Federico Marini’s research while affiliated with Johannes Gutenberg University Mainz and other places

The hematological system is impacted in nearly all ionizing radiation exposure scenarios. Whole transcriptome data offer detailed insights into blood's radiation response, crucial for radiotherapy and biodosimetry. We conducted genome-wide RNA-seq analysis on blood from three donors irradiated ex vivo with X-rays and incubated for 2h and 6h. Gene expression was subject to strong inter-donor variation and time post-exposure. After 0.5, 1, 2, and 4 Gy X-rays, 5, 33, 84, and 364 genes (2h) and 72, 99, 274, and 607 genes (6h) were differentially expressed (DEG), compared to 0 Gy. The corresponding number of the inferred transcription factors was 255, 253, 274, and 292 after 2h and 214, 245, 262, and 279 after 6h. In sham-irradiated blood, 924 DEGs and 165 transcription factors were affected by ex vivo incubation alone. We identified 34 radioresponsive DEGs not previously described, 8 and 9 showing significant positive or negative correlations with dose, respectively, including GPN1, MRM2, G0S2, and PTPRS. DNA damage signaling pathways were affected from the lowest dose, with doses ≥ 2 Gy additionally triggering proinflammatory responses. This first genome-wide RNA-seq study of ex vivo X-ray-exposed human blood reveals novel radiosensitive genes, transcription factors, and pathways, enhancing the understanding of the consequences of diagnostic, therapeutic, or accidental exposures on the highly radioresponsive hematological system.

The Human BioMolecular Atlas Program (HuBMAP) constructs the worldwide available platform to research the human body at the cellular level. The HuBMAP Data Portal encompasses a wide range of data resources measured on emerging experimental technologies at a spatial resolution. To broaden access to the HuBMAP Data Portal, we introduce an R client called HuBMAPR available on Bioconductor. This provides an efficient and programmatic interface that enables researchers to discover and retrieve HuBMAP data more easily and quickly. Availability and implementation HuBMAPR is available at https://bioconductor.org/packages/HuBMAPR.

The emergence of post-COVID sequelae (PCS) represents a global challenge. However, understanding of biological mechanisms and the definition of quantifiable risk factors remains limited. This study harnessed the power of predictive machine learning models to explore the potential of proteomics in predicting individual post-COVID symptoms and their collective manifestation as PCS. The analysis utilized a panel of approximately 2900 proteins measured in 495 COVID-19 patients. The study identified 235 unique proteins associated with 21 distinct post-COVID symptoms. Symptoms more closely linked by similar protein patterns tended to co-occur more frequently in patients. Six symptom clusters with distinct molecular pathway signatures were uncovered, with metabolic and inflammatory pathways prominently involved across several clusters. The relevance of the specific protein signatures for post-COVID symptoms could be demonstrated and explored by objective, quantifiable clinical tests, including cognitive and somatic assessments, and underlined their relevance. Data from various modalities, including pre-existing conditions, disease risk factors and genetic susceptibility, revealed relevant relations that may contribute to PCS heterogeneity. This work underscores the complex and multifaceted nature of post-COVID symptoms. It emphasizes the need for systematic and more specific approaches to facilitate the development of targeted therapies and treatment strategies.

Background Macrophages and tubular epithelial cell interactions are integral in kidney ischemia-incited interstitial inflammation leading to acute kidney injury. Ischemia/reperfusion injury riggers tubular epithelial cells to express IL-34, a macrophage growth factor, that promotes acute kidney injury and subsequent chronic kidney disease. IL-34 engages the cognate receptor, c-FMS, expressed by macrophages, and the recently discovered Protein-Tyrosine Phosphatase ζ (Ptprz). Ptprz, binds to multiple ligands other than IL-34 that progressively increase their expression in kidneys after ischemia/reperfusion injury. Methods We tested the hypothesis that signaling through Ptprz promotes macrophage-mediated acute kidney injury and subsequent chronic kidney disease, by comparing Ptprz knockout with wild-type mice after ischemia/reperfusion injury. Results Ptprz was expressed by leukocytes and in tubular epithelial cells after ischemia/reperfusion injury in mice. Using Ptprz knockout mice we determined that during acute kidney injury and chronic kidney disease kidney pathology, and loss of kidney function were ameliorated. Ptprz-dependent mechanisms mediated: (i) tubular epithelial cell expression of chemokines that fostered macrophage and T cell rich renal inflammation, and (ii) tubule injury and apoptosis, that resulted in the loss of tubules and interstitial fibrosis during chronic kidney disease . Mechanistically, Ptprz dependent tubule epithelial cells released mediators that:(i) promoted tubule cytotoxicity, and thereby, shortened tubule survival, and (ii) stimulated Ptprz expressing macrophages to generate mediators that induce kidney destruction. These findings are translational, as after ischemia reperfusion injury in human kidney transplants, PTPRZ and PTPRZ ligands were upregulated and expressed by the same cell populations as in mice. Moreover, PTPRZ levels in sera were elevated in kidney transplant patients. Conclusions Intra-renal Ptprz-dependent macrophage and tubular epithelial cell mediated mechanisms promote acute kidney injury and subsequent chronic kidney disease.

