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

Signal Amplification in Enzyme-Based Amperometric Biosensors

A unique mode of current amplification was investigated in reagentless biosensors based on the clinically significant enzymes including alcohol dehydrogenase, glucose 6-phosphate dehydrogenase, glycerol 3-phosphate dehydrogenase, and glucose oxidase. The biosensors were designed by sandwiching the enzyme–polymer film between an electrode and Nafion film. In particular, each enzyme and its cofactor were covalently attached to the chains of polysaccharide chitosan and mixed with carbon nanotubes on the electrode surface. The coating of such biosensors with Nafion resulted in the current increase by up to 1000%, depending on the enzyme. The results were analyzed considering the interplay between the enzyme activity–pH profiles and the Nafion-induced pH increase in the underlying chitosan film. The data were collected by using the rapid (<5 min) amperometric enzyme assays and pH-sensitive iridium oxide films. The increase in the biosensor current was attributed to the pH-driven increase in the enzyme activity inside the two-film interface. Such signal amplification should also be feasible in other biosensors based on the polyelectrolytes and sandwiched enzymes providing that a proper match is made between the enzyme activity–pH profiles and the pH of buffer solutions

Electrochemical Coupled-Enzyme Assays at Carbon Nanotubes

The recently developed internally calibrated electrochemical continuous enzyme assay (ICECEA) has proved to work well for single-enzyme systems. In the present work, its relevance to more challenging coupled-enzyme measurements was investigated by using a model enzyme pair comprising aspartate transaminase (AST) and malic dehydrogenase. The ICECEA was performed at an electrode modified with carbon nanotubes (CNTs), which were dispersed in a polysaccharide chitosan that acted as an adhesive. The 7 min assay required a 100 μL sample and relied on an AST-free calibration. It had a limit of detection equal to 5.0 pM AST (0.10 U L^–1) with no need for the incubation period. Its linear range extended up to 3500 pM (70 U L^–1). Perhaps the most promising was the fact that the assay and its calibration could be performed in the same solution even though the composition of the assay solution for the coupled-enzyme assays is typically more complex than that for the single-enzyme assays. This and the fast electrode kinetics of the signal transducing reaction of nicotinamide adenine dinucleotide at CNTs accounted for the low limit of detection. The unique shape of the ICECEA amperogram allowed for the selective determination of AST in the complex matrix of serum samples containing redox active potentially interfering species. Given these advantages, the prospects for the ICECEA in the development of other coupled-enzyme assays were also discussed

Rapid Electrochemical Enzyme Assay with Enzyme-Free Calibration

The internally calibrated electrochemical continuous enzyme assay (ICECEA, patent pending) was developed for the fast determination of enzyme activity unit (<i>U</i>). The assay depends on the integration of enzyme-free preassay calibration with the actual enzyme assay in one continuous experiment. Such integration resulted in a uniquely shaped amperometric trace that allowed for the selective picomolar determination of redox enzymes. The ICECEA worked because the preassay calibration did not interfere with the enzyme assay allowing both measurements to be performed in succession in the same solution and at the same electrode. The method displayed a good accuracy (relative error, <3%) and precision (relative standard deviation (RSD), <3%) when tested with different working electrodes (carbon nanotubes/chitosan, glassy carbon, platinum) and enzymes (alcohol dehydrogenase, ADH; lactate dehydrogenase, LDH; xanthine oxidase, XOx; glucose oxidase, GOx). The limit of detection for the ADH, LDH, XOx, and GOx was equal to 0.18, 0.14, 0.0031, and 0.11 U L^–1 (or 4.2, 0.72, 89, and 6.0 pM), respectively. The simplicity, reliability, and short analysis time make the ICECEA competitive with the optical enzyme assays currently in use

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

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 non-coding 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 Gene Ontology - a computational knowledge structure describing 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, as well as guidance on how users can best make use of the data we provide. We conclude with future directions for the project