Bioinformatics Analysis Of Data Research Articles

Integrated bioinformatic analysis of gene expression profiling data to identify combinatorial biomarkers in inflammatory skin disease

Selection of appropriate biomarker to identify inflammatory skin diseases is complicated by the involvement of thousands of differentially expressed genes (DEGs) across multiple cell types and organs. This study aimed to identify combinatorial biomarkers in inflammatory skin diseases. From one gene expression microarray profiling dataset, we performed bioinformatic analyses on dataset from lesional skin biopsies of patients with inflammatory skin diseases (atopic dermatitis [AD], contact eczema [KE], lichen planus [Li], psoriasis vulgaris [Pso]) and healthy controls to identify the involved pathways, predict upstream regulators, and potential measurable extracellular biomarkers. Overall, 434, 629, 581, and 738 DEGs were mapped in AD, KE, Li, and Pso, respectively; 238 identified DEGs were shared among four different inflammatory skin diseases. Bioinformatic analysis on four inflammatory skin diseases showed significant activation of pathways with known pathogenic relevance. Common upstream regulators, with upregulated predicted activity, identified were CNR1 and BMP4. We found the following common serum biomarkers: ACR, APOE, ASIP, CRISP1, DKK1, IL12B, IL9, MANF, MDK, NRTN, PCSK5, and VEGFC. Considerable differences of gene expression changes, involved pathways, upstream regulators, and biomarkers were found in different inflammatory skin diseases. Integrated bioinformatic analysis identified 12 potential common biomarkers of inflammatory skin diseases requiring further evaluation.

RNA editing increases the nucleotide diversity of SARS-CoV-2 in human host cells.

SARS-CoV-2 is a positive-sense, single-stranded RNA virus responsible for the COVID-19 pandemic. It remains unclear whether and to what extent the virus in human host cells undergoes RNA editing, a major RNA modification mechanism. Here we perform a robust bioinformatic analysis of metatranscriptomic data from multiple bronchoalveolar lavage fluid samples of COVID-19 patients, revealing an appreciable number of A-to-I RNA editing candidate sites in SARS-CoV-2. We confirm the enrichment of A-to-I RNA editing signals at these candidate sites through evaluating four characteristics specific to RNA editing: the inferred RNA editing sites exhibit (i) stronger ADAR1 binding affinity predicted by a deep-learning model built from ADAR1 CLIP-seq data, (ii) decreased editing levels in ADAR1-inhibited human lung cells, (iii) local clustering patterns, and (iv) higher RNA secondary structure propensity. Our results have critical implications in understanding the evolution of SARS-CoV-2 as well as in COVID-19 research, such as phylogenetic analysis and vaccine development.

KMC3 and CHTKC: Best Scenarios, Deficiencies, and Challenges in High-Throughput Sequencing Data Analysis

Background: K-mer frequency counting is an upstream process of many bioinformatics data analysis workflows. KMC3 and CHTKC are the representative partition-based k-mer counting and non-partition-based k-mer counting algorithms, respectively. This paper evaluates the two algorithms and presents their best applicable scenarios and potential improvements using multiple hardware contexts and datasets. Results: KMC3 uses less memory and runs faster than CHTKC on a regular configuration server. CHTKC is efficient on high-performance computing platforms with high available memory, multi-thread, and low IO bandwidth. When tested with various datasets, KMC3 is less sensitive to the number of distinct k-mers and is more efficient for tasks with relatively low sequencing quality and long k-mer. CHTKC performs better than KMC3 in counting assignments with large-scale datasets, high sequencing quality, and short k-mer. Both algorithms are affected by IO bandwidth, and decreasing the influence of the IO bottleneck is critical as our tests show improvement by filtering and compressing consecutive first-occurring k-mers in KMC3. Conclusions: KMC3 is more competitive for running counter on ordinary hardware resources, and CHTKC is more competitive for counting k-mers in super-scale datasets on higher-performance computing platforms. Reducing the influence of the IO bottleneck is essential for optimizing the k-mer counting algorithm, and filtering and compressing low-frequency k-mers is critical in relieving IO impact.

GXP: Analyze and Plot Plant Omics Data in Web Browsers.

