Maximilian Collatz

Hepatitis C Virus (HCV) is a positive strand RNA virus that infects hepatocytes and reprograms cellular gene expression, contributing to chronic liver disease and hepatocellular carcinoma. HCV replicates slowly and induces limited changes in the cellular transcriptome. Here, we improved analysis of low-abundance transcripts, including long non-coding RNAs (lncRNAs), in the context of HCV replication. We stringently removed genomic DNA from total RNA from Huh-7 hepatoma cell lines by a combination of TRIzol, acidic phenol, silica matrix columns and recombinant DNase I. RNA sequencing (RNA-Seq) and targeted validation were used to characterize Lnc-ITM2C-1 splice variants and expression of its neighboring gene, G protein-coupled receptor 55 (GPR55), to investigate their potential roles in HCV replication. Challenging our previous findings, under our improved experimental conditions, Lnc-ITM2C-1 and GPR55 were neither involved in a positive regulation circuit with HCV nor induced detectable transcriptome changes. Secondary analysis of RNA-Seq data according to HCV infection status revealed statistically robust HCV-induced expression of 21 host cell genes involved in pathways like stress response (in particular ATF3), lipid metabolism, cytoskeleton organization and growth regulation. In contrast, we found that expression changed for only a limited subset of previously reported HCV-associated lncRNAs (e.g., EGOT). This highlights the necessity of stringent gDNA removal for analysis of low-abundance transcripts like lncRNAs.

Antimicrobial resistance poses a significant challenge for infection control, requiring the development of accurate and high-throughput diagnostic techniques. We expanded and optimized an existing DNA microarray platform for the molecular characterization of vancomycin-resistant Enterococcus (VRE) by incorporating resistance, virulence, species-specific, and typing markers. The enhanced microarray allows for the simultaneous analysis of up to 96 strains, providing detailed genetic profiles of clinical isolates. VRE strains from Romania and Bavaria, Germany, were analyzed, and the results were compared to those obtained using traditional typing methods, such as multilocus sequence typing (MLST). Next-generation sequencing (NGS) was used in parallel to validate the microarray findings and explore genomic relationships. The microarray revealed considerable genetic diversity and potential epidemiological linkages among isolates. A novel hexadecimal-based nomenclature system was introduced for standardized and scalable strain classification. Comparative analysis demonstrated that the array profiles provided greater discriminatory power and practical resolution than MLST. Receiver operating characteristic (ROC) curve analysis of 187 target genes in 220 isolates gave diagnostic sensitivity and specificity of 100%. This integrated approach offers a cost-effective, rapid, and adaptable global VRE surveillance and infection control tool. It provides a practical alternative to conventional typing systems and facilitates early detection of outbreaks and emerging clones.

Accurate primer and probe design is essential for molecular applications, including PCR, qPCR, and molecular multiparameter assays like microarrays. The novel software tool AssayBLAST addresses this need by simulating interactions between oligonucleotides and target sequences. AssayBLAST handles large sets of primer and probe sequences simultaneously and supports comprehensive assay designs by allowing users to identify off-target binding, calculate melting temperatures, and ensure strand specificity, a critical but often overlooked aspect. AssayBLAST performs two optimized BLAST-based searches for each primer or probe sequence, checking the forward and reverse strands for off-target interactions and strand-specific binding accuracy. The results are compiled into a mapping table containing binding sites, mismatches, and strand orientation, allowing users to validate large sets of oligonucleotides across predefined custom databases for a complete and optimal theoretical assay design. AssayBLAST was evaluated against experimental Staphylococcus aureus microarray data, achieving 97.5% accuracy in predicting probe–target hybridization outcomes. This high accuracy demonstrates the method’s effectiveness in reliably using BLAST hits and mismatch counts to predict microarray results. AssayBLAST provides a reliable, scalable solution for in silico primer and probe validation, effectively supporting large-scale assay designs and optimizations. Its accurate prediction of hybridization outcomes demonstrates its utility in enhancing the efficiency and reliability of molecular assays.

Background/Objectives: Vancomycin-resistant enterococci (VRE) are one of the leading causes of antibiotic-resistant infections in the hospital setting worldwide, and this has become a major issue, because most patients infected with this strain are difficult to treat. Multiplex real-time polymerase chain reaction (RT PCR) is an advantageous technique that can amplify multiple targets in a single reaction, and can be used to quickly detect specific targets in VRE within two hours, starting from suspected colonies of bacterial cultures, without sample preparation. Methods: In this study, we selected the glycopeptide/vancomycin resistance genes that are most common in clinical settings, vanA and vanB, in combination with the species markers ddl_faecium and ddl_faecalis for the most common VRE species—Enterococcus faecium and Enterococcus faecalis. Results: DNA from forty clinical VRE strains was prepared using a fast and economic heat lysis method, and a multiplex real-time PCR assay was optimized and carried out subsequently. The results were in concordance with the results from recombinase polymerase amplification (RPA) of the same VRE samples. Conclusions: Multiplex RT PCR and RPA for VRE detection proffers a second method for the confirmation of vancomycin resistance, and it can be developed as a fast screening assay for patients before admission into high-risk settings.

