scholarly journals Comparative genomics and metabolomics analysis of Riemerella anatipestifer strain CH-1 and CH-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jibin Liu ◽  
Anchun Cheng ◽  
Mingshu Wang ◽  
Mafeng Liu ◽  
Dekang Zhu ◽  
...  
Keyword(s):  
Large Scale ◽  
Hierarchical Cluster ◽  
Pathogenic Microorganism ◽  
Sequence Database ◽  
Metabolomics Data ◽  
Intracellular Metabolites ◽  
Riemerella Anatipestifer ◽  
Genome Sequence Database ◽  
Serotype 1 ◽  
Significant Mortality

AbstractRiemerella anatipestifer is a major pathogenic microorganism in poultry causing serositis with significant mortality. Serotype 1 and 2 were most pathogenic, prevalent, and liable over the world. In this study, the intracellular metabolites in R. anatipestifer strains RA-CH-1 (serotype 1) and RA-CH-2 (serotype 2) were identified by gas chromatography-mass spectrometer (GC–MS). The metabolic profiles were performed using hierarchical clustering and partial least squares discriminant analysis (PLS-DA). The results of hierarchical cluster analysis showed that the amounts of the detected metabolites were more abundant in RA-CH-2. RA-CH-1 and RA-CH-2 were separated by the PLS-DA model. 24 potential biomarkers participated in nine metabolisms were contributed predominantly to the separation. Based on the complete genome sequence database and metabolite data, the first large-scale metabolic models of iJL463 (RA-CH-1) and iDZ470 (RA-CH-2) were reconstructed. In addition, we explained the change of purine metabolism combined with the transcriptome and metabolomics data. The study showed that it is possible to detect and differentiate between these two organisms based on their intracellular metabolites using GC–MS. The present research fills a gap in the metabolomics characteristics of R. anatipestifer.

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

scholarly journals FarRRegulates the farAB-Encoded Efflux Pump of Neisseriagonorrhoeae via an MtrR RegulatoryMechanism

2003 ◽  
Vol 185 (24) ◽  
pp. 7145-7152 ◽  
Author(s):  
E.-H. Lee ◽  
C. Rouquette-Loughlin ◽  
J. P. Folster ◽  
W. M. Shafer
Keyword(s):  
Regulatory Mechanism ◽  
Efflux Pump ◽  
Regulatory Protein ◽  
Transcriptional Repressors ◽  
Sequence Database ◽  
Mobility Shift ◽  
Pump System ◽  
Regulatory Cascade ◽  
Genome Sequence Database ◽  
Gel Mobility Shift

ABSTRACT The farAB operon of Neisseria gonorrhoeae encodes an efflux pump which mediates gonococcal resistance to antibacterial fatty acids. It was previously observed that expression of the farAB operon was positively regulated by MtrR, which is a repressor of the mtrCDE-encoded efflux pump system (E.-H. Lee and W. M. Shafer, Mol. Microbiol. 33:839-845, 1999). This regulation was believed to be indirect since MtrR did not bind to the farAB promoter. In this study, computer analysis of the gonococcal genome sequence database, lacZ reporter fusions, and gel mobility shift assays were used to elucidate the regulatory mechanism by which expression of the farAB operon is modulated by MtrR in gonococci. We identified a regulatory protein belonging to the MarR family of transcriptional repressors and found that it negatively controls expression of farAB by directly binding to the farAB promoter. We designated this regulator FarR to signify its role in regulating the farAB operon. We found that MtrR binds to the farR promoter, thereby repressing farR expression. Hence, MtrR regulates farAB in a positive fashion by modulating farR expression. This MtrR regulatory cascade seems to play an important role in adjusting levels of the FarAB and MtrCDE efflux pumps to prevent their excess expression in gonococci.

