity of all five potential probiotic strains.

PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer

MOTIVATION

As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification.

RESULTS

In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence "images". Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins.

AVAILABILITY AND IMPLEMENTATION

The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.

Phages for treatment of Escherichia coli infections

Diseases due to infections by pathogenic Escherichia coli strains are on the rise and with the growing antimicrobial resistance among bacterial pathogens, including this group. Thus, alternative therapeutic options are actively investigated. Among these alternatives is phage therapy. In the case of E. coli, the combination of the well understood biology of this species and its bacteriophages represents a good guiding example for the establishment of phage therapy principles against this and other pathogenic bacteria. In this chapter, the procedures toward the development of phage therapy against pathogenic E. coli with the use of T-even group of phages are discussed. These steps involve the isolation, purification, characterisation and large-scale production of these phages, with formulation of phage cocktails for in vitro and in vivo studies. The main emphasis is made on phage therapy of enteropathogenic E. coli O157:H, which is one of the prominent human pathogens but persists as a commensal bacterium in many food animals. The implementation of phage therapy against E. coli O157:H within the One Health framework in carrier animals and for treatment of meat, vegetables, fruits and other agricultural produce thus would allow controlling and interrupting the transmission routes of this pathogen to the human food chain and preventing human disease. Examples of successful control and elimination of E. coli O157:H are given, while the problems encountered in phage treatment of this pathogen are also discussed.

The current status of phage therapy and its advancement towards establishing standard antimicrobials for combating multi drug-resistant bacterial pathogens

Phage therapy; a revived antimicrobial weapon, has great therapeutic advantages with the main ones being its ability to eradicate multidrug-resistant pathogens as well as selective toxicity, which ensures that beneficial microbiota is not harmed, unlike antibiotics. These therapeutic properties make phage therapy a novel approach for combating resistant pathogens. Since millions of people across the globe succumb to multidrug-resistant infections, the implementation of phage therapy as a standard antimicrobial could transform global medicine as it offers greater therapeutic advantages than conventional antibiotics. Although phage therapy has incomplete clinical data, such as a lack of standard dosage and the ideal mode of administration, the conducted clinical studies report its safety and efficacy in some case studies, and therefore, this could lessen the concerns of its skeptics. Since its discovery, the development of phage therapeutics has been in a smooth progression. Concerns about phage resistance in populations of pathogenic bacteria are raised when bacteria are exposed to phages. Bacteria can use restriction-modification, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein (Cas) defense, or mutations in the phage receptors to prevent phage invasion. Phage resistance, however, is often costly for the bacteria and may lead to a reduction in its virulence. The ongoing competition between bacteria and phage, on the other hand, ensures the emergence of phage strains that have evolved to infect resistant bacteria. A phage can quickly adapt by altering one or more aspects of its mode of infection, evading a resistance mechanism through genetic modifications, or directly thwarting the CRISPR-Cas defense. Using phage-bacterium coevolution as a technique could be crucial in the development of phage therapy as well. Through its recent advancement, gene-editing tools such as CRISPR-Cas allow the bioengineering of phages to produce phage cocktails that have broad spectrum activities, which could maximize the treatment's efficacy. This review presents the current state of phage therapy and its progression toward establishing standard medicine for combating antibiotic resistance. Recent clinical trials of phage therapy, some important case studies, and other ongoing clinical studies of phage therapy are all presented in this review. Furthermore, the recent advancement in the development of phage therapeutics, its application in various sectors, and concerns regarding its implementation are also highlighted here. Phage therapy has great potential and could help the fight against drug-resistant bacterial pathogens.

DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases

Biofilm production plays a clinically significant role in the pathogenicity of many bacteria, limiting our ability to apply antimicrobial agents and contributing in particular to the pathogenesis of chronic infections. Bacteriophage depolymerases, leveraged by these viruses to circumvent biofilm mediated resistance, represent a potentially powerful weapon in the fight against antibiotic resistant bacteria. Such enzymes are able to degrade the extracellular matrix that is integral to the formation of all biofilms and as such would allow complementary therapies or disinfection procedures to be successfully applied. In this manuscript, we describe the development and application of a machine learning based approach towards the identification of phage depolymerases. We demonstrate that on the basis of a relatively limited number of experimentally proven enzymes and using an amino acid derived feature vector that the development of a powerful model with an accuracy on the order of 90% is possible, showing the value of such approaches in protein functional annotation and the discovery of novel therapeutic agents.

