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Until recently it was thought that all the erratic flints found at the Stone Age sites in the upper part of the Odra Valley were considered as silicites from the Baltic Sea. Their age of Baltic flints is associated with the Cretaceous, more precisely with the Maastrichtian or Palaeogene. However, it turns out that among these rocks, there are also silicites that are of different age and origin. They were first found in outcrops of sands and gravels around Opole-Groszowice, SW Poland. So far, these flints have not been distinguished in archaeological assemblages. Petrographic analysis presented in this article proves that these silicites of brown colour are of Jurassic age. They entered the glacial sediments via the lobes of the Drenthe glaciation. They were used in the same way as raw materials of Baltic origin. In this paper we show some examples of Palaeolithic and younger sites, where artifacts made of brown silicite occurred. In the past, these kind of flints from this region were often regarded as imports from the Kraków-Częstochowa Plateau.

Information about the impact of interactions between amyloid proteins on their fibrillization propensity is scattered among many experimental articles and presented in unstructured form. We manually curated information located in almost 200 publications (selected out of 562 initially considered), obtaining details of 883 experimentally studied interactions between 46 amyloid proteins or peptides. We also proposed a novel standardized terminology for the description of amyloid–amyloid interactions, which is included in our database, covering all currently known types of such a cross-talk, including inhibition of fibrillization, cross-seeding and other phenomena. The new approach allows for more specific studies on amyloids and their interactions, by providing very well-defined data. AmyloGraph, an online database presenting information on amyloid–amyloid interactions, is available at (http://AmyloGraph.com/). Its functionalities are also accessible as the R package (https://github.com/KotulskaLab/AmyloGraph). AmyloGraph is the only publicly available repository for experimentally determined amyloid–amyloid interactions.

Enterohaemolysin (Ehx) and alpha-haemolysin are virulence-associated factors (VAFs) causing the haemolytic phenotype in Escherichia coli . It has been shown that chromosomally and plasmid-encoded alpha-haemolysin are characteristic of specific pathotypes, virulence-associated factors and hosts. However, the prevalence of alpha- and enterohaemolysin does not overlap in the majority of pathotypes. Therefore, this study focuses on the characterization of the haemolytic E. coli population associated with multiple pathotypes in human and animal infectious diseases. Using a genomics approach, we investigated characteristic features of the enterohaemolysin-encoding strains to identify factors differentiating enterohaemolysin-positive from alpha-haemolysin-positive E. coli populations. To shed light on the functionality of Ehx subtypes, we analysed Ehx-coding genes and inferred EhxA phylogeny. The two haemolysins are associated with a different repertoire of adhesins, iron acquisition or toxin systems. Alpha-haemolysin is predominantly found in uropathogenic E. coli (UPEC) and predicted to be chromosomally encoded, or nonpathogenic and undetermined E. coli pathotypes and typically predicted to be plasmid-encoded. Enterohaemolysin is mainly associated with Shiga toxin-producing E. coli (STEC) and enterohaemorrhagic E. coli (EHEC) and predicted to be plasmid-encoded. Both types of haemolysin are found in atypical enteropathogenic E. coli (aEPEC). Moreover, we identified a new EhxA subtype present exclusively in genomes with VAFs characteristic of nonpathogenic E. coli . This study reveals a complex relationship between haemolytic E. coli of diverse pathotypes, providing a framework for understanding the potential role of haemolysin in pathogenesis.

The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.

The rapid influx of low-quality data visualizations is one of the main challenges in today’s commu- nication. Misleading, unreadable, or confusing visualizations spread misinformation. Furthermore, they fail to deliver their message to the viewer. The scale of the problem is big enough that there already exist public fora gathering tens of thousands of users criticizing graphics and charts (reddit.com/r/dataisugly) made with obvious mistakes. Current attempts at data visualization appear mostly as simple and overgeneralized checklists, and often lack systematicity and versatility. The lack of proper tooling for evaluating data visualization quality further heightens the problem. Therefore, this paper proposes VisQualdex, a systematic set of guidelines for static data visual- ization. The codex categorization is based and inspired by the theory of Grammar of Graphics. It contains dozens of criteria designed to catch various errors and mistakes of different categories and magnitude. Furthermore, it has been peer-reviewed and tested by experts of data visualization, data science, graphics design, information technology and computer science. To apply theory in the real world and increase the practical impact of VisQualdex, this contribution also introduces a practical tool. The implementation of the guidelines is available in the form of the web server, https://visqual.onrender.com, developed as a single page application in JavaScript using Vue.js and Material Design principles.

