Norfluoxetine Prevents Degeneration of Dopamine Neurons by Inhibiting Microglia-Derived Oxidative Stress in an MPTP Mouse Model of Parkinson's Disease.

Neuroinflammation is the neuropathological feature of Parkinson's disease (PD) and causes microglial activation and activated microglia-derived oxidative stress in the PD patients and PD animal models, resulting in neurodegeneration. The present study examined whether norfluoxetine (a metabolite of fluoxetine) could regulate neuroinflammation in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropypridine (MPTP) mouse model of PD and rescue dopamine neurons. Analysis by tyrosine hydroxylase (TH) immunohistochemistry demonstrated that norfluoxetine prevents degeneration of nigrostriatal dopamine neurons in vivo in MPTP-lesioned mice compared to vehicle-treated MPTP-lesioned control mice. MAC-1 immunostaining and hydroethidine histochemical staining showed that norfluoxetine neuroprotection is accompanied by inhibiting MPTP-induced microglial activation and activated microglia-derived reactive oxygen species production in vivo, respectively. In the separate experiments, treatment with norfluoxetine inhibited NADPH oxidase activation and nitrate production in LPS-treated cortical microglial cultures in vitro. Collectively, these in vivo and in vitro results suggest that norfluoxetine could be employed as a novel therapeutic agent for treating PD, which is associated with neuroinflammation and microglia-derived oxidative stress.

Using single cell sequencing data to model the evolutionary history of a tumor.

BACKGROUND: The introduction of next-generation sequencing (NGS) technology has made it possible to detect genomic alterations within tumor cells on a large scale. However, most applications of NGS show the genetic content of mixtures of cells. Recently developed single cell sequencing technology can identify variation within a single cell. Characterization of multiple samples from a tumor using single cell sequencing can potentially provide information on the evolutionary history of that tumor. This may facilitate understanding how key mutations accumulate and evolve in lineages to form a heterogeneous tumor. RESULTS: We provide a computational method to infer an evolutionary mutation tree based on single cell sequencing data. Our approach differs from traditional phylogenetic tree approaches in that our mutation tree directly describes temporal order relationships among mutation sites. Our method also accommodates sequencing errors. Furthermore, we provide a method for estimating the proportion of time from the earliest mutation event of the sample to the most recent common ancestor of the sample of cells. Finally, we discuss current limitations on modeling with single cell sequencing data and possible improvements under those limitations. CONCLUSIONS: Inferring the temporal ordering of mutational sites using current single cell sequencing data is a challenge. Our proposed method may help elucidate relationships among key mutations and their role in tumor progression.

Overfitting, generalization, and MSE in class probability estimation with high-dimensional data.

Accurate class probability estimation is important for medical decision making but is challenging, particularly when the number of candidate features exceeds the number of cases. Special methods have been developed for nonprobabilistic classification, but relatively little attention has been given to class probability estimation with numerous candidate variables. In this paper, we investigate overfitting in the development of regularized class probability estimators. We investigate the relation between overfitting and accurate class probability estimation in terms of mean square error. Using simulation studies based on real datasets, we found that some degree of overfitting can be desirable for reducing mean square error. We also introduce a mean square error decomposition for class probability estimation that helps clarify the relationship between overfitting and prediction accuracy.

Scalable Fabrication of Quasi-One-Dimensional van der Waals Ta2Pt3Se8 Nanowire Thin Films via Solution Processing for NO2 Gas Sensing over Large Areas.

Solution-based processing of van der Waals (vdW) one- (1D) and two-dimensional (2D) materials is an effective strategy to obtain high-quality molecular chains or atomic sheets in a large area with scalability. In this work, quasi-1D vdW Ta2Pt3Se8 was exfoliated via liquid phase exfoliation (LPE) to produce a stably dispersed Ta2Pt3Se8 nanowire solution. In order to screen the optimal exfoliation solvent, nine different solvents were employed with different total surface tensions and polar/dispersive (P/D) component (P/D) ratios. The LPE behavior of Ta2Pt3Se8 was elucidated by matching the P/D ratios between Ta2Pt3Se8 and the applied solvent, resulting in N-methyl-2-pyrrolidone (NMP) as an optimal solvent owing to the well-matched total surface tension and P/D ratio. Subsequently, Ta2Pt3Se8 nanowire thin films are manufactured via vacuum filtration using a Ta2Pt3Se8/NMP dispersion. Then, gas sensing devices are fabricated onto the Ta2Pt3Se8 nanowire thin films, and gas sensing property toward NO2 is evaluated at various thin-film thicknesses. A 50 nm thick Ta2Pt3Se8 thin-film device exhibited a percent response of 25.9% at room temperature and 32.4% at 100 °C, respectively. In addition, the device showed complete recovery within 14.1 min at room temperature and 3.5 min at 100 °C, respectively.

