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Franke, V.

Publications and source records attributed to Franke, V..

At least 19 recordsLinked to original sources

Supporting-like cells constitute an alternative steroidogenic lineage conserved in amniotes

In mammals, the production of sex hormones is widely considered to depend on the interstitial lineage of the gonad, which differentiates into Leydig cells in males or theca cells in females. However, certain mammalian species evidence gonadal steroidogenic activity prior to the specialization of these interstitial lineages, suggesting that alternative cell types may assume this function. Here we reveal a previously unrecognized role for supporting-like cells (SLCs), which can act as a major steroidogenic lineage during mammalian embryonic development. Through comparative single-cell transcriptomics, steroidomics and in toto organ imaging we find that in rabbits, SLCs not only contribute to the formation of gonadal rete structures, as described for other mammals, but also differentiate into a steroid-producing population. The steroidogenic program of SLCs is initially activated in both sexes but selectively maintained in ovaries, whereas in testes it is progressively replaced by that of interstitially derived Leydig cells. Evolutionary comparisons indicate that SLCs may represent an ancestral lineage that is homologous to the steroidogenic cells of non-mammalian species, which also derive from supporting precursors and share expression of cell fate regulators such as PAX2/8 and TBX1. Altogether, our findings redefine current models of gonadal lineages, revealing an unexpected plasticity in sex differentiation and exemplifying how distinct cell types can converge on analogous functions during evolution.

developmental biology↗

Local translational program at the muscle-tendon junction endows domain identity in muscle syncytia

How cells establish specialized subdomains is a fundamental question in cell and tissue biology. Skeletal muscle fibers, among the largest cells in the body, are multinucleated and form distinct regions such as the neuromuscular and myotendinous junctions (MTJ), the latter forming a critical interface between muscle and tendon that transmits contractile force. While transcriptional heterogeneity among myonuclei has been described, whether local translation contributes to domain identity remains unknown, largely due to the lack of tools for domain-specific manipulation. Here, we introduce MTJ-AAV, a viral system that enables selective genetic targeting of MTJ myonuclei. This approach allowed MTJ-specific ribosome tagging and revealed extensive translational regulation underlying MTJ biology and its remodeling during exercise. Interestingly, untranslated regions of these transcripts were sufficient to control regionalized translation. Notably, the KLF-family transcription factors emerged as translationally upregulated targets at the MTJ, where they drive local gene expression. Our findings establish local translation as a key layer of subcellular specialization and provide a versatile toolkit for dissecting spatial molecular regulation within muscle syncytia.

cell biology↗

Flexynesis: A deep learning framework for bulk multi-omics data integration for precision oncology and beyond

Accurate decision making in precision oncology depends on integration of multimodal molecular information, such as the genetic data, gene expression, protein abundance, and epigenetic measurements. Deep learning methods facilitate integration of heterogeneous datasets. However, almost all published deep learning-based bulk multi-omics integration methods have constrained usability. They suffer from lack of transparency, modularity, deployability, and are applicable exclusively to narrow tasks. To address these limitations, we introduce Flexynesis, a versatile tool designed with usability, and adaptability in mind. Flexynesis streamlines data processing, enforces structured data splitting, and ensures rigorous model evaluation. It offers unsupervised feature selection, different omics layer fusion options, and hyperparameter tuning. Users can choose from distinct architectures - fully connected networks, variational autoencoders, multi-triplet networks, graph neural networks, and cross-modality encoding networks. Each model is complemented with a straightforward input interface and standardized training, evaluation, and feature importance quantification methods, enabling easy incorporation into data integration pipelines. For improved user experience, Flexynesis supports features such as on-the-fly task determination and compatibility with regression, classification, and survival modeling. It accommodates multi-task prediction of a mixture of numerical/categorical outcome variables with a tolerance for missing labels. We also developed an extensive benchmarking pipeline, showcasing the tools capability across diverse real-life datasets. This toolset should make deep-learning based bulk multi-omics data integration in the context of clinical/pre-clinical data analysis and marker discovery more accessible to a wider audience with or without experience in deep-learning development. Flexynesis is available at https://github.com/BIMSBbioinfo/flexynesis and can be installed from https://pypi.org/project/flexynesis/. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/603606v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@17eda85org.highwire.dtl.DTLVardef@13c89ecorg.highwire.dtl.DTLVardef@182f706org.highwire.dtl.DTLVardef@127d9d9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Summary of the Flexynesis data integration and analysis workflow. C_FIG

bioinformatics↗

A single dose of cocaine rewires the 3D genome structure of midbrain dopamine neurons