Cross-seeding between amyloidogenic proteins in the gut is receiving increasing attention as a possible mechanism for initiation or acceleration of amyloid formation by aggregation-prone proteins such as αSN, which is central in the development of Parkinson's disease. This is particularly pertinent in view of the growing number of functional (i.e. benign and useful) amyloid proteins discovered in bacteria. Here we identify two amyloidogenic proteins, Pr12 and Pr17, in fecal matter from Parkinson's disease transgenic rats and their wild type counterparts, based on their stability against dissolution by formic acid. Both proteins show robust aggregation into ThT-positive aggregates that contain higher-order β-sheets and have a fibrillar morphology, indicative of amyloid proteins. In addition, Pr17 aggregates formed in vitro showed significant resistance against formic acid, suggesting an ability to form highly stable amyloid. Treatment with proteinase K revealed a protected core of approx. 9 kDa. Neither Pr12 nor Pr17, however, affected αSN aggregation in vitro. Thus, amyloidogenicity does not per se lead to an ability to cross-seed fibrillation of αSN. Our results support the use of proteomics and formic acid to identify amyloidogenic protein in complex mixtures and suggests that there may be numerous functional amyloid proteins in microbiomes. This article is protected by copyright. All rights reserved.

Motivation Hydrogen–deuterium mass spectrometry (HDX-MS) is a rapidly developing technique for monitoring dynamics and interactions of proteins. The development of new devices has to be followed with new software suites addressing emerging standards in data analysis. Results We propose HaDeX, a novel tool for processing, analysis and visualization of HDX-MS experiments. HaDeX supports a reproducible analytical process, including data exploration, quality control and generation of publication-quality figures. Availability and implementation HaDeX is available primarily as a web-server (http://mslab-ibb.pl/shiny/HaDeX/), but its all functionalities are also accessible as the R package (https://CRAN.R-project.org/package=HaDeX) and standalone software (https://sourceforge.net/projects/HaDeX/). Supplementary information Supplementary data are available at Bioinformatics online.

The rapid and simultaneous detection of DNA and protein biomarkers is necessary to detect the outbreak of a disease or to monitor a disease. For example, cardiovascular diseases are a major cause of adult mortality worldwide. We have developed a rapidly adaptable platform to assess biomarkers using a microfluidic technology. Our model mimics autoantibodies against three proteins, C-reactive protein (CRP), brain natriuretic peptide (BNP), and low-density lipoprotein (LDL). Cell-free mitochondrial DNA (cfmDNA) and DNA controls are detected via fluorescence probes. The biomarkers are covalently bound on the surface of size- (11–15 μm) and dual-color encoded microbeads and immobilized as planar layer in a microfluidic chip flow cell. Binding events of target molecules were analyzed by fluorescence measurements with a fully automatized fluorescence microscope (end-point and real-time) developed in house. The model system was optimized for buffers and immobilization strategies of the microbeads to enable the simultaneous detection of protein and DNA biomarkers. All prime target molecules (anti-CRP, anti-BNP, anti-LDL, cfmDNA) and the controls were successfully detected both in independent reactions and simultaneously. In addition, the biomarkers could also be detected in spiked human serum in a similar way as in the optimized buffer system. The detection limit specified by the manufacturer is reduced by at least a factor of five for each biomarker as a result of the antibody detection and kinetic experiments indicate that nearly 50 % of the fluorescence intensity is achieved within 7 min. For rapid data inspection, we have developed the open source software digilogger, which can be applied for data evaluation and visualization. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s00216-019-02199-x) contains supplementary material, which is available to authorized users.

Amplification curves from quantitative Real-Time PCR experiments typically exhibit a sigmoidal shape. They can roughly be divided into a ground or baseline phase, an exponential amplification phase, a linear phase and finally a plateau phase, where in the latter, the PCR product concentration no longer increases. Nevertheless, in some cases the plateau phase displays a negative trend, e.g. in hydrolysis probe assays. This cycle-to-cycle fluorescence decrease is commonly referred to in the literature as the hook effect. Other detection chemistries also exhibit this negative trend, however the underlying molecular mechanisms are different.In this study we present two approaches to automatically detect hook effect-like curvatures based on linear (hookreg) and nonlinear regression (hookregNL). As the hook effect is typical for qPCR data, both algorithms can be employed for the automated identification of regular structured qPCR curves. Therefore, our algorithms streamline quality control, but can also be used for assay optimization or machine learning. Keywords: Hook effect, QPCR, TaqMan, EvaGreen, SybrGreen, Linear, Nonlinear, RDML, Automation