Probabilistic classifiers with high-dimensional data.

For medical classification problems, it is often desirable to have a probability associated with each class. Probabilistic classifiers have received relatively little attention for small n large p classification problems despite of their importance in medical decision making. In this paper, we introduce 2 criteria for assessment of probabilistic classifiers: well-calibratedness and refinement and develop corresponding evaluation measures. We evaluated several published high-dimensional probabilistic classifiers and developed 2 extensions of the Bayesian compound covariate classifier. Based on simulation studies and analysis of gene expression microarray data, we found that proper probabilistic classification is more difficult than deterministic classification. It is important to ensure that a probabilistic classifier is well calibrated or at least not "anticonservative" using the methods developed here. We provide this evaluation for several probabilistic classifiers and also evaluate their refinement as a function of sample size under weak and strong signal conditions. We also present a cross-validation method for evaluating the calibration and refinement of any probabilistic classifier on any data set.

Multiplex immunofluorescence-guided laser capture microdissection for spatial transcriptomics of metastatic melanoma tissues.

We describe a pipeline for optimized and streamlined multiplexed immunofluorescence-guided laser capture microdissection allowing the harvest of individual cells based on their phenotype and tissue localization for transcriptomic analysis with next-generation RNA sequencing. Here, we analyze transcriptomes of CD3+ T cells, CD14+ monocytes/macrophages, and melanoma cells in non-dissociated metastatic melanoma tissue. While this protocol is described for melanoma tissues, we successfully applied it to human tonsil, skin, and breast cancer tissues as well as mouse lung tissues. For complete details on the use and execution of this protocol, please refer to Martinek et al. (2022).

Transcriptional profiling of macrophages in situ in metastatic melanoma reveals localization-dependent phenotypes and function.

Modulation of immune function at the tumor site could improve patient outcomes. Here, we analyze patient samples of metastatic melanoma, a tumor responsive to T cell-based therapies, and find that tumor-infiltrating T cells are primarily juxtaposed to CD14+ monocytes/macrophages rather than melanoma cells. Using immunofluorescence-guided laser capture microdissection, we analyze transcriptomes of CD3+ T cells, CD14 + monocytes/macrophages, and melanoma cells in non-dissociated tissue. Stromal CD14+ cells display a specific transcriptional signature distinct from CD14+ cells within tumor nests. This signature contains LY75, a gene linked with antigen capture and regulation of tolerance and immunity in dendritic cells (DCs). When applied to TCGA cohorts, this gene set can distinguish patients with significantly prolonged survival in metastatic cutaneous melanoma and other cancers. Thus, the stromal CD14+ cell signature represents a candidate biomarker and suggests that reprogramming of stromal macrophages to acquire DC function may offer a therapeutic opportunity for metastatic cancers.

Spatial clustering of array CGH features in combination with hierarchical multiple testing.

We propose a new approach for clustering DNA features using array CGH data from multiple tumor samples. We distinguish data-collapsing (joining contiguous DNA clones or probes with extremely similar data into regions) from clustering (joining contiguous, correlated regions based on a maximum likelihood principle). The model-based clustering algorithm accounts for the apparent spatial patterns in the data. We evaluate the randomness of the clustering result by a cluster stability score in combination with cross-validation. Moreover, we argue that the clustering really captures spatial genomic dependency by showing that coincidental clustering of independent regions is very unlikely.Using the region and cluster information, we combine testing of these for association with a clinical variable in a hierarchical multiple testing approach. This allows for interpreting the significance of both regions and clusters while controlling the Family-Wise Error Rate simultaneously. We prove that in the context of permutation tests and permutation-invariant clusters it is allowed to perform clustering and testing on the same data set. Our procedures are illustrated on two cancer data sets.

Human KIT+ myeloid cells facilitate visceral metastasis by melanoma.

