DNA Microarray Data Research Articles

Detection of Sulfur Oxidizing Bacteria to Oxidize Hydrogen Sulfide in Biogas from Pig Farm by NGS and DNA Microarray Technique

A high concentration of hydrogen sulfide (H2S) released from pig farming is one of the major environmental problems affecting surrounding communities. In modern pig farms, the bioscrubber is used to eliminate H2S, which is found to be driven mainly by the sulfur-oxidizing bacteria (SOB) community. Therefore, in this study, molecular biology techniques such as next-generation sequencing (NGS) and DNA microarray are proposed to study the linkage between enzyme activity and the abundance of the SOB community. The starting sludge (SFP1) and recirculating sludge (SFP2) samples were collected from the bioscrubber reactor in the pig farm. The abundance of microbial populations between the two sampling sites was considered together with the gene expression results of both soxABXYZ and fccAB. Based on the NGS analysis, the members of phylum Proteobacteria such as Halothiobacillus, Acidithiobacillus, Thiothrix, Novosphingobium, Sulfuricurvum, Sulfurovum, Sulfurimonas, Acinetobacter, Thiobacillus, Magnetospirillum, Arcobacter, and Paracoccus were predominantly found in SFP2. The presence of Cyanobacteria in SFP pig farms is associated with increased biogas yields. The microarray results showed that the expression of soxAXBYZ and fccAB genes involved in the oxidation of sulfide to sulfate was increased in Halothiobacillus, Paracoccus, Acidithiobacillus, Magnetospirillum, Sphingobium, Thiobacillus, Sulfuricurvum, Sulfuricurvum, Arcobacter, and Thiothrix. Both NGS and DNA microarray data supported the functional roles of SOB in odor elimination and the oxidation of H2S through the function of soxABXYZ and fccAB. The results also identified the key microbes for H2S odor treatment, which can be utilized to monitor the stability of biological treatment systems and the toxicity of sulfide minerals by oxidation.

Effects of mifepristone on adipocyte differentiation in mouse 3T3-L1 cells

BackgroundBoth glucocorticoid receptor and peroxisome proliferator-activated receptor-γ (PPARγ) play a critical role in adipocyte differentiation. Mifepristone is not only an antagonist of the glucocorticoid receptor but also an agonist of PPARγ. Therefore, the present study investigated the effect of mifepristone on adipocyte differentiation.MethodsMouse 3T3-L1 cells were used as a model for adipocyte differentiation. The lipid droplet formation was evaluated with Bodipy493/503 staining and the expression of adipocyte markers [adiponectin and adipocyte fatty acid binding protein-4 (Fabp4)] was evaluated with quantitative PCR and immunoblot analyses for indication of adipocyte differentiation. siRNA and neutralizing antibodies were used to elucidate the molecular mechanism of mifepristone-induced adipocyte differentiation. Luciferase reporter assay was used to examine the effect of mifepristone on the promoter activity of PPAR-response element (PPRE). The DNA microarray analysis was used to characterize the transcriptome of the mifepristone-induced adipocytes. In vivo adipogenic effect of mifepristone was examined in mice.ResultsMifepristone not only enhanced adipocyte differentiation induced by the conventional protocol consisting of insulin, dexamethasone and 3-isobutyl-1-methylxanthine but also induced adipocyte differentiation alone, as evidenced by lipid droplets formation and induction of the expression of adiponectin and Fabp4. These effects were inhibited by an adiponectin-neutralizing antibody and a PPARγ antagonist. Mifepristone activated the promoter activity of PPRE in a manner sensitive to PPARγ antagonist. A principal component analysis (PCA) of DNA microarray data revealed that the mifepristone-induced adipocytes represent some characteristics of the in situ adipocytes in normal adipose tissues to a greater extent than those induced by the conventional protocol. Mifepristone administration induced an increase in the weight of epididymal, perirenal and gluteofemoral adipose tissues.ConclusionsMifepristone alone is capable of inducing adipocyte differentiation in 3T3-L1 cells and adipogenesis in vivo. PPARγ plays a critical role in the mifepristone-induced adipocyte differentiation. Mifepristone-induced adipocytes are closer to the in situ adipocytes than those induced by the conventional protocol. The present study proposes a single treatment with mifepristone as a novel protocol to induce more physiologically relevant adipocytes in 3T3-L1 cells than the conventional protocol.

Transcriptional dysregulation of autophagy in the muscle of a mouse model of Duchenne muscular dystrophy

It has been reported that autophagic activity is disturbed in the skeletal muscles of dystrophin-deficient mdx mice and patients with Duchenne muscular dystrophy (DMD). Transcriptional regulations of autophagy by FoxO transcription factors (FoxOs) and transcription factor EB (TFEB) play critical roles in adaptation to cellular stress conditions. Here, we investigated whether autophagic activity is dysregulated at the transcription level in dystrophin-deficient muscles. Expression levels of autophagy-related genes were globally decreased in tibialis anterior and soleus muscles of mdx mice compared with those of wild-type mice. DNA microarray data from the NCBI database also showed that genes related to autophagy were globally downregulated in muscles from patients with DMD. These downregulated genes are known as targets of FoxOs and TFEB. Immunostaining showed that nuclear localization of FoxO1 and FoxO3a was decreased in mdx mice. Western blot analyses demonstrated increases in phosphorylation levels of FoxO1 and FoxO3a in mdx mice. Nuclear localization of TFEB was also reduced in mdx mice, which was associated with elevated phosphorylation levels of TFEB. Collectively, the results suggest that autophagy is disturbed in dystrophin-deficient muscles via transcriptional downregulation due to phosphorylation-mediated suppression of FoxOs and TFEB.

Clonal Hematopoiesis Associated with Rheumatoid Arthritis

Introduction: Clonal hematopoiesis (CH) becomes common with age and can play a role in the pathophysiology of inflammatory diseases by modifying immune cell function. CH is characterized by the presence of somatic mutations in genes such as DNMT3A and TET2 (CH of indeterminate potential, CHIP), and as mosaic chromosomal alterations (mCAs). While CHIP, particularly in the context of TET2 mutations, can promote inflammation in cardiovascular diseases, the association between clonal hematopoiesis and autoimmunity has not been comprehensively studied to date. Rheumatoid arthritis (RA) is one of the most common autoimmune diseases primarily affecting joints. Seropositive RA is defined by the presence of anti-cyclic citrullinated peptide antibodies (ACPA) and/or rheumatoid factor (RF), whereas seronegative RA is a more heterogeneous inflammatory disease entity involving patients with clinical RA manifestations in the absence of ACPA and RF. Understanding of the association between CH and inflammatory processes may yield novel insights into prevention and management of autoimmune diseases. Methods: To comprehensively evaluate the association between CH and autoimmune diseases, we identified CHIP variants in whole exome sequencing (WES) data from the FINRISK study (N=10 129). We also detected mCAs and CHIP in the nationwide FinnGen study (N=340 173), where we manually identified CHIP carriers as outliers in SNP intensity plots at known CHIP loci in DNA microarray data. Only known CHIP loci with variant prevalence comparable to UK Biobank WES data and significant positive associations with age and hematological malignancies were included for downstream analyses. To further characterize the link between CHIP and RA phenotypes, we performed targeted panel sequencing of 69 myeloid driver genes in 675 RA patient blood samples (91% collected at diagnosis) and 60 healthy controls. We studied the association between CH and RA in multivariable models excluding participants with prevalent hematologic malignancies and adjusting for sex, age, smoking, and principal components of ancestry. We performed uni- and multivariable analyses to evaluate the link between CHIP and RA patient phenotypes. Results: We identified CHIP in 381 (3.8%) FINRISK participants. As expected, the prevalence of CHIP increased with age, and CHIP variants were associated with prevalent and incident hematologic malignancies. C-reactive protein values were higher in participants with CHIP (P<0.001). In multivariable models, CHIP status (OR 1.98, 1.13-3.45, P=0.017) was more common in participants with questionnaire-based history of RA (N=151) (Fig 1A). In the FinnGen study, CHIP carrier status was associated with prevalent autoimmune diseases (OR 1.26, 1.05-1.53, P=0.015). We validated the association between prevalent RA (N=10 990) and CHIP carrier status (N=2 393) in the FinnGen study (OR 1.58, 1.18-2.13, P=0.002) (Fig 1A). In particular, CHIP (OR 2.17, 1.40-3.36, P<0.001), and GNB1 variants (OR 4.71, 1.87-11.82, P=0.001) were associated with the seronegative RA, whereas DNMT3A was associated with seropositive RA (OR 1.83, 1.08-3.09, P=0.024). We observed no association between autosomal mCAs and RA. Using targeted panel sequencing, we found CHIP in 22% (146/675) of RA patients. RA patients with CHIP variants were older than those without a CHIP variant (median, 69 vs 63 years, P<0.001). Erythrocyte sedimentation rate (ESR) was higher in those with CHIP (median, 42.5 vs 26.0 mm/h, P=0.043). RA patients with TET2 variants were more likely to have seronegative disease compared with TET2 wild-type patients (proportion of seronegative patients, 56% vs 33%, OR 2.62; P=0.008) (Fig 1B). The association remained significant (OR 2.38, 1.14-4.96, P=0.02) when adjusting for age, sex, and smoking. Evaluation of longitudinal disease outcomes by CHIP status are currently ongoing. Conclusion: CHIP variants were more common in participants with prevalent RA in two population-level cohorts, and the association was specific to seronegative RA in the large FinnGen cohort. In newly diagnosed RA patients, TET2 mutations were more common in seronegative than in seropositive RA. While the causality and the functional basis of these observations remain to be explored in future studies, our results provide additional evidence for the association between CHIP and inflammation in various disease contexts, including autoimmune diseases.

A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of DNA microarray data

A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of DNA microarray data

Feature Selection of Microarray Data Using Simulated Kalman Filter with Mutation

Microarrays have been proven to be beneficial for understanding the genetics of disease. They are used to assess many different types of cancers. Machine learning algorithms, like the artificial neural network (ANN), can be trained to determine whether a microarray sample is cancerous or not. The classification is performed using the features of DNA microarray data, which are composed of thousands of gene values. However, most of the gene values have been proven to be uninformative and redundant. Meanwhile, the number of the samples is significantly smaller in comparison to the number of genes. Therefore, this paper proposed the use of a simulated Kalman filter with mutation (SKF-MUT) for the feature selection of microarray data to enhance the classification accuracy of ANN. The algorithm is based on a metaheuristics optimization algorithm, inspired by the famous Kalman filter estimator. The mutation operator is proposed to enhance the performance of the original SKF in the selection of microarray features. Eight different benchmark datasets were used, which comprised: diffuse large b-cell lymphomas (DLBCL); prostate cancer; lung cancer; leukemia cancer; “small, round blue cell tumor” (SRBCT); brain tumor; nine types of human tumors; and 11 types of human tumors. These consist of both binary and multiclass datasets. The accuracy is taken as the performance measurement by considering the confusion matrix. Based on the results, SKF-MUT effectively selected the number of features needed, leading toward a higher classification accuracy ranging from 95% to 100%.

A Bimodal Extension of the Log-Normal Distribution on the Real Line with an Application to DNA Microarray Data

A bimodal double log-normal distribution on the real line is proposed using the random sign mixture transform. Its associated statistical inferences are derived. Its parameters are estimated by the maximum likelihood method. The performance of the estimators and the corresponding confidence intervals is checked by simulation studies. Application of the proposed distribution to a real data set from a DNA microarray is presented.

Experimental Evaluation of the Possibility to Detect Cross-Contaminated DNA Samples Based on Genetic Data

The problems of cross-contamination and swap samples are extremely relevant during large-scale genetic studies. In this study several approaches of detecting cross-contaminated DNA samples were checked: the ratio of reads per reference and alternative allele (allele ratio, AR), the amount of heterozygos to homozygous variants ratio, the CallRate value for the DNA microarrays data, the Picard CrosscheckFingerprints (CrossCheck) program. Contaminated samples (mixtures) were created by mixing ordinary “pure” DNA samples in different ratios. Samples’ quality parameters were analyzed after whole genome sequencing and genotyping with the Illumina microarray BeadArray technology CoreExome (CE) DNA microarray. It has been experimentally established that all of these approaches can be used to detect genotyping errors associated with sample contamination.

Stepwise activation of p63 and the MEK/ERK pathway induces the expression of ARL4C to promote oral squamous cell carcinoma cell proliferation.

Carcinogenesis is a multistep process wherein cells accumulate multiple genetic alterations and progress to a more malignant phenotype. It has been proposed that sequential accumulation of gene abnormalities in specific genes drives the transition from non-tumorous epithelia through a preneoplastic lesion/benign tumor to cancer. Histologically, oral squamous cell carcinoma (OSCC) progresses in multiple ordered steps that begin with mucosal epithelial cell hyperplasia, which is followed by dysplasia, carcinoma in situ and invasive carcinoma. It is therefore hypothesized that genetic alteration-mediated multistep carcinogenesis would be involved in the development of OSCC; however, the detailed molecular mechanisms are unknown. We clarified the comprehensive gene expression patterns and carried out an enrichment analysis using DNA microarray data from a pathological specimen of OSCC (including a non-tumor region, carcinoma in situ lesion and invasive carcinoma lesion). The expression of numerous genes and signal activation were altered in the development of OSCC. Among these, the p63 expression was increased and the MEK/ERK-MAPK pathway was activated in carcinoma in situ lesion and in invasive carcinoma lesion. Immunohistochemical analyses revealed that p63 was initially upregulated in carcinoma in situ and ERK was sequentially activated in invasive carcinoma lesions in OSCC specimens. ADP-ribosylation factor (ARF)-like 4c (ARL4C), the expression of which is reportedly induced by p63 and/or the MEK/ERK-MAPK pathway in OSCC cells, has been shown to promote tumorigenesis. Immunohistochemically, in OSCC specimens, ARL4C was more frequently detected in tumor lesions, especially in invasive carcinoma lesions, than in carcinoma in situ lesions. Additionally, ARL4C and phosphorylated ERK were frequently merged in invasive carcinoma lesions. Loss-of-function experiments using inhibitors and siRNAs revealed that p63 and MEK/ERK-MAPK cooperatively induce the expression of ARL4C and cell growth in OSCC cells. These results suggest that the stepwise activation of p63 and MEK/ERK-MAPK contributes to OSCC tumor cell growth through regulation of ARL4C expression.

MP14-20 LATROPHILINS AS DOWNSTREAM EFFECTORS OF THE ANDROGEN RECEPTOR INDUCE BLADDER CANCER PROGRESSION

You have accessJournal of UrologyCME1 Apr 2023MP14-20 LATROPHILINS AS DOWNSTREAM EFFECTORS OF THE ANDROGEN RECEPTOR INDUCE BLADDER CANCER PROGRESSION Takuro Goto, Yuki Teramoto, Masato Yasui, Guiyang Jiang, Yujiro Nagata, and Hiroshi Miyamoto Takuro GotoTakuro Goto More articles by this author , Yuki TeramotoYuki Teramoto More articles by this author , Masato YasuiMasato Yasui More articles by this author , Guiyang JiangGuiyang Jiang More articles by this author , Yujiro NagataYujiro Nagata More articles by this author , and Hiroshi MiyamotoHiroshi Miyamoto More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003234.20AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We and others have indicated that androgen receptor (AR) activity is strongly associated with urothelial cancer outgrowth. Meanwhile, our DNA microarray data suggest that latrophilin-3 (LPHN3), one of latrophilins (LPHNs) that are a group of the G-protein-coupled receptor where a spider venom latrotoxin (LTX) is known to bind, represents a downstream target of AR in bladder cancer cells. However, the involvement of LPHNs in neoplastic diseases has not been documented. The present study aimed to determine the functional role of LPHNs, including LPHN1, LPHN2, and LPHN3, in the progression of bladder cancer. METHODS: We determined the levels of LPHN expression in 145 bladder tumors (by immunohistochemistry) as well as in human bladder cancer lines (by RT-PCR/western blot). We then assessed the effects of ligand (e.g. LTX, FLRT3) treatment, as well as silencing of each LPHN via shRNA virus infection, on the growth of bladder cancer cells. RESULTS: Bladder tumors showed immunoreactivity for LPHN1 (86%), LPHN2 (66%), and LPHN3 (52%). The positive rates for LPHN1 (92%/95% vs. 69%/78%, p<0.001/p=0.004) and LPHN2 (77%/81% vs. 46%/55%, p<0.001/p=0.002) were significantly higher in high-grade/muscle-invasive tumors than in low-grade/non-muscle-invasive tumors, respectively. In addition, LPHN1 (HR=3.026, 95% CI=1.008-9.084, p=0.048) or LPHN3 (HR=2.314, 95% CI=1.123-4.771, p=0.023) positivity in muscle-invasive tumors showed significance for disease-specific mortality even in a multivariate setting. In AR-positive bladder cancer lines, androgen treatment considerably increased the expression levels of LPHN1, LPHN2, and LPHN3. Chromatin immunoprecipitation assay further revealed the binding of AR to the promoter region of each LPHN in bladder cancer cells. Meanwhile, treatment with LTX or FLRT3 induced the expression of each LPHN. MTT assay then showed that LTX or FLRT3 increased the cell viability of bladder cancer lines, while knockdown of LPHN1, LPHN2, or LPHN3 reduced it. Similarly, induction by LTX/FLRT3 treatment and inhibition by LPHN1/LPHN2/LPHN3 knockdown in the ability of cell migration were seen. CONCLUSIONS: LPHNs, as downstream effectors of the AR, are thus likely to be activated in some bladder cancers, which may result in the promotion of tumor growth. Moreover, immunohistochemistry for LPHNs may be useful for predicting tumor progression, particularly in those with muscle-invasive disease. Accordingly, LPHN inhibition, along with AR inactivation, has the potential of being an effective therapeutic approach for urothelial cancer. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e190 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Takuro Goto More articles by this author Yuki Teramoto More articles by this author Masato Yasui More articles by this author Guiyang Jiang More articles by this author Yujiro Nagata More articles by this author Hiroshi Miyamoto More articles by this author Expand All Advertisement PDF downloadLoading ...