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1–R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment’s propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

Cross-seeding between amyloidogenic proteins in the gut is receiving increasing attention as a possible mechanism for initiation or acceleration of amyloid formation by aggregation-prone proteins such as αSN, which is central in the development of Parkinson’s disease. This is particularly pertinent in view of the growing number of functional (i.e. benign and useful) amyloid proteins discovered in bacteria. Here we identify two functional amyloid proteins, Pr12 and Pr17, in fecal matter from Parkinson’s disease transgenic rats and their wild type counterparts, based on their stability against dissolution by formic acid. Both proteins show robust aggregation into ThT-positive aggregates that contain higher-order β-sheets and have a fibrillar morphology, indicative of amyloid proteins. In addition, Pr17 aggregates formed in vitro showed significant resistance against formic acid, suggesting an ability to form highly stable amyloid. Treatment with proteinase K revealed a protected core of approx. 9 kDa. Neither Pr12 nor Pr17, however, affected αSN aggregation in vitro. Thus, amyloidogenicity does not per se lead to an ability to cross-seed fibrillation of αSN. Our results support the use of proteomics and formic acid to identify amyloid protein in complex mixtures and indicates the existence of numerous functional amyloid proteins in microbiomes.IMPORTANCEThe bacterial microbiome in the gastrointestinal tract is increasingly seen as important for human health and disease. One area of particular interest is that of neurodegenerative diseases such as Parkinson’s which involve pathological aggregation into amyloid of human proteins such as α- synuclein (αSN). Bacteria are known to form benign or functional amyloid, some of which may initiate unwanted aggregation of e.g. αSN in the enteric nervous system through cross-seeding via contact with the microbiome. Here we show that the rat microbiome contains several proteins which form this type of amyloid aggregate both in vivo and in vitro. Although the two proteins we investigate in depth do not directly promote αSN aggregation, our work shows that the microbiome potentially harbors a significant number of bacterial amyloid which could play a role in human physiology at various levels.

MotivationQuantitative Real-time PCR (qPCR) is a widely used -omics method for the precise quantification of nucleic acids, in which the result is associated with the presence/absence or quantity of a specific nucleic acid sequence. As the amount of qPCR data increases worldwide, the manual assessment of results becomes challenging and difficult to reproduce. To overcome this, some automatable characteristics of amplification curves have been described in the literature, often with an appropriate “rule of thumb”.ResultsWe developed PCRedux to analyze and calculate 90 numerical qPCR amplification curve descriptors (‘‘features”) from large datasets of qPCR amplification curves that are aimed for interpretable machine learning and development of decision support systems. In a case study of a diverse dataset with 3181 positive, negative and ambiguous amplification curves, as assessed by three human raters, we demonstrate a sensitivity >99 % and specificity >97 % in detecting positive and negative amplification. PCRedux is unique as it goes beyond traditional qPCR analysis to capture curvature properties that improve the characterization and classification of amplification curves. The calculation of the features is reproducible and objective, since R is used as a controllable working environment. PCRedux is not a black box, but open source software following on the principle of mathematically interpretable features. These can be combined with user-defined labels for automatic multi-category classification and regression in machine learning.Availabilityhttps://cran.r-project.org/package=PCRedux. Web server: http://shtest.evrogen.net/PCRedux-app/. Documentation: https://PCRuniversum.github.io/PCRedux/.

Several disorders are related to amyloid aggregation of proteins, for example Alzheimer’s or Parkinson’s diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers—the most cytotoxic species. Determining amyloidogenicity is tedious and costly. The most reliable identification of amyloids is obtained with high resolution microscopies, such as electron microscopy or atomic force microscopy (AFM). More frequently, less expensive and faster methods are used, especially infrared (IR) spectroscopy or Thioflavin T staining. Different experimental methods are not always concurrent, especially when amyloid peptides do not readily form fibrils but oligomers. This may lead to peptide misclassification and mislabeling. Several bioinformatics methods have been proposed for in-silico identification of amyloids, many of them based on machine learning. The effectiveness of these methods heavily depends on accurate annotation of the reference training data obtained from in-vitro experiments. We study how robust are bioinformatics methods to weak supervision, encountering imperfect training data. AmyloGram and three other amyloid predictors were applied. The results proved that a certain degree of misannotation in the reference data can be eliminated by the bioinformatics tools, even if they belonged to their training set. The computational results are supported by new experiments with IR and AFM methods.

