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Infection research largely relies on classical cell culture or mouse models. Despite having delivered invaluable insights into host-pathogen interactions, both have limitations in translating mechanistic principles to human pathologies. Alternatives can be derived from modern Tissue Engineering approaches, allowing the reconstruction of functional tissue models in vitro. Here, we combined a biological extracellular matrix with primary tissue-derived enteroids to establish an in vitro model of the human small intestinal epithelium exhibiting in vivo-like characteristics. Using the foodborne pathogen Salmonella enterica serovar Typhimurium, we demonstrated the applicability of our model to enteric infection research in the human context. Infection assays coupled to spatio-temporal readouts recapitulated the established key steps of epithelial infection by this pathogen in our model. Besides, we detected the upregulation of olfactomedin 4 in infected cells, a hitherto unrecognized aspect of the host response to Salmonella infection. Together, this primary human small intestinal tissue model fills the gap between simplistic cell culture and animal models of infection, and shall prove valuable in uncovering human-specific features of host-pathogen interplay.
In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.
Highlights
• Data awareness and data integrity underpins the trustworthiness of results and subsequent further analysis.
• Big data and bioinformatics enable efficient resource use by repurposing publicly available RNA-Sequencing data.
• Manual checks of data quality and integrity are insufficient due to the overwhelming volume and rapidly growing data.
• Automation and artificial intelligence provide cost-effective and efficient solutions for data integrity and quality checks.
• FAIR data management, various software solutions and analysis tools assist metadata maintenance.
Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.
CRISPR/Cas9 gene editing has revolutionised loss-of-function experiments in Leishmania, the causative agent of leishmaniasis. As Leishmania lack a functional non-homologous DNA end joining pathway however, obtaining null mutants typically requires additional donor DNA, selection of drug resistance-associated edits or time-consuming isolation of clones. Genome-wide loss-of-function screens across different conditions and across multiple Leishmania species are therefore unfeasible at present. Here, we report a CRISPR/Cas9 cytosine base editor (CBE) toolbox that overcomes these limitations. We employed CBEs in Leishmania to introduce STOP codons by converting cytosine into thymine and created http://www.leishbaseedit.net/ for CBE primer design in kinetoplastids. Through reporter assays and by targeting single- and multi-copy genes in L. mexicana, L. major, L. donovani, and L. infantum, we demonstrate how this tool can efficiently generate functional null mutants by expressing just one single-guide RNA, reaching up to 100% editing rate in non-clonal populations. We then generated a Leishmania-optimised CBE and successfully targeted an essential gene in a plasmid library delivered loss-of-function screen in L. mexicana. Since our method does not require DNA double-strand breaks, homologous recombination, donor DNA, or isolation of clones, we believe that this enables for the first time functional genetic screens in Leishmania via delivery of plasmid libraries.
Increased intestinal permeability and inflammation, both fueled by dysbiosis, appear to contribute to rheumatoid arthritis (RA) pathogenesis. This single-center pilot study aimed to investigate zonulin, a marker of intestinal permeability, and calprotectin, a marker of intestinal inflammation, measured in serum and fecal samples of RA patients using commercially available kits. We also analyzed plasma lipopolysaccharide (LPS) levels, a marker of intestinal permeability and inflammation. Furthermore, univariate, and multivariate regression analyses were carried out to determine whether or not there were associations of zonulin and calprotectin with LPS, BMI, gender, age, RA-specific parameters, fiber intake, and short-chain fatty acids in the gut. Serum zonulin levels were more likely to be abnormal with a longer disease duration and fecal zonulin levels were inversely associated with age. A strong association between fecal and serum calprotectin and between fecal calprotectin and LPS were found in males, but not in females, independent of other biomarkers, suggesting that fecal calprotectin may be a more specific biomarker than serum calprotectin is of intestinal inflammation in RA. Since this was a proof-of-principle study without a healthy control group, further research is needed to validate fecal and serum zonulin as valid biomarkers of RA in comparison with other promising biomarkers.
