Chronic kidney disease (CKD) is characterized by persistent inflammation and tubulointerstitial fibrosis leading to end-stage renal disease. Transient receptor potential canonical 6 (TRPC6) channel inhibition mitigates tubular injury and renal fibrosis in murine models of unilateral ureteral obstruction (UUO) and 2-month chronic post-ischemia-reperfusion injury (2m post-I/R). Through integrated analysis of single-cell-RNA-sequencing (scRNA-Seq) data from UUO mice treated with the selective TRPC6 inhibitor SH045, here the renoprotective cell composition and cell type-specific transcriptional programs are defined. We explored translational aspects by conducting an in-depth scRNA-Seq analysis of kidney samples from patients with CKD. These results reveal global transcriptional shifts with a dramatic diversification of inflammatory cells, endothelial cells and fibroblasts. Notably, a distinct subpopulation of novel endothelial cells is delineated, which is termed ECRIN, that regulate inflammatory networks implicating VEGF and GAS signaling pathways. The data also indicates that inhibition of TRPC6 channels triggers a Prnp transcription factor regulatory network, which contributes to the alleviation of renal fibrosis. The key findings are supported at the protein level by immunofluorescence and western blot analysis. We observed similar patterns in the chronic 2m postI/R injury model. These findings provide novel insights into the potential therapeutic benefits of TRPC6 inhibition in CKD.

Aging is a complex process that significantly contributes to age-related diseases and poses significant challenges for effective interventions, with few holistic anti-aging approaches successfully reversing its signs. Heterochronic parabiosis studies illuminated the potential for rejuvenation through blood-borne factors, yet the specific drivers including underlying mechanisms remain largely unknown and until today insights have not been successfully translated to humans. In this study, we were able to recreate rejuvenation of the human skin via systemic factors using a microphysiological system including a 3D skin model and a 3D bone marrow model. Addition of young human serum in comparison to aged human serum resulted in an improvement of proliferation and a reduction of the biological age as measured by methylation-based age clocks in the skin tissue. Interestingly, this effect was only visible in the presence of bone marrow-derived cells. Further investigation of the bone marrow model revealed changes in the cell population in response to young versus aged human serum treatment. Using proteome analysis, we identified 55 potential systemic rejuvenating proteins produced by bone marrow-derived cells. For seven of these proteins, we were able to verify a rejuvenating effect on human skin cells using hallmarks of aging assays, supporting their role as systemic factors rejuvenating human skin tissue.

BACKGROUND: Alterations in pre-mRNA splicing are crucial to the pathophysiology of various diseases. However, the effects of alternative splicing of mRNA on podocytes in hypertensive nephropathy are still unknown. The Sys_CARE project aimed to identify alternative splicing events involved in the development and progression of glomerular hypertension. METHODS: Murine podocytes were exposed to mechanical stretch, after which proteins and mRNA were analyzed by proteomics, RNA sequencing and several bioinformatic alternative splicing tools. RESULTS: Using transcriptomic and proteomic analysis, we identified significant changes in gene expression and protein abundance due to mechanical stretch. RNA-Seq identified over 3,000 alternative spliced genes after mechanical stretch, including all types of alternative splicing events. Among these, 17 genes exhibited an alternative splicing event across four different splicing analysis tools. From this group, we focused on Myl6, a component of the myosin protein complex, and Shroom3, an actin-binding protein essential for podocyte function. We identified two Shroom3 isoforms with significant expression changes under mechanical stretch, which was validated by qRT-PCR and in situ hybridization. Additionally, we observed an expression switch of two Myl6 isoforms after mechanical stretch, accompanied by an alteration in the C-terminal amino acid sequence. CONCLUSIONS: A comprehensive RNA-Seq analysis of mechanically stretched podocytes identified novel potential podocyte-specific biomarkers and highlighted significant alternative splicing events, notably in the mRNA of Shroom3 and Myl6.