Next-generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever-growing number of life science research projects. Typically, established software pipelines analyze raw data and produce quantitative data informing about gene expression or concentrations of metabolites. These results need to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some of these tools already exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNAseq and Metabolomics data visualization and analysis tool entirely running in the user’s web browser, thus not needing any custom installation, manual programming or uploading of confidential data to third party servers. Consequently, upon receiving the bioinformatic raw data analysis of RNAseq or other omics results, GXP immediately enables the user to interact with the data according to biological questions by performing knowledge-driven, in-depth data analyses and candidate identification via visualization and data exploration. Thereby, GXP can support and accelerate complex interdisciplinary omics projects and downstream analyses. GXP offers an easy way to publish data, plots, and analysis results either as a simple exported file or as a custom website. GXP is freely available on GitHub (see introduction)

Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1.

Ample evidence indicates that the gut microbiome is a tumor-extrinsic factor associated with antitumor response to anti-programmed cell death protein-1 (PD-1) therapy, but inconsistencies exist between published microbial signatures associated with clinical outcomes. To resolve this, we evaluated a new melanoma cohort, along with four published datasets. Time-to-event analysis showed that baseline microbiota composition was optimally associated with clinical outcome at approximately 1 year after initiation of treatment. Meta-analysis and other bioinformatic analyses of the combined data show that bacteria associated with favorable response are confined within the Actinobacteria phylum and the Lachnospiraceae/Ruminococcaceae families of Firmicutes. Conversely, Gram-negative bacteria were associated with an inflammatory host intestinal gene signature, increased blood neutrophil-to-lymphocyte ratio, and unfavorable outcome. Two microbial signatures, enriched for Lachnospiraceae spp. and Streptococcaceae spp., were associated with favorable and unfavorable clinical response, respectively, and with distinct immune-related adverse effects. Despite between-cohort heterogeneity, optimized all-minus-one supervised learning algorithms trained on batch-corrected microbiome data consistently predicted outcomes to programmed cell death protein-1 therapy in all cohorts. Gut microbial communities (microbiotypes) with nonuniform geographical distribution were associated with favorable and unfavorable outcomes, contributing to discrepancies between cohorts. Our findings shed new light on the complex interaction between the gut microbiome and response to cancer immunotherapy, providing a roadmap for future studies.

Molecular characterization of renal cell carcinoma with venous tumor thrombus: Genomic aberrations with renal vein wall invasion.

385 Background: Renal cell carcinoma (RCC) patients with tumor thrombus (TT) have been observed distinct phenotypes of TT: deeply invading into the wall of renal vein/inferior vena cava (IVC) (DITT) or minimal/non-invasion (NITT), with different surgical strategy and post-thrombectomy outcome. However, the molecular characters of DITT/NITT have not been investigated, due to operational challenges and limited patient access. Methods: In this study, primary tumor and TT samples from 68 patients having IVC thrombectomy were prospectively collected for DNA mutation profiling and RNA expression profiling, using Predicine panel-based next generation sequencing and transcriptome RNA-SEQ, respectively. Next-generation sequencing and bioinformatics data analysis was conducted in Colleges of American Pathologist (CAP)-accredited laboratory in Shanghai, China (Huidu Shanghai Medical Sciences Ltd.). Kaplan–Meier survival analysis was performed to analyze the correlation between DITT/NITT and clinical outcome, and p-values were calculated using log-rank test. Results: When metastasis was detected, patients with DITT showed significantly shorter overall survival comparing to patients with NITT (P < 0.05, log-rank test). However, this difference was not observed in non-metastatic group. Successful sequencing has been achieved in 100% (68/68) patients. NITT tumor bulks more frequently carried mutations in PTEN, TP53 and epigenetic regulators such as SETD2, PBRM1 and KDM5C and had higher expression of cell cycle related pathway genes. In contrast, DITT had lower expression of genes encoding cell adhesion and extracellular matrix molecules such as ADAM33, NCAM1 and FLRT2, which could contribute to higher invasiveness. Conclusions: To our knowledge, this is the first large study to systematically interrogate genomic landscape and molecular features of different TT phenotypes in RCC patients. The differential genomic mutation and gene expression landscapes across the two groups may reflect biologic differences and clinical implications such as tumor invasiveness and aggressiveness.

