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- Graduate School of Life Sciences (130)
- Theodor-Boveri-Institut für Biowissenschaften (64)
- Institut für Informatik (43)
- Universität - Fakultätsübergreifend (40)
- Medizinische Klinik und Poliklinik I (35)
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- Institut für Geographie und Geologie (28)
- Medizinische Klinik und Poliklinik II (28)
- Lehrstuhl für Orthopädie (27)
- Institut für Organische Chemie (26)
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- Akademie der Wissenschaften und der Literatur, Mainz (1)
- Anthropology Department University of Tennessee, Knoxville (1)
- Bungando Medical Centre, Mwanza, Tanzania (1)
- CHC Würzburg (Comprehensive Hearing Center) (1)
- CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg (1)
- CUHAS Catholic university of health and allied science, Mwanza, Tanzania (1)
Scalability is often mentioned in literature, but a stringent definition is missing. In particular, there is no general scalability assessment which clearly indicates whether a system scales or not or whether a system scales better than another. The key contribution of this article is the definition of a scalability index (SI) which quantifies if a system scales in comparison to another system, a hypothetical system, e.g., linear system, or the theoretically optimal system. The suggested SI generalizes different metrics from literature, which are specialized cases of our SI. The primary target of our scalability framework is, however, benchmarking of two systems, which does not require any reference system. The SI is demonstrated and evaluated for different use cases, that are (1) the performance of an IoT load balancer depending on the system load, (2) the availability of a communication system depending on the size and structure of the network, (3) scalability comparison of different location selection mechanisms in fog computing with respect to delays and energy consumption; (4) comparison of time-sensitive networking (TSN) mechanisms in terms of efficiency and utilization. Finally, we discuss how to use and how not to use the SI and give recommendations and guidelines in practice. To the best of our knowledge, this is the first work which provides a general SI for the comparison and benchmarking of systems, which is the primary target of our scalability analysis.
In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%.
Background: Due to the importance of radiologic examinations, such as X-rays or computed tomography scans, for many clinical diagnoses, the optimal use of the radiology department is 1 of the primary goals of many hospitals.
Objective: This study aims to calculate the key metrics of this use by creating a radiology data warehouse solution, where data from radiology information systems (RISs) can be imported and then queried using a query language as well as a graphical user interface (GUI).
Methods: Using a simple configuration file, the developed system allowed for the processing of radiology data exported from any kind of RIS into a Microsoft Excel, comma-separated value (CSV), or JavaScript Object Notation (JSON) file. These data were then imported into a clinical data warehouse. Additional values based on the radiology data were calculated during this import process by implementing 1 of several provided interfaces. Afterward, the query language and GUI of the data warehouse were used to configure and calculate reports on these data. For the most common types of requested reports, a web interface was created to view their numbers as graphics.
Results: The tool was successfully tested with the data of 4 different German hospitals from 2018 to 2021, with a total of 1,436,111 examinations. The user feedback was good, since all their queries could be answered if the available data were sufficient. The initial processing of the radiology data for using them with the clinical data warehouse took (depending on the amount of data provided by each hospital) between 7 minutes and 1 hour 11 minutes. Calculating 3 reports of different complexities on the data of each hospital was possible in 1-3 seconds for reports with up to 200 individual calculations and in up to 1.5 minutes for reports with up to 8200 individual calculations.
Conclusions: A system was developed with the main advantage of being generic concerning the export of different RISs as well as concerning the configuration of queries for various reports. The queries could be configured easily using the GUI of the data warehouse, and their results could be exported into the standard formats Excel and CSV for further processing.
Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group.
ABSTRACT
The highly conserved heterotrimeric protein kinase SNF1 is important for metabolic adaptations in the pathogenic yeast Candida albicans. A key function of SNF1 is to inactivate the repressor protein Mig1 and thereby allow the expression of genes that are required for the utilization of alternative carbon sources when the preferred carbon source, glucose, is absent or becomes limiting. However, how SNF1 controls Mig1 activity in C. albicans has remained elusive. Using a phosphoproteomics approach, we found that Mig1 is phosphorylated at multiple serine residues. Replacement of these serine residues by nonphosphorylatable alanine residues strongly increased the repressor activity of Mig1 in cells lacking a functional SNF1 complex, indicating that additional protein kinases are involved in the regulation of Mig1. Unlike wild-type Mig1, whose levels strongly decreased when the cells were grown on sucrose or glycerol instead of glucose, the levels of a mutant Mig1 protein lacking nine phosphorylation sites remained high under these conditions. Despite the increased protein levels and the absence of multiple phosphorylation sites, cells with a functional SNF1 complex could still sufficiently inhibit the hyperactive Mig1 to enable wild-type growth on alternative carbon sources. In line with this, phosphorylated forms of the mutant Mig1 were still detected in the presence and absence of a functional SNF1, demonstrating that Mig1 contains additional, unidentified phosphorylation sites and that downstream protein kinases are involved in the control of Mig1 activity by SNF1.