Making judgments of learning (JOLs) after studying can directly improve learning. This JOL reactivity has been shown for simple materials but has scarcely been investigated with educationally relevant materials such as expository texts. The few existing studies have not yet reported any consistent gains in text comprehension due to providing JOLs. In the present study, we hypothesized that increasing the chances of covert retrieval attempts when making JOLs after each of five to-be-studied text passages would produce comprehension benefits at 1 week compared to restudy. In a between-subjects design, we manipulated both whether participants (N = 210) were instructed to covertly retrieve the texts, and whether they made delayed target-absent JOLs. The results indicated that delayed, target-absent JOLs did not improve text comprehension after 1 week, regardless of whether prior instructions to engage in covert retrieval were provided. Based on the two-stage model of JOLs, we reasoned that participants’ retrieval attempts during metacomprehension judgments were either insufficient (i.e., due to a quick familiarity assessment) or were ineffective (e.g., due to low retrieval success).
Background: The COVID-19 pandemic has led to a flood of — often contradictory — evidence. HCWs had to develop strategies to locate information that supported their work. We investigated the information-seeking of different HCW groups in Germany. Methods: In December 2020, we conducted online surveys on COVID-19 information sources, strategies, assigned trustworthiness, and barriers — and in February 2021, on COVID-19 vaccination information sources. Results were analyzed descriptively; group comparisons were performed using χ\(^2\)-tests. Results: For general COVID-19-related medical information (413 participants), non-physicians most often selected official websites (57%), TV (57%), and e-mail/newsletters (46%) as preferred information sources — physicians chose official websites (63%), e-mail/newsletters (56%), and professional journals (55%). Non-physician HCWs used Facebook/YouTube more frequently. The main barriers were insufficient time and access issues. Non-physicians chose abstracts (66%), videos (45%), and webinars (40%) as preferred information strategy; physicians: overviews with algorithms (66%), abstracts (62%), webinars (48%). Information seeking on COVID-19 vaccination (2700 participants) was quite similar, however, with newspapers being more often used by non-physicians (63%) vs. physician HCWs (70%). Conclusion: Non-physician HCWs more often consulted public information sources. Employers/institutions should ensure the supply of professional, targeted COVID-19 information for different HCW groups.
PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.
In recent years, various forms of caloric restriction (CR) and amino acid or protein restriction (AAR or PR) have shown not only success in preventing age-associated diseases, such as type II diabetes and cardiovascular diseases, but also potential for cancer therapy. These strategies not only reprogram metabolism to low-energy metabolism (LEM), which is disadvantageous for neoplastic cells, but also significantly inhibit proliferation. Head and neck squamous cell carcinoma (HNSCC) is one of the most common tumour types, with over 600,000 new cases diagnosed annually worldwide. With a 5-year survival rate of approximately 55%, the poor prognosis has not improved despite extensive research and new adjuvant therapies. Therefore, for the first time, we analysed the potential of methionine restriction (MetR) in selected HNSCC cell lines. We investigated the influence of MetR on cell proliferation and vitality, the compensation for MetR by homocysteine, the gene regulation of different amino acid transporters, and the influence of cisplatin on cell proliferation in different HNSCC cell lines.
Alveolar (AE) and cystic (CE) echinococcosis are two parasitic diseases caused by the tapeworms Echinococcus multilocularis and E. granulosus sensu lato (s. l.), respectively. Currently, AE and CE are mainly diagnosed by means of imaging techniques, serology, and clinical and epidemiological data. However, no viability markers that indicate parasite state during infection are available. Extracellular small RNAs (sRNAs) are short non-coding RNAs that can be secreted by cells through association with extracellular vesicles, proteins, or lipoproteins. Circulating sRNAs can show altered expression in pathological states; hence, they are intensively studied as biomarkers for several diseases. Here, we profiled the sRNA transcriptomes of AE and CE patients to identify novel biomarkers to aid in medical decisions when current diagnostic procedures are inconclusive. For this, endogenous and parasitic sRNAs were analyzed by sRNA sequencing in serum from disease negative, positive, and treated patients and patients harboring a non-parasitic lesion. Consequently, 20 differentially expressed sRNAs associated with AE, CE, and/or non-parasitic lesion were identified. Our results represent an in-depth characterization of the effect E. multilocularis and E. granulosus s. l. exert on the extracellular sRNA landscape in human infections and provide a set of novel candidate biomarkers for both AE and CE detection.