A whole-genome SNP association study of NCI60 cell line panel indicates a role of Ca2+ signaling in selenium resistance

Features of the cytoprotective effect of selenium nanoparticles on primary cortical neurons and astrocytes during oxygen-glucose deprivation and reoxygenation

The study is aimed at elucidating the effect of selenium nanoparticles (SeNPs) on the death of cells in the primary culture of mouse cerebral cortex during oxygen and glucose deprivation (OGD). A primary cell culture of the cerebral cortex containing neurons and astrocytes was subjected to OGD and reoxygenation to simulate cerebral ischemia-like conditions in vitro. To evaluate the neuroprotective effect of SeNPs, cortical astrocytes and neurons were incubated for 24 h with SeNPs, and then subjected to 2-h OGD, followed by 24-h reoxygenation. Vitality tests, fluorescence microscopy, and real-time PCR have shown that incubation of primary cultured neurons and astrocytes with SeNPs at concentrations of 2.5–10 µg/ml under physiological conditions has its own characteristics depending on the type of cells (astrocytes or neurons) and leads to a dose-dependent increase in apoptosis. At low concentration SeNPs (0.5 µg/ml), on the contrary, almost completely suppressed the processes of basic necrosis and apoptosis. Both high (5 µg/ml) and low (0.5 µg/ml) concentrations of SeNPs, added for 24 h to the cells of cerebral cortex, led to an increase in the expression level of genes Bcl-2, Bcl-xL, Socs3, while the expression of Bax was suppressed. Incubation of the cells with 0.5 µg/ml SeNPs led to a decrease in the expression of SelK and SelT. On the contrary, 5 µg/ml SeNPs caused an increase in the expression of SelK, SelN, SelT, SelP. In the ischemic model, after OGD/R, there was a significant death of brain cells by the type of necrosis and apoptosis. OGD/R also led to an increase in mRNA expression of the Bax, SelK, SelN, and SelT genes and suppression of the Bcl-2, Bcl-xL, Socs3, SelP genes. Pre-incubation of cell cultures with 0.5 and 2.5 µg/ml SeNPs led to almost complete inhibition of OGD/R-induced necrosis and greatly reduced apoptosis. Simultaneously with these processes we observed suppression of caspase-3 activation. We hypothesize that the mechanisms of the protective action of SeNPs involve the activation of signaling cascades recruiting nuclear factors Nrf2 and SOCS3/STAT3, as well as the activation of adaptive pathways of ESR signaling of stress arising during OGD and involving selenoproteins SelK and SelT, proteins of the Bcl-2 family ultimately leading to inactivation of caspase-3 and inhibition of apoptosis. Thus, our results demonstrate that SeNPs can act as neuroprotective agents in the treatment of ischemic brain injuries.

An emerging spectrum of variants and clinical features in KCNMA1-linked channelopathy

KCNMA1-linked channelopathy is an emerging neurological disorder characterized by heterogeneous and overlapping combinations of movement disorder, seizure, developmental delay, and intellectual disability. KCNMA1 encodes the BK K+ channel, which contributes to both excitatory and inhibitory neuronal and muscle activity. Understanding the basis of the disorder is an important area of active investigation; however, the rare prevalence has hampered the development of large patient cohorts necessary to establish genotype-phenotype correlations. In this review, we summarize 37 KCNMA1 alleles from 69 patients currently defining the channelopathy and assess key diagnostic and clinical hallmarks. At present, 3 variants are classified as gain-of-function with respect to BK channel activity, 14 loss-of-function, 15 variants of uncertain significance, and putative benign/VUS. Symptoms associated with these variants were curated from patient-provided information and prior publications to define the spectrum of clinical phenotypes. In this newly expanded cohort, seizures showed no differential distribution between patients harboring GOF and LOF variants, while movement disorders segregated by mutation type. Paroxysmal non-kinesigenic dyskinesia was predominantly observed among patients with GOF alleles of the BK channel, although not exclusively so, while additional movement disorders were observed in patients with LOF variants. Neurodevelopmental and structural brain abnormalities were prevalent in patients with LOF mutations. In contrast to mutations, disease-associated KCNMA1 single nucleotide polymorphisms were not predominantly related to neurological phenotypes but covered a wider set of peripheral physiological functions. Together, this review provides additional evidence exploring the genetic and biochemical basis for KCNMA1-linked channelopathy and summarizes the clinical repository of patient symptoms across multiple types of KCNMA1 gene variants.

