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Engineering, decoding and systems-level characterization of chimpanzee cytomegalovirus.

ABSTRACT: The chimpanzee cytomegalovirus (CCMV) is the closest relative of human CMV (HCMV). Because of the high conservation between these two species and the ability of human cells to fully support CCMV replication, CCMV holds great potential as a model system for HCMV. To make the CCMV genome available for precise and rapid gene manipulation techniques, we captured the genomic DNA of CCMV strain Heberling as a bacterial artificial chromosome (BAC). Selected BAC clones were reconstituted to infectious viruses, growing to similar high titers as parental CCMV. DNA sequencing confirmed the integrity of our clones and led to the identification of two polymorphic loci and a deletion-prone region within the CCMV genome. To re-evaluate the CCMV coding potential, we analyzed the viral transcriptome and proteome and identified several novel ORFs, splice variants, and regulatory RNAs. We further characterized the dynamics of CCMV gene expression and found that viral proteins cluster into five distinct temporal classes. In addition, our datasets revealed that the host response to CCMV infection and the de-regulation of cellular pathways are in line with known hallmarks of HCMV infection. In a first functional experiment, we investigated a proposed frameshift mutation in UL128 that was suspected to restrict CCMV's cell tropism. In fact, repair of this frameshift re-established productive CCMV infection in endothelial and epithelial cells, expanding the options of CCMV as an infection model. Thus, BAC-cloned CCMV can serve as a powerful tool for systematic approaches in comparative functional genomics, exploiting the close phylogenetic relationship between CCMV and HCMV.

SUBMITTER: Phan QV

PROVIDER: S-EPMC8759705 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Cloning and Decoding of Chimpanzee Cytomegalovirus - A Resource for Comparative Functional Genomics

Project description:The chimpanzee cytomegalovirus (CCMV) is the closest relative of human CMV (HCMV). Because of the high conservation between these two species and the ability of human cells to fully support CCMV replication, CCMV holds great potential as a model system for HCMV. To make the CCMV genome available for precise and rapid gene manipulation techniques, we captured the genomic DNA of CCMV strain Heberling as a bacterial artificial chromosome(BAC). Selected BAC clones were reconstituted to infectious viruses, growing to similar high titers as parental CCMV. DNA sequencing confirmed the integrity of our clones and led to the identification of two polymorphic loci within the CCMV genome. To re-evaluate the CCMV coding potential, we analyzed the transcriptome and proteome of infected cells and identified several novel ORFs, splice variants, and regulatory RNAs. We further characterized the dynamics of viral gene expression and found that in strong parallel to HCMV, CCMV proteins cluster into five distinct temporal classes. In addition, our datasets revealed that the host response to CCMV infection and the de-regulation of cellular pathways are in line with known hallmarks of HCMV infection. In a first functional experiment, we investigated a proposed frameshift mutation in UL128 that was suspected to restrict CCMV’s cell tropism. In fact, repair of this frameshift re-established productive CCMV infection in endothelial and epithelial cells, expanding the options of CCMV as an infection model. Thus, BAC-cloned CCMV can serve as a powerful tool for systematic approaches in comparative functional genomics, exploiting the close phylogenetic relationship between CCMV and HCMV.

2021-12-20 | PXD027434 | Pride

Decoding human cytomegalovirus.

Project description:The human cytomegalovirus (HCMV) genome was sequenced 20 years ago. However, like those of other complex viruses, our understanding of its protein coding potential is far from complete. We used ribosome profiling and transcript analysis to experimentally define the HCMV translation products and follow their temporal expression. We identified hundreds of previously unidentified open reading frames and confirmed a fraction by means of mass spectrometry. We found that regulated use of alternative transcript start sites plays a broad role in enabling tight temporal control of HCMV protein expression and allowing multiple distinct polypeptides to be generated from a single genomic locus. Our results reveal an unanticipated complexity to the HCMV coding capacity and illustrate the role of regulated changes in transcript start sites in generating this complexity.

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Project description:This SuperSeries is composed of the SubSeries listed below.

2023-04-26 | GSE212289 | GEO

Decoding murine cytomegalovirus

Project description:Decoding murine cytomegalovirus

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Systems-level engineering and characterization of Clostridium autoethanogenum through heterologous production of poly-3-hydroxybutyrate (PHB)

Project description:Gas fermentation is emerging as an economically attractive option for the sustainable production of fuels and chemicals from gaseous waste feedstocks. Clostridium autoethanogenum can use CO and/or CO2 + H2 as its sole carbon and energy sources. Fermentation of C. autoethanogenum is currently being deployed on a commercial scale for ethanol production. Expanding the product spectrum of acetogens will enhance the economics of gas fermentation. To achieve efficient heterologous product synthesis, limitations in redox and energy metabolism must be overcome. Here, we engineered and characterised at a systems-level, a recombinant poly-3-hydroxybutyrate (PHB)-producing strain of C. autoethanogenum. Cells were grown in CO-limited steady-state chemostats on two gas mixtures, one resembling syngas (20% H2) and the other steel mill off-gas (2% H2). Results were characterised using metabolomics and transcriptomics, and then integrated using a genome-scale metabolic model reconstruction. PHB-producing cells had an increased expression of the Rnf complex, suggesting energy limitations for heterologous production. Subsequent optimisation of the bioprocess led to a 12-fold increase in the cellular PHB content. The data suggest that the cellular redox state, rather than the acetyl-CoA pool, was limiting PHB production. Integration of the data into the genome-scale metabolic model showed that ATP availability limits PHB production. Altogether, the data presented here advances the fundamental understanding of heterologous product synthesis in gas-fermenting acetogens.