Urea stress is more akin to EGF exposure than to hypertonic stress in renal medullary cells

2002 ◽  
Vol 283 (3) ◽  
pp. F388-F398 ◽  
Author(s):  
Wei Tian ◽  
David M. Cohen
Keyword(s):  
Cluster Analysis ◽  
Large Scale ◽  
Hierarchical Cluster ◽  
Immunoblot Analysis ◽  
Activating Transcription Factor 3 ◽  
Expression Array ◽  
Hypertonic Stress ◽  
Activating Transcription Factor ◽  
Medullary Cells

Although urea is considered to be a cell stressor even in renal medullary cells perpetually exposed to this solute in vivo by virtue of the renal concentrating mechanism, aspects of urea signaling resemble that of a peptide mitogen. Urea was compared with epidermal growth factor and hypertonic NaCl or hypertonic mannitol using a large-scale expression array-based approach. The expression profile in response to urea stress more closely resembled that of EGF treatment than hypertonic stress, as determined by hierarchical cluster analysis; the effect of urea+NaCl was equidistant from that of either solute applied individually. Among the most highly urea- and hypertonicity-responsive transcripts were genes that had previously been shown to be responsive to these solutes, validating this approach. Increased expression of the activating transcription factor 3 by urea was newly detected via expression array and confirmed via immunoblot analysis. Earlier, we noted an abrogation of tonicity-dependent gene regulation by urea, primarily in a transient transfection-based model (Tian W and Cohen DM. Am J Physiol Renal Physiol 280: F904–F912, 2001). Here we applied K-means cluster analysis to demonstrate that the genes most profoundly up- or downregulated by hypertonic stress were partially restored toward basal levels in the presence of urea pretreatment. These global expression data are consistent with our earlier biochemical studies suggesting that urea affords cytoprotection in this context. In the aggregate, these data strongly support the hypothesis that the urea effect in renal medullary cells resembles that of a peptide mitogen in terms of the adaptive program of gene expression and in terms of cytoprotection from hypertonicity.

scholarly journals Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

2018 ◽  
Vol 38 (6) ◽  
Author(s):  
Georg Basler ◽  
Alisdair R. Fernie ◽  
Zoran Nikoloski
Keyword(s):  
Large Scale ◽  
Metabolic Flux ◽  
Cell Types ◽  
Flux Analysis ◽  
Metabolic Labeling ◽  
Modeling Framework ◽  
Metabolomics Data ◽  
Technological Advances ◽  
A Genome ◽  
Genome Scale

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

scholarly journals Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

2019 ◽  
Vol 48 (3) ◽  
pp. 978-993 ◽  
Author(s):  
Tuulia Tynkkynen ◽  
Qin Wang ◽  
Jussi Ekholm ◽  
Olga Anufrieva ◽  
Pauli Ohukainen ◽  
...  
Keyword(s):  
Nmr Spectroscopy ◽  
Large Scale ◽  
Molecular Data ◽  
Population Based ◽  
Urine Samples ◽  
Metabolomics Data ◽  
Urine Metabolites ◽  
Nmr Metabolomics ◽  
Genome Wide ◽  
Metabolic Information

Abstract Background Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available. Methods We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548). Results Intra-assay metabolite variations were mostly <5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance. Conclusion Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.

scholarly journals Genome Sequence Resource of “Candidatus Liberibacter asiaticus” strain Myan16 from Myanmar

Plant Disease ◽  
2020 ◽  
Author(s):  
Yongqin Zheng ◽  
jun guo ◽  
Xiaoling Deng ◽  
Zheng Zheng
Keyword(s):  
Genome Sequence ◽  
Draft Genome ◽  
Sequence Database ◽  
Candidatus Liberibacter Asiaticus ◽  
Genome Sequence Database ◽  
Citrus Production ◽  
Candidatus Liberibacter ◽  
Citrus Huanglongbing ◽  
Liberibacter Asiaticus

“Candidatus Liberibacter asiaticus” (CaLas), an uncultured α-proteobacterium, is associated with citrus Huanglongbing (HLB, yellow shoot disease), a destructive disease threatening citrus production worldwide. Here, we reported the draft genome sequence of CaLas strain Myan16 from a HLB-affected lime tree in Myitkyina, Kachin State, Myanmar. The strain Myan16 genome is 1,229,102 bp with an average G+C content of 36.4%, along with a circular prophage: P-Myan16-2 (36,303 bp, Type 2). This is the first genome sequence of CaLas strain from Myanmar, which will enrich the current CaLas genome sequence database and facilitate HLB epidemiology research in Asia and world.