Effect of Selenium Nanoparticle Supplementation on Tissue Inflammation, Blood Cell Count, and IGF-1 Levels in Spinal Cord Injury-Induced Rats

Selenium is known to be a neuroprotective agent in respect to a number of neuronal diseases and pain. The aim of this study was to evaluate the neuroprotective effect of the oral administration of selenium nanoparticles in rats with spinal cord injury (SCI). Forty adult female rats were randomly assigned to two equal groups as experimental and control. Under general inhalation anesthesia, in both groups, SCI was created, at the T9-10 level of the column. On the third day after the operation, a supplement of selenium nanoparticle was administered to the experimental group at 0.2 mg/kg per day. The histology of the site of injury, IGF-1 serum concentrations, and changes in the white blood cells were examined in both groups at different pre-surgical and post-surgical times. The results of the current study showed a significant decrease in the total white blood cells, including lymphocyte, neutrophil, and monocyte in the experimental group compared to the control group. Histological evaluation showed that the inflammatory responses reduced significantly in the experimental group compared to the control group. In conclusion, we speculate that the decrease in the number of inflammatory cells after oral administration of the selenium nanoparticles is due to the neuroprotective effects of this nanoparticle.

Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study

BACKGROUND

Prostate cancer (PCa) is the most common male cancer in the United Kingdom and we aimed to identify clinically relevant biomarkers corresponding to stage progression of the disease.

METHODS

We used enhanced proteomic profiling of PCa progression using iTRAQ 3D LC mass spectrometry on high-quality serum samples to identify biomarkers of PCa.

RESULTS

We identified >1000 proteins. Following specific inclusion/exclusion criteria we targeted seven proteins of which two were validated by ELISA and six potentially interacted forming an 'interactome' with only a single protein linking each marker. This network also includes accepted cancer markers, such as TNF, STAT3, NF-κB and IL6.

CONCLUSIONS

Our linked and interrelated biomarker network highlights the potential utility of six of our seven markers as a panel for diagnosing PCa and, critically, in determining the stage of the disease. Our validation analysis of the MS-identified proteins found that SAA alongside KLK3 may improve categorisation of PCa than by KLK3 alone, and that TSR1, although not significant in this model, might also be a clinically relevant biomarker.

Signatures of Evolutionary Adaptation in Quantitative Trait Loci Influencing Trace Element Homeostasis in Liver

Essential trace elements possess vital functions at molecular, cellular, and physiological levels in health and disease, and they are tightly regulated in the human body. In order to assess variability and potential adaptive evolution of trace element homeostasis, we quantified 18 trace elements in 150 liver samples, together with the expression levels of 90 genes and abundances of 40 proteins involved in their homeostasis. Additionally, we genotyped 169 single nucleotide polymorphism (SNPs) in the same sample set. We detected significant associations for 8 protein quantitative trait loci (pQTL), 10 expression quantitative trait loci (eQTLs), and 15 micronutrient quantitative trait loci (nutriQTL). Six of these exceeded the false discovery rate cutoff and were related to essential trace elements: 1) one pQTL for GPX2 (rs10133290); 2) two previously described eQTLs for HFE (rs12346) and SELO (rs4838862) expression; and 3) three nutriQTLs: The pathogenic C282Y mutation at HFE affecting iron (rs1800562), and two SNPs within several clustered metallothionein genes determining selenium concentration (rs1811322 and rs904773). Within the complete set of significant QTLs (which involved 30 SNPs and 20 gene regions), we identified 12 SNPs with extreme patterns of population differentiation (FST values in the top 5% percentile in at least one HapMap population pair) and significant evidence for selective sweeps involving QTLs at GPX1, SELENBP1, GPX3, SLC30A9, and SLC39A8. Overall, this detailed study of various molecular phenotypes illustrates the role of regulatory variants in explaining differences in trace element homeostasis among populations and in the human adaptive response to environmental pressures related to micronutrients.