Lipidomic signatures from physically frail and robust older adults at hospital admission

Identifying serum biomarkers that can predict physical frailty in older adults would have tremendous clinical value for primary care, as this condition is inherently related to poor quality of life and premature mortality. We compared the serum lipid profile of physically frail and robust older adults to identify specific lipid biomarkers that could be used to assess physical frailty in older patients at hospital admission. Forty-three older adults (58.1% male), mean (range) age 86.4 (78–100 years) years, were classified as physically frail (n = 18) or robust (n = 25) based on scores from the Short Physical Performance Battery (≤ 6 points). Non-targeted metabolomic study by ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) analysis with later bioinformatics data analysis. Once the significantly different metabolites were identified, the KEGG database was used on them to establish which were the metabolic pathways mainly involved. Area under receiver-operating curve (AUROC) analysis was used to test the discriminatory ability of lipid biomarkers for frailty based on the Short Physical Performance Battery. We identified a panel of five metabolites including ceramides Cer (40:2), Cer (d18:1/20:0), Cer (d18:1/23:0), cholesterol, and phosphatidylcholine (PC) (14:0/20:4) that were significantly increased in physically frail older adults compared with robust older adults at hospital admission. The most interesting in the physically frail metabolome study found with the KEGG database were the metabolic pathways, vitamin digestion and absorption, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance. In addition, Cer (40:2) (AUROC 0.747), Cer (d18:1/23:0) (AUROC 0.720), and cholesterol (AUROC 0.784) were identified as higher values of physically frail at hospital admission. The non-targeted metabolomic study can open a wide view of the physically frail features changes at the plasma level, which would be linked to the physical frailty phenotype at hospital admission. Also, we propose that metabolome analysis will have a suitable niche in personalized medicine for physically frail older adults.

Invariable Ribosome Stoichiometry During Murine Erythroid Differentiation: Implications for Understanding Ribosomopathies.

Heterogeneity of the main ribosomal composition represents an emerging, yet debatable, mechanism of gene expression regulation with a purported role in ribosomopathies, a group of disorders caused by mutations in ribosomal protein genes (RPs). Ribosomopathies, mysteriously relate with tissue-specific symptoms (mainly anemia and cancer predisposition), despite the ubiquitous expression and necessity of the associated RPs. An outstanding question that may shed light into disease pathogenicity and provide potential pharmacological interventions, is whether and how the ribosomal composition is modified during, the highly affected by RP mutations, process of erythroid differentiation. To address this issue, we analyzed ribosome stoichiometry using an established model of erythroid differentiation, through sucrose gradient ultracentrifugation and quantitative proteomics. We found that differentiation associates with an extensive reprogramming of the overall ribosomal levels, characterized by an increase in monosomes and a decrease in polysomes. However, by calculating a stoichiometry score for each independent ribosomal protein, we found that the main ribosomal architecture remained invariable between immature and differentiated cells. In total, none of the 78 Ribosomal Proteins (RPs- 74 core RPs, Rack1, Fau and 2 paralogs) detected was statistically different between the samples. This data was further verified through antibody-mediated quantification of 6 representative RPs. Moreover, bioinformatic analysis of whole cell proteomic data derived out of 4 additional models of erythropoiesis revealed that RPs were co-regulated across these cell types, too. In conclusion, ribosomes maintain an invariant protein stoichiometry during differentiation, thus excluding ribosome heterogeneity from a potential mechanism of toxicity in ribosomopathies and other erythroid disorders.

Identification of a novel large duplication (exon2_6dup): copy number variation in the LDLR gene in a large family with familial hypercholesterolemia by whole-genome sequencing

We found a large family with familial hypercholesterolemia (FH) that included 7 siblings who all developed myocardial infarction. The aim of this study was to identify the pathogenic gene underlying FH in the family. Whole-genome sequencing (WGS) was performed in 12 affected and 10 unaffected individuals in the family. WGS identified a novel large duplication: copy number variation (CNV) in the LDLR gene, exon2_6dup (c.68-499_940+252dup), that was present in the 12 affected family members but not in any of the 10 unaffected family members. The exact extent and genomic breakpoint sequence of the duplication caused by nonallelic homologous recombination between Alu sequences were identified based on bioinformatic analysis of WGS data for the LDLR gene. A novel c.68-499_940+252dup variant in the LDLR gene was identified based on bioinformatic analysis of WGS data. WGS is a powerful tool that can be used to precisely identify CNVs in addition to small-scale variations in FH-related genes.

