TAX1BP1 contributes to deoxypodophyllotoxin-induced glioma cell parthanatos via inducing nuclear translocation of AIF by activation of mitochondrial respiratory chain complex I.

Parthanatos is a type of programmed cell death initiated by over-activated poly (ADP-ribose) polymerase 1 (PARP1). Nuclear translocation of apoptosis inducing factor (AIF) is a prominent feature of parthanatos. But it remains unclear how activated nuclear PARP1 induces mitochondrial AIF translocation into nuclei. Evidence has shown that deoxypodophyllotoxin (DPT) induces parthanatos in glioma cells via induction of excessive ROS. In this study we explored the downstream signal of activated PARP1 to induce nuclear translocation of AIF in DPT-triggered glioma cell parthanatos. We showed that treatment with DPT (450 nM) induced PARP1 over-activation and Tax1 binding protein 1 (TAX1BP1) distribution to mitochondria in human U87, U251 and U118 glioma cells. PARP1 activation promoted TAX1BP1 distribution to mitochondria by depleting nicotinamide adenine dinucleotide (NAD+). Knockdown of TAX1BP1 with siRNA not only inhibited TAX1BP1 accumulation in mitochondria, but also alleviated nuclear translocation of AIF and glioma cell death. We demonstrated that TAX1BP1 enhanced the activity of respiratory chain complex I not only by upregulating the expression of ND1, ND2, NDUFS2 and NDUFS4, but also promoting their assemblies into complex I. The activated respiratory complex I generated more superoxide to cause mitochondrial depolarization and nuclear translocation of AIF, while the increased mitochondrial superoxide reversely reinforced PARP1 activation by inducing ROS-dependent DNA double strand breaks. In mice bearing human U87 tumor xenograft, administration of DPT (10 mg· kg-1 ·d-1, i.p., for 8 days) markedly inhibited the tumor growth accompanied by NAD+ depletion, TAX1BP1 distribution to mitochondria, AIF distribution to nuclei as well as DNA DSBs and PARP1 activation in tumor tissues. Taken together, these data suggest that TAX1BP1 acts as a downstream signal of activated PARP1 to trigger nuclear translocation of AIF by activation of mitochondrial respiratory chain complex I.

Activated SIRT1 contributes to DPT-induced glioma cell parthanatos by upregulation of NOX2 and NAT10.

Parthanatos is a type of programmed cell death dependent on hyper-activation of poly (ADP-ribose) polymerase 1 (PARP-1). SIRT1 is a highly conserved nuclear deacetylase and often acts as an inhibitor of parthanatos by deacetylation of PARP1. Our previous study showed that deoxypodophyllotoxin (DPT), a natural compound isolated from the traditional herb Anthriscus sylvestris, triggered glioma cell death via parthanatos. In this study, we investigated the role of SIRT1 in DPT-induced human glioma cell parthanatos. We showed that DPT (450 nmol/L) activated both PARP1 and SIRT1, and induced parthanatos in U87 and U251 glioma cells. Activation of SIRT1 with SRT2183 (10 µmol/L) enhanced, while inhibition of SIRT1 with EX527 (200 µmol/L) or knockdown of SIRT1 attenuated DPT-induced PARP1 activation and glioma cell death. We demonstrated that DPT (450 nmol/L) significantly decreased intracellular NAD+ levels in U87 and U251 cells. Further decrease of NAD+ levels with FK866 (100 µmol/L) aggravated, but supplement of NAD+ (0.5, 2 mmol/L) attenuated DPT-induced PARP1 activation. We found that NAD+ depletion enhanced PARP1 activation via two ways: one was aggravating ROS-dependent DNA DSBs by upregulation of NADPH oxidase 2 (NOX2); the other was reinforcing PARP1 acetylation via increase of N-acetyltransferase 10 (NAT10) expression. We found that SIRT1 activity was improved when being phosphorylated by JNK at Ser27, the activated SIRT1 in reverse aggravated JNK activation via upregulating ROS-related ASK1 signaling, thus forming a positive feedback between JNK and SIRT1. Taken together, SIRT1 activated by JNK contributed to DPT-induced human glioma cell parthanatos via initiation of NAD+ depletion-dependent upregulation of NOX2 and NAT10.

Self-adaption and texture generation: A hybrid loss function for low-dose CT denoising.

