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Phaneuf, P. V.

Publications and source records attributed to Phaneuf, P. V..

18 recordsLinked to original sources

Four-species Aspergillus pan-GWAS reveals rare genome expansion in pathogenicity and contraction in domestication

Aspergillus species are ecologically diverse and deeply entangled with human health and industry. A. fumigatus and A. flavus are the two principal species of invasive aspergillosis. A. niger and A. oryzae, on the other hand, are responsible for global enzyme production, organic acid production, and koji-based fermentation industries. The question of whether these similar phenotypes share the same genomic mechanisms across the genus is not yet understood. To address this, we constructed per-species pangenomes for the four Aspergillus species (929 initial genomes filtered to 210 ANI-verified, high-quality assemblies for a total of 88 A. fumigatus, 70 A. flavus, 33 A. oryzae, and 19 A. niger assemblies) alongside a genus-level pangenome of 15,163 orthogroups, and conducted phenotype-labeled pan-genome-wide association studies (pan-GWAS) with kinship correction across all species. Pan-GWAS identified up to 117 significant orthogroup presence/absence associations per species-phenotype comparison. However, convergence analysis showed that among the 92 and 62 distinct gene families significant for human pathogenicity in A. fumigatus and A. flavus respectively, the two species seldom agreed on whether the pathogenicity was associated with the enrichment or the depletion of a specific gene family. Convergence analysis of the functional annotations also yielded zero significant results at FDR < 0.05. A literature-curated gene panel analysis also showed that a species labeled pathogenic and another labeled GRAS carried the same aflatoxin and virulence genes, suggesting that gene presence alone cannot readily explain their phenotypic differences. Instead, we propose that niche adaptation operates through the use of the pangenomic rare genome. Reclassifying rare genes by homology identified truly rare subsets (156 to 391 orthogroups per species) distinct from paralogs and gene fragments. Human-pathogenic strains showed significant rare genome expansion of 2.44-fold for both A. fumigatus and A. flavus (kinship corrected, p = 6.6 e-08). Conversely, industrial strains showed rare genome contraction where both A. niger and A. oryzae industrial strains carried 0.57-fold (kinship corrected, p = 0.015) fewer rare genes than their non-industrial counterparts. Hence, we claim that Aspergillus niche evolution proceeds through directional rare genome changes, where there is expansion under pathogenic selection, and contraction under industrial domestication. The rare genome, often discarded as noise, may represent the primary evolutionary source for clinical and biotechnological adaptation in this genus.

genomics↗

A Diverse Genetic Basis for Metabolic Reactions is Revealed Through Pangenome analysis

Sequenced genomes for thousands of strains of a bacterial species allow for a comprehensive analysis of its pangenome. We present a pangenome study of Escherichia colis metabolism by formulating gene-to-protein-to-reaction associations (GPRs) for about 2,700 metabolic reactions in 2,377 fully sequenced strains. On one hand, these GPRs reconstruct strain-specific networks that allow computational predictions (and experimental validation) of metabolic phenotypes, while on the other hand, they give the genetic basis for a given metabolic reaction in every strain. A pangenome-wide analysis of GPRs shows that: 1) We can reveal the genetic basis for a specific metabolic property at the species level; 2) The genetic basis for many metabolic reactions is diverse; 3) Many rare genes show variation in the genes genomic neighborhood which often contain genes from transposable elements, 4) Many rare genes show large-scale fragmentation and horizontal gene transfer (>11,000 rare genes in 2,377 strains); and 5) The aromatic amino acids and branched chain amino acids pathways are enriched with rare genes, with Acetolactate synthase having 29 distinct genes. Thus, analysis of GPRs across the pangenome reveals a complex dynamic evolutionary history of metabolism, revealing the role of conserved, fragmented, and horizontally transferred metabolic genes.