Background/Objectives: The prediction of cancer types is primarily reliant on driver genes and their specific mutations. The advancement in novel omics technologies has led to the acquisition of additional genetic data. When integrated with artificial intelligence models, there is considerable potential for this to enhance the accuracy of cancer diagnosis. As mutational signatures can provide insights into repair mechanism malfunctions, they also have the potential for more accurate cancer diagnosis. Methods: First, we compared unsupervised and supervised machine learning approaches to predict cancer types. We employed deep and artificial neural network architectures with an explainable component like layerwise relevance propagation to extract the most relevant features for the cancer-type prediction. Ten-fold cross-validation and an extensive grid search were used to optimize the neural network architecture using driver gene mutations, mutational signatures and topological mutation information as input. The PCAWG dataset was used as input to discriminate between 17 primary sites and 24 cancer types. Results: Overall, our approach showed that the most relevant mutation information to discriminate between cancer types is increased by >10% using the whole genome or intergenic and intronic genome regions instead of exome information. Furthermore, the most relevant features for most cancer types, except for two, are in the mutational signatures and not the topological mutation information. Conclusions: Informative mutational signatures outperformed the prediction of cancer types in comparison to driver gene mutations and added a new layer of diagnostic information. As the degree of information within the mutational signatures is not solely based on the frequency of occurrence, it is even possible to separate cancer types from the same primary site by the different relevant mutations. Furthermore, the comparison of informative mutational signatures allowed the cancer-type assignment of specific impaired repair mechanisms.

Functional revascularization is key to stroke recovery and requires remodeling and regeneration of blood vessels around which is located the brain’s only stromal compartment. Stromal progenitor cells (SPCs) are critical for tissue regeneration following injury in many organs, yet their identity in the brain remains elusive. Here we show that the perivascular niche of brain SPCs includes pericytes, venular smooth muscle cells and perivascular fibroblasts that together help cerebral microvasculature regenerate following experimental stroke. Ischemic injury triggers amplification of pericytes and perivascular fibroblasts in the infarct region where they associate with endothelial cells inside a reactive astrocyte border. Fate-tracking of Hic1+ SPCs uncovered a transient functional and transcriptional phenotype of stroke-activated pericytes and perivascular fibroblasts. Both populations of these cells remained segregated, displaying distinct angiogenic and fibrogenic profiles. Therefore, pericytes and perivascular fibroblasts are distinct subpopulations of SPCs in the adult brain that coordinate revascularization and scar formation after injury.

Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance.Methods DICOM-structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration with interactive filtering capabilities, thereby simplifying the process of creation of patient cohorts over several sites with consistent multi-modal data.Results In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data include imaging and waveform data (i.e., computed tomography images, electrocardiography scans) as well as annotations (i.e., calcification segmentations, and pointsets), and metadata (i.e., prostheses and pacemaker dependency).Conclusion Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for multi-centric data analysis. The graphical interface as well as example structured report templates are available at https://github.com/Cardio-AI/fl-multi-modal-dataset-creation .

Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data unused. Leveraging these could enhance transformer architectures’ ability in regimes with small and diversely annotated sets. We conduct the largest federated cardiac CT analysis to date (n = 8, 104) in a real-world setting across eight hospitals. Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. This improves predictive accuracy and enables simultaneous learning of all partial labels across the federation, and outperforms UNet-based models in generalizability on downstream tasks. Code and model weights are made openly available for leveraging future cardiac CT analysis.

Renal cell carcinoma (RCC) is the most common form of kidney cancer, known for its immune evasion and resistance to chemotherapy. Evidence indicates that the SARS-CoV-2 virus may worsen outcomes for RCC patients, as well as patients with diminished renal function. Evidence suggests that the SARS-CoV-2 virus may exacerbate outcomes in RCC patients and those with impaired renal function. This study explored the unidirectional effects of RCC cells and the SARS-CoV-2 spike protein (S protein) on human renal proximal tubule epithelial cells (RPTECs) using a microphysiological approach. We co-cultured RCC cells (Caki-1) with RPTEC and exposed them to the SARS-CoV-2 S protein under dynamic 3D conditions. The impact on metabolic activity, gene expression, immune secretions, and S protein internalization was evaluated. The SARS-CoV-2 S protein was internalized by RPTEC but poorly interacted with RCC cells. RPTECs exposed to RCC cells and the S protein exhibited upregulated expression of genes involved in immunogenic pathways, particularly those related to antigen processing and presentation via the major histocompatibility complex I (MHCI). Additionally, increased TNF-α secretion suggested a pro-inflammatory response. Metabolic shifts toward glycolysis were observed in RCC co-culture, while the presence of the S protein led to minor changes. The presence of RCC cells amplified the immune-modulatory effects of the SARS-CoV-2 S protein on the renal epithelium, potentially exacerbating renal inflammation and fostering tumor-supportive conditions. These findings suggest that COVID-19 infections can impact renal function in the presence of kidney cancer.

