scholarly journals Essential metabolism for a minimal cell

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Marian Breuer ◽  
Tyler M Earnest ◽  
Chuck Merryman ◽  
Kim S Wise ◽  
Lijie Sun ◽  
...  
Keyword(s):  
Experimental Information ◽  
Enzymatic Reactions ◽  
Proteomics Data ◽  
Biological Mechanisms ◽  
Minimal Cell ◽  
Open Questions ◽  
Mycoplasma Mycoides ◽  
Data Yield ◽  
Genome Scale

JCVI-syn3A, a robust minimal cell with a 543 kbp genome and 493 genes, provides a versatile platform to study the basics of life. Using the vast amount of experimental information available on its precursor, Mycoplasma mycoides capri, we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment. The model agrees well with genome-scale in vivo transposon mutagenesis experiments, showing a Matthews correlation coefficient of 0.59. The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% (68% essential), compared to 79% in silico essentiality. This coherent model of the minimal metabolism in JCVI-syn3A at the same time also points toward specific open questions regarding the minimal genome of JCVI-syn3A, which still contains many genes of generic or completely unclear function. In particular, the model, its comparison to in vivo essentiality and proteomics data yield specific hypotheses on gene functions and metabolic capabilities; and provide suggestions for several further gene removals. In this way, the model and its accompanying data guide future investigations of the minimal cell. Finally, the identification of 30 essential genes with unclear function will motivate the search for new biological mechanisms beyond metabolism.

scholarly journals Conformational entropy of a single peptide controlled under force governs protease recognition and catalysis

2018 ◽  
Vol 115 (45) ◽  
pp. 11525-11530 ◽  
Author(s):  
Marcelo E. Guerin ◽  
Guillaume Stirnemann ◽  
David Giganti
Keyword(s):  
Single Molecule ◽  
Conformational Dynamics ◽  
Conformational Sampling ◽  
Enzymatic Reactions ◽  
Sequence Specificity ◽  
Proteomics Data ◽  
Conformational Entropy ◽  
Substrate Peptide ◽  
The Impact

An immense repertoire of protein chemical modifications catalyzed by enzymes is available as proteomics data. Quantifying the impact of the conformational dynamics of the modified peptide remains challenging to understand the decisive kinetics and amino acid sequence specificity of these enzymatic reactions in vivo, because the target peptide must be disordered to accommodate the specific enzyme-binding site. Here, we were able to control the conformation of a single-molecule peptide chain by applying mechanical force to activate and monitor its specific cleavage by a model protease. We found that the conformational entropy impacts the reaction in two distinct ways. First, the flexibility and accessibility of the substrate peptide greatly increase upon mechanical unfolding. Second, the conformational sampling of the disordered peptide drives the specific recognition, revealing force-dependent reaction kinetics. These results support a mechanism of peptide recognition based on conformational selection from an ensemble that we were able to quantify with a torsional free-energy model. Our approach can be used to predict how entropy affects site-specific modifications of proteins and prompts conformational and mechanical selectivity.

scholarly journals Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

2019 ◽  
Author(s):  
David Heckmann ◽  
Anaamika Campeau ◽  
Colton J. Lloyd ◽  
Patrick Phaneuf ◽  
Ying Hefner ◽  
...  
Keyword(s):  
Metabolic Adaptation ◽  
Proteomics Data ◽  
Cellular Models ◽  
Regulatory Changes ◽  
Genetic Perturbations ◽  
Data Driven Approach ◽  
Enzyme Turnover ◽  
Genome Scale

AbstractEnzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, non-physiological, inconsistent, and labor-intensive to obtain.We use a data-driven approach to estimate in vivo kcats using metabolic specialist E. coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of noisy and inconsistent parameterizations of cellular models.

scholarly journals Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

2020 ◽  
Vol 117 (37) ◽  
pp. 23182-23190 ◽  
Author(s):  
David Heckmann ◽  
Anaamika Campeau ◽  
Colton J. Lloyd ◽  
Patrick V. Phaneuf ◽  
Ying Hefner ◽  
...  
Keyword(s):  
Metabolic Adaptation ◽  
Proteomics Data ◽  
Cellular Models ◽  
Genetic Perturbations ◽  
Data Driven Approach ◽  
Enzyme Turnover ◽  
Low Coverage ◽  
Genome Scale

Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo kcats using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.