The CC genotype of the delta-sarcoglycan gene polymorphism rs13170573 is associated with obstructive sleep apnea in the Chinese population

Obstructive sleep apnea (OSA) is a highly heterogeneous sleep disorder, and increasing evidence suggests that genetic factors play a role in the etiology of OSA. Airway muscle dysfunction might promote pharyngeal collapsibility, mutations or single nucleotide polymorphisms (SNPs) in the delta-sarcoglycan (SCGD) gene associated with muscle dysfunction. To evaluate if SCGD gene SNPs are associated with OSA, 101 individuals without OSA and 97 OSA patients were recruited randomly. The genotype distributions of SNPs (rs157350, rs7715464, rs32076, rs13170573 and rs1835919) in case and control populations were evaluated. The GG, GC and CC genotypes of rs13170573 in control and OSA groups were 51.5% and 37.1%, 36.6% and 35.1%, and 11.9% and 27.8%, respectively. Significantly fewer OSA patients possessed the GG genotype and significantly more possessed the CC genotype compared with controls. Further multivariate logistic regression analysis showed that the CC genotype was an independent risk factor for OSA, with an odds ratio (OR) of 2.17 (95% confidence interval [CI]: 1.19-6.01). Other factors, such as age ≥ 50 years, male gender, body mass index (BMI) ≥ 25 kg/m(2), low-density lipoprotein cholesterol (LDL-C) level ≥ 3.33 mg/dL, smoking and hypertension, were also independent risk factors for OSA in our multivariate logistic regression model.

Feature selection and classifier performance on diverse bio- logical datasets

Background

There is an ever-expanding range of technologies that generate very large numbers of biomarkers for research and clinical applications. Choosing the most informative biomarkers from a high-dimensional data set, combined with identifying the most reliable and accurate classification algorithms to use with that biomarker set, can be a daunting task. Existing surveys of feature selection and classification algorithms typically focus on a single data type, such as gene expression microarrays, and rarely explore the model's performance across multiple biological data types.

Results

This paper presents the results of a large scale empirical study whereby a large number of popular feature selection and classification algorithms are used to identify the tissue of origin for the NCI-60 cancer cell lines. A computational pipeline was implemented to maximize predictive accuracy of all models at all parameters on five different data types available for the NCI-60 cell lines. A validation experiment was conducted using external data in order to demonstrate robustness.

Conclusions

As expected, the data type and number of biomarkers have a significant effect on the performance of the predictive models. Although no model or data type uniformly outperforms the others across the entire range of tested numbers of markers, several clear trends are visible. At low numbers of biomarkers gene and protein expression data types are able to differentiate between cancer cell lines significantly better than the other three data types, namely SNP, array comparative genome hybridization (aCGH), and microRNA data.

Interestingly, as the number of selected biomarkers increases best performing classifiers based on SNP data match or slightly outperform those based on gene and protein expression, while those based on aCGH and microRNA data continue to perform the worst. It is observed that one class of feature selection and classifier are consistently top performers across data types and number of markers, suggesting that well performing feature-selection/classifier pairings are likely to be robust in biological classification problems regardless of the data type used in the analysis.

