Lung endothelium exploits susceptible tumor cell states to instruct metastatic latency.

In metastasis, cancer cells travel around the circulation to colonize distant sites. Due to the rarity of these events, the immediate fates of metastasizing tumor cells (mTCs) are poorly understood while the role of the endothelium as a dissemination interface remains elusive. Using a newly developed combinatorial mTC enrichment approach, we provide a transcriptional blueprint of the early colonization process. Following their arrest at the metastatic site, mTCs were found to either proliferate intravascularly or extravasate, thereby establishing metastatic latency. Endothelial-derived angiocrine Wnt factors drive this bifurcation, instructing mTCs to follow the extravasation-latency route. Surprisingly, mTC responsiveness towards niche-derived Wnt was established at the epigenetic level, which predetermined tumor cell behavior. Whereas hypomethylation enabled high Wnt activity leading to metastatic latency, methylated mTCs exhibited low activity and proliferated intravascularly. Collectively the data identify the predetermined methylation status of disseminated tumor cells as a key regulator of mTC behavior in the metastatic niche.

Endothelial GATA4 controls liver fibrosis and regeneration by preventing a pathogenic switch in angiocrine signaling.

BACKGROUND & AIMS: Angiocrine signaling by liver sinusoidal endothelial cells (LSECs) regulates hepatic functions such as growth, metabolic maturation, and regeneration. Recently, we identified GATA4 as the master regulator of LSEC specification during development. Herein, we studied the role of endothelial GATA4 in the adult liver and in hepatic pathogenesis. METHODS: We generated adult Clec4g-icretg/0xGata4fl/fl (Gata4LSEC-KO) mice with LSEC-specific depletion of Gata4. Livers were analyzed by histology, electron microscopy, immunohistochemistry/immunofluorescence, in situ hybridization, and LSECs were isolated for gene expression profiling, ChIP- and ATAC-sequencing. Partial hepatectomy was performed to assess regeneration. We used choline-deficient, l-amino acid-defined (CDAA) diet and chronic carbon tetrachloride exposure to model liver fibrosis. Human single cell RNA-seq data sets were analyzed for endothelial alterations in healthy and cirrhotic livers. RESULTS: Genetic Gata4 deficiency in LSECs of adult mice caused perisinusoidal liver fibrosis, hepatopathy and impaired liver regeneration. Sinusoidal capillarization and LSEC-to-continuous endothelial transdifferentiation were accompanied by a profibrotic angiocrine switch involving de novo endothelial expression of hepatic stellate cell-activating cytokine PDGFB. Increased chromatin accessibility and amplification by activated MYC mediated angiocrine Pdgfb expression. As observed in Gata4LSEC-KO livers, CDAA diet-induced perisinusoidal liver fibrosis was associated with GATA4 repression, MYC activation and a profibrotic angiocrine switch in LSECs. Comparison of CDAA-fed Gata4LSEC-KO and control mice demonstrated that endothelial GATA4 indeed protects against dietary-induced perisinusoidal liver fibrosis. In human cirrhotic livers, GATA4-positive LSECs and endothelial GATA4 target genes were reduced, while non-LSEC endothelial cells and MYC target genes including PDGFB were enriched. CONCLUSIONS: Endothelial GATA4 protects against perisinusoidal liver fibrosis by repressing MYC activation and profibrotic angiocrine signaling at the chromatin level. Therapies targeting the GATA4/MYC/PDGFB/PDGFRß axis offer a promising strategy for prevention and treatment of liver fibrosis. LAY SUMMARY: The liver vasculature is supposed to play a major role in the development of liver fibrosis and cirrhosis, which can lead to liver failure and liver cancer. Herein, we discovered that structural and transcriptional changes induced by genetic deletion of the transcription factor GATA4 in the hepatic endothelium were sufficient to cause liver fibrosis. Activation of the transcription factor MYC and de novo expression of the "angiocrine" growth factor PDGFB were identified as downstream drivers of fibrosis and as potential therapeutic targets for this potentially fatal disease.

Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels.

Tumour heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is known to affect the efficacy of cancer therapy, most personalized treatment approaches do not account for intratumour heterogeneity. We addressed this issue by studying the heterogeneity of nodal B-cell lymphomas by single-cell RNA-sequencing and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subpopulations and compared their drug-response and genomic profiles. Malignant subpopulations from the same patient responded strikingly differently to anti-cancer drugs ex vivo, which recapitulated subpopulation-specific drug sensitivity during in vivo treatment. Infiltrating T cells represented the majority of non-malignant cells, whose gene-expression signatures were similar across all donors, whereas the frequencies of T-cell subsets varied significantly between the donors. Our data provide insights into the heterogeneity of nodal B-cell lymphomas and highlight the relevance of intratumour heterogeneity for personalized cancer therapy.

On the optimal design of metabolic RNA labeling experiments.