The tricellular tight junctions are crucial for the regulation of paracellular flux at tricellular junctions, where tricellulin (MARVELD2) and angulins (ILDR1, ILDR2, or LSR) are localized. The role of ILDR2 in podocytes, specialized epithelial cells in the kidney, is still unknown. We investigated the role of ILDR2 in glomeruli and its influence on blood filtration. Western blots, single-cell RNA sequencing (scRNA-seq), and superresolution microscopy showed a strong expression of ILDR2 in podocytes that colocalized with the podocyte-specific claudin CLDN5. Co-immunoprecipitation revealed that ILDR2 interacts with CLDN5. In glomerulopathies, induced by nephrotoxic serum and by desoxycorticosterone acetate (DOCA)-salt heminephrectomy, ILDR2 was strongly up-regulated. Furthermore, Ildr2 knockout mice exhibited glomerular hypertrophy and decreased podocyte density. However, they did not develop effacement of podocyte foot processes or proteinuria. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic analysis of isolated glomeruli showed an increase in matrix proteins, such as fibronectin and collagens. This suggests a protective role of ILDR2 in glomerulopathies.

The Explainable Modular Neural Network (XModNN) enables the identification of biomarkers, facilitating the classification of diseases and clinical parameters in transcriptomic datasets. The modules within XModNN represent specific pathways or genes of a functional hierarchy. The incorporation of biological insights into the architectural design reduced the number of parameters. This is further reinforced by the weighted multi-loss progressive training, which enables successful classification with a reduced number of replicates. The combination of this workflow with layer-wise relevance propagation ensures a robust post hoc explanation of the individual module contribution. Two use cases were employed to predict sex and neuroblastoma cell states, demonstrating that XModNN, in contrast to standard statistical approaches, results in a reduced number of candidate biomarkers. Moreover, the architecture enables the training on a limited number of examples, attaining the same performance and robustness as support vector machine and random forests. The integrated pathway relevance analysis improves a standard gene set overrepresentation analysis, which relies solely on gene assignment. Two crucial genes and three pathways were identified for sex classification, while 26 genes and six pathways are highly important to discriminate adrenergic-mesenchymal cell states in neuroblastoma cancer.

Identifying effective drugs for focal segmental glomerulosclerosis (FSGS) treatment holds significant importance. Our high-content drug screening on zebrafish larvae relies on nitroreductase/metronidazole (NTR/MTZ)-induced podocyte ablation to generate FSGS-like injury. A crucial factor for successful drug screenings is minimizing variability in injury induction. For this, we introduce nifurpirinol (NFP) as a more reliable prodrug for targeted podocyte depletion. NFP showed a 2.3-fold increase in efficiency at concentrations 1,600-fold lower compared with MTZ-mediated injury induction. Integration into the screening workflow validated its suitability for the high-content drug screening. The presence of crucial FSGS hallmarks, such as podocyte foot process effacement, proteinuria, and activation of parietal epithelial cells, was observed. After the isolation of the glomeruli from the larvae, we identified essential pathways by proteomic analysis. This study shows that NFP serves as a highly effective prodrug to induce the FSGS-like disease in zebrafish larvae and is well-suited for a high-content drug screening to identify new candidates for the treatment of FSGS.NEW & NOTEWORTHY This research investigated the use of nifurpirinol in nanomolar amounts as a prodrug to reliably induce focal segmental glomerulosclerosis (FSGS)-like damage in transgenic zebrafish larvae. Through proteomic analysis of isolated zebrafish glomeruli, we were further able to identify proteins that are significantly regulated after the manifestation of FSGS. These results are expected to expand our knowledge of the pathomechanism of FSGS.