A Survey of Feature Selection Strategies for DNA Microarray Classification

Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, cross-validation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features. Keywords: Brief survey; DNA microarray data; feature selection; filter methods; wrapper methods; embedded methods; and hybrid methods. DOI: 10.7176/CEIS/14-2-01 Publication date: March 31 st 2023

Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare

BackgroundIdentifying candidates responsive to treatment is important in lupus nephritis (LN) at the renal flare (RF) because an effective treatment can lower the risk of progression to end-stage kidney disease. However, machine learning (ML)-based models that address this issue are lacking.MethodsTranscriptomic profiles based on DNA microarray data were extracted from the GSE32591 and GSE112943 datasets. Comprehensive bioinformatics analyses were performed to identify disease-defining genes (DDGs). Peripheral blood samples (GSE81622, GSE99967, and GSE72326) were used to evaluate the effect of DDGs. Single-sample gene set enrichment analysis (ssGSEA) scores of the DDGs were calculated and correlated with specific immunology genes listed in the nCounter panel. GSE60681 and GSE69438 were used to examine the ability of the DDGs to discriminate LN from other renal diseases. K-means clustering was used to obtain the separate gene sets. The clustering results were extended to data derived using the nCounter technique. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first RF in each cluster. LASSO models with tenfold validation were built in GSE200306 and assessed by receiver operating characteristic (ROC) analysis with area under curve (AUC). The models were validated by using an independent dataset (GSE113342).ResultsForty-five hub genes specific to LN were identified. Eight optimal disease-defining clusters (DDCs) were identified in this study. Th1 and Th2 cell differentiation pathway was significantly enriched in DDC-6. LCK in DDC-6, whose expression positively correlated with various subsets of T cell infiltrations, was found to be differentially expressed between responders and non-responders and was ranked high in regulatory network analysis. Based on DDC-6, the prediction model had the best performance (AUC: 0.75; 95% confidence interval: 0.44–1 in the testing set) and high precision (0.83), recall (0.71), and F1 score (0.77) in the validation dataset.ConclusionsOur study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.

Shc Is Implicated in Calreticulin-Mediated Sterile Inflammation in Alcoholic Hepatitis.

Src homology and collagen (Shc) proteins are major adapters to extracellular signals, however, the regulatory role of Shc isoforms in sterile inflammatory responses in alcoholic hepatitis (AH) has not been fully investigated. We hypothesized that in an isoform-specific manner Shc modulates pre-apoptotic signals, calreticulin (CRT) membrane exposure, and recruitment of inflammatory cells. Liver biopsy samples from patients with AH vs healthy subjects were studied for Shc expression using DNA microarray data and immunohistochemistry. Shc knockdown (hypomorph) and age-matched wild-type mice were pair-fed according to the chronic-plus-binge alcohol diet. To analyze hepatocyte-specific effects, adeno-associated virus 8-thyroxine binding globulin-Cre (hepatocyte-specific Shc knockout)-mediated deletion was performed in flox/flox Shc mice. Lipid peroxidation, proinflammatory signals, redox radicals, reduced nicotinamide adenine dinucleotide/oxidized nicotinamide adenine dinucleotide ratio, as well as cleaved caspase 8, B-cell-receptor-associated protein 31 (BAP31), Bcl-2-associated X protein (Bax), and Bcl-2 homologous antagonist killer (Bak), were assessed invivo. CRT translocation was studied in ethanol-exposed p46ShcẟSH2-transfected hepatocytes by membrane biotinylation in conjunction with phosphorylated-eukaryotic initiation factor 2 alpha, BAP31, caspase 8, and Bax/Bak. The effects of idebenone, a novel Shc inhibitor, was studied in alcohol/pair-fed mice. Shc was significantly induced in patients with AH (P< .01). Alanine aminotransferase, reduced nicotinamide adenine dinucleotide/oxidized nicotinamide adenine dinucleotide ratios, production of redox radicals, and lipid peroxidation improved (P < .05), and interleukin 1β, monocyte chemoattractant protein 1, and C-X-C chemokine ligand 10 were reduced in Shc knockdown and hepatocyte-specific Shc knockout mice. Invivo, Shc-dependent induction, and, in hepatocytes, a p46Shc-dependent increase in pre-apoptotic proteins Bax/Bak, caspase 8, BAP31 cleavage, and membrane translocation of CRT/endoplasmic reticulum-resident protein 57 were seen. Idebenone protected against alcohol-mediated liver injury. Alcohol induces p46Shc-dependent activation of pre-apoptotic pathways and translocation of CRT to the membrane, where it acts as a damage-associated molecular pattern, instigating immunogenicity. Shc inhibition could be a novel treatment strategy in AH.

