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.

BACKGROUND: FSGS affects the complex three-dimensional morphology of podocytes, resulting in loss of filtration barrier function and the development of sclerotic lesions. Therapies to treat FSGS are limited, and podocyte-specific drugs are unavailable. To address the need for treatments to delay or stop FSGS progression, researchers are exploring the repurposing of drugs that have been approved by the US Food and Drug Administration (FDA) for other purposes. METHODS: To identify drugs with potential to treat FSGS, we used a specific zebrafish screening strain to combine a high-content screening (HCS) approach with an in vivo model. This zebrafish screening strain expresses nitroreductase and the red fluorescent protein mCherry exclusively in podocytes (providing an indicator for podocyte depletion), as well as a circulating 78 kDa vitamin D-binding enhanced green fluorescent protein fusion protein (as a readout for proteinuria). To produce FSGS-like lesions in the zebrafish, we added 80 µ M metronidazole into the fish water. We used a specific screening microscope in conjunction with advanced image analysis methods to screen a library of 138 drugs and compounds (including some FDA-approved drugs) for podocyte-protective effects. Promising candidates were validated to be suitable for translational studies. RESULTS: After establishing this novel in vivo HCS assay, we identified seven drugs or compounds that were protective in our FSGS-like model. Validation experiments confirmed that the FDA-approved drug belinostat was protective against larval FSGS. Similar pan-histone deacetylase inhibitors also showed potential to reproduce this effect. CONCLUSIONS: Using an FSGS-like zebrafish model, we developed a novel in vivo HCS assay that identified belinostat and related pan-histone deacetylase inhibitors as potential candidates for treating FSGS.

Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify potential diagnostic and therapeutic biomarkers. To improve the classification of ncRNAs, we investigated different approaches of utilizing primary sequences and secondary structures as well as the late integration of both using machine learning models, including different neural network architectures. As input, we used the newest version of RNAcentral, focusing on six ncRNA classes, including lncRNA, rRNA, tRNA, miRNA, snRNA and snoRNA. The late integration of graph-encoded structural features and primary sequences in our MncR classifier achieved an overall accuracy of \textgreater97%, which could not be increased by more fine-grained subclassification. In comparison to the actual best-performing tool ncRDense, we had a minimal increase of 0.5% in all four overlapping ncRNA classes on a similar test set of sequences. In summary, MncR is not only more accurate than current ncRNA prediction tools but also allows the prediction of long ncRNA classes (lncRNAs, certain rRNAs) up to 12.000 nts and is trained on a more diverse ncRNA dataset retrieved from RNAcentral.

The evolutionary conserved NEAT1-MALAT1 gene cluster generates large noncoding transcripts remaining nuclear, while tRNA-like transcripts (mascRNA, menRNA) enzymatically generated from these precursors translocate to the cytosol. Whereas functions have been assigned to the nuclear transcripts, data on biological functions of the small cytosolic transcripts are sparse. We previously found NEAT1-/- and MALAT1-/- mice to display massive atherosclerosis and vascular inflammation. Here, employing selective targeted disruption of menRNA or mascRNA, we investigate the tRNA-like molecules as critical components of innate immunity. CRISPR-generated human ΔmascRNA and ΔmenRNA monocytes/macrophages display defective innate immune sensing, loss of cytokine control, imbalance of growth/angiogenic factor expression impacting upon angiogenesis, and altered cell-cell interaction systems. Antiviral response, foam cell formation/oxLDL uptake, and M1/M2 polarization are defective in ΔmascRNA/ΔmenRNA macrophages, defining first biological functions of menRNA and describing new functions of mascRNA. menRNA and mascRNA represent novel components of innate immunity arising from the noncoding genome. They appear as prototypes of a new class of noncoding RNAs distinct from others (miRNAs, siRNAs) by biosynthetic pathway and intracellular kinetics. Their NEAT1-MALAT1 region of origin appears as archetype of a functionally highly integrated RNA processing system.

Aging of the skin is accompanied by cellular as well as tissue environmental changes, ultimately reducing the ability of the tissue to regenerate and adequately respond to external stressors. Macrophages are important gatekeepers of tissue homeostasis, and it has been reported that their number and phenotype change during aging in a site-specific manner. How aging affects human skin macrophages and what implications this has for the aging process in the tissue are still not fully understood. Using single-cell RNA-sequencing analysis, we show that there is at least a 50% increase of macrophages in human aged skin, which appear to have developed from monocytes and exhibit more proinflammatory M1-like characteristics. In contrast, the cell-intrinsic ability of aged monocytes to differentiate into M1 macrophages was reduced. Using coculture experiments with aged dermal fibroblasts, we show that it is the aged microenvironment that drives a more proinflammatory phenotype of macrophages in the skin. This proinflammatory M1-like phenotype in turn negatively influenced the expression of extracellular matrix proteins by fibroblasts, emphasizing the impact of the aged macrophages on the skin phenotype.

