The Gene Ontology knowledgebase in 2023.

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Heterogeneous Distribution of Proton Motive Force in Nonheritable Antibiotic Resistance.

Bacterial infections that are difficult to eradicate are often treated by sequentially exposing the bacteria to different antibiotics. Although effective, this approach can give rise to epigenetic or other phenomena that may help some cells adapt to and tolerate the antibiotics. Characteristics of such adapted cells are dormancy and low energy levels, which promote survival without lending long-term genetic resistance against antibiotics. In this work, we quantified motility in cells of Escherichia coli that adapted and survived sequential exposure to lethal doses of antibiotics. In populations that adapted to transcriptional inhibition by rifampicin, we observed that ~1 of 3 cells continued swimming for several hours in the presence of lethal concentrations of ampicillin. As motility is powered by proton motive force (PMF), our results suggested that many adapted cells retained a high PMF. Single-cell growth assays revealed that the high-PMF cells resuscitated and divided upon the removal of ampicillin, just as the low-PMF cells did, a behavior reminiscent of persister cells. Our results are consistent with the notion that cells in a clonal population may employ multiple different mechanisms to adapt to antibiotic stresses. Variable PMF is likely a feature of a bet-hedging strategy: a fraction of the adapted cell population lies dormant while the other fraction retains high PMF to be able to swim out of the deleterious environment. IMPORTANCE Bacterial cells with low PMF may survive antibiotic stress due to dormancy, which favors nonheritable resistance without genetic mutations or acquisitions. On the other hand, cells with high PMF are less tolerant, as PMF helps in the uptake of certain antibiotics. Here, we quantified flagellar motility as an indirect measure of the PMF in cells of Escherichia coli that had adapted to ampicillin. Despite the disadvantage of maintaining a high PMF in the presence of antibiotics, we observed high PMF in ~30% of the cells, as evidenced by their ability to swim rapidly for several hours. These and other results were consistent with the idea that antibiotic tolerance can arise via different mechanisms in a clonal population.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO).

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

Insights from the reanalysis of high-throughput chemical genomics data for Escherichia coli K-12.

Despite the demonstrated success of genome-wide genetic screens and chemical genomics studies at predicting functions for genes of unknown function or predicting new functions for well-characterized genes, their potential to provide insights into gene function has not been fully explored. We systematically reanalyzed a published high-throughput phenotypic dataset for the model Gram-negative bacterium Escherichia coli K-12. The availability of high-quality annotation sets allowed us to compare the power of different metrics for measuring phenotypic profile similarity to correctly infer gene function. We conclude that there is no single best method; the three metrics tested gave comparable results for most gene pairs. We also assessed how converting quantitative phenotypes to discrete, qualitative phenotypes affected the association between phenotype and function. Our results indicate that this approach may allow phenotypic data from different studies to be combined to produce a larger dataset that may reveal functional connections between genes not detected in individual studies.

Use of a Fluorescence-Based Assay To Measure Escherichia coli Membrane Potential Changes in High Throughput.

Bacterial membrane potential is difficult to measure using classical electrophysiology techniques due to the small cell size and the presence of the peptidoglycan cell wall. Instead, chemical probes are often used to study membrane potential changes under conditions of interest. Many of these probes are fluorescent molecules that accumulate in a charge-dependent manner, and the resulting fluorescence change can be analyzed via flow cytometry or using a fluorescence microplate reader. Although this technique works well in many Gram-positive bacteria, it generates fairly low signal-to-noise ratios in Gram-negative bacteria due to dye exclusion by the outer membrane. We detail an optimized workflow that uses the membrane potential probe, 3,3'-diethyloxacarbocyanine iodide [DiOC2(3)], to measure Escherichia coli membrane potential changes in high throughput and describe the assay conditions that generate significant signal-to-noise ratios to detect membrane potential changes using a fluorescence microplate reader. A valinomycin calibration curve demonstrates this approach can robustly report membrane potentials over at least an ∼144-mV range with an accuracy of ∼12 mV. As a proof of concept, we used this approach to characterize the effects of some commercially available small molecules known to elicit membrane potential changes in other systems, increasing the repertoire of compounds known to perturb E. coli membrane energetics. One compound, the eukaryotic Ca2+ channel blocker amlodipine, was found to alter E. coli membrane potential and decrease the MIC of kanamycin, further supporting the value of this screening approach. This detailed methodology permits studying E. coli membrane potential changes quickly and reliably at the population level.