IMPORTANCE
The SNF1 protein kinase signaling pathway, which is highly conserved in eukaryotic cells, is important for metabolic adaptations in the pathogenic yeast Candida albicans. However, so far, it has remained elusive how SNF1 controls the activity of one of its main effectors, the repressor protein Mig1 that inhibits the expression of genes required for the utilization of alternative carbon sources when glucose is available. In this study, we have identified multiple phosphorylation sites in Mig1 that contribute to its inactivation. Mutation of these sites strongly increased Mig1 repressor activity in the absence of SNF1, but SNF1 could still sufficiently inhibit the hyperactive Mig1 to enable growth on alternative carbon sources. These findings reveal features of Mig1 that are important for controlling its repressor activity. Furthermore, they demonstrate that both SNF1 and additional protein kinases regulate Mig1 in this pathogenic yeast.
Abstract
Protein kinases are central components of almost all signaling pathways that control cellular activities. In the model organism Saccharomyces cerevisiae, the paralogous protein kinases Ypk1 and Ypk2, which control membrane lipid homeostasis, are essential for viability, and previous studies strongly indicated that this is also the case for their single ortholog Ypk1 in the pathogenic yeast Candida albicans. Here, using FLP-mediated inducible gene deletion, we reveal that C. albicans ypk1Δ mutants are viable but slow-growing, explaining prior failures to obtain null mutants. Phenotypic analyses of the mutants showed that the functions of Ypk1 in regulating sphingolipid biosynthesis and cell membrane lipid asymmetry are conserved, but the consequences of YPK1 deletion are milder than in S. cerevisiae. Mutational studies demonstrated that the highly conserved PDK1 phosphorylation site T548 in its activation loop is essential for Ypk1 function, whereas the TORC2 phosphorylation sites S687 and T705 at the C-terminus are important for Ypk1-dependent resistance to membrane stress. Unexpectedly, Pkh1, the single C. albicans orthologue of Pkh1/Pkh2, which mediate Ypk1 phosphorylation at the PDK1 site in S. cerevisiae, was not required for normal growth of C. albicans under nonstressed conditions, and Ypk1 phosphorylation at T548 was only slightly reduced in pkh1Δ mutants. We found that another protein kinase, Pkh3, whose ortholog in S. cerevisiae cannot substitute Pkh1/2, acts redundantly with Pkh1 to activate Ypk1 in C. albicans. No phenotypic effects were observed in cells lacking Pkh3 alone, but pkh1Δ pkh3Δ double mutants had a severe growth defect and Ypk1 phosphorylation at T548 was completely abolished. These results establish that Ypk1 is not essential for viability in C. albicans and that, despite its generally conserved function, the Ypk1 signaling pathway is rewired in this pathogenic yeast and includes a novel upstream kinase to activate Ypk1 by phosphorylation at the PDK1 site.
Author summary
Protein kinases are key components of cellular signaling pathways, and elucidating the specific roles of individual kinases is important to understand how organisms adapt to changes in their environment. The protein kinase Ypk1 is highly conserved in eukaryotic organisms and crucial for the maintenance of cell membrane homeostasis. It was previously thought that Ypk1 is essential for viability in the pathogenic yeast Candida albicans, as in the model organism Saccharomyces cerevisiae. Here, by using forced, inducible gene deletion, we reveal that C. albicans mutants lacking Ypk1 are viable but have a strong growth defect. The phenotypes of the mutants indicate that the known functions of Ypk1 are conserved in C. albicans, but loss of this kinase has less severe consequences than in S. cerevisiae. We also unravel the puzzling previous observation that C. albicans mutants lacking the Ypk1-activating kinase Pkh1, which is essential in S. cerevisiae, have no obvious growth defects. We show that the protein kinase Pkh3, which has not previously been implicated in the Ypk1 signaling pathway, can substitute Pkh1 and activate Ypk1 in C. albicans. These findings provide novel insights into this conserved signaling pathway and how it is rewired in a human-pathogenic fungus.
