Dataset Information

The Minimal Translation Machinery: What We Can Learn From Naturally and Experimentally Reduced Genomes.

ABSTRACT: The current theoretical proposals of minimal genomes have not attempted to outline the essential machinery for proper translation in cells. Here, we present a proposal of a minimal translation machinery based on (1) a comparative analysis of bacterial genomes of insects' endosymbionts using a machine learning classification algorithm, (2) the empiric genomic information obtained from Mycoplasma mycoides JCVI-syn3.0 the first minimal bacterial genome obtained by design and synthesis, and (3) a detailed functional analysis of the candidate genes based on essentiality according to the DEG database (Escherichia coli and Bacillus subtilis) and the literature. This proposed minimal translational machinery is composed by 142 genes which must be present in any synthetic prokaryotic cell designed for biotechnological purposes, 76.8% of which are shared with JCVI-syn3.0. Eight additional genes were manually included in the proposal for a proper and efficient translation.

SUBMITTER: Garzon MJ

PROVIDER: S-EPMC9035817 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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The Minimal Translation Machinery: What We Can Learn From Naturally and Experimentally Reduced Genomes.

Garzón María José MJ Reyes-Prieto Mariana M Gil Rosario R

Frontiers in microbiology 20220411

The current theoretical proposals of minimal genomes have not attempted to outline the essential machinery for proper translation in cells. Here, we present a proposal of a minimal translation machinery based on (1) a comparative analysis of bacterial genomes of insects' endosymbionts using a machine learning classification algorithm, (2) the empiric genomic information obtained from <i>Mycoplasma mycoides</i> JCVI-syn3.0 the first minimal bacterial genome obtained by design and synthesis, and ( ...[more]

PMID: 35479634

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Project description:BACKGROUND: Understanding the topology and dynamics of the human protein-protein interaction (PPI) network will significantly contribute to biomedical research, therefore its systematic reconstruction is required. Several meta-databases integrate source PPI datasets, but the protein node sets of their networks vary depending on the PPI data combined. Due to this inherent heterogeneity, the way in which the human PPI network expands via multiple dataset integration has not been comprehensively analyzed. We aim at assembling the human interactome in a global structured way and exploring it to gain insights of biological relevance. RESULTS: First, we defined the UniProtKB manually reviewed human "complete" proteome as the reference protein-node set and then we mined five major source PPI datasets for direct PPIs exclusively between the reference proteins. We updated the protein and publication identifiers and normalized all PPIs to the UniProt identifier level. The reconstructed interactome covers approximately 60% of the human proteome and has a scale-free structure. No apparent differentiating gene functional classification characteristics were identified for the unrepresented proteins. The source dataset integration augments the network mainly in PPIs. Polyubiquitin emerged as the highest-degree node, but the inclusion of most of its identified PPIs may be reconsidered. The high number (>300) of connections of the subsequent fifteen proteins correlates well with their essential biological role. According to the power-law network structure, the unrepresented proteins should mainly have up to four connections with equally poorly-connected interactors. CONCLUSIONS: Reconstructing the human interactome based on the a priori definition of the protein nodes enabled us to identify the currently included part of the human "complete" proteome, and discuss the role of the proteins within the network topology with respect to their function. As the network expansion has to comply with the scale-free theory, we suggest that the core of the human interactome has essentially emerged. Thus, it could be employed in systems biology and biomedical research, despite the considerable number of currently unrepresented proteins. The latter are probably involved in specialized physiological conditions, justifying the scarcity of related PPI information, and their identification can assist in designing relevant functional experiments and targeted text mining algorithms.

Project description:Reactive oxygen and nitrogen species (ROS-RNS) are important defence substances involved in the immune response against pathogens. An excessive increase in ROS-RNS, however, can damage the organism causing oxidative stress (OS). The organism is able to neutralise OS by the production of antioxidant enzymes (AE); hence, tissue damage is the result of an imbalance between oxidant and antioxidant status. Though some work has been carried out in humans, there is a lack of information about the oxidant/antioxidant status in the presence of tuberculosis (TB) in wild reservoirs. In the Mediterranean Basin, wild boar (Sus scrofa) is the main reservoir of TB. Wild boar showing severe TB have an increased risk to Mycobacterium spp. shedding, leading to pathogen spreading and persistence. If OS is greater in these individuals, oxidant/antioxidant balance in TB-affected boars could be used as a biomarker of disease severity. The present work had a two-fold objective: i) to study the effects of bovine TB on different OS biomarkers (namely superoxide dismutase (SOD), catalasa (CAT), glutathione peroxidase (GPX), glutathione reductase (GR) and thiobarbituric acid reactive substances (TBARS)) in wild boar experimentally challenged with Mycobacterium bovis, and ii) to explore the role of body weight, sex, population and season in explaining the observed variability of OS indicators in two populations of free-ranging wild boar where TB is common. For the first objective, a partial least squares regression (PLSR) approach was used whereas, recursive partitioning with regression tree models (RTM) were applied for the second. A negative relationship between antioxidant enzymes and bovine TB (the more severe lesions, the lower the concentration of antioxidant biomarkers) was observed in experimentally infected animals. The final PLSR model retained the GPX, SOD and GR biomarkers and showed that 17.6% of the observed variability of antioxidant capacity was significantly correlated with the PLSR X's component represented by both disease status and the age of boars. In the samples from free-ranging wild boar, however, the environmental factors were more relevant to the observed variability of the OS biomarkers than the TB itself. For each OS biomarker, each RTM was defined as a maximum by one node due to the population effect. Along the same lines, the ad hoc tree regression on boars from the population with a higher prevalence of severe TB confirmed that disease status was not the main factor explaining the observed variability in OS biomarkers. It was concluded that oxidative damage caused by TB is significant, but can only be detected in the absence of environmental variation in wild boar.

Dataset Information

The Minimal Translation Machinery: What We Can Learn From Naturally and Experimentally Reduced Genomes.

Publications

The Minimal Translation Machinery: What We Can Learn From Naturally and Experimentally Reduced Genomes.

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