Telomere length analysis in amyotrophic lateral sclerosis using large-scale whole genome sequence data.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the loss of upper and lower motor neurons, leading to progressive weakness of voluntary muscles, with death following from neuromuscular respiratory failure, typically within 3 to 5 years. There is a strong genetic contribution to ALS risk. In 10% or more, a family history of ALS or frontotemporal dementia is obtained, and the Mendelian genes responsible for ALS in such families have now been identified in about 50% of cases. Only about 14% of apparently sporadic ALS is explained by known genetic variation, suggesting that other forms of genetic variation are important. Telomeres maintain DNA integrity during cellular replication, differ between sexes, and shorten naturally with age. Sex and age are risk factors for ALS and we therefore investigated telomere length in ALS. Samples were from Project MinE, an international ALS whole genome sequencing consortium that includes phenotype data. For validation we used donated brain samples from motor cortex from people with ALS and controls. Ancestry and relatedness were evaluated by principal components analysis and relationship matrices of DNA microarray data. Whole genome sequence data were from Illumina HiSeq platforms and aligned using the Isaac pipeline. TelSeq was used to quantify telomere length using whole genome sequence data. We tested the association of telomere length with ALS and ALS survival using Cox regression. There were 6,580 whole genome sequences, reducing to 6,195 samples (4,315 from people with ALS and 1,880 controls) after quality control, and 159 brain samples (106 ALS, 53 controls). Accounting for age and sex, there was a 20% (95% CI 14%, 25%) increase of telomere length in people with ALS compared to controls (p = 1.1 × 10-12), validated in the brain samples (p = 0.03). Those with shorter telomeres had a 10% increase in median survival (p = 5.0×10-7). Although there was no difference in telomere length between sporadic ALS and familial ALS (p=0.64), telomere length in 334 people with ALS due to expanded C9orf72 repeats was shorter than in those without expanded C9orf72 repeats (p = 5.0×10-4). Although telomeres shorten with age, longer telomeres are a risk factor for ALS and worsen prognosis. Longer telomeres are associated with ALS.

Clustering column-mean quantile median: a new methodology for imputing missing data

DNA microarray data sets have been widely explored and used to analyze data without any previous biological background. However, analyzing them becomes challenging if data are missing. Thus, machine learning techniques are applied because microarray technology is promising in genomics, especially in the analysis of gene expression data. Furthermore, gene expression data can describe the transcription and translation processes of each genetic information in detail. In this study, a new system was proposed to impute more realizable values for missing data in a microarray dataset. This system was validated and evaluated on 42 samples of rectal cancer. Several evaluation tests were also conducted to confirm the effectiveness of the new system and compare it with highly known imputing algorithms. The proposed clustering column-mean quantile median technique could predict highly informative missing genes, thereby reducing the difference between the original and imputed datasets and demonstrating its efficiency.

Penalized logistic regression with prior information for microarray gene expression classification.

Cancer classification and gene selection are important applications in DNA microarray gene expression data analysis. Since DNA microarray data suffers from the high-dimensionality problem, automatic gene selection methods are used to enhance the classification performance of expert classifier systems. In this paper, a new penalized logistic regression method that performs simultaneous gene coefficient estimation and variable selection in DNA microarray data is discussed. The method employs prior information about the gene coefficients to improve the classification accuracy of the underlying model. The coordinate descent algorithm with screening rules is given to obtain the gene coefficient estimates of the proposed method efficiently. The performance of the method is examined on five high-dimensional cancer classification datasets using the area under the curve, the number of selected genes, misclassification rate and F-score measures. The real data analysis results indicate that the proposed method achieves a good cancer classification performance with a small misclassification rate, large area under the curve and F-score by trading off some sparsity level of the underlying model. Hence, the proposed method can be seen as a reliable penalized logistic regression method in the scope of high-dimensional cancer classification.