Bulk RNA sequencing technologies have provided invaluable insights into host and bacterial gene expression and associated regulatory networks. Nevertheless, the majority of these approaches report average expression across cell populations, hiding the true underlying expression patterns that are often heterogeneous in nature. Due to technical advances, single-cell transcriptomics in bacteria has recently become reality, allowing exploration of these heterogeneous populations, which are often the result of environmental changes and stressors. In this work, we have improved our previously published bacterial single-cell RNA sequencing (scRNA-seq) protocol that is based on multiple annealing and deoxycytidine (dC) tailing-based quantitative scRNA-seq (MATQ-seq), achieving a higher throughput through the integration of automation. We also selected a more efficient reverse transcriptase, which led to reduced cell loss and higher workflow robustness. Moreover, we successfully implemented a Cas9-based rRNA depletion protocol into the MATQ-seq workflow. Applying our improved protocol on a large set of single Salmonella cells sampled over different growth conditions revealed improved gene coverage and a higher gene detection limit compared to our original protocol and allowed us to detect the expression of small regulatory RNAs, such as GcvB or CsrB at a single-cell level. In addition, we confirmed previously described phenotypic heterogeneity in Salmonella in regard to expression of pathogenicity-associated genes. Overall, the low percentage of cell loss and high gene detection limit makes the improved MATQ-seq protocol particularly well suited for studies with limited input material, such as analysis of small bacterial populations in host niches or intracellular bacteria.
IMPORTANCE: Gene expression heterogeneity among isogenic bacteria is linked to clinically relevant scenarios, like biofilm formation and antibiotic tolerance. The recent development of bacterial single-cell RNA sequencing (scRNA-seq) enables the study of cell-to-cell variability in bacterial populations and the mechanisms underlying these phenomena. Here, we report a scRNA-seq workflow based on MATQ-seq with increased robustness, reduced cell loss, and improved transcript capture rate and gene coverage. Use of a more efficient reverse transcriptase and the integration of an rRNA depletion step, which can be adapted to other bacterial single-cell workflows, was instrumental for these improvements. Applying the protocol to the foodborne pathogen Salmonella, we confirmed transcriptional heterogeneity across and within different growth phases and demonstrated that our workflow captures small regulatory RNAs at a single-cell level. Due to low cell loss and high transcript capture rates, this protocol is uniquely suited for experimental settings in which the starting material is limited, such as infected tissues.
The development of retrogressive thaw slumps (RTS) is known to be strongly influenced by relief-related parameters, permafrost characteristics, and climatic triggers. To deepen the understanding of RTS, this study examines the subsurface characteristics in the vicinity of an active thaw slump, located in the Richardson Mountains (Western Canadian Arctic). The investigations aim to identify relationships between the spatiotemporal slump development and the influence of subsurface structures. Information on these were gained by means of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The spatiotemporal development of the slump was revealed by high-resolution satellite imagery and unmanned aerial vehicle–based digital elevation models (DEMs). The analysis indicated an acceleration of slump expansion, especially since 2018. The comparison of the DEMs enabled the detailed balancing of erosion and accumulation within the slump area between August 2018 and August 2019. In addition, manual frost probing and GPR revealed a strong relationship between the active layer thickness, surface morphology, and hydrology. Detected furrows in permafrost table topography seem to affect the active layer hydrology and cause a canalization of runoff toward the slump. The three-dimensional ERT data revealed a partly unfrozen layer underlying a heterogeneous permafrost body. This may influence the local hydrology and affect the development of the RTS. The results highlight the complex relationships between slump development, subsurface structure, and hydrology and indicate a distinct research need for other RTSs.
Young grapevines (Vitis vinifera) suffer and eventually can die from the crown gall disease caused by the plant pathogen Allorhizobium vitis (Rhizobiaceae). Virulent members of A. vitis harbor a tumor-inducing plasmid and induce formation of crown galls due to the oncogenes encoded on the transfer DNA. The expression of oncogenes in transformed host cells induces unregulated cell proliferation and metabolic and physiological changes. The crown gall produces opines uncommon to plants, which provide an important nutrient source for A. vitis harboring opine catabolism enzymes. Crown galls host a distinct bacterial community, and the mechanisms establishing a crown gall–specific bacterial community are currently unknown. Thus, we were interested in whether genes homologous to those of the tumor-inducing plasmid coexist in the genomes of the microbial species coexisting in crown galls. We isolated 8 bacterial strains from grapevine crown galls, sequenced their genomes, and tested their virulence and opine utilization ability in bioassays. In addition, the 8 genome sequences were compared with 34 published bacterial genomes, including closely related plant-associated bacteria not from crown galls. Homologous genes for virulence and opine anabolism were only present in the virulent Rhizobiaceae. In contrast, homologs of the opine catabolism genes were present in all strains including the nonvirulent members of the Rhizobiaceae and non-Rhizobiaceae. Gene neighborhood and sequence identity of the opine degradation cluster of virulent and nonvirulent strains together with the results of the opine utilization assay support the important role of opine utilization for cocolonization in crown galls, thereby shaping the crown gall community.
