Gene expression profiles based flux balance model to predict the carbon source for Bacillus subtilis

2019 ◽  
Author(s):  
Kulwadee Thanamit ◽  
Franziska Hoerhold ◽  
Marcus Oswald ◽  
Rainer Koenig
Keyword(s):  
Gene Expression ◽  
Carbon Source ◽  
Drug Targets ◽  
Expression Profiles ◽  
Carbon Sources ◽  
Gene Expression Profiles ◽  
Fast Method ◽  
Flux Balance ◽  
Metabolic Fluxes

ABSTRACTFinding drug targets for antimicrobial treatment is a central focus in biomedical research. To discover new drug targets, we developed a method to identify which nutrients are essential for microorganisms. Using 13C labeled metabolites to infer metabolic fluxes is the most informative way to infer metabolic fluxes to date. However, the data can get difficult to acquire in complicated environments, for example, if the pathogen homes in host cells. Although data from gene expression profiling is less informative compared to metabolic tracer derived data, its generation is less laborious, and may still provide the relevant information. Besides this, metabolic fluxes have been successfully predicted by flux balance analysis (FBA). We developed an FBA based approach using the stoichiometric knowledge of the metabolic reactions of a cell combining them with expression profiles of the coding genes. We aimed to identify essential drug targets for specific nutritional uptakes of microorganisms. As a case study, we predicted each single carbon source out of a pool of eight different carbon sources for B. subtilis based on gene expression profiles. The models were in good agreement to models basing on 13C metabolic flux data of the same conditions. We could well predict every carbon source. Later, we applied successfully the model to unseen data from a study in which the carbon source was shifted from glucose to malate and vice versa. Technically, we present a new and fast method to reduce thermodynamically infeasible loops, which is a necessary preprocessing step for such model-building algorithms.SIGNIFICANCEIdentifying metabolic fluxes using 13C labeled tracers is the most informative way to gain insight into metabolic fluxes. However, obtaining the data can be laborious and challenging in a complex environment. Though transcriptional data is an indirect mean to estimate the fluxes, it can help to identify this. Here, we developed a new method employing constraint-based modeling to predict metabolic fluxes embedding gene expression profiles in a linear regression model. As a case study, we used the data from Bacillus subtilis grown under different carbon sources. We could well predict the correct carbon source. Additionally, we established a novel and fast method to remove thermodynamically infeasible loops.

scholarly journals Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models

PLoS ONE ◽  
2012 ◽  
Vol 7 (5) ◽  
pp. e36947 ◽  
Author(s):  
Aaron Brandes ◽  
Desmond S. Lun ◽  
Kuhn Ip ◽  
Jeremy Zucker ◽  
Caroline Colijn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Flux ◽  
Expression Profiles ◽  
Carbon Sources ◽  
Gene Expression Profiles

scholarly journals Identification of key genes related to heart failure by analysis of expression profiles

2021 ◽  
Author(s):  
Che Wang ◽  
Qingmin Li ◽  
Honghui Yang ◽  
Chuanyu Gao ◽  
Qiubo Du ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Drug Targets ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Type I ◽  
Ppi Network ◽  
Hub Genes ◽  
And Control

IntroductionTo elucidate the candidate biomarkers involved in the patho�genesis process of heart failure (HF) via analysis of differentially expressed genes (DEGs) of the dataset from the Gene Expression Omnibus (GEO).Material and methodsThe GSE76701 gene expression profiles regarding the HF and control subjects were respectively analysed. Briefly, DEGs were firstly identified and subjected to Cytoscape plug-in ClueGO + CluePedia and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network was then built to analyse the in�teraction between DEGs, followed by the construction of an interaction net�work by combining with hub genes with the targeted miRNA genes of DEGs to identify the key molecules of HF. In addition, potential drugs targeting key DEGs were sought using the drug-gene interaction database (DGIdb), and a drug-mRNA-miRNA interaction network was also constructed.ResultsA total of 489 DEGs were verified between HF and control, which mainly enriched in type I interferon and leukocyte migration according to molecular function. Significantly increased levels of GAPDH, GALM1, MMP9, CCL5, and GNAL2 were found in the HF setting and were identified as the hub genes based on the PPI network. Furthermore, according to the drug-mRNA-miRNA network, FCGR2B, CCND1, and NF-κb, as well as corre�sponding miRNA-605-5p, miRNA-147a, and miRNA-671-5p were identified as the drug targets of HF.ConclusionsThe hub genes GAPDH, GALM1, MMP9, CCL5, and GNAL2 were significantly increased in HF. miRNA-605-5p, miRNA-147a, and miRNA-671-5p were predicted as the drug target-interacted gene-miRNA of HF.

scholarly journals Lanthanide-dependent cross-feeding of methane-derived carbon is linked by microbial community interactions

2016 ◽  
Vol 114 (2) ◽  
pp. 358-363 ◽  
Author(s):  
Sascha M. B. Krause ◽  
Timothy Johnson ◽  
Yasodara Samadhi Karunaratne ◽  
Yanfen Fu ◽  
David A. C. Beck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microbial Community ◽  
Expression Profiles ◽  
Carbon Sources ◽  
Gene Expression Profiles ◽  
Microbial Interactions ◽  
Community Interactions ◽  
Carbon Cycles ◽  
Calcium Dependent ◽  
Pure Cultures

