Natural phenolic compounds as biofilm inhibitors of multidrug-resistant Escherichia coli - the role of similar biological processes despite structural diversity.

Multidrug-resistant gram-negative pathogens such as Escherichia coli have become increasingly difficult to treat and therefore alternative treatment options are needed. Targeting virulence factors like biofilm formation could be one such option. Inhibition of biofilm-related structures like curli and cellulose formation in E. coli has been shown for different phenolic natural compounds like epigallocatechin gallate. This study demonstrates this effect for other structurally unrelated phenolics, namely octyl gallate, scutellarein and wedelolactone. To verify whether these structurally different compounds influence identical pathways of biofilm formation in E. coli a broad comparative RNA-sequencing approach was chosen with additional RT-qPCR to gain initial insights into the pathways affected at the transcriptomic level. Bioinformatical analysis of the RNA-Seq data was performed using DESeq2, BioCyc and KEGG Mapper. The comparative bioinformatics analysis on the pathways revealed that, irrespective of their structure, all compounds mainly influenced similar biological processes. These pathways included bacterial motility, chemotaxis, biofilm formation as well as metabolic processes like arginine biosynthesis and tricarboxylic acid cycle. Overall, this work provides the first insights into the potential mechanisms of action of novel phenolic biofilm inhibitors and highlights the complex regulatory processes of biofilm formation in E. coli.

Applications of Neural Networks in Biomedical Data Analysis.

Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields.

LEDGF/p75 Is Required for an Efficient DNA Damage Response.

Lens epithelium-derived growth factor splice variant of 75 kDa (LEDGF/p75) plays an important role in cancer, but its DNA-damage repair (DDR)-related implications are still not completely understood. Different LEDGF model cell lines were generated: a complete knock-out of LEDGF (KO) and re-expression of LEDGF/p75 or LEDGF/p52 using CRISPR/Cas9 technology. Their proliferation and migration capacity as well as their chemosensitivity were determined, which was followed by investigation of the DDR signaling pathways by Western blot and immunofluorescence. LEDGF-deficient cells exhibited a decreased proliferation and migration as well as an increased sensitivity toward etoposide. Moreover, LEDGF-depleted cells showed a significant reduction in the recruitment of downstream DDR-related proteins such as replication protein A 32 kDa subunit (RPA32) after exposure to etoposide. The re-expression of LEDGF/p75 rescued all knock-out effects. Surprisingly, untreated LEDGF KO cells showed an increased amount of DNA fragmentation combined with an increased formation of γH2AX and BRCA1. In contrast, the protein levels of ubiquitin-conjugating enzyme UBC13 and nuclear proteasome activator PA28γ were substantially reduced upon LEDGF KO. This study provides for the first time an insight that LEDGF is not only involved in the recruitment of CtIP but has also an effect on the ubiquitin-dependent regulation of DDR signaling molecules and highlights the role of LEDGF/p75 in homology-directed DNA repair.

countfitteR: efficient selection of count distributions to assess DNA damage.

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.

Analysis of three-dimensional biofilms on different material surfaces.

Biofilms cause complications and high costs in both industry and medicine. Of particular interest are bacterial infections of prosthetic materials, which usually cannot be eliminated due to the high antibiotic resistance known for bacteria forming biofilms. The search for new materials and coatings with lower colonization potential and antibacterial activity is of great importance to reduce biofilm formation. However, there is no standardized procedure to examine the colonization characteristics of bacteria in the biofilm state in situ. Here, we describe an automated epifluorescence microscopy system for the semi-quantitative analysis of three-dimensional (3D) biofilms on various surfaces. To analyze adherent bacteria, three materials (glass, steel and titanium) were incubated with bacteria in a flow chamber system. After fluorescence staining of the bacteria, automated image capturing, quantification of the bacteria, measurement of the colonized area and determination of the 3D biofilm height were carried out by using novel software. Furthermore, the materials were examined for their surface topography using white light scanning interferometry. Titanium compared to glass showed a significantly higher number of adherent bacteria. We argue that this was due to the higher microroughness of titanium. The colonized area was in accordance with the number of adherent bacteria and was also significantly larger on titanium coupons compared to glass. Maximum 3D biofilm height on glass coupons was significantly lower compared to the ones on steel and titanium. This novel method enables the standardized, automated investigation of the colonization with bacteria on different materials. This approach can considerably support the characterization of new material surfaces and their innovative coatings by analyzing the amount of attached bacteria and thickness of biofilms in situ and eliminates the need of conventional cultivation.

An explorative study of polymers for 3D printing of bioanalytical test systems.

BACKGROUND: The 3D printing is relevant as a manufacturing technology of functional models for forensic, pharmaceutical and bioanalytical applications such as drug delivery systems, sample preparation and point-of-care tests. OBJECTIVE: Melting behavior and autofluorescence of materials are decisive for optimal printing and applicability of the product which are influenced by varying unknown additives. METHODS: We have produced devices for bioanalytical applications from commercially available thermoplastic polymers using a melt-layer process. We characterized them by differential scanning calorimetry, fluorescence spectroscopy and functional assays (DNA capture assay, model for cell adhesion, bacterial adhesion and biofilm formation test). RESULTS: From 14 tested colored, transparent and black materials we found only deep black acrylonitrile-butadiene-styrene (ABS) and some black polylactic acid (PLA) useable for fluorescence-based assays, with low autofluorescence only in the short-wave range of 300-400 nm. PLA was suitable for standard bioanalytical purposes due to a glass transition temperature of approximately 60°C, resistance to common laboratory chemicals and easy print processing. For temperature-critical methods, such as hybridization reactions up to 90°C, ABS was better suited. CONCLUSIONS: Autofluorescence was not a disadvantage per se but can also be used as a reference signal in assays. The rapid development of individual protocols for sample processing and analysis required the availability of a material with consistent quality over time. For fluorescence-based assays, the use of commercial standard materials did not seem to meet this requirement.