BACKGROUND: Deep learning has been successfully applied to low-dose CT (LDCT) denoising. But the training of the model is very dependent on an appropriate loss function. Existing denoising models often use per-pixel loss, including mean abs error (MAE) and mean square error (MSE). This ignores the difference in denoising difficulty between different regions of the CT images and leads to the loss of large texture information in the generated image. PURPOSE: In this paper, we propose a new hybrid loss function that adapts to the noise in different regions of CT images to balance the denoising difficulty and preserve texture details, thus acquiring CT images with high-quality diagnostic value using LDCT images, providing strong support for condition diagnosis. METHODS: We propose a hybrid loss function consisting of weighted patch loss (WPLoss) and high-frequency information loss (HFLoss). To enhance the model's denoising ability of the local areas which are difficult to denoise, we improve the MAE to obtain WPLoss. After the generated image and the target image are divided into several patches, the loss weight of each patch is adaptively and dynamically adjusted according to its loss ratio. In addition, considering that texture details are contained in the high-frequency information of the image, we use HFLoss to calculate the difference between CT images in the high-frequency information part. RESULTS: Our hybrid loss function improves the denoising performance of several models in the experiment, and obtains a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Moreover, through visual inspection of the generated results of the comparison experiment, the proposed hybrid function can effectively suppress noise and retain image details. CONCLUSIONS: We propose a hybrid loss function for LDCT image denoising, which has good interpretation properties and can improve the denoising performance of existing models. And the validation results of multiple models using different datasets show that it has good generalization ability. By using this loss function, high-quality CT images with low radiation are achieved, which can avoid the hazards caused by radiation and ensure the disease diagnosis for patients.

BNIP3 contributes to silibinin-induced DNA double strand breaks in glioma cells via inhibition of mTOR.

BNIP3 is found to eliminate cancer cells via causing mitochondrial damage and endoplasmic reticulum stress, but it remains elusive of its role in regulating DNA double strand breaks (DSBs). In this study, we find that silibinin triggers DNA DSBs, ROS accumulation and expressional upregulation of BNIP3 in glioma cells. Mitigation of ROS with antioxidant GSH significantly inhibits silibinin-induced DNA DSBs and glioma cell death. Then, we find knockdown of BNIP3 with SiRNA obviously prevents silibinin-induced DNA DSBs and ROS accumulation. Mechanistically, BNIP3 knockdown not only reverses silibinin-triggered depletion of cysteine and GSH via maintaining xCT level, but also abrogates catalase decrease. Notably, silibinin-induced dephosphorylation of mTOR is also prevented when BNIP3 is knocked down. Given that activated mTOR could promote xCT expression and inhibit autophagic degradation of catalase, our data suggest that BNIP3 contributes to silibinin-induced DNA DSBs via improving intracellular ROS by inhibition of mTOR.

ATF3 contributes to brucine-triggered glioma cell ferroptosis via promotion of hydrogen peroxide and iron.

Ferroptotic cell death is characterized by iron-dependent lipid peroxidation that is initiated by ferrous iron and H2O2 via Fenton reaction, in which the role of activating transcription factor 3 (ATF3) remains elusive. Brucine is a weak alkaline indole alkaloid extracted from the seeds of Strychnos nux-vomica, which has shown potent antitumor activity against various tumors, including glioma. In this study, we showed that brucine inhibited glioma cell growth in vitro and in vivo, which was paralleled by nuclear translocation of ATF3, lipid peroxidation, and increases of iron and H2O2. Furthermore, brucine-induced lipid peroxidation was inhibited or exacerbated when intracellular iron was chelated by deferoxamine (500 µM) or improved by ferric ammonium citrate (500 µM). Suppression of lipid peroxidation with lipophilic antioxidants ferrostatin-1 (50 µM) or liproxstatin-1 (30 µM) rescued brucine-induced glioma cell death. Moreover, knockdown of ATF3 prevented brucine-induced accumulation of iron and H2O2 and glioma cell death. We revealed that brucine induced ATF3 upregulation and translocation into nuclei via activation of ER stress. ATF3 promoted brucine-induced H2O2 accumulation via upregulating NOX4 and SOD1 to generate H2O2 on one hand, and downregulating catalase and xCT to prevent H2O2 degradation on the other hand. H2O2 then contributed to brucine-triggered iron increase and transferrin receptor upregulation, as well as lipid peroxidation. This was further verified by treating glioma cells with exogenous H2O2 alone. Moreover, H2O2 reversely exacerbated brucine-induced ER stress. Taken together, ATF3 contributes to brucine-induced glioma cell ferroptosis via increasing H2O2 and iron.

FOXO3a protects glioma cells against temozolomide-induced DNA double strand breaks via promotion of BNIP3-mediated mitophagy.