P718 Characterization of microbiota and metabolomic profile in human colonic surgical resections of UC patients

Abstract Background Ulcerative colitis (UC) is characterized by a diffuse, continuous, and chronic inflammation of mucosa and submucosa layers in the colon whose etiology is still unknown. Intestinal microbiota dysbiosis and alterations in the metabolomic profile have been reported in mucosal biopsies from Inflammatory Bowel Disease (IBD) patients. We aim to characterize the microbiota composition and metabolomic profile in colonic resections of UC patients. Methods Colonic resections from UC (n=18) and non-IBD (n=20) patients were obtained. Microbiota identification, composition and classification was performed by 16S rRNA gene Illumina Miseq sequencing. The bioinformatic analysis of sequencing data was performed using constrained correspondence analysis (CCA) and non-parametric Wilcoxon test to compare species and genera proportions. Bacterial load was estimated by qPCR. Metabolomic analysis was performed using Nuclear Magnetic Resonance (NMR) to study polar metabolites and Gas-Chromatography or Liquid-Chromatography Mass-Spectrometry to study non-polar metabolites. Results Microbiota analysis revealed differences at genus and species level between UC and non-IBD patients. Cellulosimicrobium was found at 10-fold higher levels in UC samples while Escherichia genus was 3.88 times more abundant in controls. A reduction in bacterial load was seen in UC samples. Levels of short-chain fatty acids (FA), medium-chain FA, long-chain FA, carboxylic acids and amino acids were obtained from the metabolomics analysis and a positive correlation between propionic, aspartic and hydroxybutyric acids and Cellulosimicrobium and Pseudomonas was found (Fig1B). Gut commensals such as unclassified Agathobacter, Blautia faecis, Oscillospiraceae or Faecalibacterium prausnitzii positively correlated with the abundance of butyric acid, acetic acid and succinic acid; meanwhile Subdoligranulum, Lachnospireace or unclassified Blautia negatively correlated with some medium or long chain fatty acids (Fig1B). Fig1. Correlations between bacterial species and metabolites in (A) controls and (B) UC patients. Performed with mixOmics library in R. Conclusion Significant differences in microbiota composition and abundance are found in UC patients, which undergo a clear gut dysbiosis in colonic tissue. Correlation analysis suggests Cellulosimicrobium and Pseudomonas as sources of propionic, aspartic and hydroxybutyric acid. These results point at Cellulosimicrobium as a potential candidate in UC pathology.

Renal mTORC1 activation is associated with disease activity and prognosis in lupus nephritis.

This study was initiated to evaluate mammalian target of rapamycin (mTOR) activation in renal tissue of LN patients. This retrospective study included 187 LN patients, 20 diabetic nephropathy (DN) patients, 10 minimal change disease (MCD) patients and 10 normal controls (NCs). Seven of 187 LN patients had repeated renal biopsies. mTORC1/2 activation was evaluated by immunohistochemistry and multiplexed immunofluorescence. The association of mTORC1/2 activation with the clinicopathologic indices and prognostic outcomes was analysed among 187 LN patients. Proteomics was performed in renal biopsies of 20 LN patients. Proteomics was employed to comprehensively evaluate the impact of mTOR activation on intrarenal gene expression. mTORC1/2 was significantly activated in podocytes, mesangial cells, endothelial cells and tubular epithelial cells of LN patients as compared with those with MCD or NC. The glomerular mTORC1 activation was higher in LN patients compared with DN patients. mTORC1, but not mTORC2, activation strongly correlated with serum albumin, complement C3, proteinuria and the following pathological biomarkers of LN: crescent formation, interstitial inflammation and fibrosis. Moreover, mTORC1 activation was identified as a prognostic marker in LN patients. Bioinformatic analyses of proteomics and immunohistochemical data unveiled increased complement activation, antigen presentation and phagocytosis in LN patients with mTORC1 activation. Renal mTORC1 activation could be a biomarker to reveal disease activity and predict clinical prognosis in LN patients.