The utilization of methane, a potent greenhouse gas, is an important component of local and global carbon cycles that is characterized by tight linkages between methane-utilizing (methanotrophic) and nonmethanotrophic bacteria. It has been suggested that the methanotroph sustains these nonmethanotrophs by cross-feeding, because subsequent products of the methane oxidation pathway, such as methanol, represent alternative carbon sources. We established cocultures in a microcosm model system to determine the mechanism and substrate that underlay the observed cross-feeding in the environment. Lanthanum, a rare earth element, was applied because of its increasing importance in methylotrophy. We used co-occurring strains isolated from Lake Washington sediment that are involved in methane utilization: a methanotroph and two nonmethanotrophic methylotrophs. Gene-expression profiles and mutant analyses suggest that methanol is the dominant carbon and energy source the methanotroph provides to support growth of the nonmethanotrophs. However, in the presence of the nonmethanotroph, gene expression of the dominant methanol dehydrogenase (MDH) shifts from the lanthanide-dependent MDH (XoxF)-type, to the calcium-dependent MDH (MxaF)-type. Correspondingly, methanol is released into the medium only when the methanotroph expresses the MxaF-type MDH. These results suggest a cross-feeding mechanism in which the nonmethanotrophic partner induces a change in expression of methanotroph MDHs, resulting in release of methanol for its growth. This partner-induced change in gene expression that benefits the partner is a paradigm for microbial interactions that cannot be observed in studies of pure cultures, underscoring the importance of synthetic microbial community approaches to understand environmental microbiomes.

scholarly journals Inferring propensity amongst lung and breast carcinomas via overlapped gene expression profiles

2017 ◽  
Author(s):  
Rajni Jaiswal ◽  
Sabin Dhakal ◽  
Shaurya Jauhari
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Biological Networks ◽  
Drug Targets ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Cancer Data ◽  
Gene Regulatory

ABSTRACTReconstruction of biological networks for topological analyses helps in correlation identification between various types of biomarkers. These networks have been vital components of System Biology in present era. Genes are the basic physical and structural unit of heredity. Genes act as instructions to make molecules called proteins. Alterations in the normal sequence of these genes are the root cause of various diseases and cancer is the prominent example disease caused by gene alteration or mutation. These slight alterations can be detected by microarray analysis. The high throughput data obtained by microarray experiments aid scientists in reconstructing cancer specific gene regulatory networks. The purpose of experiment performed is to find out the overlapping of the gene expression profiles of breast and lung cancer data, so that the common hub genes can be sifted and utilized as drug targets which could be used for the treatment of diseased conditions. In this study, first the differentially expressed genes have been identified (lung cancer and breast cancer), followed by a filtration approach and most significant genes are chosen using paired t-test and gene regulatory network construction. The obtained result has been checked and validated with the available databases and literature.

scholarly journals Correction: Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models

PLoS ONE ◽  
2012 ◽  
Vol 7 (8) ◽  
Author(s):  
Aaron Brandes ◽  
Desmond S. Lun ◽  
Kuhn Ip ◽  
Jeremy Zucker ◽  
Caroline Colijn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Flux ◽  
Expression Profiles ◽  
Carbon Sources ◽  
Gene Expression Profiles

scholarly journals Differential gene expression in scrub typhus compared to other acute febrile infections by bioinformatic approaches

2020 ◽  
Author(s):  
Manisha Mandal ◽  
Shyamapada Mandal
Keyword(s):  
Gene Expression ◽  
Drug Targets ◽  
Immunological Synapse ◽  
Scrub Typhus ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Febrile Illness ◽  
Enrichment Analysis ◽  
Microarray Gene Expression ◽  
Functional Pathways

AbstractScrub typhus (ST), caused with the infection of Orientia tsutsugamushi, without eschar, is a febrile illness that mimics malaria (ML), dengue (DG), and other rickettsioses such as murine typhus (MT). Comparative analysis of microarray gene expression profiles of GSE16463 dataset, from O. tsutsugamushi infected monocytes, was performed to identify transcriptional signatures in ST discriminated from other acute febrile infections, accompanied by functional pathways and enrichment analysis in disease pathogenesis. A unique 31 ST-associated signature genes obtained in this study could help distinguish ST from other febrile illnesses DG, ML and MT. The functional pathways significantly enriched in ST disease group included translocation of ZAP-70 to immunological synapse, and phosphorylation of CD3 and TCR zeta chains, involving PTPN22 and CD3G genes, which could further help in the understanding of molecular pathophysiology of ST and discovering novel drug targets as well as vaccine developments.

1327: Gene Expression Profiles in Benign Prostatic Hyperplasia

2004 ◽  
Vol 171 (4S) ◽  
pp. 349-350
Author(s):  
Gaelle Fromont ◽  
Michel Vidaud ◽  
Alain Latil ◽  
Guy Vallancien ◽  
Pierre Validire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Benign Prostatic Hyperplasia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostatic Hyperplasia
Sign in / Sign up

Export Citation Format

Share Document