128 research outputs found

    Imaging Immune and Metabolic Cells of Visceral Adipose Tissues with Multimodal Nonlinear Optical Microscopy

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    Visceral adipose tissue (VAT) inflammation is recognized as a mechanism by which obesity is associated with metabolic diseases. The communication between adipose tissue macrophages (ATMs) and adipocytes is important to understanding the interaction between immunity and energy metabolism and its roles in obesity-induced diseases. Yet visualizing adipocytes and macrophages in complex tissues is challenging to standard imaging methods. Here, we describe the use of a multimodal nonlinear optical (NLO) microscope to characterize the composition of VATs of lean and obese mice including adipocytes, macrophages, and collagen fibrils in a label-free manner. We show that lipid metabolism processes such as lipid droplet formation, lipid droplet microvesiculation, and free fatty acids trafficking can be dynamically monitored in macrophages and adipocytes. With its versatility, NLO microscopy should be a powerful imaging tool to complement molecular characterization of the immunity-metabolism interface

    Influence of Market Type and Time of Purchase on Bacterial Counts and Salmonella and Listeria Prevalence in Whole Chickens in Vietnam

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    The objective of the current study was to determine the influence of market type and sampling time on Salmonella and Listeria prevalence and bacterial counts of 180 whole chicken carcasses collected from 6 supermarkets (SM), 6 indoor markets (IM), and 6 open markets (OM) in Vietnam, at opening (T0) and 4 h after the opening (T4). Salmonella and Listeria prevalence was at least 25.6% and 42.7%, respectively. Whole birds in IM had greater Salmonella prevalence than birds from both SM and OM by 28.4% and 23.0% (P = 0.006 and 0.022, respectively). Listeria prevalence was lower in whole chickens from SM, at 56.6%, than those in IM and OM (78.6% and 73.2%, P = 0.024 and 0.089, respectively). Whole chicken carcasses had more than 10.1, 7.5, and 9.4 log colony-forming units (CFU)/g of aerobic bacteria, Escherichia coli (E. coli), and coliforms, respectively. Both E. coli and coliform counts were greater in IM than in SM (P = 0.002 and 0.006). However, only E. coli counts differed between SM (7.7 log CFU/g) and OM (8.3 log CFU/g; P = 0.024). These results highlighted high levels of bacteria and high prevalence of Salmonella and Listeria in whole chickens in retail establishments in Vietnam, posing potential food safety and public health risks

    Neuroinflammation, Mast Cells, and Glia: Dangerous Liaisons

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    The perspective of neuroinflammation as an epiphenomenon following neuron damage is being replaced by the awareness of glia and their importance in neural functions and disorders. Systemic inflammation generates signals that communicate with the brain and leads to changes in metabolism and behavior, with microglia assuming a pro-inflammatory phenotype. Identification of potential peripheral-to-central cellular links is thus a critical step in designing effective therapeutics. Mast cells may fulfill such a role. These resident immune cells are found close to and within peripheral nerves and in brain parenchyma/meninges, where they exercise a key role in orchestrating the inflammatory process from initiation through chronic activation. Mast cells and glia engage in crosstalk that contributes to accelerate disease progression; such interactions become exaggerated with aging and increased cell sensitivity to stress. Emerging evidence for oligodendrocytes, independent of myelin and support of axonal integrity, points to their having strong immune functions, innate immune receptor expression, and production/response to chemokines and cytokines that modulate immune responses in the central nervous system while engaging in crosstalk with microglia and astrocytes. In this review, we summarize the findings related to our understanding of the biology and cellular signaling mechanisms of neuroinflammation, with emphasis on mast cell-glia interactions

    The cardiomyocyte disrupts pyrimidine biosynthesis in non-myocytes to regulate heart repair

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    Various populations of cells are recruited to the heart after cardiac injury, but little is known about whether cardiomyocytes directly regulate heart repair. Using a murine model of ischemic cardiac injury, we demonstrate that cardiomyocytes play a pivotal role in heart repair by regulating nucleotide metabolism and fates of nonmyocytes. Cardiac injury induced the expression of the ectonucleotidase ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), which hydrolyzes extracellular ATP to form AMP. In response to AMP, cardiomyocytes released adenine and specific ribonucleosides that disrupted pyrimidine biosynthesis at the orotidine monophosphate (OMP) synthesis step and induced genotoxic stress and p53-mediated cell death of cycling nonmyocytes. As nonmyocytes are critical for heart repair, we showed that rescue of pyrimidine biosynthesis by administration of uridine or by genetic targeting of the ENPP1/AMP pathway enhanced repair after cardiac injury. We identified ENPP1 inhibitors using small molecule screening and showed that systemic administration of an ENPP1 inhibitor after heart injury rescued pyrimidine biosynthesis in nonmyocyte cells and augmented cardiac repair and postinfarct heart function. These observations demonstrate that the cardiac muscle cell regulates pyrimidine metabolism in nonmuscle cells by releasing adenine and specific nucleosides after heart injury and provide insight into how intercellular regulation of pyrimidine biosynthesis can be targeted and monitored for augmenting tissue repair

    Co-targeting of convergent nucleotide biosynthetic pathways for leukemia eradication

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    Pharmacological targeting of metabolic processes in cancer must overcome redundancy in biosynthetic pathways. Deoxycytidine (dC) triphosphate (dCTP) can be produced both by the de novo pathway (DNP) and by the nucleoside salvage pathway (NSP). However, the role of the NSP in dCTP production and DNA synthesis in cancer cells is currently not well understood. We show that acute lymphoblastic leukemia (ALL) cells avoid lethal replication stress after thymidine (dT)-induced inhibition of DNP dCTP synthesis by switching to NSP-mediated dCTP production. The metabolic switch in dCTP production triggered by DNP inhibition is accompanied by NSP up-regulation and can be prevented using DI-39, a new high-affinity small-molecule inhibitor of the NSP rate-limiting enzyme dC kinase (dCK). Positron emission tomography (PET) imaging was useful for following both the duration and degree of dCK inhibition by DI-39 treatment in vivo, thus providing a companion pharmacodynamic biomarker. Pharmacological co-targeting of the DNP with dT and the NSP with DI-39 was efficacious against ALL models in mice, without detectable host toxicity. These findings advance our understanding of nucleotide metabolism in leukemic cells, and identify dCTP biosynthesis as a potential new therapeutic target for metabolic interventions in ALL and possibly other hematological malignancies

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    A novel single-cell based method for breast cancer prognosis

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    Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Xiaomei Li, Lin Liu, Gregory J. Goodall, Andreas Schreiber, Taosheng Xu, Jiuyong Li, Thuc D. L
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