Biology subjects

Heinemann, A.

Publications and source records attributed to Heinemann, A..

5 recordsLinked to original sources

bioRxiv2023

Myeloperoxidase promotes a tumorigenic microenvironment in non-small cell lungcancer

Myeloperoxidase (MPO) is a heme peroxidase that is mainly expressed and secreted by neutrophils. MPOs role in inflammatory diseases has been highlighted in recent years, but its role in tumor development remains unclear. Therefore, we investigated the role of MPO in non-small cell lung cancer (NSCLC). In silico analysis revealed a survival benefit in patients with NSCLC and low MPO expression. Furthermore, a syngeneic tumor model using MPO knockout (KO) mice revealed that mice lacking MPO had lower tumor growth than controls. The reduction in tumor size was accompanied by an increase in lymphoid populations, including natural killer cells and CD8+ T cells, suggesting a shift to a more anti-tumorigenic immune environment in MPO-KO mouse tumors. The T cell induced interferon-gamma (IFN-{gamma}) expression was increased in MPO-KO tumors, indicating increased tumoricidal activity. CD8 depletion abolished the previously observed reduction in tumor size in MPO-KO mice, indicating that CD8+ T cells play an important role. In vitro, T cells treated with MPO showed reduced proliferation and IFN-{gamma} expression. Furthermore, MPO could be internalized into T cells. Heparin pretreatment of T cells blocked MPO binding and internalization into T cells and reversed MPO-induced proliferation reduction. Interestingly, MPO+ lymphocytes were found in tumor samples from patients with NSCLC. Our findings suggest that MPO plays an immunosuppressive role in NSCLC. One Sentence SummaryHigh myeloperoxidase (MPO) expression in non-small cell lung cancer patients is a predictor for adverse outcome and mice lacking MPO showed enhanced anti-tumorigenic leukocyte infiltration, suggesting a pro-tumorigenic role of MPO.

cancer biology↗

bioRxiv2023

Myeloperoxidase enhances the migration of human choriocarcinoma JEG-3 cells

Myeloperoxidase (MPO) is one of the most abundant proteins in neutrophil granules. It catalyzes the production of reactive oxygen species, which are important in inflammation and immune defense. MPO also binds to several proteins, lipids, and DNA to alter their function. MPO is present at the feto-maternal interface during pregnancy, where neutrophils are abundant. In this study, we determined the effect of MPO on JEG-3 human choriocarcinoma cells as a model of extravillous trophoblasts (EVTs) during early pregnancy. We found that MPO was internalized by JEG-3 cells and localized to the cytoplasm and nuclei. MPO internalization and activity enhanced JEG-3 cell migration, whereas this effect was impaired by pre-treating cells with heparin, to block cellular uptake, and MPO-activity inhibitor 4-ABAH. This study identifies a novel mechanism for the effect of MPO on EVT function during normal pregnancy and suggests a potential role of MPO in abnormal pregnancies. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/526039v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@5fd67borg.highwire.dtl.DTLVardef@1fa4f7dorg.highwire.dtl.DTLVardef@78a0c3org.highwire.dtl.DTLVardef@1828760_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Gestational diabetes mellitus dysregulates the PD-1/PD-L1 axis at the feto-maternal interface

The most common pregnancy complication is gestational diabetes mellitus (GDM), which is a glucose tolerance disorder. Obesity and older maternal age, which are associated with low-grade systemic inflammation, are the main risk factors for GDM. To evaluate the complexity and differences in the immune landscape at the fetal-maternal interface, we examined the maternally derived tissue, decidua basalis (DB), from healthy women, women with obesity, and women with GDM using flow cytometry, western blot, and gene expression analysis. Our results showed that the immune cell composition of DB is not altered by obesity; however, in GDM pregnancies, the DB displays a dysregulated PD-1/PD-L1 axis and significantly reduced regulatory T cell (Treg) infiltration, suggesting reduced local immunosuppression. Our study provides a detailed picture of the immune landscape at the fetal-maternal interface in normal, obese, and GDM pregnancies. This will aid our understanding of possible dysfunctional immune mechanisms in GDM.

immunology↗

bioRxiv2022

Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil

The sustainability of irrigated rice (Oryza sativa L.) production systems in Brazilian tropical region highly depends on the success of developing stable cultivars. To achieve this goal, many steps in product development must address the environmental variability and genotype by environment interactions (GE), which makes difficult the design and development of local-specific adapted cultivars. Thus, the adoption of new strategies for characterizing environmental-phenotype relations are the key for optimizing this process. In addition, it could also benefit post-breeding stages of seed production. To overcome this situation, we implemented a data-driven approach to link environmental characterization to yield clustering using historical data (1982-2017, 31 locations, 471 genotypes), 42 envirotyping covariables and machine learning (ML), combining two unsupervised (K-means and decision tree models, DTC) algorithms. Additionally, linear mixed models (LMM) were applied to explore the relations between the outcomes of our approach and GE analysis for irrigated rice yield in Brazilian tropical region. Four environments were identified: Very Low Yield (1.7 Mg.ha-1), Low Yield (5.1 Mg.ha-1), High Yield (7.2 Mg.ha-1), and Very High Yield (9.0 Mg.ha-1), considering all genotypes and regions. Our approach allows the prediction of environments (yield clusters) for a diverse set of growing conditions and revealed geographic and climatic causes of environmental quality, which differ according to each region and genotype group. From the LMM analysis, we found that the current relation between genetics (G), environmental variation (E), and GE for rainfed rice in Brazil is 1:6:2, but when we introduced our data-driven clusters (ME), the ratio decreased to 1:5:1. Consequently, the selection reliability for local adaptability across an extensive region increases. Our approach helps to identify mega-environments in Brazil that could be used as a target population of environments (TPE) of breeding programs. Additionally, it helps to identify more productive and stable seed production fields. HighlightsO_LIA nationwide environmental characterization and its relation to the genotype by environment interaction (GE) for grain yield of rainfed rice growing regions in Brazil. C_LIO_LIA data-driven approach capable to identifying clusters of yield levels and a machine learning approach to relate those clusters with environmental typologies. C_LIO_LIUnrevealed geographic and climatic causes of environmental quality for a group of genotypes or cultivar-specific predictions. C_LIO_LIThe strategy benefits diverse stages of breeding (multiple environmental trial analysis) and post-breeding (selection of fields for seed production) as an alternative approach to reduce costs and support decisions on cultivar planting locations. C_LI

genetics↗

bioRxiv2021

Bone Mineral Density modelled as a Random Field: a Feasibility Study on Pelvic Bone

Background and ObjectiveCapturing the population variability of bone properties is of paramount importance to biomedical engineering. The aim of the present paper is to describe variability and correlations in bone mineral density with a spatial random field inferred from routine computed tomography data. MethodsRandom fields were simulated by transforming pairwise uncorrelated Gaussian random variables into correlated variables through the spectral decomposition of an age-detrended correlation matrix. The validity of the random field model was demonstrated in the spatiotemporal analysis of bone mineral density. The similarity between the computed tomography samples and those generated via random fields was analyzed with the energy distance metric. ResultsThe random field of bone mineral density was found to be approximately Gaussian/slightly left-skewed/strongly right-skewed at various locations. However, average bone density could be simulated well with the proposed Gaussian random field for which the energy distance, i.e., a measure that quantifies discrepancies between two distribution functions, is convergent with respect to the number of correlation eigenpairs. ConclusionsThe proposed random field model allows the enhancement of computational biomechanical models with variability in bone mineral density, which could increase the usability of the model and provides a step forward in in-silico medicine.

biophysics↗