scholarly journals The Genome Sequence DataBase: towards an integrated functional genomics resource

1999 ◽  
Vol 27 (1) ◽  
pp. 35-38 ◽  
Author(s):  
M. P. Skupski ◽  
M. Booker ◽  
A. Farmer ◽  
M. Harpold ◽  
W. Huang ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Genome Sequence ◽  
Sequence Database ◽  
Genome Sequence Database

scholarly journals The Mutual Impact of Smoking and Low Cholesterol on All-Cause, Non-Cardiovascular, and Cardiovascular Mortalities in Males

2018 ◽  
Vol 12 (6) ◽  
pp. 2128-2135
Author(s):  
Nader Parsa ◽  
Samira Taravatmanesh ◽  
Maurizio Trevisan ◽  
Pari Mahlagha Zaheri
Keyword(s):  
Mortality Risk ◽  
Large Scale ◽  
Italian Population ◽  
Cvd Risk ◽  
Protective Association ◽  
Hazard Ratios ◽  
Never Smokers ◽  
Low Cholesterol ◽  
Risk Association ◽  
Significant Mortality

The aim of the current study was to examine the possible relationship between the mutual effects of smoking and low cholesterol on all-cause, non-cardiovascular, and cardiovascular mortalities in males. This is a prospective cohort study of 30,179 males sampled from the Risk Factors and Life Expectancy (RIFLE) studies in the Italian population. The RIFLE data are from 19 different large-scale studies over a 9.5-year follow-up period. The Cox Proportional Hazard model was applied to analyze the data. The associations are presented as hazard ratios (HRs) with 95% confidence interval (CI). Cholesterol data were reported in categories. There were significant mortality risk mutual associations for never-smokers and those in the low cholesterol category (<160 mg/dl) for all-cause (HR = 3.13, 95% CI [1.69, 5.80]), and non-cardiovascular disease (CVD) (HR = 6.51, 95% CI [2.19, 19.33]) mortality in men with an insignificant risk for CVD mortality (HR = 1.90, 95% CI [0.85, 4.22]). There were significant mortality risk associations of the mutual effects of ex-smokers and low cholesterol for non-CVD in the first to third cholesterol categories (HR = 2.50, 95% CI [1.40, 4.46]; HR = 2.65, 95% CI [1.50, 4.71]; HR = 2.12, 95% CI [1.17, 3.82], respectively), but no significant findings for all-cause and CVD deaths. Furthermore, there were significant mortality risk association of mutual effects of current-smokers and low cholesterol for non-CVD (HR = 1.56, 95% CI [1.11, 2.28]) in the first category of cholesterol level, but insignificant risk associations for all-cause deaths (HR = 1.21, 95% CI [0.89, 1.66]). Interestingly, findings indicate a mutual protective association for current-smokers and low cholesterol (<160 mg/dl) for CVD risk in males (HR = 0.42, 95% CI [0.19, 0.91]). Findings of this study identified significant mortality risk association for mutual effects of never-smokers, ex-smokers, and low cholesterol for non-CVD. However, there is significant protective association for current-smokers and low cholesterol for CVD.

scholarly journals Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline

Metabolites ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 568
Author(s):  
Brechtje Hoegen ◽  
Alan Zammit ◽  
Albert Gerritsen ◽  
Udo F. H. Engelke ◽  
Steven Castelein ◽  
...  
Keyword(s):  
Data Analysis ◽  
Clinical Application ◽  
Inborn Errors Of Metabolism ◽  
Large Scale ◽  
Clinical Diagnostics ◽  
Untargeted Metabolomics ◽  
Computational Pipeline ◽  
Inborn Errors ◽  
Metabolomics Data ◽  
Metabolic Fingerprint

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

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