PhaTYP: predicting the lifestyle for bacteriophages using BERT

Bacteriophages (or phages), which infect bacteria, have two distinct lifestyles: virulent and temperate. Predicting the lifestyle of phages helps decipher their interactions with their bacterial hosts, aiding phages' applications in fields such as phage therapy. Because experimental methods for annotating the lifestyle of phages cannot keep pace with the fast accumulation of sequenced phages, computational method for predicting phages' lifestyles has become an attractive alternative. Despite some promising results, computational lifestyle prediction remains difficult because of the limited known annotations and the sheer amount of sequenced phage contigs assembled from metagenomic data. In particular, most of the existing tools cannot precisely predict phages' lifestyles for short contigs. In this work, we develop PhaTYP (Phage TYPe prediction tool) to improve the accuracy of lifestyle prediction on short contigs. We design two different training tasks, self-supervised and fine-tuning tasks, to overcome lifestyle prediction difficulties. We rigorously tested and compared PhaTYP with four state-of-the-art methods: DeePhage, PHACTS, PhagePred and BACPHLIP. The experimental results show that PhaTYP outperforms all these methods and achieves more stable performance on short contigs. In addition, we demonstrated the utility of PhaTYP for analyzing the phage lifestyle on human neonates' gut data. This application shows that PhaTYP is a useful means for studying phages in metagenomic data and helps extend our understanding of microbial communities.

Multiomic spatial analysis reveals a distinct mucosa-associated virome

The human gut virome has been increasingly explored in recent years. However, nearly all virome-sequencing efforts rely solely on fecal samples and few studies leverage multiomic approaches to investigate phage-host relationships. Here, we combine metagenomics, metaviromics, and metatranscriptomics to study virome-bacteriome interactions at the colonic mucosal-luminal interface in a cohort of three individuals with inflammatory bowel disease; non-IBD controls were not included in this study. We show that the mucosal viral population is distinct from the stool virome and houses abundant crAss-like phages that are undetectable by fecal sampling. Through viral protein prediction and metatranscriptomic analysis, we explore viral gene transcription, prophage activation, and the relationship between the presence of integrase and temperate phages in IBD subjects. We also show the impact of deep sequencing on virus recovery and offer guidelines for selecting optimal sequencing depths in future metaviromic studies. Systems biology approaches such as those presented in this report will enhance our understanding of the human virome and its interactions with our microbiome and our health.

Probiotic potential characterization and clustering using unsupervised algorithms of lactic acid bacteria from saltwater fish samples

This research aimed to isolate lactic acid bacteria from the bowel of saltwater fish to assess their potential probiotic properties. Nineteen isolates of LAB including Lactiplantibacillus plantarum, Lactiplantibacillus pentosus, Lactobacillus acidophilus, Levilactobacillus brevis, Pediococcus pentosaceus, and Pediococcus acidilactici were recognized using molecular tools. All the isolates survived in the simulated conditions of the GI tract. Auto-aggregation ranged from 01.3 ± 0.5 to 82.6 ± 1.4% and hydrophobicity with toluene ranged from 3.7 ± 1.6 to 69.4 ± 1.3%, while the range of hydrophobicity with xylene was from 02.2 ± 1.6 to 56.4 ± 2.1%. All the isolates of lactobacilli, pediococci, enterococci, and lactococci indicated variable sensitivity and resistance towards clinical antibiotics. Non-neutralized cell free supernatant of isolates F12 and F15 showed antimicrobial activity against all the 8 evaluated enteric pathogens. Cluster analysis of identified potential probiotic bacteria based on heat-map and PCA methods also highlighted the priority of isolates F3, F7, F12, and F15 as bio-control agents in fishery industry. The findings of this study may essentially contribute to the understanding of the probiotic potential of LAB in saltwater fish, in order to access their probiotic characterization for use as biocontrol in fishery.

Genome Mining Approach Reveals the Occurrence and Diversity Pattern of Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-Associated Systems in Lactobacillus brevis Strains

Clustered regularly interspaced short palindromic repeats (CRISPR) together with their CRISPR-associated (Cas) genes are widely distributed in prokaryotes that provide an adaptive defense mechanism against foreign invasive DNA. There is relatively little knowledge about the CRISPR-Cas diversity and evolution in Lactobacillus brevis strains. Therefore, in this study, a genome-mining approach was employed to investigate the diversity and occurrence of the CRISPR-Cas system in 83 L. brevis strains. Moreover, trans-activating CRISPR RNA (tracrRNA) and protospacer adjacent motif (PAM) as pivotal elements for the successful targeting and inference of phages by the subtype II CRISPR-Cas systems were surveyed. Finally, evolutionary paths of L. brevis strains under selective pressure from foreign invasive DNA such as plasmids and phages of studied strains were surveyed using acquisition and deletion events analysis of spacers. A total of 127 confirmed CRISPRs were identified, which were distributed in 69 strains. Among strains with confirmed CRISPRs, 35 strains only contained one CRISPR locus, 23 strains contained two CRISPR loci, and 12 strains contained three to six CRISPR loci. L. brevis strains frequently harbor more than one CRISPR system. Analysis of confirmed CRISPR arrays showed that 31 out of 127 confirmed CRISPRs included Cas genes which were categorized as one of the II-A, II-C, and I-E subtypes. Analysis of subtype II-A spacers reflected divergent evolution for 18 strains into 16 unique groups. Additional analysis of spacer sequences also confirmed the implication of characterizing CRISPR-Cas systems in targeting of phages and plasmids. The current study highlighted the potential of utilizing CRISPR spacer polymorphism in genotyping lactobacillus strains. Moreover, it provides deep insights into the occurrence, diversity, and functional impacts of the CRISPR-Cas system in L. brevis strains.

