Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning.

With the improvement of single-cell measurement techniques, there is a growing awareness that individual differences exist among cells, and protein expression distribution can vary across cells in the same tissue or cell line. Pinpointing the protein subcellular locations in single cells is crucial for mapping functional specificity of proteins and studying related diseases. Currently, research about single-cell protein location is still in its infancy, and most studies and databases do not annotate proteins at the cell level. For example, in the human protein atlas database, an immunofluorescence image stained for a particular protein shows multiple cells, but the subcellular location annotation is for the whole image, ignoring intercellular difference. In this study, we used large-scale immunofluorescence images and image-level subcellular locations to develop a deep-learning-based pipeline that could accurately recognize protein localizations in single cells. The pipeline consisted of two deep learning models, i.e. an image-based model and a cell-based model. The former used a multi-instance learning framework to comprehensively model protein distribution in multiple cells in each image, and could give both image-level and cell-level predictions. The latter firstly used clustering and heuristics algorithms to assign pseudo-labels of subcellular locations to the segmented cell images, and then used the pseudo-labels to train a classification model. Finally, the image-based model was fused with the cell-based model at the decision level to obtain the final ensemble model for single-cell prediction. Our experimental results showed that the ensemble model could achieve higher accuracy and robustness on independent test sets than state-of-the-art methods.

Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks.

Location proteomics seeks to provide automated high-resolution descriptions of protein location patterns within cells. Many efforts have been undertaken in location proteomics over the past decades, thereby producing plenty of automated predictors for protein subcellular localization. However, most of these predictors are trained solely from high-throughput microscopic images or protein amino acid sequences alone. Unifying heterogeneous protein data sources has yet to be exploited. In this paper, we present a pipeline called sequence, image, network-based protein subcellular locator (SIN-Locator) that constructs a multi-view description of proteins by integrating multiple data types including images of protein expression in cells or tissues, amino acid sequences and protein-protein interaction networks, to classify the patterns of protein subcellular locations. Proteins were encoded by both handcrafted features and deep learning features, and multiple combining methods were implemented. Our experimental results indicated that optimal integrations can considerately enhance the classification accuracy, and the utility of SIN-Locator has been demonstrated through applying to new released proteins in the human protein atlas. Furthermore, we also investigate the contribution of different data sources and influence of partial absence of data. This work is anticipated to provide clues for reconciliation and combination of multi-source data for protein location analysis.

Arginine Methylation of MDH1 by CARM1 Inhibits Glutamine Metabolism and Suppresses Pancreatic Cancer.

Distinctive from their normal counterparts, cancer cells exhibit unique metabolic dependencies on glutamine to fuel anabolic processes. Specifically, pancreatic ductal adenocarcinoma (PDAC) cells rely on an unconventional metabolic pathway catalyzed by aspartate aminotransferase, malate dehydrogenase 1 (MDH1), and malic enzyme 1 to rewire glutamine metabolism and support nicotinamide adenine dinucleotide phosphate (NADPH) production. Here, we report that methylation on arginine 248 (R248) negatively regulates MDH1. Protein arginine methyltransferase 4 (PRMT4/CARM1) methylates and inhibits MDH1 by disrupting its dimerization. Knockdown of MDH1 represses mitochondria respiration and inhibits glutamine metabolism, which sensitizes PDAC cells to oxidative stress and suppresses cell proliferation. Meanwhile, re-expression of wild-type MDH1, but not its methylation-mimetic mutant, protects cells from oxidative injury and restores cell growth and clonogenic activity. Importantly, MDH1 is hypomethylated at R248 in clinical PDAC samples. Our study reveals that arginine methylation of MDH1 by CARM1 regulates cellular redox homeostasis and suppresses glutamine metabolism of pancreatic cancer.

DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features.

MOTIVATION: Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with complex cellular processes. Currently, the subcellular location annotations of proteins in most studies and databases are descriptive terms, which fail to capture the protein amount or fractions across different locations. This highly limits the understanding of complex spatial distribution and functional mechanism of multi-label proteins. Thus, quantitatively analyzing the multiplex location patterns of proteins is an urgent and challenging task. RESULTS: In this study, we developed a deep-learning-based pattern unmixing pipeline for protein subcellular localization (DULoc) to quantitatively estimate the fractions of proteins localizing in different subcellular compartments from immunofluorescence images. This model used a deep convolutional neural network to construct feature representations, and combined multiple nonlinear decomposing algorithms as the pattern unmixing method. Our experimental results showed that the DULoc can achieve over 0.93 correlation between estimated and true fractions on both real and synthetic datasets. In addition, we applied the DULoc method on the images in the human protein atlas database on a large scale, and showed that 70.52% of proteins can achieve consistent location orders with the database annotations. AVAILABILITY AND IMPLEMENTATION: The datasets and code are available at: https://github.com/PRBioimages/DULoc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images.

