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Integration of comprehensive data and biotechnological tools for industrial applications of Kluyveromyces marxianus.
Nurcholis, M, Lertwattanasakul, N, Rodrussamee, N, Kosaka, T, Murata, M, Yamada, M
Applied microbiology and biotechnology. 2020;(2):475-488
Abstract
Among the so-called non-conventional yeasts, Kluyveromyces marxianus has extremely potent traits that are suitable for industrial applications. Indeed, it has been used for the production of various enzymes, chemicals, and macromolecules in addition to utilization of cell biomass as nutritional materials, feed and probiotics. The yeast is expected to be an efficient ethanol producer with advantages over Saccharomyces cerevisiae in terms of high growth rate, thermotolerance and a wide sugar assimilation spectrum. Results of comprehensive analyses of its genome and transcriptome may accelerate studies for applications of the yeast and may further increase its potential by combination with recent biotechnological tools including the CRISPR/Cas9 system. We thus review published studies by merging with information obtained from comprehensive data including genomic and transcriptomic data, which would be useful for future applications of K. marxianus.
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Expression Patterns of Xenobiotic-Metabolizing Enzymes in Tumor and Adjacent Normal Mucosa Tissues among Patients with Colorectal Cancer: The ColoCare Study.
Beyerle, J, Holowatyj, AN, Haffa, M, Frei, E, Gigic, B, Schrotz-King, P, Boehm, J, Habermann, N, Stiborova, M, Scherer, D, et al
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2020;(2):460-469
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Abstract
BACKGROUND Xenobiotic-metabolizing enzymes (XME) play a critical role in the activation and detoxification of several carcinogens. However, the role of XMEs in colorectal carcinogenesis is unclear. METHODS We investigated the expression of XMEs in human colorectal tissues among patients with stage I-IV colorectal cancer (n = 71) from the ColoCare Study. Transcriptomic profiling using paired colorectal tumor and adjacent normal mucosa tissues of XMEs (GSTM1, GSTA1, UGT1A8, UGT1A10, CYP3A4, CYP2C9, GSTP1, and CYP2W1) by RNA microarray was compared using Wilcoxon rank-sum tests. We assessed associations between clinicopathologic, dietary, and lifestyle factors and XME expression with linear regression models. RESULTS GSTM1, GSTA1, UGT1A8, UGT1A10, and CYP3A4 were all statistically significantly downregulated in colorectal tumor relative to normal mucosa tissues (all P ≤ 0.03). Women had significantly higher expression of GSTM1 in normal tissues compared with men (β = 0.37, P = 0.02). By tumor site, CYP2C9 expression was lower in normal mucosa among patients with rectal cancer versus colon cancer cases (β = -0.21, P = 0.0005). Smokers demonstrated higher CYP2C9 expression levels in normal mucosa (β = 0.17, P = 0.02) when compared with nonsmokers. Individuals who used NSAIDs had higher GSTP1 tumor expression compared with non-NSAID users (β = 0.17, P = 0.03). Higher consumption of cooked vegetables (>1×/week) was associated with higher CYP3A4 expression in colorectal tumor tissues (β = 0.14, P = 0.007). CONCLUSIONS XMEs have lower expression in colorectal tumor relative to normal mucosa tissues and may modify colorectal carcinogenesis via associations with clinicopathologic, lifestyle, and dietary factors. IMPACT Better understanding into the role of drug-metabolizing enzymes in colorectal cancer may reveal biological differences that contribute to cancer development, as well as treatment response, leading to clinical implications in colorectal cancer prevention and management.
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Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.
Bouziane, H, Chouarfia, A
Journal of integrative bioinformatics. 2020;(1):51-79
Abstract
To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key importance for protein function elucidation. However, it remains a challenging task, especially for multiple sites proteins known to shuttle between cell compartments to perform their proper biological functions and proteins which do not have significant homology to proteins of known subcellular locations. Due to their low-cost and reasonable accuracy, machine learning-based methods have gained much attention in this context with the availability of a plethora of biological databases and annotated proteins for analysis and benchmarking. Various predictive models have been proposed to tackle the SCL problem, using different protein sequence features pertaining to the subcellular localization, however, the overwhelming majority of them focuses on single localization and cover very limited cellular locations. The prediction was basically established on sorting signals, amino acids compositions, and homology. To improve the prediction quality, focus is actually on knowledge information extracted from annotation databases, such as protein-protein interactions and Gene Ontology (GO) functional domains annotation which has been recently a widely adopted and essential information for learning systems. To deal with such problem, in the present study, we considered SCL prediction task as a multi-label learning problem and tried to label both single site and multiple sites unannotated bacterial protein sequences by mining proteins homology relationships using both GO terms of protein homologs and PSI-BLAST profiles. The experiments using 5-fold cross-validation tests on the benchmark datasets showed a significant improvement on the results obtained by the proposed consensus multi-label prediction model which discriminates six compartments for Gram-negative and five compartments for Gram-positive bacterial proteins.