Midbrain dopamine neurons (DNs) respond to a first exposure to addictive drugs and play key roles in chronic drug usage1-3. As the synaptic and transcriptional changes that follow an acute cocaine exposure are mostly resolved within a few days4,5, the molecular changes that encode the long-term cellular memory of the exposure within DNs remain unknown. To investigate whether a single cocaine exposure induces long-term changes in the 3D genome structure of DNs, we applied Genome Architecture Mapping and single nucleus transcriptomic analyses in the mouse midbrain. We found extensive rewiring of 3D genome architecture at 24 hours past exposure which remains or worsens by 14 days, outlasting transcriptional responses. The cocaine-induced chromatin rewiring occurs at all genomic scales and affects genes with major roles in cocaine-induced synaptic changes. A single cocaine exposure triggers extensive long-lasting changes in chromatin condensation in post-synaptic and post-transcriptional regulatory genes, for example the unfolding of Rbfox1 which becomes most prominent 14 days post exposure. Finally, structurally remodeled genes are most expressed in a specific DN sub-type characterized by low expression of the dopamine auto-receptor Drd2, a key feature of highly cocaine-sensitive cells. These results reveal an important role for long-lasting 3D genome remodelling in the cellular memory of a single cocaine exposure, providing new hypotheses for understanding the inception of drug addiction and 3D genome plasticity.

genomics↗

DNA virus infections shape transposable elements activity in vitro and in vivo

Transposable elements (TEs) are implicated in a variety of processes including placental and preimplantation development and a variety of human diseases. TEs are known to be activated in the context of some viral infections, but the mechanisms and consequences are not understood. We show strong activation of TEs upon DNA virus infection, in particular the MLT- and THE1-class of LTR-containing retrotransposons as well as a subset of LINE-1-, Alu-elements and HERVs. Mechanistically, two key pathways induce TEs upregulation: inhibition of the KAP1/TRIM28 repressive complex by phosphorylation, and expression of the pioneer transcription factor double-homeobox 4 (DUX4), which is known to be involved in TE-induction during zygotic genome activation in embryonic development. DUX4 is induced by DNA viruses, it binds to TEs upon infection and analysis of genes adjacent to TEs shows pathways that are important for DNA virus infections. Analysis of knockdown, knockout and overexpression data reveal that almost all TEs expressed upon herpesviral infection are regulated by KAP1/TRIM28 and DUX4. Interestingly, analysis of single cell sequencing data from patients with DNA virus-associated cancers showed that in vivo TEs expression strongly correlates with virus infection, indicating a possible role in viral oncogenesis.

genomics↗

A new type of transcriptional reprogramming by an IRF4 mutation in lymphoma

SUMMARY PARAGRAPHDisease-causing mutations in genes encoding transcription factors (TFs) are a recurrent finding in hematopoietic malignancies and might involve key regulators of lineage adherence and cellular differentiation1-3. Such mutations can affect TF-interactions with their cognate DNA-binding motifs4, 5. Whether and how TF-mutations impact upon the nature of binding to TF composite elements (CE) and influence their interaction with other TFs is unclear. Here, we report a new mechanism of TF alteration in human lymphomas with perturbed B cell identity. It is caused by a recurrent somatic missense mutation c.295T>C (p.Cys99Arg; p.C99R) targeting the center of the DNA-binding domain of Interferon Regulatory Factor 4 (IRF4), a key TF in immune cell-differentiation and -activation6, 7. IRF4-C99R fundamentally alters IRF4 DNA-binding, with loss-of-binding to canonical IRF motifs and neomorphic gain-of-binding to canonical and non-canonical IRF composite elements (CEs). Furthermore, IRF4-C99R thoroughly modifies IRF4 function, by blocking IRF4-dependent plasma cell induction, and up-regulating disease-specific genes in a non-canonical Activator Protein-1 (AP-1)-IRF-CE (AICE)-dependent manner. Our data explain how a single arginine mutation creates a complex switch of TF specificity and gene regulation. These data open the possibility of designing specific inhibitors to block the neomorphic, disease-causing DNA-binding activities of a mutant transcription factor.

cancer biology↗

Multi-omics alleviates the limitations of panel-sequencing for cancer drug response prediction