systems biology↗

ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering

AO_SCPLOWBSTRACTC_SCPLOWProtein design and engineering are crucial for advancements in biotechnology, medicine, and sustainability. Machine learning (ML) models are used to design or enhance protein properties such as stability, catalytic activity, and selectivity. However, many existing ML tools require specialized expertise or lack open-source availability, limiting broader use and further development. To address this, we developed ProteusAI, a user-friendly and open-source ML platform to streamline protein engineering and design tasks. ProteusAI offers modules to support researchers in various stages of the design-build-test-learn (DBTL) cycle, including protein discovery, structure-based design, zero-shot predictions, and ML-guided directed evolution (MLDE). Our benchmarking results demonstrate ProteusAIs efficiency in improving proteins and enyzmes within a few DBTL-cycle iterations. ProteusAI democratizes access to ML-guided protein engineering and is freely available for academic and commercial use. Future work aims to expand and integrate novel methods in computational protein and enzyme design to further develop ProteusAI.

bioinformatics↗

PanKB: An interactive microbial pangenome knowledgebase for research, biotechnological innovation, and knowledge mining

The exponential growth of microbial genome data presents unprecedented opportunities for mining the potential of microorganisms. The burgeoning field of pangenomics offers a framework for extracting insights from this big biological data. Recent advances in microbial pangenomic research have generated substantial data and literature, yielding valuable knowledge across diverse microbial species. PanKB (pankb.org), a knowledgebase designed for microbial pangenomics research and biotechnological applications, was built to capitalize on this wealth of information. PanKB currently includes 51 pangenomes on 8 industrially relevant microbial families, comprising 8, 402 genomes, over 500, 000 genes, and over 7M mutations. To describe this data, PanKB implements four main components: 1) Interactive pangenomic analytics to facilitate exploration, intuition, and potential discoveries; 2) Alleleomic analytics, a pangenomic- scale analysis of variants, providing insights into intra-species sequence variation and potential mutations for applications; 3) A global search function enabling broad and deep investigations across pangenomes to power research and bioengineering workflows; 4) A bibliome of 833 open- access pangenomic papers and an interface with an LLM that can answer in-depth questions using their knowledge. PanKB empowers researchers and bioengineers to harness the full potential of microbial pangenomics and serves as a valuable resource bridging the gap between pangenomic data and practical applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/608241v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@4d3daaorg.highwire.dtl.DTLVardef@10b6d49org.highwire.dtl.DTLVardef@133da4forg.highwire.dtl.DTLVardef@141ae7e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

The Pangenome of Escherichia coli

Thousands of complete genome sequences for strains of a species that are now available enable the advancement of pangenome analytics to a new level of sophistication. We collected 2,377 publicly-available complete genomes of Escherichia coli for detailed pangenome analysis. The core genome and accessory genomes consisted of 2,398 and 5,182 genes, respectively. We developed a machine learning approach to define the accessory genes characterizing the major phylogroups of E. coli plus Shigella: A, B1, B2, C, D, E, F, G, and Shigella. The analysis resulted in a detailed structure of the genetic basis of the phylogroups differential traits. This pangenome structure was largely consistent with a housekeeping-gene-based MLST distribution, sequence-based Mash distance, and the Clermont quadruplex classification. The rare genome consisted of 163,619 genes, about 79% of which represented variations of 315 underlying transposon elements. This analysis generated a mathematical definition of the genetic basis for a species.

genomics↗

Pangenome mining of the Streptomyces genus redefines their biosynthetic potential

BackgroundStreptomyces is a highly diverse genus known for the production of secondary or specialized metabolites with a wide range of applications in the medical and agricultural industries. Several thousand complete or nearly-complete Streptomyces genome sequences are now available, affording the opportunity to deeply investigate the biosynthetic potential within these organisms and to advance natural product discovery initiatives. ResultWe performed pangenome analysis on 2,371 Streptomyces genomes, including approximately 1,200 complete assemblies. Employing a data-driven approach based on genome similarities, the Streptomyces genus was classified into 7 primary and 42 secondary MASH-clusters, forming the basis for a comprehensive pangenome mining. A refined workflow for grouping biosynthetic gene clusters (BGCs) redefined their diversity across different MASH-clusters. This workflow also reassigned 2,729 known BGC families to only 440 families, a reduction caused by inaccuracies in BGC boundary detections. When the genomic location of BGCs is included in the analysis, a conserved genomic structure (synteny) among BGCs becomes apparent within species and MASH-clusters. This synteny suggests that vertical inheritance is a major factor in the acquisition of new BGCs. ConclusionOur analysis of a genomic dataset at a scale of thousands of genomes refined predictions of BGC diversity using MASH-clusters as a basis for pangenome analysis. The observed conservation in the order of BGCs genomic locations showed that the BGCs are vertically inherited. The presented workflow and the in-depth analysis pave the way for large-scale pangenome investigations and enhance our understanding of the biosynthetic potential of the Streptomyces genus.