Wnt signaling promotes tooth germ development through YAP1-TGF-β signaling

Tooth germ development involves continuous and sequential steps with reciprocal interactions between odontogenic epithelium and the adjacent mesenchyme. Several growth factors, including Wnt, are essential for tooth germ development. Molecular mechanisms underlying Wnt/β-catenin-regulated tooth germ development are poorly understood. In tooth germ rudiments culture, we recently demonstrated that Semaphorin 3A (Sema3A), an axonal guidance factor, stimulation reversed Wnt/β-catenin signaling-dependent decreased cell proliferation but did not completely rescue the morphological anomalies of tooth germ, suggesting that an uncharacterized signaling pathway may be essential in Wnt/β-catenin signaling-dependent tooth germ development. Herein, an enrichment analysis using DNA microarray data, which was obtained in our previous research, revealed that Wnt/β-catenin signaling negatively regulates YAP1 and/or TGF-β signalings. In odontogenic epithelial cells and tooth germ rudiments, Wnt/β-catenin signaling activation reduced YAP1 expression, thereby suppressing YAP1 and TGF-β signalings sequentially. Additionally, YAP1 signaling induced TGF-β2 expression to promote TGF-β signaling in the cells. Finally, Wnt/β-catenin signaling-dependent disorganized tooth germ development, in which YAP1 signaling was suppressed, was reversed by TGF-β stimulation. These results suggest that Wnt/β-catenin signaling contributes to the tooth germ development through YAP1-TGF-β signaling.

Photothermal-Controlled Release of IL-4 in IL-4/PDA-Immobilized Black Titanium Dioxide (TiO2) Nanotubes Surface to Enhance Osseointegration: An In Vivo Study.

Host immune response has gradually been accepted as a critical factor in achieving successful implant osseointegration. The aim of this study is to create a favorable immune microenvironment by the dominant release of IL-4 during the initial few days after implant insertion to mitigate early inflammatory reactions and facilitate osseointegration. Herein, the B-TNT/PDA/IL-4 substrate was established by immobilizing an interleukin-4 (IL-4)/polydopamine (PDA) coating on a black TiO2 nanotube (B-TNT) surface, achieving on-demand IL-4 release under near infrared (NIR) irradiation. Gene Ontology (GO) enrichment analyses based on high-throughput DNA microarray data revealed that IL-4 addition inhibited osteoclast differentiation and function. Animal experiment results suggested that the B-TNT/PDA/IL-4+Laser substrate induced the least inflammatory, tartrate-resistant acid phosphatase, inducible nitric oxide synthase and the most CD163 positive cells, compared to the Ti group at 7 days post-implantation. In addition, 28 days post-implantation, micro-computed tomography results showed the highest bone volume/total volume, trabecular thickness, trabecular number and the lowest trabecular separation, while Hematoxylin-eosin and Masson-trichrome staining revealed the largest amount of new bone formation for the B-TNT/PDA/IL-4+Laser group. This study revealed the osteoimmunoregulatory function of the novel B-TNT/PDA/IL-4 surface by photothermal release of IL-4 at an early period post-implantation, thus paving a new way for dental implant surface modification.

A comparison of strategies for generating artificial replicates in RNA-seq experiments

Due to the overall high costs, technical replicates are usually omitted in RNA-seq experiments, but several methods exist to generate them artificially. Bootstrapping reads from FASTQ-files has recently been used in the context of other NGS analyses and can be used to generate artificial technical replicates. Bootstrapping samples from the columns of the expression matrix has already been used for DNA microarray data and generates a new artificial replicate of the whole experiment. Mixing data of individual samples has been used for data augmentation in machine learning. The aim of this comparison is to evaluate which of these strategies are best suited to study the reproducibility of differential expression and gene-set enrichment analysis in an RNA-seq experiment. To study the approaches under controlled conditions, we performed a new RNA-seq experiment on gene expression changes upon virus infection compared to untreated control samples. In order to compare the approaches for artificial replicates, each of the samples was sequenced twice, i.e. as true technical replicates, and differential expression analysis and GO term enrichment analysis was conducted separately for the two resulting data sets. Although we observed a high correlation between the results from the two replicates, there are still many genes and GO terms that would be selected from one replicate but not from the other. Cluster analyses showed that artificial replicates generated by bootstrapping reads produce it p values and fold changes that are close to those obtained from the true data sets. Results generated from artificial replicates with the approaches of column bootstrap or mixing observations were less similar to the results from the true replicates. Furthermore, the overlap of results among replicates generated by column bootstrap or mixing observations was much stronger than among the true replicates. Artificial technical replicates generated by bootstrapping sequencing reads from FASTQ-files are better suited to study the reproducibility of results from differential expression and GO term enrichment analysis in RNA-seq experiments than column bootstrap or mixing observations. However, FASTQ-bootstrapping is computationally more expensive than the other two approaches. The FASTQ-bootstrapping may be applicable to other applications of high-throughput sequencing.