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Genome sequence and spore germination-associated transcriptome analysis of Corynespora cassiicola from cucumber.
Gao, S, Zeng, R, Xu, L, Song, Z, Gao, P, Dai, F
BMC microbiology. 2020;(1):199
Abstract
BACKGROUND Corynespora cassiicola, as a necrotrophic phytopathogenic ascomycetous fungus, can infect hundreds of species of plants and rarely causes human diseases. This pathogen infects cucumber species and causes cucumber target spot, which has recently caused large cucumber yield losses in China. Genome sequence and spore germination-associated transcriptome analysis will contribute to the understanding of the molecular mechanism of pathogenicity and spore germination of C. cassiicola. RESULTS First, we reported the draft genome sequences of the cucumber-sampled C. cassiicola isolate HGCC with high virulence. Although conspecific, HGCC exhibited distinct genome sequence differences from a rubber tree-sampled isolate (CCP) and a human-sampled isolate (UM591). The proportion of secreted proteins was 7.2% in HGCC. A total of 28.9% (4232) of HGCC genes, 29.5% (4298) of CCP genes and 28.6% (4214) of UM591 genes were highly homologous to experimentally proven virulence-associated genes, respectively, which were not significantly different (P = 0.866) from the average (29.7%) of 10 other phytopathogenic fungi. Thousands of putative virulence-associated genes in various pathways or families were identified in C. cassiicola. Second, a global view of the transcriptome of C. cassiicola spores during germination was evaluated using RNA sequencing (RNA-Seq). A total of 3288 differentially expressed genes (DEGs) were identified. The majority of KEGG-annotated DEGs were involved in metabolism, genetic information processing, cellular processes, the organismal system, human diseases and environmental information processing. CONCLUSIONS These results facilitate the exploration of the molecular pathogenic mechanism of C. cassiicola in cucumbers and the understanding of molecular and cellular processes during spore germination.
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Liver transcriptomics highlights interleukin-32 as novel NAFLD-related cytokine and candidate biomarker.
Baselli, GA, Dongiovanni, P, Rametta, R, Meroni, M, Pelusi, S, Maggioni, M, Badiali, S, Pingitore, P, Maurotti, S, Montalcini, T, et al
Gut. 2020;(10):1855-1866
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Abstract
OBJECTIVE Efforts to manage non-alcoholic fatty liver disease (NAFLD) are limited by the incomplete understanding of the pathogenic mechanisms and the absence of accurate non-invasive biomarkers. The aim of this study was to identify novel NAFLD therapeutic targets andbiomarkers by conducting liver transcriptomic analysis in patients stratified by the presence of the PNPLA3 I148M genetic risk variant. DESIGN We sequenced the hepatic transcriptome of 125 obese individuals. 'Severe NAFLD' was defined as the presence of steatohepatitis, NAFLD activity score ≥4 or fibrosis stage ≥2. The circulating levels of the most upregulated transcript, interleukin-32 (IL32), were measured by ELISA. RESULTS Carriage of the PNPLA3 I148M variant correlated with the two major components of hepatic transcriptome variability and broadly influenced gene expression. In patients with severe NAFLD, there was an upregulation of inflammatory and lipid metabolism pathways. IL32 was the most robustly upregulated gene in the severe NAFLD group (adjusted p=1×10-6), and its expression correlated with steatosis severity, both in I148M variant carriers and non-carriers. In 77 severely obese, and in a replication cohort of 160 individuals evaluated at the hepatology service, circulating IL32 levels were associated with both NAFLD and severe NAFLD independently of aminotransferases (p<0.01 for both). A linear combination of IL32-ALT-AST showed a better performance than ALT-AST alone in NAFLD diagnosis (area under the curve=0.92 vs 0.81, p=5×10-5). CONCLUSION Hepatic IL32 is overexpressed in NAFLD, correlates with hepatic fat and liver damage, and is detectable in the circulation, where it is independently associated with the presence and severity of NAFLD.
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The role of long noncoding RNAs in atrial fibrillation.
Babapoor-Farrokhran, S, Gill, D, Rasekhi, RT
Heart rhythm. 2020;(6):1043-1049
Abstract
Atrial fibrillation (AF) is a common arrhythmia with serious clinical sequelae, yet little is known about its genetic origins. Recently, the untranscribed 98% of the human genome has been increasingly implicated in important processes such as cardiac organogenesis, physiology, and pathophysiology. Specifically, long noncoding RNAs (lncRNAs) have been shown to interact with the transcriptome in various ways that alter gene expression. Previously, multiple lncRNAs have been identified in disease processes such as heart failure, coronary artery disease, and diabetes. Multiple studies now show lncRNAs are involved in each fundamental mechanism leading to the development of AF, including structural remodeling, electrical remodeling, renin angiotensin system effects, and calcium handling abnormalities. The altered expression of lncRNAs offers genetic targets for the diagnosis and treatment of AF. This article discusses the role of lncRNAs in AF and its pathogenesis.