MOTIVATION: Recognition of protein subcellular distribution patterns and identification of location biomarker proteins in cancer tissues are important for understanding protein functions and related diseases. Immunohistochemical (IHC) images enable visualizing the distribution of proteins at the tissue level, providing an important resource for the protein localization studies. In the past decades, several image-based protein subcellular location prediction methods have been developed, but the prediction accuracies still have much space to improve due to the complexity of protein patterns resulting from multi-label proteins and the variation of location patterns across cell types or states. RESULTS: Here, we propose a multi-label multi-instance model based on deep graph convolutional neural networks, GraphLoc, to recognize protein subcellular location patterns. GraphLoc builds a graph of multiple IHC images for one protein, learns protein-level representations by graph convolutions and predicts multi-label information by a dynamic threshold method. Our results show that GraphLoc is a promising model for image-based protein subcellular location prediction with model interpretability. Furthermore, we apply GraphLoc to the identification of candidate location biomarkers and potential members for protein networks. A large portion of the predicted results have supporting evidence from the existing literatures and the new candidates also provide guidance for further experimental screening. AVAILABILITY AND IMPLEMENTATION: The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/GraphLoc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modulating tumour vascular normalisation using triptolide-loaded NGR-functionalized liposomes for enhanced cancer radiotherapy.

Radiotherapy is an effective therapy in tumour treatment. However, the characteristics of the tumour microenvironment, including hypoxia, low pH, and interstitial fluid pressure bring about radioresistance. To improve the anti-tumour effect of radiotherapy, it has been demonstrated that antiangiogenic therapy can be employed to repair the structural and functional defects of tumour angiogenic vessels, thereby preventing radioresistance or poor therapeutic drug delivery. In this study, we prepared triptolide (TP)-loaded Asn-Gly-Arg (NGR) peptide conjugated mPEG2000-DSPE-targeted liposomes (NGR-PEG-TP-LPs) to induce tumour blood vessel normalisation, to the end of increasing the sensitivity of tumour cells to radiotherapy. Further, to quantify the tumour vessel normalisation window, the structure and functionality of tumour blood vessels post NGR-PEG-TP-LPs treatment were evaluated. Thereafter, the anti-tumour effect of radiotherapy following these treatments was evaluated using HCT116 xenograft-bearing mouse models based on the tumour vessel normalisation period window. The results obtained showed that NGR-PEG-TP-LPs could modulate tumour vascular normalisation to increase the oxygen content of the tumour microenvironment and enhance the efficacy of radiotherapy. Further, liver and kidney toxicity tests indicated that NGR-PEG-TP-LPs are safe for application in cancer treatment.

Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers.

BACKGROUND: The expression changes of some proteins are associated with cancer progression, and can be used as biomarkers in cancer diagnosis. Automated systems have been frequently applied in the large-scale detection of protein biomarkers and have provided a valuable complement for wet-laboratory experiments. For example, our previous work used an immunohistochemical image-based machine learning classifier of protein subcellular locations to screen biomarker proteins that change locations in colon cancer tissues. The tool could recognize the location of biomarkers but did not consider the effect of protein expression level changes on the screening process. RESULTS: In this study, we built an automated classification model that recognizes protein expression levels in immunohistochemical images, and used the protein expression levels in combination with subcellular locations to screen cancer biomarkers. To minimize the effect of non-informative sections on the immunohistochemical images, we employed the representative image patches as input and applied a Wasserstein distance method to determine the number of patches. For the patches and the whole images, we compared the ability of color features, characteristic curve features, and deep convolutional neural network features to distinguish different levels of protein expression and employed deep learning and conventional classification models. Experimental results showed that the best classifier can achieve an accuracy of 73.72% and an F1-score of 0.6343. In the screening of protein biomarkers, the detection accuracy improved from 63.64 to 95.45% upon the incorporation of the protein expression changes. CONCLUSIONS: Machine learning can distinguish different protein expression levels and speed up their annotation in the future. Combining information on the expression patterns and subcellular locations of protein can improve the accuracy of automatic cancer biomarker screening. This work could be useful in discovering new cancer biomarkers for clinical diagnosis and research.