Massively parallel RNA sequencing (RNA-seq) in combination with metabolic labeling has become the de facto standard approach to study alterations in RNA transcription, processing or decay. Regardless of advances in the experimental protocols and techniques, every experimentalist needs to specify the key aspects of experimental design: For example, which protocol should be used (biochemical separation vs. nucleotide conversion) and what is the optimal labeling time? In this work, we provide approximate answers to these questions using the asymptotic theory of optimal design. Specifically, we investigate, how the variance of degradation rate estimates depends on the time and derive the optimal time for any given degradation rate. Subsequently, we show that an increase in sample numbers should be preferred over an increase in sequencing depth. Lastly, we provide some guidance on use cases when laborious biochemical separation outcompetes recent nucleotide conversion based methods (such as SLAMseq) and show, how inefficient conversion influences the precision of estimates. Code and documentation can be found at https://github.com/dieterich-lab/DesignMetabolicRNAlabeling.

circtools-a one-stop software solution for circular RNA research.

MOTIVATION: Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. RESULTS: Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, screening for enrichment of RBP binding sites, differential exon RNase R resistance and circRNA-specific primer design. circtools supports researchers with visualization options and data export into commonly used formats. AVAILABILITY AND IMPLEMENTATION: circtools is available via https://github.com/dieterich-lab/circtools and http://circ.tools under GPLv3.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

pulseR: Versatile computational analysis of RNA turnover from metabolic labeling experiments.

MOTIVATION: Metabolic labelling of RNA is a well-established and powerful method to estimate RNA synthesis and decay rates. The pulseR R package simplifies the analysis of RNA-seq count data that emerge from corresponding pulse-chase experiments. RESULTS: The pulseR package provides a flexible interface and readily accommodates numerous different experimental designs. To our knowledge, it is the first publicly available software solution that models count data with the more appropriate negative-binomial model. Moreover, pulseR handles labelled and unlabelled spike-in sets in its workflow and accounts for potential labeling biases (e.g. number of uridine residues). AVAILABILITY AND IMPLEMENTATION: The pulseR package is freely available at https://github.com/dieterich-lab/pulseR under the GPLv3.0 licence. CONTACT: a.uvarovskii@uni-heidelberg.de or christoph.dieterich@uni-heidelberg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In Vivo Killing Capacity of Cytotoxic T Cells Is Limited and Involves Dynamic Interactions and T Cell Cooperativity.

According to in vitro assays, T cells are thought to kill rapidly and efficiently, but the efficacy and dynamics of cytotoxic T lymphocyte (CTL)-mediated killing of virus-infected cells in vivo remains elusive. We used two-photon microscopy to quantify CTL-mediated killing in mice infected with herpesviruses or poxviruses. On average, one CTL killed 2-16 virus-infected cells per day as determined by real-time imaging and by mathematical modeling. In contrast, upon virus-induced MHC class I downmodulation, CTLs failed to destroy their targets. During killing, CTLs remained migratory and formed motile kinapses rather than static synapses with targets. Viruses encoding the calcium sensor GCaMP6s revealed strong heterogeneity in individual CTL functional capacity. Furthermore, the probability of death of infected cells increased for those contacted by more than two CTLs, indicative of CTL cooperation. Thus, direct visualization of CTLs during killing of virus-infected cells reveals crucial parameters of CD8(+) T cell immunity.

Effects of aging on influenza virus infection dynamics.

UNLABELLED: The consequences of influenza virus infection are generally more severe in individuals over 65 years of age (the elderly). Immunosenescence enhances the susceptibility to viral infections and renders vaccination less effective. Understanding age-related changes in the immune system is crucial in order to design prophylactic and immunomodulatory strategies to reduce morbidity and mortality in the elderly. Here, we propose different mathematical models to provide a quantitative understanding of the immune strategies in the course of influenza virus infection using experimental data from young and aged mice. Simulation results suggested a central role of CD8(+) T cells for adequate viral clearance kinetics in young and aged mice. Adding the removal of infected cells by natural killer cells did not improve the model fit in either young or aged animals. We separately examined the infection-resistant state of cells promoted by the cytokines alpha/beta interferon (IFN-α/ß), IFN-γ, and tumor necrosis factor alpha (TNF-α). The combination of activated CD8(+) T cells with any of the cytokines provided the best fits in young and aged animals. During the first 3 days after infection, the basic reproductive number for aged mice was 1.5-fold lower than that for young mice (P < 0.05). IMPORTANCE: The fits of our models to the experimental data suggest that the increased levels of IFN-α/ß, IFN-γ, and TNF-α (the "inflammaging" state) promote slower viral growth in aged mice, which consequently limits the stimulation of immune cells and contributes to the reported impaired responses in the elderly. A quantitative understanding of influenza virus pathogenesis and its shift in the elderly is the key contribution of this work.