Development of Synchrotron Footprinting at NSLS and NSLS-II

2019 ◽  
Vol 26 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Jen Bohon
Keyword(s):  
Solvent Accessibility ◽  
Biological Systems ◽  
In Vivo Studies ◽  
Aqueous Samples ◽  
Biological Mechanisms ◽  
Macromolecular Assembly ◽  
Structure And Dynamics ◽  
Synchrotron Light ◽  
Ideal Technique

Background: First developed in the 1990’s at the National Synchrotron Light Source, xray synchrotron footprinting is an ideal technique for the analysis of solution-state structure and dynamics of macromolecules. Hydroxyl radicals generated in aqueous samples by intense x-ray beams serve as fine probes of solvent accessibility, rapidly and irreversibly reacting with solvent exposed residues to provide a “snapshot” of the sample state at the time of exposure. Over the last few decades, improvements in instrumentation to expand the technology have continuously pushed the boundaries of biological systems that can be studied using the technique. Conclusion: Dedicated synchrotron beamlines provide important resources for examining fundamental biological mechanisms of folding, ligand binding, catalysis, transcription, translation, and macromolecular assembly. The legacy of synchrotron footprinting at NSLS has led to significant improvement in our understanding of many biological systems, from identifying key structural components in enzymes and transporters to in vivo studies of ribosome assembly. This work continues at the XFP (17-BM) beamline at NSLS-II and facilities at ALS, which are currently accepting proposals for use.

scholarly journals Ways into Understanding HIF Inhibition

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 159
Author(s):  
Tina Schönberger ◽  
Joachim Fandrey ◽  
Katrin Prost-Fingerle
Keyword(s):  
Protein Interactions ◽  
Target Genes ◽  
Protein Protein Interactions ◽  
Low Oxygen ◽  
Drug Candidates ◽  
Open Questions ◽  
Wide Range ◽  
Hif Pathway

Hypoxia is a key characteristic of tumor tissue. Cancer cells adapt to low oxygen by activating hypoxia-inducible factors (HIFs), ensuring their survival and continued growth despite this hostile environment. Therefore, the inhibition of HIFs and their target genes is a promising and emerging field of cancer research. Several drug candidates target protein–protein interactions or transcription mechanisms of the HIF pathway in order to interfere with activation of this pathway, which is deregulated in a wide range of solid and liquid cancers. Although some inhibitors are already in clinical trials, open questions remain with respect to their modes of action. New imaging technologies using luminescent and fluorescent methods or nanobodies to complement widely used approaches such as chromatin immunoprecipitation may help to answer some of these questions. In this review, we aim to summarize current inhibitor classes targeting the HIF pathway and to provide an overview of in vitro and in vivo techniques that could improve the understanding of inhibitor mechanisms. Unravelling the distinct principles regarding how inhibitors work is an indispensable step for efficient clinical applications and safety of anticancer compounds.

scholarly journals Genetic stability of genome-scale deoptimized RNA virus vaccine candidates under selective pressure

2017 ◽  
Vol 114 (3) ◽  
pp. E386-E395 ◽  
Author(s):  
Cyril Le Nouën ◽  
Thomas McCarty ◽  
Michael Brown ◽  
Melissa Laird Smith ◽  
Roberto Lleras ◽  
...  
Keyword(s):  
Virus Vaccine ◽  
Genetic Stability ◽  
Selective Pressure ◽  
Rna Virus ◽  
Temperature Sensitive ◽  
Phenotypic Analysis ◽  
Vaccine Candidates ◽  
Synonymous Mutations ◽  
Genome Scale

Recoding viral genomes by numerous synonymous but suboptimal substitutions provides live attenuated vaccine candidates. These vaccine candidates should have a low risk of deattenuation because of the many changes involved. However, their genetic stability under selective pressure is largely unknown. We evaluated phenotypic reversion of deoptimized human respiratory syncytial virus (RSV) vaccine candidates in the context of strong selective pressure. Codon pair deoptimized (CPD) versions of RSV were attenuated and temperature-sensitive. During serial passage at progressively increasing temperature, a CPD RSV containing 2,692 synonymous mutations in 9 of 11 ORFs did not lose temperature sensitivity, remained genetically stable, and was restricted at temperatures of 34 °C/35 °C and above. However, a CPD RSV containing 1,378 synonymous mutations solely in the polymerase L ORF quickly lost substantial attenuation. Comprehensive sequence analysis of virus populations identified many different potentially deattenuating mutations in the L ORF as well as, surprisingly, many appearing in other ORFs. Phenotypic analysis revealed that either of two competing mutations in the virus transcription antitermination factor M2-1, outside of the CPD area, substantially reversed defective transcription of the CPD L gene and substantially restored virus fitness in vitro and in case of one of these two mutations, also in vivo. Paradoxically, the introduction into Min L of one mutation each in the M2-1, N, P, and L proteins resulted in a virus with increased attenuation in vivo but increased immunogenicity. Thus, in addition to providing insights on the adaptability of genome-scale deoptimized RNA viruses, stability studies can yield improved synthetic RNA virus vaccine candidates.