MP06-14 ROLE OF LATROPHILINS AS KEY DOWNSTREAM EFFECTORS OF THE ANDROGEN RECEPTOR IN MODULATING UROTHELIAL TUMORIGENESIS

You have accessJournal of UrologyCME1 May 2022MP06-14 ROLE OF LATROPHILINS AS KEY DOWNSTREAM EFFECTORS OF THE ANDROGEN RECEPTOR IN MODULATING UROTHELIAL TUMORIGENESIS Takuro Goto, Yuki Teramoto, Masato Yasui, Guiyang Jiang, Yujiro Nagata, George Netto, and Hiroshi Miyamoto Takuro GotoTakuro Goto More articles by this author , Yuki TeramotoYuki Teramoto More articles by this author , Masato YasuiMasato Yasui More articles by this author , Guiyang JiangGuiyang Jiang More articles by this author , Yujiro NagataYujiro Nagata More articles by this author , George NettoGeorge Netto More articles by this author , and Hiroshi MiyamotoHiroshi Miyamoto More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002523.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We and others have indicated that androgen receptor (AR) signaling plays a critical role in urothelial cancer outgrowth. Meanwhile, our DNA microarray data suggest that latrophilin-3 (LPHN3), one of latrophilins (LPHNs) that are a group of the G-protein-coupled receptor where a spider venom latrotoxin (LTX) is known to bind, represents a downstream target of AR in bladder cancer cells. The present study aimed to determine the functional role of LPHNs, including LPHN1, LPHN2, and LPHN3, in the development of urothelial cancer. METHODS: We compared the levels of LPHN expression in 83 bladder tumors vs. paired normal bladder tissues (by immunohistochemistry) as well as in human normal urothelial SVHUC sublines stably expressing wild-type AR vs. vector only (by western blot). We then assessed the effects of ligand (e.g. LTX, FLRT3) treatment, as well as silencing of each LPHN via shRNA virus infection, on the neoplastic transformation of SVHUC-derived cells induced by a chemical carcinogen 3-methylcholanthrene (MCA). Bladder tumor development was also compared in C57BL/6 mice treated with a carcinogen BBN and LTX/FLRT3. RESULTS: The rates of immunoreactivity for LPHN1 (87% vs. 69%, P=0.012), LPHN2 (55% vs. 31%, P=0.002), and LPHN3 (51% vs. 37%, P=0.087) were significantly higher in bladder tumors than in non-neoplastic urothelial tissues. Moreover, LPHN3 positivity in non-muscle-invasive tumors was associated with the risk of disease recurrence after transurethral surgery (P=0.051). The expression levels of each LPHN were also higher in SVHUC-AR cells than in AR-negative SVHUC-vector cells, and androgen treatment in SVHUC-AR further increased them. Chromatin immunoprecipitation assay then revealed the binding of AR to the promoter region of each LPHN in SVHUC-derived cells. Meanwhile, induction in LPHN expression by LTX or FLRT3 treatment was seen in SVHUC cells. The in vitro transformation assay showed that LTX and FLRT3 induced the MCA-mediated oncogenic activity particularly in SVHUC-vector cells, while knockdown of LPHN1, LPHN2, or LPHN3 resulted in similar inhibition in that of MCA-SVHUC-AR cells. LTX (P=0.002) and FLRT3 (P=0.001) also accelerated the development of bladder tumors in BBN-treated female mice. CONCLUSIONS: These findings suggest that LPHNs, which are activated in bladder tumors as key downstream effectors of the AR, promote urothelial tumorigenesis. Accordingly, LPHN inhibition, along with AR inactivation, has the potential of being an effective chemopreventive approach for urothelial cancer. Source of Funding: Ferring Research Institute © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e82 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Takuro Goto More articles by this author Yuki Teramoto More articles by this author Masato Yasui More articles by this author Guiyang Jiang More articles by this author Yujiro Nagata More articles by this author George Netto More articles by this author Hiroshi Miyamoto More articles by this author Expand All Advertisement PDF DownloadLoading ...