Today, there is a continuous worldwide battle against antimicrobial resistance (AMR) and that includes vancomycin-resistant enterococci (VRE). Methods that can adequately and quickly detect transmission chains in outbreaks are needed to trace and manage this problem fast and cost-effectively. In this study, DNA-microarray-based technology was developed for this purpose. It commenced with the bioinformatic design of specific oligonucleotide sequences to obtain amplification primers and hybridization probes. Microarrays were manufactured using these synthesized oligonucleotides. A highly parallel and stringent labeling and hybridization protocol was developed and employed using isolated genomic DNA from previously sequenced (referenced) clinical VRE strains for optimal sensitivity and specificity. Microarray results showed the detection of virulence, resistance, and species-specific genes in the VRE strains. Theoretical predictions of the microarray results were also derived from the sequences of the same VRE strain and were compared to array results while optimizing protocols until the microarray result and theoretical predictions were a match. The study concludes that DNA microarray technology can be used to quickly, accurately, and economically detect specifically and massively parallel target genes in enterococci.

Background: The effectiveness and reliability of diagnostic tests that detect DNA sequences largely hinge on the quality of the used primers and probes. This importance is especially evident when considering the specific sample being analyzed, as it affects the molecular background and potential for cross-reactivity, ultimately determining the test’s performance. Methods: Predicting primers based on the consensus sequence of the target has multiple advantages, including high specificity, diagnostic reliability, broad applicability, and long-term validity. Automated curation of the input sequences ensures high-quality primers and probes. Results: Here, we present a use case for developing a set of consensus primers and probes to identify antibiotic resistance and virulence genes in Staphylococcus (S.) aureus using the ConsensusPrime pipeline. Extensive qPCR experiments with several S. aureus strains confirm the exceptional quality of the primers designed using the pipeline. Conclusions: By improving the quality of the input sequences and using the consensus sequence as a basis, the ConsensusPrime pipeline pipeline ensures high-quality primers and probes, which should be the basis of molecular assays.

Staphylococcus aureus is a versatile pathogen that does not only occur in humans but also in various wild and domestic animals, including several avian species. When characterizing S. aureus isolates from waterfowl, isolates were identified as atypical CC133 by DNA microarray analysis. They differed from previously sequenced CC133 strains in the presence of the collagen adhesin gene cna; some also showed a different capsule type and a deviant spa type. Thus, they were subjected to whole-genome sequencing. This revealed multiple insertions of large regions of DNA from other S. aureus lineages into a CC133-derived backbone genome. Three distinct strains were identified based on the size and extent of these inserts. One strain comprised two small inserts of foreign DNA up- and downstream of oriC; one of about 7000 nt or 0.25% originated from CC692 and the other, at ca. 38,000 nt or 1.3% slightly larger one was of CC522 provenance. The second strain carried a larger CC692 insert (nearly 257,000 nt or 10% of the strain’s genome), and its CC522-derived insert was also larger, at about 53,500 nt or 2% of the genome). The third strain carried an identical CC692-derived region (in which the same mutations were observed as in the second strain), but it had a considerably larger CC522-like insertion of about 167,000 nt or 5.9% of the genome. Both isolates of the first, and two out of four isolates of the second strain also harbored a hemolysin-beta-integrating prophage carrying “bird-specific” virulence factors, ornithine cyclodeaminase D0K6J8 and a putative protease D0K6J9. Furthermore, isolates had two different variants of SCC elements that lacked mecA/mecC genes. These findings highlight the role of horizontal gene transfer in the evolution of S. aureus facilitated by SCC elements, by phages, and by a yet undescribed mechanism for large-scale exchange of core genomic DNA.

Background: High-quality oligonucleotides for molecular amplification and detection procedures of diverse target sequences depend on sequence homology. Processing input sequences and identifying homogeneous regions in alignments can be carried out by hand only if they are small and contain sequences of high similarity. Finding the best regions for large and inhomogeneous alignments needs to be automated. Results: The ConsensusPrime pipeline was developed to sort out redundant and technical interfering data in multiple sequence alignments and detect the most homologous regions from multiple sequences. It automates the prediction of optimal consensus primers for molecular analytical and sequence-based procedures/assays. Conclusion: ConsensusPrime is a fast and easy-to-use pipeline for predicting optimal consensus primers that is executable on local systems without depending on external resources and web services. An implementation in a Docker image ensures platform-independent executability and installability despite the combination of multiple programs. The source code and installation instructions are publicly available on GitHub.

High quality oligonucleotides for molecular amplification and detection procedures of diverse target sequences depend on sequence homology. The processing of input sequences and the identification of homogeneous regions in alignments can be done manually only if alignments are small and if they contain sequences of high similarity. For large and inhomogeneous alignments, the process of finding the best regions needs to be automated.The ConsensusPrime pipeline was developed sort out redundant and technical interfering data in multiple sequence alignments and detect the most homolog regions from multiple sequences. It automates the prediction of optimal consensus primers for molecular analytical and sequence-based procedures/assays.ConsensusPrime is a fast and easy to use pipeline for predicting optimal consensus primers that is executable on local systems without depending on external resources and webservices. An implementation in a Docker image ensures platform-independent executability and installability despite the combination of multiple programs. The source code and installation instructions are publicly available on GitHub (https://github.com/mcollatz/ConsensusPrime).

Motivation: By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly reduced using in silico prediction. Results: Here, we present EpiDope, a python tool which uses a deep neural network to detect linear B-cell epitope regions on individual protein sequences. With an area under the curve between 0.67 ± 0.07 in the receiver operating characteristic curve, EpiDope exceeds all other currently used linear B-cell epitope prediction tools. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach. Availabilityand implementation: EpiDope is available on GitHub (http://github.com/mcollatz/EpiDope). Supplementary information: Supplementary data are available at Bioinformatics online.