Escherichia coli bacteria are the most common causes of diarrhea and septicemia in calves. Moreover, calves form a major reservoir for transmission of pathogenic E. coli to humans. Systematic reviews and meta-analyses of publications on Escherichia coli as calf pathogens and the role of calves as reservoir have not been done so far. We reviewed studies between 1951 and 2013 reporting the presence of virulence associated factors (VAFs) in calf E. coli and extracted the following information: year(s) and country of sampling, animal number, health status, isolate number, VAF prevalence, serotypes, diagnostic methods and biological assays. The prevalence of VAFs or E. coli pathotypes was compared between healthy and diarrheic animals and was analysed for time courses. Together, 106 papers with 25982 E. coli isolates from 27 countries tested for VAFs were included. F5, F17 and F41 fimbriae and heat-stable enterotoxin (ST) – VAFs of enterotoxigenic E. coli (ETEC) were significantly associated with calf diarrhea. On the contrary, ETEC VAF F4 fimbriae and heat-labile enterotoxin as well as enteropathogenic (EPEC), Shiga toxin-producing (STEC), and enterohaemorrhagic E. coli (EHEC) were not associated with diarrhea. The prevalence increased overtime for ST-positive isolates, but decreased for F5- and STEC-positive isolates. Our study provides useful information about the history of scientific investigations performed in this domain so far, and helps to define etiological agents of calf disease, and to evaluate calves as reservoir hosts for human pathogenic E. coli.

Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.

Amyloids are proteins associated with several clinical disorders, including Alzheimer’s, and Creutzfeldt-Jakob’s. Despite their diversity, all amyloid proteins can undergo aggregation initiated by short segments called hot spots. To find the patterns defining the hot spots, we trained predictors of amyloidogenicity, using n-grams and random forest classifiers. Since the amyloidogenicity may not depend on the exact sequence of amino acids but on their more general properties, we tested 524,284 reduced amino acid alphabets of different lengths (three to six letters) to find the alphabet providing the best performance in cross-validation. The predictor based on this alphabet, called AmyloGram, was benchmarked against the most popular tools for the detection of amyloid peptides using an external data set and obtained the highest values of performance measures (AUC: 0.90, MCC: 0.63). Our results showed sequential patterns in the amyloids which are strongly correlated with hydrophobicity, a tendency to form β-sheets, and lower flexibility of amino acid residues. Among the most informative n-grams of AmyloGram we identified 15 that were previously confirmed experimentally. AmyloGram is available as the web-server: http://smorfland.uni.wroc.pl/shiny/AmyloGram/ and as the R package AmyloGram. R scripts and data used to produce the results of this manuscript are available at http://github.com/michbur/AmyloGramAnalysis.

Bacterial biofilm formation is a widespread phenomenon and a complex process requiring a set of genes facilitating the initial adhesion, maturation, and production of the extracellular polymeric matrix and subsequent dispersal of bacteria. Most studies on Escherichia coli biofilm formation have investigated nonpathogenic E. coli K-12 strains. Due to the extensive focus on laboratory strains in most studies, there is poor information regarding biofilm formation by pathogenic E. coli isolates. In this study, we genotypically and phenotypically characterized 187 human clinical E. coli isolates representing various pathotypes (e.g., uropathogenic, enteropathogenic, and enteroaggregative E. coli ). We investigated the presence of biofilm-associated genes (“genotype”) and phenotypically analyzed the isolates for motility and curli and cellulose production (“phenotype”). We developed a new screening method to examine the in vitro biofilm formation ability. In summary, we found a high prevalence of biofilm-associated genes. However, we could not detect a biofilm-associated gene or specific phenotype correlating with the biofilm formation ability. In contrast, we did identify an association of increased biofilm formation with a specific E. coli pathotype. Enteroaggregative E. coli (EAEC) was found to exhibit the highest capacity for biofilm formation. Using our image-based technology for the screening of biofilm formation, we demonstrated the characteristic biofilm formation pattern of EAEC, consisting of thick bacterial aggregates. In summary, our results highlight the fact that biofilm-promoting factors shown to be critical for biofilm formation in nonpathogenic strains do not reflect their impact in clinical isolates and that the ability of biofilm formation is a defined characteristic of EAEC. IMPORTANCE Bacterial biofilms are ubiquitous and consist of sessile bacterial cells surrounded by a self-produced extracellular polymeric matrix. They cause chronic and device-related infections due to their high resistance to antibiotics and the host immune system. In nonpathogenic Escherichia coli , cell surface components playing a pivotal role in biofilm formation are well known. In contrast, there is poor information for their role in biofilm formation of pathogenic isolates. Our study provides insights into the correlation of biofilm-associated genes or specific phenotypes with the biofilm formation ability of commensal and pathogenic E. coli . Additionally, we describe a newly developed method enabling qualitative biofilm analysis by automated image analysis, which is beneficial for high-throughput screenings. Our results help to establish a better understanding of E. coli biofilm formation.

Antimicrobial peptides (AMPs) constitute a diverse group of bioactive molecules that provide multicellular organisms with protection against microorganisms, and microorganisms with weaponry for competition. Some AMPs can target cancer cells; thus, they are called anticancer peptides (ACPs). Due to their small size, positive charge, hydrophobicity and amphipathicity, AMPs and ACPs interact with negatively charged components of biological membranes. AMPs preferentially permeabilize microbial membranes, but ACPs additionally target mitochondrial and plasma membranes of cancer cells. The preference towards mitochondrial membranes is explained by their membrane potential, membrane composition resulting from $\alpha$-proteobacterial origin and the fact that mitochondrial targeting signals could have evolved from AMPs. Taking into account the therapeutic potential of ACPs and millions of deaths due to cancer annually, it is of vital importance to find new cationic peptides that selectively destroy cancer cells. Therefore, to reduce the costs of experimental research, we have created a robust computational tool, CancerGram, that uses n-grams and random forests for predicting ACPs. Compared to other ACP classifiers, CancerGram is the first three-class model that effectively classifies peptides into: ACPs, AMPs and non-ACPs/non-AMPs, with AU1U amounting to 0.89 and a Kappa statistic of 0.65. CancerGram is available as a web server and R package on GitHub.