Motivation Immune molecules such as B and T cell receptors, human leukocyte antigens (HLAs), or killer Ig-like receptors (KIRs) are encoded in the most genetically diverse loci of the human genome. Many of these immune genes exhibit remarkable allelic diversity across populations. While computational methods for HLA typing from bulk RNA sequencing data have emerged, streamlined solutions for allele-specific quantification in single-cell RNA sequencing (scRNA-seq) are lacking. Moreover, no standardized data structure or analytical framework has been established to handle allele-specific immune gene expression data at single-cell level. Results We present a comprehensive workflow to (1) automate allele-typing and allele-specific expression quantification of HLA transcripts in scRNA-seq data using a Snakemake workflow, scIGD (single-cell ImmunoGenomic Diversity), and (2) represent and interactively explore immune gene expression at different annotation levels using a multi-layer data structure implemented as an R/Bioconductor software package, SingleCellAlleleExperiment . We validated our approach on a diverse spectrum of scRNA-seq datasets, and found that it performs consistently across different sequencing platforms and experimental setups. We illustrate how our method can be utilized to study loss of HLA expression in tumor cells or discover differential HLA allele expression in specific immune cell subtypes. By capturing such allele-specific expression patterns and their variation, our workflow offers novel insights into human immunogenomic diversity. Availability and implementation scIGD is available under the MIT license at: https://github.com/AGImkeller/scIGD . SingleCellAlleleExperiment is available under the MIT license at: https://bioconductor.org/packages/SingleCellAlleleExperiment . scaeData provides validation datasets and is available under the MIT license at: https://bioconductor.org/packages/scaeData . Data processed with scIGD are available at: https://doi.org/10.5281/zenodo.14033960 . Contact Katharina Imkeller. E-mail: imkeller@rz.uni-frankfurt.de . Supplementary information Supplementary data are available within the same submission.

Introduction:T cell-based cancer immunotherapy has demonstrated unprecedented efficacy in hematologic malignancies. However, only a minority of patients experienced long-term remissions with the existing treatment modalities. Dysfunction of infused T cells is regarded as one of the main reasons for failure of T cell therapies. Identification and insight into the mechanisms driving T cell dysfunction will offer additional tools to improve cellular and metabolic fitness of genetically engineered T cells for cancer therapy. Aim: In contrast to exhaustion, aging and senescence-driven T cell dysfunction is less well understood and can impede cellular functions critical to efficient anti-tumor responses in elderly or pre-treated patients. Identification of senescent T cells has mainly relied on cell surface markers that have been used inconsistently in the literature. Here, we aim to provide a detailed molecular and functional signature of senescent T cells exhibiting all hallmarks of senescence including proliferation arrest, cellular phenotype, senescence-associated secretory phenotype (SASP) and β-galactosidase activity to abrogate and reprogram senescence-induced T cell dysfunction. Methods: T cells were isolated from young (< 30 years old) and older (45-90 years old) healthy donors. CD8+ T cells were extensively analyzed for cellular phenotype (flow cytometry), mitochondrial function (metabolic flux assays, electron microscopy, flow cytometry), β-galactosidase activity and cellular functions including proliferation and apoptosis. Anti-tumor cytotoxic responses of different T cell subsets were tested by introduction of a tumor-antigen specific T cell receptor (TCR) via electroporation. Senescence was further characterized at the molecular level by analyzing the TCR repertoire and transcriptome by single-cell RNA sequencing. Results: The detailed examination of different senescence hallmarks revealed a subset of T cells within a population of late-differentiated cells. Compared to other subsets, this T cell sub-population showed a complete lack of proliferation under different stimulation conditions and exhibited known surface markers of senescence such as CD57, KLRG1 and re-expression of CD45RA. Failure of proliferation was accompanied by signs of mitochondrial dysfunction such as low mitochondrial mass and membrane potential, altered shape in electron microscopy and reduced mitochondrial respiration. In turn, emergence of these features could be observed in non-senescent T cells upon senescence-inducing treatments. Importantly, senescent T cells exhibited superior cytotoxic potential as determined by degranulation assay. A more direct proof of tumor cell lysis by TCR gene transfer approach confirmed a potent cytolytic activity of these cells, despite their inability to proliferate. Finally, RNA sequencing on a single-cell level was performed and confirmed perturbations in mitochondrial gene expression and showed upregulation of signaling pathways linked to cytotoxicity and activation. Validation of these new findings are currently underway across different experimental approaches. Conclusion: The deep characterization of senescence in T cells at the cellular and molecular level allows a more precise identification of these cells in patients undergoing adoptive T cell therapies. Furthermore, the results of this study suggest that these T cells could elicit strong anti-tumor cytotoxicity despite defective proliferation and might be useful for cancer treatment. The ongoing analysis of mechanisms involved in senescence induction can be used to restore proliferative potential in this T cell subset and hence improve T cell-based cancer immunotherapy.