Abstract
Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran’s index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.
Author summary
Mammalian embryo development relies on organized differentiation of stem cells into different lineages. Particularly at the early stages of embryogenesis, cells of different fates form three-dimensional spatial patterns that are difficult to identify by eye. Pattern quantification and mathematical modeling have produced first insights into potential mechanisms for the cell fate arrangements. However, these approaches have relied on classifications of the patterns such as inside-out or random, or used summary statistics such as pair correlation functions or cluster radii. Deep neural networks allow characterizing patterns directly. Since the tissue context can be readily reproduced by a graph, we implemented a graph neural network to characterize the patterns of embryonic stem cell organoids as a whole. In addition, we implemented a multilayer perceptron model to reconstruct the fate of a given cell based on its neighbors. To train and test the models, we used synthetic data generated by our mathematical model for cell-cell communication. This interplay of deep learning and mathematical modeling in combination with summary statistics allowed us to identify a potential mechanism for cell fate determination in mouse embryonic stem cells. Our results agree with a mechanism with a dispersion of the intercellular signal that links a cell’s fate to those of the local neighborhood.
Summary
Embryos develop in a concerted sequence of spatiotemporal arrangements of cells. In the preimplantation mouse embryo, the distribution of the cells in the inner cell mass evolves from a salt-and-pepper pattern to spatial segregation of two distinct cell types. The exact properties of the salt-and-pepper pattern have not been analyzed so far. We investigate the spatiotemporal distribution of NANOG- and GATA6-expressing cells in the ICM of the mouse blastocysts with quantitative three-dimensional single-cell-based neighborhood analyses. A combination of spatial statistics and agent-based modeling reveals that the cell fate distribution follows a local clustering pattern. Using ordinary differential equations modeling, we show that this pattern can be established by a distance-based signaling mechanism enabling cells to integrate information from the whole inner cell mass into their cell fate decision. Our work highlights the importance of longer-range signaling to ensure coordinated decisions in groups of cells to successfully build embryos.
Highlights
• The local cell neighborhood and global ICM population composition correlate
• ICM cells show characteristics of local clustering in early and mid mouse blastocysts
• ICM patterning requires integration of signals from cells beyond the first neighbors
Background:
Cancer patients often suffer from psychological symptoms and need psychological support. Especially during the COVID-19 pandemic, eHealth interventions might be helpful to overcome the obstacles of the pandemic. This study evaluates the effectiveness of a video sequence-based eHealth intervention on anxiety, fatigue, and depression in cancer patients.
Methods:
Patients (N = 157) with different tumor entities were randomly assigned to the video intervention group (IG) and the waiting control group (CG). Patients in the IG received a video intervention comprising 8 video sequences over 4 weeks. The videos included psychoeducation on distress and psychological symptoms, Acceptance and Commitment Therapy elements, and Yoga and Qigong exercises. Patients’ anxiety and fear of progression (primary outcomes) and secondary outcomes were assessed before randomization (T1) and after the end of the intervention for IG or the waiting period for CG (T2) using self-reported questionnaires (GAD-7, PA-F-KF, EORTC QLQ-FA12, PHQ-8).
Results:
Patients of the IG showed no significant improvement in anxiety (GAD-7; P = .75), fear of progression (FoP-Q-SF; P = .29), fatigue (EORTC QLQ-FA12; P = .72), and depression (PHQ-8; P = .95) compared to patients in the waiting CG. However, symptoms of anxiety, fatigue, and depression decreased in both groups. Exploratory subgroup analysis regarding sex, therapy status, therapy goal, and tumor entity showed no effects. Overall, the intervention had a high level of acceptance.
Conclusions:
The video intervention was ineffective in reducing the psychological burden compared to a waiting CG. The findings support prior observations of the value of therapeutic guidance and promoting self-management for improving patients’ psychological burdens. Further studies are required to evaluate the effectiveness of psycho-oncological eHealth delivered through video sequences.
Cooperative, connected and automated mobility (CCAM) systems depend on a reliable communication to provide their service and more crucially to ensure the safety of users. One way to ensure the reliability of a data transmission is to use multiple transmission technologies in combination with redundant flows. In this paper, we describe a system requiring multipath communication in the context of CCAM. To this end, we introduce a data plane-based scheduler that uses replication and integration modules to provide redundant and transparent multipath communication. We provide an analytical model for the full replication module of the system and give an overview of how and where the data-plane scheduler components can be realized.
The ATPase p97 (also known as VCP, Cdc48) has crucial functions in a variety of important cellular processes such as protein quality control, organellar homeostasis, and DNA damage repair, and its de-regulation is linked to neuromuscular diseases and cancer. p97 is tightly controlled by numerous regulatory cofactors, but the full range and function of the p97–cofactor network is unknown. Here, we identify the hitherto uncharacterized FAM104 proteins as a conserved family of p97 interactors. The two human family members VCP nuclear cofactor family member 1 and 2 (VCF1/2) bind p97 directly via a novel, alpha-helical motif and associate with p97-UFD1-NPL4 and p97-UBXN2B complexes in cells. VCF1/2 localize to the nucleus and promote the nuclear import of p97. Loss of VCF1/2 results in reduced nuclear p97 levels, slow growth, and hypersensitivity to chemical inhibition of p97 in the absence and presence of DNA damage, suggesting that FAM104 proteins are critical regulators of nuclear p97 functions.
YAP, the key protein effector of the Hippo pathway, is a transcriptional co-activator that controls the expression of cell cycle genes, promotes cell growth and proliferation and regulates organ size. YAP modulates gene transcription by binding to distal enhancers, but the mechanisms of gene regulation by YAP-bound enhancers remain poorly understood. Here we show that constitutive active YAP5SA leads to widespread changes in chromatin accessibility in untransformed MCF10A cells. Newly accessible regions include YAP-bound enhancers that mediate activation of cycle genes regulated by the Myb-MuvB (MMB) complex. By CRISPR-interference we identify a role for YAP-bound enhancers in phosphorylation of Pol II at Ser5 at MMB-regulated promoters, extending previously published studies that suggested YAP primarily regulates the pause-release step and transcriptional elongation. YAP5SA also leads to less accessible ‘closed’ chromatin regions, which are not directly YAP-bound but which contain binding motifs for the p53 family of transcription factors. Diminished accessibility at these regions is, at least in part, a consequence of reduced expression and chromatin-binding of the p53 family member ΔNp63 resulting in downregulation of ΔNp63-target genes and promoting YAP-mediated cell migration. In summary, our studies uncover changes in chromatin accessibility and activity that contribute to the oncogenic activities of YAP.
Addition of heparin binding sites strongly increases the bone forming capabilities of BMP9 in vivo
(2023)
Highlights
• Despite not being crucial for bone development BMP9 can induce bone growth in vivo.
• BMP9 induced bone formation is strongly enhanced by introduced heparin binding sites.
• BMP9s bone forming capabilities are triggered by extracellular matrix binding.
• Heparin binding BMP9 (BMP9 HB) can improve the current therapies in treating bone fractures.
Abstract
Bone Morphogenetic proteins (BMPs) like BMP2 and BMP7 have shown great potential in the treatment of severe bone defects. In recent in vitro studies, BMP9 revealed the highest osteogenic potential compared to other BMPs, possibly due to its unique signaling pathways that differs from other osteogenic BMPs. However, in vivo the bone forming capacity of BMP9-adsorbed scaffolds is not superior to BMP2 or BMP7. In silico analysis of the BMP9 protein sequence revealed that BMP9, in contrast to other osteogenic BMPs such as BMP2, completely lacks so-called heparin binding motifs that enable extracellular matrix (ECM) interactions which in general might be essential for the BMPs' osteogenic function. Therefore, we genetically engineered a new BMP9 variant by adding BMP2-derived heparin binding motifs to the N-terminal segment of BMP9′s mature part. The resulting protein (BMP9 HB) showed higher heparin binding affinity than BMP2, similar osteogenic activity in vitro and comparable binding affinities to BMPR-II and ALK1 compared to BMP9. However, remarkable differences were observed when BMP9 HB was adsorbed to collagen scaffolds and implanted subcutaneously in the dorsum of rats, showing a consistent and significant increase in bone volume and density compared to BMP2 and BMP9. Even at 10-fold lower BMP9 HB doses bone tissue formation was observed. This innovative approach of significantly enhancing the osteogenic properties of BMP9 simply by addition of ECM binding motifs, could constitute a valuable replacement to the commonly used BMPs. The possibility to use lower protein doses demonstrates BMP9 HB's high translational potential.
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.