45 research outputs found
The genetic basis for adaptation of model-designed syntrophic co-cultures.
Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities
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Aledb 1.0: A database of mutations from adaptive laboratory evolution experimentation
Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have been published, providing a strong impetus for developing databases to warehouse experimental evolution information and make it retrievable for large-scale analysis. Here, the first step towards establishing this resource is presented: ALEdb (http://aledb.org). This initial release contains over 11Â 000 mutations that have been discovered from eleven ALE publications. ALEdb (i) is a web-based platform that comprehensively reports on ALE acquired mutations and their conditions, (ii) reports key mutations using previously established trends, (iii) enables a search-driven workflow to enhance user mutation functional analysis through mutation cross-reference, (iv) allows exporting of mutation query results for custom analysis, (v) includes a bibliome describing the databased experiment publications and (vi) contains experimental evolution mutations from multiple model organisms. Thus, ALEdb is an informative platform which will become increasingly revealing as the number of reported ALE experiments and identified mutations continue to expand
Reframing gene essentiality in terms of adaptive flexibility
Abstract Background Essentiality assays are important tools commonly utilized for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are also often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. One explanation for this inconsistency is that the model contains the wrong information, possibly an incorrectly annotated alternative pathway or isozyme reaction. Inconsistencies could also be attributed to experimental limitations, such as growth tests with arbitrary time cut-offs. The focus of this study was to resolve such inconsistencies to better understand isozyme activities and gene essentiality. Results In this study, we explored the definition of conditional essentiality from a phenotypic and genomic perspective. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for Escherichia coli were targeted for extended growth tests followed by population sequencing and transcriptome analysis. Of the twenty false positive strains available and confirmed from the Keio single gene knock-out collection, 11 strains were shown to grow with longer incubation periods making these actual true positives. These strains grew reproducibly with a diverse range of growth phenotypes. The lag phase observed for these strains ranged from less than one day to more than 7 days. It was found that 9 out of 11 of the false positive strains that grew acquired mutations in at least one replicate experiment and the types of mutations ranged from SNPs and small indels associated with regulatory or metabolic elements to large regions of genome duplication. Comparison of the detected adaptive mutations, modeling predictions of alternate pathways and isozymes, and transcriptome analysis of KO strains suggested agreement for the observed growth phenotype for 6 out of the 9 cases where mutations were observed. Conclusions Longer-term growth experiments followed by whole genome sequencing and transcriptome analysis can provide a better understanding of conditional gene essentiality and mechanisms of adaptation to such perturbations. Compensatory mutations are largely reproducible mechanisms and are in agreement with genome-scale modeling predictions to loss of function gene deletion events
Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTICâHF: baseline characteristics and comparison with contemporary clinical trials
Aims:
The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTICâHF) trial. Here we describe the baseline characteristics of participants in GALACTICâHF and how these compare with other contemporary trials.
Methods and Results:
Adults with established HFrEF, New York Heart Association functional class (NYHA)ââ„âII, EF â€35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokineticâguided dosing: 25, 37.5 or 50âmg bid). 8256 patients [male (79%), nonâwhite (22%), mean age 65âyears] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NTâproBNP 1971âpg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTICâHF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressureâ<â100âmmHg (n = 1127), estimated glomerular filtration rate <â30âmL/min/1.73 m2 (n = 528), and treated with sacubitrilâvalsartan at baseline (n = 1594).
Conclusions:
GALACTICâHF enrolled a wellâtreated, highârisk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation
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ALE Analytics: A Software Pipeline and Web Platform for the Analysis of Microbial Genomic Data from Adaptive Laboratory Evolution Experiments
Adaptive Laboratory Evolution (ALE) methodologies are used for studying microbial adaptive mutations that optimize host metabolism. The Systems Biology Research Group (SBRG) at the University of California, San Diego, has implemented high-throughput ALE experiment automation that enables the group to expand their experimental evolutions to scales previously infeasible with manual workflows. The data generated by the high-throughput automation now requires a post-processing, content management and analysis framework that can operate on the same scale. We developed a software system which solves the SBRG's specific ALE big data to knowledge challenges. The software system is comprised of a post-processing protocol for quality control, a software framework and database for data consolidation and a web platform named ALE Analytics for report generation and automated key mutation analysis. The automated key mutation analysis is evaluated against published ALE experiment key mutation results from the SBRG and maintains an average recall of 89.6% and an average precision of 71.2%. The consolidation of all ALE experiments into a unified resource has enabled the development of web applications that compare key mutations across multiple experiments. These features find the genomic regions rph, hns/tdk, rpoB, rpoC and pykF mutated in more than one ALE experiment published by the SBRG. We reason that leveraging this software system relieves the bottleneck in ALE experiment analysis and generates new data mining opportunities for research in understanding system-level mechanisms that govern adaptive evolution
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Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data
Microbes are increasingly engineered for a large variety of valuable applications. Designing microbial strains remains challenging due to the complexity and knowledge gaps with biological systems. Bioengineers have a unique advantage over other engineering disciplines: biological systems have been solving challenges long before human intervention through adaptive evolution. Adaptive Laboratory Evolution (ALE) methods leverage this natural problem-solving process to elucidate biological functions and generate application solutions. The promise of ALE methods has resulted in the generation of a substantial amount of public ALE data, which in aggregate, could contribute new insights towards ALE-derived strain design. The work of this dissertation combined aggregated public ALE data and rich mutation annotations to design strain variants with possible value in applications. Public ALE mutational data was consolidated into a web-accessible database that can report and export the aggregated ALE data. ALE metadata was used to statistically associate mutations to experimental conditions, reducing the amount of conditions to consider during mutation functional analysis and therefore deconvoluting the context of adaptive mutations. Multi-scale and multi-dimensional mutation annotations were used to identify the mutation effects, structural features, regulatory features, and cellular subsystems that ALE mutations converged upon. Meta-analysis of the aggregated ALE data revealed the principles underlying adaptive mutation trends, which were then used to design novel strain variants. The results of this dissertation demonstrate how strain design principles can be extracted from aggregated ALE data to enhance microbial engineering efforts