systems biology↗

Genomic insights into Lactobacillaceae: Analyzing the Alleleome of core pangenomes for enhanced understanding of strain diversity and revealing Phylogroup-specific unique variants

The Lactobacillaceae familys significance in food and health, combined with available strain-specific genomes, enables genome assessment through pangenome analysis. The Alleleome of the core pangenomes of the Lactobacillaceae family, which identifies natural sequence variations, was reconstructed from the amino acid and nucleotide sequences of the core genes across 2,447 strains of 26 species. It comprised 3.71 million amino acid variants in 29,448 core genes across the family. The alleleome analysis of the Lactobacillaceae family revealed key findings: 1) In the core pangenome, amino acid substitutions prevailed over rare insertions and deletions, 2) Purifying negative selection primarily influenced core gene variations in the family, with diversifying selection noted in L. helveticus. L. plantarums core alleleome was investigated due to its industrial importance. In L. plantarum, the defining characteristics of its core alleleome included: 1) It is highly conserved; 2) Among 235 isolation sources, the primary categories displaying variant prevalence were fermented food, feces, and unidentified sources; 3) It is predominantly characterized by conservative and moderately conservative mutations; and 4) Phylogroup-specific core variant gene analysis identified unique variants (DltX, FabZ1, Pts23B, CspP) in phylogroups I and B which could be used as identifier or validation markers of strain or phylogroup.

genomics↗

Pangenome reconstruction of Lactobacillaceae metabolism predicts species-specific metabolic traits

Strains across the Lactobacillaceae family form the basis for a trillion-dollar industry. Our understanding of the genomic basis for their key traits is fragmented, however, including the metabolism that is foundational to their industrial uses. Pangenome analysis of publicly available Lactobacillaceae genomes allowed us to generate genome-scale metabolic network reconstructions for 26 species of industrial importance. Their manual curation led to more than 75,000 gene-protein-reaction associations that were deployed to generate 2,446 genome-scale metabolic models. Cross-referencing genomes and known metabolic traits allowed for manual metabolic network curation and validation of the metabolic models. As a result, we provide the first pangenomic basis for metabolism in the Lactobacillaceae family and a collection of predictive computational metabolic models that enable a variety of practical uses.

systems biology↗

Data-Driven Strain Design Towards Mitigating Biomanufacturing Stresses

Microbial strains used in large-scale biomanufacturing of melatonin often experience stresses like reactive oxygen species (ROS), SOS response, and acid stress, which can reduce productivity. This study leveraged a data-driven workflow to identify mutations that could improve robustness to these stresses for an industrially important melatonin production strain. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 available conditions. oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute to fitness in ROS stress. Across these genes, 259 total mutations were inspected and 10 were chosen for reintroduction based on mutation clustering and transcriptional signals as evidence of fitness impact. Strains engineered with mutations in oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress, and reduced SOS response suggesting improved genetic stability. Additionally, new evidence was generated towards understand the function of ygfZ, a gene of relatively uncertain function. This meta-analysis approach utilized interoperable multi-omics datasets to identify targeted mutations conferring industrially-relevant phenotypes, describing an approach for data-driven strain engineering to optimize microbial cell factories. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/558093v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1c8b4f6org.highwire.dtl.DTLVardef@1e08eacorg.highwire.dtl.DTLVardef@1b6fcb7org.highwire.dtl.DTLVardef@89554f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