Incorporating label correlations into deep neural networks to classify protein subcellular location patterns in immunohistochemistry images.

Analysis of protein subcellular localization is a critical part of proteomics. In recent years, as both the number and quality of microscopic images are increasing rapidly, many automated methods, especially convolutional neural networks (CNN), have been developed to predict protein subcellular location(s) based on bioimages, but their performance always suffers from some inherent properties of the problem. First, many microscopic images have non-informative or noisy sections, like unstained stroma and unspecific background, which affect the extraction of protein expression information. Second, the patterns of protein subcellular localization are very complex, as a lot of proteins locate in more than one compartment. In this study, we propose a new label-correlation enhanced deep neural network, laceDNN, to classify the subcellular locations of multi-label proteins from immunohistochemistry images. The model uses small representative patches as input to alleviate the image noise issue, and its backbone is a hybrid architecture of CNN and recurrent neural network, where the former network extracts representative image features and the latter learns the organelle dependency relationships. Our experimental results indicate that the proposed model can improve the performance of multi-label protein subcellular classification.

Antenna effect of pyridoxal phosphate on the fluorescence of mitoxantrone-silicon nanoparticles and its application in alkaline phosphatase assay.

As a kind of sensing and imaging fluorescent probe with the merit of low toxicity, good stability, and environment-friendly, silicon nanoparticles (SiNPs) are currently attracting extensive research. In this work, we obtained mitoxantrone-SiNPs (MXT-SiNPs) with green emission by one-pot synthesis under mild temperature condition. The antenna based on pyridoxal phosphate (PLP) was designed for light-harvesting to enhance the luminescence of MXT-SiNPs and to establish a novel sensing strategy for alkaline phosphatase (ALP). PLP transfers the absorbed photon energy to MXT-SiNPs by forming Schiff base. When PLP is dephosphorized by ALP, the released free hydroxyl group reacts with aldehyde group to form internal hemiacetal, which leads to the failure of Schiff base formation. Based on the relationship between antenna formation ability and PLP hydrolysis degree, the activity of ALP can be measured. A good linear relationship was obtained from 0.2 to 3.0 U/L, with a limit of detection of 0.06 U/L. Furthermore, the sensing platform was successfully used to detect ALP in human serum with recovery of 97.6-106.2%. The rational design of antenna elements for fluorescent nanomaterials can not only provide a new pathway to manipulate the luminescence, but also provide a new direction for fluorescence sensing strategy.

Immunofluorescent-aggregation assay based on anti-Salmonella typhimurium IgG-AuNCs, for rapid detection of Salmonella typhimurium.

Sensitive and rapid detection of pathogenic bacteria plays an important role in avoiding food poisoning. However, the practical application value of conventional assays for detection of foodborne bacteria, are limited by major drawbacks; these include the laboriousness of pure culture preparation, complexity of DNA extraction for polymerase chain reaction, and low sensitivity of enzyme-linked immunosorbent assay. Herein, we designed a non-complex strategy for the sensitive, quantitative, and rapid detection of Salmonella typhimurium with high specificity, using an anti-Salmonella typhimurium IgG-AuNC-based immunofluorescent-aggregation assay. Salmonella typhimurium was agglutinated with fluorescent anti-Salmonella typhimurium IgG-AuNC on a glass slide, and observed using a fluorescence microscope with photoexcitation and photoemission at 560 nm and 620 nm, respectively. Under optimized reaction conditions, the AuNC-based immunofluorescent-aggregation assay had a determination range between 7.0 × 103 and 3.0 × 108 CFU/mL, a limit of detection of 1.0 × 103 CFU/mL and an assay response time of 3 min. The technique delivered good results in assessing real samples.

Consistency and variation of protein subcellular location annotations.