OCT1 and OCT2 are polyspecific membrane transporters that are involved in hepatic and renal drug clearance in humans and mice. In this study, we cloned dog OCT1 and OCT2 and compared their function to the human and mouse orthologs. We used liver and kidney RNA to clone dog OCT1 and OCT2. The cloned and the publicly available RNA-Seq sequences differed from the annotated exon-intron structure of OCT1 in the dog genome CanFam3.1. An additional exon between exons 2 and 3 was identified and confirmed by sequencing in six additional dog breeds. Next, dog OCT1 and OCT2 were stably overexpressed in HEK293 cells and the transport kinetics of five drugs were analyzed. We observed strong differences in the transport kinetics between dog and human orthologs. Dog OCT1 transported fenoterol with 12.9-fold higher capacity but 14.3-fold lower affinity (higher KM) than human OCT1. Human OCT1 transported ipratropium with 5.2-fold higher capacity but 8.4-fold lower affinity than dog OCT1. Compared to human OCT2, dog OCT2 showed 10-fold lower transport of fenoterol and butylscopolamine. In conclusion, the functional characterization of dog OCT1 and OCT2 reported here may have implications when using dogs as pre-clinical models as well as for drug therapy in dogs.

Specialized surveillance mechanisms are essential to maintain the genetic integrity of germ cells, which are not only the source of all somatic cells but also of the germ cells of the next generation. DNA damage and chromosomal aberrations are, therefore, not only detrimental for the individual but affect the entire species. In oocytes, the surveillance of the structural integrity of the DNA is maintained by the p53 family member TAp63α. The TAp63α protein is highly expressed in a closed and inactive state and gets activated to the open conformation upon the detection of DNA damage, in particular DNA double-strand breaks. To understand the cellular response to DNA damage that leads to the TAp63α triggered oocyte death we have investigated the RNA transcriptome of oocytes following irradiation at different time points. The analysis shows enhanced expression of pro-apoptotic and typical p53 target genes such as CDKn1a or Mdm2, concomitant with the activation of TAp63α. While DNA repair genes are not upregulated, inflammation-related genes become transcribed when apoptosis is initiated by activation of STAT transcription factors. Furthermore, comparison with the transcriptional profile of the ΔNp63α isoform from other studies shows only a minimal overlap, suggesting distinct regulatory programs of different p63 isoforms.

BCL11B, an essential transcription factor for thymopoiesis, regulates also vital processes in post-thymic lymphocytes. Increased expression of BCL11B was recently correlated with the maturation of NK cells, whereas reduced BCL11B levels were observed in native and induced T cell subsets displaying NK cell features. We show that BCL11B-depleted CD8+ T cells stimulated with IL-15 acquired remarkable innate characteristics. These induced innate CD8+ (iiT8) cells expressed multiple innate receptors like NKp30, CD161, and CD16 as well as factors regulating migration and tissue homing while maintaining their T cell phenotype. The iiT8 cells effectively killed leukemic cells spontaneously and neuroblastoma spheroids in the presence of a tumor-specific monoclonal antibody mediated by CD16 receptor activation. These iiT8 cells integrate the innate natural killer cell activity with adaptive T cell longevity, promising an interesting therapeutic potential. Our study demonstrates that innate T cells, albeit of limited clinical applicability given their low frequency, can be efficiently generated from peripheral blood and applied for adoptive transfer, CAR therapy, or combined with therapeutic antibodies.

Within the last years, more and more noncoding RNAs (ncRNAs) became the focal point to understand cell regulatory mechanisms because one class of ncRNAs, microRNAs (miRNAs), plays an essential role in translation repression or degradation of specific mRNAs and is implicated in disease etiology. miRNAs can serve as oncomiRs (oncogenic miRNAs) and tumor suppressor miRNAs, thus, miRNA therapeutics in clinical trials have become a vital component with respect to cancer treatment. To circumvent side-effects and allow an accurate effect it is crucial to accurately predict miRNAs and their mRNA targets. Over the last two decades, different approaches for miRNA prediction as well as miRNA target prediction have been developed and improved based on sequence and structure features. Nowadays, the abundance of high-throughput sequencing data and databases of miRNAs and miRNA targets from different species allow the training, testing, and validation of predicted miRNAs and miRNA targets with machine learning methods. This book chapter focuses on the important requirements for miRNA target prediction tools using ML like common features used for miRNA-binding site prediction. Furthermore, best practices for the prediction and validation of miRNA-mRNA targets are presented and set in the context of possible applications for cancer diagnosis and therapeutics.