Using the E. coli Alleleome in Strain Design

Leveraging observed variants in strain design is a promising technique for creating strains with specific properties. Adaptive laboratory evolution (ALE) experiments generate variants that enhance fitness under specific conditions and can contribute to application-specific strain designs. Further, the wild-type (WT) coding alleleome of an organism, the complete set of its genes WT alleles, can provide an additional amount and diversity of variants not yet accessible from the aggregation of ALE experiment results. This study used both an ALE mutation database (3093 genomes) and a large set of WT genomes (12,661 genomes) to explore the sequence solution space of genes involved in tolerance to 10 conditions of industrial importance. To accomplish this, ALE variants for 22 genes previously identified as potentially important for industrial chemical tolerance were collected and supplemented with all available variants from the WT coding alleleome. A total of 4879 variants were reintroduced and used in 10 selection experiments. Both ALE and WT contributed highly enriched variants, where the enrichment and benefits depended on the conditions, genes, and gene product regions. The results also revealed that variants not originating from the initial experiment could potentially confer substantially greater benefits. Additionally, ALE and WT variants rarely overlapped on AA positions, but their clustering did coincide with where highly enriched variants were ultimately located. For genes primarily hosting potential gain-of-function variations, substitutions predicted to have a conservative impact frequently outperformed more radical substitutions. Case studies demonstrated that maximizing the amount of variants enabled easier identification of variant trends, which in turn can be used to better understand areas and characteristics of genes that can be feasibly varied, representing what could be thought of as a genome design variable. The combination of ALE and WT variants is a promising approach for use in future projects to better constrain and ultimately achieve practical coverage in the exploration of feasible sequence solution space. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/558058v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1a8e843org.highwire.dtl.DTLVardef@28a134org.highwire.dtl.DTLVardef@16f6849org.highwire.dtl.DTLVardef@127b43c_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

BGCFlow: Systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as; i) data selection, ii) functional annotation, iii) phylogenetic analysis, iv) genome mining, and v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge in the ever-growing genomic datasets of biotechnologically relevant bacterial species. BGCFlow is available for downloading at https://github.com/NBChub/bgcflow.

bioinformatics↗

Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress tolerance mechanisms: 1) modification of transport, 2) activation of ROS stress responses, 3) use of ROS-sensitive iron regulation, 4) motility, 5) broad transcriptional reallocation toward growth, and 6) metabolic rewiring to decrease NADH production. This work thus reveals the genome-scale systems biology of ROS tolerance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/521246v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@187f455org.highwire.dtl.DTLVardef@ba0798org.highwire.dtl.DTLVardef@148b175org.highwire.dtl.DTLVardef@17a8f8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

Laboratory-acquired mutations fall outside the wild-type alleleome of Escherichia coli

Inexpensive DNA sequencing has led to a rapidly increasing number of whole genome sequences in the public domain. Natural sequence variation can now be assessed across a large number of sequenced strains of a bacterial species, resulting in the definition of the wild-type alleleome (the collection of alleles for every gene found in the species). Concurrently, laboratory evolution emerged as a new approach to address biological questions and to develop new phenotypic traits, and a large number of laboratory acquired mutations can be found in databases. The availability of this large-scale sequence variation data now allows for a detailed comparison of mutations fixed in natural versus laboratory evolutions. Such comparison shows that laboratory-acquired mutations are rarely found in the wild-type alleleome of Escherichia coli. The E. coli alleleome is highly conserved as most of the sequence variation is concentrated in about 2% of the coding region. We find that there are typically two alternate amino acids coded for in the variable locations, and switches between the two are found in the data sets. Finally, we find that adaptive laboratory mutations, unlike wild-type mutations, do not utilize the redundancy built into the genetic code: they are less likely to be synonymous and rely on changing a single nucleotide in a codon. However, the uniqueness of mutations fixed in laboratory evolutions bodes well for synthetic biology by revealing novel exploitable sequence space untouched by natural evolution.

bioengineering↗

Laboratory evolution of synthetic electron transport system variants reveals a larger metabolic respiratory system and its plasticity