MIC-Drop: A platform for large-scale in vivo CRISPR screens

Science ◽  
2021 ◽  
pp. eabi8870
Author(s):  
Saba Parvez ◽  
Chelsea Herdman ◽  
Manu Beerens ◽  
Korak Chakraborti ◽  
Zachary P. Harmer ◽  
...  
Keyword(s):  
Large Scale ◽  
Cultured Cells ◽  
Cardiac Development ◽  
Droplet Microfluidics ◽  
Model Organisms ◽  
Genetic Screens ◽  
Large Numbers ◽  
And Function ◽  
Genome Scale

CRISPR-Cas9 can be scaled up for large-scale screens in cultured cells, but CRISPR screens in animals have been challenging because generating, validating, and keeping track of large numbers of mutant animals is prohibitive. Here, we report Multiplexed Intermixed CRISPR Droplets (MIC-Drop), a platform combining droplet microfluidics, single-needle en masse CRISPR ribonucleoprotein injections, and DNA barcoding to enable large-scale functional genetic screens in zebrafish. The platform can efficiently identify genes responsible for morphological or behavioral phenotypes. In one application, we show MIC-Drop can identify small molecule targets. Furthermore, in a MIC-Drop screen of 188 poorly characterized genes, we discover several genes important for cardiac development and function. With the potential to scale to thousands of genes, MIC-Drop enables genome-scale reverse-genetic screens in model organisms.

scholarly journals Genome-scale metabolic network reconstruction of model animals as a platform for translational research

2021 ◽  
Vol 118 (30) ◽  
pp. e2102344118
Author(s):  
Hao Wang ◽  
Jonathan L. Robinson ◽  
Pinar Kocabas ◽  
Johan Gustafsson ◽  
Mihail Anton ◽  
...  
Keyword(s):  
Transgenic Mice ◽  
Metabolic Network ◽  
Model Organisms ◽  
Protein Overexpression ◽  
Sequencing Data ◽  
Proteomics Data ◽  
Gm2 Ganglioside ◽  
Species Specific ◽  
Specific Reactions ◽  
Genome Scale

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

scholarly journals PPM1D is a neuroblastoma oncogene and therapeutic target in childhood neural tumors

2020 ◽  
Author(s):  
Jelena Milosevic ◽  
Susanne Fransson ◽  
Miklos Gulyas ◽  
Gabriel Gallo-Oller ◽  
Thale K Olsen ◽  
...  
Keyword(s):  
Therapeutic Target ◽  
Genetic Manipulation ◽  
Clonal Evolution ◽  
Neuroblastoma Cell ◽  
Tumor Formation ◽  
T Cell Lymphomas ◽  
Wip1 Phosphatase ◽  
Bona Fide ◽  
Genome Scale

SUMMARYMajority of cancers harbor alterations of the tumor suppressor TP53. However, childhood cancers, including unfavorable neuroblastoma, often lack TP53 mutations despite frequent loss of p53 function, suggesting alternative p53 inactivating mechanisms.Here we show that p53-regulating PPM1D at chromosome 17q22.3 is linked to aggressive tumors and poor prognosis in neuroblastoma. We identified that WIP1-phosphatase encoded by PPM1D, is activated by frequent segmental 17q-gain further accumulated during clonal evolution, gene-amplifications, gene-fusions or gain-of-function somatic and germline mutations. Pharmacological and genetic manipulation established WIP1 as a druggable target in neuroblastoma. Genome-scale CRISPR-Cas9 screening demonstrated PPM1D genetic dependency in TP53 wild-type neuroblastoma cell lines, and shRNA PPM1D knockdown significantly delayed in vivo tumor formation. Establishing a transgenic mouse model overexpressing PPM1D showed that these mice develop cancers phenotypically and genetically similar to tumors arising in mice with dysfunctional p53 when subjected to low-dose irradiation. Tumors include T-cell lymphomas harboring Notch1-mutations, Pten-deletions and p53-accumulation, adenocarcinomas and PHOX2B-expressing neuroblastomas establishing PPM1D as a bona fide oncogene in wtTP53 cancer and childhood neuroblastoma. Pharmacological inhibition of WIP1 suppressed the growth of neural tumors in nude mice proposing WIP1 as a therapeutic target in neural childhood tumors.

scholarly journals Revisiting the in vivo GlnR-binding sites at the genome scale in Bacillus subtilis

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Paola Randazzo ◽  
Anne Aucouturier ◽  
Olivier Delumeau ◽  
Sandrine Auger
Keyword(s):  
Bacillus Subtilis ◽  
Binding Sites ◽  
Genome Scale
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