Can Bacteriophages Replace Antibiotics?

Increasing antibiotic resistance numbers force both scientists and politicians to tackle the problem, and preferably without any delay. The application of bacteriophages as precision therapy to treat bacterial infections, phage therapy, has received increasing attention during the last two decades. While it looks like phage therapy is here to stay, there is still a lot to do. Medicine regulatory authorities are working to deliver clear instructions to carry out phage therapy. Physicians need to get more practical experience on treatments with phages. In this opinion article I try to place phage therapy in the context of the health care system and state that the use phages for precision treatments will require a seamless chain of events from the patient to the phage therapy laboratory to allow for the immediate application of phages therapeutically. It is not likely that phages will replace antibiotics, however, they will be valuable in the treatment of infections caused by multidrug resistant bacteria. Antibiotics will nevertheless remain the main treatment for a majority of infections.

Combination of MALDI-TOF Mass Spectrometry and Machine Learning for Rapid Antimicrobial Resistance Screening: The Case of Campylobacter spp

While MALDI-TOF mass spectrometry (MS) is widely considered as the reference method for the rapid and inexpensive identification of microorganisms in routine laboratories, less attention has been addressed to its ability for detection of antimicrobial resistance (AMR). Recently, some studies assessed its potential application together with machine learning for the detection of AMR in clinical pathogens. The scope of this study was to investigate MALDI-TOF MS protein mass spectra combined with a prediction approach as an AMR screening tool for relevant foodborne pathogens, such as Campylobacter coli and Campylobacter jejuni. A One-Health panel of 224 C. jejuni and 116 C. coli strains was phenotypically tested for seven antimicrobial resistances, i.e., ciprofloxacin, erythromycin, tetracycline, gentamycin, kanamycin, streptomycin, and ampicillin, independently, and were submitted, after an on- and off-plate protein extraction, to MALDI Biotyper analysis, which yielded one average spectra per isolate and type of extraction. Overall, high performance was observed for classifiers detecting susceptible as well as ciprofloxacin- and tetracycline-resistant isolates. A maximum sensitivity and a precision of 92.3 and 81.2%, respectively, were reached. No significant prediction performance differences were observed between on- and off-plate types of protein extractions. Finally, three putative AMR biomarkers for fluoroquinolones, tetracyclines, and aminoglycosides were identified during the current study. Combination of MALDI-TOF MS and machine learning could be an efficient and inexpensive tool to swiftly screen certain AMR in foodborne pathogens, which may enable a rapid initiation of a precise, targeted antibiotic treatment.

Large-scale comparative review and assessment of computational methods for phage virion proteins identification

Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.

Computational Prediction of Bacteriophage Host Ranges

Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.

Integrative Systems Biology Analysis Elucidates Mastitis Disease Underlying Functional Modules in Dairy Cattle

Background: Mastitis is the most prevalent disease in dairy cattle and one of the most significant bovine pathologies affecting milk production, animal health, and reproduction. In addition, mastitis is the most common, expensive, and contagious infection in the dairy industry. Methods: A meta-analysis of microarray and RNA-seq data was conducted to identify candidate genes and functional modules associated with mastitis disease. The results were then applied to systems biology analysis via weighted gene coexpression network analysis (WGCNA), Gene Ontology, enrichment analysis for the Kyoto Encyclopedia of Genes and Genomes (KEGG), and modeling using machine-learning algorithms. Results: Microarray and RNA-seq datasets were generated for 2,089 and 2,794 meta-genes, respectively. Between microarray and RNA-seq datasets, a total of 360 meta-genes were found that were significantly enriched as "peroxisome," "NOD-like receptor signaling pathway," "IL-17 signaling pathway," and "TNF signaling pathway" KEGG pathways. The turquoise module (n = 214 genes) and the brown module (n = 57 genes) were identified as critical functional modules associated with mastitis through WGCNA. PRDX5, RAB5C, ACTN4, SLC25A16, MAPK6, CD53, NCKAP1L, ARHGEF2, COL9A1, and PTPRC genes were detected as hub genes in identified functional modules. Finally, using attribute weighting and machine-learning methods, hub genes that are sufficiently informative in Escherichia coli mastitis were used to optimize predictive models. The constructed model proposed the optimal approach for the meta-genes and validated several high-ranked genes as biomarkers for E. coli mastitis using the decision tree (DT) method. Conclusion: The candidate genes and pathways proposed in this study may shed new light on the underlying molecular mechanisms of mastitis disease and suggest new approaches for diagnosing and treating E. coli mastitis in dairy cattle.