Comprehensive genomic profiling using cancer gene panels has been shown to improve treatment options for a variety of cancer types. However, genomic aberrations detected via such gene panels dont necessarily serve as strong predictors of drug sensitivity. In this study, using pharmacogenomics datasets of cell lines, patient-derived xenografts, and ex-vivo treated fresh tumor specimens, we demonstrate that utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer.

bioinformatics↗

The SPOC domain is a phosphoserine binding module that bridges transcription machinery with co- and post-transcriptional regulators

The heptarepeats of the C-terminal domain (CTD) of RNA polymerase II (Pol II) are extensively modified throughout the transcription cycle. The CTD coordinates RNA synthesis and processing by recruiting transcription regulation factors as well as RNA capping, splicing and 3end processing factors. The SPOC domain of PHF3 was recently identified as a new CTD reader domain specifically binding to phosphorylated serine-2 residues in adjacent CTD repeats. Here, we establish the SPOC domains of the human proteins DIDO, SHARP and RBM15 as phosphoserine binding modules that can act as CTD readers but also recognize other phosphorylated binding partners. We report the crystal structure of SHARP (SPEN) SPOC-CTD and identify the molecular determinants for its specific binding to phosphorylated serine-5. PHF3 and DIDO SPOC domains preferentially interact with the Pol II elongation complex, while RBM15 and SHARP SPOC domains engage with the m6A writer and reader proteins. Our findings establish the SPOC domain as a major interface between the transcription machinery and regulators of transcription and co-transcriptional processes.

biochemistry↗

Identifying tumor cells at the single cell level

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. The single cell sequencing technology enables an effective molecular characterization of single cells within the tumor. This technology can help deconvolute heterogeneous tumor samples and thus revolutionize personalized medicine. However, a governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. One of the critical cell type annotation challenges is identification of tumor cells within single cell or spatial sequencing experiments.This is a critical limiting step for a multitude of research, clinical, and commercial applications. A reliable method addressing that challenge is a prerequisite for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. Here, we propose Ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single cell level. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

cancer biology↗

Multi-omics and deep learning provide a multifaceted view of cancer

Cancer is a complex disease with a large financial and healthcare burden on society. One hallmark of the disease is the uncontrolled growth and proliferation of malignant cells. Unlike Mendelian diseases which may be explained by a few genomic loci, a deeper molecular and mechanistic understanding of the development of cancer is needed. Such an endeavor requires the integration of tens of thousands of molecular features across multiple layers of information encoded in the cells. In practical terms, this implies integration of multi omics information from the genome, transcriptome, epigenome, proteome, metabolome, and even micro-environmental factors such as the microbiome. Finding mechanistic insights and biomarkers in such a high dimensional space is a challenging task. Therefore, efficient machine learning techniques are needed to reduce the dimensionality of the data while simultaneously discovering complex but meaningful biomarkers. These markers then can lead to testable hypotheses in research and clinical applications. In this study, we applied advanced deep learning methods to uncover multi-omic fingerprints that are associated with a wide range of clinical and molecular features of tumor samples. Using these fingerprints, we can accurately classify different cancer types, and their subtypes. Non-linear multi-omic fingerprints can uncover clinical features associated with patient survival and response to treatment, ranging from chemotherapy to immunotherapy. In addition, multi-omic fingerprints may be deconvoluted into a meaningful subset of genes and genomic alterations to support clinically relevant decisions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/462364v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@4b53d0org.highwire.dtl.DTLVardef@16b9a96org.highwire.dtl.DTLVardef@1720212org.highwire.dtl.DTLVardef@1e5f096_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Herpesviral induction of germline transcription factor DUX4 is critical for viral gene expression

DUX4 is a transcription factor and a master regulator of embryonic genome activation (EGA). During early embryogenesis, EGA is crucial for maternal to zygotic transition at the 8-cell stage in order to overcome silencing of genes and enable transcription from the zygotic genome. In adult somatic cells, DUX4 expression is largely silenced. Activation is likely pathogenic, and in adult muscle cells causes genetic disorder Facioscapulohumeral Muscular Dystrophy (FSHD). We identified activation of DUX4 expression upon lytic replication of the herpesviruses HSV-1, HCMV, EBV and KSHV, but not of adenoviruses, negative strand RNA viruses or positive strand RNA viruses. We demonstrate by RNA-Seq analysis that DUX4 expression upon herpesviral replication leads to the induction of hundreds of DUX4 target genes including germline-specific retroelements as well as several members of the TRIM, PRAMEF and ZSCAN protein families. Moreover, we show that DUX4 expression is a direct consequence of herpesviral infection. DUX4 can be stimulated by overexpression of HSV-1 immediate early proteins, indicating active induction of EGA genes by herpesviral infection. We further show that DUX4 expression is critical for driving HSV-1 gene expression. Our results show that viruses from alpha-, beta- and gamma-herpesvirus subfamilies induce DUX4 expression and downstream germline-specific genes and retroelements. We hypothesize that herpesviruses induce DUX4 expression in order to induce an early embryonic-like transcriptional program that prevents epigenetic silencing of the viral genome and facilitates herpesviral gene expression.