A major challenge for protein databases is reconciling information from diverse sources. This is especially difficult when some information consists of secondary, human-interpreted rather than primary data. For example, the Swiss-Prot database contains curated annotations of subcellular location that are based on predictions from protein sequence, statements in scientific articles, and published experimental evidence. The Human Protein Atlas (HPA) consists of millions of high-resolution microscopic images that show protein spatial distribution on a cellular and subcellular level. These images are manually annotated with protein subcellular locations by trained experts. The image annotations in HPA can capture the variation of subcellular location across different cell lines, tissues, or tissue states. Systematic investigation of the consistency between HPA and Swiss-Prot assignments of subcellular location, which is important for understanding and utilizing protein location data from the two databases, has not been described previously. In this paper, we quantitatively evaluate the consistency of subcellular location annotations between HPA and Swiss-Prot at multiple levels, as well as variation of protein locations across cell lines and tissues. Our results show that annotations of these two databases differ significantly in many cases, leading to proposed procedures for deriving and integrating the protein subcellular location data. We also find that proteins having highly variable locations are more likely to be biomarkers of diseases, providing support for incorporating analysis of subcellular location in protein biomarker identification and screening.

Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images.

MOTIVATION: Systematic and comprehensive analysis of protein subcellular location as a critical part of proteomics ('location proteomics') has been studied for many years, but annotating protein subcellular locations and understanding variation of the location patterns across various cell types and states is still challenging. RESULTS: In this work, we used immunohistochemistry images from the Human Protein Atlas as the source of subcellular location information, and built classification models for the complex protein spatial distribution in normal and cancerous tissues. The models can automatically estimate the fractions of protein in different subcellular locations, and can help to quantify the changes of protein distribution from normal to cancer tissues. In addition, we examined the extent to which different annotated protein pathways and complexes showed similarity in the locations of their member proteins, and then predicted new potential proteins for these networks. AVAILABILITY AND IMPLEMENTATION: The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/complexsubcellularpatterns. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Role of CD40 in Allergic Rhinitis and Airway Remodelling.

BACKGROUND: Allergic rhinitis (AR) affects millions of people and is lack of effective treatment. CD40 is an important costimulatory molecule in immunity. However, few studies have focused on the role of CD40 in AR. METHODS: In this study, we built mouse model of chronic AR. The mice were divided into the AR, control, intravenous CD40 siRNA, and nasal CD40 siRNA groups (n = 6 each). We detected OVA-sIgE, IL-4, IL-5, IL-13, IL-10, IFN-γ, and TGF-ß levels in serum and supernatant by ELISA, CD40+ splenic DCs, and Foxp3+ Tregs by flow cytometry and CD40 mRNA by RT2-PCR. We also used PAS and MT stains to assess tissue remodelling. RESULTS: (1) The OVA-sIgE, IL-4, IL-5, and IL-13 levels in the serum or supernatant of nasal septal membrane of AR mice were significantly higher than control. After treated with CD40 siRNA, those indicators were significantly decreased. The IFN-γ, IL-10, and TGF-ß levels in AR mice were significantly lower than that in control and were increased by administration of CD40 siRNA. (2) AR mice had significantly fewer Foxp3+ Tregs in the spleen than control mice. After treated with CD40 siRNA, AR mice had significantly more Foxp3+ Tregs. (3) AR mice exhibited a significantly higher CD40 mRNA levels than control. Administration of CD40 siRNA significantly reduced the CD40 mRNA level. (4) The AR mice showed significantly greater collagen deposition than the control in MT staining. Applications of CD40 siRNA significantly reduced the collagen deposition in AR mice. CONCLUSION: CD40 siRNA therapy shows promise for chronic AR as it significantly attenuated allergic symptoms and Th2-related inflammation and upregulated Foxp3+ Tregs. CD40 plays a role in tissue remodelling in AR, which can be inhibited by CD40 siRNA application.

Unilateral facial injection of Botulinum neurotoxin A attenuates bilateral trigeminal neuropathic pain and anxiety-like behaviors through inhibition of TLR2-mediated neuroinflammation in mice.

