The role of the immune checkpoint molecules PD-1/PD-L1 and TIM-3/Gal-9 in the pathogenesis of preeclampsia—a narrative review

Preeclampsia is a pregnancy-specific disease which is characterized by abnormal placentation, endothelial dysfunction, and systemic inflammation. Several studies have shown that the maternal immune system, which is crucial for maintaining the pregnancy by ensuring maternal-fetal-tolerance, is disrupted in preeclamptic patients. Besides different immune cells, immune checkpoint molecules such as the programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1 system) and the T-cell immunoglobulin and mucin domain-containing protein 3/Galectin-9 (TIM-3/Gal-9 system) are key players in upholding the balance between pro-inflammatory and anti-inflammatory signals. Therefore, a clear understanding about the role of these immune checkpoint molecules in preeclampsia is essential. This review discusses the role of these two immune checkpoint systems in pregnancy and their alterations in preeclampsia

Prion Protein in Milk

BACKGROUND: Prions are known to cause transmissible spongiform encephalopathies (TSE) after accumulation in the central nervous system. There is increasing evidence that prions are also present in body fluids and that prion infection by blood transmission is possible. The low concentration of the proteinaceous agent in body fluids and its long incubation time complicate epidemiologic analysis and estimation of spreading and thus the risk of human infection. This situation is particularly unsatisfactory for food and pharmaceutical industries, given the lack of sensitive tools for monitoring the infectious agent. METHODOLOGY/PRINCIPAL FINDINGS: We have developed an adsorption matrix, Alicon PrioTrap®, which binds with high affinity and specificity to prion proteins. Thus we were able to identify prion protein (PrP(C))–the precursor of prions (PrP(Sc))–in milk from humans, cows, sheep, and goats. The absolute amount of PrP(C) differs between the species (from µg/l range in sheep to ng/l range in human milk). PrP(C) is also found in homogenised and pasteurised off-the-shelf milk, and even ultrahigh temperature treatment only partially diminishes endogenous PrP(C) concentration. CONCLUSIONS/SIGNIFICANCE: In view of a recent study showing evidence of prion replication occurring in the mammary gland of scrapie infected sheep suffering from mastitis, the appearance of PrP(C) in milk implies the possibility that milk of TSE-infected animals serves as source for PrP(Sc)

Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor

Genomic resources for wild populations of the house mouse, Mus musculus and its close relative Mus spretus

WOS: 000390231600001PubMed ID: 27622383Wild populations of the house mouse (Mus musculus) represent the raw genetic material for the classical inbred strains in biomedical research and are a major model system for evolutionary biology. We provide whole genome sequencing data of individuals representing natural populations of M. m. domesticus (24 individuals from 3 populations), M. m. helgolandicus (3 individuals), M. m. musculus (22 individuals from 3 populations) and M. spretus (8 individuals from one population). We use a single pipeline to map and call variants for these individuals and also include 10 additional individuals of M. m. castaneus for which genomic data are publically available. In addition, RNAseq data were obtained from 10 tissues of up to eight adult individuals from each of the three M. m. domesticus populations for which genomic data were collected. Data and analyses are presented via tracks viewable in the UCSC or IGV genome browsers. We also provide information on available outbred stocks and instructions on how to keep them in the laboratory.Max-Planck Society; DFG [HA 3139/4-1]; ERC [322564]; contract-research-project for the Bundeswehr Medical Service [M/SABX/005]This work was mostly financed by institutional resources of the Max-Planck Society, a DFG grant to B.H. and M.T. (HA 3139/4-1) and an ERC grant to D.T. (NewGenes, 322564). We thank Sonja Ihle, Susanne Krochter, Ruth Rottscheidt for contributing to collecting animals in the wild and our animal care takers for active involvement of optimizing the scheme for wild mouse keeping. The initial analysis of mice from Afghanistan was funded by contract-research-project for the Bundeswehr Medical Service M/SABX/005. We thank Bastian Pfeifer for help with software package PopGenome, Leslie Turner for discussion and Daniel M. Hooper and Trevor Price for helpful comments on the manuscript. D.T. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis

Long-read sequencing identifies a common transposition haplotype predisposing for CLCNKB deletions

BACKGROUND: Long-read sequencing is increasingly used to uncover structural variants in the human genome, both functionally neutral and deleterious. Structural variants occur more frequently in regions with a high homology or repetitive segments, and one rearrangement may predispose to additional events. Bartter syndrome type 3 (BS 3) is a monogenic tubulopathy caused by deleterious variants in the chloride channel gene CLCNKB, a high proportion of these being large gene deletions. Multiplex ligation-dependent probe amplification, the current diagnostic gold standard for this type of mutation, will indicate a simple homozygous gene deletion in biallelic deletion carriers. However, since the phenotypic spectrum of BS 3 is broad even among biallelic deletion carriers, we undertook a more detailed analysis of precise breakpoint regions and genomic structure. METHODS: Structural variants in 32 BS 3 patients from 29 families and one BS4b patient with CLCNKB deletions were investigated using long-read and synthetic long-read sequencing, as well as targeted long-read sequencing approaches. RESULTS: We report a ~3 kb duplication of 3'-UTR CLCNKB material transposed to the corresponding locus of the neighbouring CLCNKA gene, also found on ~50 % of alleles in healthy control individuals. This previously unknown common haplotype is significantly enriched in our cohort of patients with CLCNKB deletions (45 of 51 alleles with haplotype information, 2.2 kb and 3.0 kb transposition taken together, p=9.16×10-9). Breakpoint coordinates for the CLCNKB deletion were identifiable in 28 patients, with three being compound heterozygous. In total, eight different alleles were found, one of them a complex rearrangement with three breakpoint regions. Two patients had different CLCNKA/CLCNKB hybrid genes encoding a predicted CLCNKA/CLCNKB hybrid protein with likely residual function. CONCLUSIONS: The presence of multiple different deletion alleles in our cohort suggests that large CLCNKB gene deletions originated from many independently recurring genomic events clustered in a few hot spots. The uncovered associated sequence transposition haplotype apparently predisposes to these additional events. The spectrum of CLCNKB deletion alleles is broader than expected and likely still incomplete, but represents an obvious candidate for future genotype/phenotype association studies. We suggest a sensitive and cost-efficient approach, consisting of indirect sequence capture and long-read sequencing, to analyse disease-relevant structural variant hotspots in general

Micro-RNA networks in T-cell prolymphocytic leukemia reflect T-cell activation and shape DNA damage response and survival pathways

T-cell prolymphocytic leukemia (T-PLL) is a poor-prognostic mature T-cell malignancy. It typically presents with exponentially rising lymphocyte counts, splenomegaly, and bone marrow infiltration. Effective treatment options are scarce and a better understanding of T-PLL's pathogenesis is desirable. Activation of the TCL1 proto-oncogene and loss-of-function perturbations of the tumor suppressor ATM are T-PLL's genomic hallmarks. The leukemic cell reveals a phenotype of active T-cell receptor (TCR) signaling and aberrant DNA-damage responses. Regulatory networks based on the profile of micro-RNAs (miRs) have not been described for T-PLL. In a combined approach of small-RNA and transcriptome sequencing in 46 clinically and moleculary well-characterized T-PLL, we identified a global T-PLL-specific miR expression profile that involves 34 significantly deregulated miR species. This pattern strikingly resembled miR-ome signatures of TCR-activated T-cells. By integrating these T-PLL miR profiles with transcriptome data, we uncovered regulatory networks associated with cell survival signaling and DNA-damage response pathways. Despite a miR-ome that discerned leukemic from normal T-cells, there were also robust subsets of T-PLL defined by a small set of specific miRs. Most prominently, miR-141 and the miR-200c-cluster separated cases into two major subgroups. Furthermore, increased expression of miR-223-3p as well as reduced expression of miR-21 and the miR-29 cluster were associated with more activated T-cell phenotypes and more aggressive disease presentations. Based on the implicated pathobiological role of these miR deregulations, targeting strategies around their effectors appear worth pursuing. We also established a combinatorial miR-based overall survival score for T-PLL (miROS-T-PLL), that might improve current clinical stratifications

In vivo migration of labeled autologous natural killer cells to liver metastases in patients with colon carcinoma

BACKGROUND: Besides being the effectors of native anti-tumor cytotoxicity, NK cells participate in T-lymphocyte responses by promoting the maturation of dendritic cells (DC). Adherent NK (A-NK) cells constitute a subset of IL-2-stimulated NK cells which show increased expression of integrins and the ability to adhere to solid surface and to migrate, infiltrate, and destroy cancer. A critical issue in therapy of metastatic disease is the optimization of NK cell migration to tumor tissues and their persistence therein. This study compares localization to liver metastases of autologous A-NK cells administered via the systemic (intravenous, i.v.) versus locoregional (intraarterial, i.a.) routes. PATIENTS AND METHODS: A-NK cells expanded ex-vivo with IL-2 and labeled with (111)In-oxine were injected i.a. in the liver of three colon carcinoma patients. After 30 days, each patient had a new preparation of (111)In-A-NK cells injected i.v. Migration of these cells to various organs was evaluated by SPET and their differential localization to normal and neoplastic liver was demonstrated after i.v. injection of (99m)Tc-phytate. RESULTS: A-NK cells expressed a donor-dependent CD56(+)CD16(+)CD3(- )(NK) or CD56(+)CD16(+)CD3(+ )(NKT) phenotype. When injected i.v., these cells localized to the lung before being visible in the spleen and liver. By contrast, localization of i.a. injected A-NK cells was virtually confined to the spleen and liver. Binding of A-NK cells to liver neoplastic tissues was observed only after i.a. injections. CONCLUSION: This unique study design demonstrates that A-NK cells adoptively transferred to the liver via the intraarterial route have preferential access and substantial accumulation to the tumor site

Cell type-specific transcriptomics of esophageal adenocarcinoma as a scalable alternative for single cell transcriptomics

Single-cell transcriptomics have revolutionized our understanding of the cell composition of tumors and allowed us to identify new subtypes of cells. Despite rapid technological advancements, single-cell analysis remains resource-intense hampering the scalability that is required to profile a sufficient number of samples for clinical associations. Therefore, more scalable approaches are needed to understand the contribution of individual cell types to the development and treatment response of solid tumors such as esophageal adenocarcinoma where comprehensive genomic studies have only led to a small number of targeted therapies. Due to the limited treatment options and late diagnosis, esophageal adenocarcinoma has a poor prognosis. Understanding the interaction between and dysfunction of individual cell populations provides an opportunity for the development of new interventions. In an attempt to address the technological and clinical needs, we developed a protocol for the separation of esophageal carcinoma tissue into leukocytes (CD45+), epithelial cells (EpCAM+), and fibroblasts (two out of PDGFRα, CD90, anti-fibroblast) by fluorescence-activated cell sorting and subsequent RNA sequencing. We confirm successful separation of the three cell populations by mapping their transcriptomic profiles to reference cell lineage expression data. Gene-level analysis further supports the isolation of individual cell populations with high expression of CD3, CD4, CD8, CD19, and CD20 for leukocytes, CDH1 and MUC1 for epithelial cells, and FAP, SMA, COL1A1, and COL3A1 for fibroblasts. As a proof of concept, we profiled tumor samples of nine patients and explored expression differences in the three cell populations between tumor and normal tissue. Interestingly, we found that angiogenesis-related genes were upregulated in fibroblasts isolated from tumors compared with normal tissue. Overall, we suggest our protocol as a complementary and more scalable approach compared with single-cell RNA sequencing to investigate associations between clinical parameters and transcriptomic alterations of specific cell populations in esophageal adenocarcinoma

Cell cycle regulation in hematopoietic stem cells

Hematopoietic stem cells (HSCs) give rise to all lineages of blood cells. Because HSCs must persist for a lifetime, the balance between their proliferation and quiescence is carefully regulated to ensure blood homeostasis while limiting cellular damage. Cell cycle regulation therefore plays a critical role in controlling HSC function during both fetal life and in the adult. The cell cycle activity of HSCs is carefully modulated by a complex interplay between cell-intrinsic mechanisms and cell-extrinsic factors produced by the microenvironment. This fine-tuned regulatory network may become altered with age, leading to aberrant HSC cell cycle regulation, degraded HSC function, and hematological malignancy