The study of microbiomes has gained in importance over the past few years, and has led to the fields of metagenomics, metatranscriptomics and metaproteomics. While initially focused on the study of biodiversity within these communities the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute functional similarity between two data sets. MegaGO is highly performant: each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license, and is available at https://github.com/MEGA-GO/.

Salmonella is a foodborne pathogen affecting over 200 million people and resulting in over 200,000 fatal cases per year. Its adhesion to and invasion into intestinal epithelial cells represent one of the first and key steps in the pathogenesis of salmonellosis. Still, around 35 to 40% of bacterial genes have no experimentally validated function, and their contribution to bacterial virulence, including adhesion and invasion, remains largely unknown. Therefore, the significance of this study is in the identification of new genes or gene allelic variants previously not associated with adhesion and invasion. It is well established that blocking adhesion and/or invasion would stop or hamper bacterial infection; therefore, the new findings from this study could be used in future developments of anti-Salmonella therapy targeting genes involved in these key processes. Such treatment could be a valuable alternative, as the prevalence of antibiotic-resistant bacteria is increasing very rapidly., The initial steps of Salmonella pathogenesis involve adhesion to and invasion into host epithelial cells. While well-studied for Salmonella enterica serovar Typhimurium, the factors contributing to this process in other, host-adapted serovars remains unexplored. Here, we screened clinical isolates of serovars Gallinarum, Dublin, Choleraesuis, Typhimurium, and Enteritidis for adhesion to and invasion into intestinal epithelial cell lines of human, porcine, and chicken origins. Thirty isolates with altered infectivity were used for genomic analyses, and 14 genes and novel mutations associated with high or low infectivity were identified. The functions of candidate genes included virulence gene expression regulation and cell wall or membrane synthesis and components. The role of several of these genes in Salmonella adhesion to and invasion into cells has not previously been investigated. The genes dksA (encoding a stringent response regulator) and sanA (encoding a vancomycin high-temperature exclusion protein) were selected for further analyses, and we confirmed their roles in adhesion to and invasion into host cells. Furthermore, transcriptomic analyses were performed for S. Enteritidis and S. Typhimurium, with two highly infective and two marginally infective isolates for each serovar. Expression profiles for the isolates with altered infection phenotypes revealed the importance of type 3 secretion system expression levels in the determination of an isolate’s infection phenotype. Taken together, these data indicate a new role in cell host infection for genes or gene variants previously not associated with adhesion to and invasion into the epithelial cells. IMPORTANCE Salmonella is a foodborne pathogen affecting over 200 million people and resulting in over 200,000 fatal cases per year. Its adhesion to and invasion into intestinal epithelial cells represent one of the first and key steps in the pathogenesis of salmonellosis. Still, around 35 to 40% of bacterial genes have no experimentally validated function, and their contribution to bacterial virulence, including adhesion and invasion, remains largely unknown. Therefore, the significance of this study is in the identification of new genes or gene allelic variants previously not associated with adhesion and invasion. It is well established that blocking adhesion and/or invasion would stop or hamper bacterial infection; therefore, the new findings from this study could be used in future developments of anti-Salmonella therapy targeting genes involved in these key processes. Such treatment could be a valuable alternative, as the prevalence of antibiotic-resistant bacteria is increasing very rapidly.

Background DNA double-strand breaks can be counted as discrete foci by imaging techniques. In personalized medicine and pharmacology, the analysis of counting data is relevant for numerous applications, e.g., for cancer and aging research and the evaluation of drug efficacy. By default, it is assumed to follow the Poisson distribution. This assumption, however, may lead to biased results and faulty conclusions in datasets with excess zero values (zero-inflation), a variance larger than the mean (overdispersion), or both. In such cases, the assumption of a Poisson distribution would skew the estimation of mean and variance, and other models like the negative binomial (NB), zero-inflated Poisson or zero-inflated NB distributions should be employed. The model chosen has an influence on the parameter estimation (mean value and confidence interval). Yet the choice of the suitable distribution model is not trivial. Methods To support, simplify and objectify this process, we have developed the countfitteR software as an R package. We used a Bayesian approach for distribution model selection and the shiny web application framework for interactive data analysis. Results We show the application of our software based on examples of DNA double-strand break count data from phenotypic imaging by multiplex fluorescence microscopy. In analyzing numerous datasets of molecular pharmacological markers (phosphorylated histone H2AX and p53 binding protein), countfitteR demonstrated an equal or superior statistical performance compared to the usually employed two-step procedure, with an overall power of up to 98%. In addition, it still gave information in cases with no result at all from the two-step procedure. In our data sample we found that the NB distribution was the most frequent, with the Poisson distribution taking second place. Conclusions countfitteR can perform an automated distribution model selection and thus support the data analysis and lead to objective statistically verifiable estimated values. Originally designed for the analysis of foci in biomedical image data, countfitteR can be used in a variety of areas where non-Poisson distributed counting data is prevalent.