2019-02-03 | GSE115837 | GEO

Decoding human cytomegalovirus using ribosome profiling

Project description:The human cytomegalovirus (HCMV) genome was sequenced 20 years ago. However, like other complex viruses, our understanding of its protein coding potential is far from complete. Here, we use ribosome profiling and transcript analysis to experimentally define the HCMV translation products and follow their temporal expression. We identified several hundred previously unidentified open reading frames and confirmed a fraction by mass spectrometry. We found that regulated use of alternative transcript start sites plays a broad role in enabling tight temporal control of HCMV protein expression and allowing multiple distinct polypeptides to be generated from a single genomic locus. Our results reveal an unanticipated complexity to the HCMV coding capacity and illustrate the role of regulated changes in transcript start sites in generating this complexity. Ribosome profiling and mRNA-seq from 3 time points (5hr, 24hr, 72hr) along HCMV infection. The supplementary file 'GSE41605_merlin_final_orfs.bed.gz' includes the 751 ORFs of HCMV that were identified in this study.

2012-10-17 | E-GEOD-41605 | biostudies-arrayexpress

Decoding human cytomegalovirus using ribosome profiling

2012-10-17 | GSE41605 | GEO

Systems engineering of Escherichia coli for high-level glutarate production from glucose.

Project description:Glutarate is a key monomer in polyester and polyamide production. The low efficiency of the current biosynthetic pathways hampers its production by microbial cell factories. Herein, through metabolic simulation, a lysine-overproducing E. coli strain Lys5 is engineered, achieving titer, yield, and productivity of 195.9 g/L, 0.67 g/g glucose, and 5.4 g/L·h, respectively. Subsequently, the pathway involving aromatic aldehyde synthase, monoamine oxidase, and aldehyde dehydrogenase (AMA pathway) is introduced into E. coli Lys5 to produce glutarate from glucose. To enhance the pathway's efficiency, rational mutagenesis on the aldehyde dehydrogenase is performed, resulting in the development of variant Mu5 with a 50-fold increase in catalytic efficiency. Finally, a glutarate tolerance gene cbpA is identified and genomically overexpressed to enhance glutarate productivity. With enzyme expression optimization, the glutarate titer, yield, and productivity of E. coli AMA06 reach 88.4 g/L, 0.42 g/g glucose, and 1.8 g/L·h, respectively. These findings hold implications for improving glutarate biosynthesis efficiency in microbial cell factories.

| S-EPMC10838341 | biostudies-literature

Group-level brain decoding with deep learning.

Project description:Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group-level models to outperform subject-specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between-subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject- and group-level decoding models. Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group-level models to perform better than subject-level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (https://github.com/ricsinaruto/MEG-group-decode).

| S-EPMC10619368 | biostudies-literature

Robust prognostic biomarkers for EAC identified by systems-level characterization of tumor transcriptome

Project description:Despite continual efforts to rationalize a prognostic stratification of patients with esophageal adenocarcinoma (EAC) before treatment, current staging system only shows limited success owing to the lack of molecular and genetic markers that reflect prognostic features of the tumor. To develop molecular predictors of prognosis, we used systems-level characterization of tumor transcriptome. Using DNA microarray, genome-wide gene expression profiling was performed on 75 biopsy samples from patients with untreated EAC. Various statistical and informatical methods were applied to gene expression data to identify potential biomarkers associated with prognosis. Potential marker genes were validated in an independent cohort using quantitiative RT-PCR to measure gene expression. Distinct subgroups of EAC were uncovered by systems-level characterization of tumor transcriptome. We also identified a six-gene expression signature that could be used to predict overall survival (OS) of EAC patients. In particular, expression of SPARC and SPP1 was a strong independent predictor of OS, and a combined gene expression signature with these two genes was associated with prognosis (P < 0.024), even when all relevant pathological variables were considered together in multivariate Cox hazard regression analysis. Our findings suggest that molecular features reflected in gene expression signatures may dictate the prognosis of EAC patients, and these gene expression signatures can be used to predict the likelihood of prognosis at the time of diagnosis and before treatment. 64 primary esophageal adenocarcinoma, 15 Barrett's esophagus and 28 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at UT M.D. Anderson Cancer Center (MDACC). Microarray experiment and data analysis were done in the Dept. of systems biology at MDACC DNA microarray (Illumina human V2)

2011-01-04 | E-GEOD-13898 | biostudies-arrayexpress

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