Phenotype annotation with the ontology of microbial phenotypes (OMP).

BACKGROUND: Microbial genetics has formed a foundation for understanding many aspects of biology. Systematic annotation that supports computational data mining should reveal further insights for microbes, microbiomes, and conserved functions beyond microbes. The Ontology of Microbial Phenotypes (OMP) was created to support such annotation. RESULTS: We define standards for an OMP-based annotation framework that supports the capture of a variety of phenotypes and provides flexibility for different levels of detail based on a combination of pre- and post-composition using OMP and other Open Biomedical Ontology (OBO) projects. A system for entering and viewing OMP annotations has been added to our online, public, web-based data portal. CONCLUSIONS: The annotation framework described here is ready to support projects to capture phenotypes from the experimental literature for a variety of microbes. Defining the OMP annotation standard should support the development of new software tools for data mining and analysis in comparative phenomics.

Bacteria-to-Human Protein Networks Reveal Origins of Endogenous DNA Damage.

DNA damage provokes mutations and cancer and results from external carcinogens or endogenous cellular processes. However, the intrinsic instigators of endogenous DNA damage are poorly understood. Here, we identify proteins that promote endogenous DNA damage when overproduced: the DNA "damage-up" proteins (DDPs). We discover a large network of DDPs in Escherichia coli and deconvolute them into six function clusters, demonstrating DDP mechanisms in three: reactive oxygen increase by transmembrane transporters, chromosome loss by replisome binding, and replication stalling by transcription factors. Their 284 human homologs are over-represented among known cancer drivers, and their RNAs in tumors predict heavy mutagenesis and a poor prognosis. Half of the tested human homologs promote DNA damage and mutation when overproduced in human cells, with DNA damage-elevating mechanisms like those in E. coli. Our work identifies networks of DDPs that provoke endogenous DNA damage and may reveal DNA damage-associated functions of many human known and newly implicated cancer-promoting proteins.

ECO, the Evidence & Conclusion Ontology: community standard for evidence information.

The Evidence and Conclusion Ontology (ECO) contains terms (classes) that describe types of evidence and assertion methods. ECO terms are used in the process of biocuration to capture the evidence that supports biological assertions (e.g. gene product X has function Y as supported by evidence Z). Capture of this information allows tracking of annotation provenance, establishment of quality control measures and query of evidence. ECO contains over 1500 terms and is in use by many leading biological resources including the Gene Ontology, UniProt and several model organism databases. ECO is continually being expanded and revised based on the needs of the biocuration community. The ontology is freely available for download from GitHub (https://github.com/evidenceontology/) or the project's website (http://evidenceontology.org/). Users can request new terms or changes to existing terms through the project's GitHub site. ECO is released into the public domain under CC0 1.0 Universal.

The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations.

The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientific study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made.

An ontology for microbial phenotypes.

BACKGROUND: Phenotypic data are routinely used to elucidate gene function in organisms amenable to genetic manipulation. However, previous to this work, there was no generalizable system in place for the structured storage and retrieval of phenotypic information for bacteria. RESULTS: The Ontology of Microbial Phenotypes (OMP) has been created to standardize the capture of such phenotypic information from microbes. OMP has been built on the foundations of the Basic Formal Ontology and the Phenotype and Trait Ontology. Terms have logical definitions that can facilitate computational searching of phenotypes and their associated genes. OMP can be accessed via a wiki page as well as downloaded from SourceForge. Initial annotations with OMP are being made for Escherichia coli using a wiki-based annotation capture system. New OMP terms are being concurrently developed as annotation proceeds. CONCLUSIONS: We anticipate that diverse groups studying microbial genetics and associated phenotypes will employ OMP for standardizing microbial phenotype annotation, much as the Gene Ontology has standardized gene product annotation. The resulting OMP resource and associated annotations will facilitate prediction of phenotypes for unknown genes and result in new experimental characterization of phenotypes and functions.