The estimated mean copy per partition (λ) is the essential information from a digital PCR (dPCR) experiment because λ can be used to calculate the target concentration in a sample. However, little information is available how to statistically compare dPCR runs of multiple runs or reduplicates. The comparison of λ values from several runs is a multiple comparison problem, which can be solved using the binary structure of dPCR data. We propose and evaluate two novel methods based on Generalized Linear Models (GLM) and Multiple Ratio Tests (MRT) for comparison of digital PCR experiments. We enriched our MRT framework with computation of simultaneous confidence intervals suitable for comparing multiple dPCR runs. The evaluation of both statistical methods support that MRT is faster and more robust for dPCR experiments performed in large scale. Our theoretical results were confirmed by the analysis of dPCR measurements of dilution series.Both methods were implemented in the dpcR package (v. 0.2) for the open source R statistical computing environment.

Motivation Reproducibility, a cornerstone of research, requires defined data formats, which include the setup and output of experiments. The real-time PCR data markup language (RDML) is a recommended standard of the minimum information for publication of quantitative real-time PCR experiments guidelines. Despite the popularity of the RDML format for analysis of quantitative PCR data, handling of RDML files is not yet widely supported in all PCR curve analysis softwares. Results This study describes the open-source RDML package for the statistical computing language R. RDML is compatible with RDML versions ≤ 1.2 and provides functionality to (i) import RDML data; (ii) extract sample information (e.g. targets and concentration); (iii) transform data to various formats of the R environment; (iv) generate human-readable run summaries; and (v) to create RDML files from user data. In addition, RDML offers a graphical user interface to read, edit and create RDML files. Availability and implementation https://cran.r-project.org/package=RDML. rdmlEdit server http://shtest.evrogen.net/rdmlEdit/. Documentation: http://kablag.github.io/RDML/. Supplementary information Supplementary data are available at Bioinformatics online.

Sex determination in mammals is strongly linked to sex chromosomes. In most cases, females possess two copies of X chromosome while males have one X and one Y chromosome. It is assumed that these chromosomes originated from a pair of homologous autosomes, which diverged when recombination between them was suppressed. However, it is still debated why the sex chromosomes stopped recombining and how this process spread out over most part of the chromosomes. To study this problem, we developed a simulation model, in which the recombination rate between the sex chromosomes can freely evolve. We found that the suppression of recombination between the X and Y is spontaneous and proceeds very quickly during the evolution of population, which leads to the degeneration of the Y in males. Interestingly, the degeneration happens only when mating pairs are unfaithful. This evolutionary strategy purifies the X chromosome from defective alleles and leads to the larger number of females than males in the population. In consequence, the reproductive potential of the whole population increases. Our results imply that both the suppression of recombination and the degeneration of Y chromosome may be associated with reproductive strategy and favoured in polygamous populations with faithless mating partners.

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.

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.

Real-time quantitative polymerase chain reaction (qPCR) data are found to display periodic patterns in the fluorescence intensity as a function of sample number for fixed cycle number. This behavior is seen for technical replicate datasets recorded on several different commercial instruments; it occurs in the baseline region and typically increases with increasing cycle number in the growth and plateau regions. Autocorrelation analysis reveals periodicities of 12 for 96-well systems and 24 for a 384-well system, indicating a correlation with block architecture. Passive dye experiments show that the effect may be from optical detector bias. Importantly, the signal periodicity manifests as periodicity in quantification cycle (Cq) values when these are estimated by the widely applied fixed threshold approach, but not when scale-insensitive markers like first- and second-derivative maxima are used. Accordingly, any scale variability in the growth curves will lead to bias in constant-threshold-based Cqs, making it mandatory that workers should either use scale-insensitive Cqs or normalize their growth curves to constant amplitude before applying the constant threshold method.