FOXO3a (forkhead box transcription factor 3a) is involved in regulating multiple biological processes in cancer cells. BNIP3 (Bcl-2/adenovirus E1B 19-kDa-interacting protein 3) is a receptor accounting for priming damaged mitochondria for autophagic removal. In this study we investigated the role of FOXO3a in regulating the sensitivity of glioma cells to temozolomide (TMZ) and its relationship with BNIP3-mediated mitophagy. We showed that TMZ dosage-dependently inhibited the viability of human U87, U251, T98G, LN18 and rat C6 glioma cells with IC50 values of 135.75, 128.26, 142.65, 155.73 and 111.60 µM, respectively. In U87 and U251 cells, TMZ (200 µM) induced DNA double strand breaks (DSBs) and nuclear translocation of apoptosis inducing factor (AIF), which was accompanied by BNIP3-mediated mitophagy and FOXO3a accumulation in nucleus. TMZ treatment induced intracellular ROS accumulation in U87 and U251 cells via enhancing mitochondrial superoxide, which not only contributed to DNA DSBs and exacerbated mitochondrial dysfunction, but also upregulated FOXO3a expression. Knockdown of FOXO3a aggravated TMZ-induced DNA DSBs and mitochondrial damage, as well as glioma cell death. TMZ treatment not only upregulated BNIP3 and activated autophagy, but also triggered mitophagy by prompting BNIP3 translocation to mitochondria and reinforcing BNIP3 interaction with LC3BII. Inhibition of mitophagy by knocking down BNIP3 with SiRNA or blocking autophagy with 3MA or bafilomycin A1 exacerbated mitochondrial superoxide and intracellular ROS accumulation. Moreover, FOXO3a knockdown inhibited TMZ-induced BNIP3 upregulation and autophagy activation. In addition, we showed that treatment with TMZ (100 mg·kg-1·d-1, ip) for 12 days in C6 cell xenograft mice markedly inhibited tumor growth accompanied by inducing FOXO3a upregulation, oxidative stress and BNIP3-mediated mitophagy in tumor tissues. These results demonstrate that FOXO3a attenuates temozolomide-induced DNA double strand breaks in human glioma cells via promoting BNIP3-mediated mitophagy.

A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.

SUMMARY: There is an increasing interest in joint analysis of multiple phenotypes for genome-wide association studies (GWASs) based on the following reasons. First, cohorts usually collect multiple phenotypes and complex diseases are usually measured by multiple correlated intermediate phenotypes. Second, jointly analyzing multiple phenotypes may increase statistical power for detecting genetic variants associated with complex diseases. Third, there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. In this paper, we develop a clustering linear combination (CLC) method to jointly analyze multiple phenotypes for GWASs. In the CLC method, we first cluster individual statistics into positively correlated clusters and then, combine the individual statistics linearly within each cluster and combine the between-cluster terms in a quadratic form. CLC is not only robust to different signs of the means of individual statistics, but also reduce the degrees of freedom of the test statistic. We also theoretically prove that if we can cluster the individual statistics correctly, CLC is the most powerful test among all tests with certain quadratic forms. Our simulation results show that CLC is either the most powerful test or has similar power to the most powerful test among the tests we compared, and CLC is much more powerful than other tests when effect sizes align with inferred clusters. We also evaluate the performance of CLC through a real case study. AVAILABILITY AND IMPLEMENTATION: R code for implementing our method is available at http://www.math.mtu.edu/∼shuzhang/software.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association Between Nitrite and Nitrate Intake and Risk of Gastric Cancer: A Systematic Review and Meta-Analysis.

BACKGROUND Studies have shown inconsistent associations of nitrite and nitrate intake with the risk of gastric cancer or its associated mortality. We performed a meta-analysis of observational studies to evaluate the correlation of nitrite and nitrate intake with the risk of gastric cancer. MATERIAL AND METHODS We searched for studies reporting effect estimates and 95% confidence intervals (CIs) of gastric cancer in PubMed, EMBASE, and the Cochrane Library through November 2018. The summary results of the included studies were pooled using a random-effects model. RESULTS Eighteen case-control and 6 prospective cohort studies recruiting 800 321 participants were included in this study. The summary results indicated that the highest (odds ratio [OR], 1.27; 95%CI, 1.03-1.55; P=0.022) or moderate (OR: 1.12; 95%CI, 1.01-1.26; P=0.037) nitrite intake were associated with a higher risk of gastric cancer. However, we noted that high (OR, 0.81; 95%CI, 0.68-0.97; P=0.021) or moderate (OR, 0.86; 95%CI, 0.75-0.99; P=0.036) nitrate intakes were associated with a reduced risk of gastric cancer. These associations differed when stratified by publication year, study design, country, the percentage of male participants, assessment of exposure, adjusted model, and study quality. CONCLUSIONS High or moderate nitrite intake was associated with higher risk of gastric cancer, whereas high or moderate nitrate intake was correlated with lower risk of gastric cancer.

Joint Analysis of Multiple Traits in Rare Variant Association Studies.

The joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, the majority of existing methods for the joint analysis of multiple traits test association between one common variant and multiple traits. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. Current statistical methods for rare variant association studies are for one single trait only. In this paper, we propose an adaptive weighting reverse regression (AWRR) method to test association between multiple traits and rare variants in a genomic region. AWRR is robust to the directions of effects of causal variants and is also robust to the directions of association of traits. Using extensive simulation studies, we compare the performance of AWRR with canonical correlation analysis (CCA), Single-TOW, and the weighted sum reverse regression (WSRR). Our results show that, in all of the simulation scenarios, AWRR is consistently more powerful than CCA. In most scenarios, AWRR is more powerful than Single-TOW and WSRR.

SIRT1 activated by AROS sensitizes glioma cells to ferroptosis via induction of NAD+ depletion-dependent activation of ATF3.

Ferroptosis is a type of programmed cell death resulting from iron overload-dependent lipid peroxidation, and could be promoted by activating transcription factor 3 (ATF3). SIRT1 is an enzyme accounting for removing acetylated lysine residues from target proteins by consuming NAD+, but its role remains elusive in ferroptosis and activating ATF3. In this study, we found SIRT1 was activated during the process of RSL3-induced glioma cell ferroptosis. Moreover, the glioma cell death was aggravated by SIRT1 activator SRT2183, but suppressed by SIRT inhibitor EX527 or when SIRT1 was silenced with siRNA. These indicated SIRT1 sensitized glioma cells to ferroptosis. Furthermore, we found SIRT1 promoted RSL3-induced expressional upregulation and nuclear translocation of ATF3. Silence of ATF3 with siRNA attenuated RSL3-induced increases of ferrous iron and lipid peroxidation, downregulation of SLC7A11 and GPX4 and depletion of cysteine and GSH. Thus, SIRT1 promoted glioma cell ferroptosis by inducting ATF3 activation. Mechanistically, ATF3 activation was reinforced when RSL3-induced decline of NAD+ was aggravated by FK866 that could inhibit NAD + synthesis via salvage pathway, but suppressed when intracellular NAD+ was maintained at higher level by supplement of exogenous NAD+. Notably, the NAD + decline caused by RSL3 was enhanced when SIRT1 was further activated by SRT2183, but attenuated when SIRT1 activation was inhibited by EX527. These indicated SIRT1 promoted ATF3 activation via consumption of NAD+. Finally, we found RSL3 activated SIRT1 by inducing reactive oxygen species-dependent upregulation of AROS. Together, our study revealed SIRT1 activated by AROS sensitizes glioma cells to ferroptosis via activation of ATF3-dependent inhibition of SLC7A11 and GPX4.

Testing an optimally weighted combination of common and/or rare variants with multiple traits.

Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.

Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test.

The joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods use all of the traits for testing the association between multiple traits and a single variant. However, those methods for association studies may lose power in the presence of a large number of noise traits. In this paper, we propose an "optimal" maximum heritability test (MHT-O) to test the association between multiple traits and a single variant. MHT-O includes a procedure of deleting traits that have weak or no association with the variant. Using extensive simulation studies, we compare the performance of MHT-O with MHT, Trait-based Association Test uses Extended Simes procedure (TATES), SUM_SCORE and MANOVA. Our results show that, in all of the simulation scenarios, MHT-O is either the most powerful test or comparable to the most powerful test among the five tests we compared.

A novel statistical method for rare-variant association studies in general pedigrees.

Both population-based and family-based designs are commonly used in genetic association studies to identify rare variants that underlie complex diseases. For any type of study design, the statistical power will be improved if rare variants can be enriched in the samples. Family-based designs, with ascertainment based on phenotype, may enrich the sample for causal rare variants and thus can be more powerful than population-based designs. Therefore, it is important to develop family-based statistical methods that can account for ascertainment. In this paper, we develop a novel statistical method for rare-variant association studies in general pedigrees for quantitative traits. This method uses a retrospective view that treats the traits as fixed and the genotypes as random, which allows us to account for complex and undefined ascertainment of families. We then apply the newly developed method to the Genetic Analysis Workshop 19 data set and compare the power of the new method with two other methods for general pedigrees. The results show that the newly proposed method increases power in most of the cases we consider, more than the other two methods.

An Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies.

Currently, the analyses of most genome-wide association studies (GWAS) have been performed on a single phenotype. There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Therefore, using only one single phenotype may lose statistical power to identify the underlying genetic mechanism. There is an increasing need to develop and apply powerful statistical tests to detect association between multiple phenotypes and a genetic variant. In this paper, we develop an Adaptive Fisher's Combination (AFC) method for joint analysis of multiple phenotypes in association studies. The AFC method combines p-values obtained in standard univariate GWAS by using the optimal number of p-values which is determined by the data. We perform extensive simulations to evaluate the performance of the AFC method and compare the power of our method with the powers of TATES, Tippett's method, Fisher's combination test, MANOVA, MultiPhen, and SUMSCORE. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful test. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.