Metastasis of melanoma significantly worsens prognosis; thus, therapeutic interventions that prevent metastasis could improve patient outcomes. Here, we show using humanized mice that colonization of distant visceral organs with melanoma is dependent upon a human CD33+CD11b+CD117+ progenitor cell subset comprising <4% of the human CD45+ leukocytes. Metastatic tumor-infiltrating CD33+ cells from patients and humanized (h)NSG-SGM3 mice showed converging transcriptional profiles. Single-cell RNA-seq analysis identified a gene signature of a KIT/CD117-expressing CD33+ subset that correlated with decreased overall survival in a TCGA melanoma cohort. Thus, human CD33+CD11b+CD117+ myeloid cells represent a novel candidate biomarker as well as a therapeutic target for metastatic melanoma.

Modeling performance of sample collection sites using whole exome sequencing metrics.

Although next-generation sequencing assays are routinely carried out using samples from cancer trials, the sequencing data are not always of the required quality. There is a need to evaluate the performance of tissue collection sites and provide feedback about the quality of next-generation sequencing data. This study used a modeling approach based on whole exome sequencing quality control (QC) metrics to evaluate the relative performance of sites participating in the Bristol Myers Squibb Immuno-Oncology clinical trials sample collection. We identified several events for the sample swap. Overall, most sites performed well and few showed poor performance. These findings can increase awareness of sample failure and improve the quality of samples.

Effects of dependence in high-dimensional multiple testing problems.

BACKGROUND: We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices. RESULTS: We study the robustness against dependence of several FDR procedures that are popular in microarray studies, such as Benjamin-Hochberg FDR, Storey's q-value, SAM and resampling based FDR procedures. False Non-discovery Rates and estimates of the number of null hypotheses are computed from those methods and compared. Our simulation study shows that methods such as SAM and the q-value do not adequately control the FDR to the level claimed under dependence conditions. On the other hand, the adaptive Benjamini-Hochberg procedure seems to be most robust while remaining conservative. Finally, the estimates of the number of true null hypotheses under various dependence conditions are variable. CONCLUSION: We discuss a new method for efficient guided simulation of dependent data, which satisfy imposed network constraints as conditional independence structures. Our simulation set-up allows for a structural study of the effect of dependencies on multiple testing criterions and is useful for testing a potentially new method on pi0 or FDR estimation in a dependency context.

CGHcall: calling aberrations for array CGH tumor profiles.

UNLABELLED: CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint information from segmentation and by inclusion of several biological concepts that are ignored by existing algorithms. The algorithm is validated for simulated and verified real array CGH data. By incorporating more than three classes, CGHcall improves detection of single copy gains and amplifications. Moreover, it allows effective inclusion of chromosome arm information. AVAILABILITY: An R-package (GUI), a manual and an example data set are available at http://www.few.vu.nl/~mavdwiel/CGHcall.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A pan-cancer analysis of driver gene mutations, DNA methylation and gene expressions reveals that chromatin remodeling is a major mechanism inducing global changes in cancer epigenomes.

BACKGROUND: Recent large-scale cancer sequencing studies have discovered many novel cancer driver genes (CDGs) in human cancers. Some studies also suggest that CDG mutations contribute to cancer-associated epigenomic and transcriptomic alterations across many cancer types. Here we aim to improve our understanding of the connections between CDG mutations and altered cancer cell epigenomes and transcriptomes on pan-cancer level and how these connections contribute to the known association between epigenome and transcriptome. METHOD: Using multi-omics data including somatic mutation, DNA methylation, and gene expression data of 20 cancer types from The Cancer Genome Atlas (TCGA) project, we conducted a pan-cancer analysis to identify CDGs, when mutated, have strong associations with genome-wide methylation or expression changes across cancer types, which we refer as methylation driver genes (MDGs) or expression driver genes (EDGs), respectively. RESULTS: We identified 32 MDGs, among which, eight are known chromatin modification or remodeling genes. Many of the remaining 24 MDGs are connected to chromatin regulators through either regulating their transcription or physically interacting with them as potential co-factors. We identified 29 EDGs, 26 of which are also MDGs. Further investigation on target genes' promoters methylation and expression alteration patterns of these 26 overlapping driver genes shows that hyper-methylation of target genes' promoters are significantly associated with down-regulation of the same target genes and hypo-methylation of target genes' promoters are significantly associated with up-regulation of the same target genes. CONCLUSION: This finding suggests a pivotal role for genetically driven changes in chromatin remodeling in shaping DNA methylation and gene expression patterns during tumor development.