We evaluated prevalence and distribution of antibiotic resistant E. coli in three categories: animals (goats, pigs, poultry, cattle, sheep), humans (butchers, meat sellers, animal farm workers, buyers) and animal food products (milk, cheese, beef, chicken, yoghurt) from selected animal farms in South-West Nigeria. The biofilm formation, phylogrouping and detection of biofilm-associated genes were further analyzed. Out of a total number of 280 samples that were collected, 216 E. coli strains were isolated. The prevalence of isolating E. coli from humans (96%) was higher than from animals (89%) and about 38.8% were isolated from animal food products. Out of these 216 E. coli isolates that were tested for antibiotic sensitivity, 37 were multidrug-resistant. The prevalence of isolating these multi-resistant strains was highest in the animal category (23.6%) followed by the animal food products (16.1%) and the human category (11.5%). All the multidrug-resistant isolates were capable of forming biofilm, which varied significantly with each isolate. The majority of isolates belonged to phylogroup B1. All biofilm-associated genes were detected in the antibiotic resistant isolates. The results suggest the need for continuous surveillance of antibiotic resistance, biofilm formation and virulence determinants in E. coli in the whole food chain. Keywords: E. coli, Antibiotic resistance, Multidrug resistance, Nigeria, Human, Animals, Food products

Amyloids are proteins associated with the number of clinical disorders (e.g., Alzheimer's, Creutzfeldt-Jakob's and Huntington's diseases). Despite their diversity, all amyloid proteins can undergo aggregation initiated by 6- to 15-residue segments, called hot spots. To find the patterns defining the hot-spots, we trained predictors of amyloidogenicity, using n-grams and random forest classifiers, based on data collected in the AmyLoad database. Only the most informative n-grams, selected by our Quick Permutation Test, were considered. Since the amyloidogenicity may not depend on the exact sequence of amino acids but on more general properties of amino acids, 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 ß-sheets and rigidity of amino acid residues. Among the most informative n-grams of AmyloGram we identified 15 that were already confirmed experimentally. AmyloGram is available as a web-server: www.smorfland.uni.wroc.pl/amylogram/. The code and results are publicly available at: www.github.com/michbur/prediction_amyloidogenicity_ngram/.

Signal peptides are N-terminal presequences responsible for targeting proteins to the endomembrane system, and subsequent subcellular or extracellular compartments, and consequently condition their proper function. The significance of signal peptides stimulates development of new computational methods for their detection. These methods employ learning systems trained on datasets comprising signal peptides from different types of proteins and taxonomic groups. As a result, the accuracy of predictions are high in the case of signal peptides that are well-represented in databases, but might be low in other, atypical cases. Such atypical signal peptides are present in proteins found in apicomplexan parasites, causative agents of malaria and toxoplasmosis. Apicomplexan proteins have a unique amino acid composition due to their AT-biased genomes. Therefore, we designed a new, more flexible and universal probabilistic model for recognition of atypical eukaryotic signal peptides. Our approach called signalHsmm includes knowledge about the structure of signal peptides and physicochemical properties of amino acids. It is able to recognize signal peptides from the malaria parasites and related species more accurately than popular programs. Moreover, it is still universal enough to provide prediction of other signal peptides on par with the best preforming predictors.

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.

BACKGROUND Quantification cycle (Cq) and amplification efficiency (AE) are parameters mathematically extracted from raw data to characterize quantitative PCR (qPCR) reactions and quantify the copy number in a sample. Little attention has been paid to the effects of preprocessing and the use of smoothing or filtering approaches to compensate for noisy data. Existing algorithms largely are taken for granted, and it is unclear which of the various methods is most informative. We investigated the effect of smoothing and filtering algorithms on amplification curve data. METHODS We obtained published high-replicate qPCR data sets from standard block thermocyclers and other cycler platforms and statistically evaluated the impact of smoothing on Cq and AE. RESULTS Our results indicate that selected smoothing algorithms affect estimates of Cq and AE considerably. The commonly used moving average filter performed worst in all qPCR scenarios. The Savitzky–Golay smoother, cubic splines, and Whittaker smoother resulted overall in the least bias in our setting and exhibited low sensitivity to differences in qPCR AE, whereas other smoothers, such as running mean, introduced an AE-dependent bias. CONCLUSIONS The selection of a smoothing algorithm is an important step in developing data analysis pipelines for real-time PCR experiments. We offer guidelines for selection of an appropriate smoothing algorithm in diagnostic qPCR applications. The findings of our study were implemented in the R packages chipPCR and qpcR as a basis for the implementation of an analytical strategy.

Brak