Analysis of LINE1 Retrotransposons in Huntington's Disease.

Transposable elements (TEs) are mobile genetic elements that made up about half the human genome. Among them, the autonomous non-LTR retrotransposon long interspersed nuclear element-1 (L1) is the only currently active TE in mammals and covers about 17% of the mammalian genome. L1s exert their function as structural elements in the genome, as transcribed RNAs to influence chromatin structure and as retrotransposed elements to shape genomic variation in somatic cells. L1s activity has been shown altered in several diseases of the nervous system. Huntington disease (HD) is a dominantly inherited neurodegenerative disorder caused by an expansion of a CAG repeat in the HTT gene which leads to a gradual loss of neurons most prominently in the striatum and, to a lesser extent, in cortical brain regions. The length of the expanded CAG tract is related to age at disease onset, with longer repeats leading to earlier onset. Here we carried out bioinformatic analysis of public RNA-seq data of a panel of HD mouse models showing that a decrease of L1 RNA expression recapitulates two hallmarks of the disease: it correlates to CAG repeat length and it occurs in the striatum, the site of neurodegeneration. Results were then experimentally validated in HttQ111 knock-in mice. The expression of L1-encoded proteins was independent from L1 RNA levels and differentially regulated in time and tissues. The pattern of expression L1 RNAs in human HD post-mortem brains showed similarity to mouse models of the disease. This work suggests the need for further study of L1s in HD and adds support to the current hypothesis that dysregulation of TEs may be involved in neurodegenerative diseases.

Nonwoven-based gelatin/polycaprolactone membrane loaded with ERK inhibitor U0126 for treatment of tendon defects

BackgroundTendon is a major component of musculoskeletal system connecting the muscles to the bone. Tendon injuries are very common orthopedics problems leading to impeded motion. Up to now, there still lacks effective treatments for tendon diseases.MethodsTendon stem/progenitor cells (TSPCs) were isolated from the patellar tendons of SD rats. The expression levels of genes were evaluated by quantitative RT-PCR. Immunohistochemistry staining was performed to confirm the presence of tendon markers in tendon tissues. Bioinformatics analysis of data acquired by RNA-seq was used to find out the differentially expressed genes. Rat patellar tendon injury model was used to evaluate the effect of U0126 on tendon injury healing. Biomechanical testing was applied to evaluate the mechanical properties of newly formed tendon tissues.ResultsIn this study, we have shown that ERK inhibitor U0126 rather PD98059 could effectively increase the expression of tendon-related genes and promote the tenogenesis of TSPCs in vitro. To explore the underlying mechanisms, RNA sequencing was performed to identify the molecular difference between U0126-treated and control TSPCs. The result showed that GDF6 was significantly increased by U0126, which is an important factor of the TGFβ superfamily regulating tendon development and tenogenesis. In addition, NBM (nonwoven-based gelatin/polycaprolactone membrane) which mimics the native microenvironment of the tendon tissue was used as an acellular scaffold to carry U0126. The results demonstrated that when NBM was used in combination with U0126, tendon healing was significantly promoted with better histological staining outcomes and mechanical properties.ConclusionTaken together, we have found U0126 promoted tenogenesis in TSPCs through activating GDF6, and NBM loaded with U0126 significantly promoted tendon defect healing, which provides a new treatment for tendon injury.

FGF signalling facilitates cervical cancer progression.