In actual pandemic situations like COVID-19, it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an agent-based model (ABM) to simulate the spread of SARS-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly during the first wave. In our model, we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. In this context, we focused on parameters of local events as possible mitigation measures and ran simulations, including varying size, duration, frequency and the proportion of events. The majority of changes to single event parameters, with the exception of frequency, showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations, we could observe drastic changes in the number of infections per day. Depending on the lockdown strategy, we even observed a delayed peak in infection numbers of the second wave. As an advantage of the developed ABM, it is possible to analyze the individual risk of single agents during the pandemic. In contrast to standard or adjusted ODEs, we observed a 21% (with masks) / 48% (without masks) increased risk for single reappearing participants on local events, with a linearly increasing risk based on the length of the events.

The advent of micro-physiological systems (MPS) in biomedical research has enabled the introduction of more complex and relevant physiological into in vitro models. The recreation of complex morphological features in three-dimensional environments can recapitulate otherwise absent dynamic interactions in conventional models. In this study we developed an advanced in vitro Renal Cell Carcinoma (RCC) that mimics the interplay between healthy and malignant renal tissue. Based on the TissUse Humimic platform our model combines healthy renal proximal tubule epithelial cells (RPTEC) and RCC. Co-culturing reconstructed RPTEC tubules with RCC spheroids in a closed micro-perfused circuit resulted in significant phenotypical changes to the tubules. Expression of immune factors revealed that interleukin-8 (IL-8) and tumor necrosis factor-alfa (TNF-α) were upregulated in the non-malignant cells while neutrophil gelatinase-associated lipocalin (NGAL) was downregulated in both RCC and RPTEC. Metabolic analysis showed that RCC prompted a shift in the energy production of RPTEC tubules, inducing glycolysis, in a metabolic adaptation that likely supports RCC growth and immunogenicity. In contrast, RCC maintained stable metabolic activity, emphasizing their resilience to external factors. RNA-seq and biological process analysis of primary RTPTEC tubules demonstrated that the 3D tubular architecture and MPS conditions reverted cells to a predominant oxidative phosphorylate state, a departure from the glycolytic metabolism observed in 2D culture. This dynamic RCC co-culture model, approximates the physiology of healthy renal tubules to that of RCC, providing new insights into tumor-host interactions. Our approach can show that an RCC-MPS can expand the complexity and scope of pathophysiology and biomarker studies in kidney cancer research.

Podocyte detachment due to mechanical stress is a common issue in hypertension-induced kidney disease. This study highlights the role of zyxin for podocyte stability and function. We have found that zyxin is significantly up-regulated in podocytes after mechanical stretch and relocalizes from focal adhesions to actin filaments. In zyxin knockout podocytes, we found that the loss of zyxin reduced the expression of vinculin and VASP as well as the expression of matrix proteins, such as fibronectin. This suggests that zyxin is a central player in the translation of mechanical forces in podocytes. In vivo, zyxin is highly up-regulated in patients suffering from diabetic nephropathy and in hypertensive DOCA-salt treated mice. Furthermore, zyxin loss in mice resulted in proteinuria and effacement of podocyte foot processes that was measured by super resolution microscopy. This highlights the essential role of zyxin for podocyte maintenance in vitro and in vivo, especially under mechanical stretch.

Arabidopsis contains hundreds of ribosomal DNA copies organized within the nucleolar organizing regions (NORs) in chromosomes 2 and 4. There are four major types of variants of rDNA, VAR1-4, based on the polymorphisms of 3’ external transcribed sequences. The variants are known to be differentially expressed during plant development. We created a mutant by the CRISPR-Cas9-mediated excision of ~ 25 nt from predominantly NOR4 ribosomal DNA copies, obtaining mosaic mutational events on ~ 5% of all rDNA copies. The excised region consists of P-loop and Helix-82 segments of 25S rRNA. The mutation led to allelic, dosage-dependent defects marked by lateral root inhibition, reduced size, and pointy leaves, all previously observed for defective ribosomal function. The mutation in NOR4 led to dosage compensation from the NOR2 copies by elevated expression of VAR1 in mutants and further associated single-nucleotide variants, thus, resulting in altered rRNA sub-population. Furthermore, the mutants exhibited rRNA maturation defects specifically in the minor pathway typified by 32S pre-rRNA accumulation. Density-gradient fractionation and subsequent RT-PCR of rRNA analyses revealed that mutated copies were not incorporated into the translating ribosomes. The mutants in addition displayed an elevated autophagic flux as shown by the autophagic marker GFP-ATG8e, likely related to ribophagy.