Background Glycosphingolipids (GSLs) are membrane lipids composed of a ceramide backbone linked to a glycan moiety. Ganglioside biosynthesis is a part of the GSL metabolism, which involves sequential reactions catalyzed by specific enzymes that in part have a poor substrate specificity. GSLs are deregulated in cancer, thus playing a role as potential biomarkers for personalized therapy or subtype classification. However, the analysis of GSL profiles is complex and requires dedicated technologies, that are currently not included in the commonly utilized high-throughput assays adopted in contexts such as molecular tumor boards. Methods In this study, we developed a method to discriminate the enzyme activity among the four series of the ganglioside metabolism pathway by incorporating transcriptome data and topological information of the metabolic network. We introduced three adjustment options for reaction activity scores (RAS) and demonstrated their application in both exploratory and comparative analyses by applying the method on neuroblastic tumors (NTs), encompassing neuroblastoma (NB), ganglioneuroblastoma (GNB), and ganglioneuroma (GN). Furthermore, we interpreted the results in the context of earlier published GSL measurements in the same tumors. Results By adjusting RAS values using a weighting scheme based on network topology and transition probabilities (TPs), the individual series of ganglioside metabolism can be differentiated, enabling a refined analysis of the GSL profile in NT entities. Notably, the adjustment method we propose reveals the differential engagement of the ganglioside series between NB and GNB. Moreover, MYCN gene expression, a well-known prognostic marker in NTs, appears to correlate with the expression of therapeutically relevant gangliosides, such as GD2. Using unsupervised learning, we identified subclusters within NB based on altered GSL metabolism. Conclusion Our study demonstrates the utility of adjusting RAS values in discriminating ganglioside metabolism subtypes, highlighting the potential for identifying novel cancer subgroups based on sphingolipid profiles. These findings contribute to a better understanding of ganglioside dysregulation in NT and may have implications for stratification and targeted therapeutic strategies in these tumors and other tumor entities with a deregulated GSL metabolism.

The Human BioMolecular Atlas Program (HuBMAP) constructs the worldwide available platform to research the human body at the cellular level. The HuBMAP Data Portal encompasses a wide range of data resources measured on emerging experimental technologies at a spatial resolution. To broaden access to the HuBMAP Data Portal, we introduce an R client called HuBMAPR available on Bioconductor. This gives an efficient and programmatic interface, enabling researchers to discover and retrieve HuBMAP data easier and faster. Availability This package is available on GitHub ( https://github.com/christinehou11/HuBMAPR ) and has been submitted to Bioconductor.

The field of single-cell biology is growing rapidly and is generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO, allow researchers to access large volumes of curated datasets. Although the majority of the data is from scRNAseq experiments, a wide range of other modalities are represented as well. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse, and for extracting novel biological insights. Here, we highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome.