Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data.

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature's problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel Escherichia coli strains with fitnesses comparable to ALE mutants. These designs were achieved through a meta-analysis of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13 957 observed mutations), which additionally revealed global ALE mutation trends that inform on ALE-derived strain design principles. Such informative trends anticipate ALE-derived strain designs as largely gene-centric, as opposed to noncoding, and composed of a relatively small number of beneficial variants (approximately 6). These results demonstrate how strain design efforts can be enhanced by the meta-analysis of aggregated ALE data.

Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity.

BACKGROUND: Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures. RESULTS: Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine. CONCLUSIONS: The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

A Novel Homozygous Mutation of the AIRE Gene in an APECED Patient From Pakistan: Case Report and Review of the Literature.

Autoimmune-poly-endocrinopathy-candidiasis-ectodermal-dystrophy syndrome (APECED) is a rare monogenic recessive disorder caused by mutations in the autoimmune regulator (AIRE) gene. Criteria for the diagnosis of APECED are the presence of two of the following disorders: chronic mucocutaneous candidiasis (CMC), chronic hypoparathyroidism (CHP), and Addison's disease. APECED develops at high incidence in Finns, Sardinians, and Iranian Jews and presents with a wide range of clinical phenotypes and genotypes. In this manuscript, we report the clinical, endocrinological, and molecular features of a 16-year-old female patient from Pakistan living in Italy and presenting the major APECED clinical manifestations CMC, CHP, and primary adrenal insufficiency. Premature ovarian failure, chronic bronchopneumopathy, vitiligo, Hashimoto's thyroiditis emerged as associated diseases. In our patient, AIRE gene screening revealed the novel c.396G>C (p.Arg132Ser; p.R132S) mutation in homozygosity thus confirming APECED diagnosis. This is the first reported mutation within the nuclear localization signal (NLS) that is associated with APECED. The NLS mutation affects the nuclear import of classical transcription factors through nuclear pore by recognition of nuclear import receptors, the importin α molecules. By displaying crystal structures of the peptide containing the KRK basic residue cluster bound to α importins, we show that p.R132S replacement in 131-KRK-133 does not reproduce these interactions. Thus, we propose that the novel mutation exerts its pathogenetic effect by impairing the nuclear import of the Aire protein. The present case report is added to a limited series of Pakistani APECED patients who we reviewed from the scientific literature, mostly diagnosed on clinical findings.