Cervical cancer is one of the most frequently diagnosed cancers in women worldwide. While cervical cancer is caused by human papillomavirus (HPV), not all females infected with HPV develop the disease, suggesting that other factors might facilitate its progression. Growing evidence supports the involvement of the fibroblast growth factor receptor (FGFR) axis in several cancers, including gynecological. However, for cervical cancer, the molecular mechanisms that underpin the disease remain poorly understood, including the role of FGFR signaling. The aim of this study was to investigate FGF(R) signaling in cervical cancer through bioinformatic analysis of cell line and patient data and through detailed expression profiling, manipulation of the FGFR axis, and downstream phenotypic analysis in cell lines (HeLa, SiHa, and CaSki). Expression (protein and mRNA) analysis demonstrated that FGFR1b/c, FGFR2b/c, FGFR4, FGF2, FGF4, and FGF7 were expressed in all three lines. Interestingly, FGFR1 and 2 localized to the nucleus, supporting that nuclear FGFRs could act as transcription factors. Importantly, 2D and 3D cell cultures demonstrated that FGFR activation can facilitate cell functions correlated with invasive disease. Collectively, this study supports an association between FGFR signaling and cervical cancer progression, laying the foundations for the development of therapeutic approaches targeting FGFR in this disease.

Identification of Key microRNAs in Diabetes Mellitus Erectile Dysfunction Rats with Stem Cell Therapy by Bioinformatic Analysis of Deep Sequencing Data.

PurposeDiabetes mellitus erectile dysfunction (DMED) is a common resulting complication of diabetes. Studies have shown mesenchymal stem cell (MSC)-based therapy was beneficial in alleviating erectile function of DMED rats. While the pathogenesis of DMED and the mechanism MSCs actions are unclear.Materials and MethodsWe constructed a rat model of DMED with or without intracavernous injection of MSCs, and performed microRNA (miRNA) sequencing of corpora cavernosa tissues.ResultsWe identified three overlapping differentially expressed miRNAs (rno-miR-1298, rno-miR-122-5p, and rno-miR-6321) of the normal control group, DMED group, and DMED+MSCs group. We predicted 285 target genes of three miRNAs through RNAhybrid and miRanda database and constructed a miRNA-target gene network through Cytoscape. Next, we constructed protein-protein interaction networks through STRING database and identified the top 10 hub genes with highest connectivity scores. Five GO terms including cellular response to growth factor stimulus (GO:0071363), ossification (GO:0001503), response to steroid hormone (GO:0048545), angiogenesis (GO:0001525), positive regulation of apoptotic process (GO:0043065), and one Reactome pathway (Innate Immune System) were significantly enriched by 10 hub genes using the Metascape database. We selected the GSE2457 dataset to validate the expression of hub genes and found only the expression of B4galt1 was statistically different (p<0.001). B4galt1 was highly expressed in penile tissues of diabetic rats and would be negatively regulated by rno-miR-1298.ConclusionsThree key miRNAs were identified in DMED rats with stem cell therapy and the miR-1298/B4GalT1 axis might exert function in stem cell therapy for ED.

Bioinformatics Methods for ChIP-seq Histone Analysis.

The field of genomics and genome-wide analysis has exploded since around 2008 with the development of high-throughput omics approaches, largely driven by the emergence of the next-generation sequencing technologies. Among the different biological applications supported by recent sequencing technologies, ChIP-seq (Chromatin ImmunoPrecipitation followed by Sequencing) is one of the most powerful techniques which has dramatically changed our view of the epigenetics landscape of cells.In this chapter, I will present and discuss the main steps of bioinformatic and biostatistical analysis of ChIP-seq data (Fig. 1). While this technique has been widely used to study transcription factor binding sites, I will focus here on the analysis of histone modifications.

Exploring the Role of Oxidative Stress in Sperm Motility: A Proteomic Network Approach.

Significance: Infertility is a major global health problem, with nearly half of the cases being associated with male factors. Although reactive oxygen species (ROS) are crucial for sperm cell normal physiological processes, an imbalance between ROS production and antioxidants can lead to oxidative stress that can impair sperm function. Indeed, high semen ROS levels are reported in 30%-80% of infertile men. Recent Advances: Male oxidative stress infertility is an uprising classification for idiopathic infertility. Proteomic approaches, including quantitative mass spectrometry (MS)-based proteomics, are being utilized to explore the molecular mechanisms associated with oxidative stress in male infertility. Critical Issues: In this review, proteome data were collected from articles available on PubMed centered on MS-based proteomic studies, performed in seminal plasma and sperm cell samples, and enrolling men with impaired semen parameters. The bioinformatic analysis of proteome data with Cytoscape (ClueGO+CluePedia) and STRING tools allowed the identification of the biological processes more prevalent in asthenozoospermia, with focus on the ones related to oxidative stress. Future Directions: The identification of the antioxidant proteins in seminal plasma and sperm cells that can protect sperm cells from oxidative stress is crucial not only for a better understanding of the molecular mechanisms associated with male infertility but specially to guide new therapeutic possibilities. Antioxid. Redox Signal. 37, 501-520.