OBJECTIVES: In this study, we investigated the possible analgesic effects of Botulinum toxin type A (BoNT/A) on trigeminal neuralgia (TN). A modified TN mouse model was established by chronic constriction injury of the distal infraorbital nerve (dIoN-CCI) in mice, and the possible roles of microglia toll-like receptor 2 (TLR2) and neuroinflammation was investigated. METHODS: Male C57BL/6 mice were divided into 3 groups, including sham group, vehicle-treated TN group and BoNT/A-treated TN group. Bilateral mechanical pain hypersensitivity, anxiety-like and depressive-like behaviors were evaluated by using von Frey test, open field, elevated plus-maze testing, and forced swimming test in mice, respectively. The mRNA or protein expression levels of toll-like receptors (TLRs), glia activation markers and proinflammatory factors in the trigeminal nucleus caudalis (TNC) were tested by RT-qPCR, immunofluorescence and Western blotting. We also tested the pain behaviors of TN in Tlr2-/- mice. RESULTS: We found that unilateral subcutaneous injection of BoNT/A into the whisker pad on the ipsilateral side of dIoN-CCI mice significantly attenuated bilateral mechanical pain hypersensitivity and anxiety-like behaviors induced by dIoN-CCI surgery in mice. The dIoN-CCI surgery significantly up-regulated the expression of TLR2, MyD88, CD11b (a microglia marker), IL-1ß, TNF-α and IL-6 in the ipsilateral TNC in mice, and BoNT/A injection significantly inhibited the expression of these factors. Immunostaining results confirmed that BoNT/A injection significantly inhibited the microglia activation in the ipsilateral TNC in dIoN-CCI mice. TLR2 deficiency also alleviated bilateral mechanical pain hypersensitivity and the up-regulation of MyD88 expression in the TNC of dIoN-CCI mice. CONCLUSION: These results indicate that unilateral injection of BoNT/A attenuated bilateral mechanical pain hypersensitivity and anxiety-like behaviors in dIoN-CCI mice, and the analgesic effects of BoNT/A may be associated with the inhibition of TLR2-mediated neuroinflammation in the TNC.

Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer.

BACKGROUND: Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied. RESULTS: In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations. CONCLUSIONS: Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.

miR-448-3p alleviates diabetic vascular dysfunction by inhibiting endothelial-mesenchymal transition through DPP-4 dysregulation.

Diabetes mellitus (DM) often causes vascular endothelial damage and alters vascular microRNA (miR) expression. miR-448-3p has been reported to be involved in the development of DM, but whether miR-448-3p regulates diabetic vascular endothelial dysfunction remains unclear. To investigate the molecular mechanism of diabetic vascular endothelial dysfunction and the role of miR-448-3p therein, Sprague-Dawley rats were injected with streptozotocin (STZ) to establish diabetic animal model and the rat aortic endothelial cells were treated with high glucose to establish diabetic cell model. For the treatment group, after the induction of diabetes, the miR-448-3p levels in vivo and in vitro were upregulated by adeno-associated virus serotype 2 (AAV2)-miR-448-3p injection and miR-448-3p mimic transfection, respectively. Our results showed that AAV2-miR-448-3p injection alleviated the body weight loss and blood glucose level elevation induced by STZ injection. The miR-448-3p level was significantly decreased and the dipeptidyl peptidase-4 (DPP-4) messenger RNA level was increased in diabetic animal and cell models, which was reversed by miR-448-3p treatment. Moreover, the diabetic rats exhibited endothelial damage and endothelial-mesenchymal transition (EndMT), while AAV2-miR-448-3p injection relieved those situations. In vitro experiments demonstrated that miR-448-3p overexpression in endothelial cells alleviated endothelial damage by inhibiting EndMT through blocking the transforming growth factor-ß/Smad pathway. We further proved that miR-448-3p negatively regulated DPP-4 by binding to its 3'-untranslated region, and DPP-4 overexpression reversed the effect of miR-448-3p overexpression on EndMT. Overall, we conclude that miR-448-3p overexpression inhibits EndMT via targeting DPP-4 and further ameliorates diabetic vascular endothelial dysfunction, indicating that miR-448-3p may serve as a promising therapeutic target for diabetic endothelial dysfunction.

Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis.

Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.

Liposome-Encapsulated Zoledronate Favors Tumor Vascular Normalization and Enhances Anticancer Efficacy of Cisplatin.