PortEco: a resource for exploring bacterial biology through high-throughput data and analysis tools.

PortEco (http://porteco.org) aims to collect, curate and provide data and analysis tools to support basic biological research in Escherichia coli (and eventually other bacterial systems). PortEco is implemented as a 'virtual' model organism database that provides a single unified interface to the user, while integrating information from a variety of sources. The main focus of PortEco is to enable broad use of the growing number of high-throughput experiments available for E. coli, and to leverage community annotation through the EcoliWiki and GONUTS systems. Currently, PortEco includes curated data from hundreds of genome-wide RNA expression studies, from high-throughput phenotyping of single-gene knockouts under hundreds of annotated conditions, from chromatin immunoprecipitation experiments for tens of different DNA-binding factors and from ribosome profiling experiments that yield insights into protein expression. Conditions have been annotated with a consistent vocabulary, and data have been consistently normalized to enable users to find, compare and interpret relevant experiments. PortEco includes tools for data analysis, including clustering, enrichment analysis and exploration via genome browsers. PortEco search and data analysis tools are extensively linked to the curated gene, metabolic pathway and regulation content at its sister site, EcoCyc.

Heat shock transcription factor σ32 co-opts the signal recognition particle to regulate protein homeostasis in E. coli.

All cells must adapt to rapidly changing conditions. The heat shock response (HSR) is an intracellular signaling pathway that maintains proteostasis (protein folding homeostasis), a process critical for survival in all organisms exposed to heat stress or other conditions that alter the folding of the proteome. Yet despite decades of study, the circuitry described for responding to altered protein status in the best-studied bacterium, E. coli, does not faithfully recapitulate the range of cellular responses in response to this stress. Here, we report the discovery of the missing link. Surprisingly, we found that σ(32), the central transcription factor driving the HSR, must be localized to the membrane rather than dispersed in the cytoplasm as previously assumed. Genetic analyses indicate that σ(32) localization results from a protein targeting reaction facilitated by the signal recognition particle (SRP) and its receptor (SR), which together comprise a conserved protein targeting machine and mediate the cotranslational targeting of inner membrane proteins to the membrane. SRP interacts with σ(32) directly and transports it to the inner membrane. Our results show that σ(32) must be membrane-associated to be properly regulated in response to the protein folding status in the cell, explaining how the HSR integrates information from both the cytoplasm and bacterial cell membrane.

GONUTS: the Gene Ontology Normal Usage Tracking System.

The Gene Ontology Normal Usage Tracking System (GONUTS) is a community-based browser and usage guide for Gene Ontology (GO) terms and a community system for general GO annotation of proteins. GONUTS uses wiki technology to allow registered users to share and edit notes on the use of each term in GO, and to contribute annotations for specific genes of interest. By providing a site for generation of third-party documentation at the granularity of individual terms, GONUTS complements the official documentation of the Gene Ontology Consortium. To provide examples for community users, GONUTS displays the complete GO annotations from seven model organisms: Saccharomyces cerevisiae, Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus and Arabidopsis thaliana. To support community annotation, GONUTS allows automated creation of gene pages for gene products in UniProt. GONUTS will improve the consistency of annotation efforts across genome projects, and should be useful in training new annotators and consumers in the production of GO annotations and the use of GO terms. GONUTS can be accessed at http://gowiki.tamu.edu. The source code for generating the content of GONUTS is available upon request.

EcoliWiki: a wiki-based community resource for Escherichia coli.

EcoliWiki is the community annotation component of the PortEco (http://porteco.org; formerly EcoliHub) project, an online data resource that integrates information on laboratory strains of Escherichia coli, its phages, plasmids and mobile genetic elements. As one of the early adopters of the wiki approach to model organism databases, EcoliWiki was designed to not only facilitate community-driven sharing of biological knowledge about E. coli as a model organism, but also to be interoperable with other data resources. EcoliWiki content currently covers genes from five laboratory E. coli strains, 21 bacteriophage genomes, F plasmid and eight transposons. EcoliWiki integrates the Mediawiki wiki platform with other open-source software tools and in-house software development to extend how wikis can be used for model organism databases. EcoliWiki can be accessed online at http://ecoliwiki.net.