microbiology↗

The SEQC2 Epigenomics Quality Control (EpiQC) Study: Comprehensive Characterization of Epigenetic Methods, Reproducibility, and Quantification

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and a global resource for epigenetics research from the FDAs Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus was on 5-methylcytosine modifications. Each sample was processed in two or more technical replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), one enzymatic deamination method (EMseq), targeted methylation sequencing (Illumina Methyl Capture EPIC), and single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, BWAMeth, and GemBS), we found overall high concordance between assays (R=0.87-R0.93), differences in efficency of read mapping and CpG capture and coverage, and platform performance. The data provided herein can guide continued used of these reference materials in epigenomics assays, as well as provide best practices for epigenomics research and experimental design in future studies.

genomics↗

Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention

The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global health threat with more than two million infected people since its emergence in late 2019. Detailed knowledge of the molecular biology of the infection is indispensable for understanding of the viral replication, host responses, and disease progression. We provide gene expression profiles of SARS-CoV and SARS-CoV-2 infections in three human cell lines (H1299, Caco-2 and Calu-3 cells), using bulk and single-cell transcriptomics. Small RNA profiling showed strong expression of the immunity and inflammation-associated microRNA miRNA-155 upon infection with both viruses. SARS-CoV-2 elicited approximately two-fold higher stimulation of the interferon response compared to SARS-CoV in the permissive human epithelial cell line Calu-3, and induction of cytokines such as CXCL10 or IL6. Single cell RNA sequencing data showed that canonical interferon stimulated genes such as IFIT2 or OAS2 were broadly induced, whereas interferon beta (IFNB1) and lambda (IFNL1-4) were expressed only in a subset of infected cells. In addition, temporal resolution of transcriptional responses suggested interferon regulatory factors (IRFs) activities precede that of nuclear factor-{kappa}B (NF-{kappa}B). Lastly, we identified heat shock protein 90 (HSP90) as a protein relevant for the infection. Inhibition of the HSP90 charperone activity by Tanespimycin/17-N-allylamino-17-demethoxygeldanamycin (17-AAG) resulted in a reduction of viral replication, and of TNF and IL1B mRNA levels. In summary, our study established in vitro cell culture models to study SARS-CoV-2 infection and identified HSP90 protein as potential drug target for therapeutic intervention of SARS-CoV-2 infection.

systems biology↗

Single-nucleus transcriptomics reveals functional compartmentalization in syncytial skeletal muscle cells

Syncytial skeletal muscle cells contain hundreds of nuclei in a shared cytoplasm. We investigated nuclear heterogeneity and transcriptional dynamics in the uninjured and regenerating muscle using single-nucleus RNA-sequencing (snRNAseq) of isolated nuclei from muscle fibers. This revealed distinct nuclear subtypes unrelated to fiber type diversity, completely novel subtypes as well as the expected ones at the neuromuscular and myotendinous junctions. In fibers of the Mdx dystrophy mouse model, new subtypes emerged, among them nuclei expressing a repair signature that were also abundant in the muscle of dystrophy patients, and a nuclear population associated with necrotic fibers. Finally, modifications of our approach revealed the compartmentalization in the rare and specialized muscle spindle. Our data identifies new nuclear compartments of the myofiber and defines a molecular roadmap for their functional analyses; the data can be freely explored on the MyoExplorer server (https://shiny.mdc-berlin.net/MyoExplorer/).

developmental biology↗

Cell-type specialization in the brain is encoded by specific long-range chromatin topologies