EZH2 targeting to improve the sensitivity of acquired radio-resistance bladder cancer cells

Among the many treatments for Bladder cancer (BCa) patients, radiotherapy is an effective way to preserve the bladder. However, as the frequency of irradiation increases, the tumor cells appear "acquired radio-resistance" (ARR) and loss the sensitivity to radiotherapy. To explore the molecular mechanism of ARR, two BCa cell lines, 5637 and T24, were enrolled here and their ARR counterparts, 5637R and T24R, were obtained by exposure to γ-ray of 2 Gy for 30 times. Compared to parental cells, ARR cells have significantly enhanced stem cell-like phenotype, robust DNA damage repair capabilities and elevated expression of zeste homolog 2 (EZH2). Decreasing EZH2 expression, both parental and ARR cells exhibited reduced abilities of forming microsphere and repairing DNA damage, but enhanced cells radio-sensitivity and intracellular autophagy compared to untreated cells. Down-regulation the expression of EZH2 induced an increasing of both LC3 and P62 in parental cells, while in ARR cells, only LC3 increased upon EZH2 reduction. On the other hand, UNC1999 treatment caused the increasing of LC3B and P62 in all cells, suggested that siEZH2 and UNC1999 affect ARR cells autophagy through different mechanisms. In vivo study showed that pre-treated with UNC1999 greatly enhanced T24R cells sensitivity to IR, and knocking down the expression of EZH2 significantly suppressed the tumor growth. Combined with bioinformatics data analysis, we speculate that EZH2 is an important biomolecule linking the diagnosis, radiotherapy and prognosis of BCa. EZH2 targeted therapy may be an effective way to overcome ARR of BCa, and is worthy of in-depth study.

Current Methods and Role of Next-Generation Sequencing in the Diagnosis of Antimicrobial Resistance in Tuberculosis

Rapid diagnosis of tuberculosis (TB) and identification of antimicrobial resistance (AR) are critical to improving patient outcomes, especially for multidrug- and extensively drug-resistant TB (DR-TB) infections. Diagnosis can take weeks with traditional culture and growth-based antimicrobial susceptibility testing, delaying appropriate treatment, impacting transmission of resistant strains, and leading to additional health care costs. Next-generation sequencing (NGS) techniques are revolutionizing the diagnostic laboratory and provide rapid, comprehensive, and accurate detection of AR in one test. With NGS, the whole genome can be sequenced in as little as 4 days from identification of TB in a positive culture. Subsequent bioinformatic analysis of sequence data assesses nucleotide changes compared to a wild-type reference genome and can identify mutations known to cause AR. Additionally, data can be analyzed to determine the species and provide a genotype. Targeted NGS methods designed to detect DR-TB directly from clinical specimens hold promise for the future.

Diagnosis of Viral Diseases Using Deep Sequencing and Metagenomics Analyses.

Viruses are ubiquitous in nature and exist in a variety of habitats. The advancement in sequencing technologies has revolutionized the understanding of viral biodiversity associated with plant diseases. Deep sequencing combined with metagenomics is a powerful approach that has proven to be revolutionary in the last decade and involves the direct analysis of viral genomes present in a diseased tissue sample. This protocol describes the details of RNA extraction and purification from wild rice plant and their yield, RNA purity, and integrity assessment. As a final step, bioinformatics data analysis including demultiplexing, quality control, de novo transcriptome assembly, taxonomic allocation and read mapping following Illumina HiSeq small and total RNA sequencing are described. Furthermore, the total RNAs extraction protocol and an additional ribosomal rRNAs depletion step which are significantly important for viral genomes construction are provided.