The aim of this study was to examine the effectiveness of alanine-proline-arginine-proline-glycine (APRPG) peptide-conjugated PEGylated cationic liposomes-encapsulated zoledronic acid (ZOL) (APRPG-PEG-ZOL-CLPs) in achieving vascular normalization. Cisplatin (diamminedichloroplatinum, DDP) was used to improve anticancer efficacy. The present study showed that APRPG-PEG-ZOL-CLPs increased anticancer efficacy, which was regarded as vascular normalization. Our results demonstrated that the viability, migration, and tube formation of human umbilical vein endothelial cells (HUVECs) were evidently repressed by APRPG-PEG-ZOL-CLPs. Moreover, APRPG-PEG-ZOL-CLPs could decrease vessel density, as well as hypoxia-inducible factor 1α (HIF-1α), and increase thrombospondin 1 (TSP-1) expression of tumors. Therefore, the anticancer efficacy of APRPG-PEG-ZOL-CLPs combined with DDP was superior to that of PEG-ZOL-CLP or ZOL treatment combined with DDP schemes, as demonstrated by the obviously evident reduction in tumor volume. These results indicated that APRPG-PEG-ZOL-CLPs were most effective in normalizing tumor vasculature to elevate the therapeutic effect of antitumor drugs.

Repression of TXNIP-NLRP3 axis restores intestinal barrier function via inhibition of myeloperoxidase activity and oxidative stress in nonalcoholic steatohepatitis.

Dysfunction of the intestinal barrier function occurs in hepatic injury, but the specific mechanisms responsible are largely unknown. Recently, NOD-like receptor 3 (NLRP3) inflammasome functions in impairing endothelial barrier function. In this study, we test the hypothesis that TXNIP-NLRP3 axis repression prevents against intestinal barrier function disruption in nonalcoholic steatohepatitis (NASH). First, lipopolysaccharide (LPS)-induced alterations in expression of ZO-1 and occludin, myeloperoxidase (MPO) activity, reactive oxygen species (ROS) level, and transepithelial electric resistance (TEER) in intestinal epithelial cells (IECs) isolated from C57BL/6 wild-type (WT) and TXNIP-/- mice were evaluated. The underlying regulatory mechanisms of TXNIP knockout in vivo were investigated with the detection of expressions of TXNIP, NLRP3 and ZO-1, and occludin, the interaction of TXNIP-NLRP3, MPO activity, ROS level, permeability of intestinal mucosa, levels of inflammatory factors in serum, and LPS concentration. We identified that TXNIP knockout promoted ZO-1 and occludin expression, yet reduced MPO activity, ROS level, and cell permeability in IECs, indicating restored the intestinal barrier function. However, LPS upregulated TXNIP and NLRP3 expression, as well as contributed to the interaction between TXNIP and NLRP3 in vitro. Furthermore, TXNIP was significantly upregulated in the intestinal mucosa of NASH mice and its knockout repaired the intestinal barrier disrupt, inhibited expression of inflammatory factors, and reduced LPS concentration as well as hepatic injury in vivo. Taken together, our findings demonstrated that inhibited the activation of the TXNIP-NLRP3 axis reduced MPO activity and oxidative stress and thus restoring the intestinal barrier function in NASH. TXNIP-NLRP3 axis may be a promising therapeutic strategy for the NASH treatment.

Smoking status and subsequent gastric cancer risk in men compared with women: a meta-analysis of prospective observational studies.

BACKGROUND: Smoking is one of the well-established risk factors for gastric cancer incidence, yet whether men are more or equally susceptible to gastric cancer due to smoking compared with women is a matter of controversy. The aim of this study was to investigate and compare the effect of sex on gastric cancer risk associated with smoking. METHODS: We conducted a systemic literature search in MEDLINE, EMBASE, and the Cochrane CENTRAL databases to identify studies published from inception to December 2018. We included prospective observational studies which reported effect estimates with 95% confidence intervals (CIs) for associations of current or former smokers with the incidence of gastric cancer by sex. We calculated the ratio of relative risk (RRR) with corresponding 95% CI based on sex-specific effect estimates for current or former smokers versus non-smokers on the risk of gastric cancer. RESULTS: We included 10 prospective studies with 3,381,345 participants in our analysis. Overall, the summary RRR (male to female) for gastric cancer risk in current smokers was significantly increased compared with non-smokers (RRR: 1.30; 95% CI: 1.05-1.63; P = 0.019). Furthermore, there was no significant sex difference for the association between former smokers and gastric cancer risk (RRR: 1.20; 95% CI: 0.92-1.55; P = 0.178). However, the result of sensitivity analysis indicated the pooled result was not stable, which was altered by excluding a nested case-control study (RRR: 1.31; 95% CI: 1.10-1.57; P = 0.002). CONCLUSION: This systematic review showed a potential sex difference association between current smokers and the risk of gastric cancer. The sex differential in smokers can give important clues for the etiology of gastric cancers and should be examined in further studies.