Neurons and oligodendrocytes are terminally differentiated cells that sustain cascades of gene activation and repression to execute highly specialized functions, while retaining homeostatic control. To study long-range chromatin folding without disturbing the native tissue environment, we developed Genome Architecture Mapping in combination with immunoselection (immunoGAM), and applied it to three cell types from the adult murine brain: dopaminergic neurons (DNs) from the midbrain, pyramidal glutamatergic neurons (PGNs) from the hippocampus, and oligodendroglia (OLGs) from the cortex. We find cell-type specific 3D chromatin structures that relate with patterns of gene expression at multiple genomic scales, including extensive reorganization of topological domains (TADs) and chromatin compartments. We discover the loss of TAD insulation, or TAD melting, at long genes (>400 kb) when they are highly transcribed. We find many neuron-specific contacts which contain accessible chromatin regions enriched for putative binding sites for multiple neuronal transcription factors, and which connect cell-type specific genes that are associated with neurodegenerative disorders such as Parkinsons disease, or specialized functions such as synaptic plasticity and memory. Lastly, sensory receptor genes exhibit increased membership in heterochromatic compartments that establish strong contacts in brain cells. However, their silencing is compromised in a subpopulation of PGNs with molecular signatures of long-term potentiation. Overall, our work shows that the 3D organization of the genome is highly cell-type specific, and essential to better understand mechanisms of gene regulation in highly specialized tissues such as the brain.

molecular biology↗

PHF3 regulates neuronal gene expression through the new Pol II CTD reader domain SPOC

The C-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) is a regulatory hub for transcription and RNA processing. Here, we identify PHD-finger protein 3 (PHF3) as a new CTD-binding factor that negatively regulates transcription and mRNA stability. The PHF3 SPOC domain preferentially binds to CTD repeats phosphorylated on Serine-2 and PHF3 tracks with Pol II across the length of genes. PHF3 competes with TFIIS for Pol II binding through its TFIIS-like domain (TLD), thus inhibiting TFIIS-mediated rescue of backtracked Pol II. PHF3 knock-out or PHF3 SPOC deletion in human cells result in gene upregulation and a global increase in mRNA stability, with marked derepression of neuronal genes. Key neuronal genes are aberrantly expressed in Phf3 knock-out mouse embryonic stem cells, resulting in impaired neuronal differentiation. Our data suggest that PHF3 is a prominent effector of neuronal gene regulation at the interface of transcription elongation and mRNA decay.

biochemistry↗

Single-cell RNA-sequencing of Herpes simplex virus 1-infected cells identifies NRF2 activation as an antiviral program

Herpesvirus infection initiates a range of perturbations in the host cell, which remain poorly understood at the level of individual cells. Here, we quantified the transcrips of single human primary fibroblasts during the first hours of lytic infection with HSV-1. By applying a generalizable analysis scheme, we defined a precise temporal order of early viral gene expression and found unexpected bifurcations and bottlenecks. We identified individual host cell genes and pathways relevant in early infection by combining three different computational approaches: gene and pathway overdispersion analysis, prediction of cell-state transition probabilities as well as future cell states. One transcriptional program, which was turned on in infected cells and correlated with increased resistance to infection, implicated the transcription factor NRF2. Consequently, Bardoxolone methyl, a known NRF2 agonist, impaired virus production, suggesting that NRF2 activation restricts the progression of viral infection. Our study provides novel insights into early stages of HSV-1 infection and serves as a general blueprint for the investigation of heterogenous cell states in virus infection.

systems biology↗

Reproducible genomics analysis pipelines with GNU Guix

In bioinformatics, as well as other computationally-intensive research fields, there is a need for workflows that can reliably produce consistent output, independent of the software environment or configuration settings of the machine on which they are executed. Indeed, this is essential for controlled comparison between different observations or for the wider dissemination of workflows. Providing this type of reproducibility, however, is often complicated by the need to accommodate the myriad dependencies included in a larger body of software, each of which generally come in various versions. Moreover, in many fields (bioinformatics being a prime example), these versions are subject to continual change due to rapidly evolving technologies, further complicating problems related to reproducibility. Here, we propose a principled approach for building analysis pipelines and managing their dependencies. As a case study to demonstrate the utility of our approach, we present a set of highly reproducible pipelines for the analysis of RNA-seq, ChIP-seq, Bisulfite-seq, and single-cell RNA-seq. All pipelines process raw experimental data, and generate reports containing publication-ready plots and figures, with interactive report elements and standard observables. Users may install these highly reproducible packages and apply them to their own datasets without any special computational expertise beyond the use of the command line. We hope such a toolkit will provide immediate benefit to laboratory workers wishing to process their own data sets or bioinformaticians seeking to automate all, or parts of, their analyses. In the long term, we hope our approach to reproducibility will serve as a blueprint for reproducible workflows in other areas. Our pipelines, along with their corresponding documentation and sample reports, are available at http://bioinformatics.mdc-berlin.de/pigx

bioinformatics↗