Respiration requires organisms to have an electron transport system (ETS) for the generation of proton motive force across the membrane that drives ATP synthase. Although the molecular details of the ETS are well studied and constitute textbook material, few studies have appeared to elucidate its systems biology. The most thermodynamically efficient ETS consists of two enzymes, an NADH: quinone oxidoreductase (NqRED) and a dioxygen reductase (O2RED), which facilitate the shuttling of electrons from NADH to oxygen. However, evolution has produced variations within ETS which modulate the overall energy efficiency of the system even within the same organism 1-3. The system-level impact of these variations and their individual physiological optimality remain poorly determined. To mimic varying ETS efficiency we generated four Escherichia coli deletion strains (named ETS-1H, 2H, 3H, and 4H) harboring unbranched ETS variants that pump 1, 2, 3, or 4 proton(s) per electron respectively. We then used a combination of synergistic methods (laboratory evolution, multi-omic analyses, and computation of proteome allocation) to characterize these ETS variants. We found that: (a) all four ETS variants evolved to a similar optimized growth rate, (b) the evolution of ETS variants was enabled by specific rewiring of major energy-generating pathways that couple to the ETS to optimize their ATP production capability, (c) proteome allocation per ATP generated was the same for all the variants, (d) the aero-type, that designates the overall ATP generation strategy 4 of a variant, remained conserved during its laboratory evolution, with the exception of the ETS-4H variant, and (e) integrated computational analysis of then data supported a proton-to-ATP ratio of 10 protons per 3 ATP for ATP synthase for all four ETS variants. We thus have defined the Aero-Type System (ATS) as a generalization of the aerobic bioenergetics, which is descriptive of the metabolic systems biology of respiration and demonstrates its plasticity.

systems biology↗

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 system. Adaptive Laboratory Evolution (ALE) leverages natures 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 presents a novel and first of its kind meta-analysis workflow to derive data-driven strain designs from aggregate ALE mutational data using rich mutation annotations, statistical and structural biology methods. The mutational dataset consolidated and utilized in this study contained 63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13,957 observed mutations. High-level trends across the entire dataset were established and revealed that ALE-derived strain designs will largely be gene-centric, as opposed to non-coding, and a relatively small number of variants (approx. 4) can significantly alter cellular states and provide benefits which range from an increase in fitness to a complete necessity for survival. Three novel experimentally validated designs relevant to metabolic engineering applications are presented as use cases for the workflow. Specifically, these designs increased growth rates with glycerol as a carbon source through a point mutation to glpK and a truncation to cyaA or increased tolerance to toxic levels of isobutyric acid through a pykF truncation. These results demonstrate how strain designs can be extracted from aggregated ALE data to enhance strain design efforts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/452699v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@8e1759org.highwire.dtl.DTLVardef@9ee7d5org.highwire.dtl.DTLVardef@866f04org.highwire.dtl.DTLVardef@1e27b29_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (E. coli, S. aureus, and B. subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

systems biology↗

Adaptive laboratory evolution of Escherichia coli under acid stress

The ability of Escherichia coli to tolerate acid stress is important for its survival and colonization in the human digestive tract. Here, we performed adaptive laboratory evolution of the laboratory strain E. coli K-12 MG1655 at pH 5.5 in glucose minimal medium. By 800 generations, six independent populations under evolution reached 18.0% higher growth rates than their starting strain at pH 5.5, while maintaining comparable growth rates to the starting strain at pH 7. We characterized the evolved strains to find that: (1) whole genome sequencing of isolated clones from each evolved population revealed mutations in rpoC appearing in 5 of 6 sequenced clones; (2) gene expression profiles revealed different strategies to mitigate acid stress, that are related to amino acid metabolism and energy production and conversion. Thus, a combination of adaptive laboratory evolution, genome resequencing, and expression profiling reveals, on a genome-scale, the strategies that E. coli deploys to mitigate acid stress.

microbiology↗

ALEdb 1.0: A Database of Mutations from Adaptive Laboratory Evolution

Full genomic sequences are readily available, but their functional interpretation remains a fundamental challenge. Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. Thus, ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but it also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have appeared in the literature providing a strong impetus for developing structured databases to warehouse experimental evolution information and make it retrievable for large-scale analysis. Here, the first step towards establishing this capability is presented: ALEdb (http://aledb.org). This initial release contains over 11,000 mutations that have been discovered in ALE experiments. ALEdb is the first of its kind; (1) it is a web-based platform that comprehensively reports on ALE acquired mutations and their conditions, (2) it reports key mutations using previously established trends, (3) it enables a search-driven workflow to enhance user mutation functional analysis, (4) it allows exporting of mutation query results for custom analysis, (5) it has a bibliome that describes the underlying published literature, and (6) 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.

bioinformatics↗