Prediction of individual response to anticancer therapy: historical and future perspectives

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

Cell line modeling for systems medicine in cancers (review)

Unexpected drug efficacy or resistance is poorly understood in cancers because of the lack of systematic analyses of drug response profiles in cancer tissues of various genotypic backgrounds. The recent development of high-throughput technologies has allowed massive screening of chemicals and drugs against panels of heterogeneous cancer cell lines. In parallel, multi-level omics datasets, including genome-wide genetic alterations, gene expression and protein regulation, have been generated from diverse sets of cancer cell lines, thus providing a surrogate system, known as cancer cell line modeling, that can represent cancer diversity. Taken together, recent efforts with cancer cell line modeling have enabled a systematic understanding of the causal factors of varied drug responses in cancers. These large-scale association studies could potentially predict and optimize target windows for drug treatment in cancer patients. The present review provides an overview of the major types of cell line-based large datasets and their applications in cancer studies. Moreover, this review discusses recent integrated approaches that use multi-level datasets to discover synergistic drug combination or repositioning for cancer treatment.

The oxidative damage and disbalance of calcium homeostasis in brain of chicken induced by selenium deficiency

Dietary selenium (Se) deficiency can influence the function of the brain. Our objective was to investigate the effects of Se deficiency on oxidative damage and calcium (Ca) homeostasis in brain of chicken. In the present study, 1-day-old chickens were fed either a commercial diet (as control group) with 0.15 mg/kg Se or a Se-deficient diet (as L group) with 0.033 mg/kg Se for 75 days. Then, brain injury biomarkers were examined, including histological analysis, ultrastructure assay, and apoptosis assay. We also examined the effect of Se deficiency on the Se-containing antioxidative enzyme glutathione peroxidase (GSH-Px), the level of glutathione (GSH), and the Ca homeostasis in brain of chicken. The results showed that the levels of Se and GSH and activity of GSH-Px are seriously reduced by 33.8-96 % (P < 0.001), 24.51-27.84 % (P < 0.001), and 20.70-64.24 % (P < 0.01), respectively. In the present study, we also perform histological analysis and ultrastructure assay and find that Se deficiency caused disorganized histological structure, damage to the mitochondria, fusion of nuclear membrane and nucleus shrinkage, higher apoptosis rate (P < 0.001), and increase of Ca homeostasis (P < 0.05 or P < 0.01 or P < 0.001) in the brain of chicken. In conclusion, the results demonstrated that Se deficiency induced oxidative damage and disbalance of Ca homeostasis in the brain of chicken. Similar to mammals, chickens brain is also extremely susceptible to oxidative damage and selenium deficiency.

Expression of microRNAs in the NCI-60 cancer cell-lines

The NCI-60 panel of 60 human cancer cell-lines of nine different tissues of origin has been extensively characterized in biological, molecular and pharmacological studies. Analyses of data from such studies have provided valuable information for understanding cellular processes and developing strategies for the diagnosis and treatment of cancer. Here, Affymetrix® GeneChip™ miRNA version 1 oligonucleotide microarrays were used to quantify 847 microRNAs to generate an expression dataset of 495 (58.4%) microRNAs that were identified as expressed in at least one cell-line of the NCI-60 panel. Accuracy of the microRNA measurements was partly confirmed by reverse transcription and polymerase chain reaction assays. Similar to that seen among the four existing NCI-60 microRNA datasets, the concordance of the new expression dataset with the other four was modest, with mean Pearson correlation coefficients of 0.37–0.54. In spite of this, comparable results with different datasets were noted in clustering of the cell-lines by their microRNA expression, differential expression of microRNAs by the lines’ tissue of origin, and correlation of specific microRNAs with the doubling-time of cells or their radiation sensitivity. Mutation status of the cell-lines for the TP53, PTEN and BRAF but not CDKN2A or KRAS cancer-related genes was found to be associated with changes in expression of specific microRNAs. The microRNA dataset generated here should be valuable to those working in the field of microRNAs as well as in integromic studies of the NCI-60 panel.

Selenium modulates oxidative stress-induced TRPM2 cation channel currents in transfected Chinese hamster ovary cells

It has been recently reported that the essential antioxidant element selenium has protective effects on cytosolic Ca(2+) levels in cell lines. However, the effects of selenium on like transient receptor potential melastatin 2 (TRPM2) in response to oxidative stress (H(2) O(2) ) are not well understood. We investigated the effects of selenium on H(2) O(2) -induced TRPM2 channel currents in the Chinese hamster ovary (CHO) cell line using patch-clamp and fura-2 fluorescence imaging techniques.

NCI60 cancer cell line panel data and RNAi analysis help identify EAF2 as a modulator of simvastatin and lovastatin response in HCT-116 cells

Simvastatin and lovastatin are statins traditionally used for lowering serum cholesterol levels. However, there exists evidence indicating their potential chemotherapeutic characteristics in cancer. In this study, we used bioinformatic analysis of publicly available data in order to systematically identify the genes involved in resistance to cytotoxic effects of these two drugs in the NCI60 cell line panel. We used the pharmacological data available for all the NCI60 cell lines to classify simvastatin or lovastatin resistant and sensitive cell lines, respectively. Next, we performed whole-genome single marker case-control association tests for the lovastatin and simvastatin resistant and sensitive cells using their publicly available Affymetrix 125K SNP genomic data. The results were then evaluated using RNAi methodology. After correction of the p-values for multiple testing using False Discovery Rate, our results identified three genes (NRP1, COL13A1, MRPS31) and six genes (EAF2, ANK2, AKAP7, STEAP2, LPIN2, PARVB) associated with resistance to simvastatin and lovastatin, respectively. Functional validation using RNAi confirmed that silencing of EAF2 expression modulated the response of HCT-116 colon cancer cells to both statins. In summary, we have successfully utilized the publicly available data on the NCI60 cell lines to perform whole-genome association studies for simvastatin and lovastatin. Our results indicated genes involved in the cellular response to these statins and siRNA studies confirmed the role of the EAF2 in response to these drugs in HCT-116 colon cancer cells.

Bioinformatic analyses identifies novel protein-coding pharmacogenomic markers associated with paclitaxel sensitivity in NCI60 cancer cell lines

BACKGROUND

Paclitaxel is a microtubule-stabilizing drug that has been commonly used in treating cancer. Due to genetic heterogeneity within patient populations, therapeutic response rates often vary. Here we used the NCI60 panel to identify SNPs associated with paclitaxel sensitivity. Using the panel's GI50 response data available from Developmental Therapeutics Program, cell lines were categorized as either sensitive or resistant. PLINK software was used to perform a genome-wide association analysis of the cellular response to paclitaxel with the panel's SNP-genotype data on the Affymetrix 125 k SNP array. FastSNP software helped predict each SNP's potential impact on their gene product. mRNA expression differences between sensitive and resistant cell lines was examined using data from BioGPS. Using Haploview software, we investigated for haplotypes that were more strongly associated with the cellular response to paclitaxel. Ingenuity Pathway Analysis software helped us understand how our identified genes may alter the cellular response to paclitaxel.

RESULTS

43 SNPs were found significantly associated (FDR<0.005) with paclitaxel response, with 10 belonging to protein-coding genes (CFTR, ROBO1, PTPRD, BTBD12, DCT, SNTG1, SGCD, LPHN2, GRIK1, ZNF607). SNPs in GRIK1, DCT, SGCD and CFTR were predicted to be intronic enhancers, altering gene expression, while SNPs in ZNF607 and BTBD12 cause conservative missense mutations. mRNA expression analysis supported these findings as GRIK1, DCT, SNTG1, SGCD and CFTR showed significantly (p<0.05) increased expression among sensitive cell lines. Haplotypes found in GRIK1, SGCD, ROBO1, LPHN2, and PTPRD were more strongly associated with response than their individual SNPs.

CONCLUSIONS

Our study has taken advantage of available genotypic data and its integration with drug response data obtained from the NCI60 panel. We identified 10 SNPs located within protein-coding genes that were not previously shown to be associated with paclitaxel response. As only five genes showed differential mRNA expression, the remainder would not have been detected solely based on expression data. The identified haplotypes highlight the role of utilizing SNP combinations within genomic loci of interest to improve the risk determination associated with drug response. These genetic variants represent promising biomarkers for predicting paclitaxel response and may play a significant role in the cellular response to paclitaxel.