The Memory-Enhancing Effects of Liquiritigenin by Activation of NMDA Receptors and the CREB Signaling Pathway in Mice.

Liquiritigenin (LQ) is a flavonoid that can be isolated from Glycyrrhiza radix. It is frequently used as a tranditional oriental medicine herbal treatment for swelling and injury and for detoxification. However, the effects of LQ on cognitive function have not been fully explored. In this study, we evaluated the memory-enhancing effects of LQ and the underlying mechanisms with a focus on the N-methyl-D-aspartic acid receptor (NMDAR) in mice. Learning and memory ability were evaluated with the Y-maze and passive avoidance tests following administration of LQ. In addition, the expression of NMDAR subunits 1, 2A, and 2B; postsynaptic density-95 (PSD-95); phosphorylation of Ca2+/calmodulin-dependent protein kinase II (CaMKII); phosphorylation of extracellular signal-regulated kinase 1/2 (ERK 1/2); and phosphorylation of cAMP response element binding (CREB) proteins were examined by Western blot. In vivo, we found that treatment with LQ significantly improved memory performance in both behavioral tests. In vitro, LQ significantly increased NMDARs in the hippocampus. Furthermore, LQ significantly increased PSD-95 expression as well as CaMKII, ERK, and CREB phosphorylation in the hippocampus. Taken together, our results suggest that LQ has cognition enhancing activities and that these effects are mediated, in part, by activation of the NMDAR and CREB signaling pathways.

IL1 Receptor Antagonist Controls Transcriptional Signature of Inflammation in Patients with Metastatic Breast Cancer.

Inflammation affects tumor immune surveillance and resistance to therapy. Here, we show that production of IL1ß in primary breast cancer tumors is linked with advanced disease and originates from tumor-infiltrating CD11c+ myeloid cells. IL1ß production is triggered by cancer cell membrane-derived TGFß. Neutralizing TGFß or IL1 receptor prevents breast cancer progression in humanized mouse model. Patients with metastatic HER2- breast cancer display a transcriptional signature of inflammation in the blood leukocytes, which is attenuated after IL1 blockade. When present in primary breast cancer tumors, this signature discriminates patients with poor clinical outcomes in two independent public datasets (TCGA and METABRIC).Significance: IL1ß orchestrates tumor-promoting inflammation in breast cancer and can be targeted in patients using an IL1 receptor antagonist. Cancer Res; 78(18); 5243-58. ©2018 AACRSee related commentary by Dinarello, p. 5200.

Memory-improving effect of formulation-MSS by activation of hippocampal MAPK/ERK signaling pathway in rats.

MSS, a comprising mixture of maesil (Prunus mume Sieb. et Zucc) concentrate, disodium succinate and Span80 (3.6:4.6 :1 ratio) showed a significant improvement of memory when daily administered (460 mg/kg day, p.o.) into the normal rats for 3 weeks. During the spatial learning of 4 days in Morris water maze test, both working memory and short-term working memory index were significantly increased when compared to untreated controls. We investigated a molecular signal transduction mechanism of MSS on the behaviors of spatial learning and memory. MSS treatment increased hippocampal mRNA levels of NR2B and TrkB without changes of NR1, NR2A, ERK1, ERK2 and CREB. However, the protein levels of pERK/ERK and pCREB/CREB were all significantly increased to 1.5+/-0.17 times. These results suggest that the improving effect of spatial memory for MSS is linked to MAPK/ERK signaling pathway that ends up in the phosphorylation of CREB through TrkB and/or NR2B of NMDA receptor.

Estimating the false discovery rate using nonparametric deconvolution.

Given a set of microarray data, the problem is to detect differentially expressed genes, using a false discovery rate (FDR) criterion. As opposed to common procedures in the literature, we do not base the selection criterion on statistical significance only, but also on the effect size. Therefore, we select only those genes that are significantly more differentially expressed than some f-fold (e.g., f = 2). This corresponds to use of an interval null domain for the effect size. Based on a simple error model, we discuss a naive estimator for the FDR, interpreted as the probability that the parameter of interest lies in the null-domain (e.g., mu < log(2)(2) = 1) given that the test statistic exceeds a threshold. We improve the naive estimator by using deconvolution. That is, the density of the parameter of interest is recovered from the data. We study performance of the methods using simulations and real data.