Mutations in the flhD gene of Escherichia coli K-12 do not cause the reported effect on cell division.

The hetero-oligomeric FlhD/FlhC complex is a global regulator of transcription in Escherichia coli. FlhD alone, independent of FlhC, has also been reported to control when E. coli cells stop dividing and enter the stationary phase. This work is frequently cited as evidence that FlhD regulates cell division; however, our data indicate that this is not the case. The results presented here show that the previously observed phenotype is not due to the flhD locus, but is instead due to differences in the thyA alleles present in the flhD(+) and flhD(-) strains used in the original studies. We find that when the strains being compared have the same thyA allele (wild type or mutant), flhD mutations have no effect on growth.

A previously uncharacterized gene, yjfO (bsmA), influences Escherichia coli biofilm formation and stress response.

Bacteria growing as surface-adherent biofilms are better able to withstand chemical and physical stresses than their unattached, planktonic counterparts. Using transcriptional profiling and quantitative PCR, we observed a previously uncharacterized gene, yjfO to be upregulated during Escherichia coli MG1655 biofilm growth in a chemostat on serine-limited defined medium. A yjfO mutant, developed through targeted-insertion mutagenesis, and a yjfO-complemented strain, were obtained for further characterization. While bacterial surface colonization levels (c.f.u. cm(-2)) were similar in all three strains, the mutant strain exhibited reduced microcolony formation when observed in flow cells, and greatly enhanced flagellar motility on soft (0.3 %) agar. Complementation of yjfO restored microcolony formation and flagellar motility to wild-type levels. Cell surface hydrophobicity and twitching motility were unaffected by the presence or absence of yjfO. In contrast to the parent strain, biofilms from the mutant strain were less able to resist acid and peroxide stresses. yjfO had no significant effect on E. coli biofilm susceptibility to alkali or heat stress. Planktonic cultures from all three strains showed similar responses to these stresses. Regardless of the presence of yjfO, planktonic E. coli withstood alkali stress better than biofilm populations. Complementation of yjfO restored viability following exposure to peroxide stress, but did not restore acid resistance. Based on its influence on biofilm maturation and stress response, and effects on motility, we propose renaming the uncharacterized gene, yjfO, as bsmA (biofilm stress and motility).

What we can learn about Escherichia coli through application of Gene Ontology.

How we classify the genes, products and complexes that are present or absent in genomes, transcriptomes, proteomes and other datasets helps us place biological objects into subsystems with common functions, see how molecular functions are used to implement biological processes and compare the biology of different species and strains. Gene Ontology (GO) is one of the most successful systems for classifying biological function. Although GO is widely used for eukaryotic genomics, it has not yet been widely used for bacterial systems. The potential applications of GO are currently limited by the need to improve the annotation of bacterial genomes with GO and to improve how prokaryotic biology is represented in the ontology. Here, we discuss why GO should be adopted by microbiologists, and describe recent efforts to build and maintain high-quality GO annotation for Escherichia coli as a model system.

High-throughput, quantitative analyses of genetic interactions in E. coli.

Large-scale genetic interaction studies provide the basis for defining gene function and pathway architecture. Recent advances in the ability to generate double mutants en masse in Saccharomyces cerevisiae have dramatically accelerated the acquisition of genetic interaction information and the biological inferences that follow. Here we describe a method based on F factor-driven conjugation, which allows for high-throughput generation of double mutants in Escherichia coli. This method, termed genetic interaction analysis technology for E. coli (GIANT-coli), permits us to systematically generate and array double-mutant cells on solid media in high-density arrays. We show that colony size provides a robust and quantitative output of cellular fitness and that GIANT-coli can recapitulate known synthetic interactions and identify previously unidentified negative (synthetic sickness or lethality) and positive (suppressive or epistatic) relationships. Finally, we describe a complementary strategy for genome-wide suppressor-mutant identification. Together, these methods permit rapid, large-scale genetic interaction studies in E. coli.