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A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.
Long, G, Yang, C
Molecular medicine reports. 2020;(3):1561-1571
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Abstract
Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25‑50%. The present study explored the mechanisms of PSS using four microarray datasets (GSE26378, GSE26440, GSE13904 and GSE4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease‑associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.
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BDNF Val66Met Polymorphism, the Allele-Specific Analysis by qRT-PCR - a Novel Protocol.
de Assis, GG, Hoffman, JR, Gasanov, EV
International journal of medical sciences. 2020;(18):3058-3064
Abstract
Background: Alteration in brain-derived neurotrophic factor (BDNF) production is a marker of neuropathological conditions, which has led to the investigation of Val66Met polymorphism occurring in the human BDNF gene (BDNF). Presently, there are no reported methods available for the analysis of Val66Met impact on human BDNF functioning. Purpose: To develop a qRT-PCR protocol for the allele-specific expression evaluation of the Val66Met polymorphism in BDNF. Methods: Using RNA extracted from muscle samples of 9 healthy volunteers (32.9 ± 10.3 y) at rest and following a maximal effort aerobic capacity exercise test, a protocol was developed for the detection of Val66/Met66 allele-specific BDNF expression in Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) - relative to housekeeping genes - and validated by absolute quantification in Droplet Digital Polymerase Chain Reaction (ddPCR). Results: Differences in the relative values of BDNF mRNA were confirmed by ddPCR analysis. HPRT1 and B2M were the most stable genes expressed in muscle tissue among different metabolic conditions, while GAPDH revealed to be metabolic responsive. Conclusion: Our qRT-PCR protocol successfully determines the allele-specific detection and changes in BDNF expression regarding the Val66Met polymorphism.
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Transcriptome profiling of hiPSC-derived LSECs with nanoCAGE.
Danoy, M, Poulain, S, Koui, Y, Tauran, Y, Scheidecker, B, Kido, T, Miyajima, A, Sakai, Y, Plessy, C, Leclerc, E
Molecular omics. 2020;(2):138-146
Abstract
Liver Sinusoidal Endothelial Cells (LSECs) are an important component of the liver as they compose the microvasculature which allows the supply of oxygen, blood, and nutrients. However, maintenance of these cells in vitro remains challenging as they tend to rapidly lose some of their characteristics such as fenestration or as their immortalized counterparts present poor characteristics. In this work, human induced pluripotent stem cells (hiPSCs) have been differentiated toward an LSEC phenotype. After differentiation, the RNA quantification allowed demonstration of high expression of specific vascular markers (CD31, CD144, and STAB2). Immunostaining performed on the cells was found to be positive for both Stabilin-1 and Stabilin-2. Whole transcriptome analysis performed with the nanoCAGE method further confirmed the overall vascular commitment of the cells. The gene expression profile revealed the upregulation of the APLN, LYVE1, VWF, ESAM and ANGPT2 genes while VEGFA appeared to be downregulated. Analysis of promoter motif activities highlighted several transcription factors (TFs) of interest in LSECs (IRF2, ERG, MEIS2, SPI1, IRF7, WRNIP1, HIC2, NFIX_NFIB, BATF, and PATZ1). Based on this investigation, we compiled the regulatory network involving the relevant TFs, their target genes as well as their related signaling pathways. The proposed hiPSC-derived LSEC model and its regulatory network were then confirmed by comparing the experimental data to primary human LSEC reference datasets. Thus, the presented model appears as a promising tool to generate more complex in vitro liver multi-cellular tissues.
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Methods of Gene Expression Profiling to Understand Abiotic Stress Perception and Response in Legume Crops.
Bala, M, Sinha, R, Mallick, MA, Sharma, TR, Singh, AK
Methods in molecular biology (Clifton, N.J.). 2020;:99-126
Abstract
Legume crops offer a wide genetic diversity that can be exploited to raise improved crop varieties with higher tolerance against adverse climatic conditions. In order to achieve food and nutritional security, legume breeding programs should also incorporate advanced genomics tools. Genomes of many model and nonmodel legume crops have been sequenced, which provide opportunities to identify and characterize candidate genes to develop abiotic stress tolerant crops. Gene expression profiling is a powerful tool to identify candidate genes and understand their function. The present chapter describes two such strategies, that is, candidate gene expression profiling approach and global transcriptome profiling approach. The methods like RT-PCR and qRT-PCR that are being traditionally used to study expression of target genes under defined experimental conditions are discussed. In addition, global transcriptome analysis approach and its advancements are discussed. Details of next-generation sequencing (NGS) based RNA-sequencing (RNA-seq) and associated advanced bioinformatics tools to identify differentially expressing genes at a global level are also described.